Original Research

From Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA.

Abstracts

• Objective: To identify patient factors associated with willingness to take daily weight loss medication and weight loss expectations using these medications.
• Methods: A random sample of 331 primary care patients aged 18–65 years with a BMI ≥ 35 kg/m² were recruited from 4 diverse primary care practices in Boston, MA. We conducted telephone interviews and chart reviews to assess patients’ willingness to take a weight loss medication and their expectations for weight loss. We used sequential logistic regression models to identify demographic, clinical, and quality of life (QOL) factors associated with this willingness.
• Results: Of 331 subjects, 69% were women, 35% were white, 35% were black, and 25% were Hispanic; 249 (75%) of patients were willing to take a daily weight loss medication if recommended by their doctor but required a median weight loss of 15% to 24%; only 17% of patients were willing to take a medication for ≤ 10% weight loss. Men were significantly more willing than women (1.2 [95% CI 1.0–1.4]). Diabetes was the only comorbidity associated with willingness to consider pharmacotherapy (1.2 [1.0–1.3]) but only modestly improved model performance (C-statistic increased from 0.59 to 0.60). In contrast, lower QOL, especially low self-esteem and sex life, were stronger correlates (C-statistic 0.72).
• Conclusion: A majority of obese primary care patients were willing to take a daily weight loss pill; however, most required more than 10% weight loss to consider pharmacotherapy worthwhile. Poor QOL, especially low self-esteem and poor sex life, were stronger correlates than having diabetes.

Key words: obesity; primary care; weight loss medication.

In the United States, obesity continues to be unrelentingly prevalent, affecting more than one-third of adults (34.9%) [1]. This statistic has ominous implications when considering that obesity is a risk factor for numerous chronic diseases, such as coronary heart disease, diabetes, sleep apnea, osteoarthritis, and some types of cancers [2]. Moreover, it is associated with increased risk of all-cause and cardiovascular disease mortality. Promisingly, an initial 5% to 10% weight loss over 6 months has been associated with improvement in LDL, HDL, triglycerides, glucose, hemoglobin A1C, diabetes risk, blood pressure, and medication use [2]. Therefore, although patients may not be able to achieve their ideal body weight or normal BMI, modest weight loss can still have beneficial health effects.

Weight loss medications are effective adjunctive therapies in helping patients lose up to 10% of their body weight on average when combined with diet and exercise [3–5]. There are currently 5 medications approved by the Food and Drug Administration for long-term use for weight loss: orlistat, lorcaserin, phentermine-topiramate, bupropion-naltrexone, and liraglutide. Despite their proven efficacy, there are barriers to initiating a long-term weight loss medication. Insurance reimbursement is limited for these medications, thus resulting in high out-of-pocket cost for patients that they may be unable or unwilling to pay [6]. There may also be safety concerns given that several weight loss medications, including fenfluramine, sibutramine, and rimonabant, have been withdrawn from the market because of adverse effects [7]. Thus, in deciding whether to initiate a pharmacologic weight loss regimen, patients must believe that the weight loss benefits will exceed the potential risks.

Little is known, however, about patients’ willingness to take weight loss medications or the minimum weight loss they expect to lose to make pharmacotherapy worthwhile. Only a few studies have investigated patient willingness to adopt pharmacotherapy as part of a weight loss regimen, and only one investigated obese patients in the United States [8]. In this context, we surveyed a sociodemographically diverse group of primary care patients with moderate to severe obesity to examine patient characteristics associated with willingness to pursue weight loss pharmacotherapy. We also aimed to evaluate how much weight patients expected to lose in order to make taking a daily medication worth the effort. Characterizing patients seen in primary care who are willing to adopt pharmacotherapy to lose weight may guide weight loss counseling in the primary care setting. Furthermore, determining whether patients have realistic weight loss expectations can help clinicians better counsel their patients on weight loss goals.

Methods

Study Sample

We recruited 337 subjects from 4 diverse primary care practices in Boston, Massachusetts: a large hospital-based academic practice, a community practice in a working-class suburb, a community practice in an affluent suburb, and a health center serving a predominantly socially disadvantaged population. The primary goal of the parent study was to understand the preferences of patients for weight loss treatment, especially bariatric surgery. Therefore, to be included, patients needed to have a BMI ≥ 35 kg/m² at the time of recruitment, been seen in clinic within the past year, be aged 18–65 years, and be English or Spanish speaking. By design, African-American and Hispanic patients were oversampled from an electronic list of potentially eligible patient groups so that we could examine for racial differences in treatment preferences. Study details have been previously described [9].

Data Collection and Measures

Trained interviewers conducted a 45- to 60-minute telephone interview with each participant in either English or Spanish. To assess willingness to use a daily weight loss medication, subjects were asked, “If your doctor recommended it, would you be willing to take a pill or medication every day in order to lose weight?” Those who answered affirmatively were then asked the minimum amount of weight they would have to lose to make taking a pill everyday worthwhile.

Subjects were also asked about demographic information (age, race, education, marital status) and comorbid health conditions commonly associated with obesity (diabetes mellitus, hypertension, asthma, obstructive sleep apnea, GERD, depression, anxiety, back pain, and cardiovascular problems). We assessed quality of life (QOL) using the Impact of Weight on Quality of Life-Lite (IWQOL-Lite), a 31-item instrument designed specifically to assess the impact of obesity on QOL capturing 5 domains (physical function, self-esteem, sexual life, public distress, and work). Subjects were asked to rate a series of statements beginning with “Because of my weight…” as “always true,” “usually true,” “sometimes true,” “rarely true,” or “never true.” Global and domain scores ranged from 0 to 100; higher scores reflected better QOL [10].

Data Analysis

We used descriptive statistics to characterize the proportion of subjects willing to use a daily weight loss medication and the weight loss required for patients to be willing to consider pharmacotherapy. We used a stepwise logistic model to examine demographic, QOL, and clinical factors associated with the willingness to take a weight loss medication as the outcome, with an entry criteria of P value of 0.1 and an exit criteria of 0.05. Log-Poisson distribution using the sandwich estimator was used to obtain relative risks for each significant variable. Adjusted models included age, BMI, sex, and race and any significant comorbidities. We added overall QOL score and individual QOL scores in subsequent models to examine the relative influence of overall vs. domain-specific QOL. Statistical analyses were conducted with SAS (SAS Institute, Cary, NC). We considered the change in model C-statistic when specific variables were added to the model to determine the importance of these factors in contributing to patients’ willingness to consider pharmacotherapy; larger changes in model C-statistic signifies a greater contribution.

Results

Table 2 displays sequentially adjusted models examining various demographic, clinical, and QOL factors associated with willingness to take a weight loss medication. 

Discussion

In our study, we found that a large proportion (75%) of primary care patients with at least moderate obesity were willing to take a daily weight loss medication if their doctor recommended it. After full adjustment, men, those with lower quality of life (QOL), and patients with diabetes were more likely to pursue weight loss pharmacotherapy than their counterparts. Moreover, QOL appeared more important than comorbid diagnoses in contributing to whether patients would consider taking a weight loss medication. Most patients expected to lose more than 10% of their weight to make taking a daily medication worthwhile.

