Original Research

Skilled nursing facilities (SNFs) play a crucial role in the hospital readmission process.Approximately 1 in 4 Medicare beneficiaries discharged from an acute care hospital is admitted to a SNF instead of returning directly home. Of these patients, 1 in 4 will be readmitted within 30 days,¹ a rate significantly higher than the readmission rate of the inpatient population as a whole.² The 2014 Protecting Access to Medicare Act created a value-based purchasing program that will use quality measures to steer funds to, or away from, individual SNFs. When the program takes effect in 2018, the Centers for Medicare & Medicaid Services will use SNFs’ 30-day all-cause readmission rate to determine which SNFs receive payments and which receive penalties.³ The Affordable Care Act, passed in 2010, has also established penalties for hospitals with higher than expected readmission rates for Medicare patients.⁴

Despite this intensifying regulatory focus, relatively little is known about the factors that drive readmissions from SNFs. A prospective review of data from SNFs in 4 states has shown that SNFs staffed by nurse practitioners or physician assistants and those equipped to provide intravenous therapy were less likely to transfer patients to the hospital for ambulatory care-sensitive diagnoses.⁵ Qualitative studies have provided useful insight into the causes of SNF-to-hospital transfers but have not focused on 30-day readmissions.^6,7 A single survey-based study has examined the causes of SNF-to-hospital readmissions.⁸ However, survey-based methodologies have limited ability to capture the complex perspectives of SNF clinicians, who play a critical role in determining which SNF patients require evaluation or treatment in an acute care setting.

To address this gap in knowledge about factors contributing to SNF readmissions, we conducted a qualitative study examining SNF clinicians’ perspectives on patients readmitted to the hospital within 30 days of discharge. We used a structured interview tool to explore the root causes of readmission with frontline SNF staff, with the goal of using this knowledge to inform future hospital quality improvement (QI) efforts.

Case Identification

Hospital data-tracking software (Allscripts) was used to identify patients who experienced a 30-day, unplanned readmission from SNFs to an academic medical center. We restricted our search to patients whose index admission and readmission were to the medical center’s inpatient general medicine service. A study team member (BWC) monitored the dataset on a weekly basis and contacted SNF clinicians by e-mail and telephone to arrange interviews at times of mutual convenience. To mitigate against recall bias, interviews were conducted within 30 days of the readmission in question. A total of 32 cases were identified. No SNF clinicians refused a request for interview. For 8 of these cases, it was not possible to find a time of mutual convenience within the specified 30-day window. The remaining 24 cases involved patients from 15 SNFs across Connecticut. Interviews were conducted from August 2015 to November 2015.

The project was reviewed by our institution’s Human Investigation Committee and was exempted from Institutional Review Board review.

Study Participants

Interviews were conducted on-site at SNFs with groups of 1 to 4 SNF clinicians and administrators. SNF participants were informed of interviewer credentials and the study’s QI goals prior to participation. Participation was voluntary and did not affect the clinician’s relationship with the hospital or the SNF. Participants were not paid.

Interventions to Reduce Acute Care Transfers (INTERACT) is a QI program that includes training for clinicians, communication tools, and advance care planning tools.⁹ INTERACT is currently used in 138 Connecticut SNFs as part of a statewide QI effort funded by the Connecticut State Department of Public Health. In prospective QI studies,^10,11 implementation of INTERACT has been associated with decreased transfers from SNFs to acute care hospitals. The INTERACT Quality Improvement Tool, one part of the INTERACT bundle of interventions, is a 26-item questionnaire used to identify root causes of transfers from SNFs to acute care hospitals. It includes both checklists and open-ended questions about patient factors, SNF procedures, and SNF clinical decision-making.

We used the INTERACT QI Tool¹² to conduct structured interviews with nurses and administrators at SNFs. Interviewers used a hard copy of the tool to maintain field notes, and all parts of the questionnaire were completed in each interview. Although the questionnaire elicits baseline demographic and medical information, such as the patient’s age and vital signs prior to readmission, the majority of each interview was dedicated to discussion of the open-ended questions in Table 1. Upon completion of the INTERACT QI Tool, the interviewer asked 2 open-ended questions about reducing readmissions and 4 closed-ended questions regarding SNF admission procedures. (Table 1) The supplemental questions were added after preliminary interviews with SNF clinicians revealed concerns about the SNF referral process and about communication between the hospital, emergency department (ED) and SNFs—issues not included in the INTERACT questionnaire. Interviewers used phatic communication, probing questions, and follow-up questions to elicit detailed information from participants, and participant responses were not limited to topics in the questionnaire and the list of supplemental questions.

Interviews were conducted by a hospital clinical integration coordinator, social worker, and a physician (KB, MCB, BWC). All interviewers received formal training in qualitative research methods prior to the study.

All interviews were audio recorded, with permission from the participants, and were professionally transcribed. Field notes were maintained to ensure accuracy of INTERACT QI Tool data. Participant interviews covered no more than two cases per session and lasted from 18 to 71 minutes (mean duration, 38 minutes).

Analysis

Analysis of transcripts was inductive and informed by grounded theory methodology, in which data is reviewed for repeating ideas, which are then analyzed and grouped to develop a theoretical understanding of the phenomenon under investigation.^13,14

A preliminary codebook was developed using transcripts of the first 11 interviews. All statements relevant to the readmission process were extracted from the raw interview transcript and collected into a single list. This list was then reviewed for statements sharing a particular idea or concern. Such statements were grouped together under the heading of a repeating idea, and each repeating idea was assigned a code. Using this codebook, each transcript was independently reviewed and coded by three study team members with formal training in inductive qualitative analysis (KB, KTM, BWC). Reviewers assigned codes to sections of relevant text. Discrepancies in code assignment were discussed among the 3 analysts until consensus was reached. Using the method of constant comparison described in grounded theory,the codebook was updated continuously as the process of coding transcripts proceeded.¹² Changes to the codebook were discussed among the coding team until consensus was achieved. The process of data acquisition and coding continued until theoretical saturation was reached. Themes relating to underlying factors associated with readmissions were then identified based on shared properties among repeating ideas. ATLAS.ti (Scientific Software, Berlin, Germany, Version 7) was used to facilitate data organization and retrieval.

The SNFs in our study included 12 for-profit and 3 non-profit facilities. The number of licensed beds in each facility ranged from 73 to 360, with a mean of 148 beds. The SNFs had CMS Nursing Home Compare ratings ranging from 1 star, the lowest possible rating, to 5 stars, the highest possible,¹⁵ with a mean rating of 2.9 stars. Our analysis did not reveal differences in perceived contributions to readmissions from large vs. small or highly rated vs poorly rated SNFs.

Emergent transitions: Lack of coordination between ED and SNF

SNF clinicians frequently encountered situations in which a relatively stable patient was readmitted to the hospital after being transferred to the ED, despite the fact that SNF clinicians believed the patient should have returned to the SNF once a specific test was performed or service rendered at the ED. Commonly cited clinical scenarios that resulted in such readmissions included placement of urinary catheters and evaluation for cystitis. An assistant director of nursing reported that “the ER doesn’t want to hear my side of the story,” making it difficult for her to provide information that would prevent such readmissions. Other SNF clinicians reported similar difficulties in communicating with ED clinicians.

Code status: Incompletely addressed goals of care

The SNF clinicians in our study described cases in which patients with end-stage lung disease and disseminated cancer were readmitted to the hospital, despite SNF efforts to prevent readmission and provide palliative care within the SNF. For example, a SNF advanced practice nurse described a case in which a patient with widely metastatic cancer requested readmission to the hospital for treatment of deep vein thrombosis, despite longstanding recommendations from SNF staff that the patient forego hospitalization and enroll in hospice care. After discussion of code status and goals of care with hospital clinicians, the patient chose to enroll in hospice care and not to continue anticoagulation. SNF clinicians often perceived that, in the words of one administrator, “the palliative talks in the hospital outweigh our talks by a lot.” Numerous SNF clinicians believed that in-depth clarification of goals of care prior to discharge could prevent some readmissions.

Wrong patient, wrong place: Mismatch between clinical needs and SNF capabilities

One director of nursing stated that “[when] you read a referral, there’s a huge difference sometimes between what you read and what you see.” SNF clinicians reported that this discrepancy between clinical report and clinical reality often leads to patients being placed in SNFs that are unequipped to care for them. Many patients were perceived as being too ill for discharge from the acute-care setting in the first place. A nurse manager described this as a pattern of “pushing patients out of the hospital.” However, mismatches in clinical disposition were also seen as contributing to readmissions for medically stable patients, such as those with dementia, for whom SNFs frequently lack adequate staffing and physical safeguards.

Missing links: Important clinical information not effectively communicated by hospital

SNF clinicians described numerous challenges in formulating plans of care based on hospital discharge documentation. Discrepancies between discharge summaries and patient instructions were perceived as common and potential causes of readmissions. For patients discharged from the academic medical center in this study, medication instructions are included in both the discharge summary sent to the SNF and in a patient instruction packet. Several SNF clinicians said that it was common for a course of antibiotics to be listed on the discharge summary but not the patient instruction packet, or vice versa. SNF clinicians, who usually lack access to the hospital’s electronic medical record, have limited means for determining the correct document. Other important clinical data points, such as intermittent intravenous (IV) furosemide dosing and suppressive antibiotic regimens, were omitted from discharge paperwork altogether. SNF clinicians had difficulty reaching hospital clinicians who could clarify these clinical questions. “Good luck finding the person that took care of [the patient] three days before,” said one director of nursing.

Change starts at home: Challenges in SNF processes and culture

Many clinicians in our study reported that their facilities had recently added clinical capabilities in an effort to care for patients with complex medical problems. For example, to prevent transfers of patients with decompensated heart failure, several facilities in our study had recently obtained certification to give IV diuretics. However, as one director of nursing stated, these efforts require “buy-in” from doctors to decrease readmissions. That buy-in has not always been forthcoming. SNF clinicians also reported difficulty convincing patients and families that their facilities are capable of providing care that, in the past, might only have been available in acute-care settings.

These themes, along with associated sub-themes and representative quotations, are shown above (Table 4).

Our study suggests that the interaction between EDs and SNFs is an important and understudied domain in the spectrum of events leading to readmission. Prior studies have documented inadequacies in patient information provided by SNFs to EDs.^16,17 Efforts to improve SNF-to-ED information sharing have focused on making sure that ED clinicians have important baseline information about patients transferred from a SNF.^18,19 However, many of the clinicians in our study reported taking proactive steps to communicate with ED clinicians. These efforts encountered logistical and cultural barriers, with information that might have prevented readmission failing to reach ED providers. Many of the SNF clinicians in our study perceived this failure as a common cause of readmission, especially for relatively stable SNF patients.

Previous studies have pointed to a role for goals of care discussions in reducing hospital readmissions.²⁰ Our data underscore an important qualification to these findings: Location matters. The SNF clinicians in our study reported frequent and detailed goals of care discussions with their patients. However, they also reported that goals of care discussions held in the subacute setting carried less weight with patients and families than discussions held in the hospital. SNF clinicians described a number of cases in which patients were willing to adjust code status or goals of care only after being readmitted to the hospital.

Our study also points to the implications of existing research showing that patients are discharged from acute care hospitals “quicker and sicker” than they had been prior to the 1983 adoption of Medicare’s prospective payment system.²¹ Specifically, the SNF clinicians we interviewed perceived a strong link between patient acuity at the time of transfer and SNFs’ persistently high readmission rates. As SNFs have worked to expand their clinical capabilities, they struggle to win buy-in from physicians and families, many of whom view SNFs as incapable of managing acute illness. Many SNF clinicians also pointed to deficiencies in their own referral and admission processes as a recurring cause of readmissions. For example, several patients in our analysis suffered from dementia. Although these patients were stable enough to leave the acute care setting, the SNF clinicians responsible for their readmissions felt that their SNFs were not well-equipped to care for patients with dementia and that the patients should instead have been transferred to facilities with more robust resources for dementia care.

Finally, our findings highlight a fundamental tension between hospitals and SNFs: Which facility ought to shoulder the responsibility and cost for services that may prevent a readmission—the hospital or the SNF? For example, does responsibility for coordinating subspecialist evaluation of a patient’s chronic condition fall to the hospital or to the SNF? If such an evaluation is undertaken during a hospitalization, it prolongs the patient’s hospital stay and happens at the hospital’s expense. If the patient is discharged to a SNF and sees the subspecialist in clinic, then the SNF must pay for transportation to and from the clinic appointment. SNF clinicians expressed near unanimity that fragmented models of care and high barriers to communication made it difficult to design solutions to these dilemmas.

Strengths and limitations

To our knowledge, this is the first interview-based study examining SNF clinicians’ perspectives on unplanned, 30-day hospital readmissions. We gathered information from clinicians with a range of clinical experience, all of whom had cared directly for the patient who had been readmitted. Our data came from clinicians at 15 SNFs of varying sizes and quality ratings, allowing us to identify a broad range of factors contributing to readmissions.

Because this study relied on qualitative methods, it should be viewed as hypothesis-generating rather than hypothesis-confirming. Further research is needed to determine whether variables related to the themes above are causally linked to SNF readmissions. We identified cases for review using convenience sampling of a cohort of readmitted patients at a single tertiary-care hospital, and all participating SNFs were located in Connecticut. These factors may limit the generalizability of our findings. Although the clinicians we interviewed occupied diverse roles within their respective SNFs, our sample did not include direct-care staff without managerial responsibility, such as certified nursing assistants or licensed practical nurses. This prevented our study from identifying themes into which managers would have limited insight, especially those involving cultural and management practices leading to poor communication between them and their staff. Because our study examines cases in which discharge and readmission were to a general medicine service, it may not describe factors relevant to patients discharged from subspecialist or surgical services.

Implications for future QI efforts and research

Several clinicians we interviewed suggested that readmissions might be reduced by dedicating the services of a hospital professional, such as a nurse or case manager, to monitoring the clinical course of medically complex patients after discharge. A dedicated “transition coach” could clarify deficiencies in discharge paperwork, facilitate necessary follow-up appointments, liaise with staff at both the hospital and the SNF, or coordinate acquisition of necessary equipment. Prospective trials have demonstrated that such interventions can decrease readmission rates among hospitalized patients,^22,23 but formal studies have not been carried out among cohorts of SNF patients.

Prior efforts to improve SNF-ED information sharing have focused on making sure that ED clinicians have important baseline information about patients transferred from a SNF.^24,25 The experiences of SNF clinicians in our study suggest that important information also fails to make its way from ED providers to SNFs and that this failure results in unnecessary readmissions of relatively stable SNF patients. Thus, hospitals may be able to prevent SNF readmissions by creating lines of communication between EDs and SNFs and by ensuring that ED physicians and mid-level providers are familiar with the clinical capabilities of local SNFs.

Future research and QI work should also investigate approaches to care coordination that ensure that complex patients are placed in SNFs with resources adequate to address their comorbidities. Potential interventions might include increased use of SNF “liaisons,” who would evaluate patients in-person prior to approving transfer to a given SNF. As has been previously suggested,²⁶ hospitals might also reduce readmissions by narrowing the pool of facilities to which they transfer patients, thereby building more robust, interconnected relationships with a smaller number of SNFs.

SNF clinicians identified areas for improvement at almost every point in the chain of events spanning hospitalization, discharge, and transfer. Among the most frequently cited contributors to readmissions were clinical instability at the time of discharge and omission of clinically important information from discharge documentation. Improved communication between hospitals, ED clinicians, and SNFs, as well as more thoroughly defined goals of care at the time of discharge, were seen as promising ways of decreasing readmissions. Successful interventions for reducing readmissions from SNFs will likely require multifaceted approaches to these problems.

Disclosure: This research was supported by a grant (#P30HS023554-01) from the Agency for Healthcare Research and Quality (AHRQ) and received support from Yale New Haven Hospital and the Claude D. Pepper Older Americans Independence Center at Yale University School of Medicine (#P30AG021342 NIH/NIA).

Skilled nursing facilities (SNFs) play a crucial role in the hospital readmission process.Approximately 1 in 4 Medicare beneficiaries discharged from an acute care hospital is admitted to a SNF instead of returning directly home. Of these patients, 1 in 4 will be readmitted within 30 days,¹ a rate significantly higher than the readmission rate of the inpatient population as a whole.² The 2014 Protecting Access to Medicare Act created a value-based purchasing program that will use quality measures to steer funds to, or away from, individual SNFs. When the program takes effect in 2018, the Centers for Medicare & Medicaid Services will use SNFs’ 30-day all-cause readmission rate to determine which SNFs receive payments and which receive penalties.³ The Affordable Care Act, passed in 2010, has also established penalties for hospitals with higher than expected readmission rates for Medicare patients.⁴

Despite this intensifying regulatory focus, relatively little is known about the factors that drive readmissions from SNFs. A prospective review of data from SNFs in 4 states has shown that SNFs staffed by nurse practitioners or physician assistants and those equipped to provide intravenous therapy were less likely to transfer patients to the hospital for ambulatory care-sensitive diagnoses.⁵ Qualitative studies have provided useful insight into the causes of SNF-to-hospital transfers but have not focused on 30-day readmissions.^6,7 A single survey-based study has examined the causes of SNF-to-hospital readmissions.⁸ However, survey-based methodologies have limited ability to capture the complex perspectives of SNF clinicians, who play a critical role in determining which SNF patients require evaluation or treatment in an acute care setting.

To address this gap in knowledge about factors contributing to SNF readmissions, we conducted a qualitative study examining SNF clinicians’ perspectives on patients readmitted to the hospital within 30 days of discharge. We used a structured interview tool to explore the root causes of readmission with frontline SNF staff, with the goal of using this knowledge to inform future hospital quality improvement (QI) efforts.

Case Identification

Hospital data-tracking software (Allscripts) was used to identify patients who experienced a 30-day, unplanned readmission from SNFs to an academic medical center. We restricted our search to patients whose index admission and readmission were to the medical center’s inpatient general medicine service. A study team member (BWC) monitored the dataset on a weekly basis and contacted SNF clinicians by e-mail and telephone to arrange interviews at times of mutual convenience. To mitigate against recall bias, interviews were conducted within 30 days of the readmission in question. A total of 32 cases were identified. No SNF clinicians refused a request for interview. For 8 of these cases, it was not possible to find a time of mutual convenience within the specified 30-day window. The remaining 24 cases involved patients from 15 SNFs across Connecticut. Interviews were conducted from August 2015 to November 2015.

The project was reviewed by our institution’s Human Investigation Committee and was exempted from Institutional Review Board review.

Study Participants

Interviews were conducted on-site at SNFs with groups of 1 to 4 SNF clinicians and administrators. SNF participants were informed of interviewer credentials and the study’s QI goals prior to participation. Participation was voluntary and did not affect the clinician’s relationship with the hospital or the SNF. Participants were not paid.

Interventions to Reduce Acute Care Transfers (INTERACT) is a QI program that includes training for clinicians, communication tools, and advance care planning tools.⁹ INTERACT is currently used in 138 Connecticut SNFs as part of a statewide QI effort funded by the Connecticut State Department of Public Health. In prospective QI studies,^10,11 implementation of INTERACT has been associated with decreased transfers from SNFs to acute care hospitals. The INTERACT Quality Improvement Tool, one part of the INTERACT bundle of interventions, is a 26-item questionnaire used to identify root causes of transfers from SNFs to acute care hospitals. It includes both checklists and open-ended questions about patient factors, SNF procedures, and SNF clinical decision-making.

We used the INTERACT QI Tool¹² to conduct structured interviews with nurses and administrators at SNFs. Interviewers used a hard copy of the tool to maintain field notes, and all parts of the questionnaire were completed in each interview. Although the questionnaire elicits baseline demographic and medical information, such as the patient’s age and vital signs prior to readmission, the majority of each interview was dedicated to discussion of the open-ended questions in Table 1. Upon completion of the INTERACT QI Tool, the interviewer asked 2 open-ended questions about reducing readmissions and 4 closed-ended questions regarding SNF admission procedures. (Table 1) The supplemental questions were added after preliminary interviews with SNF clinicians revealed concerns about the SNF referral process and about communication between the hospital, emergency department (ED) and SNFs—issues not included in the INTERACT questionnaire. Interviewers used phatic communication, probing questions, and follow-up questions to elicit detailed information from participants, and participant responses were not limited to topics in the questionnaire and the list of supplemental questions.

Interviews were conducted by a hospital clinical integration coordinator, social worker, and a physician (KB, MCB, BWC). All interviewers received formal training in qualitative research methods prior to the study.

All interviews were audio recorded, with permission from the participants, and were professionally transcribed. Field notes were maintained to ensure accuracy of INTERACT QI Tool data. Participant interviews covered no more than two cases per session and lasted from 18 to 71 minutes (mean duration, 38 minutes).

Analysis

Analysis of transcripts was inductive and informed by grounded theory methodology, in which data is reviewed for repeating ideas, which are then analyzed and grouped to develop a theoretical understanding of the phenomenon under investigation.^13,14

A preliminary codebook was developed using transcripts of the first 11 interviews. All statements relevant to the readmission process were extracted from the raw interview transcript and collected into a single list. This list was then reviewed for statements sharing a particular idea or concern. Such statements were grouped together under the heading of a repeating idea, and each repeating idea was assigned a code. Using this codebook, each transcript was independently reviewed and coded by three study team members with formal training in inductive qualitative analysis (KB, KTM, BWC). Reviewers assigned codes to sections of relevant text. Discrepancies in code assignment were discussed among the 3 analysts until consensus was reached. Using the method of constant comparison described in grounded theory,the codebook was updated continuously as the process of coding transcripts proceeded.¹² Changes to the codebook were discussed among the coding team until consensus was achieved. The process of data acquisition and coding continued until theoretical saturation was reached. Themes relating to underlying factors associated with readmissions were then identified based on shared properties among repeating ideas. ATLAS.ti (Scientific Software, Berlin, Germany, Version 7) was used to facilitate data organization and retrieval.

The SNFs in our study included 12 for-profit and 3 non-profit facilities. The number of licensed beds in each facility ranged from 73 to 360, with a mean of 148 beds. The SNFs had CMS Nursing Home Compare ratings ranging from 1 star, the lowest possible rating, to 5 stars, the highest possible,¹⁵ with a mean rating of 2.9 stars. Our analysis did not reveal differences in perceived contributions to readmissions from large vs. small or highly rated vs poorly rated SNFs.

Emergent transitions: Lack of coordination between ED and SNF

SNF clinicians frequently encountered situations in which a relatively stable patient was readmitted to the hospital after being transferred to the ED, despite the fact that SNF clinicians believed the patient should have returned to the SNF once a specific test was performed or service rendered at the ED. Commonly cited clinical scenarios that resulted in such readmissions included placement of urinary catheters and evaluation for cystitis. An assistant director of nursing reported that “the ER doesn’t want to hear my side of the story,” making it difficult for her to provide information that would prevent such readmissions. Other SNF clinicians reported similar difficulties in communicating with ED clinicians.

Code status: Incompletely addressed goals of care

The SNF clinicians in our study described cases in which patients with end-stage lung disease and disseminated cancer were readmitted to the hospital, despite SNF efforts to prevent readmission and provide palliative care within the SNF. For example, a SNF advanced practice nurse described a case in which a patient with widely metastatic cancer requested readmission to the hospital for treatment of deep vein thrombosis, despite longstanding recommendations from SNF staff that the patient forego hospitalization and enroll in hospice care. After discussion of code status and goals of care with hospital clinicians, the patient chose to enroll in hospice care and not to continue anticoagulation. SNF clinicians often perceived that, in the words of one administrator, “the palliative talks in the hospital outweigh our talks by a lot.” Numerous SNF clinicians believed that in-depth clarification of goals of care prior to discharge could prevent some readmissions.

Wrong patient, wrong place: Mismatch between clinical needs and SNF capabilities

One director of nursing stated that “[when] you read a referral, there’s a huge difference sometimes between what you read and what you see.” SNF clinicians reported that this discrepancy between clinical report and clinical reality often leads to patients being placed in SNFs that are unequipped to care for them. Many patients were perceived as being too ill for discharge from the acute-care setting in the first place. A nurse manager described this as a pattern of “pushing patients out of the hospital.” However, mismatches in clinical disposition were also seen as contributing to readmissions for medically stable patients, such as those with dementia, for whom SNFs frequently lack adequate staffing and physical safeguards.

Missing links: Important clinical information not effectively communicated by hospital

SNF clinicians described numerous challenges in formulating plans of care based on hospital discharge documentation. Discrepancies between discharge summaries and patient instructions were perceived as common and potential causes of readmissions. For patients discharged from the academic medical center in this study, medication instructions are included in both the discharge summary sent to the SNF and in a patient instruction packet. Several SNF clinicians said that it was common for a course of antibiotics to be listed on the discharge summary but not the patient instruction packet, or vice versa. SNF clinicians, who usually lack access to the hospital’s electronic medical record, have limited means for determining the correct document. Other important clinical data points, such as intermittent intravenous (IV) furosemide dosing and suppressive antibiotic regimens, were omitted from discharge paperwork altogether. SNF clinicians had difficulty reaching hospital clinicians who could clarify these clinical questions. “Good luck finding the person that took care of [the patient] three days before,” said one director of nursing.

Change starts at home: Challenges in SNF processes and culture

Many clinicians in our study reported that their facilities had recently added clinical capabilities in an effort to care for patients with complex medical problems. For example, to prevent transfers of patients with decompensated heart failure, several facilities in our study had recently obtained certification to give IV diuretics. However, as one director of nursing stated, these efforts require “buy-in” from doctors to decrease readmissions. That buy-in has not always been forthcoming. SNF clinicians also reported difficulty convincing patients and families that their facilities are capable of providing care that, in the past, might only have been available in acute-care settings.

These themes, along with associated sub-themes and representative quotations, are shown above (Table 4).

Our study suggests that the interaction between EDs and SNFs is an important and understudied domain in the spectrum of events leading to readmission. Prior studies have documented inadequacies in patient information provided by SNFs to EDs.^16,17 Efforts to improve SNF-to-ED information sharing have focused on making sure that ED clinicians have important baseline information about patients transferred from a SNF.^18,19 However, many of the clinicians in our study reported taking proactive steps to communicate with ED clinicians. These efforts encountered logistical and cultural barriers, with information that might have prevented readmission failing to reach ED providers. Many of the SNF clinicians in our study perceived this failure as a common cause of readmission, especially for relatively stable SNF patients.

Previous studies have pointed to a role for goals of care discussions in reducing hospital readmissions.²⁰ Our data underscore an important qualification to these findings: Location matters. The SNF clinicians in our study reported frequent and detailed goals of care discussions with their patients. However, they also reported that goals of care discussions held in the subacute setting carried less weight with patients and families than discussions held in the hospital. SNF clinicians described a number of cases in which patients were willing to adjust code status or goals of care only after being readmitted to the hospital.

Our study also points to the implications of existing research showing that patients are discharged from acute care hospitals “quicker and sicker” than they had been prior to the 1983 adoption of Medicare’s prospective payment system.²¹ Specifically, the SNF clinicians we interviewed perceived a strong link between patient acuity at the time of transfer and SNFs’ persistently high readmission rates. As SNFs have worked to expand their clinical capabilities, they struggle to win buy-in from physicians and families, many of whom view SNFs as incapable of managing acute illness. Many SNF clinicians also pointed to deficiencies in their own referral and admission processes as a recurring cause of readmissions. For example, several patients in our analysis suffered from dementia. Although these patients were stable enough to leave the acute care setting, the SNF clinicians responsible for their readmissions felt that their SNFs were not well-equipped to care for patients with dementia and that the patients should instead have been transferred to facilities with more robust resources for dementia care.

Finally, our findings highlight a fundamental tension between hospitals and SNFs: Which facility ought to shoulder the responsibility and cost for services that may prevent a readmission—the hospital or the SNF? For example, does responsibility for coordinating subspecialist evaluation of a patient’s chronic condition fall to the hospital or to the SNF? If such an evaluation is undertaken during a hospitalization, it prolongs the patient’s hospital stay and happens at the hospital’s expense. If the patient is discharged to a SNF and sees the subspecialist in clinic, then the SNF must pay for transportation to and from the clinic appointment. SNF clinicians expressed near unanimity that fragmented models of care and high barriers to communication made it difficult to design solutions to these dilemmas.

Strengths and limitations

To our knowledge, this is the first interview-based study examining SNF clinicians’ perspectives on unplanned, 30-day hospital readmissions. We gathered information from clinicians with a range of clinical experience, all of whom had cared directly for the patient who had been readmitted. Our data came from clinicians at 15 SNFs of varying sizes and quality ratings, allowing us to identify a broad range of factors contributing to readmissions.

Because this study relied on qualitative methods, it should be viewed as hypothesis-generating rather than hypothesis-confirming. Further research is needed to determine whether variables related to the themes above are causally linked to SNF readmissions. We identified cases for review using convenience sampling of a cohort of readmitted patients at a single tertiary-care hospital, and all participating SNFs were located in Connecticut. These factors may limit the generalizability of our findings. Although the clinicians we interviewed occupied diverse roles within their respective SNFs, our sample did not include direct-care staff without managerial responsibility, such as certified nursing assistants or licensed practical nurses. This prevented our study from identifying themes into which managers would have limited insight, especially those involving cultural and management practices leading to poor communication between them and their staff. Because our study examines cases in which discharge and readmission were to a general medicine service, it may not describe factors relevant to patients discharged from subspecialist or surgical services.

Implications for future QI efforts and research

Several clinicians we interviewed suggested that readmissions might be reduced by dedicating the services of a hospital professional, such as a nurse or case manager, to monitoring the clinical course of medically complex patients after discharge. A dedicated “transition coach” could clarify deficiencies in discharge paperwork, facilitate necessary follow-up appointments, liaise with staff at both the hospital and the SNF, or coordinate acquisition of necessary equipment. Prospective trials have demonstrated that such interventions can decrease readmission rates among hospitalized patients,^22,23 but formal studies have not been carried out among cohorts of SNF patients.

Prior efforts to improve SNF-ED information sharing have focused on making sure that ED clinicians have important baseline information about patients transferred from a SNF.^24,25 The experiences of SNF clinicians in our study suggest that important information also fails to make its way from ED providers to SNFs and that this failure results in unnecessary readmissions of relatively stable SNF patients. Thus, hospitals may be able to prevent SNF readmissions by creating lines of communication between EDs and SNFs and by ensuring that ED physicians and mid-level providers are familiar with the clinical capabilities of local SNFs.

Future research and QI work should also investigate approaches to care coordination that ensure that complex patients are placed in SNFs with resources adequate to address their comorbidities. Potential interventions might include increased use of SNF “liaisons,” who would evaluate patients in-person prior to approving transfer to a given SNF. As has been previously suggested,²⁶ hospitals might also reduce readmissions by narrowing the pool of facilities to which they transfer patients, thereby building more robust, interconnected relationships with a smaller number of SNFs.

SNF clinicians identified areas for improvement at almost every point in the chain of events spanning hospitalization, discharge, and transfer. Among the most frequently cited contributors to readmissions were clinical instability at the time of discharge and omission of clinically important information from discharge documentation. Improved communication between hospitals, ED clinicians, and SNFs, as well as more thoroughly defined goals of care at the time of discharge, were seen as promising ways of decreasing readmissions. Successful interventions for reducing readmissions from SNFs will likely require multifaceted approaches to these problems.

Disclosure: This research was supported by a grant (#P30HS023554-01) from the Agency for Healthcare Research and Quality (AHRQ) and received support from Yale New Haven Hospital and the Claude D. Pepper Older Americans Independence Center at Yale University School of Medicine (#P30AG021342 NIH/NIA).

Skilled nursing facilities (SNFs) play a crucial role in the hospital readmission process.Approximately 1 in 4 Medicare beneficiaries discharged from an acute care hospital is admitted to a SNF instead of returning directly home. Of these patients, 1 in 4 will be readmitted within 30 days,¹ a rate significantly higher than the readmission rate of the inpatient population as a whole.² The 2014 Protecting Access to Medicare Act created a value-based purchasing program that will use quality measures to steer funds to, or away from, individual SNFs. When the program takes effect in 2018, the Centers for Medicare & Medicaid Services will use SNFs’ 30-day all-cause readmission rate to determine which SNFs receive payments and which receive penalties.³ The Affordable Care Act, passed in 2010, has also established penalties for hospitals with higher than expected readmission rates for Medicare patients.⁴

Despite this intensifying regulatory focus, relatively little is known about the factors that drive readmissions from SNFs. A prospective review of data from SNFs in 4 states has shown that SNFs staffed by nurse practitioners or physician assistants and those equipped to provide intravenous therapy were less likely to transfer patients to the hospital for ambulatory care-sensitive diagnoses.⁵ Qualitative studies have provided useful insight into the causes of SNF-to-hospital transfers but have not focused on 30-day readmissions.^6,7 A single survey-based study has examined the causes of SNF-to-hospital readmissions.⁸ However, survey-based methodologies have limited ability to capture the complex perspectives of SNF clinicians, who play a critical role in determining which SNF patients require evaluation or treatment in an acute care setting.

To address this gap in knowledge about factors contributing to SNF readmissions, we conducted a qualitative study examining SNF clinicians’ perspectives on patients readmitted to the hospital within 30 days of discharge. We used a structured interview tool to explore the root causes of readmission with frontline SNF staff, with the goal of using this knowledge to inform future hospital quality improvement (QI) efforts.

Case Identification

Hospital data-tracking software (Allscripts) was used to identify patients who experienced a 30-day, unplanned readmission from SNFs to an academic medical center. We restricted our search to patients whose index admission and readmission were to the medical center’s inpatient general medicine service. A study team member (BWC) monitored the dataset on a weekly basis and contacted SNF clinicians by e-mail and telephone to arrange interviews at times of mutual convenience. To mitigate against recall bias, interviews were conducted within 30 days of the readmission in question. A total of 32 cases were identified. No SNF clinicians refused a request for interview. For 8 of these cases, it was not possible to find a time of mutual convenience within the specified 30-day window. The remaining 24 cases involved patients from 15 SNFs across Connecticut. Interviews were conducted from August 2015 to November 2015.

The project was reviewed by our institution’s Human Investigation Committee and was exempted from Institutional Review Board review.

Study Participants

Interviews were conducted on-site at SNFs with groups of 1 to 4 SNF clinicians and administrators. SNF participants were informed of interviewer credentials and the study’s QI goals prior to participation. Participation was voluntary and did not affect the clinician’s relationship with the hospital or the SNF. Participants were not paid.

Interventions to Reduce Acute Care Transfers (INTERACT) is a QI program that includes training for clinicians, communication tools, and advance care planning tools.⁹ INTERACT is currently used in 138 Connecticut SNFs as part of a statewide QI effort funded by the Connecticut State Department of Public Health. In prospective QI studies,^10,11 implementation of INTERACT has been associated with decreased transfers from SNFs to acute care hospitals. The INTERACT Quality Improvement Tool, one part of the INTERACT bundle of interventions, is a 26-item questionnaire used to identify root causes of transfers from SNFs to acute care hospitals. It includes both checklists and open-ended questions about patient factors, SNF procedures, and SNF clinical decision-making.

We used the INTERACT QI Tool¹² to conduct structured interviews with nurses and administrators at SNFs. Interviewers used a hard copy of the tool to maintain field notes, and all parts of the questionnaire were completed in each interview. Although the questionnaire elicits baseline demographic and medical information, such as the patient’s age and vital signs prior to readmission, the majority of each interview was dedicated to discussion of the open-ended questions in Table 1. Upon completion of the INTERACT QI Tool, the interviewer asked 2 open-ended questions about reducing readmissions and 4 closed-ended questions regarding SNF admission procedures. (Table 1) The supplemental questions were added after preliminary interviews with SNF clinicians revealed concerns about the SNF referral process and about communication between the hospital, emergency department (ED) and SNFs—issues not included in the INTERACT questionnaire. Interviewers used phatic communication, probing questions, and follow-up questions to elicit detailed information from participants, and participant responses were not limited to topics in the questionnaire and the list of supplemental questions.

Interviews were conducted by a hospital clinical integration coordinator, social worker, and a physician (KB, MCB, BWC). All interviewers received formal training in qualitative research methods prior to the study.

All interviews were audio recorded, with permission from the participants, and were professionally transcribed. Field notes were maintained to ensure accuracy of INTERACT QI Tool data. Participant interviews covered no more than two cases per session and lasted from 18 to 71 minutes (mean duration, 38 minutes).

Analysis

Analysis of transcripts was inductive and informed by grounded theory methodology, in which data is reviewed for repeating ideas, which are then analyzed and grouped to develop a theoretical understanding of the phenomenon under investigation.^13,14

A preliminary codebook was developed using transcripts of the first 11 interviews. All statements relevant to the readmission process were extracted from the raw interview transcript and collected into a single list. This list was then reviewed for statements sharing a particular idea or concern. Such statements were grouped together under the heading of a repeating idea, and each repeating idea was assigned a code. Using this codebook, each transcript was independently reviewed and coded by three study team members with formal training in inductive qualitative analysis (KB, KTM, BWC). Reviewers assigned codes to sections of relevant text. Discrepancies in code assignment were discussed among the 3 analysts until consensus was reached. Using the method of constant comparison described in grounded theory,the codebook was updated continuously as the process of coding transcripts proceeded.¹² Changes to the codebook were discussed among the coding team until consensus was achieved. The process of data acquisition and coding continued until theoretical saturation was reached. Themes relating to underlying factors associated with readmissions were then identified based on shared properties among repeating ideas. ATLAS.ti (Scientific Software, Berlin, Germany, Version 7) was used to facilitate data organization and retrieval.

The SNFs in our study included 12 for-profit and 3 non-profit facilities. The number of licensed beds in each facility ranged from 73 to 360, with a mean of 148 beds. The SNFs had CMS Nursing Home Compare ratings ranging from 1 star, the lowest possible rating, to 5 stars, the highest possible,¹⁵ with a mean rating of 2.9 stars. Our analysis did not reveal differences in perceived contributions to readmissions from large vs. small or highly rated vs poorly rated SNFs.

Emergent transitions: Lack of coordination between ED and SNF

SNF clinicians frequently encountered situations in which a relatively stable patient was readmitted to the hospital after being transferred to the ED, despite the fact that SNF clinicians believed the patient should have returned to the SNF once a specific test was performed or service rendered at the ED. Commonly cited clinical scenarios that resulted in such readmissions included placement of urinary catheters and evaluation for cystitis. An assistant director of nursing reported that “the ER doesn’t want to hear my side of the story,” making it difficult for her to provide information that would prevent such readmissions. Other SNF clinicians reported similar difficulties in communicating with ED clinicians.

Code status: Incompletely addressed goals of care

The SNF clinicians in our study described cases in which patients with end-stage lung disease and disseminated cancer were readmitted to the hospital, despite SNF efforts to prevent readmission and provide palliative care within the SNF. For example, a SNF advanced practice nurse described a case in which a patient with widely metastatic cancer requested readmission to the hospital for treatment of deep vein thrombosis, despite longstanding recommendations from SNF staff that the patient forego hospitalization and enroll in hospice care. After discussion of code status and goals of care with hospital clinicians, the patient chose to enroll in hospice care and not to continue anticoagulation. SNF clinicians often perceived that, in the words of one administrator, “the palliative talks in the hospital outweigh our talks by a lot.” Numerous SNF clinicians believed that in-depth clarification of goals of care prior to discharge could prevent some readmissions.

Wrong patient, wrong place: Mismatch between clinical needs and SNF capabilities

One director of nursing stated that “[when] you read a referral, there’s a huge difference sometimes between what you read and what you see.” SNF clinicians reported that this discrepancy between clinical report and clinical reality often leads to patients being placed in SNFs that are unequipped to care for them. Many patients were perceived as being too ill for discharge from the acute-care setting in the first place. A nurse manager described this as a pattern of “pushing patients out of the hospital.” However, mismatches in clinical disposition were also seen as contributing to readmissions for medically stable patients, such as those with dementia, for whom SNFs frequently lack adequate staffing and physical safeguards.

Missing links: Important clinical information not effectively communicated by hospital

SNF clinicians described numerous challenges in formulating plans of care based on hospital discharge documentation. Discrepancies between discharge summaries and patient instructions were perceived as common and potential causes of readmissions. For patients discharged from the academic medical center in this study, medication instructions are included in both the discharge summary sent to the SNF and in a patient instruction packet. Several SNF clinicians said that it was common for a course of antibiotics to be listed on the discharge summary but not the patient instruction packet, or vice versa. SNF clinicians, who usually lack access to the hospital’s electronic medical record, have limited means for determining the correct document. Other important clinical data points, such as intermittent intravenous (IV) furosemide dosing and suppressive antibiotic regimens, were omitted from discharge paperwork altogether. SNF clinicians had difficulty reaching hospital clinicians who could clarify these clinical questions. “Good luck finding the person that took care of [the patient] three days before,” said one director of nursing.

Change starts at home: Challenges in SNF processes and culture

Many clinicians in our study reported that their facilities had recently added clinical capabilities in an effort to care for patients with complex medical problems. For example, to prevent transfers of patients with decompensated heart failure, several facilities in our study had recently obtained certification to give IV diuretics. However, as one director of nursing stated, these efforts require “buy-in” from doctors to decrease readmissions. That buy-in has not always been forthcoming. SNF clinicians also reported difficulty convincing patients and families that their facilities are capable of providing care that, in the past, might only have been available in acute-care settings.

These themes, along with associated sub-themes and representative quotations, are shown above (Table 4).

Our study suggests that the interaction between EDs and SNFs is an important and understudied domain in the spectrum of events leading to readmission. Prior studies have documented inadequacies in patient information provided by SNFs to EDs.^16,17 Efforts to improve SNF-to-ED information sharing have focused on making sure that ED clinicians have important baseline information about patients transferred from a SNF.^18,19 However, many of the clinicians in our study reported taking proactive steps to communicate with ED clinicians. These efforts encountered logistical and cultural barriers, with information that might have prevented readmission failing to reach ED providers. Many of the SNF clinicians in our study perceived this failure as a common cause of readmission, especially for relatively stable SNF patients.

Previous studies have pointed to a role for goals of care discussions in reducing hospital readmissions.²⁰ Our data underscore an important qualification to these findings: Location matters. The SNF clinicians in our study reported frequent and detailed goals of care discussions with their patients. However, they also reported that goals of care discussions held in the subacute setting carried less weight with patients and families than discussions held in the hospital. SNF clinicians described a number of cases in which patients were willing to adjust code status or goals of care only after being readmitted to the hospital.

Our study also points to the implications of existing research showing that patients are discharged from acute care hospitals “quicker and sicker” than they had been prior to the 1983 adoption of Medicare’s prospective payment system.²¹ Specifically, the SNF clinicians we interviewed perceived a strong link between patient acuity at the time of transfer and SNFs’ persistently high readmission rates. As SNFs have worked to expand their clinical capabilities, they struggle to win buy-in from physicians and families, many of whom view SNFs as incapable of managing acute illness. Many SNF clinicians also pointed to deficiencies in their own referral and admission processes as a recurring cause of readmissions. For example, several patients in our analysis suffered from dementia. Although these patients were stable enough to leave the acute care setting, the SNF clinicians responsible for their readmissions felt that their SNFs were not well-equipped to care for patients with dementia and that the patients should instead have been transferred to facilities with more robust resources for dementia care.

Finally, our findings highlight a fundamental tension between hospitals and SNFs: Which facility ought to shoulder the responsibility and cost for services that may prevent a readmission—the hospital or the SNF? For example, does responsibility for coordinating subspecialist evaluation of a patient’s chronic condition fall to the hospital or to the SNF? If such an evaluation is undertaken during a hospitalization, it prolongs the patient’s hospital stay and happens at the hospital’s expense. If the patient is discharged to a SNF and sees the subspecialist in clinic, then the SNF must pay for transportation to and from the clinic appointment. SNF clinicians expressed near unanimity that fragmented models of care and high barriers to communication made it difficult to design solutions to these dilemmas.

Strengths and limitations

To our knowledge, this is the first interview-based study examining SNF clinicians’ perspectives on unplanned, 30-day hospital readmissions. We gathered information from clinicians with a range of clinical experience, all of whom had cared directly for the patient who had been readmitted. Our data came from clinicians at 15 SNFs of varying sizes and quality ratings, allowing us to identify a broad range of factors contributing to readmissions.