Few studies have examined patients’ willingness to take a medication to lose weight. Tan et al [11] found that only about half of their surveyed outpatients were likely to take a medication to lose weight; however, approximately a quarter of the patients in that study were of normal BMI. In contrast, our study interviewed patients with at least a BMI of 35 kg/m² and the majority of these patients reported a willingness to take a weight loss medication. Nevertheless, patients appear to have unrealistic expectations of the weight loss potential of pharmacotherapy. Only a minority of patients in our study would be willing to take a weight loss medication if the weight loss was no more than 10%, a level that is more consistent with the outcomes achievable in most clinical trials of weight loss medications [12]. Prior studies have also shown that patients often have unrealistic weight loss expectations and are unable to achieve their ideal body weight using diet, exercise, or pharmacotherapy [13,14]. Doyle et al found that percentage of weight loss was the most important treatment attribute when considering weight loss pharmacotherapy when compared to cost, health improvements, side effects, diet and exercise requirements, and method of medication administration [8]. Thus it is important to educate patients on realistic goal setting and the benefits of modest weight loss when considering pharmacotherapy. The weight loss preferences expressed in our study may also influence the weight loss outcomes targets pursued in pharmaceutical development. Interestingly, after full adjustment, BMI did not correlate with willingness to take a weight loss medication. Given that all patients in our study had a BMI of ≥ 35 kg/m², this may imply that variations beyond this BMI threshold did not significantly affect a patient’s willingness to use pharmacotherapy. In contrast, weight-related QOL was an important correlate.

Men were slightly more likely than women to be willing to take a weight loss medication, which is interesting since men have been shown to be less likely to participate in behavioral weight loss programs and diets [15]. One reason may be that many weight loss programs are delivered in group settings which may deter men from participating. Whether this hypothetical willingness to undergo pharmacotherapy would translate to actual use is unclear, especially since there are barriers to pharmacotherapy including out-of-pocket costs. In a prior study in the United Kingdom, women were more likely to have reported prior weight loss medication use than men [16].

Our study did not find differences in willingness to pursue weight loss medication by race or educational attainment. This is consistent with our prior work demonstrating that racial and ethnic minorities were no less likely to consider bariatric surgery if the treatment were recommended by their doctor [9]. However, our other work did suggest that clinicians may be less likely to recommend bariatric surgery to their medically eligible minority patients as compared to their Caucasian patients. Whether this may be the case for pharmacotherapy is unclear since this was not explicitly queried in our current study [9].

Our study also found that patients with diabetes but not other comorbidities were more likely to consider weight loss medication after adjusting for QOL. This may reflect a stronger link between diabetes and obesity perceived by patients. Our result is consistent with our earlier data showing that diabetes but not other comorbid conditions was associated with a higher likelihood of considering weight loss surgery [9]. Nevertheless, having diabetes contributed only modestly to the variation in patient preferences regarding pharmacotherapy as reflected by the trivial change in model C-statistic when diabetes status was added to the model.

In contrast, lower QOL scores, especially in the domains of self-esteem and sex life, were associated with increased willingness to take a weight loss medication and appeared to be a stronger predictor than individual comorbidities. This is consistent with other studies showing that patients seeking treatment for obesity tend to have lower health-related QOL [9,17]. Our findings are also consistent with our previous research demonstrating that impairments in specific QOL domains are often more important to patients and stronger drivers of diminished well-being than measures of overall QOL [18]. Hence, given their importance to patients, clinicians need to consider QOL benefits when counseling patients about the risks and benefits of various obesity treatments.

This study is the first to our knowledge to systematically characterize demographic factors associated with the likelihood of primary care patients with obesity considering weight loss pharmacotherapy. This information may aid outpatient weight loss counseling by increasing awareness of gender and patient specific preferences. The fact that many patients with obesity appear to be interested in pursuing weight loss medication may also support public health initiatives in providing equitable access to weight loss pharmacotherapy. As our study characterizes patients who are willing to pursue weight loss medications, future studies may include retrospective analyses on actual use of weight loss medications among various demographic groups. Further investigation on specific reasons why patients choose whether or not to use weight loss medication may also be helpful.

This study has important limitations. The sample size was modest and potentially underpowered to detect small differences across different subgroups. Our sample was also limited to practices in Boston, which limits generalizability; although, by design, we oversampled racial and ethnic subjects to ensure diverse representation. Finally, our study examined patients’ hypothetical willingness to take weight loss medications rather than their actual adherence to treatment if offered.

Conclusion

In this sample of obese primary care patients, we found that the majority of patients were willing to take a daily medication to lose weight; however, patients had expectations for weight loss that far exceeded the level achievable by patients in pharmaceutical trials of these agents. Men and patients with diabetes were more likely to be willing to pursue weight loss medication; however, lower weight-related QOL, especially low self-esteem and impaired sexual function, appeared to be a stronger correlate of willingness to consider pharmacotherapy than comorbid diagnoses.

Corresponding author: Christina C. Wee, MD, MPH, Beth Israel Deaconess Medical Center, 330 Brookline Ave., Boston, MA 02215, cwee@bidmc.harvard.edu.

Funding/support: This study was funded by the National Institute of Diabetes, Digestive and Kidney Diseases (R01 DK073302, PI Wee). Dr. Wee is also supported by an NIH midcareer mentorship award (K24DK087932). The sponsor had no role in the design or conduct of the study; the collection, management, analysis, and interpretation of the data; or the preparation, review, or approval of the manuscript.

Financial disclosures: None reported.

From Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA.

Abstracts

• Objective: To identify patient factors associated with willingness to take daily weight loss medication and weight loss expectations using these medications.
• Methods: A random sample of 331 primary care patients aged 18–65 years with a BMI ≥ 35 kg/m² were recruited from 4 diverse primary care practices in Boston, MA. We conducted telephone interviews and chart reviews to assess patients’ willingness to take a weight loss medication and their expectations for weight loss. We used sequential logistic regression models to identify demographic, clinical, and quality of life (QOL) factors associated with this willingness.
• Results: Of 331 subjects, 69% were women, 35% were white, 35% were black, and 25% were Hispanic; 249 (75%) of patients were willing to take a daily weight loss medication if recommended by their doctor but required a median weight loss of 15% to 24%; only 17% of patients were willing to take a medication for ≤ 10% weight loss. Men were significantly more willing than women (1.2 [95% CI 1.0–1.4]). Diabetes was the only comorbidity associated with willingness to consider pharmacotherapy (1.2 [1.0–1.3]) but only modestly improved model performance (C-statistic increased from 0.59 to 0.60). In contrast, lower QOL, especially low self-esteem and sex life, were stronger correlates (C-statistic 0.72).
• Conclusion: A majority of obese primary care patients were willing to take a daily weight loss pill; however, most required more than 10% weight loss to consider pharmacotherapy worthwhile. Poor QOL, especially low self-esteem and poor sex life, were stronger correlates than having diabetes.

Key words: obesity; primary care; weight loss medication.

In the United States, obesity continues to be unrelentingly prevalent, affecting more than one-third of adults (34.9%) [1]. This statistic has ominous implications when considering that obesity is a risk factor for numerous chronic diseases, such as coronary heart disease, diabetes, sleep apnea, osteoarthritis, and some types of cancers [2]. Moreover, it is associated with increased risk of all-cause and cardiovascular disease mortality. Promisingly, an initial 5% to 10% weight loss over 6 months has been associated with improvement in LDL, HDL, triglycerides, glucose, hemoglobin A1C, diabetes risk, blood pressure, and medication use [2]. Therefore, although patients may not be able to achieve their ideal body weight or normal BMI, modest weight loss can still have beneficial health effects.