Because this study relied on qualitative methods, it should be viewed as hypothesis-generating rather than hypothesis-confirming. Further research is needed to determine whether variables related to the themes above are causally linked to SNF readmissions. We identified cases for review using convenience sampling of a cohort of readmitted patients at a single tertiary-care hospital, and all participating SNFs were located in Connecticut. These factors may limit the generalizability of our findings. Although the clinicians we interviewed occupied diverse roles within their respective SNFs, our sample did not include direct-care staff without managerial responsibility, such as certified nursing assistants or licensed practical nurses. This prevented our study from identifying themes into which managers would have limited insight, especially those involving cultural and management practices leading to poor communication between them and their staff. Because our study examines cases in which discharge and readmission were to a general medicine service, it may not describe factors relevant to patients discharged from subspecialist or surgical services.

Implications for future QI efforts and research

Several clinicians we interviewed suggested that readmissions might be reduced by dedicating the services of a hospital professional, such as a nurse or case manager, to monitoring the clinical course of medically complex patients after discharge. A dedicated “transition coach” could clarify deficiencies in discharge paperwork, facilitate necessary follow-up appointments, liaise with staff at both the hospital and the SNF, or coordinate acquisition of necessary equipment. Prospective trials have demonstrated that such interventions can decrease readmission rates among hospitalized patients,^22,23 but formal studies have not been carried out among cohorts of SNF patients.

Prior efforts to improve SNF-ED information sharing have focused on making sure that ED clinicians have important baseline information about patients transferred from a SNF.^24,25 The experiences of SNF clinicians in our study suggest that important information also fails to make its way from ED providers to SNFs and that this failure results in unnecessary readmissions of relatively stable SNF patients. Thus, hospitals may be able to prevent SNF readmissions by creating lines of communication between EDs and SNFs and by ensuring that ED physicians and mid-level providers are familiar with the clinical capabilities of local SNFs.

Future research and QI work should also investigate approaches to care coordination that ensure that complex patients are placed in SNFs with resources adequate to address their comorbidities. Potential interventions might include increased use of SNF “liaisons,” who would evaluate patients in-person prior to approving transfer to a given SNF. As has been previously suggested,²⁶ hospitals might also reduce readmissions by narrowing the pool of facilities to which they transfer patients, thereby building more robust, interconnected relationships with a smaller number of SNFs.

SNF clinicians identified areas for improvement at almost every point in the chain of events spanning hospitalization, discharge, and transfer. Among the most frequently cited contributors to readmissions were clinical instability at the time of discharge and omission of clinically important information from discharge documentation. Improved communication between hospitals, ED clinicians, and SNFs, as well as more thoroughly defined goals of care at the time of discharge, were seen as promising ways of decreasing readmissions. Successful interventions for reducing readmissions from SNFs will likely require multifaceted approaches to these problems.

Disclosure: This research was supported by a grant (#P30HS023554-01) from the Agency for Healthcare Research and Quality (AHRQ) and received support from Yale New Haven Hospital and the Claude D. Pepper Older Americans Independence Center at Yale University School of Medicine (#P30AG021342 NIH/NIA).

Respiratory illness (RI) is one of the most common reasons for pediatric hospitalization.¹ Examples of RI include acute illness, such as bronchiolitis, bacterial pneumonia, and asthma, as well as chronic conditions, such as obstructive sleep apnea and chronic respiratory insufficiency. Hospital care for RI includes monitoring and treatment to optimize oxygenation, ventilation, hydration, and other body functions. Most previously healthy children hospitalized with RI stay in the hospital for a limited duration (eg, a few days) because the severity of their illness is short lived and they quickly return to their previous healthy status.² However, hospital care is increasing for children with fragile and tenuous health due to complex medical conditions.³ RI is a common reason for hospitalization among these children as well and recovery of respiratory health and function can be slow and protracted for some of them.⁴ Weeks, months, or longer periods of time may be necessary for the children to return to their previous respiratory baseline health and function after hospital discharge; other children may not return to their baseline.^5,6

Hospitalized older adults with high-severity RI are routinely streamlined for transfer to post-acute facility care (PAC) shortly (eg, a few days) after acute-care hospitalization. Nearly 70% of elderly Medicare beneficiaries use PAC following a brief length of stay (LOS) in the acute-care hospital.⁷ It is believed that PAC helps optimize the patients’ health and functional status and relieves the family caregiving burden that would have occurred at home.^8-10 PAC use also helps to shorten acute-care hospitalization for RI while avoiding readmission.^8-10 In contrast with adult patients, use of PAC for hospitalized children is not routine.¹¹ While PAC use in children is infrequent, RI is one of the most common reasons for acute admission among children who use it.¹²

For some children with RI, PAC might be positioned to offer a safe, therapeutic, and high-value setting for pulmonary rehabilitation, as well as related medical, nutritional, functional, and family cares.⁶ PAC, by design, could possibly help some of the children transition back into their homes and communities. As studies continue to emerge that assess the value of PAC in children, it is important to learn more about the use of PAC in children hospitalized with RI. The objectives were to (1) assess which children admitted with RI are the most likely to use PAC services for recovery and (2) estimate how many hospitalized children not using PAC had the same characteristics as those who did.

Study Design, Setting, and Population

We conducted a retrospective cohort analysis of 609,800 hospitalizations for RI occurring from January 1, 2010 to December 31, 2015, in 43 freestanding children’s hospitals in the Pediatric Health Information Systems (PHIS) dataset. All hospitals participating in PHIS are members of the Children’s Hospital Association.¹³ The Boston Children’s Hospital Institutional Review Board approved this study with a waiver for informed consent.

RI was identified using the Agency for Healthcare Research and Quality (AHRQ) Clinical Classification System (CCS).¹⁴ Using diagnosis CCS category 8 (“Diseases of the Respiratory System”) and the procedure CCS category 6 (“Operations on the Respiratory System”), we identified all hospitalizations from the participating hospitals with a principal diagnosis or procedure International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code for an RI.

Main Outcome Measure

Discharge disposition following the acute-care hospitalization for RI was the main outcome measure. We used PHIS uniform disposition coding to classify the discharge disposition as transfer to PAC (ie, rehabilitation facility, skilled nursing facility, etc.) vs all other dispositions (ie, routine to home, against medical advice, etc.).¹² The PAC disposition category was derived from the Centers for Medicare & Medicaid Services Patient Discharge Status Codes and Hospital Transfer Policies as informed by the National Uniform Billing Committee Official UB-04 Data Specifications Manual, 2008. PAC transfer included disposition to external PAC facilities, as well as to internal, embedded PAC units residing in a few of the acute-care children’s hospitals included in the cohort.

Demographic and Clinical Characteristics

We assessed patient demographic and clinical characteristics that might correlate with PAC use following acute-care hospitalization for RI. Demographic characteristics included gender, age at admission in years, payer (public, private, and other), and race/ethnicity (Hispanic, non-Hispanic black, non-Hispanic white, other).

Clinical characteristics included chronic conditions (type and number) and assistance with medical technology. Chronic condition and medical technology characteristics were assessed with ICD-9-CM diagnosis codes. PHIS contain up to 41 ICD-9-CM diagnosis codes per hospital discharge record. To identify the presence and number of chronic conditions, we used the AHRQ Chronic Condition Indicator system, which categorizes over 14,000 ICD-9-CM diagnosis codes into chronic vs non-chronic conditions.^14,15 Children hospitalized with a chronic condition were further classified as having a complex chronic condition (CCC) using Feudtner and colleagues’ ICD-9-CM diagnosis classification scheme.¹⁶ CCCs represent defined diagnosis groupings expected to last longer than 12 months and involving either a single organ system, severe enough to require specialty pediatric care and hospitalization, or multiple organ systems.^17,18 Hospitalized children who were assisted with medical technology were identified with ICD-9-CM codes indicating the use of a medical device to manage and treat a chronic illness (eg, ventricular shunt to treat hydrocephalus) or to maintain basic body functions necessary for sustaining life (eg, a tracheostomy tube for breathing).^19,20 We distinguished children undergoing tracheotomy during hospitalization using ICD-9-CM procedure codes 31.1 and 31.2.

Acute-Care Hospitalization Characteristics

We also assessed the relationship between acute-care hospitalization characteristics and use of PAC after discharge, including US census region, LOS, use of intensive care, number of medication classes administered, and use of enhanced respiratory support. Enhanced respiratory support was defined as use of continuous or bilevel positive airway pressure (CPAP or BiPAP) or mechanical ventilation during the acute-care hospitalization for RI. These respiratory supports were identified using billing data in PHIS.

Statistical Analysis

In bivariable analysis, we compared demographic, clinical, and hospitalization characteristics of hospitalized children with vs without discharge to PAC using Rao-Scott chi-square tests and Wilcoxon rank-sum tests as appropriate. In multivariable analysis, we derived a generalized linear mix effects model with fixed effects for demographic, clinical, and hospitalization characteristics that were associated with PAC at P < 0.1 in bivariable analysis (ie, age, gender, race/ethnicity, payer, medical technology, use of intensive care unit [ICU], use of positive pressure or mechanical ventilation, hospital region, LOS, new tracheostomy, existing tracheostomy, other technologies, number of medications, number of chronic conditions [of any complexity], and type of complex chronic conditions). We controlled for clustering of patients within hospitals by including a random intercept for each hospital. We also assessed combinations of patient characteristics on the likelihood of PAC use with classification and regression tree (CART) modeling. Using CART, we determined which characteristic combinations were associated with the highest and lowest use of PAC using binary split and post-pruning, goodness of fit rules.²¹ All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, NC), and R v.3.2 (R Foundation for Statistical Computing, Vienna, Austria) using the “party” package. The threshold for statistical significance was set at P < 0.05.

Of the 609,800 hospitalizations for RI, PAC use after discharge occurred for 2660 (0.4%). RI discharges to PAC accounted for 2.1% (n = 67,405) of hospital days and 2.7% ($280 million) of hospital cost of all RI hospitalizations. For discharges to PAC, the most common RI were pneumonia (29.1% [n = 773]), respiratory failure or insufficiency (unspecified reason; 22.0% [n = 584]), and upper respiratory infection (12.2% [n = 323]).

Demographic Characteristics

Median age at acute-care admission was higher for PAC vs non-PAC discharges (6 years [interquartile range {IQR} 1-15] vs 2 years [0-7], P < 0.001; Table 1). Hispanic patients accounted for a smaller percentage of RI discharges to PAC vs non-PAC (14.1% vs 21.8%, P < 0.001) and a higher percentage to PAC were for patients with public insurance (75.9% vs 62.5, P < 0.001; Table 1).

Clinical Characteristics

A greater percentage of RI hospitalizations discharged to PAC vs not-PAC had ≥1 CCC (94.9% vs 33.5%), including a neuromuscular CCC (57.5% vs 8.9%) or respiratory CCC (62.5% vs 12.0%), P < 0.001 for all (Table 2). A greater percentage discharged to PAC was assisted with medical technology (83.2% vs 15.1%), including respiratory technology (eg, tracheostomy; 53.8% vs 5.4%) and gastrointestinal technology (eg, gastrostomy; 71.9% vs 11.8%), P < 0.001 for all. Of the children with respiratory technology, 14.8% (n = 394) underwent tracheotomy during the acute-care hospitalization. Children discharged to PAC had a higher percentage of multiple chronic conditions. For example, the percentages of children discharged to PAC vs not with ≥7 conditions were 54.5% vs 7.0% (P < 0.001; Table 2). The most common chronic conditions experienced by children discharged to PAC included epilepsy (41.2%), gastroesophageal reflux (36.6%), cerebral palsy (28.2%), and asthma (18.2%).

Hospitalization Characteristics

Acute-care RI hospitalization median LOS was longer for discharges to PAC vs non-PAC (10 days [IQR 4-27] vs 2 days [IQR 1-4], P < 0.001; Table 1). A greater percentage of discharges to PAC were administered medications from multiple classes during the acute-care RI admission (eg, 54.8% vs 13.4% used medications from ≥7 classes, P < 0.001). A greater percentage of discharges to PAC used intensive care services during the acute-care admission (65.6% vs 22.4%, P < 0.001). A greater percentage of discharges to PAC received CPAP (10.6 vs 5.0%), BiPAP (19.8% vs 11.4%), or mechanical ventilation (52.7% vs 9.1%) during the acute-care RI hospitalization (P < 0.001 for all; Table 1).

Multivariable Analysis of the Likelihood of Post-Acute Care Use Following Discharge

In multivariable analysis, the patient characteristics associated with the highest likelihood of discharge to PAC included ≥11 vs no chronic conditions (odds ratio [OR] 11.8 , 95% CI, 8.0-17.2), ≥9 classes vs no classes of medications administered during the acute-care hospitalization (OR 4.8 , 95% CI, 1.8-13.0), and existing tracheostomy (OR 3.0, [95% CI, 2.6-3.5; Figure 2 and eTable). Patient characteristics associated with a more modest likelihood of discharge to PAC included public vs private insurance (OR 1.8, 95% CI, 1.6-2.0), neuromuscular complex chronic condition (OR 1.6, 95% CI, 1.5-1.8), new tracheostomy (OR 1.9, 95% CI, 1.7-2.2), and use of any enhanced respiratory support (ie, CPAP/BiPAP/mechanical ventilation) during the acute-care hospitalization (OR 1.4, 95% CI, 1.3-1.6; Figure 2 and Supplementary Table).

Classification and Regression Tree Analysis

In the CART analysis, the highest percentage (6.3%) of children hospitalized with RI who were discharged to PAC had the following combination of characteristics: ≥6 chronic conditions, ≥7 classes of medications administered, and respiratory technology. Median (IQR) length of acute-care LOS for children with these attributes who were transferred to PAC was 19 (IQR 8-56; range 1-1005) days; LOS remained long (median 13 days [IQR 6-41, range 1-1413]) for children with the same attributes not transferred to PAC (n = 9448). Between these children transferred vs not to PAC, 79.3% vs 65.9% received ICU services; 74.4% vs 73.5% received CPAP, BiPAP, or mechanical ventilation; and 31.0% vs 22.7% underwent tracheotomy during the acute-care hospitalization. Of these children who were not transferred to PAC, 18.9% were discharged to home nursing services.

The findings from the present study suggest that patients with RI hospitalization in children’s hospitals who use PAC are medically complex, with high rates of multiple chronic conditions—including cerebral palsy, asthma, chronic respiratory insufficiency, dysphagia, epilepsy, and gastroesophageal reflux—and high rates of technology assistance including enterostomy and tracheostomy. The characteristics of patients most likely to use PAC include long LOS, a large number of chronic conditions, many types of medications administered during the acute-care hospitalization, respiratory technology use, and an underlying neuromuscular condition. Specifically, the highest percentage of children hospitalized with RI who were discharged to PAC had ≥6 chronic conditions, ≥7 classes of medications administered, and respiratory technology. Our analysis suggests that there may be a large population of children with these same characteristics who experienced a prolonged LOS but were not transferred to PAC.

Acknowledgments

Dr. Berry was supported by the Agency for Healthcare Research and Quality (R21 HS023092-01), the Lucile Packard Foundation for Children’s Health, and Franciscan Hospital for Children. The funders were not involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclosure: The authors have no financial relationships relevant to this article to disclose.

Respiratory illness (RI) is one of the most common reasons for pediatric hospitalization.¹ Examples of RI include acute illness, such as bronchiolitis, bacterial pneumonia, and asthma, as well as chronic conditions, such as obstructive sleep apnea and chronic respiratory insufficiency. Hospital care for RI includes monitoring and treatment to optimize oxygenation, ventilation, hydration, and other body functions. Most previously healthy children hospitalized with RI stay in the hospital for a limited duration (eg, a few days) because the severity of their illness is short lived and they quickly return to their previous healthy status.² However, hospital care is increasing for children with fragile and tenuous health due to complex medical conditions.³ RI is a common reason for hospitalization among these children as well and recovery of respiratory health and function can be slow and protracted for some of them.⁴ Weeks, months, or longer periods of time may be necessary for the children to return to their previous respiratory baseline health and function after hospital discharge; other children may not return to their baseline.^5,6

Hospitalized older adults with high-severity RI are routinely streamlined for transfer to post-acute facility care (PAC) shortly (eg, a few days) after acute-care hospitalization. Nearly 70% of elderly Medicare beneficiaries use PAC following a brief length of stay (LOS) in the acute-care hospital.⁷ It is believed that PAC helps optimize the patients’ health and functional status and relieves the family caregiving burden that would have occurred at home.^8-10 PAC use also helps to shorten acute-care hospitalization for RI while avoiding readmission.^8-10 In contrast with adult patients, use of PAC for hospitalized children is not routine.¹¹ While PAC use in children is infrequent, RI is one of the most common reasons for acute admission among children who use it.¹²

For some children with RI, PAC might be positioned to offer a safe, therapeutic, and high-value setting for pulmonary rehabilitation, as well as related medical, nutritional, functional, and family cares.⁶ PAC, by design, could possibly help some of the children transition back into their homes and communities. As studies continue to emerge that assess the value of PAC in children, it is important to learn more about the use of PAC in children hospitalized with RI. The objectives were to (1) assess which children admitted with RI are the most likely to use PAC services for recovery and (2) estimate how many hospitalized children not using PAC had the same characteristics as those who did.

Study Design, Setting, and Population

We conducted a retrospective cohort analysis of 609,800 hospitalizations for RI occurring from January 1, 2010 to December 31, 2015, in 43 freestanding children’s hospitals in the Pediatric Health Information Systems (PHIS) dataset. All hospitals participating in PHIS are members of the Children’s Hospital Association.¹³ The Boston Children’s Hospital Institutional Review Board approved this study with a waiver for informed consent.

RI was identified using the Agency for Healthcare Research and Quality (AHRQ) Clinical Classification System (CCS).¹⁴ Using diagnosis CCS category 8 (“Diseases of the Respiratory System”) and the procedure CCS category 6 (“Operations on the Respiratory System”), we identified all hospitalizations from the participating hospitals with a principal diagnosis or procedure International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code for an RI.

Main Outcome Measure

Discharge disposition following the acute-care hospitalization for RI was the main outcome measure. We used PHIS uniform disposition coding to classify the discharge disposition as transfer to PAC (ie, rehabilitation facility, skilled nursing facility, etc.) vs all other dispositions (ie, routine to home, against medical advice, etc.).¹² The PAC disposition category was derived from the Centers for Medicare & Medicaid Services Patient Discharge Status Codes and Hospital Transfer Policies as informed by the National Uniform Billing Committee Official UB-04 Data Specifications Manual, 2008. PAC transfer included disposition to external PAC facilities, as well as to internal, embedded PAC units residing in a few of the acute-care children’s hospitals included in the cohort.

Demographic and Clinical Characteristics

We assessed patient demographic and clinical characteristics that might correlate with PAC use following acute-care hospitalization for RI. Demographic characteristics included gender, age at admission in years, payer (public, private, and other), and race/ethnicity (Hispanic, non-Hispanic black, non-Hispanic white, other).

Clinical characteristics included chronic conditions (type and number) and assistance with medical technology. Chronic condition and medical technology characteristics were assessed with ICD-9-CM diagnosis codes. PHIS contain up to 41 ICD-9-CM diagnosis codes per hospital discharge record. To identify the presence and number of chronic conditions, we used the AHRQ Chronic Condition Indicator system, which categorizes over 14,000 ICD-9-CM diagnosis codes into chronic vs non-chronic conditions.^14,15 Children hospitalized with a chronic condition were further classified as having a complex chronic condition (CCC) using Feudtner and colleagues’ ICD-9-CM diagnosis classification scheme.¹⁶ CCCs represent defined diagnosis groupings expected to last longer than 12 months and involving either a single organ system, severe enough to require specialty pediatric care and hospitalization, or multiple organ systems.^17,18 Hospitalized children who were assisted with medical technology were identified with ICD-9-CM codes indicating the use of a medical device to manage and treat a chronic illness (eg, ventricular shunt to treat hydrocephalus) or to maintain basic body functions necessary for sustaining life (eg, a tracheostomy tube for breathing).^19,20 We distinguished children undergoing tracheotomy during hospitalization using ICD-9-CM procedure codes 31.1 and 31.2.

Acute-Care Hospitalization Characteristics

We also assessed the relationship between acute-care hospitalization characteristics and use of PAC after discharge, including US census region, LOS, use of intensive care, number of medication classes administered, and use of enhanced respiratory support. Enhanced respiratory support was defined as use of continuous or bilevel positive airway pressure (CPAP or BiPAP) or mechanical ventilation during the acute-care hospitalization for RI. These respiratory supports were identified using billing data in PHIS.

Statistical Analysis

In bivariable analysis, we compared demographic, clinical, and hospitalization characteristics of hospitalized children with vs without discharge to PAC using Rao-Scott chi-square tests and Wilcoxon rank-sum tests as appropriate. In multivariable analysis, we derived a generalized linear mix effects model with fixed effects for demographic, clinical, and hospitalization characteristics that were associated with PAC at P < 0.1 in bivariable analysis (ie, age, gender, race/ethnicity, payer, medical technology, use of intensive care unit [ICU], use of positive pressure or mechanical ventilation, hospital region, LOS, new tracheostomy, existing tracheostomy, other technologies, number of medications, number of chronic conditions [of any complexity], and type of complex chronic conditions). We controlled for clustering of patients within hospitals by including a random intercept for each hospital. We also assessed combinations of patient characteristics on the likelihood of PAC use with classification and regression tree (CART) modeling. Using CART, we determined which characteristic combinations were associated with the highest and lowest use of PAC using binary split and post-pruning, goodness of fit rules.²¹ All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, NC), and R v.3.2 (R Foundation for Statistical Computing, Vienna, Austria) using the “party” package. The threshold for statistical significance was set at P < 0.05.

Of the 609,800 hospitalizations for RI, PAC use after discharge occurred for 2660 (0.4%). RI discharges to PAC accounted for 2.1% (n = 67,405) of hospital days and 2.7% ($280 million) of hospital cost of all RI hospitalizations. For discharges to PAC, the most common RI were pneumonia (29.1% [n = 773]), respiratory failure or insufficiency (unspecified reason; 22.0% [n = 584]), and upper respiratory infection (12.2% [n = 323]).

Demographic Characteristics

Median age at acute-care admission was higher for PAC vs non-PAC discharges (6 years [interquartile range {IQR} 1-15] vs 2 years [0-7], P < 0.001; Table 1). Hispanic patients accounted for a smaller percentage of RI discharges to PAC vs non-PAC (14.1% vs 21.8%, P < 0.001) and a higher percentage to PAC were for patients with public insurance (75.9% vs 62.5, P < 0.001; Table 1).

Clinical Characteristics

A greater percentage of RI hospitalizations discharged to PAC vs not-PAC had ≥1 CCC (94.9% vs 33.5%), including a neuromuscular CCC (57.5% vs 8.9%) or respiratory CCC (62.5% vs 12.0%), P < 0.001 for all (Table 2). A greater percentage discharged to PAC was assisted with medical technology (83.2% vs 15.1%), including respiratory technology (eg, tracheostomy; 53.8% vs 5.4%) and gastrointestinal technology (eg, gastrostomy; 71.9% vs 11.8%), P < 0.001 for all. Of the children with respiratory technology, 14.8% (n = 394) underwent tracheotomy during the acute-care hospitalization. Children discharged to PAC had a higher percentage of multiple chronic conditions. For example, the percentages of children discharged to PAC vs not with ≥7 conditions were 54.5% vs 7.0% (P < 0.001; Table 2). The most common chronic conditions experienced by children discharged to PAC included epilepsy (41.2%), gastroesophageal reflux (36.6%), cerebral palsy (28.2%), and asthma (18.2%).

Hospitalization Characteristics

Acute-care RI hospitalization median LOS was longer for discharges to PAC vs non-PAC (10 days [IQR 4-27] vs 2 days [IQR 1-4], P < 0.001; Table 1). A greater percentage of discharges to PAC were administered medications from multiple classes during the acute-care RI admission (eg, 54.8% vs 13.4% used medications from ≥7 classes, P < 0.001). A greater percentage of discharges to PAC used intensive care services during the acute-care admission (65.6% vs 22.4%, P < 0.001). A greater percentage of discharges to PAC received CPAP (10.6 vs 5.0%), BiPAP (19.8% vs 11.4%), or mechanical ventilation (52.7% vs 9.1%) during the acute-care RI hospitalization (P < 0.001 for all; Table 1).

Multivariable Analysis of the Likelihood of Post-Acute Care Use Following Discharge

In multivariable analysis, the patient characteristics associated with the highest likelihood of discharge to PAC included ≥11 vs no chronic conditions (odds ratio [OR] 11.8 , 95% CI, 8.0-17.2), ≥9 classes vs no classes of medications administered during the acute-care hospitalization (OR 4.8 , 95% CI, 1.8-13.0), and existing tracheostomy (OR 3.0, [95% CI, 2.6-3.5; Figure 2 and eTable). Patient characteristics associated with a more modest likelihood of discharge to PAC included public vs private insurance (OR 1.8, 95% CI, 1.6-2.0), neuromuscular complex chronic condition (OR 1.6, 95% CI, 1.5-1.8), new tracheostomy (OR 1.9, 95% CI, 1.7-2.2), and use of any enhanced respiratory support (ie, CPAP/BiPAP/mechanical ventilation) during the acute-care hospitalization (OR 1.4, 95% CI, 1.3-1.6; Figure 2 and Supplementary Table).

Classification and Regression Tree Analysis

In the CART analysis, the highest percentage (6.3%) of children hospitalized with RI who were discharged to PAC had the following combination of characteristics: ≥6 chronic conditions, ≥7 classes of medications administered, and respiratory technology. Median (IQR) length of acute-care LOS for children with these attributes who were transferred to PAC was 19 (IQR 8-56; range 1-1005) days; LOS remained long (median 13 days [IQR 6-41, range 1-1413]) for children with the same attributes not transferred to PAC (n = 9448). Between these children transferred vs not to PAC, 79.3% vs 65.9% received ICU services; 74.4% vs 73.5% received CPAP, BiPAP, or mechanical ventilation; and 31.0% vs 22.7% underwent tracheotomy during the acute-care hospitalization. Of these children who were not transferred to PAC, 18.9% were discharged to home nursing services.

The findings from the present study suggest that patients with RI hospitalization in children’s hospitals who use PAC are medically complex, with high rates of multiple chronic conditions—including cerebral palsy, asthma, chronic respiratory insufficiency, dysphagia, epilepsy, and gastroesophageal reflux—and high rates of technology assistance including enterostomy and tracheostomy. The characteristics of patients most likely to use PAC include long LOS, a large number of chronic conditions, many types of medications administered during the acute-care hospitalization, respiratory technology use, and an underlying neuromuscular condition. Specifically, the highest percentage of children hospitalized with RI who were discharged to PAC had ≥6 chronic conditions, ≥7 classes of medications administered, and respiratory technology. Our analysis suggests that there may be a large population of children with these same characteristics who experienced a prolonged LOS but were not transferred to PAC.

Acknowledgments

Dr. Berry was supported by the Agency for Healthcare Research and Quality (R21 HS023092-01), the Lucile Packard Foundation for Children’s Health, and Franciscan Hospital for Children. The funders were not involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclosure: The authors have no financial relationships relevant to this article to disclose.

Respiratory illness (RI) is one of the most common reasons for pediatric hospitalization.¹ Examples of RI include acute illness, such as bronchiolitis, bacterial pneumonia, and asthma, as well as chronic conditions, such as obstructive sleep apnea and chronic respiratory insufficiency. Hospital care for RI includes monitoring and treatment to optimize oxygenation, ventilation, hydration, and other body functions. Most previously healthy children hospitalized with RI stay in the hospital for a limited duration (eg, a few days) because the severity of their illness is short lived and they quickly return to their previous healthy status.² However, hospital care is increasing for children with fragile and tenuous health due to complex medical conditions.³ RI is a common reason for hospitalization among these children as well and recovery of respiratory health and function can be slow and protracted for some of them.⁴ Weeks, months, or longer periods of time may be necessary for the children to return to their previous respiratory baseline health and function after hospital discharge; other children may not return to their baseline.^5,6

Hospitalized older adults with high-severity RI are routinely streamlined for transfer to post-acute facility care (PAC) shortly (eg, a few days) after acute-care hospitalization. Nearly 70% of elderly Medicare beneficiaries use PAC following a brief length of stay (LOS) in the acute-care hospital.⁷ It is believed that PAC helps optimize the patients’ health and functional status and relieves the family caregiving burden that would have occurred at home.^8-10 PAC use also helps to shorten acute-care hospitalization for RI while avoiding readmission.^8-10 In contrast with adult patients, use of PAC for hospitalized children is not routine.¹¹ While PAC use in children is infrequent, RI is one of the most common reasons for acute admission among children who use it.¹²

For some children with RI, PAC might be positioned to offer a safe, therapeutic, and high-value setting for pulmonary rehabilitation, as well as related medical, nutritional, functional, and family cares.⁶ PAC, by design, could possibly help some of the children transition back into their homes and communities. As studies continue to emerge that assess the value of PAC in children, it is important to learn more about the use of PAC in children hospitalized with RI. The objectives were to (1) assess which children admitted with RI are the most likely to use PAC services for recovery and (2) estimate how many hospitalized children not using PAC had the same characteristics as those who did.

Study Design, Setting, and Population

We conducted a retrospective cohort analysis of 609,800 hospitalizations for RI occurring from January 1, 2010 to December 31, 2015, in 43 freestanding children’s hospitals in the Pediatric Health Information Systems (PHIS) dataset. All hospitals participating in PHIS are members of the Children’s Hospital Association.¹³ The Boston Children’s Hospital Institutional Review Board approved this study with a waiver for informed consent.

RI was identified using the Agency for Healthcare Research and Quality (AHRQ) Clinical Classification System (CCS).¹⁴ Using diagnosis CCS category 8 (“Diseases of the Respiratory System”) and the procedure CCS category 6 (“Operations on the Respiratory System”), we identified all hospitalizations from the participating hospitals with a principal diagnosis or procedure International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code for an RI.

Main Outcome Measure

Discharge disposition following the acute-care hospitalization for RI was the main outcome measure. We used PHIS uniform disposition coding to classify the discharge disposition as transfer to PAC (ie, rehabilitation facility, skilled nursing facility, etc.) vs all other dispositions (ie, routine to home, against medical advice, etc.).¹² The PAC disposition category was derived from the Centers for Medicare & Medicaid Services Patient Discharge Status Codes and Hospital Transfer Policies as informed by the National Uniform Billing Committee Official UB-04 Data Specifications Manual, 2008. PAC transfer included disposition to external PAC facilities, as well as to internal, embedded PAC units residing in a few of the acute-care children’s hospitals included in the cohort.

Demographic and Clinical Characteristics

We assessed patient demographic and clinical characteristics that might correlate with PAC use following acute-care hospitalization for RI. Demographic characteristics included gender, age at admission in years, payer (public, private, and other), and race/ethnicity (Hispanic, non-Hispanic black, non-Hispanic white, other).

Clinical characteristics included chronic conditions (type and number) and assistance with medical technology. Chronic condition and medical technology characteristics were assessed with ICD-9-CM diagnosis codes. PHIS contain up to 41 ICD-9-CM diagnosis codes per hospital discharge record. To identify the presence and number of chronic conditions, we used the AHRQ Chronic Condition Indicator system, which categorizes over 14,000 ICD-9-CM diagnosis codes into chronic vs non-chronic conditions.^14,15 Children hospitalized with a chronic condition were further classified as having a complex chronic condition (CCC) using Feudtner and colleagues’ ICD-9-CM diagnosis classification scheme.¹⁶ CCCs represent defined diagnosis groupings expected to last longer than 12 months and involving either a single organ system, severe enough to require specialty pediatric care and hospitalization, or multiple organ systems.^17,18 Hospitalized children who were assisted with medical technology were identified with ICD-9-CM codes indicating the use of a medical device to manage and treat a chronic illness (eg, ventricular shunt to treat hydrocephalus) or to maintain basic body functions necessary for sustaining life (eg, a tracheostomy tube for breathing).^19,20 We distinguished children undergoing tracheotomy during hospitalization using ICD-9-CM procedure codes 31.1 and 31.2.

Acute-Care Hospitalization Characteristics

We also assessed the relationship between acute-care hospitalization characteristics and use of PAC after discharge, including US census region, LOS, use of intensive care, number of medication classes administered, and use of enhanced respiratory support. Enhanced respiratory support was defined as use of continuous or bilevel positive airway pressure (CPAP or BiPAP) or mechanical ventilation during the acute-care hospitalization for RI. These respiratory supports were identified using billing data in PHIS.

Statistical Analysis

In bivariable analysis, we compared demographic, clinical, and hospitalization characteristics of hospitalized children with vs without discharge to PAC using Rao-Scott chi-square tests and Wilcoxon rank-sum tests as appropriate. In multivariable analysis, we derived a generalized linear mix effects model with fixed effects for demographic, clinical, and hospitalization characteristics that were associated with PAC at P < 0.1 in bivariable analysis (ie, age, gender, race/ethnicity, payer, medical technology, use of intensive care unit [ICU], use of positive pressure or mechanical ventilation, hospital region, LOS, new tracheostomy, existing tracheostomy, other technologies, number of medications, number of chronic conditions [of any complexity], and type of complex chronic conditions). We controlled for clustering of patients within hospitals by including a random intercept for each hospital. We also assessed combinations of patient characteristics on the likelihood of PAC use with classification and regression tree (CART) modeling. Using CART, we determined which characteristic combinations were associated with the highest and lowest use of PAC using binary split and post-pruning, goodness of fit rules.²¹ All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, NC), and R v.3.2 (R Foundation for Statistical Computing, Vienna, Austria) using the “party” package. The threshold for statistical significance was set at P < 0.05.

Of the 609,800 hospitalizations for RI, PAC use after discharge occurred for 2660 (0.4%). RI discharges to PAC accounted for 2.1% (n = 67,405) of hospital days and 2.7% ($280 million) of hospital cost of all RI hospitalizations. For discharges to PAC, the most common RI were pneumonia (29.1% [n = 773]), respiratory failure or insufficiency (unspecified reason; 22.0% [n = 584]), and upper respiratory infection (12.2% [n = 323]).

Demographic Characteristics

Median age at acute-care admission was higher for PAC vs non-PAC discharges (6 years [interquartile range {IQR} 1-15] vs 2 years [0-7], P < 0.001; Table 1). Hispanic patients accounted for a smaller percentage of RI discharges to PAC vs non-PAC (14.1% vs 21.8%, P < 0.001) and a higher percentage to PAC were for patients with public insurance (75.9% vs 62.5, P < 0.001; Table 1).

Clinical Characteristics

A greater percentage of RI hospitalizations discharged to PAC vs not-PAC had ≥1 CCC (94.9% vs 33.5%), including a neuromuscular CCC (57.5% vs 8.9%) or respiratory CCC (62.5% vs 12.0%), P < 0.001 for all (Table 2). A greater percentage discharged to PAC was assisted with medical technology (83.2% vs 15.1%), including respiratory technology (eg, tracheostomy; 53.8% vs 5.4%) and gastrointestinal technology (eg, gastrostomy; 71.9% vs 11.8%), P < 0.001 for all. Of the children with respiratory technology, 14.8% (n = 394) underwent tracheotomy during the acute-care hospitalization. Children discharged to PAC had a higher percentage of multiple chronic conditions. For example, the percentages of children discharged to PAC vs not with ≥7 conditions were 54.5% vs 7.0% (P < 0.001; Table 2). The most common chronic conditions experienced by children discharged to PAC included epilepsy (41.2%), gastroesophageal reflux (36.6%), cerebral palsy (28.2%), and asthma (18.2%).

Hospitalization Characteristics

Acute-care RI hospitalization median LOS was longer for discharges to PAC vs non-PAC (10 days [IQR 4-27] vs 2 days [IQR 1-4], P < 0.001; Table 1). A greater percentage of discharges to PAC were administered medications from multiple classes during the acute-care RI admission (eg, 54.8% vs 13.4% used medications from ≥7 classes, P < 0.001). A greater percentage of discharges to PAC used intensive care services during the acute-care admission (65.6% vs 22.4%, P < 0.001). A greater percentage of discharges to PAC received CPAP (10.6 vs 5.0%), BiPAP (19.8% vs 11.4%), or mechanical ventilation (52.7% vs 9.1%) during the acute-care RI hospitalization (P < 0.001 for all; Table 1).

Multivariable Analysis of the Likelihood of Post-Acute Care Use Following Discharge

In multivariable analysis, the patient characteristics associated with the highest likelihood of discharge to PAC included ≥11 vs no chronic conditions (odds ratio [OR] 11.8 , 95% CI, 8.0-17.2), ≥9 classes vs no classes of medications administered during the acute-care hospitalization (OR 4.8 , 95% CI, 1.8-13.0), and existing tracheostomy (OR 3.0, [95% CI, 2.6-3.5; Figure 2 and eTable). Patient characteristics associated with a more modest likelihood of discharge to PAC included public vs private insurance (OR 1.8, 95% CI, 1.6-2.0), neuromuscular complex chronic condition (OR 1.6, 95% CI, 1.5-1.8), new tracheostomy (OR 1.9, 95% CI, 1.7-2.2), and use of any enhanced respiratory support (ie, CPAP/BiPAP/mechanical ventilation) during the acute-care hospitalization (OR 1.4, 95% CI, 1.3-1.6; Figure 2 and Supplementary Table).

Classification and Regression Tree Analysis

In the CART analysis, the highest percentage (6.3%) of children hospitalized with RI who were discharged to PAC had the following combination of characteristics: ≥6 chronic conditions, ≥7 classes of medications administered, and respiratory technology. Median (IQR) length of acute-care LOS for children with these attributes who were transferred to PAC was 19 (IQR 8-56; range 1-1005) days; LOS remained long (median 13 days [IQR 6-41, range 1-1413]) for children with the same attributes not transferred to PAC (n = 9448). Between these children transferred vs not to PAC, 79.3% vs 65.9% received ICU services; 74.4% vs 73.5% received CPAP, BiPAP, or mechanical ventilation; and 31.0% vs 22.7% underwent tracheotomy during the acute-care hospitalization. Of these children who were not transferred to PAC, 18.9% were discharged to home nursing services.

The findings from the present study suggest that patients with RI hospitalization in children’s hospitals who use PAC are medically complex, with high rates of multiple chronic conditions—including cerebral palsy, asthma, chronic respiratory insufficiency, dysphagia, epilepsy, and gastroesophageal reflux—and high rates of technology assistance including enterostomy and tracheostomy. The characteristics of patients most likely to use PAC include long LOS, a large number of chronic conditions, many types of medications administered during the acute-care hospitalization, respiratory technology use, and an underlying neuromuscular condition. Specifically, the highest percentage of children hospitalized with RI who were discharged to PAC had ≥6 chronic conditions, ≥7 classes of medications administered, and respiratory technology. Our analysis suggests that there may be a large population of children with these same characteristics who experienced a prolonged LOS but were not transferred to PAC.

Acknowledgments

Dr. Berry was supported by the Agency for Healthcare Research and Quality (R21 HS023092-01), the Lucile Packard Foundation for Children’s Health, and Franciscan Hospital for Children. The funders were not involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclosure: The authors have no financial relationships relevant to this article to disclose.

Given growing incentives to reduce readmission rates, predischarge checklists and bundles have recommended that inpatient providers schedule postdischarge follow-up visits (PDFVs) for their hospitalized patients.^1-4 PDFVs have been linked to lower readmission rates in patients with chronic conditions, including congestive heart failure, psychiatric illnesses, and chronic obstructive pulmonary disease.^5-8 In contrast, the impact of PDFVs on readmissions in hospitalized general medicine populations has been mixed.^9-12 Beyond the presence or absence of PDFVs, it may be a patient’s inability to keep scheduled PDFVs that contributes more strongly to preventable readmissions.¹¹

This challenge, dealing with the 12% to 37% of patients who miss their visits (“no-shows”), is not new.^13-17 In high-risk patient populations, such as those with substance abuse, diabetes, or human immunodeficiency virus, no-shows (NSs) have been linked to poorer short-term and long-term clinical outcomes.^16,18-20 Additionally, NSs pose a challenge for outpatient clinics and the healthcare system at large. The financial cost of NSs ranges from approximately $200 per patient in 2 analyses to $7 million in cumulative lost revenue per year at 1 large academic health system.^13,17,21 As such, increasing attendance at PDFVs is a potential target for improving both patient outcomes and clinic productivity.

Most prior PDFV research has focused on readmission risk rather than PDFV attendance as the primary outcome.^5-12 However, given the patient-oriented benefits of attending PDFVs and the clinic-oriented benefits of avoiding vacant time slots, NS PDFVs represent an important missed opportunity for our healthcare delivery system. To our knowledge, risk factors for PDFV nonattendance have not yet been systematically studied. The aim of our study was to analyze PDFV nonattendance, particularly NSs and same-day cancellations (SDCs), for hospitalizations and clinics within our healthcare system.

Study Design

We conducted an observational cohort study of adult patients from 10 medical units at the Hospital of the University of Pennsylvania (a 789-bed quaternary-care hospital within an urban, academic medical system) who were scheduled with at least 1 PDFV. Specifically, the patients included in our analysis were hospitalized on general internal medicine services or medical subspecialty services with discharge dates between April 1, 2014, and March 31, 2015. Hospitalizations included in our study had at least 1 PDFV scheduled with an outpatient provider affiliated with the University of Pennsylvania Health System (UPHS). PDFVs scheduled with unaffiliated providers were not examined.

Each PDFV was requested by a patient’s inpatient care team. Once the care team had determined that a PDFV was clinically warranted, a member of the team (generally a resident, advanced practice provider, medical student, or designee) either called the UPHS clinic to schedule an appointment time or e-mailed the outpatient UPHS provider directly to facilitate a more urgent PDFV appointment time. Once a PDFV time was confirmed, PDFV details (ie, date, time, location, and phone number) were electronically entered into the patient’s discharge instructions by the inpatient care team. At the time of discharge, nurses reviewed these instructions with their patients. All patients left the hospital with a physical copy of these instructions. As part of routine care at our institution, patients then received automated telephone reminders from their UPHS-affiliated outpatient clinic 48 hours prior to each PDFV.

Data Collection

Our study was determined to meet criteria for quality improvement by the University of Pennsylvania’s Institutional Review Board. We used our healthcare system’s integrated electronic medical record system to track the dates of initial PDFV requests, the dates of hospitalization, and actual PDFV dates. PDFVs were included if the appointment request was made while a patient was hospitalized, including the day of discharge. Our study methodology only allowed us to investigate PDFVs scheduled with UPHS outpatient providers. We did not review discharge instructions or survey non-UPHS clinics to quantify visits scheduled with other providers, for example, community health centers or external private practices.

Exclusion criteria included the following: (1) office visits with nonproviders, for example, scheduled diagnostic procedures or pharmacist appointments for warfarin dosing; (2) visits cancelled by inpatient providers prior to discharge; (3) visits for patients not otherwise eligible for UPHS outpatient care because of insurance reasons; and (4) visits scheduled for dates after a patient’s death. Our motivation for the third exclusion criterion was the infrequent and irregular process by which PDFVs were authorized for these patients. These patients and their characteristics are described in Supplementary Table 1 in more detail.

For each PDFV, we recorded age, gender, race, insurance status, driving distance, length of stay for index hospitalization, discharging service (general internal medicine vs subspecialty), postdischarge disposition (home, home with home care services such as nursing or physical therapy, or facility), the number of PDFVs scheduled per index hospitalization, PDFV specialty type (oncologic subspecialty, nononcologic medical subspecialty, nononcologic surgical subspecialty, primary care, or other specialty), PDFV season, and PDFV lead time (the number of days between the discharge date and PDFV). We consolidated oncologic specialties into 1 group given the integrated nature of our healthcare system’s comprehensive cancer center. “Other” PDFV specialty subtypes are described in Supplementary Table 2. Driving distances between patient postal codes and our hospital were calculated using Excel VBA Master (Salt Lake City, Utah) and were subsequently categorized into patient-level quartiles for further analysis. For cancelled PDFVs, we collected dates of cancellation relative to the date of the appointment itself.

Study Outcomes

The primary study outcome was PDFV attendance. Each PDFV’s status was categorized by outpatient clinic staff as attended, cancelled, or NS. For cancelled appointments, cancellation dates and reasons (if entered by clinic representatives) were collected. In keeping with prior studies investigating outpatient nonattendance,we calculated collective NS/SDC rates for the variables listed above.^17,22-25 We additionally calculated NS/SDC and attendance-as-scheduled rates stratified by the number of PDFVs per patient to assess for a “high-utilizer” effect with regard to PDFV attendance.