Weight loss medications are effective adjunctive therapies in helping patients lose up to 10% of their body weight on average when combined with diet and exercise [3–5]. There are currently 5 medications approved by the Food and Drug Administration for long-term use for weight loss: orlistat, lorcaserin, phentermine-topiramate, bupropion-naltrexone, and liraglutide. Despite their proven efficacy, there are barriers to initiating a long-term weight loss medication. Insurance reimbursement is limited for these medications, thus resulting in high out-of-pocket cost for patients that they may be unable or unwilling to pay [6]. There may also be safety concerns given that several weight loss medications, including fenfluramine, sibutramine, and rimonabant, have been withdrawn from the market because of adverse effects [7]. Thus, in deciding whether to initiate a pharmacologic weight loss regimen, patients must believe that the weight loss benefits will exceed the potential risks.

Little is known, however, about patients’ willingness to take weight loss medications or the minimum weight loss they expect to lose to make pharmacotherapy worthwhile. Only a few studies have investigated patient willingness to adopt pharmacotherapy as part of a weight loss regimen, and only one investigated obese patients in the United States [8]. In this context, we surveyed a sociodemographically diverse group of primary care patients with moderate to severe obesity to examine patient characteristics associated with willingness to pursue weight loss pharmacotherapy. We also aimed to evaluate how much weight patients expected to lose in order to make taking a daily medication worth the effort. Characterizing patients seen in primary care who are willing to adopt pharmacotherapy to lose weight may guide weight loss counseling in the primary care setting. Furthermore, determining whether patients have realistic weight loss expectations can help clinicians better counsel their patients on weight loss goals.

Methods

Study Sample

We recruited 337 subjects from 4 diverse primary care practices in Boston, Massachusetts: a large hospital-based academic practice, a community practice in a working-class suburb, a community practice in an affluent suburb, and a health center serving a predominantly socially disadvantaged population. The primary goal of the parent study was to understand the preferences of patients for weight loss treatment, especially bariatric surgery. Therefore, to be included, patients needed to have a BMI ≥ 35 kg/m² at the time of recruitment, been seen in clinic within the past year, be aged 18–65 years, and be English or Spanish speaking. By design, African-American and Hispanic patients were oversampled from an electronic list of potentially eligible patient groups so that we could examine for racial differences in treatment preferences. Study details have been previously described [9].

Data Collection and Measures

Trained interviewers conducted a 45- to 60-minute telephone interview with each participant in either English or Spanish. To assess willingness to use a daily weight loss medication, subjects were asked, “If your doctor recommended it, would you be willing to take a pill or medication every day in order to lose weight?” Those who answered affirmatively were then asked the minimum amount of weight they would have to lose to make taking a pill everyday worthwhile.

Subjects were also asked about demographic information (age, race, education, marital status) and comorbid health conditions commonly associated with obesity (diabetes mellitus, hypertension, asthma, obstructive sleep apnea, GERD, depression, anxiety, back pain, and cardiovascular problems). We assessed quality of life (QOL) using the Impact of Weight on Quality of Life-Lite (IWQOL-Lite), a 31-item instrument designed specifically to assess the impact of obesity on QOL capturing 5 domains (physical function, self-esteem, sexual life, public distress, and work). Subjects were asked to rate a series of statements beginning with “Because of my weight…” as “always true,” “usually true,” “sometimes true,” “rarely true,” or “never true.” Global and domain scores ranged from 0 to 100; higher scores reflected better QOL [10].

Data Analysis

We used descriptive statistics to characterize the proportion of subjects willing to use a daily weight loss medication and the weight loss required for patients to be willing to consider pharmacotherapy. We used a stepwise logistic model to examine demographic, QOL, and clinical factors associated with the willingness to take a weight loss medication as the outcome, with an entry criteria of P value of 0.1 and an exit criteria of 0.05. Log-Poisson distribution using the sandwich estimator was used to obtain relative risks for each significant variable. Adjusted models included age, BMI, sex, and race and any significant comorbidities. We added overall QOL score and individual QOL scores in subsequent models to examine the relative influence of overall vs. domain-specific QOL. Statistical analyses were conducted with SAS (SAS Institute, Cary, NC). We considered the change in model C-statistic when specific variables were added to the model to determine the importance of these factors in contributing to patients’ willingness to consider pharmacotherapy; larger changes in model C-statistic signifies a greater contribution.

Results

Table 2 displays sequentially adjusted models examining various demographic, clinical, and QOL factors associated with willingness to take a weight loss medication. 

Discussion

In our study, we found that a large proportion (75%) of primary care patients with at least moderate obesity were willing to take a daily weight loss medication if their doctor recommended it. After full adjustment, men, those with lower quality of life (QOL), and patients with diabetes were more likely to pursue weight loss pharmacotherapy than their counterparts. Moreover, QOL appeared more important than comorbid diagnoses in contributing to whether patients would consider taking a weight loss medication. Most patients expected to lose more than 10% of their weight to make taking a daily medication worthwhile.

Few studies have examined patients’ willingness to take a medication to lose weight. Tan et al [11] found that only about half of their surveyed outpatients were likely to take a medication to lose weight; however, approximately a quarter of the patients in that study were of normal BMI. In contrast, our study interviewed patients with at least a BMI of 35 kg/m² and the majority of these patients reported a willingness to take a weight loss medication. Nevertheless, patients appear to have unrealistic expectations of the weight loss potential of pharmacotherapy. Only a minority of patients in our study would be willing to take a weight loss medication if the weight loss was no more than 10%, a level that is more consistent with the outcomes achievable in most clinical trials of weight loss medications [12]. Prior studies have also shown that patients often have unrealistic weight loss expectations and are unable to achieve their ideal body weight using diet, exercise, or pharmacotherapy [13,14]. Doyle et al found that percentage of weight loss was the most important treatment attribute when considering weight loss pharmacotherapy when compared to cost, health improvements, side effects, diet and exercise requirements, and method of medication administration [8]. Thus it is important to educate patients on realistic goal setting and the benefits of modest weight loss when considering pharmacotherapy. The weight loss preferences expressed in our study may also influence the weight loss outcomes targets pursued in pharmaceutical development. Interestingly, after full adjustment, BMI did not correlate with willingness to take a weight loss medication. Given that all patients in our study had a BMI of ≥ 35 kg/m², this may imply that variations beyond this BMI threshold did not significantly affect a patient’s willingness to use pharmacotherapy. In contrast, weight-related QOL was an important correlate.

Men were slightly more likely than women to be willing to take a weight loss medication, which is interesting since men have been shown to be less likely to participate in behavioral weight loss programs and diets [15]. One reason may be that many weight loss programs are delivered in group settings which may deter men from participating. Whether this hypothetical willingness to undergo pharmacotherapy would translate to actual use is unclear, especially since there are barriers to pharmacotherapy including out-of-pocket costs. In a prior study in the United Kingdom, women were more likely to have reported prior weight loss medication use than men [16].

Our study did not find differences in willingness to pursue weight loss medication by race or educational attainment. This is consistent with our prior work demonstrating that racial and ethnic minorities were no less likely to consider bariatric surgery if the treatment were recommended by their doctor [9]. However, our other work did suggest that clinicians may be less likely to recommend bariatric surgery to their medically eligible minority patients as compared to their Caucasian patients. Whether this may be the case for pharmacotherapy is unclear since this was not explicitly queried in our current study [9].

Our study also found that patients with diabetes but not other comorbidities were more likely to consider weight loss medication after adjusting for QOL. This may reflect a stronger link between diabetes and obesity perceived by patients. Our result is consistent with our earlier data showing that diabetes but not other comorbid conditions was associated with a higher likelihood of considering weight loss surgery [9]. Nevertheless, having diabetes contributed only modestly to the variation in patient preferences regarding pharmacotherapy as reflected by the trivial change in model C-statistic when diabetes status was added to the model.