Statistical Analysis

We used multivariable mixed-effects regression with a logit link to assess associations between age, gender, race, insurance, driving distance quartile, length of stay, discharging service, postdischarge disposition, the number of PDFVs per hospitalization, PDFV specialty type, PDFV season, PDFV lead time, and our NS/SDC outcome. The mixed-effects approach was used to account for correlation structures induced by patients who had multiple visits and for patients with multiple hospitalizations. Specifically, our model specified 2 levels of nesting (PDFVs nested within each hospitalization, which were nested within each patient) to obtain appropriate standard error estimates for our adjusted odds ratios (ORs). Correlation matrices and multivariable variance inflation factors were used to assess collinearity among the predictor variables. These assessments did not indicate strong collinearity; hence, all predictors were included in the model. Only driving distance had a small amount of missing data (0.18% of driving distances were unavailable), so multiple imputation was not undertaken. Analyses were performed using R version 3.3.1 (R Foundation for Statistical Computing, Vienna, Austria).

Baseline Characteristics

During the 1-year study period, there were 11,829 discrete hospitalizations in medical units at our hospital. Of these hospitalizations, 6136 (52%) had at least 1 UPHS-affiliated PDFV meeting our inclusion and exclusion criteria, as detailed in the Figure. Across these hospitalizations, 9258 PDFVs were scheduled on behalf of 4653 patients. Demographic characteristics for these patients, hospitalizations, and visits are detailed in Table 1. The median age of patients in our cohort was 61 years old (interquartile range [IQR] 49-70, range 18-101). The median driving distance was 17 miles (IQR 4.3-38.8, range 0-2891). For hospitalizations, the median length of stay was 5 days (IQR 3-10, range 0-97). The median PDFV lead time, which is defined as the number of days between discharge and PDFV, was 12 days (IQR 6-23, range 1-60). Overall, 41% of patients (n = 1927) attended all of their PDFVs as scheduled; Supplementary Figure 1 lists patient-level PDFV attendance-as-scheduled percentages in more detail.

Incidence of NSs and SDCs

Twenty-five percent of PDFVs (n = 2303) were ultimately NS/SDCs; this included 1658 NSs (18% of all appointments) and 645 SDCs (7% of all appointments). Fifty-two percent of PDFVs (n = 4847) were kept as scheduled, while 23% (n = 2108) were cancelled before the day of the visit. Of the 2558 cancellations with valid cancellation dates, 49% (n = 1252) were cancelled 2 or fewer days beforehand, as shown in Supplementary Figure 2.

When comparing PDFV characteristics themselves, oncologic visits had the lowest NS/SDC incidence of any group analyzed in our study. This may be related to the inherent life-altering nature of a cancer diagnosis or our cancer center’s use of patient navigators.^23,30 In contrast, primary care clinics suffered from NS/SDC rates approaching 40%, which is a concerning finding given the importance of primary care coordination in the posthospitalization period.^9,31 Why are primary care appointments so commonly missed? Some studies suggest that forgetting about a primary care appointment is a leading reason.^15,32,33 For PDFVs, this phenomenon may be augmented because the visits are not scheduled by patients themselves. Additionally, patients may paradoxically undervalue the benefit of an all-encompassing primary care visit, compared to a PDFV focused on a specific problem, (eg, a cardiology follow-up appointment for a patient with congestive heart failure). In particular, patients with limited health literacy may potentially undervalue the capabilities of their primary care clinics.^34,35

The low absolute number of primary care PDFVs (only 8% of all visits) scheduled for patients at our hospital was an unexpected finding. This low percentage is likely a function of the patient population hospitalized at our large, urban quaternary-care facility. First, an unknown number of patients may have had PDFVs manually scheduled with primary care providers external to our health system; these PDFVs were not captured within our study. Second, 71% of the hospitalizations in our study occurred in subspecialty services, for which specific primary care follow-up may not be as urgent. Supporting this fact, further analysis of the 6136 hospitalizations in our study (data not shown) revealed that 28% of the hospitalizations in general internal medicine were scheduled with at least 1 primary care PDFV as opposed to only 5% of subspecialty-service hospitalizations.

In contrast to several previous studies of outpatient nonattendance,we did not find that visits scheduled for time points further in the future were more likely to be missed.^{14,24,25,36,37} Unlike other appointments, it may be that PDFV lead time does not affect attendance because of the unique manner in which PDFV times are scheduled and conveyed to patients. Unlike other appointments, patients do not schedule PDFVs themselves but instead learn about their PDFV dates as part of a large set of discharge instructions. This practice may result in poor recall of PDFV dates in recently hospitalized patients³⁸, regardless of the lead time between discharge and the visit itself.

Supplementary Table 1 details a 51% NS/SDC rate for the small number of PDFVs (n = 65) that were excluded a priori from our analysis because of general ineligibility for UPHS outpatient care. We specifically chose to exclude this population because of the infrequent and irregular process by which these PDFVs were authorized on a case-by-case basis, typically via active engagement by our hospital’s social work department. We did not study this population further but postulate that the 51% NS/SDC rate may reflect other social determinants of health that contribute to appointment nonadherence in a predominantly uninsured population.

Beyond their effect on patient outcomes, improving PDFV-related processes has the potential to boost both inpatient and outpatient provider satisfaction. From the standpoint of frontline inpatient providers (often resident physicians), calling outpatient clinics to request PDFVs is viewed as 1 of the top 5 administrative tasks that interfere with house staff education.³⁹ Future interventions that involve patients in the PDFV scheduling process may improve inpatient workflow while simultaneously engaging patients in their own care. For example, asking clinic representatives to directly schedule PDFVs with hospitalized patients, either by phone or in person, has been shown in pilot studies to improve PDFV attendance and decrease readmissions.^40-42 Conversely, NS/SDC visits harm outpatient provider productivity and decrease provider availability for other patients.^13,17,43 Strategies to mitigate the impact of unfilled appointment slots (eg, deliberately overbooking time slots in advance) carry their own risks, including provider burnout.⁴⁴ As such, preventing NSs may be superior to curing their adverse impacts. Many such strategies exist in the ambulatory setting,^13,43,45 for example, better communication with patients through texting or goal-directed, personalized phone reminders.^46-48Our study methodology has several limitations. Most importantly, we were unable to measure PDFVs made with providers unaffiliated with UPHS. As previously noted, our low proportion of primary care PDFVs may specifically reflect patients with primary care providers outside of our health system. Similarly, our low percentage of Medicaid patients receiving PDFVs may be related to follow-up visits with nonaffiliated community health centers. We were unable to measure patient acuity and health literacy as potential predictors of NS/SDC rates. Driving distances were calculated from patient postal codes to our hospital, not to individual outpatient clinics. However, the majority of our hospital-affiliated clinics are located adjacent to our hospital; additionally, we grouped driving distances into quartiles for our analysis. We had initially attempted to differentiate between clinic-initiated and patient-initiated cancellations, but unfortunately, we found that the data were too unreliable to be used for further analysis (outlined in Supplementary Table 3). Lastly, because we studied patients in medical units at a single large, urban, academic center, our results are not generalizable to other settings (eg, community hospitals, hospitals with smaller networks of outpatient providers, or patients being discharged from surgical services or observation units).

Given national efforts to enhance postdischarge transitions of care, we aimed to analyze attendance at provider-scheduled PDFV appointments. Our finding that 25% of PDFVs resulted in NS/SDCs raises both questions and opportunities for inpatient and outpatient providers. Further research is needed to understand why so many patients miss their PDFVs, and we should work as a field to develop creative solutions to improve PDFV scheduling and attendance.

Acknowledgments

The authors acknowledge Marie Synnestvedt, PhD, and Manik Chhabra, MD, for their assistance with data gathering and statistical analysis. They also acknowledge Allison DeKosky, MD, Michael Serpa, BS, Michael McFall, and Scott Schlegel, MBA, for their assistance with researching this topic. They did not receive external compensation for their assistance outside of their usual salary support.

DISCLOSURE

Nothing to report.

Given growing incentives to reduce readmission rates, predischarge checklists and bundles have recommended that inpatient providers schedule postdischarge follow-up visits (PDFVs) for their hospitalized patients.^1-4 PDFVs have been linked to lower readmission rates in patients with chronic conditions, including congestive heart failure, psychiatric illnesses, and chronic obstructive pulmonary disease.^5-8 In contrast, the impact of PDFVs on readmissions in hospitalized general medicine populations has been mixed.^9-12 Beyond the presence or absence of PDFVs, it may be a patient’s inability to keep scheduled PDFVs that contributes more strongly to preventable readmissions.¹¹

This challenge, dealing with the 12% to 37% of patients who miss their visits (“no-shows”), is not new.^13-17 In high-risk patient populations, such as those with substance abuse, diabetes, or human immunodeficiency virus, no-shows (NSs) have been linked to poorer short-term and long-term clinical outcomes.^16,18-20 Additionally, NSs pose a challenge for outpatient clinics and the healthcare system at large. The financial cost of NSs ranges from approximately $200 per patient in 2 analyses to $7 million in cumulative lost revenue per year at 1 large academic health system.^13,17,21 As such, increasing attendance at PDFVs is a potential target for improving both patient outcomes and clinic productivity.

Most prior PDFV research has focused on readmission risk rather than PDFV attendance as the primary outcome.^5-12 However, given the patient-oriented benefits of attending PDFVs and the clinic-oriented benefits of avoiding vacant time slots, NS PDFVs represent an important missed opportunity for our healthcare delivery system. To our knowledge, risk factors for PDFV nonattendance have not yet been systematically studied. The aim of our study was to analyze PDFV nonattendance, particularly NSs and same-day cancellations (SDCs), for hospitalizations and clinics within our healthcare system.

Study Design

We conducted an observational cohort study of adult patients from 10 medical units at the Hospital of the University of Pennsylvania (a 789-bed quaternary-care hospital within an urban, academic medical system) who were scheduled with at least 1 PDFV. Specifically, the patients included in our analysis were hospitalized on general internal medicine services or medical subspecialty services with discharge dates between April 1, 2014, and March 31, 2015. Hospitalizations included in our study had at least 1 PDFV scheduled with an outpatient provider affiliated with the University of Pennsylvania Health System (UPHS). PDFVs scheduled with unaffiliated providers were not examined.

Each PDFV was requested by a patient’s inpatient care team. Once the care team had determined that a PDFV was clinically warranted, a member of the team (generally a resident, advanced practice provider, medical student, or designee) either called the UPHS clinic to schedule an appointment time or e-mailed the outpatient UPHS provider directly to facilitate a more urgent PDFV appointment time. Once a PDFV time was confirmed, PDFV details (ie, date, time, location, and phone number) were electronically entered into the patient’s discharge instructions by the inpatient care team. At the time of discharge, nurses reviewed these instructions with their patients. All patients left the hospital with a physical copy of these instructions. As part of routine care at our institution, patients then received automated telephone reminders from their UPHS-affiliated outpatient clinic 48 hours prior to each PDFV.

Data Collection

Our study was determined to meet criteria for quality improvement by the University of Pennsylvania’s Institutional Review Board. We used our healthcare system’s integrated electronic medical record system to track the dates of initial PDFV requests, the dates of hospitalization, and actual PDFV dates. PDFVs were included if the appointment request was made while a patient was hospitalized, including the day of discharge. Our study methodology only allowed us to investigate PDFVs scheduled with UPHS outpatient providers. We did not review discharge instructions or survey non-UPHS clinics to quantify visits scheduled with other providers, for example, community health centers or external private practices.

Exclusion criteria included the following: (1) office visits with nonproviders, for example, scheduled diagnostic procedures or pharmacist appointments for warfarin dosing; (2) visits cancelled by inpatient providers prior to discharge; (3) visits for patients not otherwise eligible for UPHS outpatient care because of insurance reasons; and (4) visits scheduled for dates after a patient’s death. Our motivation for the third exclusion criterion was the infrequent and irregular process by which PDFVs were authorized for these patients. These patients and their characteristics are described in Supplementary Table 1 in more detail.

For each PDFV, we recorded age, gender, race, insurance status, driving distance, length of stay for index hospitalization, discharging service (general internal medicine vs subspecialty), postdischarge disposition (home, home with home care services such as nursing or physical therapy, or facility), the number of PDFVs scheduled per index hospitalization, PDFV specialty type (oncologic subspecialty, nononcologic medical subspecialty, nononcologic surgical subspecialty, primary care, or other specialty), PDFV season, and PDFV lead time (the number of days between the discharge date and PDFV). We consolidated oncologic specialties into 1 group given the integrated nature of our healthcare system’s comprehensive cancer center. “Other” PDFV specialty subtypes are described in Supplementary Table 2. Driving distances between patient postal codes and our hospital were calculated using Excel VBA Master (Salt Lake City, Utah) and were subsequently categorized into patient-level quartiles for further analysis. For cancelled PDFVs, we collected dates of cancellation relative to the date of the appointment itself.

Study Outcomes

The primary study outcome was PDFV attendance. Each PDFV’s status was categorized by outpatient clinic staff as attended, cancelled, or NS. For cancelled appointments, cancellation dates and reasons (if entered by clinic representatives) were collected. In keeping with prior studies investigating outpatient nonattendance,we calculated collective NS/SDC rates for the variables listed above.^17,22-25 We additionally calculated NS/SDC and attendance-as-scheduled rates stratified by the number of PDFVs per patient to assess for a “high-utilizer” effect with regard to PDFV attendance.

Statistical Analysis

We used multivariable mixed-effects regression with a logit link to assess associations between age, gender, race, insurance, driving distance quartile, length of stay, discharging service, postdischarge disposition, the number of PDFVs per hospitalization, PDFV specialty type, PDFV season, PDFV lead time, and our NS/SDC outcome. The mixed-effects approach was used to account for correlation structures induced by patients who had multiple visits and for patients with multiple hospitalizations. Specifically, our model specified 2 levels of nesting (PDFVs nested within each hospitalization, which were nested within each patient) to obtain appropriate standard error estimates for our adjusted odds ratios (ORs). Correlation matrices and multivariable variance inflation factors were used to assess collinearity among the predictor variables. These assessments did not indicate strong collinearity; hence, all predictors were included in the model. Only driving distance had a small amount of missing data (0.18% of driving distances were unavailable), so multiple imputation was not undertaken. Analyses were performed using R version 3.3.1 (R Foundation for Statistical Computing, Vienna, Austria).

Baseline Characteristics

During the 1-year study period, there were 11,829 discrete hospitalizations in medical units at our hospital. Of these hospitalizations, 6136 (52%) had at least 1 UPHS-affiliated PDFV meeting our inclusion and exclusion criteria, as detailed in the Figure. Across these hospitalizations, 9258 PDFVs were scheduled on behalf of 4653 patients. Demographic characteristics for these patients, hospitalizations, and visits are detailed in Table 1. The median age of patients in our cohort was 61 years old (interquartile range [IQR] 49-70, range 18-101). The median driving distance was 17 miles (IQR 4.3-38.8, range 0-2891). For hospitalizations, the median length of stay was 5 days (IQR 3-10, range 0-97). The median PDFV lead time, which is defined as the number of days between discharge and PDFV, was 12 days (IQR 6-23, range 1-60). Overall, 41% of patients (n = 1927) attended all of their PDFVs as scheduled; Supplementary Figure 1 lists patient-level PDFV attendance-as-scheduled percentages in more detail.

Incidence of NSs and SDCs

Twenty-five percent of PDFVs (n = 2303) were ultimately NS/SDCs; this included 1658 NSs (18% of all appointments) and 645 SDCs (7% of all appointments). Fifty-two percent of PDFVs (n = 4847) were kept as scheduled, while 23% (n = 2108) were cancelled before the day of the visit. Of the 2558 cancellations with valid cancellation dates, 49% (n = 1252) were cancelled 2 or fewer days beforehand, as shown in Supplementary Figure 2.

When comparing PDFV characteristics themselves, oncologic visits had the lowest NS/SDC incidence of any group analyzed in our study. This may be related to the inherent life-altering nature of a cancer diagnosis or our cancer center’s use of patient navigators.^23,30 In contrast, primary care clinics suffered from NS/SDC rates approaching 40%, which is a concerning finding given the importance of primary care coordination in the posthospitalization period.^9,31 Why are primary care appointments so commonly missed? Some studies suggest that forgetting about a primary care appointment is a leading reason.^15,32,33 For PDFVs, this phenomenon may be augmented because the visits are not scheduled by patients themselves. Additionally, patients may paradoxically undervalue the benefit of an all-encompassing primary care visit, compared to a PDFV focused on a specific problem, (eg, a cardiology follow-up appointment for a patient with congestive heart failure). In particular, patients with limited health literacy may potentially undervalue the capabilities of their primary care clinics.^34,35

The low absolute number of primary care PDFVs (only 8% of all visits) scheduled for patients at our hospital was an unexpected finding. This low percentage is likely a function of the patient population hospitalized at our large, urban quaternary-care facility. First, an unknown number of patients may have had PDFVs manually scheduled with primary care providers external to our health system; these PDFVs were not captured within our study. Second, 71% of the hospitalizations in our study occurred in subspecialty services, for which specific primary care follow-up may not be as urgent. Supporting this fact, further analysis of the 6136 hospitalizations in our study (data not shown) revealed that 28% of the hospitalizations in general internal medicine were scheduled with at least 1 primary care PDFV as opposed to only 5% of subspecialty-service hospitalizations.

In contrast to several previous studies of outpatient nonattendance,we did not find that visits scheduled for time points further in the future were more likely to be missed.^{14,24,25,36,37} Unlike other appointments, it may be that PDFV lead time does not affect attendance because of the unique manner in which PDFV times are scheduled and conveyed to patients. Unlike other appointments, patients do not schedule PDFVs themselves but instead learn about their PDFV dates as part of a large set of discharge instructions. This practice may result in poor recall of PDFV dates in recently hospitalized patients³⁸, regardless of the lead time between discharge and the visit itself.

Supplementary Table 1 details a 51% NS/SDC rate for the small number of PDFVs (n = 65) that were excluded a priori from our analysis because of general ineligibility for UPHS outpatient care. We specifically chose to exclude this population because of the infrequent and irregular process by which these PDFVs were authorized on a case-by-case basis, typically via active engagement by our hospital’s social work department. We did not study this population further but postulate that the 51% NS/SDC rate may reflect other social determinants of health that contribute to appointment nonadherence in a predominantly uninsured population.

Beyond their effect on patient outcomes, improving PDFV-related processes has the potential to boost both inpatient and outpatient provider satisfaction. From the standpoint of frontline inpatient providers (often resident physicians), calling outpatient clinics to request PDFVs is viewed as 1 of the top 5 administrative tasks that interfere with house staff education.³⁹ Future interventions that involve patients in the PDFV scheduling process may improve inpatient workflow while simultaneously engaging patients in their own care. For example, asking clinic representatives to directly schedule PDFVs with hospitalized patients, either by phone or in person, has been shown in pilot studies to improve PDFV attendance and decrease readmissions.^40-42 Conversely, NS/SDC visits harm outpatient provider productivity and decrease provider availability for other patients.^13,17,43 Strategies to mitigate the impact of unfilled appointment slots (eg, deliberately overbooking time slots in advance) carry their own risks, including provider burnout.⁴⁴ As such, preventing NSs may be superior to curing their adverse impacts. Many such strategies exist in the ambulatory setting,^13,43,45 for example, better communication with patients through texting or goal-directed, personalized phone reminders.^46-48Our study methodology has several limitations. Most importantly, we were unable to measure PDFVs made with providers unaffiliated with UPHS. As previously noted, our low proportion of primary care PDFVs may specifically reflect patients with primary care providers outside of our health system. Similarly, our low percentage of Medicaid patients receiving PDFVs may be related to follow-up visits with nonaffiliated community health centers. We were unable to measure patient acuity and health literacy as potential predictors of NS/SDC rates. Driving distances were calculated from patient postal codes to our hospital, not to individual outpatient clinics. However, the majority of our hospital-affiliated clinics are located adjacent to our hospital; additionally, we grouped driving distances into quartiles for our analysis. We had initially attempted to differentiate between clinic-initiated and patient-initiated cancellations, but unfortunately, we found that the data were too unreliable to be used for further analysis (outlined in Supplementary Table 3). Lastly, because we studied patients in medical units at a single large, urban, academic center, our results are not generalizable to other settings (eg, community hospitals, hospitals with smaller networks of outpatient providers, or patients being discharged from surgical services or observation units).

Given national efforts to enhance postdischarge transitions of care, we aimed to analyze attendance at provider-scheduled PDFV appointments. Our finding that 25% of PDFVs resulted in NS/SDCs raises both questions and opportunities for inpatient and outpatient providers. Further research is needed to understand why so many patients miss their PDFVs, and we should work as a field to develop creative solutions to improve PDFV scheduling and attendance.

Acknowledgments

The authors acknowledge Marie Synnestvedt, PhD, and Manik Chhabra, MD, for their assistance with data gathering and statistical analysis. They also acknowledge Allison DeKosky, MD, Michael Serpa, BS, Michael McFall, and Scott Schlegel, MBA, for their assistance with researching this topic. They did not receive external compensation for their assistance outside of their usual salary support.

DISCLOSURE

Nothing to report.

Given growing incentives to reduce readmission rates, predischarge checklists and bundles have recommended that inpatient providers schedule postdischarge follow-up visits (PDFVs) for their hospitalized patients.^1-4 PDFVs have been linked to lower readmission rates in patients with chronic conditions, including congestive heart failure, psychiatric illnesses, and chronic obstructive pulmonary disease.^5-8 In contrast, the impact of PDFVs on readmissions in hospitalized general medicine populations has been mixed.^9-12 Beyond the presence or absence of PDFVs, it may be a patient’s inability to keep scheduled PDFVs that contributes more strongly to preventable readmissions.¹¹

This challenge, dealing with the 12% to 37% of patients who miss their visits (“no-shows”), is not new.^13-17 In high-risk patient populations, such as those with substance abuse, diabetes, or human immunodeficiency virus, no-shows (NSs) have been linked to poorer short-term and long-term clinical outcomes.^16,18-20 Additionally, NSs pose a challenge for outpatient clinics and the healthcare system at large. The financial cost of NSs ranges from approximately $200 per patient in 2 analyses to $7 million in cumulative lost revenue per year at 1 large academic health system.^13,17,21 As such, increasing attendance at PDFVs is a potential target for improving both patient outcomes and clinic productivity.

Most prior PDFV research has focused on readmission risk rather than PDFV attendance as the primary outcome.^5-12 However, given the patient-oriented benefits of attending PDFVs and the clinic-oriented benefits of avoiding vacant time slots, NS PDFVs represent an important missed opportunity for our healthcare delivery system. To our knowledge, risk factors for PDFV nonattendance have not yet been systematically studied. The aim of our study was to analyze PDFV nonattendance, particularly NSs and same-day cancellations (SDCs), for hospitalizations and clinics within our healthcare system.

Study Design

We conducted an observational cohort study of adult patients from 10 medical units at the Hospital of the University of Pennsylvania (a 789-bed quaternary-care hospital within an urban, academic medical system) who were scheduled with at least 1 PDFV. Specifically, the patients included in our analysis were hospitalized on general internal medicine services or medical subspecialty services with discharge dates between April 1, 2014, and March 31, 2015. Hospitalizations included in our study had at least 1 PDFV scheduled with an outpatient provider affiliated with the University of Pennsylvania Health System (UPHS). PDFVs scheduled with unaffiliated providers were not examined.

Each PDFV was requested by a patient’s inpatient care team. Once the care team had determined that a PDFV was clinically warranted, a member of the team (generally a resident, advanced practice provider, medical student, or designee) either called the UPHS clinic to schedule an appointment time or e-mailed the outpatient UPHS provider directly to facilitate a more urgent PDFV appointment time. Once a PDFV time was confirmed, PDFV details (ie, date, time, location, and phone number) were electronically entered into the patient’s discharge instructions by the inpatient care team. At the time of discharge, nurses reviewed these instructions with their patients. All patients left the hospital with a physical copy of these instructions. As part of routine care at our institution, patients then received automated telephone reminders from their UPHS-affiliated outpatient clinic 48 hours prior to each PDFV.

Data Collection

Our study was determined to meet criteria for quality improvement by the University of Pennsylvania’s Institutional Review Board. We used our healthcare system’s integrated electronic medical record system to track the dates of initial PDFV requests, the dates of hospitalization, and actual PDFV dates. PDFVs were included if the appointment request was made while a patient was hospitalized, including the day of discharge. Our study methodology only allowed us to investigate PDFVs scheduled with UPHS outpatient providers. We did not review discharge instructions or survey non-UPHS clinics to quantify visits scheduled with other providers, for example, community health centers or external private practices.

Exclusion criteria included the following: (1) office visits with nonproviders, for example, scheduled diagnostic procedures or pharmacist appointments for warfarin dosing; (2) visits cancelled by inpatient providers prior to discharge; (3) visits for patients not otherwise eligible for UPHS outpatient care because of insurance reasons; and (4) visits scheduled for dates after a patient’s death. Our motivation for the third exclusion criterion was the infrequent and irregular process by which PDFVs were authorized for these patients. These patients and their characteristics are described in Supplementary Table 1 in more detail.

For each PDFV, we recorded age, gender, race, insurance status, driving distance, length of stay for index hospitalization, discharging service (general internal medicine vs subspecialty), postdischarge disposition (home, home with home care services such as nursing or physical therapy, or facility), the number of PDFVs scheduled per index hospitalization, PDFV specialty type (oncologic subspecialty, nononcologic medical subspecialty, nononcologic surgical subspecialty, primary care, or other specialty), PDFV season, and PDFV lead time (the number of days between the discharge date and PDFV). We consolidated oncologic specialties into 1 group given the integrated nature of our healthcare system’s comprehensive cancer center. “Other” PDFV specialty subtypes are described in Supplementary Table 2. Driving distances between patient postal codes and our hospital were calculated using Excel VBA Master (Salt Lake City, Utah) and were subsequently categorized into patient-level quartiles for further analysis. For cancelled PDFVs, we collected dates of cancellation relative to the date of the appointment itself.

Study Outcomes

The primary study outcome was PDFV attendance. Each PDFV’s status was categorized by outpatient clinic staff as attended, cancelled, or NS. For cancelled appointments, cancellation dates and reasons (if entered by clinic representatives) were collected. In keeping with prior studies investigating outpatient nonattendance,we calculated collective NS/SDC rates for the variables listed above.^17,22-25 We additionally calculated NS/SDC and attendance-as-scheduled rates stratified by the number of PDFVs per patient to assess for a “high-utilizer” effect with regard to PDFV attendance.

Statistical Analysis

We used multivariable mixed-effects regression with a logit link to assess associations between age, gender, race, insurance, driving distance quartile, length of stay, discharging service, postdischarge disposition, the number of PDFVs per hospitalization, PDFV specialty type, PDFV season, PDFV lead time, and our NS/SDC outcome. The mixed-effects approach was used to account for correlation structures induced by patients who had multiple visits and for patients with multiple hospitalizations. Specifically, our model specified 2 levels of nesting (PDFVs nested within each hospitalization, which were nested within each patient) to obtain appropriate standard error estimates for our adjusted odds ratios (ORs). Correlation matrices and multivariable variance inflation factors were used to assess collinearity among the predictor variables. These assessments did not indicate strong collinearity; hence, all predictors were included in the model. Only driving distance had a small amount of missing data (0.18% of driving distances were unavailable), so multiple imputation was not undertaken. Analyses were performed using R version 3.3.1 (R Foundation for Statistical Computing, Vienna, Austria).

Baseline Characteristics

During the 1-year study period, there were 11,829 discrete hospitalizations in medical units at our hospital. Of these hospitalizations, 6136 (52%) had at least 1 UPHS-affiliated PDFV meeting our inclusion and exclusion criteria, as detailed in the Figure. Across these hospitalizations, 9258 PDFVs were scheduled on behalf of 4653 patients. Demographic characteristics for these patients, hospitalizations, and visits are detailed in Table 1. The median age of patients in our cohort was 61 years old (interquartile range [IQR] 49-70, range 18-101). The median driving distance was 17 miles (IQR 4.3-38.8, range 0-2891). For hospitalizations, the median length of stay was 5 days (IQR 3-10, range 0-97). The median PDFV lead time, which is defined as the number of days between discharge and PDFV, was 12 days (IQR 6-23, range 1-60). Overall, 41% of patients (n = 1927) attended all of their PDFVs as scheduled; Supplementary Figure 1 lists patient-level PDFV attendance-as-scheduled percentages in more detail.

Incidence of NSs and SDCs

Twenty-five percent of PDFVs (n = 2303) were ultimately NS/SDCs; this included 1658 NSs (18% of all appointments) and 645 SDCs (7% of all appointments). Fifty-two percent of PDFVs (n = 4847) were kept as scheduled, while 23% (n = 2108) were cancelled before the day of the visit. Of the 2558 cancellations with valid cancellation dates, 49% (n = 1252) were cancelled 2 or fewer days beforehand, as shown in Supplementary Figure 2.

When comparing PDFV characteristics themselves, oncologic visits had the lowest NS/SDC incidence of any group analyzed in our study. This may be related to the inherent life-altering nature of a cancer diagnosis or our cancer center’s use of patient navigators.^23,30 In contrast, primary care clinics suffered from NS/SDC rates approaching 40%, which is a concerning finding given the importance of primary care coordination in the posthospitalization period.^9,31 Why are primary care appointments so commonly missed? Some studies suggest that forgetting about a primary care appointment is a leading reason.^15,32,33 For PDFVs, this phenomenon may be augmented because the visits are not scheduled by patients themselves. Additionally, patients may paradoxically undervalue the benefit of an all-encompassing primary care visit, compared to a PDFV focused on a specific problem, (eg, a cardiology follow-up appointment for a patient with congestive heart failure). In particular, patients with limited health literacy may potentially undervalue the capabilities of their primary care clinics.^34,35

The low absolute number of primary care PDFVs (only 8% of all visits) scheduled for patients at our hospital was an unexpected finding. This low percentage is likely a function of the patient population hospitalized at our large, urban quaternary-care facility. First, an unknown number of patients may have had PDFVs manually scheduled with primary care providers external to our health system; these PDFVs were not captured within our study. Second, 71% of the hospitalizations in our study occurred in subspecialty services, for which specific primary care follow-up may not be as urgent. Supporting this fact, further analysis of the 6136 hospitalizations in our study (data not shown) revealed that 28% of the hospitalizations in general internal medicine were scheduled with at least 1 primary care PDFV as opposed to only 5% of subspecialty-service hospitalizations.

In contrast to several previous studies of outpatient nonattendance,we did not find that visits scheduled for time points further in the future were more likely to be missed.^{14,24,25,36,37} Unlike other appointments, it may be that PDFV lead time does not affect attendance because of the unique manner in which PDFV times are scheduled and conveyed to patients. Unlike other appointments, patients do not schedule PDFVs themselves but instead learn about their PDFV dates as part of a large set of discharge instructions. This practice may result in poor recall of PDFV dates in recently hospitalized patients³⁸, regardless of the lead time between discharge and the visit itself.

Supplementary Table 1 details a 51% NS/SDC rate for the small number of PDFVs (n = 65) that were excluded a priori from our analysis because of general ineligibility for UPHS outpatient care. We specifically chose to exclude this population because of the infrequent and irregular process by which these PDFVs were authorized on a case-by-case basis, typically via active engagement by our hospital’s social work department. We did not study this population further but postulate that the 51% NS/SDC rate may reflect other social determinants of health that contribute to appointment nonadherence in a predominantly uninsured population.

Beyond their effect on patient outcomes, improving PDFV-related processes has the potential to boost both inpatient and outpatient provider satisfaction. From the standpoint of frontline inpatient providers (often resident physicians), calling outpatient clinics to request PDFVs is viewed as 1 of the top 5 administrative tasks that interfere with house staff education.³⁹ Future interventions that involve patients in the PDFV scheduling process may improve inpatient workflow while simultaneously engaging patients in their own care. For example, asking clinic representatives to directly schedule PDFVs with hospitalized patients, either by phone or in person, has been shown in pilot studies to improve PDFV attendance and decrease readmissions.^40-42 Conversely, NS/SDC visits harm outpatient provider productivity and decrease provider availability for other patients.^13,17,43 Strategies to mitigate the impact of unfilled appointment slots (eg, deliberately overbooking time slots in advance) carry their own risks, including provider burnout.⁴⁴ As such, preventing NSs may be superior to curing their adverse impacts. Many such strategies exist in the ambulatory setting,^13,43,45 for example, better communication with patients through texting or goal-directed, personalized phone reminders.^46-48Our study methodology has several limitations. Most importantly, we were unable to measure PDFVs made with providers unaffiliated with UPHS. As previously noted, our low proportion of primary care PDFVs may specifically reflect patients with primary care providers outside of our health system. Similarly, our low percentage of Medicaid patients receiving PDFVs may be related to follow-up visits with nonaffiliated community health centers. We were unable to measure patient acuity and health literacy as potential predictors of NS/SDC rates. Driving distances were calculated from patient postal codes to our hospital, not to individual outpatient clinics. However, the majority of our hospital-affiliated clinics are located adjacent to our hospital; additionally, we grouped driving distances into quartiles for our analysis. We had initially attempted to differentiate between clinic-initiated and patient-initiated cancellations, but unfortunately, we found that the data were too unreliable to be used for further analysis (outlined in Supplementary Table 3). Lastly, because we studied patients in medical units at a single large, urban, academic center, our results are not generalizable to other settings (eg, community hospitals, hospitals with smaller networks of outpatient providers, or patients being discharged from surgical services or observation units).

Given national efforts to enhance postdischarge transitions of care, we aimed to analyze attendance at provider-scheduled PDFV appointments. Our finding that 25% of PDFVs resulted in NS/SDCs raises both questions and opportunities for inpatient and outpatient providers. Further research is needed to understand why so many patients miss their PDFVs, and we should work as a field to develop creative solutions to improve PDFV scheduling and attendance.

Acknowledgments

The authors acknowledge Marie Synnestvedt, PhD, and Manik Chhabra, MD, for their assistance with data gathering and statistical analysis. They also acknowledge Allison DeKosky, MD, Michael Serpa, BS, Michael McFall, and Scott Schlegel, MBA, for their assistance with researching this topic. They did not receive external compensation for their assistance outside of their usual salary support.

DISCLOSURE

Nothing to report.

INTRODUCTION

According to Centers for Medicare & Medicaid Services (CMS), approximately 1 in 5 patients discharged from a hospital will be readmitted within 30 days.¹ The Hospital Readmission Reduction Program (HRRP) is designed to reduce readmission by withholding up to 3% of all Medicare reimbursement from hospitals with “excess” readmissions; however, absent from the HRRP is adjustment for socioeconomic status (SES), which CMS holds may undermine incentives to reduce health disparities and institutionalize lower standards for hospitals serving disadvantaged populations.²

Lack of SES adjustment has been criticized by those who point to evidence highlighting postdischarge environment and patient SES as drivers of readmission and suggest hospitals that serve low SES individuals will bear a disproportionate share of penalties.^3-6 Single-center,^3,7,8 regional,^9,10 and nationwide^6,11 studies highlight census tract level socioeconomic variables as predictive of readmission. Single-center studies, robust in controlling for confounders, including staffing, training, electronic medical record utilization, and transitional care processes, do not allow comparisons between hospitals, limiting utility in HRRP evaluation. Multicenter cohorts, on the other hand, allow for comparisons between high and low penalty hospitals, pioneered by Joynt et al¹² after the first round of HRRP penalties; yet this technique may not account for confounding caused by extensive demographic, socioeconomic, and hospital characteristic heterogeneity inherent in a national cohort. Analysis of the 2015 HRRP penalty data by Sjoding et al.⁶ revealed higher chronic obstructive pulmonary disease (COPD) readmission rates in the Mid-Atlantic, Midwest, and South relative to other regions; however, the magnitude of small-area variation and its relationship to population SES have yet to be characterized.

Therefore, we conducted a matched case-control design, whereby each maximum penalty hospital was matched to a nonpenalty hospital using key hospital characteristics. We then used geographic matching to isolate SES factors predictive of readmission within specific geographies in an effort to control for regional population differences. We hypothesized that, among both matched and localized hospital pairs, the disparities in population SES are the most significant predictors of a maximum penalty. Now in the 3rd year of the HRRP with approximately 75% of eligible hospitals to receive penalties worth an estimated $428 million in the 2015 fiscal year,¹³ we offer a small-area analysis of bipolar extremes to inform debate surrounding the HRRP with evidence regarding the causes and implications of readmission penalties.

Study Design and Sample

This study relies on a case-control design. The cases were defined as US hospitals to receive the maximum 3% HRRP penalty in fiscal year 2015. Controls were drawn from the cohort of hospitals potentially subject to HRRP penalties that received no readmission penalty in the 2015 fiscal year with at least 1 admission for any of the following conditions: heart failure (HF), acute myocardial infarction (AMI), pneumonia (PN), total knee arthroscopy or total hip arthroscopy (THA/TKA), or chronic obstructive pulmonary disease (COPD).

Data Sources

Penalty data were drawn from the 2015 master penalty file,¹⁴ which were accessed via CMS.gov. County-level demographic and socioeconomic data were gathered from the 2015 American Community Survey (ACS), a subsidiary of the US Census. Data on hospital characteristics, capacity, and regional healthcare utilization were drawn from 2012 Dartmouth Atlas,¹⁵ 2012 Medicare Cost Report,¹⁶ 2012 American Hospital Association Hospital Statistics Database, and 2014 Hospital Care Downloadable Database.

Hospital-level CMS data were linked to the master 2015 penalty file. Dartmouth Atlas data were subsequently linked to the file using the Dartmouth Atlas “Hospital to HSA/HRR Crosswalk” file (accessed via DartmouthAtlas.org.) Each hospital was assigned the profile of the hospital service area (HSA) and hospital referral region (HRR) in which it is located. An HSA is a geographic region defined by hospital admissions; the majority, but not entirety, of residents of a given HSA utilize the corresponding hospital. Similarly, an HRR is a geographic region defined by referrals for major cardiovascular and neurosurgery procedures. County-level socioeconomic data were linked to the dataset by county name; thus, hospital socioeconomic profiles are based on the county in which they are located.

Case-Control Matching

In the primary analysis, coarsened exact matching (CEM) matched controls to cases by potential confounding hospital characteristics, including the following: ownership, number of beds, case mix index (measure of acuity), ambulatory care visit rates within 14 days of discharge, and total number of penalty-eligible cases, including HF, AMI, COPD, PN, and THA/TKA.

In the secondary analysis, hospitals were geocoded by zip code. Geographic Information Systems mapping software (ESRI ArcGIS, Redlands, CA) relied upon Euclidean allocation distance spatial analysis^17,18 to match each maximum-penalty hospital to the nearest nonpenalty hospital. Each case was matched to a distinct control; duplicate controls were replaced with the nearest unmatched no-penalty hospital.

Statistical Analysis

Univariate analyses utilized unpaired Student t tests (primary analysis) and paired Student t tests (secondary analysis). The CEM algorithm matches by strata rather than pairs, precluding paired Student t tests in the primary analysis. Statistical analyses were conducted using STATA (StataCorp. 2013. Stata Statistical Software: Release 13. College Station, TX).

Maximum Penalty and Nonpenalty Hospital Matching

Of 3383 hospitals eligible for the HRRP, 39 received the maximum penalty and 770 received no penalty. Thirty-eight control hospitals were identified using CEM algorithm; 1 maximum-penalty hospital could not be matched and was excluded from primary analy

Hospital Characteristics

Case and control profiles are presented in Table 1. Cases and controls were matched by characteristics which may impact readmission rates (Table 1). CEM yielded cohorts similar across a spectrum of metrics, and identical in terms of matching criteria including ownership, beds (quartile), case mix index (above median), ambulatory care visit within 14 days of discharge (above median), and total number of penalty-eligible cases (above median). Relative to no-penalty hospitals, maximum-penalty hospitals were more likely rural (n = 9 vs n = 2, P = 0.022) and have a less profitable operating margin (0.1% vs 6.9%), and location within HSAs with higher age, sex, and race adjusted hospital-wide mortality rate (5.3% vs 4.9%, P = 0.009) and higher rates of discharge for ambulatory care sensitive conditions (108 vs 63 discharges per 1000 Medicare enrollees).

Demographic and Socioeconomic Characteristics

As presented in Table 2, cases a

Secondary Analysis: Geographical Matching

Secondary analysis matched each maximum-penalty hospital to the nearest no-penalty hospital using a global information system vector analysis algorithm. As shown in the Figure, median distance between the case and the control was 42.5 miles (interquartile range: 25th percentile, 15.4 miles; 75th percentile, 98.4 miles). Seventeen pairs (44%) were in the same HRR, 6 of which were in the same HSA. Seven pairs (18%) were within the same county

Secondary Analysis: Economic and Demographic Profiles of Geographically Matched Pairs

Demographic and socioeconomic profiles are presented in Table 3. The cases and controls are in counties with similar age, sex, and ethnicity distributions. Relative to no-penalty hospitals, maximum-penalty hospitals are in counties with lower socioeconomic profiles, including increased rates of poverty (15.6% vs 19.2%, P = 0.007) and lower rates of high school (86.4% vs 82.1%, P = 0.005) or college graduation (22.3% vs 28.1%, P = 0.002). Seven pairs were in the same county; a sensitivity analysis excluding these hospitals revealed similarly lower SES profile in cases relative to controls (Supplementary Table 1).

Our analysis reveals that county-level socioeconomic profiles are predictors of maximum HRRP penalties. Specifically, after matching cases and controls on 5 hospital characteristics that may influence readmission, maximum-penalty hospitals were more likely to be in rural counties with higher rates of poverty and lower rates of education relative to no-penalty hospitals. We observed no difference between cases and controls with respect to age, sex, or ethnicity.

Our study complement

Maximum Penalties as a Function of Population Health

The Dartmouth Atlas of Healthcare measures health outcomes, which are regionally aggregated among local hospitals by either HSA or HRR; see Methods. Such small-area aggregation does not precisely reflect outcomes from a specific hospital, but rather it describes the health status of localities. Disparities in health outcomes exist between maximum-penalty and no-penalty HSAs. Complication rates were slightly higher in maximum penalty HSAs, consistent with studies highlighting complications as drivers of surgical readmissions.^22,23 Moreover, hospital-wide mortality rates were higher in maximum-penalty areas relative to nonpenalty HSAs (5.3 vs 4.9, P = 0.009).

Using national data, Krumholz et al. found no correlation between rates of readmission and mortality for HF, AMI, and PN²⁴, which is a phenomenon acknowledged by the Medicare Payment Advisory Commission (MedPac) in a 2013 report titled, “Refining the hospital readmission reduction program.”²⁵ In large national studies, others have shown low SES to be associated with elevated readmission but not mortality.^10,11 In contrast, we restricted our analysis to matched cohorts and are, to our knowledge, the first to present evidence of an association between readmission and hospital-wide mortality adjusted for age, sex, and ethnicity.

Our results suggest maximum readmission penalties are a function of population health and public health capacity. The rates of ambulatory care sensitive condition (ACSC) discharges were substantially higher in HSAs of maximum penalty hospitals relative to nonpenalty hospitals (108 vs 63 per 1000 Medicare enrollees, P < 0.001). ACSC discharges have been used to measure primary care quality for 30 years, with the assumption being that admission for chronic conditions, such as HF, can be prevented with effective primary care.^26,27 Moreover, patients discharged from maximum-penalty hospitals were more likely to have an emergency room visit within 30 days of discharge (20.8% vs 18.4%, P < 0.001). Higher rates of ACSCs and postdischarge emergency department visits suggest outpatient resources in maximum-penalty service areas struggle to manage the disease burden of high-risk populations. Geography may be a contributor; maximum-penalty hospitals were more likely to be rural than no-penalty hospitals (24% vs 5%, P = 0.022).

Our findings suggest hospitals providing care to vulnerable communities (defined by low income, low education, and high rates of ambulatory sensitive discharges) are disproportionately penalized. McHugh et al. revealed high nurse staffing levels to be protective against readmission penalties²⁸, yet high penalties to low-margin hospitals may encourage reduced rather than increased staff. It may be better policy to direct resources rather than penalties to underserved communities; our findings echo others with concern about disproportionate penalties to hospitals serving low SES patients.^2,5,6,29

Secondary Analysis: Geographic Matching

Geographic matching paired each maximum-penalty hospital to the nearest no-penalty hospital in an attempt to control for unmeasured regional factors that may confound an association between socioeconomic profile and health outcomes. For example, cost of living ^{30, 31} and obesity ^32,33 vary regionally. Our study was unequipped to assess potential regional confounders; we attempted to control for them with geographical matching.

Median distance between maximum-penalty and no-penalty hospitals was 42.5 miles. Seven pairs were located within the same county, thus both case and control were assigned the same socioeconomic profile. Despite close proximity and identical SES profile in 7 of 39 pairs, maximum-penalty hospitals were in counties with lower income and lower rates of education, strengthening the association between SES and maximum readmission penalties.

Implications and Future Directions

In response to criticism surrounding the HRRP, the National Quality Forum endorsed the general concept of SES adjustment for hospital quality measures.³⁴ Subsequently, in a briefing dated March 24, 2015, MedPAC, a government agency which provides Medicare policy analysis to Congress, proposed an SES adjustment methodology of “dividing hospitals into peer groups based on their overall share of low-income Medicare patients, and then setting a benchmark readmissions target for each peer group”;³⁵ in other words, lower standards for hospitals that serve low-income populations. MedPAC’s proposal will reduce penalties to “safety net” institutions, which is progress but not a solution. Although the HRRP appears to be working, according to the US Department of Health and Human Services, readmissions fell by 150,000 between January 2012 and February 2013,³⁶ we are concerned neither the HRRP nor the MedPac revision proposal considers geographic and environmental components of readmission. The HRRP promotes national improvement in exchange for regional regression.

Fair quality measures are key to value-based reimbursement models; yet, implicit in penalties for excess readmissions is the assumed ability to calculate hospital performance targets. Benchmarks for safety, timely care, and patient satisfaction can be uniform; rates of central line infections should not be influenced by patient mix. However, 9 of the 39 maximum-penalty hospitals under the HRRP are in rural Kentucky; one could hypothesize many reasons why rural Kentucky is a hotbed for excess readmission, including the regional production of tobacco and bourbon.

The fundamental question raised by our study is whether poor performance on quality measures is a function of underperforming hospitals or a manifestation of underserved communities. Moving forward, we encourage data systems and study designs that focus research on geospatial distribution of population health within the context of social and behavioral health determinants.³⁷ Small-area studies of factors that drive health outcomes will inform rational alignment of healthcare policies and resources (including penalties and incentives) with underlying population needs.

Strengths and Weaknesses

Matching is a strength of the study. Primary analysis matched case and controls by hospital characteristics, generating cohorts similar across a spectrum of hospital metrics. Therefore, variation in readmission rates was less likely confounded by hospital characteristics. The secondary analysis was matched by geography in an effort to adjust for unmeasured, regional factors, including obesity and cost of living that may confound an association between SES and health outcomes. Geographic matching adds strength to our assertion that SES drives distinction between maximum-penalty hospitals and nonpenalty hospitals.

One weakness was the regional unit of analysis for socioeconomic and Dartmouth Atlas data, which is not a precise profile of the corresponding hospital. Each hospital was assigned a county-level socioeconomic profile. A more robust methodology would analyze patient-level SES data; this was impractical given a cohort of 78 hospitals. Regional health outcomes data limits analysis of readmission penalties as a function of hospital quality. Instead, regional data facilitated associations between readmission and population health, consistent with the aim of our study.

We analyzed 116 of 3668 hospitals eligible for the HRRP (3.2%), limiting the generalizability of our findings. Eighty-four percent of hospitals in the primary analysis have below the median number of beds, and none of them are teaching hospitals. Our analysis, restricted to maximum-penalty and no-penalty cohorts, does not address potential association between submaximal readmission penalties and socioeconomics.

Both matching techniques potentially controlled for similar SES factors and skewed our results towards null, especially in terms of race and ethnicity. Geographic matching generated 7 pairs (18%) within in the same county; both maximum-penalty and no-penalty hospitals were assigned the same socioeconomic profile, as well as 6 pairs (15%) within the same HSA, and both cases and controls had identical Dartmouth Atlas health outcomes profiles. We retained these pairs in our analysis to avoid artificially inflating SES and population health differences between cohorts.

Thirty-nine hospitals received a maximum penalty in the 3rd year of the HRRP. Relative to geographically matched no-penalty hospitals, maximum-penalty hospitals were more likely to be rural and located in counties with less educational attainment, more poverty and more poorly controlled chronic disease. In contrast to nationwide studies, a matched analysis plan revealed no difference between the cohorts in terms of race and ethnicity and provided evidence that maximum penalty hospitals had higher rates of age-, sex-, and race-adjusted hospital-wide mortality.

Our results highlight potential consequences of nationally derived benchmarks for phenomena underpinned by social, behavioral, and environmental factors and raise the question of whether maximum HRRP penalties are a consequence of underperforming hospitals or a manifestation of underserved communities. We are encouraged by MedPAC’s proposal to stratify HRRP by SES, yet recommend further small-area geographic analyses to better align quality measures, penalties, and incentives with resources and needs of distinct populations.

Acknowledgments

The authors thank William Hisey, who laid the foundation for the analysis and without whom the project would not have been possible.

DISCLOSURE

The authors certify that none of the material in this manuscript has been previously published and that none of this material is currently under consideration for publication elsewhere. This project received no funding. None of the authors on this manuscript have any commercial relationships to disclose in relation to this manuscript. All authors have reviewed and approved this manuscript and have contributed significantly to the design, conduct, and/or analysis of the research. No authors have any financial interests to disclose. No authors have any potential conflicts of interest to disclose. No authors have financial or personal relationships with any of the subject material presented in the manuscript.

INTRODUCTION

According to Centers for Medicare & Medicaid Services (CMS), approximately 1 in 5 patients discharged from a hospital will be readmitted within 30 days.¹ The Hospital Readmission Reduction Program (HRRP) is designed to reduce readmission by withholding up to 3% of all Medicare reimbursement from hospitals with “excess” readmissions; however, absent from the HRRP is adjustment for socioeconomic status (SES), which CMS holds may undermine incentives to reduce health disparities and institutionalize lower standards for hospitals serving disadvantaged populations.²

Lack of SES adjustment has been criticized by those who point to evidence highlighting postdischarge environment and patient SES as drivers of readmission and suggest hospitals that serve low SES individuals will bear a disproportionate share of penalties.^3-6 Single-center,^3,7,8 regional,^9,10 and nationwide^6,11 studies highlight census tract level socioeconomic variables as predictive of readmission. Single-center studies, robust in controlling for confounders, including staffing, training, electronic medical record utilization, and transitional care processes, do not allow comparisons between hospitals, limiting utility in HRRP evaluation. Multicenter cohorts, on the other hand, allow for comparisons between high and low penalty hospitals, pioneered by Joynt et al¹² after the first round of HRRP penalties; yet this technique may not account for confounding caused by extensive demographic, socioeconomic, and hospital characteristic heterogeneity inherent in a national cohort. Analysis of the 2015 HRRP penalty data by Sjoding et al.⁶ revealed higher chronic obstructive pulmonary disease (COPD) readmission rates in the Mid-Atlantic, Midwest, and South relative to other regions; however, the magnitude of small-area variation and its relationship to population SES have yet to be characterized.

Therefore, we conducted a matched case-control design, whereby each maximum penalty hospital was matched to a nonpenalty hospital using key hospital characteristics. We then used geographic matching to isolate SES factors predictive of readmission within specific geographies in an effort to control for regional population differences. We hypothesized that, among both matched and localized hospital pairs, the disparities in population SES are the most significant predictors of a maximum penalty. Now in the 3rd year of the HRRP with approximately 75% of eligible hospitals to receive penalties worth an estimated $428 million in the 2015 fiscal year,¹³ we offer a small-area analysis of bipolar extremes to inform debate surrounding the HRRP with evidence regarding the causes and implications of readmission penalties.

Study Design and Sample

This study relies on a case-control design. The cases were defined as US hospitals to receive the maximum 3% HRRP penalty in fiscal year 2015. Controls were drawn from the cohort of hospitals potentially subject to HRRP penalties that received no readmission penalty in the 2015 fiscal year with at least 1 admission for any of the following conditions: heart failure (HF), acute myocardial infarction (AMI), pneumonia (PN), total knee arthroscopy or total hip arthroscopy (THA/TKA), or chronic obstructive pulmonary disease (COPD).

Data Sources

Penalty data were drawn from the 2015 master penalty file,¹⁴ which were accessed via CMS.gov. County-level demographic and socioeconomic data were gathered from the 2015 American Community Survey (ACS), a subsidiary of the US Census. Data on hospital characteristics, capacity, and regional healthcare utilization were drawn from 2012 Dartmouth Atlas,¹⁵ 2012 Medicare Cost Report,¹⁶ 2012 American Hospital Association Hospital Statistics Database, and 2014 Hospital Care Downloadable Database.

Hospital-level CMS data were linked to the master 2015 penalty file. Dartmouth Atlas data were subsequently linked to the file using the Dartmouth Atlas “Hospital to HSA/HRR Crosswalk” file (accessed via DartmouthAtlas.org.) Each hospital was assigned the profile of the hospital service area (HSA) and hospital referral region (HRR) in which it is located. An HSA is a geographic region defined by hospital admissions; the majority, but not entirety, of residents of a given HSA utilize the corresponding hospital. Similarly, an HRR is a geographic region defined by referrals for major cardiovascular and neurosurgery procedures. County-level socioeconomic data were linked to the dataset by county name; thus, hospital socioeconomic profiles are based on the county in which they are located.

Case-Control Matching

In the primary analysis, coarsened exact matching (CEM) matched controls to cases by potential confounding hospital characteristics, including the following: ownership, number of beds, case mix index (measure of acuity), ambulatory care visit rates within 14 days of discharge, and total number of penalty-eligible cases, including HF, AMI, COPD, PN, and THA/TKA.

In the secondary analysis, hospitals were geocoded by zip code. Geographic Information Systems mapping software (ESRI ArcGIS, Redlands, CA) relied upon Euclidean allocation distance spatial analysis^17,18 to match each maximum-penalty hospital to the nearest nonpenalty hospital. Each case was matched to a distinct control; duplicate controls were replaced with the nearest unmatched no-penalty hospital.

Statistical Analysis

Univariate analyses utilized unpaired Student t tests (primary analysis) and paired Student t tests (secondary analysis). The CEM algorithm matches by strata rather than pairs, precluding paired Student t tests in the primary analysis. Statistical analyses were conducted using STATA (StataCorp. 2013. Stata Statistical Software: Release 13. College Station, TX).

Maximum Penalty and Nonpenalty Hospital Matching

Of 3383 hospitals eligible for the HRRP, 39 received the maximum penalty and 770 received no penalty. Thirty-eight control hospitals were identified using CEM algorithm; 1 maximum-penalty hospital could not be matched and was excluded from primary analy

Hospital Characteristics

Case and control profiles are presented in Table 1. Cases and controls were matched by characteristics which may impact readmission rates (Table 1). CEM yielded cohorts similar across a spectrum of metrics, and identical in terms of matching criteria including ownership, beds (quartile), case mix index (above median), ambulatory care visit within 14 days of discharge (above median), and total number of penalty-eligible cases (above median). Relative to no-penalty hospitals, maximum-penalty hospitals were more likely rural (n = 9 vs n = 2, P = 0.022) and have a less profitable operating margin (0.1% vs 6.9%), and location within HSAs with higher age, sex, and race adjusted hospital-wide mortality rate (5.3% vs 4.9%, P = 0.009) and higher rates of discharge for ambulatory care sensitive conditions (108 vs 63 discharges per 1000 Medicare enrollees).

Demographic and Socioeconomic Characteristics

As presented in Table 2, cases a

Secondary Analysis: Geographical Matching

Secondary analysis matched each maximum-penalty hospital to the nearest no-penalty hospital using a global information system vector analysis algorithm. As shown in the Figure, median distance between the case and the control was 42.5 miles (interquartile range: 25th percentile, 15.4 miles; 75th percentile, 98.4 miles). Seventeen pairs (44%) were in the same HRR, 6 of which were in the same HSA. Seven pairs (18%) were within the same county

Secondary Analysis: Economic and Demographic Profiles of Geographically Matched Pairs

Demographic and socioeconomic profiles are presented in Table 3. The cases and controls are in counties with similar age, sex, and ethnicity distributions. Relative to no-penalty hospitals, maximum-penalty hospitals are in counties with lower socioeconomic profiles, including increased rates of poverty (15.6% vs 19.2%, P = 0.007) and lower rates of high school (86.4% vs 82.1%, P = 0.005) or college graduation (22.3% vs 28.1%, P = 0.002). Seven pairs were in the same county; a sensitivity analysis excluding these hospitals revealed similarly lower SES profile in cases relative to controls (Supplementary Table 1).

Our analysis reveals that county-level socioeconomic profiles are predictors of maximum HRRP penalties. Specifically, after matching cases and controls on 5 hospital characteristics that may influence readmission, maximum-penalty hospitals were more likely to be in rural counties with higher rates of poverty and lower rates of education relative to no-penalty hospitals. We observed no difference between cases and controls with respect to age, sex, or ethnicity.

Our study complement

Maximum Penalties as a Function of Population Health

The Dartmouth Atlas of Healthcare measures health outcomes, which are regionally aggregated among local hospitals by either HSA or HRR; see Methods. Such small-area aggregation does not precisely reflect outcomes from a specific hospital, but rather it describes the health status of localities. Disparities in health outcomes exist between maximum-penalty and no-penalty HSAs. Complication rates were slightly higher in maximum penalty HSAs, consistent with studies highlighting complications as drivers of surgical readmissions.^22,23 Moreover, hospital-wide mortality rates were higher in maximum-penalty areas relative to nonpenalty HSAs (5.3 vs 4.9, P = 0.009).

Using national data, Krumholz et al. found no correlation between rates of readmission and mortality for HF, AMI, and PN²⁴, which is a phenomenon acknowledged by the Medicare Payment Advisory Commission (MedPac) in a 2013 report titled, “Refining the hospital readmission reduction program.”²⁵ In large national studies, others have shown low SES to be associated with elevated readmission but not mortality.^10,11 In contrast, we restricted our analysis to matched cohorts and are, to our knowledge, the first to present evidence of an association between readmission and hospital-wide mortality adjusted for age, sex, and ethnicity.

Our results suggest maximum readmission penalties are a function of population health and public health capacity. The rates of ambulatory care sensitive condition (ACSC) discharges were substantially higher in HSAs of maximum penalty hospitals relative to nonpenalty hospitals (108 vs 63 per 1000 Medicare enrollees, P < 0.001). ACSC discharges have been used to measure primary care quality for 30 years, with the assumption being that admission for chronic conditions, such as HF, can be prevented with effective primary care.^26,27 Moreover, patients discharged from maximum-penalty hospitals were more likely to have an emergency room visit within 30 days of discharge (20.8% vs 18.4%, P < 0.001). Higher rates of ACSCs and postdischarge emergency department visits suggest outpatient resources in maximum-penalty service areas struggle to manage the disease burden of high-risk populations. Geography may be a contributor; maximum-penalty hospitals were more likely to be rural than no-penalty hospitals (24% vs 5%, P = 0.022).

Our findings suggest hospitals providing care to vulnerable communities (defined by low income, low education, and high rates of ambulatory sensitive discharges) are disproportionately penalized. McHugh et al. revealed high nurse staffing levels to be protective against readmission penalties²⁸, yet high penalties to low-margin hospitals may encourage reduced rather than increased staff. It may be better policy to direct resources rather than penalties to underserved communities; our findings echo others with concern about disproportionate penalties to hospitals serving low SES patients.^2,5,6,29

Secondary Analysis: Geographic Matching

Geographic matching paired each maximum-penalty hospital to the nearest no-penalty hospital in an attempt to control for unmeasured regional factors that may confound an association between socioeconomic profile and health outcomes. For example, cost of living ^{30, 31} and obesity ^32,33 vary regionally. Our study was unequipped to assess potential regional confounders; we attempted to control for them with geographical matching.

Median distance between maximum-penalty and no-penalty hospitals was 42.5 miles. Seven pairs were located within the same county, thus both case and control were assigned the same socioeconomic profile. Despite close proximity and identical SES profile in 7 of 39 pairs, maximum-penalty hospitals were in counties with lower income and lower rates of education, strengthening the association between SES and maximum readmission penalties.

Implications and Future Directions

In response to criticism surrounding the HRRP, the National Quality Forum endorsed the general concept of SES adjustment for hospital quality measures.³⁴ Subsequently, in a briefing dated March 24, 2015, MedPAC, a government agency which provides Medicare policy analysis to Congress, proposed an SES adjustment methodology of “dividing hospitals into peer groups based on their overall share of low-income Medicare patients, and then setting a benchmark readmissions target for each peer group”;³⁵ in other words, lower standards for hospitals that serve low-income populations. MedPAC’s proposal will reduce penalties to “safety net” institutions, which is progress but not a solution. Although the HRRP appears to be working, according to the US Department of Health and Human Services, readmissions fell by 150,000 between January 2012 and February 2013,³⁶ we are concerned neither the HRRP nor the MedPac revision proposal considers geographic and environmental components of readmission. The HRRP promotes national improvement in exchange for regional regression.

Fair quality measures are key to value-based reimbursement models; yet, implicit in penalties for excess readmissions is the assumed ability to calculate hospital performance targets. Benchmarks for safety, timely care, and patient satisfaction can be uniform; rates of central line infections should not be influenced by patient mix. However, 9 of the 39 maximum-penalty hospitals under the HRRP are in rural Kentucky; one could hypothesize many reasons why rural Kentucky is a hotbed for excess readmission, including the regional production of tobacco and bourbon.

The fundamental question raised by our study is whether poor performance on quality measures is a function of underperforming hospitals or a manifestation of underserved communities. Moving forward, we encourage data systems and study designs that focus research on geospatial distribution of population health within the context of social and behavioral health determinants.³⁷ Small-area studies of factors that drive health outcomes will inform rational alignment of healthcare policies and resources (including penalties and incentives) with underlying population needs.

Strengths and Weaknesses

Matching is a strength of the study. Primary analysis matched case and controls by hospital characteristics, generating cohorts similar across a spectrum of hospital metrics. Therefore, variation in readmission rates was less likely confounded by hospital characteristics. The secondary analysis was matched by geography in an effort to adjust for unmeasured, regional factors, including obesity and cost of living that may confound an association between SES and health outcomes. Geographic matching adds strength to our assertion that SES drives distinction between maximum-penalty hospitals and nonpenalty hospitals.

One weakness was the regional unit of analysis for socioeconomic and Dartmouth Atlas data, which is not a precise profile of the corresponding hospital. Each hospital was assigned a county-level socioeconomic profile. A more robust methodology would analyze patient-level SES data; this was impractical given a cohort of 78 hospitals. Regional health outcomes data limits analysis of readmission penalties as a function of hospital quality. Instead, regional data facilitated associations between readmission and population health, consistent with the aim of our study.

We analyzed 116 of 3668 hospitals eligible for the HRRP (3.2%), limiting the generalizability of our findings. Eighty-four percent of hospitals in the primary analysis have below the median number of beds, and none of them are teaching hospitals. Our analysis, restricted to maximum-penalty and no-penalty cohorts, does not address potential association between submaximal readmission penalties and socioeconomics.

Both matching techniques potentially controlled for similar SES factors and skewed our results towards null, especially in terms of race and ethnicity. Geographic matching generated 7 pairs (18%) within in the same county; both maximum-penalty and no-penalty hospitals were assigned the same socioeconomic profile, as well as 6 pairs (15%) within the same HSA, and both cases and controls had identical Dartmouth Atlas health outcomes profiles. We retained these pairs in our analysis to avoid artificially inflating SES and population health differences between cohorts.

Thirty-nine hospitals received a maximum penalty in the 3rd year of the HRRP. Relative to geographically matched no-penalty hospitals, maximum-penalty hospitals were more likely to be rural and located in counties with less educational attainment, more poverty and more poorly controlled chronic disease. In contrast to nationwide studies, a matched analysis plan revealed no difference between the cohorts in terms of race and ethnicity and provided evidence that maximum penalty hospitals had higher rates of age-, sex-, and race-adjusted hospital-wide mortality.

Our results highlight potential consequences of nationally derived benchmarks for phenomena underpinned by social, behavioral, and environmental factors and raise the question of whether maximum HRRP penalties are a consequence of underperforming hospitals or a manifestation of underserved communities. We are encouraged by MedPAC’s proposal to stratify HRRP by SES, yet recommend further small-area geographic analyses to better align quality measures, penalties, and incentives with resources and needs of distinct populations.

Acknowledgments

The authors thank William Hisey, who laid the foundation for the analysis and without whom the project would not have been possible.

DISCLOSURE

The authors certify that none of the material in this manuscript has been previously published and that none of this material is currently under consideration for publication elsewhere. This project received no funding. None of the authors on this manuscript have any commercial relationships to disclose in relation to this manuscript. All authors have reviewed and approved this manuscript and have contributed significantly to the design, conduct, and/or analysis of the research. No authors have any financial interests to disclose. No authors have any potential conflicts of interest to disclose. No authors have financial or personal relationships with any of the subject material presented in the manuscript.

INTRODUCTION

According to Centers for Medicare & Medicaid Services (CMS), approximately 1 in 5 patients discharged from a hospital will be readmitted within 30 days.¹ The Hospital Readmission Reduction Program (HRRP) is designed to reduce readmission by withholding up to 3% of all Medicare reimbursement from hospitals with “excess” readmissions; however, absent from the HRRP is adjustment for socioeconomic status (SES), which CMS holds may undermine incentives to reduce health disparities and institutionalize lower standards for hospitals serving disadvantaged populations.²

Lack of SES adjustment has been criticized by those who point to evidence highlighting postdischarge environment and patient SES as drivers of readmission and suggest hospitals that serve low SES individuals will bear a disproportionate share of penalties.^3-6 Single-center,^3,7,8 regional,^9,10 and nationwide^6,11 studies highlight census tract level socioeconomic variables as predictive of readmission. Single-center studies, robust in controlling for confounders, including staffing, training, electronic medical record utilization, and transitional care processes, do not allow comparisons between hospitals, limiting utility in HRRP evaluation. Multicenter cohorts, on the other hand, allow for comparisons between high and low penalty hospitals, pioneered by Joynt et al¹² after the first round of HRRP penalties; yet this technique may not account for confounding caused by extensive demographic, socioeconomic, and hospital characteristic heterogeneity inherent in a national cohort. Analysis of the 2015 HRRP penalty data by Sjoding et al.⁶ revealed higher chronic obstructive pulmonary disease (COPD) readmission rates in the Mid-Atlantic, Midwest, and South relative to other regions; however, the magnitude of small-area variation and its relationship to population SES have yet to be characterized.

Therefore, we conducted a matched case-control design, whereby each maximum penalty hospital was matched to a nonpenalty hospital using key hospital characteristics. We then used geographic matching to isolate SES factors predictive of readmission within specific geographies in an effort to control for regional population differences. We hypothesized that, among both matched and localized hospital pairs, the disparities in population SES are the most significant predictors of a maximum penalty. Now in the 3rd year of the HRRP with approximately 75% of eligible hospitals to receive penalties worth an estimated $428 million in the 2015 fiscal year,¹³ we offer a small-area analysis of bipolar extremes to inform debate surrounding the HRRP with evidence regarding the causes and implications of readmission penalties.

Study Design and Sample

This study relies on a case-control design. The cases were defined as US hospitals to receive the maximum 3% HRRP penalty in fiscal year 2015. Controls were drawn from the cohort of hospitals potentially subject to HRRP penalties that received no readmission penalty in the 2015 fiscal year with at least 1 admission for any of the following conditions: heart failure (HF), acute myocardial infarction (AMI), pneumonia (PN), total knee arthroscopy or total hip arthroscopy (THA/TKA), or chronic obstructive pulmonary disease (COPD).

Data Sources

Penalty data were drawn from the 2015 master penalty file,¹⁴ which were accessed via CMS.gov. County-level demographic and socioeconomic data were gathered from the 2015 American Community Survey (ACS), a subsidiary of the US Census. Data on hospital characteristics, capacity, and regional healthcare utilization were drawn from 2012 Dartmouth Atlas,¹⁵ 2012 Medicare Cost Report,¹⁶ 2012 American Hospital Association Hospital Statistics Database, and 2014 Hospital Care Downloadable Database.

Hospital-level CMS data were linked to the master 2015 penalty file. Dartmouth Atlas data were subsequently linked to the file using the Dartmouth Atlas “Hospital to HSA/HRR Crosswalk” file (accessed via DartmouthAtlas.org.) Each hospital was assigned the profile of the hospital service area (HSA) and hospital referral region (HRR) in which it is located. An HSA is a geographic region defined by hospital admissions; the majority, but not entirety, of residents of a given HSA utilize the corresponding hospital. Similarly, an HRR is a geographic region defined by referrals for major cardiovascular and neurosurgery procedures. County-level socioeconomic data were linked to the dataset by county name; thus, hospital socioeconomic profiles are based on the county in which they are located.

Case-Control Matching

In the primary analysis, coarsened exact matching (CEM) matched controls to cases by potential confounding hospital characteristics, including the following: ownership, number of beds, case mix index (measure of acuity), ambulatory care visit rates within 14 days of discharge, and total number of penalty-eligible cases, including HF, AMI, COPD, PN, and THA/TKA.

In the secondary analysis, hospitals were geocoded by zip code. Geographic Information Systems mapping software (ESRI ArcGIS, Redlands, CA) relied upon Euclidean allocation distance spatial analysis^17,18 to match each maximum-penalty hospital to the nearest nonpenalty hospital. Each case was matched to a distinct control; duplicate controls were replaced with the nearest unmatched no-penalty hospital.

Statistical Analysis

Univariate analyses utilized unpaired Student t tests (primary analysis) and paired Student t tests (secondary analysis). The CEM algorithm matches by strata rather than pairs, precluding paired Student t tests in the primary analysis. Statistical analyses were conducted using STATA (StataCorp. 2013. Stata Statistical Software: Release 13. College Station, TX).

Maximum Penalty and Nonpenalty Hospital Matching

Of 3383 hospitals eligible for the HRRP, 39 received the maximum penalty and 770 received no penalty. Thirty-eight control hospitals were identified using CEM algorithm; 1 maximum-penalty hospital could not be matched and was excluded from primary analy

Hospital Characteristics

Case and control profiles are presented in Table 1. Cases and controls were matched by characteristics which may impact readmission rates (Table 1). CEM yielded cohorts similar across a spectrum of metrics, and identical in terms of matching criteria including ownership, beds (quartile), case mix index (above median), ambulatory care visit within 14 days of discharge (above median), and total number of penalty-eligible cases (above median). Relative to no-penalty hospitals, maximum-penalty hospitals were more likely rural (n = 9 vs n = 2, P = 0.022) and have a less profitable operating margin (0.1% vs 6.9%), and location within HSAs with higher age, sex, and race adjusted hospital-wide mortality rate (5.3% vs 4.9%, P = 0.009) and higher rates of discharge for ambulatory care sensitive conditions (108 vs 63 discharges per 1000 Medicare enrollees).

Demographic and Socioeconomic Characteristics

As presented in Table 2, cases a

Secondary Analysis: Geographical Matching

Secondary analysis matched each maximum-penalty hospital to the nearest no-penalty hospital using a global information system vector analysis algorithm. As shown in the Figure, median distance between the case and the control was 42.5 miles (interquartile range: 25th percentile, 15.4 miles; 75th percentile, 98.4 miles). Seventeen pairs (44%) were in the same HRR, 6 of which were in the same HSA. Seven pairs (18%) were within the same county

Secondary Analysis: Economic and Demographic Profiles of Geographically Matched Pairs

Demographic and socioeconomic profiles are presented in Table 3. The cases and controls are in counties with similar age, sex, and ethnicity distributions. Relative to no-penalty hospitals, maximum-penalty hospitals are in counties with lower socioeconomic profiles, including increased rates of poverty (15.6% vs 19.2%, P = 0.007) and lower rates of high school (86.4% vs 82.1%, P = 0.005) or college graduation (22.3% vs 28.1%, P = 0.002). Seven pairs were in the same county; a sensitivity analysis excluding these hospitals revealed similarly lower SES profile in cases relative to controls (Supplementary Table 1).

Our analysis reveals that county-level socioeconomic profiles are predictors of maximum HRRP penalties. Specifically, after matching cases and controls on 5 hospital characteristics that may influence readmission, maximum-penalty hospitals were more likely to be in rural counties with higher rates of poverty and lower rates of education relative to no-penalty hospitals. We observed no difference between cases and controls with respect to age, sex, or ethnicity.

Our study complement

Maximum Penalties as a Function of Population Health

The Dartmouth Atlas of Healthcare measures health outcomes, which are regionally aggregated among local hospitals by either HSA or HRR; see Methods. Such small-area aggregation does not precisely reflect outcomes from a specific hospital, but rather it describes the health status of localities. Disparities in health outcomes exist between maximum-penalty and no-penalty HSAs. Complication rates were slightly higher in maximum penalty HSAs, consistent with studies highlighting complications as drivers of surgical readmissions.^22,23 Moreover, hospital-wide mortality rates were higher in maximum-penalty areas relative to nonpenalty HSAs (5.3 vs 4.9, P = 0.009).

Using national data, Krumholz et al. found no correlation between rates of readmission and mortality for HF, AMI, and PN²⁴, which is a phenomenon acknowledged by the Medicare Payment Advisory Commission (MedPac) in a 2013 report titled, “Refining the hospital readmission reduction program.”²⁵ In large national studies, others have shown low SES to be associated with elevated readmission but not mortality.^10,11 In contrast, we restricted our analysis to matched cohorts and are, to our knowledge, the first to present evidence of an association between readmission and hospital-wide mortality adjusted for age, sex, and ethnicity.

Our results suggest maximum readmission penalties are a function of population health and public health capacity. The rates of ambulatory care sensitive condition (ACSC) discharges were substantially higher in HSAs of maximum penalty hospitals relative to nonpenalty hospitals (108 vs 63 per 1000 Medicare enrollees, P < 0.001). ACSC discharges have been used to measure primary care quality for 30 years, with the assumption being that admission for chronic conditions, such as HF, can be prevented with effective primary care.^26,27 Moreover, patients discharged from maximum-penalty hospitals were more likely to have an emergency room visit within 30 days of discharge (20.8% vs 18.4%, P < 0.001). Higher rates of ACSCs and postdischarge emergency department visits suggest outpatient resources in maximum-penalty service areas struggle to manage the disease burden of high-risk populations. Geography may be a contributor; maximum-penalty hospitals were more likely to be rural than no-penalty hospitals (24% vs 5%, P = 0.022).

Our findings suggest hospitals providing care to vulnerable communities (defined by low income, low education, and high rates of ambulatory sensitive discharges) are disproportionately penalized. McHugh et al. revealed high nurse staffing levels to be protective against readmission penalties²⁸, yet high penalties to low-margin hospitals may encourage reduced rather than increased staff. It may be better policy to direct resources rather than penalties to underserved communities; our findings echo others with concern about disproportionate penalties to hospitals serving low SES patients.^2,5,6,29

Secondary Analysis: Geographic Matching

Geographic matching paired each maximum-penalty hospital to the nearest no-penalty hospital in an attempt to control for unmeasured regional factors that may confound an association between socioeconomic profile and health outcomes. For example, cost of living ^{30, 31} and obesity ^32,33 vary regionally. Our study was unequipped to assess potential regional confounders; we attempted to control for them with geographical matching.

Median distance between maximum-penalty and no-penalty hospitals was 42.5 miles. Seven pairs were located within the same county, thus both case and control were assigned the same socioeconomic profile. Despite close proximity and identical SES profile in 7 of 39 pairs, maximum-penalty hospitals were in counties with lower income and lower rates of education, strengthening the association between SES and maximum readmission penalties.

Implications and Future Directions

In response to criticism surrounding the HRRP, the National Quality Forum endorsed the general concept of SES adjustment for hospital quality measures.³⁴ Subsequently, in a briefing dated March 24, 2015, MedPAC, a government agency which provides Medicare policy analysis to Congress, proposed an SES adjustment methodology of “dividing hospitals into peer groups based on their overall share of low-income Medicare patients, and then setting a benchmark readmissions target for each peer group”;³⁵ in other words, lower standards for hospitals that serve low-income populations. MedPAC’s proposal will reduce penalties to “safety net” institutions, which is progress but not a solution. Although the HRRP appears to be working, according to the US Department of Health and Human Services, readmissions fell by 150,000 between January 2012 and February 2013,³⁶ we are concerned neither the HRRP nor the MedPac revision proposal considers geographic and environmental components of readmission. The HRRP promotes national improvement in exchange for regional regression.

Fair quality measures are key to value-based reimbursement models; yet, implicit in penalties for excess readmissions is the assumed ability to calculate hospital performance targets. Benchmarks for safety, timely care, and patient satisfaction can be uniform; rates of central line infections should not be influenced by patient mix. However, 9 of the 39 maximum-penalty hospitals under the HRRP are in rural Kentucky; one could hypothesize many reasons why rural Kentucky is a hotbed for excess readmission, including the regional production of tobacco and bourbon.

The fundamental question raised by our study is whether poor performance on quality measures is a function of underperforming hospitals or a manifestation of underserved communities. Moving forward, we encourage data systems and study designs that focus research on geospatial distribution of population health within the context of social and behavioral health determinants.³⁷ Small-area studies of factors that drive health outcomes will inform rational alignment of healthcare policies and resources (including penalties and incentives) with underlying population needs.

Strengths and Weaknesses

Matching is a strength of the study. Primary analysis matched case and controls by hospital characteristics, generating cohorts similar across a spectrum of hospital metrics. Therefore, variation in readmission rates was less likely confounded by hospital characteristics. The secondary analysis was matched by geography in an effort to adjust for unmeasured, regional factors, including obesity and cost of living that may confound an association between SES and health outcomes. Geographic matching adds strength to our assertion that SES drives distinction between maximum-penalty hospitals and nonpenalty hospitals.

One weakness was the regional unit of analysis for socioeconomic and Dartmouth Atlas data, which is not a precise profile of the corresponding hospital. Each hospital was assigned a county-level socioeconomic profile. A more robust methodology would analyze patient-level SES data; this was impractical given a cohort of 78 hospitals. Regional health outcomes data limits analysis of readmission penalties as a function of hospital quality. Instead, regional data facilitated associations between readmission and population health, consistent with the aim of our study.

We analyzed 116 of 3668 hospitals eligible for the HRRP (3.2%), limiting the generalizability of our findings. Eighty-four percent of hospitals in the primary analysis have below the median number of beds, and none of them are teaching hospitals. Our analysis, restricted to maximum-penalty and no-penalty cohorts, does not address potential association between submaximal readmission penalties and socioeconomics.

Both matching techniques potentially controlled for similar SES factors and skewed our results towards null, especially in terms of race and ethnicity. Geographic matching generated 7 pairs (18%) within in the same county; both maximum-penalty and no-penalty hospitals were assigned the same socioeconomic profile, as well as 6 pairs (15%) within the same HSA, and both cases and controls had identical Dartmouth Atlas health outcomes profiles. We retained these pairs in our analysis to avoid artificially inflating SES and population health differences between cohorts.

Thirty-nine hospitals received a maximum penalty in the 3rd year of the HRRP. Relative to geographically matched no-penalty hospitals, maximum-penalty hospitals were more likely to be rural and located in counties with less educational attainment, more poverty and more poorly controlled chronic disease. In contrast to nationwide studies, a matched analysis plan revealed no difference between the cohorts in terms of race and ethnicity and provided evidence that maximum penalty hospitals had higher rates of age-, sex-, and race-adjusted hospital-wide mortality.

Our results highlight potential consequences of nationally derived benchmarks for phenomena underpinned by social, behavioral, and environmental factors and raise the question of whether maximum HRRP penalties are a consequence of underperforming hospitals or a manifestation of underserved communities. We are encouraged by MedPAC’s proposal to stratify HRRP by SES, yet recommend further small-area geographic analyses to better align quality measures, penalties, and incentives with resources and needs of distinct populations.

Acknowledgments

The authors thank William Hisey, who laid the foundation for the analysis and without whom the project would not have been possible.

DISCLOSURE

The authors certify that none of the material in this manuscript has been previously published and that none of this material is currently under consideration for publication elsewhere. This project received no funding. None of the authors on this manuscript have any commercial relationships to disclose in relation to this manuscript. All authors have reviewed and approved this manuscript and have contributed significantly to the design, conduct, and/or analysis of the research. No authors have any financial interests to disclose. No authors have any potential conflicts of interest to disclose. No authors have financial or personal relationships with any of the subject material presented in the manuscript.

There is growing recognition that patients and family members have critical insights into healthcare experiences. As consumers of healthcare, patient experience is the definitive gauge of whether healthcare is patient centered. In addition, patients may know things about their healthcare that the care team does not. Several studies have demonstrated that patients have knowledge of adverse events and medical errors that are not detected by other methods.^1-5 For these reasons, systems designed to elicit patient perspectives of care and detect patient-perceived breakdowns in care could be used to improve healthcare safety and quality, including the patient experience.

Historically, hospitals have relied on patient-initiated reporting via complaints or legal action as the main source of information regarding patient-perceived breakdowns in care. However, many patients are hesitant to speak up about problems or uncertain about how to report concerns.^6-8 As a result, healthcare systems often only learn of the most severe breakdowns in care from a subset of activated patients, thus underestimating how widespread patient-perceived breakdowns are.

To overcome these limitations of patient-initiated reporting, hospitals could conduct outreach to patients to actively identify and learn about patient-perceived breakdowns in care. Potential benefits of outreach to patients include more reliable detection of patient-perceived breakdowns in care, identification of a broader range of types of breakdowns commonly experienced by patients, and recognition of problems in real-time when there is more opportunity for redress. Indeed, some hospitals have adopted active outreach programs such as structured nurse manager rounding or postdischarge phone calls.⁹

It is possible that outreach will not overcome patients’ reluctance to speak up, or patients may not share serious or actionable breakdowns. The manner in which outreach is conducted is likely to influence the information patients are willing to share. Prior studies examining patient perspectives of healthcare have primarily taken a structured approach with close-ended questions or a focus on specific aspects of care.^1,10,11 Limited data collected using an open-ended approach suggest patient-perceived breakdowns in care may be very common.^2,12,13 However, the impact of such breakdowns on patients has not been well characterized.

In order to design systems that can optimally detect patient-perceived breakdowns in care, additional information is needed to understand whether patients will report breakdowns in response to outreach programs, what types of problems they will report, and how these problems impact them. Understanding such issues will allow healthcare systems to respond to calls by federal health agencies to develop mechanisms for patients to report concerns about breakdowns in care, thereby providing truly patient-centered care.¹⁴ Therefore, we undertook this study with the overall goal of describing what may be learned from an open-ended outreach approach that directly asks patients about problems they have encountered during hospitalization. Specifically, we aim to (1) describe the types of problems reported by patients in response to this outreach approach and (2) characterize patients’ perceptions of the impact of these events.

Setting

We conducted this study in 2 hospitals between June 2014 and February 2015. One participating hospital is a large, urban, tertiary care medical center serving a predominantly white (78%) patient population in Baltimore, Maryland. The second hospital is a large, inner city, tertiary care medical center serving a predominantly African-American (71%) patient population in Washington, DC.

Three medical-surgical units (MSUs) at each hospital participated. We selected MSUs because MSU patients interact with a variety of clinicians, often have long stays, and are at risk for adverse events. Hospitalists were part of the clinical care team in each of the participating units, serving either as the attending of record or by comanaging patients.

Patient Eligibility

Patients were potentially eligible if they were at least 18 years old, able to speak English or Spanish, and admitted to the hospital for more than 24 hours. Ineligibility criteria included the following: imminent discharge, observation (noninpatient) status, on hospice, on infection precautions (for inpatient interviews only), psychiatric or violence concerns, prisoner status, significant confusion, or inability to provide informed consent.

Eligible patients in each unit were randomized. Interviewers consecutively approached patients according to their random assignment. If a patient was not available, the interviewer proceeded to the next room. Interviewers returned to rooms of missed patients when possible. Recruitment in the unit ended when the recruitment target for that unit was achieved.

Interviewers

Five interviewers conducted interviews. One author (KS) provided interviewer training that included didactic instruction, observation, feedback, and modeling. Interviewers participated in weekly debriefing sessions. One interviewer speaks Spanish fluently and was able to conduct interviews in Spanish. Translator services were available for the other interviewers.

Interview Process

Interviews were conducted in person while the patients were hospitalized or via telephone 7 - 30 days postdischarge. A patient who had completed an interview while hospitalized was not eligible for a postdischarge telephone interview. Family members or friends present at the time of the interviews could also participate in addition to or in lieu of the patients with the patients’ assent. Interviewers obtained verbal, informed consent at the start of each interview.

The interview domains and sample questions were developed specifically for the current study and are listed in Table 1. The goal of the interview was to elicit the patient’s (or family member’s) perception of their care experiences and their perceptions of the consequences of any problems with their care. The interviewer sought to obtain sufficient detail to understand the patient’s concerns and to determine what, if any, action might be needed to remediate problems reported by patients. Interviewers captured patient responses by taking detailed notes on a case report form or by directly entering patient responses using a computer or iPad at the time of interview at the discretion of the interviewer.

We defined a patient-perceived breakdown as something that went wrong during the hospitalization according to the patient. If a patient-perceived breakdown in care was identified, the interviewer attempted to resolve the concern. Some breakdowns had occurred in the past, making further resolution impossible (eg, a long wait in the emergency department). Other breakdowns were active and addressable, such as the patient having clinical questions that had not been answered. In such cases, the interviewer attempted to address the patient’s concerns, typically by working with unit nursing staff. For patients interviewed postdischarge, the interviewer worked to resolve ongoing patient concerns with the assistance of the patient safety, quality, and compliance teams as needed. The interviewer provided a brief narrative summary of all interviews to unit nursing leadership within 24 hours. Positive comments were sent to leadership but not captured systematically for research purposes. Further details of the process of responding to patients’ concerns will be reported elsewhere. All data were entered into REDCap to facilitate data management and reporting.¹⁵

The MedStar Health Research Institute Institutional Review Board reviewed and approved this study.

Categorizing Patients’ Responses: The Patient Experience Coding Tool

Using directed content analysis,¹⁶ we deductively created the Patient Experience Coding Tool (PECT) in order to summarize the narrative information captured during the interviews and categorize patient-perceived breakdowns in care. First, we referred to our prior interviews of patients’ views on breakdowns in cancer care⁶ and surrogate decision-makers’ views on breakdowns in intensive care units¹³ to create the initial categories. We then applied the resultant framework to the interviews in the present study and refined the categories. This involved applying the categorization to an initial set of interviews to check the sufficiency of the coding categories. We clarified the scope of each category (ie, what types of events fit under each category) and created additional categories (eg, medication-related problems) to capture patient experiences that were not included in the initial framework.

We then coded each interview using the PECT. A minimum of 2 readers reviewed the narrative notes for each interview. The first reader provided an initial categorization; the second reader reviewed the narrative and confirmed or questioned the initial categorization to improve coding accuracy. If a reader was uncertain about the correct categorization, it was discussed by three readers until an agreement was achieved. Because facilities-related problems (eg, food or parking) fall outside the realm of provider-based hospital care, such comments were not the focus of the outreach efforts and were not consistently recorded. Therefore, they were not included in the PECT and are not reported here.

Analyses

We computed simple, descriptive statistics including the number and percentage of patients identifying at least one breakdown, as well as the number and percent reporting each type of breakdown. We also computed the number and percentage of patients reporting any harm and each type of harm. We computed the percentage of patients reporting at least 1 breakdown by hospital, type of interview (postdischarge vs inpatient), selected patient demographic characteristics (eg, gender, age, education, race), and interviewee (patient vs someone other than the patient interviewed or present during the interview) using the chi-square statistic to test the statistical significance of the resulting differences. All statistical analyses were performed using SPSS version 22.

A total of 979 outreach interviews were conducted. Of these, 349 were conducted via telephone postdischarge, and 630 were conducted in person during hospitalization. The average interview duration was 8.5 minutes for telephone interviews and 12.2 minutes for in-person interviews. Of the patients approached to participate, 67% completed an interview (61% in person, 83% via telephone). Patient characteristics are summarized in Table 2.