In contrast, lower QOL scores, especially in the domains of self-esteem and sex life, were associated with increased willingness to take a weight loss medication and appeared to be a stronger predictor than individual comorbidities. This is consistent with other studies showing that patients seeking treatment for obesity tend to have lower health-related QOL [9,17]. Our findings are also consistent with our previous research demonstrating that impairments in specific QOL domains are often more important to patients and stronger drivers of diminished well-being than measures of overall QOL [18]. Hence, given their importance to patients, clinicians need to consider QOL benefits when counseling patients about the risks and benefits of various obesity treatments.

This study is the first to our knowledge to systematically characterize demographic factors associated with the likelihood of primary care patients with obesity considering weight loss pharmacotherapy. This information may aid outpatient weight loss counseling by increasing awareness of gender and patient specific preferences. The fact that many patients with obesity appear to be interested in pursuing weight loss medication may also support public health initiatives in providing equitable access to weight loss pharmacotherapy. As our study characterizes patients who are willing to pursue weight loss medications, future studies may include retrospective analyses on actual use of weight loss medications among various demographic groups. Further investigation on specific reasons why patients choose whether or not to use weight loss medication may also be helpful.

This study has important limitations. The sample size was modest and potentially underpowered to detect small differences across different subgroups. Our sample was also limited to practices in Boston, which limits generalizability; although, by design, we oversampled racial and ethnic subjects to ensure diverse representation. Finally, our study examined patients’ hypothetical willingness to take weight loss medications rather than their actual adherence to treatment if offered.

Conclusion

In this sample of obese primary care patients, we found that the majority of patients were willing to take a daily medication to lose weight; however, patients had expectations for weight loss that far exceeded the level achievable by patients in pharmaceutical trials of these agents. Men and patients with diabetes were more likely to be willing to pursue weight loss medication; however, lower weight-related QOL, especially low self-esteem and impaired sexual function, appeared to be a stronger correlate of willingness to consider pharmacotherapy than comorbid diagnoses.

Corresponding author: Christina C. Wee, MD, MPH, Beth Israel Deaconess Medical Center, 330 Brookline Ave., Boston, MA 02215, cwee@bidmc.harvard.edu.

Funding/support: This study was funded by the National Institute of Diabetes, Digestive and Kidney Diseases (R01 DK073302, PI Wee). Dr. Wee is also supported by an NIH midcareer mentorship award (K24DK087932). The sponsor had no role in the design or conduct of the study; the collection, management, analysis, and interpretation of the data; or the preparation, review, or approval of the manuscript.

Financial disclosures: None reported.

From Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA.

Abstracts

• Objective: To identify patient factors associated with willingness to take daily weight loss medication and weight loss expectations using these medications.
• Methods: A random sample of 331 primary care patients aged 18–65 years with a BMI ≥ 35 kg/m² were recruited from 4 diverse primary care practices in Boston, MA. We conducted telephone interviews and chart reviews to assess patients’ willingness to take a weight loss medication and their expectations for weight loss. We used sequential logistic regression models to identify demographic, clinical, and quality of life (QOL) factors associated with this willingness.
• Results: Of 331 subjects, 69% were women, 35% were white, 35% were black, and 25% were Hispanic; 249 (75%) of patients were willing to take a daily weight loss medication if recommended by their doctor but required a median weight loss of 15% to 24%; only 17% of patients were willing to take a medication for ≤ 10% weight loss. Men were significantly more willing than women (1.2 [95% CI 1.0–1.4]). Diabetes was the only comorbidity associated with willingness to consider pharmacotherapy (1.2 [1.0–1.3]) but only modestly improved model performance (C-statistic increased from 0.59 to 0.60). In contrast, lower QOL, especially low self-esteem and sex life, were stronger correlates (C-statistic 0.72).
• Conclusion: A majority of obese primary care patients were willing to take a daily weight loss pill; however, most required more than 10% weight loss to consider pharmacotherapy worthwhile. Poor QOL, especially low self-esteem and poor sex life, were stronger correlates than having diabetes.

Key words: obesity; primary care; weight loss medication.

In the United States, obesity continues to be unrelentingly prevalent, affecting more than one-third of adults (34.9%) [1]. This statistic has ominous implications when considering that obesity is a risk factor for numerous chronic diseases, such as coronary heart disease, diabetes, sleep apnea, osteoarthritis, and some types of cancers [2]. Moreover, it is associated with increased risk of all-cause and cardiovascular disease mortality. Promisingly, an initial 5% to 10% weight loss over 6 months has been associated with improvement in LDL, HDL, triglycerides, glucose, hemoglobin A1C, diabetes risk, blood pressure, and medication use [2]. Therefore, although patients may not be able to achieve their ideal body weight or normal BMI, modest weight loss can still have beneficial health effects.

Weight loss medications are effective adjunctive therapies in helping patients lose up to 10% of their body weight on average when combined with diet and exercise [3–5]. There are currently 5 medications approved by the Food and Drug Administration for long-term use for weight loss: orlistat, lorcaserin, phentermine-topiramate, bupropion-naltrexone, and liraglutide. Despite their proven efficacy, there are barriers to initiating a long-term weight loss medication. Insurance reimbursement is limited for these medications, thus resulting in high out-of-pocket cost for patients that they may be unable or unwilling to pay [6]. There may also be safety concerns given that several weight loss medications, including fenfluramine, sibutramine, and rimonabant, have been withdrawn from the market because of adverse effects [7]. Thus, in deciding whether to initiate a pharmacologic weight loss regimen, patients must believe that the weight loss benefits will exceed the potential risks.

Little is known, however, about patients’ willingness to take weight loss medications or the minimum weight loss they expect to lose to make pharmacotherapy worthwhile. Only a few studies have investigated patient willingness to adopt pharmacotherapy as part of a weight loss regimen, and only one investigated obese patients in the United States [8]. In this context, we surveyed a sociodemographically diverse group of primary care patients with moderate to severe obesity to examine patient characteristics associated with willingness to pursue weight loss pharmacotherapy. We also aimed to evaluate how much weight patients expected to lose in order to make taking a daily medication worth the effort. Characterizing patients seen in primary care who are willing to adopt pharmacotherapy to lose weight may guide weight loss counseling in the primary care setting. Furthermore, determining whether patients have realistic weight loss expectations can help clinicians better counsel their patients on weight loss goals.

Methods

Study Sample

We recruited 337 subjects from 4 diverse primary care practices in Boston, Massachusetts: a large hospital-based academic practice, a community practice in a working-class suburb, a community practice in an affluent suburb, and a health center serving a predominantly socially disadvantaged population. The primary goal of the parent study was to understand the preferences of patients for weight loss treatment, especially bariatric surgery. Therefore, to be included, patients needed to have a BMI ≥ 35 kg/m² at the time of recruitment, been seen in clinic within the past year, be aged 18–65 years, and be English or Spanish speaking. By design, African-American and Hispanic patients were oversampled from an electronic list of potentially eligible patient groups so that we could examine for racial differences in treatment preferences. Study details have been previously described [9].

Data Collection and Measures

Trained interviewers conducted a 45- to 60-minute telephone interview with each participant in either English or Spanish. To assess willingness to use a daily weight loss medication, subjects were asked, “If your doctor recommended it, would you be willing to take a pill or medication every day in order to lose weight?” Those who answered affirmatively were then asked the minimum amount of weight they would have to lose to make taking a pill everyday worthwhile.