Overall, 386 of 979 interviewees (39.4%) believed they had experienced at least one breakdown in care. The types of patient-perceived breakdowns reported were categorized using the PECT and are summarized in Table 3 and the Figure. The most common concern involved information exchange. Approximately 1 in 10 patients (n = 105, 10.7%) felt that they had not received the information they needed when they needed it. Medication-related concerns were reported by 12.3% (n = 120) of interviewees and predominantly included concerns about what medications were being administered (5.7%) and inadequately treated pain (5.6%). Many of the patients expressing concerns about what medications were administered questioned why they were not receiving their outpatient medications or did not understand why a different medication was being administered, suggesting that many of these instances were related to breakdowns in communication as well. Other relatively common concerns were delays in the admissions process (reported by 9.2% of interviewees), poor team communication (reported by 6.6% of interviewees), healthcare providers with a rude or uncaring manner (reported by 6.3% of interviewees), and problems related to discharge (reported by 5.7% of interviewees).

Through interviews with nearly 1000 patients, we have found that approximately 4 in 10 hospitalized patients believed they experienced a breakdown in care. Not only are patient-perceived breakdowns in care distressingly common, more than one-third of these events resulted in harm according to the patient. Patients reported a diverse range of breakdowns, including problems related to patient experience, as well as breakdowns in technical aspects of medical care. Collectively, these findings illustrate a striking need to identify and address these frequent and potentially harmful breakdowns.

Our findings are consistent with prior studies in which 20% to 50% of patients identified a problem during hospitalization. For example, Weingart et al^. interviewed patients in a single general medical unit and found that 20% experienced an adverse event, near miss, or medical error, while nearly 40% experienced what was defined as a service quality incident.^2,12 Of note, both our study and the study by Weingart et al. systematically elicited patients’ perspectives of breakdowns in care with explicit questions about problems or breakdowns in care.^2,12 Because patients are often reluctant to speak up about problems in care,without such efforts to actively identify problems, providers and leaders are likely to be unaware of the majority of these concerns.^6-8 These findings suggest that hospital-based providers should at least consider routinely asking patients about breakdowns in care to identify and respond to patients’ concerns.

Not only are patient-perceived breakdowns common, more than one-third of patients who experienced a breakdown considered it to be harmful. This suggests that our outreach approach identified predominantly nontrivial concerns. We adopted a broad definition of harm that includes emotional distress, damage to the relationship with providers, and life disruption. This differs from other studies examining patient reports of breakdowns in care, in which harm was restricted to physical injury.^1,2 We consider this inclusive definition of harm to be a strength of the present study as it provides the most complete picture of the impact of such events on patients. This approach is supported by other studies demonstrating that patients place great emphasis on the psychological consequences of adverse events.^17-19 Thus, it is clear from our work and other studies that nonphysical harm is important and warrants concerted efforts to address.

Patients in our study reported a variety of breakdowns, including breakdowns related to patient experience (eg, communication, relationship with providers) and technical aspects of healthcare delivery (eg, diagnosis, treatment). This is consistent with other studies examining patient perspectives of breakdowns in care. Weingart et al.found that hospitalized patients reported a broad range of problems, including adverse events, medical errors, communication breakdowns, and problems with food.^2,12 This variety of events suggests a need for integration between the various hospital groups that handle patient-perceived breakdowns, including bedside providers, risk management, patient relations, patient advocates, and quality and safety groups, in order to provide a coordinated and effective response to the full spectrum of patient-perceived breakdowns in care.

Patients in our study were more likely to report breakdowns related to communication and relationships with providers than those related to technical aspects of care. Similarly, Kuzel et al.found that the most common problems cited by patients in the primary care setting were breakdowns in the clinician-patient relationship and access-related problems.¹⁷This is not surprising, as patients are likely to have more direct knowledge about communication and interpersonal relationships than about technical aspects of care.

We identified several factors associated with the likelihood of reporting a breakdown in care. Most strikingly, involving a friend or family member in the interview was strongly associated with reporting a breakdown. Other work has also suggested that patients’ friends and family members are an important source of information about safety concerns.^20,21 In addition, several patient characteristics were associated with an increased likelihood of reporting a breakdown, including being younger and better educated. These findings highlight the importance of engaging patients’ friends and families in efforts to elicit patient concerns about healthcare, and they confirm recommendations for patients to bring a friend or family member to healthcare encounters.²² In addition, they illustrate the need to better understand how patient characteristics affect perceptions of breakdowns in care and their willingness to speak up, as this could inform efforts to target outreach to especially vulnerable patients.

A strength of this study is the number of interviews completed (almost 1000), which provides a diverse range of patient views and experiences, as evidenced by the demographic characteristics of participants. Interviews were conducted at two hospitals that differ substantially with regard to populations served, further enhancing the generalizability of our findings. Despite the large number of interviews and diverse patient characteristics, patients were drawn from only 3 units at 2 hospitals, which may limit generalizability.

We did not conduct medical record reviews to validate patients’ reports of problems, which may be viewed as a limitation. While such a comparison would be informative, the intent of the current study was to elicit patients’ perceptions of care, including aspects of care that are not typically captured in the medical record, such as communication. Other studies have demonstrated that patients’ reports of medical errors and adverse events tend to differ from providers’ reports of the same subjects.^19,23 Therefore, we considered the patients’ perceptions of care to be a useful endpoint in and of itself. We did not determine the extent to which providers were already aware of patients’ concerns or whether they considered patients’ concerns valid. A related limitation is our inability to determine whether the differences we identified in the rates of breakdown reporting based on patient characteristics reflect differences in willingness to report or differences in experiences. Because we included patients in an MSU, it is possible that breakdowns were related to medical care, surgical care, or both. We did not follow patients longitudinally and therefore only captured harm perceived by a patient at the time of the interview. It is possible that patients may have experienced harm later in their hospitalization or following discharge that was not measured. Lastly, we did not measure interrater reliability of the interview coding and therefore do not present the PECT as a validated instrument. These important questions should be targeted for future study.

When directly asked about their experiences, almost 4 out of 10 hospitalized patients reported a breakdown in their care, many of which were perceived to be harmful. Not all hospitals will have the resources to implement the intensive approach used in this study to elicit patient-perceived breakdowns. Therefore, further work is needed to develop sustainable methods to overcome patients’ reluctance to report breakdowns in care. Engaging patients’ families and friends may be a particularly fruitful strategy. We offer the PECT as a tool that hospitals could use to summarize a variety of sources of patient feedback such as complaints, responses to surveys, and consumer reviews. Hospitals that effectively encourage patients and their family members to speak up about perceived breakdowns will identify many opportunities to address patient concerns, potentially leading to improved patient safety and experience.

There is growing recognition that patients and family members have critical insights into healthcare experiences. As consumers of healthcare, patient experience is the definitive gauge of whether healthcare is patient centered. In addition, patients may know things about their healthcare that the care team does not. Several studies have demonstrated that patients have knowledge of adverse events and medical errors that are not detected by other methods.^1-5 For these reasons, systems designed to elicit patient perspectives of care and detect patient-perceived breakdowns in care could be used to improve healthcare safety and quality, including the patient experience.

Historically, hospitals have relied on patient-initiated reporting via complaints or legal action as the main source of information regarding patient-perceived breakdowns in care. However, many patients are hesitant to speak up about problems or uncertain about how to report concerns.^6-8 As a result, healthcare systems often only learn of the most severe breakdowns in care from a subset of activated patients, thus underestimating how widespread patient-perceived breakdowns are.

To overcome these limitations of patient-initiated reporting, hospitals could conduct outreach to patients to actively identify and learn about patient-perceived breakdowns in care. Potential benefits of outreach to patients include more reliable detection of patient-perceived breakdowns in care, identification of a broader range of types of breakdowns commonly experienced by patients, and recognition of problems in real-time when there is more opportunity for redress. Indeed, some hospitals have adopted active outreach programs such as structured nurse manager rounding or postdischarge phone calls.⁹

It is possible that outreach will not overcome patients’ reluctance to speak up, or patients may not share serious or actionable breakdowns. The manner in which outreach is conducted is likely to influence the information patients are willing to share. Prior studies examining patient perspectives of healthcare have primarily taken a structured approach with close-ended questions or a focus on specific aspects of care.^1,10,11 Limited data collected using an open-ended approach suggest patient-perceived breakdowns in care may be very common.^2,12,13 However, the impact of such breakdowns on patients has not been well characterized.

In order to design systems that can optimally detect patient-perceived breakdowns in care, additional information is needed to understand whether patients will report breakdowns in response to outreach programs, what types of problems they will report, and how these problems impact them. Understanding such issues will allow healthcare systems to respond to calls by federal health agencies to develop mechanisms for patients to report concerns about breakdowns in care, thereby providing truly patient-centered care.¹⁴ Therefore, we undertook this study with the overall goal of describing what may be learned from an open-ended outreach approach that directly asks patients about problems they have encountered during hospitalization. Specifically, we aim to (1) describe the types of problems reported by patients in response to this outreach approach and (2) characterize patients’ perceptions of the impact of these events.

Setting

We conducted this study in 2 hospitals between June 2014 and February 2015. One participating hospital is a large, urban, tertiary care medical center serving a predominantly white (78%) patient population in Baltimore, Maryland. The second hospital is a large, inner city, tertiary care medical center serving a predominantly African-American (71%) patient population in Washington, DC.

Three medical-surgical units (MSUs) at each hospital participated. We selected MSUs because MSU patients interact with a variety of clinicians, often have long stays, and are at risk for adverse events. Hospitalists were part of the clinical care team in each of the participating units, serving either as the attending of record or by comanaging patients.

Patient Eligibility

Patients were potentially eligible if they were at least 18 years old, able to speak English or Spanish, and admitted to the hospital for more than 24 hours. Ineligibility criteria included the following: imminent discharge, observation (noninpatient) status, on hospice, on infection precautions (for inpatient interviews only), psychiatric or violence concerns, prisoner status, significant confusion, or inability to provide informed consent.

Eligible patients in each unit were randomized. Interviewers consecutively approached patients according to their random assignment. If a patient was not available, the interviewer proceeded to the next room. Interviewers returned to rooms of missed patients when possible. Recruitment in the unit ended when the recruitment target for that unit was achieved.

Interviewers

Five interviewers conducted interviews. One author (KS) provided interviewer training that included didactic instruction, observation, feedback, and modeling. Interviewers participated in weekly debriefing sessions. One interviewer speaks Spanish fluently and was able to conduct interviews in Spanish. Translator services were available for the other interviewers.

Interview Process

Interviews were conducted in person while the patients were hospitalized or via telephone 7 - 30 days postdischarge. A patient who had completed an interview while hospitalized was not eligible for a postdischarge telephone interview. Family members or friends present at the time of the interviews could also participate in addition to or in lieu of the patients with the patients’ assent. Interviewers obtained verbal, informed consent at the start of each interview.

The interview domains and sample questions were developed specifically for the current study and are listed in Table 1. The goal of the interview was to elicit the patient’s (or family member’s) perception of their care experiences and their perceptions of the consequences of any problems with their care. The interviewer sought to obtain sufficient detail to understand the patient’s concerns and to determine what, if any, action might be needed to remediate problems reported by patients. Interviewers captured patient responses by taking detailed notes on a case report form or by directly entering patient responses using a computer or iPad at the time of interview at the discretion of the interviewer.

We defined a patient-perceived breakdown as something that went wrong during the hospitalization according to the patient. If a patient-perceived breakdown in care was identified, the interviewer attempted to resolve the concern. Some breakdowns had occurred in the past, making further resolution impossible (eg, a long wait in the emergency department). Other breakdowns were active and addressable, such as the patient having clinical questions that had not been answered. In such cases, the interviewer attempted to address the patient’s concerns, typically by working with unit nursing staff. For patients interviewed postdischarge, the interviewer worked to resolve ongoing patient concerns with the assistance of the patient safety, quality, and compliance teams as needed. The interviewer provided a brief narrative summary of all interviews to unit nursing leadership within 24 hours. Positive comments were sent to leadership but not captured systematically for research purposes. Further details of the process of responding to patients’ concerns will be reported elsewhere. All data were entered into REDCap to facilitate data management and reporting.¹⁵

The MedStar Health Research Institute Institutional Review Board reviewed and approved this study.

Categorizing Patients’ Responses: The Patient Experience Coding Tool

Using directed content analysis,¹⁶ we deductively created the Patient Experience Coding Tool (PECT) in order to summarize the narrative information captured during the interviews and categorize patient-perceived breakdowns in care. First, we referred to our prior interviews of patients’ views on breakdowns in cancer care⁶ and surrogate decision-makers’ views on breakdowns in intensive care units¹³ to create the initial categories. We then applied the resultant framework to the interviews in the present study and refined the categories. This involved applying the categorization to an initial set of interviews to check the sufficiency of the coding categories. We clarified the scope of each category (ie, what types of events fit under each category) and created additional categories (eg, medication-related problems) to capture patient experiences that were not included in the initial framework.

We then coded each interview using the PECT. A minimum of 2 readers reviewed the narrative notes for each interview. The first reader provided an initial categorization; the second reader reviewed the narrative and confirmed or questioned the initial categorization to improve coding accuracy. If a reader was uncertain about the correct categorization, it was discussed by three readers until an agreement was achieved. Because facilities-related problems (eg, food or parking) fall outside the realm of provider-based hospital care, such comments were not the focus of the outreach efforts and were not consistently recorded. Therefore, they were not included in the PECT and are not reported here.

Analyses

We computed simple, descriptive statistics including the number and percentage of patients identifying at least one breakdown, as well as the number and percent reporting each type of breakdown. We also computed the number and percentage of patients reporting any harm and each type of harm. We computed the percentage of patients reporting at least 1 breakdown by hospital, type of interview (postdischarge vs inpatient), selected patient demographic characteristics (eg, gender, age, education, race), and interviewee (patient vs someone other than the patient interviewed or present during the interview) using the chi-square statistic to test the statistical significance of the resulting differences. All statistical analyses were performed using SPSS version 22.

A total of 979 outreach interviews were conducted. Of these, 349 were conducted via telephone postdischarge, and 630 were conducted in person during hospitalization. The average interview duration was 8.5 minutes for telephone interviews and 12.2 minutes for in-person interviews. Of the patients approached to participate, 67% completed an interview (61% in person, 83% via telephone). Patient characteristics are summarized in Table 2.

Overall, 386 of 979 interviewees (39.4%) believed they had experienced at least one breakdown in care. The types of patient-perceived breakdowns reported were categorized using the PECT and are summarized in Table 3 and the Figure. The most common concern involved information exchange. Approximately 1 in 10 patients (n = 105, 10.7%) felt that they had not received the information they needed when they needed it. Medication-related concerns were reported by 12.3% (n = 120) of interviewees and predominantly included concerns about what medications were being administered (5.7%) and inadequately treated pain (5.6%). Many of the patients expressing concerns about what medications were administered questioned why they were not receiving their outpatient medications or did not understand why a different medication was being administered, suggesting that many of these instances were related to breakdowns in communication as well. Other relatively common concerns were delays in the admissions process (reported by 9.2% of interviewees), poor team communication (reported by 6.6% of interviewees), healthcare providers with a rude or uncaring manner (reported by 6.3% of interviewees), and problems related to discharge (reported by 5.7% of interviewees).

Through interviews with nearly 1000 patients, we have found that approximately 4 in 10 hospitalized patients believed they experienced a breakdown in care. Not only are patient-perceived breakdowns in care distressingly common, more than one-third of these events resulted in harm according to the patient. Patients reported a diverse range of breakdowns, including problems related to patient experience, as well as breakdowns in technical aspects of medical care. Collectively, these findings illustrate a striking need to identify and address these frequent and potentially harmful breakdowns.

Our findings are consistent with prior studies in which 20% to 50% of patients identified a problem during hospitalization. For example, Weingart et al^. interviewed patients in a single general medical unit and found that 20% experienced an adverse event, near miss, or medical error, while nearly 40% experienced what was defined as a service quality incident.^2,12 Of note, both our study and the study by Weingart et al. systematically elicited patients’ perspectives of breakdowns in care with explicit questions about problems or breakdowns in care.^2,12 Because patients are often reluctant to speak up about problems in care,without such efforts to actively identify problems, providers and leaders are likely to be unaware of the majority of these concerns.^6-8 These findings suggest that hospital-based providers should at least consider routinely asking patients about breakdowns in care to identify and respond to patients’ concerns.

Not only are patient-perceived breakdowns common, more than one-third of patients who experienced a breakdown considered it to be harmful. This suggests that our outreach approach identified predominantly nontrivial concerns. We adopted a broad definition of harm that includes emotional distress, damage to the relationship with providers, and life disruption. This differs from other studies examining patient reports of breakdowns in care, in which harm was restricted to physical injury.^1,2 We consider this inclusive definition of harm to be a strength of the present study as it provides the most complete picture of the impact of such events on patients. This approach is supported by other studies demonstrating that patients place great emphasis on the psychological consequences of adverse events.^17-19 Thus, it is clear from our work and other studies that nonphysical harm is important and warrants concerted efforts to address.

Patients in our study reported a variety of breakdowns, including breakdowns related to patient experience (eg, communication, relationship with providers) and technical aspects of healthcare delivery (eg, diagnosis, treatment). This is consistent with other studies examining patient perspectives of breakdowns in care. Weingart et al.found that hospitalized patients reported a broad range of problems, including adverse events, medical errors, communication breakdowns, and problems with food.^2,12 This variety of events suggests a need for integration between the various hospital groups that handle patient-perceived breakdowns, including bedside providers, risk management, patient relations, patient advocates, and quality and safety groups, in order to provide a coordinated and effective response to the full spectrum of patient-perceived breakdowns in care.

Patients in our study were more likely to report breakdowns related to communication and relationships with providers than those related to technical aspects of care. Similarly, Kuzel et al.found that the most common problems cited by patients in the primary care setting were breakdowns in the clinician-patient relationship and access-related problems.¹⁷This is not surprising, as patients are likely to have more direct knowledge about communication and interpersonal relationships than about technical aspects of care.

We identified several factors associated with the likelihood of reporting a breakdown in care. Most strikingly, involving a friend or family member in the interview was strongly associated with reporting a breakdown. Other work has also suggested that patients’ friends and family members are an important source of information about safety concerns.^20,21 In addition, several patient characteristics were associated with an increased likelihood of reporting a breakdown, including being younger and better educated. These findings highlight the importance of engaging patients’ friends and families in efforts to elicit patient concerns about healthcare, and they confirm recommendations for patients to bring a friend or family member to healthcare encounters.²² In addition, they illustrate the need to better understand how patient characteristics affect perceptions of breakdowns in care and their willingness to speak up, as this could inform efforts to target outreach to especially vulnerable patients.

A strength of this study is the number of interviews completed (almost 1000), which provides a diverse range of patient views and experiences, as evidenced by the demographic characteristics of participants. Interviews were conducted at two hospitals that differ substantially with regard to populations served, further enhancing the generalizability of our findings. Despite the large number of interviews and diverse patient characteristics, patients were drawn from only 3 units at 2 hospitals, which may limit generalizability.

We did not conduct medical record reviews to validate patients’ reports of problems, which may be viewed as a limitation. While such a comparison would be informative, the intent of the current study was to elicit patients’ perceptions of care, including aspects of care that are not typically captured in the medical record, such as communication. Other studies have demonstrated that patients’ reports of medical errors and adverse events tend to differ from providers’ reports of the same subjects.^19,23 Therefore, we considered the patients’ perceptions of care to be a useful endpoint in and of itself. We did not determine the extent to which providers were already aware of patients’ concerns or whether they considered patients’ concerns valid. A related limitation is our inability to determine whether the differences we identified in the rates of breakdown reporting based on patient characteristics reflect differences in willingness to report or differences in experiences. Because we included patients in an MSU, it is possible that breakdowns were related to medical care, surgical care, or both. We did not follow patients longitudinally and therefore only captured harm perceived by a patient at the time of the interview. It is possible that patients may have experienced harm later in their hospitalization or following discharge that was not measured. Lastly, we did not measure interrater reliability of the interview coding and therefore do not present the PECT as a validated instrument. These important questions should be targeted for future study.

When directly asked about their experiences, almost 4 out of 10 hospitalized patients reported a breakdown in their care, many of which were perceived to be harmful. Not all hospitals will have the resources to implement the intensive approach used in this study to elicit patient-perceived breakdowns. Therefore, further work is needed to develop sustainable methods to overcome patients’ reluctance to report breakdowns in care. Engaging patients’ families and friends may be a particularly fruitful strategy. We offer the PECT as a tool that hospitals could use to summarize a variety of sources of patient feedback such as complaints, responses to surveys, and consumer reviews. Hospitals that effectively encourage patients and their family members to speak up about perceived breakdowns will identify many opportunities to address patient concerns, potentially leading to improved patient safety and experience.

There is growing recognition that patients and family members have critical insights into healthcare experiences. As consumers of healthcare, patient experience is the definitive gauge of whether healthcare is patient centered. In addition, patients may know things about their healthcare that the care team does not. Several studies have demonstrated that patients have knowledge of adverse events and medical errors that are not detected by other methods.^1-5 For these reasons, systems designed to elicit patient perspectives of care and detect patient-perceived breakdowns in care could be used to improve healthcare safety and quality, including the patient experience.

Historically, hospitals have relied on patient-initiated reporting via complaints or legal action as the main source of information regarding patient-perceived breakdowns in care. However, many patients are hesitant to speak up about problems or uncertain about how to report concerns.^6-8 As a result, healthcare systems often only learn of the most severe breakdowns in care from a subset of activated patients, thus underestimating how widespread patient-perceived breakdowns are.

To overcome these limitations of patient-initiated reporting, hospitals could conduct outreach to patients to actively identify and learn about patient-perceived breakdowns in care. Potential benefits of outreach to patients include more reliable detection of patient-perceived breakdowns in care, identification of a broader range of types of breakdowns commonly experienced by patients, and recognition of problems in real-time when there is more opportunity for redress. Indeed, some hospitals have adopted active outreach programs such as structured nurse manager rounding or postdischarge phone calls.⁹

It is possible that outreach will not overcome patients’ reluctance to speak up, or patients may not share serious or actionable breakdowns. The manner in which outreach is conducted is likely to influence the information patients are willing to share. Prior studies examining patient perspectives of healthcare have primarily taken a structured approach with close-ended questions or a focus on specific aspects of care.^1,10,11 Limited data collected using an open-ended approach suggest patient-perceived breakdowns in care may be very common.^2,12,13 However, the impact of such breakdowns on patients has not been well characterized.

In order to design systems that can optimally detect patient-perceived breakdowns in care, additional information is needed to understand whether patients will report breakdowns in response to outreach programs, what types of problems they will report, and how these problems impact them. Understanding such issues will allow healthcare systems to respond to calls by federal health agencies to develop mechanisms for patients to report concerns about breakdowns in care, thereby providing truly patient-centered care.¹⁴ Therefore, we undertook this study with the overall goal of describing what may be learned from an open-ended outreach approach that directly asks patients about problems they have encountered during hospitalization. Specifically, we aim to (1) describe the types of problems reported by patients in response to this outreach approach and (2) characterize patients’ perceptions of the impact of these events.

Setting

We conducted this study in 2 hospitals between June 2014 and February 2015. One participating hospital is a large, urban, tertiary care medical center serving a predominantly white (78%) patient population in Baltimore, Maryland. The second hospital is a large, inner city, tertiary care medical center serving a predominantly African-American (71%) patient population in Washington, DC.

Three medical-surgical units (MSUs) at each hospital participated. We selected MSUs because MSU patients interact with a variety of clinicians, often have long stays, and are at risk for adverse events. Hospitalists were part of the clinical care team in each of the participating units, serving either as the attending of record or by comanaging patients.

Patient Eligibility

Patients were potentially eligible if they were at least 18 years old, able to speak English or Spanish, and admitted to the hospital for more than 24 hours. Ineligibility criteria included the following: imminent discharge, observation (noninpatient) status, on hospice, on infection precautions (for inpatient interviews only), psychiatric or violence concerns, prisoner status, significant confusion, or inability to provide informed consent.

Eligible patients in each unit were randomized. Interviewers consecutively approached patients according to their random assignment. If a patient was not available, the interviewer proceeded to the next room. Interviewers returned to rooms of missed patients when possible. Recruitment in the unit ended when the recruitment target for that unit was achieved.

Interviewers

Five interviewers conducted interviews. One author (KS) provided interviewer training that included didactic instruction, observation, feedback, and modeling. Interviewers participated in weekly debriefing sessions. One interviewer speaks Spanish fluently and was able to conduct interviews in Spanish. Translator services were available for the other interviewers.

Interview Process

Interviews were conducted in person while the patients were hospitalized or via telephone 7 - 30 days postdischarge. A patient who had completed an interview while hospitalized was not eligible for a postdischarge telephone interview. Family members or friends present at the time of the interviews could also participate in addition to or in lieu of the patients with the patients’ assent. Interviewers obtained verbal, informed consent at the start of each interview.

The interview domains and sample questions were developed specifically for the current study and are listed in Table 1. The goal of the interview was to elicit the patient’s (or family member’s) perception of their care experiences and their perceptions of the consequences of any problems with their care. The interviewer sought to obtain sufficient detail to understand the patient’s concerns and to determine what, if any, action might be needed to remediate problems reported by patients. Interviewers captured patient responses by taking detailed notes on a case report form or by directly entering patient responses using a computer or iPad at the time of interview at the discretion of the interviewer.

We defined a patient-perceived breakdown as something that went wrong during the hospitalization according to the patient. If a patient-perceived breakdown in care was identified, the interviewer attempted to resolve the concern. Some breakdowns had occurred in the past, making further resolution impossible (eg, a long wait in the emergency department). Other breakdowns were active and addressable, such as the patient having clinical questions that had not been answered. In such cases, the interviewer attempted to address the patient’s concerns, typically by working with unit nursing staff. For patients interviewed postdischarge, the interviewer worked to resolve ongoing patient concerns with the assistance of the patient safety, quality, and compliance teams as needed. The interviewer provided a brief narrative summary of all interviews to unit nursing leadership within 24 hours. Positive comments were sent to leadership but not captured systematically for research purposes. Further details of the process of responding to patients’ concerns will be reported elsewhere. All data were entered into REDCap to facilitate data management and reporting.¹⁵

The MedStar Health Research Institute Institutional Review Board reviewed and approved this study.

Categorizing Patients’ Responses: The Patient Experience Coding Tool

Using directed content analysis,¹⁶ we deductively created the Patient Experience Coding Tool (PECT) in order to summarize the narrative information captured during the interviews and categorize patient-perceived breakdowns in care. First, we referred to our prior interviews of patients’ views on breakdowns in cancer care⁶ and surrogate decision-makers’ views on breakdowns in intensive care units¹³ to create the initial categories. We then applied the resultant framework to the interviews in the present study and refined the categories. This involved applying the categorization to an initial set of interviews to check the sufficiency of the coding categories. We clarified the scope of each category (ie, what types of events fit under each category) and created additional categories (eg, medication-related problems) to capture patient experiences that were not included in the initial framework.

We then coded each interview using the PECT. A minimum of 2 readers reviewed the narrative notes for each interview. The first reader provided an initial categorization; the second reader reviewed the narrative and confirmed or questioned the initial categorization to improve coding accuracy. If a reader was uncertain about the correct categorization, it was discussed by three readers until an agreement was achieved. Because facilities-related problems (eg, food or parking) fall outside the realm of provider-based hospital care, such comments were not the focus of the outreach efforts and were not consistently recorded. Therefore, they were not included in the PECT and are not reported here.

Analyses

We computed simple, descriptive statistics including the number and percentage of patients identifying at least one breakdown, as well as the number and percent reporting each type of breakdown. We also computed the number and percentage of patients reporting any harm and each type of harm. We computed the percentage of patients reporting at least 1 breakdown by hospital, type of interview (postdischarge vs inpatient), selected patient demographic characteristics (eg, gender, age, education, race), and interviewee (patient vs someone other than the patient interviewed or present during the interview) using the chi-square statistic to test the statistical significance of the resulting differences. All statistical analyses were performed using SPSS version 22.

A total of 979 outreach interviews were conducted. Of these, 349 were conducted via telephone postdischarge, and 630 were conducted in person during hospitalization. The average interview duration was 8.5 minutes for telephone interviews and 12.2 minutes for in-person interviews. Of the patients approached to participate, 67% completed an interview (61% in person, 83% via telephone). Patient characteristics are summarized in Table 2.

Overall, 386 of 979 interviewees (39.4%) believed they had experienced at least one breakdown in care. The types of patient-perceived breakdowns reported were categorized using the PECT and are summarized in Table 3 and the Figure. The most common concern involved information exchange. Approximately 1 in 10 patients (n = 105, 10.7%) felt that they had not received the information they needed when they needed it. Medication-related concerns were reported by 12.3% (n = 120) of interviewees and predominantly included concerns about what medications were being administered (5.7%) and inadequately treated pain (5.6%). Many of the patients expressing concerns about what medications were administered questioned why they were not receiving their outpatient medications or did not understand why a different medication was being administered, suggesting that many of these instances were related to breakdowns in communication as well. Other relatively common concerns were delays in the admissions process (reported by 9.2% of interviewees), poor team communication (reported by 6.6% of interviewees), healthcare providers with a rude or uncaring manner (reported by 6.3% of interviewees), and problems related to discharge (reported by 5.7% of interviewees).

Through interviews with nearly 1000 patients, we have found that approximately 4 in 10 hospitalized patients believed they experienced a breakdown in care. Not only are patient-perceived breakdowns in care distressingly common, more than one-third of these events resulted in harm according to the patient. Patients reported a diverse range of breakdowns, including problems related to patient experience, as well as breakdowns in technical aspects of medical care. Collectively, these findings illustrate a striking need to identify and address these frequent and potentially harmful breakdowns.

Our findings are consistent with prior studies in which 20% to 50% of patients identified a problem during hospitalization. For example, Weingart et al^. interviewed patients in a single general medical unit and found that 20% experienced an adverse event, near miss, or medical error, while nearly 40% experienced what was defined as a service quality incident.^2,12 Of note, both our study and the study by Weingart et al. systematically elicited patients’ perspectives of breakdowns in care with explicit questions about problems or breakdowns in care.^2,12 Because patients are often reluctant to speak up about problems in care,without such efforts to actively identify problems, providers and leaders are likely to be unaware of the majority of these concerns.^6-8 These findings suggest that hospital-based providers should at least consider routinely asking patients about breakdowns in care to identify and respond to patients’ concerns.

Not only are patient-perceived breakdowns common, more than one-third of patients who experienced a breakdown considered it to be harmful. This suggests that our outreach approach identified predominantly nontrivial concerns. We adopted a broad definition of harm that includes emotional distress, damage to the relationship with providers, and life disruption. This differs from other studies examining patient reports of breakdowns in care, in which harm was restricted to physical injury.^1,2 We consider this inclusive definition of harm to be a strength of the present study as it provides the most complete picture of the impact of such events on patients. This approach is supported by other studies demonstrating that patients place great emphasis on the psychological consequences of adverse events.^17-19 Thus, it is clear from our work and other studies that nonphysical harm is important and warrants concerted efforts to address.

Patients in our study reported a variety of breakdowns, including breakdowns related to patient experience (eg, communication, relationship with providers) and technical aspects of healthcare delivery (eg, diagnosis, treatment). This is consistent with other studies examining patient perspectives of breakdowns in care. Weingart et al.found that hospitalized patients reported a broad range of problems, including adverse events, medical errors, communication breakdowns, and problems with food.^2,12 This variety of events suggests a need for integration between the various hospital groups that handle patient-perceived breakdowns, including bedside providers, risk management, patient relations, patient advocates, and quality and safety groups, in order to provide a coordinated and effective response to the full spectrum of patient-perceived breakdowns in care.

Patients in our study were more likely to report breakdowns related to communication and relationships with providers than those related to technical aspects of care. Similarly, Kuzel et al.found that the most common problems cited by patients in the primary care setting were breakdowns in the clinician-patient relationship and access-related problems.¹⁷This is not surprising, as patients are likely to have more direct knowledge about communication and interpersonal relationships than about technical aspects of care.

We identified several factors associated with the likelihood of reporting a breakdown in care. Most strikingly, involving a friend or family member in the interview was strongly associated with reporting a breakdown. Other work has also suggested that patients’ friends and family members are an important source of information about safety concerns.^20,21 In addition, several patient characteristics were associated with an increased likelihood of reporting a breakdown, including being younger and better educated. These findings highlight the importance of engaging patients’ friends and families in efforts to elicit patient concerns about healthcare, and they confirm recommendations for patients to bring a friend or family member to healthcare encounters.²² In addition, they illustrate the need to better understand how patient characteristics affect perceptions of breakdowns in care and their willingness to speak up, as this could inform efforts to target outreach to especially vulnerable patients.

A strength of this study is the number of interviews completed (almost 1000), which provides a diverse range of patient views and experiences, as evidenced by the demographic characteristics of participants. Interviews were conducted at two hospitals that differ substantially with regard to populations served, further enhancing the generalizability of our findings. Despite the large number of interviews and diverse patient characteristics, patients were drawn from only 3 units at 2 hospitals, which may limit generalizability.

We did not conduct medical record reviews to validate patients’ reports of problems, which may be viewed as a limitation. While such a comparison would be informative, the intent of the current study was to elicit patients’ perceptions of care, including aspects of care that are not typically captured in the medical record, such as communication. Other studies have demonstrated that patients’ reports of medical errors and adverse events tend to differ from providers’ reports of the same subjects.^19,23 Therefore, we considered the patients’ perceptions of care to be a useful endpoint in and of itself. We did not determine the extent to which providers were already aware of patients’ concerns or whether they considered patients’ concerns valid. A related limitation is our inability to determine whether the differences we identified in the rates of breakdown reporting based on patient characteristics reflect differences in willingness to report or differences in experiences. Because we included patients in an MSU, it is possible that breakdowns were related to medical care, surgical care, or both. We did not follow patients longitudinally and therefore only captured harm perceived by a patient at the time of the interview. It is possible that patients may have experienced harm later in their hospitalization or following discharge that was not measured. Lastly, we did not measure interrater reliability of the interview coding and therefore do not present the PECT as a validated instrument. These important questions should be targeted for future study.

When directly asked about their experiences, almost 4 out of 10 hospitalized patients reported a breakdown in their care, many of which were perceived to be harmful. Not all hospitals will have the resources to implement the intensive approach used in this study to elicit patient-perceived breakdowns. Therefore, further work is needed to develop sustainable methods to overcome patients’ reluctance to report breakdowns in care. Engaging patients’ families and friends may be a particularly fruitful strategy. We offer the PECT as a tool that hospitals could use to summarize a variety of sources of patient feedback such as complaints, responses to surveys, and consumer reviews. Hospitals that effectively encourage patients and their family members to speak up about perceived breakdowns will identify many opportunities to address patient concerns, potentially leading to improved patient safety and experience.

As healthcare system use and costs continue to rise, increased importance has been placed on identifying the small subgroup of patients that drive this trend.¹ It is estimated that 5% of healthcare users account for over 60% of healthcare spending.^2-6 Furthermore, care for these “high users” is expensive due to an over-reliance on inpatient services. Approximately 40% of all health spending is for inpatient care, the largest single category of health spending, which is similarly skewed toward high users.^1,3,5 Improving our understanding of this population may provide an opportunity to direct improvement efforts to a select group of patients with a potentially high benefit, as well as move care away from the costly inpatient setting.

However, the development of effective interventions to improve patient experience and outcomes while decreasing costs (referred to as the “Triple Aim” by the Institute for Health Improvement) for high users of inpatient services hinges on the methodology used to identify this high-risk population.⁷ There is substantial variability in definitions of high users; the most common definitions are based on the number of hospital encounters, days spent in the hospital, and hospital costs.^8-15 Definitions have intrinsic differences in their implications around appropriateness, efficiency, and financial sustainability of inpatient resource use. Though the constructs underlying these definitions are highly variable, direct comparisons of differences in patient capture are limited.

A recent study from a single US center explored the clinical characteristics of hospital patients based on definitions of use vs cost and observed important differences in patients’ profiles and outcomes.¹² While this suggests that the choice of definition may have major implications for whom to target (and the efficacy of any proposed interventions), this concept has not been explored at the population level. Therefore, we used population-based administrative data from a single-payer healthcare system to compare 3 common definitions of high inpatient service use and their influence on patient capture, health outcomes, and inpatient system burden.

Data Sources and Study Population

We conducted a retrospective population-based study using administrative and clinical data for the province of Alberta, including the discharge abstracts database, physician claims, ambulatory care records, population health registry file, and aggregated data from the Canadian census.¹⁶ We identified all adults who had 1 or more hospitalizations with a discharge date between April 1, 2012, and March 31, 2013, though the admission date could be prior to April 1, 2012.

Definition of High-Inpatient Use

High-inpatient use was defined using 3 metrics: number of inpatient episodes, length of stay, and cost. As in prior studies, for each definition, individuals in the upper5th percentile of the relevant distribution were designated “high users,”^2,15 while patients in the lower 95th percentile were considered “nonhigh users.” Patients could be defined as a high user in more than 1 definition.

Patients with 3 or more hospital episodes were defined as high users for the “number of inpatient episodes” definition. A hospital episode of care was defined as an event that resulted in discharge (or death) from an inpatient facility. If an individual was admitted to a hospital and transferred to another facility within 1 day of discharge, the hospitalizations were considered part of the same episode of care.

The “length of stay” definition refers to the cumulative number of days spent in an inpatient facility for all eligible episodes of care. Patients with 56 or more days in hospital during the study period were considered high users. Day of admission and discharge were considered full inpatient days, regardless of the time of admission and discharge.

The “cost” definition considered the cumulative estimated cost of every eligible episode of care. We estimated costs for each hospitalization using resource intensity weights (RIW). This is a relative weighted value for the average inpatient case after taking factors such as age, comorbidity, and procedures into account. The RIW for each episode was multiplied by the national average inpatient cost.¹⁷ Based on this definition, patients with a cumulative hospital cost of ≥ $63,597 were deemed high users. All costs were calculated in Canadian Dollars (CAD, $) and adjusted to 2013 dollars based on Statistics Canada’s Consumer Price Index.¹⁸

Demographic, Clinical, and Encounter Characteristics

Individual characteristics were measured using a combination of provincial administrative data sources. All measures were recorded as of the admission date of the first eligible hospitalization. Demographic characteristics included age, sex, First Nations status, urban/rural status (based on the individual’s residential postal code), and median neighborhood income quintile. Clinical characteristics included 28 comorbid conditions defined based on separate validated International Statistical Classification of Disease and Health Related Problems, Tenth Revision, Canada (ICD-10-CA) coding algorithms reported individually and cumulatively (categorized as 0, 1, 2–3, and 4+).¹⁹ Primary care attachment was defined as the percentage of all outpatient primary care visits made to a single practitioner in the 2-year period prior to their first hospitalization (among those with ≥3 visits). Attachment was categorized as 75%-100% (good attachment), 50%-74% (moderate attachment), or <50% (low attachment).^20,21

We also identified hospital encounter-level characteristics. These included the most responsible diagnosis, admission category (elective or urgent/emergent), and discharge disposition for each hospital episode. Reported health outcomes included the proportion of patients with in-hospital mortality and those with at least one 30-day, all-cause readmission to hospital.

Analysis

Patient characteristics were described using proportions and means (standard deviation) as appropriate for high users and nonhigh users within and across each definition. Encounter characteristics were also described and stratified by age category (18-64 or 65+ years). Comparison of patient capture was then analyzed among patients who were high use by at least 1 definition. The overlap and agreement of the 3 definitions were compared using a Venn diagram and kappa statistic. The 10 most responsible diagnoses (based on frequency) were also compared across definitions and stratified by age.

Finally, the percentage of system burden accounted for by each measure was calculated as the amount used by high users divided by the total amount used by the entire study population (x 100). To assess the potential modifying effect of age, results were stratified by age category for each definition.

All analyses were conducted using Stata 11.2 (StataCorp LP, College Station, TX).²² The Conjoint Health Research Ethics Board of the University of Calgary approved this study and granted waiver of patient consent. This manuscript is written in accordance with reporting guidelines for studies conducted using observational routinely collected health data (RECORD statement).²³

Comparison of Patient and Encounter-level Characterist

ics

A total of 219,106 adults had 283,204 inpatient episodes of care within the study timeframe. There were 12,707 (5.8%), 11,095 (5.1%), and 10,956 (5.0%) patients defined as high users based on number of inpatient episodes, length of stay, and cost, respectively (supplementary Figure 1). Regardless of definition, when compared to their non–high use counterparts, patients classified as high use were more likely to be male, older, in a lower median neighborhood income quintile, and have a higher level of comorbidity. Comparing across definitions of high use, those defined by number of inpatient episodes were more likely to be younger, live in rural areas, have better primary care attachment, and have fewer comorbidities, compared to the other definitions. High users by length of stay were more likely to be older and had a higher proportion of mental health–related comorbidities, including dementia and depression, as compared with the other definitions. Results were largely similar for those defined by cost (Table 1).

Encounter-level analyses

Comparison of Patient Capture and Inpatient Burden

Of the 22,691 individuals who were defined as high use by at least 1 definition, 2,331 (10.3%) were consistently high use across all 3 definitions (kappa = 0.38; Figure 1). Of the 13,682 individuals classified as high use by at least 1 of length of stay or cost, 8369 (61.2%) were defined as high use by both definitions (kappa = 0.75). However, of the 12,707 defined as high use by the number of inpatient episodes, only 3698 (29.1%) were also defined as high use by another definition. Exploration of the most responsible diagnoses across definitions showed that congestive heart failure (2.8%-3.5%), chronic obstructive pulmonary disease (1.6%-3.2%), and dementia (0.6%-2.2%) were the most frequent. Acute medical conditions (eg, pneumonia [1.8%] or gastroenteritis [0.7%]) that may result in multiple shorter hospitalizations were observed at higher frequencies among high users defined by inpatient episodes, while conditions commonly requiring rehabilitation (eg, fracture [1.8%] and stroke [1.7%]) were more common among high users defined by length of stay and cost (supplementary Table 2). Stratification by age showed marked differences in the diagnoses across high-use definitions. Among hi

When assessing inpatient system burden, high users by number of inpatient episodes accounted for 47,044 (16.6%) of the 283,204 episodes. High users defined by length of stay accounted for 1,286,539 (46.4%) days of 2,773,561 total days, while high users defined by cost accumulated $1.4 billion (38.9%) of the estimated $3.7 billion in inpatient expenditures. High users defined by cost and length of stay each accounted for comparatively few episode

Using a large population-based cohort of all adults with at least 1 hospitalization in the province of Alberta, Canada, within a 12-month period, we compared 3 commonly used definitions of high inpatient use. The choice of definition had a substantial influence on the types of patients categorized as high use, as well as the proportion of total inpatient utilization that was associated with high users. The definition based on number of inpatient episodes captured a distinct population of high users, while the populations identified using cumulative length of stay or cost were similar.

Differences within and between definitions were especially apparent in age-stratified analyses: Greater length of stay or higher cost among patients aged 18-64 years identifies a large proportion of psychological conditions, while a greater number of inpatient episodes identifies acute conditions and childbirth or labor-related complications. Conversely, definitions based on length of stay and cost in the elderly (65+) identified groups with chronic conditions that result in progressive functional decline (often requiring increasing supportive services upon discharge) or conditions that require significant rehabilitation prior to discharge. Regarding inpatient system burden, high users defined by number of inpatient episodes accounted for a small proportion of total inpatient episodes, while high users defined by length of stay and cost accounted for nearly half of the accumulated hospital days and cost for each. These findings highlight the need for careful consideration of how high use is defined when studying high-user populations and implications for targeting subpopulations for intervention.

Our results add to those from previous studies. A US-based, single-center study of 2566 individuals compared definitions of high inpatient use based on cost and frequency of admission and found that patients defined by cost were predominantly hospitalized for surgical conditions, while those fulfilling the episode-based definition were often hospitalized for medical conditions.¹² The most responsible diagnoses for patient hospitalizations in our study reflect this. We extended this comparison to consider the impact of age on outcomes and inpatient system burden and found that older age was also linked to poorer outcomes and increased burden. We also considered a third definition (cumulative length of stay), which provided another opportunity for comparison. The presence of chronic conditions requiring rehabilitation and possible alternate level of care days within our cohort highlights the utility of this length of stay-based approach when considering definitions. Although there were similarities between patients defined by length of stay and cost, partly due to cost being largely a function of length of stay, there were also important differences in their patient profiles. Those defined by cost tended to have conditions requiring surgical procedures not requiring extended in-hospital rehabilitation. Furthermore, the higher proportion of in-hospital mortality among those defined by cost may also reflect the fact that patients tend to accrue the majority of their healthcare expenditures during the final 120 days of life.²⁴

Each definition of high use identified complex patients; however, the differences between the various types of high users identified by these definitions suggest that they are not interchangeable. Arguably, selection of the most appropriate definition should depend on the objective of measuring high users, particularly if an intervention is planned. Interventions for high users are complex, requiring both medical and nonmedical components. The current literature in this area has often focused on case management programs, collaboration with community-based social support programs, and improving coordination and transitions of care.^25-27 While many of these approaches require considerable involvement outside of the inpatient setting, these interventions can be informed by defining who high users of inpatient services are. Our findings show several possible subgroups of high users, which could be targeted for intervention. For example, an inpatient episode-based definition, which identifies patients with frequent encounters for acute conditions (eg, pneumonia and urinary tract infections), would be informative if an intervention targeted reductions in inpatient use and readmission rates. Alternatively, an intervention designed to improve community-based mental health programs would best be informed by a definition based on length of stay in which high users with underlying mental health conditions were prevalent. Such interventions are rarely mutually exclusive and require multiple perspectives to inform their objectives. A well-designed intervention will not only address the medical characteristics of high users but also the social determinants of health that place patients at risk of high inpatient use.

Our study should be interpreted in light of its limitations. First, measures of disease severity were not available to further characterize similarities and differences across high-use groups. Furthermore, we were unable to account for other social determinants of health that may be relevant to inpatient system usage. Second, direct cost of hospitalizations was estimated based on RIW and is thus reflective of expected rather than actual costs. However, this will have minimal impact on capture, as patients defined by this metric require substantial costs to be included in the top fifth percentile, and thus deviations in individual hospitalization costs will have minimal influence on the cumulative cost. Finally, while inpatient spending makes up a large proportion of healthcare spending, there is likely a number of different high-use profiles found outside of the acute care setting. Despite these limitations, our study includes several key strengths. The use of population-level data allows for analysis that is robust and more generalizable than studies from single centers. Additionally, the comparison of 3 independent definitions allows for a greater comparison of the nuances of each definition. Our study also considers the important impact of age as an effect modifier of inpatient use in the general population and identifies distinct patient profiles that exist across each definition.

Definitions of high use of inpatient services based on number of inpatient episodes, days spent in hospital, and total hospital costs identify patient populations with different characteristics and differ substantially in their impact on health outcomes and inpatient burden. These results highlight the need for careful consideration of the context of the study or intervention and the implications of selecting a specific definition of high inpatient use at study conception. Ultimately, the performance of an intervention in high-use populations is likely to be conditional on the fit of the patient population generated by the chosen definition of high inpatient use to the objectives of a study.

Acknowledgments

This study is based in part on data provided by Alberta Health and Alberta Health Services. The interpretation and conclusions are those of the researchers and do not represent the views of the Government of Alberta. Neither the Government of Alberta nor Alberta Health express any opinion in relation to this study.

Disclosure

Dr. Hemmelgarn is supported by the Roy and Vi Baay Chair in Kidney Research. Dr. Manns is supported by the Svare Professorship in Health Economics and by a Health Scholar Award by Alberta Innovates Health Solutions (AIHS). Dr. Tonelli is supported by the David Freeze chair in Health Services Research. The Interdisciplinary Chronic Disease Collaboration is funded by AIHS—Collaborative Research and Innovation Opportunity (CRIO) Team Grants Program.

As healthcare system use and costs continue to rise, increased importance has been placed on identifying the small subgroup of patients that drive this trend.¹ It is estimated that 5% of healthcare users account for over 60% of healthcare spending.^2-6 Furthermore, care for these “high users” is expensive due to an over-reliance on inpatient services. Approximately 40% of all health spending is for inpatient care, the largest single category of health spending, which is similarly skewed toward high users.^1,3,5 Improving our understanding of this population may provide an opportunity to direct improvement efforts to a select group of patients with a potentially high benefit, as well as move care away from the costly inpatient setting.

However, the development of effective interventions to improve patient experience and outcomes while decreasing costs (referred to as the “Triple Aim” by the Institute for Health Improvement) for high users of inpatient services hinges on the methodology used to identify this high-risk population.⁷ There is substantial variability in definitions of high users; the most common definitions are based on the number of hospital encounters, days spent in the hospital, and hospital costs.^8-15 Definitions have intrinsic differences in their implications around appropriateness, efficiency, and financial sustainability of inpatient resource use. Though the constructs underlying these definitions are highly variable, direct comparisons of differences in patient capture are limited.

A recent study from a single US center explored the clinical characteristics of hospital patients based on definitions of use vs cost and observed important differences in patients’ profiles and outcomes.¹² While this suggests that the choice of definition may have major implications for whom to target (and the efficacy of any proposed interventions), this concept has not been explored at the population level. Therefore, we used population-based administrative data from a single-payer healthcare system to compare 3 common definitions of high inpatient service use and their influence on patient capture, health outcomes, and inpatient system burden.

Data Sources and Study Population

We conducted a retrospective population-based study using administrative and clinical data for the province of Alberta, including the discharge abstracts database, physician claims, ambulatory care records, population health registry file, and aggregated data from the Canadian census.¹⁶ We identified all adults who had 1 or more hospitalizations with a discharge date between April 1, 2012, and March 31, 2013, though the admission date could be prior to April 1, 2012.

Definition of High-Inpatient Use

High-inpatient use was defined using 3 metrics: number of inpatient episodes, length of stay, and cost. As in prior studies, for each definition, individuals in the upper5th percentile of the relevant distribution were designated “high users,”^2,15 while patients in the lower 95th percentile were considered “nonhigh users.” Patients could be defined as a high user in more than 1 definition.

Patients with 3 or more hospital episodes were defined as high users for the “number of inpatient episodes” definition. A hospital episode of care was defined as an event that resulted in discharge (or death) from an inpatient facility. If an individual was admitted to a hospital and transferred to another facility within 1 day of discharge, the hospitalizations were considered part of the same episode of care.

The “length of stay” definition refers to the cumulative number of days spent in an inpatient facility for all eligible episodes of care. Patients with 56 or more days in hospital during the study period were considered high users. Day of admission and discharge were considered full inpatient days, regardless of the time of admission and discharge.

The “cost” definition considered the cumulative estimated cost of every eligible episode of care. We estimated costs for each hospitalization using resource intensity weights (RIW). This is a relative weighted value for the average inpatient case after taking factors such as age, comorbidity, and procedures into account. The RIW for each episode was multiplied by the national average inpatient cost.¹⁷ Based on this definition, patients with a cumulative hospital cost of ≥ $63,597 were deemed high users. All costs were calculated in Canadian Dollars (CAD, $) and adjusted to 2013 dollars based on Statistics Canada’s Consumer Price Index.¹⁸

Demographic, Clinical, and Encounter Characteristics

Individual characteristics were measured using a combination of provincial administrative data sources. All measures were recorded as of the admission date of the first eligible hospitalization. Demographic characteristics included age, sex, First Nations status, urban/rural status (based on the individual’s residential postal code), and median neighborhood income quintile. Clinical characteristics included 28 comorbid conditions defined based on separate validated International Statistical Classification of Disease and Health Related Problems, Tenth Revision, Canada (ICD-10-CA) coding algorithms reported individually and cumulatively (categorized as 0, 1, 2–3, and 4+).¹⁹ Primary care attachment was defined as the percentage of all outpatient primary care visits made to a single practitioner in the 2-year period prior to their first hospitalization (among those with ≥3 visits). Attachment was categorized as 75%-100% (good attachment), 50%-74% (moderate attachment), or <50% (low attachment).^20,21

We also identified hospital encounter-level characteristics. These included the most responsible diagnosis, admission category (elective or urgent/emergent), and discharge disposition for each hospital episode. Reported health outcomes included the proportion of patients with in-hospital mortality and those with at least one 30-day, all-cause readmission to hospital.

Analysis

Patient characteristics were described using proportions and means (standard deviation) as appropriate for high users and nonhigh users within and across each definition. Encounter characteristics were also described and stratified by age category (18-64 or 65+ years). Comparison of patient capture was then analyzed among patients who were high use by at least 1 definition. The overlap and agreement of the 3 definitions were compared using a Venn diagram and kappa statistic. The 10 most responsible diagnoses (based on frequency) were also compared across definitions and stratified by age.

Finally, the percentage of system burden accounted for by each measure was calculated as the amount used by high users divided by the total amount used by the entire study population (x 100). To assess the potential modifying effect of age, results were stratified by age category for each definition.

All analyses were conducted using Stata 11.2 (StataCorp LP, College Station, TX).²² The Conjoint Health Research Ethics Board of the University of Calgary approved this study and granted waiver of patient consent. This manuscript is written in accordance with reporting guidelines for studies conducted using observational routinely collected health data (RECORD statement).²³

Comparison of Patient and Encounter-level Characterist

ics

A total of 219,106 adults had 283,204 inpatient episodes of care within the study timeframe. There were 12,707 (5.8%), 11,095 (5.1%), and 10,956 (5.0%) patients defined as high users based on number of inpatient episodes, length of stay, and cost, respectively (supplementary Figure 1). Regardless of definition, when compared to their non–high use counterparts, patients classified as high use were more likely to be male, older, in a lower median neighborhood income quintile, and have a higher level of comorbidity. Comparing across definitions of high use, those defined by number of inpatient episodes were more likely to be younger, live in rural areas, have better primary care attachment, and have fewer comorbidities, compared to the other definitions. High users by length of stay were more likely to be older and had a higher proportion of mental health–related comorbidities, including dementia and depression, as compared with the other definitions. Results were largely similar for those defined by cost (Table 1).

Encounter-level analyses

Comparison of Patient Capture and Inpatient Burden

Of the 22,691 individuals who were defined as high use by at least 1 definition, 2,331 (10.3%) were consistently high use across all 3 definitions (kappa = 0.38; Figure 1). Of the 13,682 individuals classified as high use by at least 1 of length of stay or cost, 8369 (61.2%) were defined as high use by both definitions (kappa = 0.75). However, of the 12,707 defined as high use by the number of inpatient episodes, only 3698 (29.1%) were also defined as high use by another definition. Exploration of the most responsible diagnoses across definitions showed that congestive heart failure (2.8%-3.5%), chronic obstructive pulmonary disease (1.6%-3.2%), and dementia (0.6%-2.2%) were the most frequent. Acute medical conditions (eg, pneumonia [1.8%] or gastroenteritis [0.7%]) that may result in multiple shorter hospitalizations were observed at higher frequencies among high users defined by inpatient episodes, while conditions commonly requiring rehabilitation (eg, fracture [1.8%] and stroke [1.7%]) were more common among high users defined by length of stay and cost (supplementary Table 2). Stratification by age showed marked differences in the diagnoses across high-use definitions. Among hi

When assessing inpatient system burden, high users by number of inpatient episodes accounted for 47,044 (16.6%) of the 283,204 episodes. High users defined by length of stay accounted for 1,286,539 (46.4%) days of 2,773,561 total days, while high users defined by cost accumulated $1.4 billion (38.9%) of the estimated $3.7 billion in inpatient expenditures. High users defined by cost and length of stay each accounted for comparatively few episode

Using a large population-based cohort of all adults with at least 1 hospitalization in the province of Alberta, Canada, within a 12-month period, we compared 3 commonly used definitions of high inpatient use. The choice of definition had a substantial influence on the types of patients categorized as high use, as well as the proportion of total inpatient utilization that was associated with high users. The definition based on number of inpatient episodes captured a distinct population of high users, while the populations identified using cumulative length of stay or cost were similar.

Differences within and between definitions were especially apparent in age-stratified analyses: Greater length of stay or higher cost among patients aged 18-64 years identifies a large proportion of psychological conditions, while a greater number of inpatient episodes identifies acute conditions and childbirth or labor-related complications. Conversely, definitions based on length of stay and cost in the elderly (65+) identified groups with chronic conditions that result in progressive functional decline (often requiring increasing supportive services upon discharge) or conditions that require significant rehabilitation prior to discharge. Regarding inpatient system burden, high users defined by number of inpatient episodes accounted for a small proportion of total inpatient episodes, while high users defined by length of stay and cost accounted for nearly half of the accumulated hospital days and cost for each. These findings highlight the need for careful consideration of how high use is defined when studying high-user populations and implications for targeting subpopulations for intervention.

Our results add to those from previous studies. A US-based, single-center study of 2566 individuals compared definitions of high inpatient use based on cost and frequency of admission and found that patients defined by cost were predominantly hospitalized for surgical conditions, while those fulfilling the episode-based definition were often hospitalized for medical conditions.¹² The most responsible diagnoses for patient hospitalizations in our study reflect this. We extended this comparison to consider the impact of age on outcomes and inpatient system burden and found that older age was also linked to poorer outcomes and increased burden. We also considered a third definition (cumulative length of stay), which provided another opportunity for comparison. The presence of chronic conditions requiring rehabilitation and possible alternate level of care days within our cohort highlights the utility of this length of stay-based approach when considering definitions. Although there were similarities between patients defined by length of stay and cost, partly due to cost being largely a function of length of stay, there were also important differences in their patient profiles. Those defined by cost tended to have conditions requiring surgical procedures not requiring extended in-hospital rehabilitation. Furthermore, the higher proportion of in-hospital mortality among those defined by cost may also reflect the fact that patients tend to accrue the majority of their healthcare expenditures during the final 120 days of life.²⁴

Each definition of high use identified complex patients; however, the differences between the various types of high users identified by these definitions suggest that they are not interchangeable. Arguably, selection of the most appropriate definition should depend on the objective of measuring high users, particularly if an intervention is planned. Interventions for high users are complex, requiring both medical and nonmedical components. The current literature in this area has often focused on case management programs, collaboration with community-based social support programs, and improving coordination and transitions of care.^25-27 While many of these approaches require considerable involvement outside of the inpatient setting, these interventions can be informed by defining who high users of inpatient services are. Our findings show several possible subgroups of high users, which could be targeted for intervention. For example, an inpatient episode-based definition, which identifies patients with frequent encounters for acute conditions (eg, pneumonia and urinary tract infections), would be informative if an intervention targeted reductions in inpatient use and readmission rates. Alternatively, an intervention designed to improve community-based mental health programs would best be informed by a definition based on length of stay in which high users with underlying mental health conditions were prevalent. Such interventions are rarely mutually exclusive and require multiple perspectives to inform their objectives. A well-designed intervention will not only address the medical characteristics of high users but also the social determinants of health that place patients at risk of high inpatient use.

Our study should be interpreted in light of its limitations. First, measures of disease severity were not available to further characterize similarities and differences across high-use groups. Furthermore, we were unable to account for other social determinants of health that may be relevant to inpatient system usage. Second, direct cost of hospitalizations was estimated based on RIW and is thus reflective of expected rather than actual costs. However, this will have minimal impact on capture, as patients defined by this metric require substantial costs to be included in the top fifth percentile, and thus deviations in individual hospitalization costs will have minimal influence on the cumulative cost. Finally, while inpatient spending makes up a large proportion of healthcare spending, there is likely a number of different high-use profiles found outside of the acute care setting. Despite these limitations, our study includes several key strengths. The use of population-level data allows for analysis that is robust and more generalizable than studies from single centers. Additionally, the comparison of 3 independent definitions allows for a greater comparison of the nuances of each definition. Our study also considers the important impact of age as an effect modifier of inpatient use in the general population and identifies distinct patient profiles that exist across each definition.

Definitions of high use of inpatient services based on number of inpatient episodes, days spent in hospital, and total hospital costs identify patient populations with different characteristics and differ substantially in their impact on health outcomes and inpatient burden. These results highlight the need for careful consideration of the context of the study or intervention and the implications of selecting a specific definition of high inpatient use at study conception. Ultimately, the performance of an intervention in high-use populations is likely to be conditional on the fit of the patient population generated by the chosen definition of high inpatient use to the objectives of a study.

Acknowledgments

This study is based in part on data provided by Alberta Health and Alberta Health Services. The interpretation and conclusions are those of the researchers and do not represent the views of the Government of Alberta. Neither the Government of Alberta nor Alberta Health express any opinion in relation to this study.

Disclosure

Dr. Hemmelgarn is supported by the Roy and Vi Baay Chair in Kidney Research. Dr. Manns is supported by the Svare Professorship in Health Economics and by a Health Scholar Award by Alberta Innovates Health Solutions (AIHS). Dr. Tonelli is supported by the David Freeze chair in Health Services Research. The Interdisciplinary Chronic Disease Collaboration is funded by AIHS—Collaborative Research and Innovation Opportunity (CRIO) Team Grants Program.

As healthcare system use and costs continue to rise, increased importance has been placed on identifying the small subgroup of patients that drive this trend.¹ It is estimated that 5% of healthcare users account for over 60% of healthcare spending.^2-6 Furthermore, care for these “high users” is expensive due to an over-reliance on inpatient services. Approximately 40% of all health spending is for inpatient care, the largest single category of health spending, which is similarly skewed toward high users.^1,3,5 Improving our understanding of this population may provide an opportunity to direct improvement efforts to a select group of patients with a potentially high benefit, as well as move care away from the costly inpatient setting.

However, the development of effective interventions to improve patient experience and outcomes while decreasing costs (referred to as the “Triple Aim” by the Institute for Health Improvement) for high users of inpatient services hinges on the methodology used to identify this high-risk population.⁷ There is substantial variability in definitions of high users; the most common definitions are based on the number of hospital encounters, days spent in the hospital, and hospital costs.^8-15 Definitions have intrinsic differences in their implications around appropriateness, efficiency, and financial sustainability of inpatient resource use. Though the constructs underlying these definitions are highly variable, direct comparisons of differences in patient capture are limited.

A recent study from a single US center explored the clinical characteristics of hospital patients based on definitions of use vs cost and observed important differences in patients’ profiles and outcomes.¹² While this suggests that the choice of definition may have major implications for whom to target (and the efficacy of any proposed interventions), this concept has not been explored at the population level. Therefore, we used population-based administrative data from a single-payer healthcare system to compare 3 common definitions of high inpatient service use and their influence on patient capture, health outcomes, and inpatient system burden.

Data Sources and Study Population

We conducted a retrospective population-based study using administrative and clinical data for the province of Alberta, including the discharge abstracts database, physician claims, ambulatory care records, population health registry file, and aggregated data from the Canadian census.¹⁶ We identified all adults who had 1 or more hospitalizations with a discharge date between April 1, 2012, and March 31, 2013, though the admission date could be prior to April 1, 2012.

Definition of High-Inpatient Use

High-inpatient use was defined using 3 metrics: number of inpatient episodes, length of stay, and cost. As in prior studies, for each definition, individuals in the upper5th percentile of the relevant distribution were designated “high users,”^2,15 while patients in the lower 95th percentile were considered “nonhigh users.” Patients could be defined as a high user in more than 1 definition.

Patients with 3 or more hospital episodes were defined as high users for the “number of inpatient episodes” definition. A hospital episode of care was defined as an event that resulted in discharge (or death) from an inpatient facility. If an individual was admitted to a hospital and transferred to another facility within 1 day of discharge, the hospitalizations were considered part of the same episode of care.

The “length of stay” definition refers to the cumulative number of days spent in an inpatient facility for all eligible episodes of care. Patients with 56 or more days in hospital during the study period were considered high users. Day of admission and discharge were considered full inpatient days, regardless of the time of admission and discharge.

The “cost” definition considered the cumulative estimated cost of every eligible episode of care. We estimated costs for each hospitalization using resource intensity weights (RIW). This is a relative weighted value for the average inpatient case after taking factors such as age, comorbidity, and procedures into account. The RIW for each episode was multiplied by the national average inpatient cost.¹⁷ Based on this definition, patients with a cumulative hospital cost of ≥ $63,597 were deemed high users. All costs were calculated in Canadian Dollars (CAD, $) and adjusted to 2013 dollars based on Statistics Canada’s Consumer Price Index.¹⁸

Demographic, Clinical, and Encounter Characteristics

Individual characteristics were measured using a combination of provincial administrative data sources. All measures were recorded as of the admission date of the first eligible hospitalization. Demographic characteristics included age, sex, First Nations status, urban/rural status (based on the individual’s residential postal code), and median neighborhood income quintile. Clinical characteristics included 28 comorbid conditions defined based on separate validated International Statistical Classification of Disease and Health Related Problems, Tenth Revision, Canada (ICD-10-CA) coding algorithms reported individually and cumulatively (categorized as 0, 1, 2–3, and 4+).¹⁹ Primary care attachment was defined as the percentage of all outpatient primary care visits made to a single practitioner in the 2-year period prior to their first hospitalization (among those with ≥3 visits). Attachment was categorized as 75%-100% (good attachment), 50%-74% (moderate attachment), or <50% (low attachment).^20,21

We also identified hospital encounter-level characteristics. These included the most responsible diagnosis, admission category (elective or urgent/emergent), and discharge disposition for each hospital episode. Reported health outcomes included the proportion of patients with in-hospital mortality and those with at least one 30-day, all-cause readmission to hospital.

Analysis

Patient characteristics were described using proportions and means (standard deviation) as appropriate for high users and nonhigh users within and across each definition. Encounter characteristics were also described and stratified by age category (18-64 or 65+ years). Comparison of patient capture was then analyzed among patients who were high use by at least 1 definition. The overlap and agreement of the 3 definitions were compared using a Venn diagram and kappa statistic. The 10 most responsible diagnoses (based on frequency) were also compared across definitions and stratified by age.

Finally, the percentage of system burden accounted for by each measure was calculated as the amount used by high users divided by the total amount used by the entire study population (x 100). To assess the potential modifying effect of age, results were stratified by age category for each definition.

All analyses were conducted using Stata 11.2 (StataCorp LP, College Station, TX).²² The Conjoint Health Research Ethics Board of the University of Calgary approved this study and granted waiver of patient consent. This manuscript is written in accordance with reporting guidelines for studies conducted using observational routinely collected health data (RECORD statement).²³

Comparison of Patient and Encounter-level Characterist

ics

A total of 219,106 adults had 283,204 inpatient episodes of care within the study timeframe. There were 12,707 (5.8%), 11,095 (5.1%), and 10,956 (5.0%) patients defined as high users based on number of inpatient episodes, length of stay, and cost, respectively (supplementary Figure 1). Regardless of definition, when compared to their non–high use counterparts, patients classified as high use were more likely to be male, older, in a lower median neighborhood income quintile, and have a higher level of comorbidity. Comparing across definitions of high use, those defined by number of inpatient episodes were more likely to be younger, live in rural areas, have better primary care attachment, and have fewer comorbidities, compared to the other definitions. High users by length of stay were more likely to be older and had a higher proportion of mental health–related comorbidities, including dementia and depression, as compared with the other definitions. Results were largely similar for those defined by cost (Table 1).

Encounter-level analyses

Comparison of Patient Capture and Inpatient Burden

Of the 22,691 individuals who were defined as high use by at least 1 definition, 2,331 (10.3%) were consistently high use across all 3 definitions (kappa = 0.38; Figure 1). Of the 13,682 individuals classified as high use by at least 1 of length of stay or cost, 8369 (61.2%) were defined as high use by both definitions (kappa = 0.75). However, of the 12,707 defined as high use by the number of inpatient episodes, only 3698 (29.1%) were also defined as high use by another definition. Exploration of the most responsible diagnoses across definitions showed that congestive heart failure (2.8%-3.5%), chronic obstructive pulmonary disease (1.6%-3.2%), and dementia (0.6%-2.2%) were the most frequent. Acute medical conditions (eg, pneumonia [1.8%] or gastroenteritis [0.7%]) that may result in multiple shorter hospitalizations were observed at higher frequencies among high users defined by inpatient episodes, while conditions commonly requiring rehabilitation (eg, fracture [1.8%] and stroke [1.7%]) were more common among high users defined by length of stay and cost (supplementary Table 2). Stratification by age showed marked differences in the diagnoses across high-use definitions. Among hi

When assessing inpatient system burden, high users by number of inpatient episodes accounted for 47,044 (16.6%) of the 283,204 episodes. High users defined by length of stay accounted for 1,286,539 (46.4%) days of 2,773,561 total days, while high users defined by cost accumulated $1.4 billion (38.9%) of the estimated $3.7 billion in inpatient expenditures. High users defined by cost and length of stay each accounted for comparatively few episode

Using a large population-based cohort of all adults with at least 1 hospitalization in the province of Alberta, Canada, within a 12-month period, we compared 3 commonly used definitions of high inpatient use. The choice of definition had a substantial influence on the types of patients categorized as high use, as well as the proportion of total inpatient utilization that was associated with high users. The definition based on number of inpatient episodes captured a distinct population of high users, while the populations identified using cumulative length of stay or cost were similar.

Differences within and between definitions were especially apparent in age-stratified analyses: Greater length of stay or higher cost among patients aged 18-64 years identifies a large proportion of psychological conditions, while a greater number of inpatient episodes identifies acute conditions and childbirth or labor-related complications. Conversely, definitions based on length of stay and cost in the elderly (65+) identified groups with chronic conditions that result in progressive functional decline (often requiring increasing supportive services upon discharge) or conditions that require significant rehabilitation prior to discharge. Regarding inpatient system burden, high users defined by number of inpatient episodes accounted for a small proportion of total inpatient episodes, while high users defined by length of stay and cost accounted for nearly half of the accumulated hospital days and cost for each. These findings highlight the need for careful consideration of how high use is defined when studying high-user populations and implications for targeting subpopulations for intervention.

Our results add to those from previous studies. A US-based, single-center study of 2566 individuals compared definitions of high inpatient use based on cost and frequency of admission and found that patients defined by cost were predominantly hospitalized for surgical conditions, while those fulfilling the episode-based definition were often hospitalized for medical conditions.¹² The most responsible diagnoses for patient hospitalizations in our study reflect this. We extended this comparison to consider the impact of age on outcomes and inpatient system burden and found that older age was also linked to poorer outcomes and increased burden. We also considered a third definition (cumulative length of stay), which provided another opportunity for comparison. The presence of chronic conditions requiring rehabilitation and possible alternate level of care days within our cohort highlights the utility of this length of stay-based approach when considering definitions. Although there were similarities between patients defined by length of stay and cost, partly due to cost being largely a function of length of stay, there were also important differences in their patient profiles. Those defined by cost tended to have conditions requiring surgical procedures not requiring extended in-hospital rehabilitation. Furthermore, the higher proportion of in-hospital mortality among those defined by cost may also reflect the fact that patients tend to accrue the majority of their healthcare expenditures during the final 120 days of life.²⁴

Each definition of high use identified complex patients; however, the differences between the various types of high users identified by these definitions suggest that they are not interchangeable. Arguably, selection of the most appropriate definition should depend on the objective of measuring high users, particularly if an intervention is planned. Interventions for high users are complex, requiring both medical and nonmedical components. The current literature in this area has often focused on case management programs, collaboration with community-based social support programs, and improving coordination and transitions of care.^25-27 While many of these approaches require considerable involvement outside of the inpatient setting, these interventions can be informed by defining who high users of inpatient services are. Our findings show several possible subgroups of high users, which could be targeted for intervention. For example, an inpatient episode-based definition, which identifies patients with frequent encounters for acute conditions (eg, pneumonia and urinary tract infections), would be informative if an intervention targeted reductions in inpatient use and readmission rates. Alternatively, an intervention designed to improve community-based mental health programs would best be informed by a definition based on length of stay in which high users with underlying mental health conditions were prevalent. Such interventions are rarely mutually exclusive and require multiple perspectives to inform their objectives. A well-designed intervention will not only address the medical characteristics of high users but also the social determinants of health that place patients at risk of high inpatient use.

Our study should be interpreted in light of its limitations. First, measures of disease severity were not available to further characterize similarities and differences across high-use groups. Furthermore, we were unable to account for other social determinants of health that may be relevant to inpatient system usage. Second, direct cost of hospitalizations was estimated based on RIW and is thus reflective of expected rather than actual costs. However, this will have minimal impact on capture, as patients defined by this metric require substantial costs to be included in the top fifth percentile, and thus deviations in individual hospitalization costs will have minimal influence on the cumulative cost. Finally, while inpatient spending makes up a large proportion of healthcare spending, there is likely a number of different high-use profiles found outside of the acute care setting. Despite these limitations, our study includes several key strengths. The use of population-level data allows for analysis that is robust and more generalizable than studies from single centers. Additionally, the comparison of 3 independent definitions allows for a greater comparison of the nuances of each definition. Our study also considers the important impact of age as an effect modifier of inpatient use in the general population and identifies distinct patient profiles that exist across each definition.

Definitions of high use of inpatient services based on number of inpatient episodes, days spent in hospital, and total hospital costs identify patient populations with different characteristics and differ substantially in their impact on health outcomes and inpatient burden. These results highlight the need for careful consideration of the context of the study or intervention and the implications of selecting a specific definition of high inpatient use at study conception. Ultimately, the performance of an intervention in high-use populations is likely to be conditional on the fit of the patient population generated by the chosen definition of high inpatient use to the objectives of a study.

Acknowledgments

This study is based in part on data provided by Alberta Health and Alberta Health Services. The interpretation and conclusions are those of the researchers and do not represent the views of the Government of Alberta. Neither the Government of Alberta nor Alberta Health express any opinion in relation to this study.

Disclosure

Dr. Hemmelgarn is supported by the Roy and Vi Baay Chair in Kidney Research. Dr. Manns is supported by the Svare Professorship in Health Economics and by a Health Scholar Award by Alberta Innovates Health Solutions (AIHS). Dr. Tonelli is supported by the David Freeze chair in Health Services Research. The Interdisciplinary Chronic Disease Collaboration is funded by AIHS—Collaborative Research and Innovation Opportunity (CRIO) Team Grants Program.

Take-Home Points

• Complication rates did not statistically significantly differ between simultaneous and staged TKA.
• Length of stay of 2 TKA admissions was greater than 1 BTKA admission.
• Transfusion requirements were greater in BTKA.
• Avoid bilateral procedures in ASA 3 patients.
• Develop institutional protocols for BTKA with multidisciplinary input.

In the United States, osteoarthritis is the most common cause of knee pain and one of the leading causes of disability.¹ Total knee arthroplasty (TKA) is an effective treatment for end-stage osteoarthritis of the knee.² Whether patients with severe, debilitating bilateral disease should undergo simultaneous bilateral TKA (BTKA) or staged BTKA (2 separate procedures during separate hospital admissions) continues to be debated. The relative risks and benefits of simultaneous BTKA relative to staged BTKA or unilateral TKA are controversial.^3-6 Proponents of simultaneous BTKA have argued that this surgery results in shorter hospital length of stay (LOS) and higher patient satisfaction without increased risk of perioperative complications,^7-9 and opponents have argued that it leads to increased perioperative mortality and complications and should not be performed routinely.^10,11

The safety of simultaneous BTKA cannot necessarily be extrapolated from data on unilateral TKA. Authors have argued that the complication rate for simultaneous BTKA is not comparable to the rate for unilateral TKA but instead is double the rate.¹² Although a doubled rate may more closely approximate the true risk of simultaneous BTKA, it still does not account for the increased surgical impact of 2 procedures (vs 1 procedure) on a patient. In this regard, comparing simultaneous and staged BTKA provides a more accurate assessment of risk, as long as the interval between surgeries is not excessive. The major stress experienced during TKA affects the cardiovascular, pulmonary, and musculoskeletal systems, and full recovery may take up to 6 months.^13-15 Outcome studies have found significant improvement in validated measures of function and pain up to but not past 6 months.^13,15 Furthermore, a large study comparing American Society of Anesthesiologists (ASA) scores with morbidity and mortality rates recorded in the New Zealand Total Joint Database established 6 months as a best approximation of postoperative mortality and morbidity risk.¹⁴ Given these data, we propose that the most accurate analysis of postoperative morbidity and mortality would be a comparison of simultaneous BTKA with BTKA staged <6 months apart. The staged procedures fall within the crucial postoperative period when increased morbidity and mortality would more likely be present. A between-surgeries interval >6 months would effectively separate the 2 procedures, rendering their risks not truly representative.

We retrospectively analyzed all simultaneous BTKA and staged BTKA (<6 months apart) surgeries performed at our orthopedic specialty hospital between 2005 and 2009. We hypothesized there would be no significant difference in perioperative morbidity or mortality between the groups.

Methods and Materials

Our institution’s Institutional Review Board approved this study. All patients who underwent either simultaneous BTKA or staged BTKA (<6 months apart) at a single orthopedic specialty hospital between 2005 and 2009 were retrospectively identified. Twenty-five surgeons performed the procedures. Which procedure to perform (simultaneous or staged) was decided by the attending surgeon in consultation with an anesthesiologist. Preoperative medical diagnostic testing was determined by the internist, who provided medical clearance, and was subject to review by the anesthesiologist. A patient was excluded from simultaneous BTKA only if the medical or anesthesiology consultant deemed the patient too high risk for bilateral procedures. Revision TKAs were excluded from the study.

Implant, approach, tourniquet use, and TKA technique were selected by the individual surgeons. Strategies for the simultaneous procedures were (1) single surgeon, single team, sequential, start second knee after closure of first, and (2) single surgeon, single team, sequential, start second knee after implantation of first but before closure. The decision to proceed with the second knee was confirmed in consultation with the anesthesiologist after implantation and deflation of the tourniquet on the first knee.

Individual electronic patient charts were reviewed for information on demographics, comorbidities, anesthesia type, antibiotics, and postoperative venous thromboembolism prophylaxis. Demographic variables included age, sex, height, weight, and body mass index (BMI). Comorbidities recorded were diabetes mellitus, coronary artery disease, prior myocardial infarction, stroke, and endocrinopathies. In addition, available ASA scores were recorded. The primary outcome was perioperative complications, defined as any complications that occurred within 6 months after surgery. These included death, pulmonary embolism (PE), and deep surgical-site infections (SSIs). Secondary outcome measures were LOS, discharge location (rehabilitation or home), and blood transfusion requirements.

The 2 groups (simultaneous BTKA, staged BTKA) were compared using Student t test for continuous variables and χ² test for categorical variables. Subgroup analysis was performed to compare healthier patients (ASA score 1 or 2) with patients who had more severe comorbidities (ASA score 3). Statistical significance was set at P < .05.

Results

Between 2005 and 2009, 371 patients had simultaneous BTKA, and 67 had staged BTKA (134 procedures) <6 months apart (Table 1).

Most surgeries (84.4% simultaneous, 90.3% staged) were performed with the patient under spinal anesthesia, and there was a trend (P = .167) toward more frequent use of general anesthesia in the simultaneous group relative to the staged group (Table 2).

Discussion

Although there was no significant difference in postoperative complication rates within 6 months after surgery between the simultaneous and staged BTKA groups, the incidence of complications in the simultaneous group was notable. The disproportionate size of the 2 comparison groups limited the power of our study to analyze individual perioperative complications. This study may be underpowered to detect differences in complications occurring relatively infrequently, which may explain why the difference in number of complications (13 in simultaneous group, 1 in staged group) did not achieve statistical significance (β = 0.89). Post hoc power analysis showed 956 patients would be needed in each group to adequately power for such small complication rates. However, our results are consistent with those of other studies.^13-15 The 1.9% PE rate in our simultaneous BTKA group does not vary from the average PE rate for TKA in the literature and is actually lower than the PE rate in a previous study at our institution.¹⁶ Fat embolism traditionally is considered more of a concern in bilateral cases than in unilateral cases. Although fat embolism surely is inherent to the physiologic alterations caused by TKA, we did not find clinically significant fat embolism in either cohort.

Similarly, the 1.08% rate of deep SSIs is within the range for postoperative TKA infections at our institution and others.¹⁷ Our staged BTKA group’s complication rate, 0.75% (1 SSI), was slightly lower than expected. However, 0.75% is in keeping with institutional norms (typical rate, ~1%). We would have expected a nonzero rate for venous thromboembolism, and perhaps such a rate would have come with an inclusion period longer than 6 months. Last, the death in the simultaneous BTKA group was not an outlier, given the published rate of mortality after elective total joint surgery.¹⁸The characteristics of our simultaneous and staged BTKA groups were very similar (Table 1), though the larger number of staged-group patients with diabetes mellitus and coronary artery disease may represent selection bias. Nevertheless, the proportions of patients with each of 3 ASA scores were similar. It is also important to note that, in this context, a high percentage of patients in each group (33.6% simultaneous, 37.5% staged) received ASA score 3 from the anesthesiologist (P > .05). This may be an important factor in explaining the larger though not statistically significant number of complications in the simultaneous group (13) relative to the staged group (1).

Conclusion

In this study, the incidence of postoperative complications was higher for simultaneous BTKA than for staged BTKA performed <6 months apart, but the difference was not significantly different. There were significant differences in LOS and blood transfusion rates between the groups, as expected. At present, only patients with ASA score 1 or 2 are considered for simultaneous BTKA at our institution. Patients with ASA score 3 or higher are not eligible.

Am J Orthop. 2017;46(4):E224-E229. Copyright Frontline Medical Communications Inc. 2017. All rights reserved.

Take-Home Points

• Complication rates did not statistically significantly differ between simultaneous and staged TKA.
• Length of stay of 2 TKA admissions was greater than 1 BTKA admission.
• Transfusion requirements were greater in BTKA.
• Avoid bilateral procedures in ASA 3 patients.
• Develop institutional protocols for BTKA with multidisciplinary input.

In the United States, osteoarthritis is the most common cause of knee pain and one of the leading causes of disability.¹ Total knee arthroplasty (TKA) is an effective treatment for end-stage osteoarthritis of the knee.² Whether patients with severe, debilitating bilateral disease should undergo simultaneous bilateral TKA (BTKA) or staged BTKA (2 separate procedures during separate hospital admissions) continues to be debated. The relative risks and benefits of simultaneous BTKA relative to staged BTKA or unilateral TKA are controversial.^3-6 Proponents of simultaneous BTKA have argued that this surgery results in shorter hospital length of stay (LOS) and higher patient satisfaction without increased risk of perioperative complications,^7-9 and opponents have argued that it leads to increased perioperative mortality and complications and should not be performed routinely.^10,11

The safety of simultaneous BTKA cannot necessarily be extrapolated from data on unilateral TKA. Authors have argued that the complication rate for simultaneous BTKA is not comparable to the rate for unilateral TKA but instead is double the rate.¹² Although a doubled rate may more closely approximate the true risk of simultaneous BTKA, it still does not account for the increased surgical impact of 2 procedures (vs 1 procedure) on a patient. In this regard, comparing simultaneous and staged BTKA provides a more accurate assessment of risk, as long as the interval between surgeries is not excessive. The major stress experienced during TKA affects the cardiovascular, pulmonary, and musculoskeletal systems, and full recovery may take up to 6 months.^13-15 Outcome studies have found significant improvement in validated measures of function and pain up to but not past 6 months.^13,15 Furthermore, a large study comparing American Society of Anesthesiologists (ASA) scores with morbidity and mortality rates recorded in the New Zealand Total Joint Database established 6 months as a best approximation of postoperative mortality and morbidity risk.¹⁴ Given these data, we propose that the most accurate analysis of postoperative morbidity and mortality would be a comparison of simultaneous BTKA with BTKA staged <6 months apart. The staged procedures fall within the crucial postoperative period when increased morbidity and mortality would more likely be present. A between-surgeries interval >6 months would effectively separate the 2 procedures, rendering their risks not truly representative.

We retrospectively analyzed all simultaneous BTKA and staged BTKA (<6 months apart) surgeries performed at our orthopedic specialty hospital between 2005 and 2009. We hypothesized there would be no significant difference in perioperative morbidity or mortality between the groups.

Methods and Materials

Our institution’s Institutional Review Board approved this study. All patients who underwent either simultaneous BTKA or staged BTKA (<6 months apart) at a single orthopedic specialty hospital between 2005 and 2009 were retrospectively identified. Twenty-five surgeons performed the procedures. Which procedure to perform (simultaneous or staged) was decided by the attending surgeon in consultation with an anesthesiologist. Preoperative medical diagnostic testing was determined by the internist, who provided medical clearance, and was subject to review by the anesthesiologist. A patient was excluded from simultaneous BTKA only if the medical or anesthesiology consultant deemed the patient too high risk for bilateral procedures. Revision TKAs were excluded from the study.

Implant, approach, tourniquet use, and TKA technique were selected by the individual surgeons. Strategies for the simultaneous procedures were (1) single surgeon, single team, sequential, start second knee after closure of first, and (2) single surgeon, single team, sequential, start second knee after implantation of first but before closure. The decision to proceed with the second knee was confirmed in consultation with the anesthesiologist after implantation and deflation of the tourniquet on the first knee.

Individual electronic patient charts were reviewed for information on demographics, comorbidities, anesthesia type, antibiotics, and postoperative venous thromboembolism prophylaxis. Demographic variables included age, sex, height, weight, and body mass index (BMI). Comorbidities recorded were diabetes mellitus, coronary artery disease, prior myocardial infarction, stroke, and endocrinopathies. In addition, available ASA scores were recorded. The primary outcome was perioperative complications, defined as any complications that occurred within 6 months after surgery. These included death, pulmonary embolism (PE), and deep surgical-site infections (SSIs). Secondary outcome measures were LOS, discharge location (rehabilitation or home), and blood transfusion requirements.

The 2 groups (simultaneous BTKA, staged BTKA) were compared using Student t test for continuous variables and χ² test for categorical variables. Subgroup analysis was performed to compare healthier patients (ASA score 1 or 2) with patients who had more severe comorbidities (ASA score 3). Statistical significance was set at P < .05.

Results

Between 2005 and 2009, 371 patients had simultaneous BTKA, and 67 had staged BTKA (134 procedures) <6 months apart (Table 1).

Most surgeries (84.4% simultaneous, 90.3% staged) were performed with the patient under spinal anesthesia, and there was a trend (P = .167) toward more frequent use of general anesthesia in the simultaneous group relative to the staged group (Table 2).

Discussion

Although there was no significant difference in postoperative complication rates within 6 months after surgery between the simultaneous and staged BTKA groups, the incidence of complications in the simultaneous group was notable. The disproportionate size of the 2 comparison groups limited the power of our study to analyze individual perioperative complications. This study may be underpowered to detect differences in complications occurring relatively infrequently, which may explain why the difference in number of complications (13 in simultaneous group, 1 in staged group) did not achieve statistical significance (β = 0.89). Post hoc power analysis showed 956 patients would be needed in each group to adequately power for such small complication rates. However, our results are consistent with those of other studies.^13-15 The 1.9% PE rate in our simultaneous BTKA group does not vary from the average PE rate for TKA in the literature and is actually lower than the PE rate in a previous study at our institution.¹⁶ Fat embolism traditionally is considered more of a concern in bilateral cases than in unilateral cases. Although fat embolism surely is inherent to the physiologic alterations caused by TKA, we did not find clinically significant fat embolism in either cohort.

Similarly, the 1.08% rate of deep SSIs is within the range for postoperative TKA infections at our institution and others.¹⁷ Our staged BTKA group’s complication rate, 0.75% (1 SSI), was slightly lower than expected. However, 0.75% is in keeping with institutional norms (typical rate, ~1%). We would have expected a nonzero rate for venous thromboembolism, and perhaps such a rate would have come with an inclusion period longer than 6 months. Last, the death in the simultaneous BTKA group was not an outlier, given the published rate of mortality after elective total joint surgery.¹⁸The characteristics of our simultaneous and staged BTKA groups were very similar (Table 1), though the larger number of staged-group patients with diabetes mellitus and coronary artery disease may represent selection bias. Nevertheless, the proportions of patients with each of 3 ASA scores were similar. It is also important to note that, in this context, a high percentage of patients in each group (33.6% simultaneous, 37.5% staged) received ASA score 3 from the anesthesiologist (P > .05). This may be an important factor in explaining the larger though not statistically significant number of complications in the simultaneous group (13) relative to the staged group (1).

Conclusion

In this study, the incidence of postoperative complications was higher for simultaneous BTKA than for staged BTKA performed <6 months apart, but the difference was not significantly different. There were significant differences in LOS and blood transfusion rates between the groups, as expected. At present, only patients with ASA score 1 or 2 are considered for simultaneous BTKA at our institution. Patients with ASA score 3 or higher are not eligible.