Subjects were also asked about demographic information (age, race, education, marital status) and comorbid health conditions commonly associated with obesity (diabetes mellitus, hypertension, asthma, obstructive sleep apnea, GERD, depression, anxiety, back pain, and cardiovascular problems). We assessed quality of life (QOL) using the Impact of Weight on Quality of Life-Lite (IWQOL-Lite), a 31-item instrument designed specifically to assess the impact of obesity on QOL capturing 5 domains (physical function, self-esteem, sexual life, public distress, and work). Subjects were asked to rate a series of statements beginning with “Because of my weight…” as “always true,” “usually true,” “sometimes true,” “rarely true,” or “never true.” Global and domain scores ranged from 0 to 100; higher scores reflected better QOL [10].

Data Analysis

We used descriptive statistics to characterize the proportion of subjects willing to use a daily weight loss medication and the weight loss required for patients to be willing to consider pharmacotherapy. We used a stepwise logistic model to examine demographic, QOL, and clinical factors associated with the willingness to take a weight loss medication as the outcome, with an entry criteria of P value of 0.1 and an exit criteria of 0.05. Log-Poisson distribution using the sandwich estimator was used to obtain relative risks for each significant variable. Adjusted models included age, BMI, sex, and race and any significant comorbidities. We added overall QOL score and individual QOL scores in subsequent models to examine the relative influence of overall vs. domain-specific QOL. Statistical analyses were conducted with SAS (SAS Institute, Cary, NC). We considered the change in model C-statistic when specific variables were added to the model to determine the importance of these factors in contributing to patients’ willingness to consider pharmacotherapy; larger changes in model C-statistic signifies a greater contribution.

Results

Table 2 displays sequentially adjusted models examining various demographic, clinical, and QOL factors associated with willingness to take a weight loss medication. 

Discussion

In our study, we found that a large proportion (75%) of primary care patients with at least moderate obesity were willing to take a daily weight loss medication if their doctor recommended it. After full adjustment, men, those with lower quality of life (QOL), and patients with diabetes were more likely to pursue weight loss pharmacotherapy than their counterparts. Moreover, QOL appeared more important than comorbid diagnoses in contributing to whether patients would consider taking a weight loss medication. Most patients expected to lose more than 10% of their weight to make taking a daily medication worthwhile.

Few studies have examined patients’ willingness to take a medication to lose weight. Tan et al [11] found that only about half of their surveyed outpatients were likely to take a medication to lose weight; however, approximately a quarter of the patients in that study were of normal BMI. In contrast, our study interviewed patients with at least a BMI of 35 kg/m² and the majority of these patients reported a willingness to take a weight loss medication. Nevertheless, patients appear to have unrealistic expectations of the weight loss potential of pharmacotherapy. Only a minority of patients in our study would be willing to take a weight loss medication if the weight loss was no more than 10%, a level that is more consistent with the outcomes achievable in most clinical trials of weight loss medications [12]. Prior studies have also shown that patients often have unrealistic weight loss expectations and are unable to achieve their ideal body weight using diet, exercise, or pharmacotherapy [13,14]. Doyle et al found that percentage of weight loss was the most important treatment attribute when considering weight loss pharmacotherapy when compared to cost, health improvements, side effects, diet and exercise requirements, and method of medication administration [8]. Thus it is important to educate patients on realistic goal setting and the benefits of modest weight loss when considering pharmacotherapy. The weight loss preferences expressed in our study may also influence the weight loss outcomes targets pursued in pharmaceutical development. Interestingly, after full adjustment, BMI did not correlate with willingness to take a weight loss medication. Given that all patients in our study had a BMI of ≥ 35 kg/m², this may imply that variations beyond this BMI threshold did not significantly affect a patient’s willingness to use pharmacotherapy. In contrast, weight-related QOL was an important correlate.

Men were slightly more likely than women to be willing to take a weight loss medication, which is interesting since men have been shown to be less likely to participate in behavioral weight loss programs and diets [15]. One reason may be that many weight loss programs are delivered in group settings which may deter men from participating. Whether this hypothetical willingness to undergo pharmacotherapy would translate to actual use is unclear, especially since there are barriers to pharmacotherapy including out-of-pocket costs. In a prior study in the United Kingdom, women were more likely to have reported prior weight loss medication use than men [16].

Our study did not find differences in willingness to pursue weight loss medication by race or educational attainment. This is consistent with our prior work demonstrating that racial and ethnic minorities were no less likely to consider bariatric surgery if the treatment were recommended by their doctor [9]. However, our other work did suggest that clinicians may be less likely to recommend bariatric surgery to their medically eligible minority patients as compared to their Caucasian patients. Whether this may be the case for pharmacotherapy is unclear since this was not explicitly queried in our current study [9].

Our study also found that patients with diabetes but not other comorbidities were more likely to consider weight loss medication after adjusting for QOL. This may reflect a stronger link between diabetes and obesity perceived by patients. Our result is consistent with our earlier data showing that diabetes but not other comorbid conditions was associated with a higher likelihood of considering weight loss surgery [9]. Nevertheless, having diabetes contributed only modestly to the variation in patient preferences regarding pharmacotherapy as reflected by the trivial change in model C-statistic when diabetes status was added to the model.

In contrast, lower QOL scores, especially in the domains of self-esteem and sex life, were associated with increased willingness to take a weight loss medication and appeared to be a stronger predictor than individual comorbidities. This is consistent with other studies showing that patients seeking treatment for obesity tend to have lower health-related QOL [9,17]. Our findings are also consistent with our previous research demonstrating that impairments in specific QOL domains are often more important to patients and stronger drivers of diminished well-being than measures of overall QOL [18]. Hence, given their importance to patients, clinicians need to consider QOL benefits when counseling patients about the risks and benefits of various obesity treatments.

This study is the first to our knowledge to systematically characterize demographic factors associated with the likelihood of primary care patients with obesity considering weight loss pharmacotherapy. This information may aid outpatient weight loss counseling by increasing awareness of gender and patient specific preferences. The fact that many patients with obesity appear to be interested in pursuing weight loss medication may also support public health initiatives in providing equitable access to weight loss pharmacotherapy. As our study characterizes patients who are willing to pursue weight loss medications, future studies may include retrospective analyses on actual use of weight loss medications among various demographic groups. Further investigation on specific reasons why patients choose whether or not to use weight loss medication may also be helpful.

This study has important limitations. The sample size was modest and potentially underpowered to detect small differences across different subgroups. Our sample was also limited to practices in Boston, which limits generalizability; although, by design, we oversampled racial and ethnic subjects to ensure diverse representation. Finally, our study examined patients’ hypothetical willingness to take weight loss medications rather than their actual adherence to treatment if offered.

Conclusion

In this sample of obese primary care patients, we found that the majority of patients were willing to take a daily medication to lose weight; however, patients had expectations for weight loss that far exceeded the level achievable by patients in pharmaceutical trials of these agents. Men and patients with diabetes were more likely to be willing to pursue weight loss medication; however, lower weight-related QOL, especially low self-esteem and impaired sexual function, appeared to be a stronger correlate of willingness to consider pharmacotherapy than comorbid diagnoses.

Corresponding author: Christina C. Wee, MD, MPH, Beth Israel Deaconess Medical Center, 330 Brookline Ave., Boston, MA 02215, cwee@bidmc.harvard.edu.

Funding/support: This study was funded by the National Institute of Diabetes, Digestive and Kidney Diseases (R01 DK073302, PI Wee). Dr. Wee is also supported by an NIH midcareer mentorship award (K24DK087932). The sponsor had no role in the design or conduct of the study; the collection, management, analysis, and interpretation of the data; or the preparation, review, or approval of the manuscript.

Financial disclosures: None reported.

The incidence of acne in adult females is rising,¹ and treatment with combined oral contraceptive pills (OCPs) is becoming an increasingly important therapy for women with acne. Prior reports have indicated that OCPs were as effective as systemic antibiotics in reducing inflammatory, noninflammatory, and total facial acne lesions after 6 months of treatment.^2,3 The acne management guidelines of the American Academy of Dermatology confer OCPs a grade A recommendation based on consistent and good-quality patient-oriented evidence.⁴

The US Food and Drug Administration (FDA) has approved 3 OCPs for the treatment of acne in adult women: norgestimate–ethinyl estradiol in 1997, norethindrone acetate–ethinyl estradiol in 2001, and drospirenone–ethinyl estradiol in 2007.⁵ However, the use of these OCPs is poorly understood by many dermatologists. One study showed that dermatologists prescribed OCPs in only 2% of visits with female patients aged 12 to 55 years who presented for acne treatment, which is less often than obstetrician/gynecologists (36%) and internists (11%),⁶ perhaps due to perceived risks or unfamiliarity with OCP formulations and guidelines among dermatologists.⁷ Adverse effects of OCPs include venous thromboembolism (VTE), myocardial infarction, and hypertension,⁸ but they generally are well tolerated.⁹

Even less is known about dermatologists’ use of drospirenone-containing OCPs (DCOCPs), which contain the only FDA-approved progestin that blocks androgen receptors. In prior studies, treatment with DCOCPs was associated with greater reductions in total lesion count and investigator-graded acne severity compared to early-generation OCPs.^10,11 However, DCOCPs have been associated with a greater risk for VTE (4.0–6.3 times higher than OCP nonuse; 1.0–3.3 times higher than levonorgestrel-containing OCPs),¹² which may explain the decline in DCOCP prescriptions among gynecologists in Germany from 23.8% of OCP prescriptions in 2007 to 11.4% in 2011.¹³

In this study, we surveyed US dermatologists about their knowledge, comfort, and prescribing practices pertaining to the use of OCPs. We compare OCP-prescribing to nonprescribing dermatologists, and those frequently prescribing DCOCPs to those who infrequently prescribe DCOCPs.

Methods

Survey Design

We performed a cross-sectional survey study using convenience sampling. The instrument was designed based on primary literature on OCPs in acne treatment and questionnaires assessing the use of OCPs in other specialties. Topics included prescribing practices, contraindications for OCPs defined by the Centers for Disease Control and Prevention (CDC),¹⁴ VTE risk, patient selection for hormonal acne therapy, comfort with prescribing OCP therapy, and participant demographics.

Skip logic was employed (ie, subsequent questions depended on prior answers). A pilot study surveyed 9 board-certified dermatologists at our home institution (Weill Cornell Medical College, New York, New York).

Data Collection

Eligible participants were board-certified US dermatologists. Data were collected and managed using an electronic data capture tool through the Weill Cornell Medical College Clinical & Translational Science Center. Surveys were distributed electronically to dermatologic society members, university alumni networks, investigators’ professional contacts, and dermatologists whose contact information was purchased from an email marketing company. Chain-referral sampling (ie, participants’ recruitment among their colleagues) was used. Surveys were distributed at a regional dermatology meeting. Responses were collected from November 2014 to April 2015. This study was approved by the institutional review board.

Statistical Analysis

For the descriptive data, all responses including pilot study participants were analyzed regardless of survey completion and were summarized using frequency counts and percentages (N=130).

For the analysis of OCP prescription predictors, the sample included all respondents answering the demographic questions and indicating if they prescribe OCPs (N=116). One respondent was excluded for answering other for current practice setting. Demographic predictors of OCP prescription were physician characteristics, geographic region, practice location population density, practice attributes, time spent on medical versus pediatric dermatology, number of weekly acne patients, and percentage of total patients who are female. Medical school graduation year was a categorical variable and was categorized as prior to 1997 (when norgestimate–ethinyl estradiol was FDA approved for acne⁵) versus 1997 or later. Respondents’ practice states were analyzed according to US regions—Northeast, Midwest, South, West/Pacific—and population density (persons per square mile) using US Census Bureau data.^15,16

Univariate logistic regressions modeling OCP prescribing probability were performed for each demographic variable; a multivariable logistic model was constructed including all variables significant at α=.20 from univariate modeling.

To compare frequent prescribers versus infrequent prescribers of DCOCPs, we included all respondents answering whether they frequently prescribe DCOCPs and whether they believed the risk for VTE associated with DCOCPs differed from other OCPs (n=68). A univariate logistic regression was performed to model the probability of responding “Yes, they pose a greater risk” versus any of the other 3 responses by whether or not the respondent frequently prescribed DCOCPs for acne, and an unadjusted odds ratio was obtained. All P values were 2-tailed with statistical significance evaluated at α=.05. Ninety-five percent confidence intervals were calculated to assess precision of obtained estimates. Analyses were performed using SAS software version 9.4.

Results

Demographics

Participant demographics as predictors of OCP prescription practices are described in Table 1.

Knowledge

Oral contraceptive pills were endorsed as effective in the treatment of acne in women by 95.4% (124/130) of respondents. Among prescribers of OCPs for acne, 94.2% (65/69) believed OCPs were associated with an increased risk for VTE, no respondents thought OCPs were associated with a decreased VTE risk, 2.9% (2/69) believed OCPs did not affect VTE risk, and 2.9% (2/69) were unsure.

Among prescribers of OCPs for acne, 46.4% (32/69) believed DCOCPs posed a greater VTE risk than other OCPs. Odds of this response did not differ with frequent DCOCP prescribers versus infrequent prescribers (odds ratio, 0.731 [95% confidence interval, 0.272-1.964]; P=.5342). Participant responses on VTE risk and DCOCPs are provided in Table 2.

Dermatologists prescribing OCPs for acne endorsed greater likelihood of doing so in cases of cyclical flares with menstrual cycle (94.2% [65/69]), acne unresponsive to conventional therapy (87.0% [60/69]), acne on the lower half of the face (78.3% [54/69]), diagnosis of polycystic ovary syndrome (PCOS)(76.8% [53/69]), clinical suspicion of PCOS (71.0% [49/69]), concomitant hirsutism (71.0% [49/69]), late- or adult-onset acne (66.7% [46/69]), laboratory evidence of hyperandrogenism (60.9% [42/69]), and concomitant androgenetic alopecia (49.3% [34/69]).

Among dermatologists who prescribed OCPs for acne, CDC-defined absolute contraindications identified correctly were blood pressure of 160/100 mm Hg (59.4% [41/69]) and history of migraine with focal neurologic symptoms (49.3% [34/69]). The CDC-defined relative contraindications identified correctly were history of deep vein thrombosis or pulmonary embolism (1.4% [1/69]), breast cancer history with 5 years of no disease (15.9% [11/69]), hyperlipidemia (42.0% [29/69]), and 36 years or older smoking fewer than 15 cigarettes per day (21.7% [15/69]).

Comfort

Dermatologist self-reported comfort levels in prescribing OCPs for acne are shown in Table 3.