Am J Orthop. 2017;46(4):E224-E229. Copyright Frontline Medical Communications Inc. 2017. All rights reserved.

Take-Home Points

• Complication rates did not statistically significantly differ between simultaneous and staged TKA.
• Length of stay of 2 TKA admissions was greater than 1 BTKA admission.
• Transfusion requirements were greater in BTKA.
• Avoid bilateral procedures in ASA 3 patients.
• Develop institutional protocols for BTKA with multidisciplinary input.

In the United States, osteoarthritis is the most common cause of knee pain and one of the leading causes of disability.¹ Total knee arthroplasty (TKA) is an effective treatment for end-stage osteoarthritis of the knee.² Whether patients with severe, debilitating bilateral disease should undergo simultaneous bilateral TKA (BTKA) or staged BTKA (2 separate procedures during separate hospital admissions) continues to be debated. The relative risks and benefits of simultaneous BTKA relative to staged BTKA or unilateral TKA are controversial.^3-6 Proponents of simultaneous BTKA have argued that this surgery results in shorter hospital length of stay (LOS) and higher patient satisfaction without increased risk of perioperative complications,^7-9 and opponents have argued that it leads to increased perioperative mortality and complications and should not be performed routinely.^10,11

The safety of simultaneous BTKA cannot necessarily be extrapolated from data on unilateral TKA. Authors have argued that the complication rate for simultaneous BTKA is not comparable to the rate for unilateral TKA but instead is double the rate.¹² Although a doubled rate may more closely approximate the true risk of simultaneous BTKA, it still does not account for the increased surgical impact of 2 procedures (vs 1 procedure) on a patient. In this regard, comparing simultaneous and staged BTKA provides a more accurate assessment of risk, as long as the interval between surgeries is not excessive. The major stress experienced during TKA affects the cardiovascular, pulmonary, and musculoskeletal systems, and full recovery may take up to 6 months.^13-15 Outcome studies have found significant improvement in validated measures of function and pain up to but not past 6 months.^13,15 Furthermore, a large study comparing American Society of Anesthesiologists (ASA) scores with morbidity and mortality rates recorded in the New Zealand Total Joint Database established 6 months as a best approximation of postoperative mortality and morbidity risk.¹⁴ Given these data, we propose that the most accurate analysis of postoperative morbidity and mortality would be a comparison of simultaneous BTKA with BTKA staged <6 months apart. The staged procedures fall within the crucial postoperative period when increased morbidity and mortality would more likely be present. A between-surgeries interval >6 months would effectively separate the 2 procedures, rendering their risks not truly representative.

We retrospectively analyzed all simultaneous BTKA and staged BTKA (<6 months apart) surgeries performed at our orthopedic specialty hospital between 2005 and 2009. We hypothesized there would be no significant difference in perioperative morbidity or mortality between the groups.

Methods and Materials

Our institution’s Institutional Review Board approved this study. All patients who underwent either simultaneous BTKA or staged BTKA (<6 months apart) at a single orthopedic specialty hospital between 2005 and 2009 were retrospectively identified. Twenty-five surgeons performed the procedures. Which procedure to perform (simultaneous or staged) was decided by the attending surgeon in consultation with an anesthesiologist. Preoperative medical diagnostic testing was determined by the internist, who provided medical clearance, and was subject to review by the anesthesiologist. A patient was excluded from simultaneous BTKA only if the medical or anesthesiology consultant deemed the patient too high risk for bilateral procedures. Revision TKAs were excluded from the study.

Implant, approach, tourniquet use, and TKA technique were selected by the individual surgeons. Strategies for the simultaneous procedures were (1) single surgeon, single team, sequential, start second knee after closure of first, and (2) single surgeon, single team, sequential, start second knee after implantation of first but before closure. The decision to proceed with the second knee was confirmed in consultation with the anesthesiologist after implantation and deflation of the tourniquet on the first knee.

Individual electronic patient charts were reviewed for information on demographics, comorbidities, anesthesia type, antibiotics, and postoperative venous thromboembolism prophylaxis. Demographic variables included age, sex, height, weight, and body mass index (BMI). Comorbidities recorded were diabetes mellitus, coronary artery disease, prior myocardial infarction, stroke, and endocrinopathies. In addition, available ASA scores were recorded. The primary outcome was perioperative complications, defined as any complications that occurred within 6 months after surgery. These included death, pulmonary embolism (PE), and deep surgical-site infections (SSIs). Secondary outcome measures were LOS, discharge location (rehabilitation or home), and blood transfusion requirements.

The 2 groups (simultaneous BTKA, staged BTKA) were compared using Student t test for continuous variables and χ² test for categorical variables. Subgroup analysis was performed to compare healthier patients (ASA score 1 or 2) with patients who had more severe comorbidities (ASA score 3). Statistical significance was set at P < .05.

Results

Between 2005 and 2009, 371 patients had simultaneous BTKA, and 67 had staged BTKA (134 procedures) <6 months apart (Table 1).

Most surgeries (84.4% simultaneous, 90.3% staged) were performed with the patient under spinal anesthesia, and there was a trend (P = .167) toward more frequent use of general anesthesia in the simultaneous group relative to the staged group (Table 2).

Discussion

Although there was no significant difference in postoperative complication rates within 6 months after surgery between the simultaneous and staged BTKA groups, the incidence of complications in the simultaneous group was notable. The disproportionate size of the 2 comparison groups limited the power of our study to analyze individual perioperative complications. This study may be underpowered to detect differences in complications occurring relatively infrequently, which may explain why the difference in number of complications (13 in simultaneous group, 1 in staged group) did not achieve statistical significance (β = 0.89). Post hoc power analysis showed 956 patients would be needed in each group to adequately power for such small complication rates. However, our results are consistent with those of other studies.^13-15 The 1.9% PE rate in our simultaneous BTKA group does not vary from the average PE rate for TKA in the literature and is actually lower than the PE rate in a previous study at our institution.¹⁶ Fat embolism traditionally is considered more of a concern in bilateral cases than in unilateral cases. Although fat embolism surely is inherent to the physiologic alterations caused by TKA, we did not find clinically significant fat embolism in either cohort.

Similarly, the 1.08% rate of deep SSIs is within the range for postoperative TKA infections at our institution and others.¹⁷ Our staged BTKA group’s complication rate, 0.75% (1 SSI), was slightly lower than expected. However, 0.75% is in keeping with institutional norms (typical rate, ~1%). We would have expected a nonzero rate for venous thromboembolism, and perhaps such a rate would have come with an inclusion period longer than 6 months. Last, the death in the simultaneous BTKA group was not an outlier, given the published rate of mortality after elective total joint surgery.¹⁸The characteristics of our simultaneous and staged BTKA groups were very similar (Table 1), though the larger number of staged-group patients with diabetes mellitus and coronary artery disease may represent selection bias. Nevertheless, the proportions of patients with each of 3 ASA scores were similar. It is also important to note that, in this context, a high percentage of patients in each group (33.6% simultaneous, 37.5% staged) received ASA score 3 from the anesthesiologist (P > .05). This may be an important factor in explaining the larger though not statistically significant number of complications in the simultaneous group (13) relative to the staged group (1).

Conclusion

In this study, the incidence of postoperative complications was higher for simultaneous BTKA than for staged BTKA performed <6 months apart, but the difference was not significantly different. There were significant differences in LOS and blood transfusion rates between the groups, as expected. At present, only patients with ASA score 1 or 2 are considered for simultaneous BTKA at our institution. Patients with ASA score 3 or higher are not eligible.

Am J Orthop. 2017;46(4):E224-E229. Copyright Frontline Medical Communications Inc. 2017. All rights reserved.

Take-Home Points

• The LHB tendon has been shown to be a significant pain generator in the shoulder.
• At our institution, the number of LHB tenodeses significantly increased from 2004 to 2014.
• The age of patients who underwent a LHB tenodesis did not change significantly over the study period.
• Furthermore, the percentage of shoulder procedures that involved a LHB tenodesis significantly increased over the study period.

• Biceps tenodesis has become a more common procedure to treat shoulder pathology.

Although the exact function of the long head of the biceps (LHB) tendon is not completely understood, it is accepted that the LHB tendon can be a significant source of pain within the shoulder.^1-4 Patients with symptoms related to biceps pathology often present with anterior shoulder pain that worsens with flexion and supination of the affected elbow and wrist.⁵ Although the sensitivity and specificity of physical examination maneuvers have been called into question, special tests have been developed to aid in the diagnosis of tendonitis of the LHB. These tests include the Speed, Yergason, bear hug, and uppercut tests as well as the O’Brien test (cross-body adduction).^6,7 Recent studies have found LHB pathology in 45% of patients who undergo rotator cuff repair and in 63% of patients with a subscapularis tear.^8,9

Pathology of the LHB tendon, including superior labrum anterior to posterior (SLAP) tears, can be treated in many ways.^5,10,11 Options include SLAP repair, biceps tenodesis, débridement, and biceps tenotomy.^11,12 Results of SLAP repairs have been less than optimal, but biceps tenodesis has been effective, and avoids the issue of cramping as can be seen with biceps tenotomy and débridement.^10,12,13 Surgical methods for biceps tenodesis include open subpectoral and all-arthroscopic.^11,12 Both methods have had good, reliable outcomes, but the all-arthroscopic technique is relatively new.^11,12,14We conducted a study to determine LHB tenodesis trends, including patient age at time of surgery. We used surgical data from fellowship-trained sports or shoulder/elbow orthopedic surgeons at a busy subspecialty-based shoulder orthopedic practice. We hypothesized that the rate of LHB tenodesis would increase significantly over time and that there would be no significant change in the age of patients who underwent LHB tenodesis.

Methods

Our Institutional Review Board exempted this study. To determine the number of LHB tenodesis procedures performed at our institution, overall and in comparison with other common arthroscopic shoulder procedures, we queried the surgical database of 4 fellowship-trained orthopedic surgeons (shoulder/elbow, Drs. Nicholson and Cole; sports, Drs. Romeo and Verma) for the period January 1, 2004 to December 31, 2014. We used Current Procedural Terminology (CPT) code 23430 to determine the number of LHB tenodesis cases, as the surgeons primarily perform an open subpectoral biceps tenodesis. Patient age at time of surgery and the date of surgery were recorded. All patients who underwent LHB tenodesis between January 1, 2004 and December 31, 2014 were included. Number of procedures performed each year by each surgeon was recorded, as were concomitant procedures performed at the same time as the LHB tenodesis. To get the denominator (and reference point) for the number of arthroscopic shoulder surgeries performed by these 4 surgeons during the study period, and thereby determine the rate of LHB tenodesis, we selected the most common shoulder arthroscopy CPT codes used in our practice: 23430, 29806, 29807, 29822, 29823, 29825, 29826, and 29827. For a patient who underwent multiple procedures on the same day (multiple CPT codes entered on the same day), only one code was counted for that day. If 23430 was among the codes, it was included, and the case was placed in the numerator; if 23430 was not among the codes, the case was placed in the denominator.

The Arthroscopy Association of North America provides descriptions for the CPT codes: 23430 (tenodesis of long tendon of biceps), 29806 (arthroscopy, shoulder, surgical; capsulorrhaphy), 29807 (arthroscopy, shoulder, surgical; repair of SLAP lesion), 29822 (arthroscopy, shoulder, surgical; débridement, limited), 29823 (arthroscopy, shoulder, surgical; débridement, extensive), 29825 (arthroscopy, shoulder, surgical; with lysis and resection of adhesions, with or without manipulation), 29826 (arthroscopy, shoulder, surgical; decompression of subacromial space with partial acromioplasty, with or without coracoacromial release), and 29827 (arthroscopy, shoulder, surgical; with rotator cuff repair).

For analysis, we divided the data into total number of arthroscopic shoulder procedures performed by each surgeon each year and number of LHB tenodesis procedures performed by each surgeon each year. Total number of patients who had an arthroscopic procedure was used to create a denominator, and number of LHB tenodesis procedures showed the percentage of arthroscopic shoulder surgery patients who underwent LHB tenodesis. (All patients who undergo biceps tenodesis also have, at the least, diagnostic shoulder arthroscopy with or without tenotomy; if the tendon is ruptured, tenotomy is unnecessary.)

Descriptive statistics were calculated as means (SDs) for continuous variables and as frequencies with percentages for categorical variables. Linear regression analysis was used to determine whether the number of LHB tenodesis procedures changed during the study period and whether patient age changed over time. Significance was set at P < .05.

Results

Of the 7640 patients who underwent arthroscopic shoulder procedures between 2004 and 2014, 2125 had LHB tenodesis (CPT code 23430).

Discussion

Tenodesis has become a common treatment option for several pathologic shoulder conditions involving the LHB tendon.⁵ We set out to determine trends in LHB tenodesis at a subspecialty-focused shoulder orthopedic practice and hypothesized that the rate of LHB tenodesis would increase significantly over time and that there would be no significant change in the age of patients who underwent LHB tenodesis. Our hypotheses were confirmed: The number of LHB tenodesis cases increased significantly without a significant change in patient age.

Treatment options for LHB pathology and SLAP tears include simple tenotomy, débridement, open biceps tenodesis, and arthroscopic tenodesis.^11,12,15

Am J Orthop. 2017;46(4):E219-E223. Copyright Frontline Medical Communications Inc. 2017. All rights reserved.

Take-Home Points

• The LHB tendon has been shown to be a significant pain generator in the shoulder.
• At our institution, the number of LHB tenodeses significantly increased from 2004 to 2014.
• The age of patients who underwent a LHB tenodesis did not change significantly over the study period.
• Furthermore, the percentage of shoulder procedures that involved a LHB tenodesis significantly increased over the study period.

• Biceps tenodesis has become a more common procedure to treat shoulder pathology.

Although the exact function of the long head of the biceps (LHB) tendon is not completely understood, it is accepted that the LHB tendon can be a significant source of pain within the shoulder.^1-4 Patients with symptoms related to biceps pathology often present with anterior shoulder pain that worsens with flexion and supination of the affected elbow and wrist.⁵ Although the sensitivity and specificity of physical examination maneuvers have been called into question, special tests have been developed to aid in the diagnosis of tendonitis of the LHB. These tests include the Speed, Yergason, bear hug, and uppercut tests as well as the O’Brien test (cross-body adduction).^6,7 Recent studies have found LHB pathology in 45% of patients who undergo rotator cuff repair and in 63% of patients with a subscapularis tear.^8,9

Pathology of the LHB tendon, including superior labrum anterior to posterior (SLAP) tears, can be treated in many ways.^5,10,11 Options include SLAP repair, biceps tenodesis, débridement, and biceps tenotomy.^11,12 Results of SLAP repairs have been less than optimal, but biceps tenodesis has been effective, and avoids the issue of cramping as can be seen with biceps tenotomy and débridement.^10,12,13 Surgical methods for biceps tenodesis include open subpectoral and all-arthroscopic.^11,12 Both methods have had good, reliable outcomes, but the all-arthroscopic technique is relatively new.^11,12,14We conducted a study to determine LHB tenodesis trends, including patient age at time of surgery. We used surgical data from fellowship-trained sports or shoulder/elbow orthopedic surgeons at a busy subspecialty-based shoulder orthopedic practice. We hypothesized that the rate of LHB tenodesis would increase significantly over time and that there would be no significant change in the age of patients who underwent LHB tenodesis.

Methods

Our Institutional Review Board exempted this study. To determine the number of LHB tenodesis procedures performed at our institution, overall and in comparison with other common arthroscopic shoulder procedures, we queried the surgical database of 4 fellowship-trained orthopedic surgeons (shoulder/elbow, Drs. Nicholson and Cole; sports, Drs. Romeo and Verma) for the period January 1, 2004 to December 31, 2014. We used Current Procedural Terminology (CPT) code 23430 to determine the number of LHB tenodesis cases, as the surgeons primarily perform an open subpectoral biceps tenodesis. Patient age at time of surgery and the date of surgery were recorded. All patients who underwent LHB tenodesis between January 1, 2004 and December 31, 2014 were included. Number of procedures performed each year by each surgeon was recorded, as were concomitant procedures performed at the same time as the LHB tenodesis. To get the denominator (and reference point) for the number of arthroscopic shoulder surgeries performed by these 4 surgeons during the study period, and thereby determine the rate of LHB tenodesis, we selected the most common shoulder arthroscopy CPT codes used in our practice: 23430, 29806, 29807, 29822, 29823, 29825, 29826, and 29827. For a patient who underwent multiple procedures on the same day (multiple CPT codes entered on the same day), only one code was counted for that day. If 23430 was among the codes, it was included, and the case was placed in the numerator; if 23430 was not among the codes, the case was placed in the denominator.

The Arthroscopy Association of North America provides descriptions for the CPT codes: 23430 (tenodesis of long tendon of biceps), 29806 (arthroscopy, shoulder, surgical; capsulorrhaphy), 29807 (arthroscopy, shoulder, surgical; repair of SLAP lesion), 29822 (arthroscopy, shoulder, surgical; débridement, limited), 29823 (arthroscopy, shoulder, surgical; débridement, extensive), 29825 (arthroscopy, shoulder, surgical; with lysis and resection of adhesions, with or without manipulation), 29826 (arthroscopy, shoulder, surgical; decompression of subacromial space with partial acromioplasty, with or without coracoacromial release), and 29827 (arthroscopy, shoulder, surgical; with rotator cuff repair).

For analysis, we divided the data into total number of arthroscopic shoulder procedures performed by each surgeon each year and number of LHB tenodesis procedures performed by each surgeon each year. Total number of patients who had an arthroscopic procedure was used to create a denominator, and number of LHB tenodesis procedures showed the percentage of arthroscopic shoulder surgery patients who underwent LHB tenodesis. (All patients who undergo biceps tenodesis also have, at the least, diagnostic shoulder arthroscopy with or without tenotomy; if the tendon is ruptured, tenotomy is unnecessary.)

Descriptive statistics were calculated as means (SDs) for continuous variables and as frequencies with percentages for categorical variables. Linear regression analysis was used to determine whether the number of LHB tenodesis procedures changed during the study period and whether patient age changed over time. Significance was set at P < .05.

Results

Of the 7640 patients who underwent arthroscopic shoulder procedures between 2004 and 2014, 2125 had LHB tenodesis (CPT code 23430).

Discussion

Tenodesis has become a common treatment option for several pathologic shoulder conditions involving the LHB tendon.⁵ We set out to determine trends in LHB tenodesis at a subspecialty-focused shoulder orthopedic practice and hypothesized that the rate of LHB tenodesis would increase significantly over time and that there would be no significant change in the age of patients who underwent LHB tenodesis. Our hypotheses were confirmed: The number of LHB tenodesis cases increased significantly without a significant change in patient age.

Treatment options for LHB pathology and SLAP tears include simple tenotomy, débridement, open biceps tenodesis, and arthroscopic tenodesis.^11,12,15

Am J Orthop. 2017;46(4):E219-E223. Copyright Frontline Medical Communications Inc. 2017. All rights reserved.

Take-Home Points

• The LHB tendon has been shown to be a significant pain generator in the shoulder.
• At our institution, the number of LHB tenodeses significantly increased from 2004 to 2014.
• The age of patients who underwent a LHB tenodesis did not change significantly over the study period.
• Furthermore, the percentage of shoulder procedures that involved a LHB tenodesis significantly increased over the study period.

• Biceps tenodesis has become a more common procedure to treat shoulder pathology.

Although the exact function of the long head of the biceps (LHB) tendon is not completely understood, it is accepted that the LHB tendon can be a significant source of pain within the shoulder.^1-4 Patients with symptoms related to biceps pathology often present with anterior shoulder pain that worsens with flexion and supination of the affected elbow and wrist.⁵ Although the sensitivity and specificity of physical examination maneuvers have been called into question, special tests have been developed to aid in the diagnosis of tendonitis of the LHB. These tests include the Speed, Yergason, bear hug, and uppercut tests as well as the O’Brien test (cross-body adduction).^6,7 Recent studies have found LHB pathology in 45% of patients who undergo rotator cuff repair and in 63% of patients with a subscapularis tear.^8,9

Pathology of the LHB tendon, including superior labrum anterior to posterior (SLAP) tears, can be treated in many ways.^5,10,11 Options include SLAP repair, biceps tenodesis, débridement, and biceps tenotomy.^11,12 Results of SLAP repairs have been less than optimal, but biceps tenodesis has been effective, and avoids the issue of cramping as can be seen with biceps tenotomy and débridement.^10,12,13 Surgical methods for biceps tenodesis include open subpectoral and all-arthroscopic.^11,12 Both methods have had good, reliable outcomes, but the all-arthroscopic technique is relatively new.^11,12,14We conducted a study to determine LHB tenodesis trends, including patient age at time of surgery. We used surgical data from fellowship-trained sports or shoulder/elbow orthopedic surgeons at a busy subspecialty-based shoulder orthopedic practice. We hypothesized that the rate of LHB tenodesis would increase significantly over time and that there would be no significant change in the age of patients who underwent LHB tenodesis.

Methods

Our Institutional Review Board exempted this study. To determine the number of LHB tenodesis procedures performed at our institution, overall and in comparison with other common arthroscopic shoulder procedures, we queried the surgical database of 4 fellowship-trained orthopedic surgeons (shoulder/elbow, Drs. Nicholson and Cole; sports, Drs. Romeo and Verma) for the period January 1, 2004 to December 31, 2014. We used Current Procedural Terminology (CPT) code 23430 to determine the number of LHB tenodesis cases, as the surgeons primarily perform an open subpectoral biceps tenodesis. Patient age at time of surgery and the date of surgery were recorded. All patients who underwent LHB tenodesis between January 1, 2004 and December 31, 2014 were included. Number of procedures performed each year by each surgeon was recorded, as were concomitant procedures performed at the same time as the LHB tenodesis. To get the denominator (and reference point) for the number of arthroscopic shoulder surgeries performed by these 4 surgeons during the study period, and thereby determine the rate of LHB tenodesis, we selected the most common shoulder arthroscopy CPT codes used in our practice: 23430, 29806, 29807, 29822, 29823, 29825, 29826, and 29827. For a patient who underwent multiple procedures on the same day (multiple CPT codes entered on the same day), only one code was counted for that day. If 23430 was among the codes, it was included, and the case was placed in the numerator; if 23430 was not among the codes, the case was placed in the denominator.

The Arthroscopy Association of North America provides descriptions for the CPT codes: 23430 (tenodesis of long tendon of biceps), 29806 (arthroscopy, shoulder, surgical; capsulorrhaphy), 29807 (arthroscopy, shoulder, surgical; repair of SLAP lesion), 29822 (arthroscopy, shoulder, surgical; débridement, limited), 29823 (arthroscopy, shoulder, surgical; débridement, extensive), 29825 (arthroscopy, shoulder, surgical; with lysis and resection of adhesions, with or without manipulation), 29826 (arthroscopy, shoulder, surgical; decompression of subacromial space with partial acromioplasty, with or without coracoacromial release), and 29827 (arthroscopy, shoulder, surgical; with rotator cuff repair).

For analysis, we divided the data into total number of arthroscopic shoulder procedures performed by each surgeon each year and number of LHB tenodesis procedures performed by each surgeon each year. Total number of patients who had an arthroscopic procedure was used to create a denominator, and number of LHB tenodesis procedures showed the percentage of arthroscopic shoulder surgery patients who underwent LHB tenodesis. (All patients who undergo biceps tenodesis also have, at the least, diagnostic shoulder arthroscopy with or without tenotomy; if the tendon is ruptured, tenotomy is unnecessary.)

Descriptive statistics were calculated as means (SDs) for continuous variables and as frequencies with percentages for categorical variables. Linear regression analysis was used to determine whether the number of LHB tenodesis procedures changed during the study period and whether patient age changed over time. Significance was set at P < .05.

Results

Of the 7640 patients who underwent arthroscopic shoulder procedures between 2004 and 2014, 2125 had LHB tenodesis (CPT code 23430).

Discussion

Tenodesis has become a common treatment option for several pathologic shoulder conditions involving the LHB tendon.⁵ We set out to determine trends in LHB tenodesis at a subspecialty-focused shoulder orthopedic practice and hypothesized that the rate of LHB tenodesis would increase significantly over time and that there would be no significant change in the age of patients who underwent LHB tenodesis. Our hypotheses were confirmed: The number of LHB tenodesis cases increased significantly without a significant change in patient age.

Treatment options for LHB pathology and SLAP tears include simple tenotomy, débridement, open biceps tenodesis, and arthroscopic tenodesis.^11,12,15

Am J Orthop. 2017;46(4):E219-E223. Copyright Frontline Medical Communications Inc. 2017. All rights reserved.

Sarcopenia is an age-related loss of skeletal muscle that may result in diminished muscle strength and functional performance. The prevalence of sarcopenia varies based on the cohort and the assessment criteria. According to the Health Aging and Body Composition (ABC) study, the prevalence of sarcopenia in community-dwelling older adults is about 14% to 18%, whereas the estimate may exceed 30% for those in longterm care.^1,2 This geriatric syndrome may disproportionately affect veterans given that they are older than the civilian population and may have disabling comorbid conditions associated with military service.³

Recently, there has been a call to action to systematically address sarcopenia by interdisciplinary organizations such as the European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis (ESCEO) and the International Working Group on Sarcopenia (IWGS).^4,5 This call to action is due to the association of sarcopenia with increased health care costs, higher disability incidence, and elevated risk of mortality.^6,7 The consequences of sarcopenia may include serious complications, such as hip fracture or a loss of functional independence.^8,9 The CDC now recognizes sarcopenia as an independently reportable medical condition. Consequently, physicians, nurse practitioners (NPs), and other associated health professionals within the VA will need to better understand clinically viable and valid methods to screen and diagnose this geriatric syndrome.

The purpose of this paper is to inform practitioners how sarcopenia screening is aided by the new ICD-10-CM code and briefly review recent VA initiatives for proactive care. Additional objectives include identifying common methods used to assess sarcopenia and providing general recommendations to the VHA National Center for Health Promotion and Disease Prevention (NCP) concerning the management of sarcopenia.

Addressing Sarcopenia

While the age-related decline in muscle size and performance has long been recognized by geriatricians, sustained advocacy by several organizations was required to realize the formal recognition of sarcopenia. Aging in Motion (AIM), a coalition of organizations focused on advancing research and treatment for conditions associated with age-related muscle dysfunction, sought the formal recognition of sarcopenia. The CDC established the ICD-10-CM code for sarcopenia in October of 2016, which allowed the syndrome to be designated as a primary or secondary condition.¹⁰

The ubiquitous nature of agerelated changes in muscle and the mandate to engage in proactive care by all levels of VA leadership led to the focus on addressing sarcopenia. The recognition of sarcopenia by the CDC comes at an opportune time given recent VA efforts to transform itself from a facilitator mainly of care delivery to an active partner in fostering the health and well-being of veterans. Initiatives that are emblematic of this attempt to shift the organizational culture across the VHA include establishing the VA Center for Innovation (VACI) and issuing guidance documents, such as the Blueprint for Excellence, which was introduced in 2014 by then VA Secretary Robert McDonald.^11,12 Many of the following Blueprint themes and strategies potentially impact sarcopenia screening and treatment within the VA:

• Delivering high-quality, veteran-centered care: A major Blueprint theme is attaining the “Triple Aims” of a health care system by promoting better health among veterans, improving the provision of care, and lowering costs through operational efficiency. The management of sarcopenia has clear clinical value given the association of age-related muscle loss with fall risk and decreased mobility.¹³ Financial value also may be associated with the effort to decrease disability related to sarcopenia and the use of a team approach featuring associated health professionals to help screen for this geriatric syndrome.^14,15 (Strategy 2)
• Leveraging health care informatics to optimize individual and population health outcomes: The inclusion of the most basic muscle performance and functional status measures in the electronic medical record (EMR), such as grip strength and gait speed, would help to identify the risk factors and determinants of sarcopenia among the veteran population. (Strategy 3)
• Advancing personalized, proactive health care that fosters a high level of health and well-being: The long-term promotion of musculoskeletal health and optimal management of sarcopenia cannot be sustained through episodic medical interactions. Instead, a contemporary approach to health services marked by the continuous promotion of health education, physical activity and exercise, and proper nutrition has demonstrated value in the management of chronic conditions.^16,17 (Strategy 6)

The new sarcopenia ICD-10-CM code along with elements of the VHA Blueprint can serve to support the systematic assessment and management of veterans with age-related muscle dysfunction. Nevertheless, renewed calls for health promotion and screening programs are often counterbalanced by the need for cost containment and the cautionary tales concerning the potential harms or errors associated with some forms of medical screening. The American Board of Internal Medicine Foundation has spearheaded the Choosing Wisely campaign to raise awareness about excessive medical testing. However, the Institute of Medicine has linked the provision of quality health care to a diagnostic process that is both timely and accurate.¹⁸ Careful consideration of these health care challenges may help guide practitioners within the VA concerning the screening and diagnosis of sarcopenia.

Sarcopenia Assessment

Sarcopenia can have several underlying causes in some individuals and result in varied patterns of clinical presentation and differing degrees of severity. The European Working Group on Sarcopenia in Older People first met in 2009 and used a consensus-based decision-making process to determine operational definitions for sarcopenia and create a staging algorithm for the syndrome.¹⁹ This consensus group developed a conceptual staging model with 3 categories: presarcopenia, sarcopenia, and severe sarcopenia (Table 1). The impetus for sarcopenia staging was the emerging research findings suggesting that lean body mass (LBM) alone did not provide a high degree of clinical value in outpatient settings due to the nonlinear relationship between LBM and muscle function in older adults.^20,21 Using the consensus model approach, an individual is classified as sarcopenic on presenting with both low LBM and low muscle function.

Screening: A Place to Start

Findings from the Health ABC Study suggested that older adults who maintained high levels of LBM were less likely to become sarcopenic. Whereas, older adults in the cohort with low levels of LBM tended to remain in a sarcopenic state.⁶ Consequently, the early detection of sarcopenia may have important health promotion implications for older adults. Sarcopenia is a syndrome with a continuum of clinical features; it is not a disease with a clear or singular etiololgy. Therefore, the result of the screening examination should identify those who would most benefit from a formal diagnostic assessment.

One approach to screening for sarcopenia involves the use of questionnaires, such as the SARC-F (sluggishness, assistance in walking, rise from a chair, climb stairs, falls), which is a brief 5-item questionnaire with Likert scoring for patient responses.¹³ In a cohort of National Health and Nutrition Examination Survey (NHANES) participants, SARC-F scores ≥ 4 were associated with slower gait speed, lower strength, and an increased likelihood of hospitalization within a year of the test response.²² However, rather than stratify patients by risk, the SARC-F exhibits a high degree of test specificity regarding the major consensus-based sarcopenia classification criteria (specificity = 94.2% to 99.1%; sensitivity = 3.8% to 9.9%).¹³ Given the known limitations of screening tools with low sensitivity, organizations such as the ESCEO have recommended supplementing the SARC-F questionnaire with other forms of assessment.⁴ Supplements to the screening examination may range from the use of “red flag” questions concerning changes in nutritional status, body weight, and physical activity, to conducting standard gait speed and grip-strength testing.^4,19,23

Performance-based testing, including habitual gait speed and grip-strength dynamometry, also may be used in both the screening and classification of sarcopenia.² Although walking speed below 1.0 m/s has been used by the IWGS as a criterion to prompt further assessment, many people within the VA health care system may have gait abnormalities independent of LBM status, and others may be nonambulatory.^24,25 As a result, grip-strength testing should be considered as a supplementary or alternate screening assessment tool.^26,27

Hand-grip dynamometry is often used diagnostically given its previous test validation, low expense, and ease of use.²³ Moreover, recent evidence suggests that muscle strength surpasses gait speed as a means of identifying people with sarcopenia. Grip strength is associated with all-cause mortality, even when adjusting for age, sex, and body size,²⁸ while slow gait speed (< .82 m/s) has a reported sensitivity of 63% and specificity of 70% for mortality in population-based studies involving older adults.²⁹

Gait speed (in those who are ambulatory) and grip-strength values could be entered into the EMR evaluation note by the primary care provider (PCP). Elements of the VA EMR, such as the ability to review the diagnosis of sarcopenia on the Problem List or the nominal enhancement of providing LBM estimates within the Cumulative Vitals and Measurements Report would support the management of sarcopenia. See Table 2 for cutoff values for frequently used sarcopenia screening and staging tests.

The pitfalls of excessive or inappropriate screening are well documented. The efforts to screen for prostate cancer have highlighted instances when inappropriate followup tests and treatment fail to alter mortality rates and ultimately yield more harm than good.³⁰ However, there are several points of departure concerning the screening for sarcopenia vs screening for prostate cancer. The screening assessments for sarcopenia are low-cost procedures that are associated with a low patient burden. These procedures may include questionnaires, functional testing, or the assessment of muscle performance. Additionally, there is a low propensity for adverse effects stemming from treatment due to disease misclassification given the common nonpharmacologic approaches used to manage sarcopenia.³¹ Nonetheless, the best screening examination—even one that has low patient burden and cost—may prove to be a poor use of medical resources if the process is not linked to a viable intervention.

Screening people aged ≥ 65 years may strike a balance between controlling health care expenditures and identifying people with the initial signs of sarcopenia early enough to begin monitoring key outcomes and providing a formalized exercise prescription. Presuming an annual age-related decline in LBM of 1.5%, and considering the standard error measurement of the most frequently used methods of strength and LBM assessment, recurrent screening could occur every 2 years.^21,32

Earlier screening may be considered for patient populations with a higher pretest probability. These groups include those with conditions associated with accelerated muscle loss, such as chronic kidney disease, peripheral artery disease, and diabetes mellitus (DM).³² Although accelerated muscle loss characterized by an inflammatory motif (eg, cancer-related cachexia) may share some features of the sarcopenia screening and assessment approach, important differences exist regarding the etiology, medical evaluation, and ICD-10-CM code designation.

Staging and Classification

Staging criteria are generally used to denote the severity of a given disease or syndrome, whereas classification criteria are used to define homogenous patient groups based on specific pathologic or clinical features of a disorder. Although classification schemes may incorporate an element of severity, they are primarily used to characterize fairly distinct phenotypic forms of disease or specific clinical presentation patterns associated with a well-defined syndrome. Although not universally adopted, the European consensus group sarcopenia staging criteria are increasingly used to provide a staging algorithm presumably driven by the severity of the condition.¹⁹

The assessment of functional performance for use in sarcopenia staging often involves measuring habitual gait speed or completing the Short Physical Performance Battery (SPPB).²³ The SPPB involves a variety of performance-based activities for balance, gait, strength, and endurance. This test has predictive validity for the onset of disability and adverse health events, and it has been extensively used in research and clinical settings.³³ Additional tests used to characterize function during the staging or diagnostic process include the timed get up and go test (TGUG) and the timed sit to stand test.^34,35 The TGUG provides an estimate of dynamic balance, and the sit to stand test has been used as very basic proxy measure of muscular power.³⁶ The sit to stand test and habitual gait speed are items included in the SPPB.³³

Accepted methods to obtain the traditional index measure of sarcopenia—based on estimates of LBM—include bioimpedance analysis (BIA) and dual X-ray absorptiometry (DXA). The BIA uses the electrical impedance of body tissues and its 2 components, resistance and reactance, to derive its body composition estimates.³⁷ Segmental BIA allows for isolated measurements of the limbs, which may be calibrated to DXA appendicular lean body mass (ALM) or magnetic resonance imaging-based estimates of LBM. This instrument is relatively safe for use, inexpensive for medical facilities, and useful for longitudinal studies, but it can be confounded by issues, such as varying levels of hydration, which may affect measurement validity in some instances.

Despite the precision of DXA for estimating densities for whole body composition analysis, the equipment is not very portable and involves low levels of radiation exposure, which limits its utility in some clinical settings. While each body composition assessment method has its advantages and disadvantages, DXA is regarded as an acceptable form of measurement for hospital settings, and BIA is frequently used in outpatient clinics and community settings. Other methods used to estimate LBM with greater accuracy, such as peripheral quantitative computed tomography, doubly labeled water, and whole body gamma ray counting, are not viable for clinical use. Other accessible methods such as anthropometric measures and skinfold measures have not been embraced by sarcopenia classification consensus groups.^23,37

Alternative methods of estimating LBM, such as diagnostic ultrasound and multifrequency electrical impedance myography, are featured outcomes in ongoing clinical trials that involve veteran participants. These modalities may soon provide a clinically viable approach to assessing muscle quality via estimates of muscle tissue composition.^37,38 Similar to the management of other geriatric syndromes, interprofessional collaboration provides an optimal approach to the assessment of sarcopenia. Physicians and other health care providers may draw on the standardized assessment of strength and function (via the SPPB and hand-grip dynamometry) by physical therapists (PTs), questionnaires administered by nursing staff (the SARC-F), or body composition estimates from other health professionals (ranging from BIA to DXA) to aid the diagnostic process and facilitate appropriate case management (Table 2).

Competing staging and classification definitions have been cited as a primary factor behind the CDC’s delayed recognition of the sarcopenia diagnosis, which in turn posed a barrier to formal clinical recognition by geriatricians.²⁴ However, this reaction to the evolving sarcopenia staging criteria also may reveal the larger misapplication of the staging process to the diagnostic process. The application of classification and staging criteria results in a homogenous group of patients, whereas the application of diagnostic criteria results in a heterogeneous group of patients to account for variations in clinical presentation associated with a given disorder. Classification criteria may be equivalent to objective measures that are used in the diagnostic process when a given disease is characterized by a well-established biomarker.³⁹

However, this is not the case for most geriatric syndromes and other disorders marked by varied clinical presentation patterns. On considering the commonly used sarcopenia staging criteria of LBM ≤ 8.50 kg/m² or grip strength < 30 kg in men and LBM ≤ 5.75 kg/m² or grip strength < 20 kg in women, it is easy to understand that such general cutoff values are far from diagnostic.^40,41 Moreover, stringent cutoff values associated with classification and staging may not adequately capture those with an atypical presentation of the syndrome (eg, someone who exhibits age-related muscle weakness but has retained adequate LBM). Such criteria often prove to have high specificity and low sensitivity, which may yield a false negative rate that is appropriate for clinical research eligibility and group assignment but inadequate for clinical care.

Screening, staging, and classification criteria with high specificity may indeed be desirable for confirmatory imaging tests associated with radiation exposure concerns or for managing risk in experimental clinical trials involving pharmacologic treatment. For example, a SARC-F score ≥ 4 may prompt the formal assessment of LBM via a DXA examination.⁴ In contrast, those with a SARC-F score ≤ 3 with low gait speed or grip strength may benefit from consultation regarding regular physical activity and nutrition recommendations. Given the challenges of establishing sarcopenia classification criteria that perform consistently across populations and geographic regions, classification and staging criteria may be best viewed as clinical reasoning tools that supplement, but not supplant, the diagnostic process.^7,42

Diagnosis

Geriatric syndromes do not lend themselves to a simple diagnostic process. Syndromes such as frailty and sarcopenia are multifactorial and lack a single distinguishing clinical feature or biomarker. The oft-cited refrain that sarcopenia is an underdiagnosed condition is partially explained by the recent ICD-10-CM code and varied classification and diagnostic criteria^.5 This circumstance highlights the need to distinctly contrast the diagnostic process with the screening and staging classifications.

The diagnostic process involves the interpretation of the patient history, signs, and symptoms within the context of individual factors, local or regional disease prevalence, and the results of the best available and most appropriate laboratory tests. After all, a patient that presents with low LBM and a gradual loss of strength without a precipitating event would necessitate further workup to rule out many clinical possibilities under the aegis of a differential diagnosis. Clinical features, such as the magnitude of weakness and pattern of strength loss or muscle atrophy along with the determination of neurologic or autoimmune involvement, are among the key elements of the differential examination for a case involving the observation of frank muscle weakness. Older adults with low muscle strength may have additional risk factors for sarcopenia such as obesity, pain, poor nutrition, previous bone fracture, and a sedentary lifestyle. However, disease etiology with lower probabilities, such as myogenic or neurogenic conditions associated with advancing age, also may be under consideration during the clinical assessment.⁶

In many instances, the cutoff scores associated with the sarcopenia staging criteria may help to guide the diagnostic process and aid clinical decision making. Since individuals with a positive screening result based on the SARC-F questionnaire (score ≥ 4) have a high likelihood of meeting the staging criteria for severe sarcopenia, a PCP may opt to obtain a confirmatory estimate of LBM both to support the clinical assessment and to monitor change over the course of rehabilitation. Whereas people who present with a decline in strength (ie, grip strength < 30 kg for a male) without an observable loss of function or a positive SARC-F score may benefit from consultation from the physician, NP, or rehabilitation health professional regarding modifiable risk factors associated with sarcopenia.

Incorporating less frequently used sarcopenia classification schemes such as identifying those with sarcopenic obesity or secondary sarcopenia due to mitigating factors such as chronic kidney disease or DM (Table 3) may engender a more comprehensive approach to intervention that targets the primary disease while also addressing important secondary sequelae. Nevertheless, staging or classification criteria cannot be deemed equivalent to diagnostic criteria for sarcopenia due to the challenges posed by syndromes that have a heterogeneous clinical presentation.

The refinement of the staging and classification criteria along with the advances in imaging technology and mechanistic research are not unique to sarcopenia. Practitioners involved in the care of people with rheumatologic conditions or osteoporosis also have contended with continued refinements to their classification criteria and approach to risk stratification.^39,43,44 Primary care providers will now have the option to use a new ICD-10-CM code (M62.84) for sarcopenia, which will allow them to properly document the clinical distinctions between people with impaired strength or function largely due to age-related muscle changes and those who have impaired muscle function due to cachexia, inflammatory myopathies, or forms of neuromuscular disease.

The ability to identify and document this geriatric syndrome in veterans will help to better define the scope of the problem within the VA health care system. The median age of veterans is 62 years compared with 43 years for nonveterans.³ Consequently, there may be value in the adoption of a formal approach to screening and diagnosis for sarcopenia among veterans who receive their primary care from VA facilities.⁷ Indeed, the exchange between the patient and the health professional regarding the screening and diagnostic process will provide valuable opportunities to promote exercise interventions before patients incur significant impairments.

One of the biggest threats burdening global health is noncommunicable diseases, and many chronic conditions, such as sarcopenia, can be prevented and managed with appropriate levels of physical activity.¹⁷ Increased physician involvement may prove to be critical given the identification of physical inactivity as a top 5 risk factor for general morbidity and mortality by World Health Organization and consensus group recommendations calling for physicians to serve a more prominent role in the provision of exercise and physical activity recommendations.^16,17

This developing health care role should include NPs, PTs, physician assistants, and other associated health professionals. It also should include collaborative efforts between physicians and rehabilitation practitioners concerning provision of the formal exercise prescriptionprescription and monitoring of patient outcomes.

Individuals with severe forms of sarcopenia rarely improve without intervention.⁶ Although no pharmacologic treatment exists to specifically address sarcopenia, strengthening exercise has been shown to be an effective mode of prevention and conservative management.⁸ Progressive resistance exercise cannot abate the expected age-related changes in skeletal muscle, but it can significantly reverse the loss of LBM and strength in untrained older adults and slow the age-related decline in muscle performance in older adult athletes and trained individuals.⁴⁵

Local senior centers and community organizations may prove to be valuable resources concerning group exercise options, and they provide the added benefit of social engagement and peer group accountability. Federal resources include the Go4Life exercise guide and online videos provided by the National Institute on Aging and the MOVE! Weight Management and Health Program provided at select VA community-based outpatient clinics. Ultimately, collaborative efforts with exercise specialists may serve to reduce the PCP burden during the provision of health services, minimize diagnostic errors associated with sarcopenia assessment and help to connect patients to valuable health promotion resources.^17,18

Conclusion

While practitioners should remain keenly aware of the pernicious effects of overdiagnosis, sarcopenia has long existed as a known, but undiagnosed, condition. Of course, geriatricians have traditionally managed poor muscle performance and mobility limitations by addressing treatable symptoms and providing referrals to physical medicine specialists when warranted. Nevertheless, the advent of ICD-10-CM code M62.⁸⁴ provides the VA with an opportunity to take a leading role in systematically addressing this geriatric syndrome within an aging veteran population.