Prescribing Practices

Among all respondents, acne medications prescribed often included oral antibiotics (76.9% [100/130]), isotretinoin (41.5% [54/130]), and spironolactone (40.8% [53/130]).

Overall, 55.4% (72/130) of respondents prescribed OCPs for the following uses: acne (95.8% [69/72]), concomitant treatment with teratogenic medication (48.6% [35/72]), PCOS (34.7% [25/72]), hirsutism (26.4% [19/72]), androgenetic alopecia (19.4% [14/72]), SAHA (seborrhea, acne, hirsutism, alopecia) syndrome (12.5% [9/72]), and HAIR-AN (hyperandrogenism, insulin resistance, acanthosis nigricans) syndrome (11.1% [8/72]). For teratogenic medications, dermatologists prescribing OCPs did so with isotretinoin (77.8% [56/72]), spironolactone (73.6% [53/72]), tetracycline antibiotics (37.5% [27/72]), and other (34.7% [25/72]).

Of dermatologists prescribing OCPs for acne, frequency included often (19% [13/69]), sometimes (45% [31/69]), and rarely (36% [25/69]). The most frequently prescribed OCPs included Ortho Tri-Cyclen (Janssen Pharmaceuticals, Inc)(80% [55/69]), Yaz (Bayer)(64% [44/69]), and Estrostep (Warner Chilcott)(19% [13/69]). Fill-in responses included Desogen (Merck & Co, Inc)(3/69 [4%]), Alesse (Wyeth Pharmaceuticals, Inc)(3/69 [4%]), Lutera (Watson Pharma, Inc)(1/69 [1%]), Loestrin (Warner Chilcott)(1/69 [1%]), and Yasmin (Bayer)(1/69 [1%]).

In univariate regressions, graduation from medical school in 1997 or later (P=.0416), academic practice setting (P=.0130), and low-density practice setting (P=.0034) were significant predictors of prescribing OCPs. In multivariable regression, only academic practice setting (P=.0295) and low-density practice setting (P=.0050) remained significant predictors. Demographic predictors are summarized in Table 1.

Comment

Our results suggest that most dermatologists (95.4%) believe OCPs effectively treat acne; however, only 54% of respondents reported prescribing them. Academic dermatologists were more likely to prescribe OCPs than nonacademic dermatologists, possibly indicating that academic dermatologists are more familiar with the literature on the efficacy and use of OCPs. Nearly half of respondents seeing 25 or more acne patients weekly did not prescribe OCPs, suggesting a notable practice gap. Dermatologists in less dense US regions were more likely to prescribe OCPs, perhaps because dermatologists may be more likely to prescribe OCPs than refer patients in health care access–limited areas, just as primary care providers treat a broader range of conditions in low-density rural areas than urban ones.¹⁷ Exploring all dermatologists’ referral patterns for OCPs is warranted.

A strong knowledge area revealed from this study was hormonal treatment of acne in women, a vital area because appropriate patient selection is key to treatment success.⁸ Weaker knowledge areas included OCP contraindications and differences in VTE risk between formulations containing drospirenone and those not containing drospirenone. Only half the sample identified CDC-defined absolute contraindications, suggesting an education target for dermatologists to ensure patient safety. In contrast, respondents were conservative about relative contraindications, with most identifying deep vein thrombosis or pulmonary embolism, remote breast cancer history, and light smoking at 36 years or older as absolute contraindications. These results could reflect weighing the risk of relative contraindications against the benefit in acne, resulting in appropriately more conservative management than overall guidelines suggest. If so, it may suggest that dermatologists are adapting overall guidelines appropriately for use of OCPs in skin conditions.

Nearly all respondents knew that OCPs are associated with an increased risk for VTE. Approximately half understood that DCOCPs are associated with a greater VTE risk than other OCPs, with no difference between frequent and infrequent prescribers. Comparing these results to the findings on OCP prescribing overall, some dermatologists’ risk-benefit calculation for VTE differs from other specialties because DCOCPs have superior efficacy in acne, whereas DCOCPs have similar contraceptive efficacy to other OCPs.¹⁸ The fact that more dermatologists believed VTE to be an absolute contraindication than hypertension suggests dermatologists have a heightened awareness of VTE risk but prescribe DCOCPs for acne despite it.

Most OCP prescribers felt very comfortable selecting good candidates for OCPs (55.5%) and counseling on treatment initiation (45.8%) and side effects (48.6%). Only 22.2%, by contrast, were very comfortable managing side effects. This finding likely reflects the notion that VTEs are not most appropriately managed by a dermatologist. Exploring if a greater comfort level in managing side effects would make dermatologists more likely to prescribe OCPs is worthwhile. Additionally, exploring why many dermatologists do not prescribe OCPs despite believing they are effective for acne is warranted.

Study limitations included the use of convenience sampling. Additionally, our study did not investigate dermatologists’ reasons for not prescribing OCPs.

Conclusion

This study demonstrates that dermatologists believe OCPs effectively treat acne in women and that most dermatologists prescribing OCPs do so for acne treatment. Academic practice setting was associated with higher odds of prescribing OCPs than a nonacademic setting, but the number of weekly acne patients did not impact the likelihood of prescribing OCPs, which suggests a treatment gap warranting education efforts for dermatologists in nonacademic settings seeing many acne patients. Our study also suggests that awareness of the increased risk for VTE associated with DCOCPs is not associated with lower likelihood of prescribing DCOCPs, suggesting dermatologists may find greater treatment efficacy to be worth the higher risk.

Acknowledgments

We are grateful to the Department of Dermatology at the Weill Cornell College of Medicine (New York, New York) for providing funding to complete this study. We also acknowledge Paul Christos, DrPH, MS (New York, New York), and Xuming Sun, MS (New York, New York), for their assistance with the survey design. We also are indebted to numerous dermatologic professional societies for allowing the survey to be distributed to their membership.

The incidence of acne in adult females is rising,¹ and treatment with combined oral contraceptive pills (OCPs) is becoming an increasingly important therapy for women with acne. Prior reports have indicated that OCPs were as effective as systemic antibiotics in reducing inflammatory, noninflammatory, and total facial acne lesions after 6 months of treatment.^2,3 The acne management guidelines of the American Academy of Dermatology confer OCPs a grade A recommendation based on consistent and good-quality patient-oriented evidence.⁴

The US Food and Drug Administration (FDA) has approved 3 OCPs for the treatment of acne in adult women: norgestimate–ethinyl estradiol in 1997, norethindrone acetate–ethinyl estradiol in 2001, and drospirenone–ethinyl estradiol in 2007.⁵ However, the use of these OCPs is poorly understood by many dermatologists. One study showed that dermatologists prescribed OCPs in only 2% of visits with female patients aged 12 to 55 years who presented for acne treatment, which is less often than obstetrician/gynecologists (36%) and internists (11%),⁶ perhaps due to perceived risks or unfamiliarity with OCP formulations and guidelines among dermatologists.⁷ Adverse effects of OCPs include venous thromboembolism (VTE), myocardial infarction, and hypertension,⁸ but they generally are well tolerated.⁹

Even less is known about dermatologists’ use of drospirenone-containing OCPs (DCOCPs), which contain the only FDA-approved progestin that blocks androgen receptors. In prior studies, treatment with DCOCPs was associated with greater reductions in total lesion count and investigator-graded acne severity compared to early-generation OCPs.^10,11 However, DCOCPs have been associated with a greater risk for VTE (4.0–6.3 times higher than OCP nonuse; 1.0–3.3 times higher than levonorgestrel-containing OCPs),¹² which may explain the decline in DCOCP prescriptions among gynecologists in Germany from 23.8% of OCP prescriptions in 2007 to 11.4% in 2011.¹³

In this study, we surveyed US dermatologists about their knowledge, comfort, and prescribing practices pertaining to the use of OCPs. We compare OCP-prescribing to nonprescribing dermatologists, and those frequently prescribing DCOCPs to those who infrequently prescribe DCOCPs.