The following items should be considered by NCP for the development of guidelines and recommendations concerning sarcopenia screening:

1. Consider screening veterans aged > 65 years for sarcopenia every 2 years. Those with mitigating systemic conditions (eg, chronic kidney disease, DM, or malnutrition) or significant mobility limitations may be screened at any age.
2. Sarcopenia screening procedures should include at a minimum the SARC-F questionnaire and gait speed (when appropriate). Including gait speed or grip strength testing in the screening exam is recommended given the low sensitivity of the SARC-F questionnaire.
3. Veterans with positive SARC-F results (≥ 4) merit a physical therapy referral. In addition, these veterans should obtain confirmatory standardized assessments for LBM and functional status.
4. Veterans at risk for sarcopenia based on patient age, medical history, and the physical examination (eg, obesity, sedentary lifestyle, a previous fracture, self-reported physical decline), but with negative SARC-F results should receive a formal exercise prescription from their PCP. Baseline assessment measures may be used for comparison with serial measures obtained during subsequent screening visits to support long-term case management.
5. Interprofessional collaboration involving geriatricians, PTs, nurses, radiologists, and other health care professionals should be involved in the screening, diagnosis, and case management of veterans with sarcopenia.
6. The VA EMR should be systematically documented with sarcopenia assessment data obtained from the gait speed tests, SARCF, SPPB, grip strength tests, and LBM estimates to better characterize this condition within the veteran population.

Any expansion in the provision of health care comes with anticipated benefits and potential costs. Broad guidance from NCP may encourage veterans to pursue selected screening tests, promote the appropriate use of preventative services, and facilitate timely treatment when needed.³¹ Clinicians who are informed about the screening, staging, classification, and diagnostic process for sarcopenia may partner with patients to make reasoned decisions about how to best manage this syndrome within the VA medical center environment.

Sarcopenia is an age-related loss of skeletal muscle that may result in diminished muscle strength and functional performance. The prevalence of sarcopenia varies based on the cohort and the assessment criteria. According to the Health Aging and Body Composition (ABC) study, the prevalence of sarcopenia in community-dwelling older adults is about 14% to 18%, whereas the estimate may exceed 30% for those in longterm care.^1,2 This geriatric syndrome may disproportionately affect veterans given that they are older than the civilian population and may have disabling comorbid conditions associated with military service.³

Recently, there has been a call to action to systematically address sarcopenia by interdisciplinary organizations such as the European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis (ESCEO) and the International Working Group on Sarcopenia (IWGS).^4,5 This call to action is due to the association of sarcopenia with increased health care costs, higher disability incidence, and elevated risk of mortality.^6,7 The consequences of sarcopenia may include serious complications, such as hip fracture or a loss of functional independence.^8,9 The CDC now recognizes sarcopenia as an independently reportable medical condition. Consequently, physicians, nurse practitioners (NPs), and other associated health professionals within the VA will need to better understand clinically viable and valid methods to screen and diagnose this geriatric syndrome.

The purpose of this paper is to inform practitioners how sarcopenia screening is aided by the new ICD-10-CM code and briefly review recent VA initiatives for proactive care. Additional objectives include identifying common methods used to assess sarcopenia and providing general recommendations to the VHA National Center for Health Promotion and Disease Prevention (NCP) concerning the management of sarcopenia.

Addressing Sarcopenia

While the age-related decline in muscle size and performance has long been recognized by geriatricians, sustained advocacy by several organizations was required to realize the formal recognition of sarcopenia. Aging in Motion (AIM), a coalition of organizations focused on advancing research and treatment for conditions associated with age-related muscle dysfunction, sought the formal recognition of sarcopenia. The CDC established the ICD-10-CM code for sarcopenia in October of 2016, which allowed the syndrome to be designated as a primary or secondary condition.¹⁰

The ubiquitous nature of agerelated changes in muscle and the mandate to engage in proactive care by all levels of VA leadership led to the focus on addressing sarcopenia. The recognition of sarcopenia by the CDC comes at an opportune time given recent VA efforts to transform itself from a facilitator mainly of care delivery to an active partner in fostering the health and well-being of veterans. Initiatives that are emblematic of this attempt to shift the organizational culture across the VHA include establishing the VA Center for Innovation (VACI) and issuing guidance documents, such as the Blueprint for Excellence, which was introduced in 2014 by then VA Secretary Robert McDonald.^11,12 Many of the following Blueprint themes and strategies potentially impact sarcopenia screening and treatment within the VA:

• Delivering high-quality, veteran-centered care: A major Blueprint theme is attaining the “Triple Aims” of a health care system by promoting better health among veterans, improving the provision of care, and lowering costs through operational efficiency. The management of sarcopenia has clear clinical value given the association of age-related muscle loss with fall risk and decreased mobility.¹³ Financial value also may be associated with the effort to decrease disability related to sarcopenia and the use of a team approach featuring associated health professionals to help screen for this geriatric syndrome.^14,15 (Strategy 2)
• Leveraging health care informatics to optimize individual and population health outcomes: The inclusion of the most basic muscle performance and functional status measures in the electronic medical record (EMR), such as grip strength and gait speed, would help to identify the risk factors and determinants of sarcopenia among the veteran population. (Strategy 3)
• Advancing personalized, proactive health care that fosters a high level of health and well-being: The long-term promotion of musculoskeletal health and optimal management of sarcopenia cannot be sustained through episodic medical interactions. Instead, a contemporary approach to health services marked by the continuous promotion of health education, physical activity and exercise, and proper nutrition has demonstrated value in the management of chronic conditions.^16,17 (Strategy 6)

The new sarcopenia ICD-10-CM code along with elements of the VHA Blueprint can serve to support the systematic assessment and management of veterans with age-related muscle dysfunction. Nevertheless, renewed calls for health promotion and screening programs are often counterbalanced by the need for cost containment and the cautionary tales concerning the potential harms or errors associated with some forms of medical screening. The American Board of Internal Medicine Foundation has spearheaded the Choosing Wisely campaign to raise awareness about excessive medical testing. However, the Institute of Medicine has linked the provision of quality health care to a diagnostic process that is both timely and accurate.¹⁸ Careful consideration of these health care challenges may help guide practitioners within the VA concerning the screening and diagnosis of sarcopenia.

Sarcopenia Assessment

Sarcopenia can have several underlying causes in some individuals and result in varied patterns of clinical presentation and differing degrees of severity. The European Working Group on Sarcopenia in Older People first met in 2009 and used a consensus-based decision-making process to determine operational definitions for sarcopenia and create a staging algorithm for the syndrome.¹⁹ This consensus group developed a conceptual staging model with 3 categories: presarcopenia, sarcopenia, and severe sarcopenia (Table 1). The impetus for sarcopenia staging was the emerging research findings suggesting that lean body mass (LBM) alone did not provide a high degree of clinical value in outpatient settings due to the nonlinear relationship between LBM and muscle function in older adults.^20,21 Using the consensus model approach, an individual is classified as sarcopenic on presenting with both low LBM and low muscle function.

Screening: A Place to Start

Findings from the Health ABC Study suggested that older adults who maintained high levels of LBM were less likely to become sarcopenic. Whereas, older adults in the cohort with low levels of LBM tended to remain in a sarcopenic state.⁶ Consequently, the early detection of sarcopenia may have important health promotion implications for older adults. Sarcopenia is a syndrome with a continuum of clinical features; it is not a disease with a clear or singular etiololgy. Therefore, the result of the screening examination should identify those who would most benefit from a formal diagnostic assessment.

One approach to screening for sarcopenia involves the use of questionnaires, such as the SARC-F (sluggishness, assistance in walking, rise from a chair, climb stairs, falls), which is a brief 5-item questionnaire with Likert scoring for patient responses.¹³ In a cohort of National Health and Nutrition Examination Survey (NHANES) participants, SARC-F scores ≥ 4 were associated with slower gait speed, lower strength, and an increased likelihood of hospitalization within a year of the test response.²² However, rather than stratify patients by risk, the SARC-F exhibits a high degree of test specificity regarding the major consensus-based sarcopenia classification criteria (specificity = 94.2% to 99.1%; sensitivity = 3.8% to 9.9%).¹³ Given the known limitations of screening tools with low sensitivity, organizations such as the ESCEO have recommended supplementing the SARC-F questionnaire with other forms of assessment.⁴ Supplements to the screening examination may range from the use of “red flag” questions concerning changes in nutritional status, body weight, and physical activity, to conducting standard gait speed and grip-strength testing.^4,19,23

Performance-based testing, including habitual gait speed and grip-strength dynamometry, also may be used in both the screening and classification of sarcopenia.² Although walking speed below 1.0 m/s has been used by the IWGS as a criterion to prompt further assessment, many people within the VA health care system may have gait abnormalities independent of LBM status, and others may be nonambulatory.^24,25 As a result, grip-strength testing should be considered as a supplementary or alternate screening assessment tool.^26,27

Hand-grip dynamometry is often used diagnostically given its previous test validation, low expense, and ease of use.²³ Moreover, recent evidence suggests that muscle strength surpasses gait speed as a means of identifying people with sarcopenia. Grip strength is associated with all-cause mortality, even when adjusting for age, sex, and body size,²⁸ while slow gait speed (< .82 m/s) has a reported sensitivity of 63% and specificity of 70% for mortality in population-based studies involving older adults.²⁹

Gait speed (in those who are ambulatory) and grip-strength values could be entered into the EMR evaluation note by the primary care provider (PCP). Elements of the VA EMR, such as the ability to review the diagnosis of sarcopenia on the Problem List or the nominal enhancement of providing LBM estimates within the Cumulative Vitals and Measurements Report would support the management of sarcopenia. See Table 2 for cutoff values for frequently used sarcopenia screening and staging tests.

The pitfalls of excessive or inappropriate screening are well documented. The efforts to screen for prostate cancer have highlighted instances when inappropriate followup tests and treatment fail to alter mortality rates and ultimately yield more harm than good.³⁰ However, there are several points of departure concerning the screening for sarcopenia vs screening for prostate cancer. The screening assessments for sarcopenia are low-cost procedures that are associated with a low patient burden. These procedures may include questionnaires, functional testing, or the assessment of muscle performance. Additionally, there is a low propensity for adverse effects stemming from treatment due to disease misclassification given the common nonpharmacologic approaches used to manage sarcopenia.³¹ Nonetheless, the best screening examination—even one that has low patient burden and cost—may prove to be a poor use of medical resources if the process is not linked to a viable intervention.

Screening people aged ≥ 65 years may strike a balance between controlling health care expenditures and identifying people with the initial signs of sarcopenia early enough to begin monitoring key outcomes and providing a formalized exercise prescription. Presuming an annual age-related decline in LBM of 1.5%, and considering the standard error measurement of the most frequently used methods of strength and LBM assessment, recurrent screening could occur every 2 years.^21,32

Earlier screening may be considered for patient populations with a higher pretest probability. These groups include those with conditions associated with accelerated muscle loss, such as chronic kidney disease, peripheral artery disease, and diabetes mellitus (DM).³² Although accelerated muscle loss characterized by an inflammatory motif (eg, cancer-related cachexia) may share some features of the sarcopenia screening and assessment approach, important differences exist regarding the etiology, medical evaluation, and ICD-10-CM code designation.

Staging and Classification

Staging criteria are generally used to denote the severity of a given disease or syndrome, whereas classification criteria are used to define homogenous patient groups based on specific pathologic or clinical features of a disorder. Although classification schemes may incorporate an element of severity, they are primarily used to characterize fairly distinct phenotypic forms of disease or specific clinical presentation patterns associated with a well-defined syndrome. Although not universally adopted, the European consensus group sarcopenia staging criteria are increasingly used to provide a staging algorithm presumably driven by the severity of the condition.¹⁹

The assessment of functional performance for use in sarcopenia staging often involves measuring habitual gait speed or completing the Short Physical Performance Battery (SPPB).²³ The SPPB involves a variety of performance-based activities for balance, gait, strength, and endurance. This test has predictive validity for the onset of disability and adverse health events, and it has been extensively used in research and clinical settings.³³ Additional tests used to characterize function during the staging or diagnostic process include the timed get up and go test (TGUG) and the timed sit to stand test.^34,35 The TGUG provides an estimate of dynamic balance, and the sit to stand test has been used as very basic proxy measure of muscular power.³⁶ The sit to stand test and habitual gait speed are items included in the SPPB.³³

Accepted methods to obtain the traditional index measure of sarcopenia—based on estimates of LBM—include bioimpedance analysis (BIA) and dual X-ray absorptiometry (DXA). The BIA uses the electrical impedance of body tissues and its 2 components, resistance and reactance, to derive its body composition estimates.³⁷ Segmental BIA allows for isolated measurements of the limbs, which may be calibrated to DXA appendicular lean body mass (ALM) or magnetic resonance imaging-based estimates of LBM. This instrument is relatively safe for use, inexpensive for medical facilities, and useful for longitudinal studies, but it can be confounded by issues, such as varying levels of hydration, which may affect measurement validity in some instances.

Despite the precision of DXA for estimating densities for whole body composition analysis, the equipment is not very portable and involves low levels of radiation exposure, which limits its utility in some clinical settings. While each body composition assessment method has its advantages and disadvantages, DXA is regarded as an acceptable form of measurement for hospital settings, and BIA is frequently used in outpatient clinics and community settings. Other methods used to estimate LBM with greater accuracy, such as peripheral quantitative computed tomography, doubly labeled water, and whole body gamma ray counting, are not viable for clinical use. Other accessible methods such as anthropometric measures and skinfold measures have not been embraced by sarcopenia classification consensus groups.^23,37

Alternative methods of estimating LBM, such as diagnostic ultrasound and multifrequency electrical impedance myography, are featured outcomes in ongoing clinical trials that involve veteran participants. These modalities may soon provide a clinically viable approach to assessing muscle quality via estimates of muscle tissue composition.^37,38 Similar to the management of other geriatric syndromes, interprofessional collaboration provides an optimal approach to the assessment of sarcopenia. Physicians and other health care providers may draw on the standardized assessment of strength and function (via the SPPB and hand-grip dynamometry) by physical therapists (PTs), questionnaires administered by nursing staff (the SARC-F), or body composition estimates from other health professionals (ranging from BIA to DXA) to aid the diagnostic process and facilitate appropriate case management (Table 2).

Competing staging and classification definitions have been cited as a primary factor behind the CDC’s delayed recognition of the sarcopenia diagnosis, which in turn posed a barrier to formal clinical recognition by geriatricians.²⁴ However, this reaction to the evolving sarcopenia staging criteria also may reveal the larger misapplication of the staging process to the diagnostic process. The application of classification and staging criteria results in a homogenous group of patients, whereas the application of diagnostic criteria results in a heterogeneous group of patients to account for variations in clinical presentation associated with a given disorder. Classification criteria may be equivalent to objective measures that are used in the diagnostic process when a given disease is characterized by a well-established biomarker.³⁹

However, this is not the case for most geriatric syndromes and other disorders marked by varied clinical presentation patterns. On considering the commonly used sarcopenia staging criteria of LBM ≤ 8.50 kg/m² or grip strength < 30 kg in men and LBM ≤ 5.75 kg/m² or grip strength < 20 kg in women, it is easy to understand that such general cutoff values are far from diagnostic.^40,41 Moreover, stringent cutoff values associated with classification and staging may not adequately capture those with an atypical presentation of the syndrome (eg, someone who exhibits age-related muscle weakness but has retained adequate LBM). Such criteria often prove to have high specificity and low sensitivity, which may yield a false negative rate that is appropriate for clinical research eligibility and group assignment but inadequate for clinical care.

Screening, staging, and classification criteria with high specificity may indeed be desirable for confirmatory imaging tests associated with radiation exposure concerns or for managing risk in experimental clinical trials involving pharmacologic treatment. For example, a SARC-F score ≥ 4 may prompt the formal assessment of LBM via a DXA examination.⁴ In contrast, those with a SARC-F score ≤ 3 with low gait speed or grip strength may benefit from consultation regarding regular physical activity and nutrition recommendations. Given the challenges of establishing sarcopenia classification criteria that perform consistently across populations and geographic regions, classification and staging criteria may be best viewed as clinical reasoning tools that supplement, but not supplant, the diagnostic process.^7,42

Diagnosis

Geriatric syndromes do not lend themselves to a simple diagnostic process. Syndromes such as frailty and sarcopenia are multifactorial and lack a single distinguishing clinical feature or biomarker. The oft-cited refrain that sarcopenia is an underdiagnosed condition is partially explained by the recent ICD-10-CM code and varied classification and diagnostic criteria^.5 This circumstance highlights the need to distinctly contrast the diagnostic process with the screening and staging classifications.

The diagnostic process involves the interpretation of the patient history, signs, and symptoms within the context of individual factors, local or regional disease prevalence, and the results of the best available and most appropriate laboratory tests. After all, a patient that presents with low LBM and a gradual loss of strength without a precipitating event would necessitate further workup to rule out many clinical possibilities under the aegis of a differential diagnosis. Clinical features, such as the magnitude of weakness and pattern of strength loss or muscle atrophy along with the determination of neurologic or autoimmune involvement, are among the key elements of the differential examination for a case involving the observation of frank muscle weakness. Older adults with low muscle strength may have additional risk factors for sarcopenia such as obesity, pain, poor nutrition, previous bone fracture, and a sedentary lifestyle. However, disease etiology with lower probabilities, such as myogenic or neurogenic conditions associated with advancing age, also may be under consideration during the clinical assessment.⁶

In many instances, the cutoff scores associated with the sarcopenia staging criteria may help to guide the diagnostic process and aid clinical decision making. Since individuals with a positive screening result based on the SARC-F questionnaire (score ≥ 4) have a high likelihood of meeting the staging criteria for severe sarcopenia, a PCP may opt to obtain a confirmatory estimate of LBM both to support the clinical assessment and to monitor change over the course of rehabilitation. Whereas people who present with a decline in strength (ie, grip strength < 30 kg for a male) without an observable loss of function or a positive SARC-F score may benefit from consultation from the physician, NP, or rehabilitation health professional regarding modifiable risk factors associated with sarcopenia.

Incorporating less frequently used sarcopenia classification schemes such as identifying those with sarcopenic obesity or secondary sarcopenia due to mitigating factors such as chronic kidney disease or DM (Table 3) may engender a more comprehensive approach to intervention that targets the primary disease while also addressing important secondary sequelae. Nevertheless, staging or classification criteria cannot be deemed equivalent to diagnostic criteria for sarcopenia due to the challenges posed by syndromes that have a heterogeneous clinical presentation.

The refinement of the staging and classification criteria along with the advances in imaging technology and mechanistic research are not unique to sarcopenia. Practitioners involved in the care of people with rheumatologic conditions or osteoporosis also have contended with continued refinements to their classification criteria and approach to risk stratification.^39,43,44 Primary care providers will now have the option to use a new ICD-10-CM code (M62.84) for sarcopenia, which will allow them to properly document the clinical distinctions between people with impaired strength or function largely due to age-related muscle changes and those who have impaired muscle function due to cachexia, inflammatory myopathies, or forms of neuromuscular disease.

The ability to identify and document this geriatric syndrome in veterans will help to better define the scope of the problem within the VA health care system. The median age of veterans is 62 years compared with 43 years for nonveterans.³ Consequently, there may be value in the adoption of a formal approach to screening and diagnosis for sarcopenia among veterans who receive their primary care from VA facilities.⁷ Indeed, the exchange between the patient and the health professional regarding the screening and diagnostic process will provide valuable opportunities to promote exercise interventions before patients incur significant impairments.

One of the biggest threats burdening global health is noncommunicable diseases, and many chronic conditions, such as sarcopenia, can be prevented and managed with appropriate levels of physical activity.¹⁷ Increased physician involvement may prove to be critical given the identification of physical inactivity as a top 5 risk factor for general morbidity and mortality by World Health Organization and consensus group recommendations calling for physicians to serve a more prominent role in the provision of exercise and physical activity recommendations.^16,17

This developing health care role should include NPs, PTs, physician assistants, and other associated health professionals. It also should include collaborative efforts between physicians and rehabilitation practitioners concerning provision of the formal exercise prescriptionprescription and monitoring of patient outcomes.

Individuals with severe forms of sarcopenia rarely improve without intervention.⁶ Although no pharmacologic treatment exists to specifically address sarcopenia, strengthening exercise has been shown to be an effective mode of prevention and conservative management.⁸ Progressive resistance exercise cannot abate the expected age-related changes in skeletal muscle, but it can significantly reverse the loss of LBM and strength in untrained older adults and slow the age-related decline in muscle performance in older adult athletes and trained individuals.⁴⁵

Local senior centers and community organizations may prove to be valuable resources concerning group exercise options, and they provide the added benefit of social engagement and peer group accountability. Federal resources include the Go4Life exercise guide and online videos provided by the National Institute on Aging and the MOVE! Weight Management and Health Program provided at select VA community-based outpatient clinics. Ultimately, collaborative efforts with exercise specialists may serve to reduce the PCP burden during the provision of health services, minimize diagnostic errors associated with sarcopenia assessment and help to connect patients to valuable health promotion resources.^17,18

Conclusion

While practitioners should remain keenly aware of the pernicious effects of overdiagnosis, sarcopenia has long existed as a known, but undiagnosed, condition. Of course, geriatricians have traditionally managed poor muscle performance and mobility limitations by addressing treatable symptoms and providing referrals to physical medicine specialists when warranted. Nevertheless, the advent of ICD-10-CM code M62.⁸⁴ provides the VA with an opportunity to take a leading role in systematically addressing this geriatric syndrome within an aging veteran population.

The following items should be considered by NCP for the development of guidelines and recommendations concerning sarcopenia screening:

1. Consider screening veterans aged > 65 years for sarcopenia every 2 years. Those with mitigating systemic conditions (eg, chronic kidney disease, DM, or malnutrition) or significant mobility limitations may be screened at any age.
2. Sarcopenia screening procedures should include at a minimum the SARC-F questionnaire and gait speed (when appropriate). Including gait speed or grip strength testing in the screening exam is recommended given the low sensitivity of the SARC-F questionnaire.
3. Veterans with positive SARC-F results (≥ 4) merit a physical therapy referral. In addition, these veterans should obtain confirmatory standardized assessments for LBM and functional status.
4. Veterans at risk for sarcopenia based on patient age, medical history, and the physical examination (eg, obesity, sedentary lifestyle, a previous fracture, self-reported physical decline), but with negative SARC-F results should receive a formal exercise prescription from their PCP. Baseline assessment measures may be used for comparison with serial measures obtained during subsequent screening visits to support long-term case management.
5. Interprofessional collaboration involving geriatricians, PTs, nurses, radiologists, and other health care professionals should be involved in the screening, diagnosis, and case management of veterans with sarcopenia.
6. The VA EMR should be systematically documented with sarcopenia assessment data obtained from the gait speed tests, SARCF, SPPB, grip strength tests, and LBM estimates to better characterize this condition within the veteran population.

Any expansion in the provision of health care comes with anticipated benefits and potential costs. Broad guidance from NCP may encourage veterans to pursue selected screening tests, promote the appropriate use of preventative services, and facilitate timely treatment when needed.³¹ Clinicians who are informed about the screening, staging, classification, and diagnostic process for sarcopenia may partner with patients to make reasoned decisions about how to best manage this syndrome within the VA medical center environment.

Sarcopenia is an age-related loss of skeletal muscle that may result in diminished muscle strength and functional performance. The prevalence of sarcopenia varies based on the cohort and the assessment criteria. According to the Health Aging and Body Composition (ABC) study, the prevalence of sarcopenia in community-dwelling older adults is about 14% to 18%, whereas the estimate may exceed 30% for those in longterm care.^1,2 This geriatric syndrome may disproportionately affect veterans given that they are older than the civilian population and may have disabling comorbid conditions associated with military service.³

Recently, there has been a call to action to systematically address sarcopenia by interdisciplinary organizations such as the European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis (ESCEO) and the International Working Group on Sarcopenia (IWGS).^4,5 This call to action is due to the association of sarcopenia with increased health care costs, higher disability incidence, and elevated risk of mortality.^6,7 The consequences of sarcopenia may include serious complications, such as hip fracture or a loss of functional independence.^8,9 The CDC now recognizes sarcopenia as an independently reportable medical condition. Consequently, physicians, nurse practitioners (NPs), and other associated health professionals within the VA will need to better understand clinically viable and valid methods to screen and diagnose this geriatric syndrome.

The purpose of this paper is to inform practitioners how sarcopenia screening is aided by the new ICD-10-CM code and briefly review recent VA initiatives for proactive care. Additional objectives include identifying common methods used to assess sarcopenia and providing general recommendations to the VHA National Center for Health Promotion and Disease Prevention (NCP) concerning the management of sarcopenia.

Addressing Sarcopenia

While the age-related decline in muscle size and performance has long been recognized by geriatricians, sustained advocacy by several organizations was required to realize the formal recognition of sarcopenia. Aging in Motion (AIM), a coalition of organizations focused on advancing research and treatment for conditions associated with age-related muscle dysfunction, sought the formal recognition of sarcopenia. The CDC established the ICD-10-CM code for sarcopenia in October of 2016, which allowed the syndrome to be designated as a primary or secondary condition.¹⁰

The ubiquitous nature of agerelated changes in muscle and the mandate to engage in proactive care by all levels of VA leadership led to the focus on addressing sarcopenia. The recognition of sarcopenia by the CDC comes at an opportune time given recent VA efforts to transform itself from a facilitator mainly of care delivery to an active partner in fostering the health and well-being of veterans. Initiatives that are emblematic of this attempt to shift the organizational culture across the VHA include establishing the VA Center for Innovation (VACI) and issuing guidance documents, such as the Blueprint for Excellence, which was introduced in 2014 by then VA Secretary Robert McDonald.^11,12 Many of the following Blueprint themes and strategies potentially impact sarcopenia screening and treatment within the VA:

• Delivering high-quality, veteran-centered care: A major Blueprint theme is attaining the “Triple Aims” of a health care system by promoting better health among veterans, improving the provision of care, and lowering costs through operational efficiency. The management of sarcopenia has clear clinical value given the association of age-related muscle loss with fall risk and decreased mobility.¹³ Financial value also may be associated with the effort to decrease disability related to sarcopenia and the use of a team approach featuring associated health professionals to help screen for this geriatric syndrome.^14,15 (Strategy 2)
• Leveraging health care informatics to optimize individual and population health outcomes: The inclusion of the most basic muscle performance and functional status measures in the electronic medical record (EMR), such as grip strength and gait speed, would help to identify the risk factors and determinants of sarcopenia among the veteran population. (Strategy 3)
• Advancing personalized, proactive health care that fosters a high level of health and well-being: The long-term promotion of musculoskeletal health and optimal management of sarcopenia cannot be sustained through episodic medical interactions. Instead, a contemporary approach to health services marked by the continuous promotion of health education, physical activity and exercise, and proper nutrition has demonstrated value in the management of chronic conditions.^16,17 (Strategy 6)

The new sarcopenia ICD-10-CM code along with elements of the VHA Blueprint can serve to support the systematic assessment and management of veterans with age-related muscle dysfunction. Nevertheless, renewed calls for health promotion and screening programs are often counterbalanced by the need for cost containment and the cautionary tales concerning the potential harms or errors associated with some forms of medical screening. The American Board of Internal Medicine Foundation has spearheaded the Choosing Wisely campaign to raise awareness about excessive medical testing. However, the Institute of Medicine has linked the provision of quality health care to a diagnostic process that is both timely and accurate.¹⁸ Careful consideration of these health care challenges may help guide practitioners within the VA concerning the screening and diagnosis of sarcopenia.

Sarcopenia Assessment

Sarcopenia can have several underlying causes in some individuals and result in varied patterns of clinical presentation and differing degrees of severity. The European Working Group on Sarcopenia in Older People first met in 2009 and used a consensus-based decision-making process to determine operational definitions for sarcopenia and create a staging algorithm for the syndrome.¹⁹ This consensus group developed a conceptual staging model with 3 categories: presarcopenia, sarcopenia, and severe sarcopenia (Table 1). The impetus for sarcopenia staging was the emerging research findings suggesting that lean body mass (LBM) alone did not provide a high degree of clinical value in outpatient settings due to the nonlinear relationship between LBM and muscle function in older adults.^20,21 Using the consensus model approach, an individual is classified as sarcopenic on presenting with both low LBM and low muscle function.

Screening: A Place to Start

Findings from the Health ABC Study suggested that older adults who maintained high levels of LBM were less likely to become sarcopenic. Whereas, older adults in the cohort with low levels of LBM tended to remain in a sarcopenic state.⁶ Consequently, the early detection of sarcopenia may have important health promotion implications for older adults. Sarcopenia is a syndrome with a continuum of clinical features; it is not a disease with a clear or singular etiololgy. Therefore, the result of the screening examination should identify those who would most benefit from a formal diagnostic assessment.

One approach to screening for sarcopenia involves the use of questionnaires, such as the SARC-F (sluggishness, assistance in walking, rise from a chair, climb stairs, falls), which is a brief 5-item questionnaire with Likert scoring for patient responses.¹³ In a cohort of National Health and Nutrition Examination Survey (NHANES) participants, SARC-F scores ≥ 4 were associated with slower gait speed, lower strength, and an increased likelihood of hospitalization within a year of the test response.²² However, rather than stratify patients by risk, the SARC-F exhibits a high degree of test specificity regarding the major consensus-based sarcopenia classification criteria (specificity = 94.2% to 99.1%; sensitivity = 3.8% to 9.9%).¹³ Given the known limitations of screening tools with low sensitivity, organizations such as the ESCEO have recommended supplementing the SARC-F questionnaire with other forms of assessment.⁴ Supplements to the screening examination may range from the use of “red flag” questions concerning changes in nutritional status, body weight, and physical activity, to conducting standard gait speed and grip-strength testing.^4,19,23

Performance-based testing, including habitual gait speed and grip-strength dynamometry, also may be used in both the screening and classification of sarcopenia.² Although walking speed below 1.0 m/s has been used by the IWGS as a criterion to prompt further assessment, many people within the VA health care system may have gait abnormalities independent of LBM status, and others may be nonambulatory.^24,25 As a result, grip-strength testing should be considered as a supplementary or alternate screening assessment tool.^26,27

Hand-grip dynamometry is often used diagnostically given its previous test validation, low expense, and ease of use.²³ Moreover, recent evidence suggests that muscle strength surpasses gait speed as a means of identifying people with sarcopenia. Grip strength is associated with all-cause mortality, even when adjusting for age, sex, and body size,²⁸ while slow gait speed (< .82 m/s) has a reported sensitivity of 63% and specificity of 70% for mortality in population-based studies involving older adults.²⁹

Gait speed (in those who are ambulatory) and grip-strength values could be entered into the EMR evaluation note by the primary care provider (PCP). Elements of the VA EMR, such as the ability to review the diagnosis of sarcopenia on the Problem List or the nominal enhancement of providing LBM estimates within the Cumulative Vitals and Measurements Report would support the management of sarcopenia. See Table 2 for cutoff values for frequently used sarcopenia screening and staging tests.

The pitfalls of excessive or inappropriate screening are well documented. The efforts to screen for prostate cancer have highlighted instances when inappropriate followup tests and treatment fail to alter mortality rates and ultimately yield more harm than good.³⁰ However, there are several points of departure concerning the screening for sarcopenia vs screening for prostate cancer. The screening assessments for sarcopenia are low-cost procedures that are associated with a low patient burden. These procedures may include questionnaires, functional testing, or the assessment of muscle performance. Additionally, there is a low propensity for adverse effects stemming from treatment due to disease misclassification given the common nonpharmacologic approaches used to manage sarcopenia.³¹ Nonetheless, the best screening examination—even one that has low patient burden and cost—may prove to be a poor use of medical resources if the process is not linked to a viable intervention.

Screening people aged ≥ 65 years may strike a balance between controlling health care expenditures and identifying people with the initial signs of sarcopenia early enough to begin monitoring key outcomes and providing a formalized exercise prescription. Presuming an annual age-related decline in LBM of 1.5%, and considering the standard error measurement of the most frequently used methods of strength and LBM assessment, recurrent screening could occur every 2 years.^21,32

Earlier screening may be considered for patient populations with a higher pretest probability. These groups include those with conditions associated with accelerated muscle loss, such as chronic kidney disease, peripheral artery disease, and diabetes mellitus (DM).³² Although accelerated muscle loss characterized by an inflammatory motif (eg, cancer-related cachexia) may share some features of the sarcopenia screening and assessment approach, important differences exist regarding the etiology, medical evaluation, and ICD-10-CM code designation.

Staging and Classification

Staging criteria are generally used to denote the severity of a given disease or syndrome, whereas classification criteria are used to define homogenous patient groups based on specific pathologic or clinical features of a disorder. Although classification schemes may incorporate an element of severity, they are primarily used to characterize fairly distinct phenotypic forms of disease or specific clinical presentation patterns associated with a well-defined syndrome. Although not universally adopted, the European consensus group sarcopenia staging criteria are increasingly used to provide a staging algorithm presumably driven by the severity of the condition.¹⁹

The assessment of functional performance for use in sarcopenia staging often involves measuring habitual gait speed or completing the Short Physical Performance Battery (SPPB).²³ The SPPB involves a variety of performance-based activities for balance, gait, strength, and endurance. This test has predictive validity for the onset of disability and adverse health events, and it has been extensively used in research and clinical settings.³³ Additional tests used to characterize function during the staging or diagnostic process include the timed get up and go test (TGUG) and the timed sit to stand test.^34,35 The TGUG provides an estimate of dynamic balance, and the sit to stand test has been used as very basic proxy measure of muscular power.³⁶ The sit to stand test and habitual gait speed are items included in the SPPB.³³

Accepted methods to obtain the traditional index measure of sarcopenia—based on estimates of LBM—include bioimpedance analysis (BIA) and dual X-ray absorptiometry (DXA). The BIA uses the electrical impedance of body tissues and its 2 components, resistance and reactance, to derive its body composition estimates.³⁷ Segmental BIA allows for isolated measurements of the limbs, which may be calibrated to DXA appendicular lean body mass (ALM) or magnetic resonance imaging-based estimates of LBM. This instrument is relatively safe for use, inexpensive for medical facilities, and useful for longitudinal studies, but it can be confounded by issues, such as varying levels of hydration, which may affect measurement validity in some instances.

Despite the precision of DXA for estimating densities for whole body composition analysis, the equipment is not very portable and involves low levels of radiation exposure, which limits its utility in some clinical settings. While each body composition assessment method has its advantages and disadvantages, DXA is regarded as an acceptable form of measurement for hospital settings, and BIA is frequently used in outpatient clinics and community settings. Other methods used to estimate LBM with greater accuracy, such as peripheral quantitative computed tomography, doubly labeled water, and whole body gamma ray counting, are not viable for clinical use. Other accessible methods such as anthropometric measures and skinfold measures have not been embraced by sarcopenia classification consensus groups.^23,37

Alternative methods of estimating LBM, such as diagnostic ultrasound and multifrequency electrical impedance myography, are featured outcomes in ongoing clinical trials that involve veteran participants. These modalities may soon provide a clinically viable approach to assessing muscle quality via estimates of muscle tissue composition.^37,38 Similar to the management of other geriatric syndromes, interprofessional collaboration provides an optimal approach to the assessment of sarcopenia. Physicians and other health care providers may draw on the standardized assessment of strength and function (via the SPPB and hand-grip dynamometry) by physical therapists (PTs), questionnaires administered by nursing staff (the SARC-F), or body composition estimates from other health professionals (ranging from BIA to DXA) to aid the diagnostic process and facilitate appropriate case management (Table 2).

Competing staging and classification definitions have been cited as a primary factor behind the CDC’s delayed recognition of the sarcopenia diagnosis, which in turn posed a barrier to formal clinical recognition by geriatricians.²⁴ However, this reaction to the evolving sarcopenia staging criteria also may reveal the larger misapplication of the staging process to the diagnostic process. The application of classification and staging criteria results in a homogenous group of patients, whereas the application of diagnostic criteria results in a heterogeneous group of patients to account for variations in clinical presentation associated with a given disorder. Classification criteria may be equivalent to objective measures that are used in the diagnostic process when a given disease is characterized by a well-established biomarker.³⁹

However, this is not the case for most geriatric syndromes and other disorders marked by varied clinical presentation patterns. On considering the commonly used sarcopenia staging criteria of LBM ≤ 8.50 kg/m² or grip strength < 30 kg in men and LBM ≤ 5.75 kg/m² or grip strength < 20 kg in women, it is easy to understand that such general cutoff values are far from diagnostic.^40,41 Moreover, stringent cutoff values associated with classification and staging may not adequately capture those with an atypical presentation of the syndrome (eg, someone who exhibits age-related muscle weakness but has retained adequate LBM). Such criteria often prove to have high specificity and low sensitivity, which may yield a false negative rate that is appropriate for clinical research eligibility and group assignment but inadequate for clinical care.

Screening, staging, and classification criteria with high specificity may indeed be desirable for confirmatory imaging tests associated with radiation exposure concerns or for managing risk in experimental clinical trials involving pharmacologic treatment. For example, a SARC-F score ≥ 4 may prompt the formal assessment of LBM via a DXA examination.⁴ In contrast, those with a SARC-F score ≤ 3 with low gait speed or grip strength may benefit from consultation regarding regular physical activity and nutrition recommendations. Given the challenges of establishing sarcopenia classification criteria that perform consistently across populations and geographic regions, classification and staging criteria may be best viewed as clinical reasoning tools that supplement, but not supplant, the diagnostic process.^7,42

Diagnosis

Geriatric syndromes do not lend themselves to a simple diagnostic process. Syndromes such as frailty and sarcopenia are multifactorial and lack a single distinguishing clinical feature or biomarker. The oft-cited refrain that sarcopenia is an underdiagnosed condition is partially explained by the recent ICD-10-CM code and varied classification and diagnostic criteria^.5 This circumstance highlights the need to distinctly contrast the diagnostic process with the screening and staging classifications.

The diagnostic process involves the interpretation of the patient history, signs, and symptoms within the context of individual factors, local or regional disease prevalence, and the results of the best available and most appropriate laboratory tests. After all, a patient that presents with low LBM and a gradual loss of strength without a precipitating event would necessitate further workup to rule out many clinical possibilities under the aegis of a differential diagnosis. Clinical features, such as the magnitude of weakness and pattern of strength loss or muscle atrophy along with the determination of neurologic or autoimmune involvement, are among the key elements of the differential examination for a case involving the observation of frank muscle weakness. Older adults with low muscle strength may have additional risk factors for sarcopenia such as obesity, pain, poor nutrition, previous bone fracture, and a sedentary lifestyle. However, disease etiology with lower probabilities, such as myogenic or neurogenic conditions associated with advancing age, also may be under consideration during the clinical assessment.⁶

In many instances, the cutoff scores associated with the sarcopenia staging criteria may help to guide the diagnostic process and aid clinical decision making. Since individuals with a positive screening result based on the SARC-F questionnaire (score ≥ 4) have a high likelihood of meeting the staging criteria for severe sarcopenia, a PCP may opt to obtain a confirmatory estimate of LBM both to support the clinical assessment and to monitor change over the course of rehabilitation. Whereas people who present with a decline in strength (ie, grip strength < 30 kg for a male) without an observable loss of function or a positive SARC-F score may benefit from consultation from the physician, NP, or rehabilitation health professional regarding modifiable risk factors associated with sarcopenia.

Incorporating less frequently used sarcopenia classification schemes such as identifying those with sarcopenic obesity or secondary sarcopenia due to mitigating factors such as chronic kidney disease or DM (Table 3) may engender a more comprehensive approach to intervention that targets the primary disease while also addressing important secondary sequelae. Nevertheless, staging or classification criteria cannot be deemed equivalent to diagnostic criteria for sarcopenia due to the challenges posed by syndromes that have a heterogeneous clinical presentation.

The refinement of the staging and classification criteria along with the advances in imaging technology and mechanistic research are not unique to sarcopenia. Practitioners involved in the care of people with rheumatologic conditions or osteoporosis also have contended with continued refinements to their classification criteria and approach to risk stratification.^39,43,44 Primary care providers will now have the option to use a new ICD-10-CM code (M62.84) for sarcopenia, which will allow them to properly document the clinical distinctions between people with impaired strength or function largely due to age-related muscle changes and those who have impaired muscle function due to cachexia, inflammatory myopathies, or forms of neuromuscular disease.

The ability to identify and document this geriatric syndrome in veterans will help to better define the scope of the problem within the VA health care system. The median age of veterans is 62 years compared with 43 years for nonveterans.³ Consequently, there may be value in the adoption of a formal approach to screening and diagnosis for sarcopenia among veterans who receive their primary care from VA facilities.⁷ Indeed, the exchange between the patient and the health professional regarding the screening and diagnostic process will provide valuable opportunities to promote exercise interventions before patients incur significant impairments.

One of the biggest threats burdening global health is noncommunicable diseases, and many chronic conditions, such as sarcopenia, can be prevented and managed with appropriate levels of physical activity.¹⁷ Increased physician involvement may prove to be critical given the identification of physical inactivity as a top 5 risk factor for general morbidity and mortality by World Health Organization and consensus group recommendations calling for physicians to serve a more prominent role in the provision of exercise and physical activity recommendations.^16,17

This developing health care role should include NPs, PTs, physician assistants, and other associated health professionals. It also should include collaborative efforts between physicians and rehabilitation practitioners concerning provision of the formal exercise prescriptionprescription and monitoring of patient outcomes.

Individuals with severe forms of sarcopenia rarely improve without intervention.⁶ Although no pharmacologic treatment exists to specifically address sarcopenia, strengthening exercise has been shown to be an effective mode of prevention and conservative management.⁸ Progressive resistance exercise cannot abate the expected age-related changes in skeletal muscle, but it can significantly reverse the loss of LBM and strength in untrained older adults and slow the age-related decline in muscle performance in older adult athletes and trained individuals.⁴⁵

Local senior centers and community organizations may prove to be valuable resources concerning group exercise options, and they provide the added benefit of social engagement and peer group accountability. Federal resources include the Go4Life exercise guide and online videos provided by the National Institute on Aging and the MOVE! Weight Management and Health Program provided at select VA community-based outpatient clinics. Ultimately, collaborative efforts with exercise specialists may serve to reduce the PCP burden during the provision of health services, minimize diagnostic errors associated with sarcopenia assessment and help to connect patients to valuable health promotion resources.^17,18

Conclusion

While practitioners should remain keenly aware of the pernicious effects of overdiagnosis, sarcopenia has long existed as a known, but undiagnosed, condition. Of course, geriatricians have traditionally managed poor muscle performance and mobility limitations by addressing treatable symptoms and providing referrals to physical medicine specialists when warranted. Nevertheless, the advent of ICD-10-CM code M62.⁸⁴ provides the VA with an opportunity to take a leading role in systematically addressing this geriatric syndrome within an aging veteran population.

The following items should be considered by NCP for the development of guidelines and recommendations concerning sarcopenia screening:

1. Consider screening veterans aged > 65 years for sarcopenia every 2 years. Those with mitigating systemic conditions (eg, chronic kidney disease, DM, or malnutrition) or significant mobility limitations may be screened at any age.
2. Sarcopenia screening procedures should include at a minimum the SARC-F questionnaire and gait speed (when appropriate). Including gait speed or grip strength testing in the screening exam is recommended given the low sensitivity of the SARC-F questionnaire.
3. Veterans with positive SARC-F results (≥ 4) merit a physical therapy referral. In addition, these veterans should obtain confirmatory standardized assessments for LBM and functional status.
4. Veterans at risk for sarcopenia based on patient age, medical history, and the physical examination (eg, obesity, sedentary lifestyle, a previous fracture, self-reported physical decline), but with negative SARC-F results should receive a formal exercise prescription from their PCP. Baseline assessment measures may be used for comparison with serial measures obtained during subsequent screening visits to support long-term case management.
5. Interprofessional collaboration involving geriatricians, PTs, nurses, radiologists, and other health care professionals should be involved in the screening, diagnosis, and case management of veterans with sarcopenia.
6. The VA EMR should be systematically documented with sarcopenia assessment data obtained from the gait speed tests, SARCF, SPPB, grip strength tests, and LBM estimates to better characterize this condition within the veteran population.

Any expansion in the provision of health care comes with anticipated benefits and potential costs. Broad guidance from NCP may encourage veterans to pursue selected screening tests, promote the appropriate use of preventative services, and facilitate timely treatment when needed.³¹ Clinicians who are informed about the screening, staging, classification, and diagnostic process for sarcopenia may partner with patients to make reasoned decisions about how to best manage this syndrome within the VA medical center environment.