Methods

Survey Design

We performed a cross-sectional survey study using convenience sampling. The instrument was designed based on primary literature on OCPs in acne treatment and questionnaires assessing the use of OCPs in other specialties. Topics included prescribing practices, contraindications for OCPs defined by the Centers for Disease Control and Prevention (CDC),¹⁴ VTE risk, patient selection for hormonal acne therapy, comfort with prescribing OCP therapy, and participant demographics.

Skip logic was employed (ie, subsequent questions depended on prior answers). A pilot study surveyed 9 board-certified dermatologists at our home institution (Weill Cornell Medical College, New York, New York).

Data Collection

Eligible participants were board-certified US dermatologists. Data were collected and managed using an electronic data capture tool through the Weill Cornell Medical College Clinical & Translational Science Center. Surveys were distributed electronically to dermatologic society members, university alumni networks, investigators’ professional contacts, and dermatologists whose contact information was purchased from an email marketing company. Chain-referral sampling (ie, participants’ recruitment among their colleagues) was used. Surveys were distributed at a regional dermatology meeting. Responses were collected from November 2014 to April 2015. This study was approved by the institutional review board.

Statistical Analysis

For the descriptive data, all responses including pilot study participants were analyzed regardless of survey completion and were summarized using frequency counts and percentages (N=130).

For the analysis of OCP prescription predictors, the sample included all respondents answering the demographic questions and indicating if they prescribe OCPs (N=116). One respondent was excluded for answering other for current practice setting. Demographic predictors of OCP prescription were physician characteristics, geographic region, practice location population density, practice attributes, time spent on medical versus pediatric dermatology, number of weekly acne patients, and percentage of total patients who are female. Medical school graduation year was a categorical variable and was categorized as prior to 1997 (when norgestimate–ethinyl estradiol was FDA approved for acne⁵) versus 1997 or later. Respondents’ practice states were analyzed according to US regions—Northeast, Midwest, South, West/Pacific—and population density (persons per square mile) using US Census Bureau data.^15,16

Univariate logistic regressions modeling OCP prescribing probability were performed for each demographic variable; a multivariable logistic model was constructed including all variables significant at α=.20 from univariate modeling.

To compare frequent prescribers versus infrequent prescribers of DCOCPs, we included all respondents answering whether they frequently prescribe DCOCPs and whether they believed the risk for VTE associated with DCOCPs differed from other OCPs (n=68). A univariate logistic regression was performed to model the probability of responding “Yes, they pose a greater risk” versus any of the other 3 responses by whether or not the respondent frequently prescribed DCOCPs for acne, and an unadjusted odds ratio was obtained. All P values were 2-tailed with statistical significance evaluated at α=.05. Ninety-five percent confidence intervals were calculated to assess precision of obtained estimates. Analyses were performed using SAS software version 9.4.

Results

Demographics

Participant demographics as predictors of OCP prescription practices are described in Table 1.

Knowledge

Oral contraceptive pills were endorsed as effective in the treatment of acne in women by 95.4% (124/130) of respondents. Among prescribers of OCPs for acne, 94.2% (65/69) believed OCPs were associated with an increased risk for VTE, no respondents thought OCPs were associated with a decreased VTE risk, 2.9% (2/69) believed OCPs did not affect VTE risk, and 2.9% (2/69) were unsure.

Among prescribers of OCPs for acne, 46.4% (32/69) believed DCOCPs posed a greater VTE risk than other OCPs. Odds of this response did not differ with frequent DCOCP prescribers versus infrequent prescribers (odds ratio, 0.731 [95% confidence interval, 0.272-1.964]; P=.5342). Participant responses on VTE risk and DCOCPs are provided in Table 2.

Dermatologists prescribing OCPs for acne endorsed greater likelihood of doing so in cases of cyclical flares with menstrual cycle (94.2% [65/69]), acne unresponsive to conventional therapy (87.0% [60/69]), acne on the lower half of the face (78.3% [54/69]), diagnosis of polycystic ovary syndrome (PCOS)(76.8% [53/69]), clinical suspicion of PCOS (71.0% [49/69]), concomitant hirsutism (71.0% [49/69]), late- or adult-onset acne (66.7% [46/69]), laboratory evidence of hyperandrogenism (60.9% [42/69]), and concomitant androgenetic alopecia (49.3% [34/69]).

Among dermatologists who prescribed OCPs for acne, CDC-defined absolute contraindications identified correctly were blood pressure of 160/100 mm Hg (59.4% [41/69]) and history of migraine with focal neurologic symptoms (49.3% [34/69]). The CDC-defined relative contraindications identified correctly were history of deep vein thrombosis or pulmonary embolism (1.4% [1/69]), breast cancer history with 5 years of no disease (15.9% [11/69]), hyperlipidemia (42.0% [29/69]), and 36 years or older smoking fewer than 15 cigarettes per day (21.7% [15/69]).

Comfort

Dermatologist self-reported comfort levels in prescribing OCPs for acne are shown in Table 3.

Prescribing Practices

Among all respondents, acne medications prescribed often included oral antibiotics (76.9% [100/130]), isotretinoin (41.5% [54/130]), and spironolactone (40.8% [53/130]).

Overall, 55.4% (72/130) of respondents prescribed OCPs for the following uses: acne (95.8% [69/72]), concomitant treatment with teratogenic medication (48.6% [35/72]), PCOS (34.7% [25/72]), hirsutism (26.4% [19/72]), androgenetic alopecia (19.4% [14/72]), SAHA (seborrhea, acne, hirsutism, alopecia) syndrome (12.5% [9/72]), and HAIR-AN (hyperandrogenism, insulin resistance, acanthosis nigricans) syndrome (11.1% [8/72]). For teratogenic medications, dermatologists prescribing OCPs did so with isotretinoin (77.8% [56/72]), spironolactone (73.6% [53/72]), tetracycline antibiotics (37.5% [27/72]), and other (34.7% [25/72]).

Of dermatologists prescribing OCPs for acne, frequency included often (19% [13/69]), sometimes (45% [31/69]), and rarely (36% [25/69]). The most frequently prescribed OCPs included Ortho Tri-Cyclen (Janssen Pharmaceuticals, Inc)(80% [55/69]), Yaz (Bayer)(64% [44/69]), and Estrostep (Warner Chilcott)(19% [13/69]). Fill-in responses included Desogen (Merck & Co, Inc)(3/69 [4%]), Alesse (Wyeth Pharmaceuticals, Inc)(3/69 [4%]), Lutera (Watson Pharma, Inc)(1/69 [1%]), Loestrin (Warner Chilcott)(1/69 [1%]), and Yasmin (Bayer)(1/69 [1%]).

In univariate regressions, graduation from medical school in 1997 or later (P=.0416), academic practice setting (P=.0130), and low-density practice setting (P=.0034) were significant predictors of prescribing OCPs. In multivariable regression, only academic practice setting (P=.0295) and low-density practice setting (P=.0050) remained significant predictors. Demographic predictors are summarized in Table 1.

Comment

Our results suggest that most dermatologists (95.4%) believe OCPs effectively treat acne; however, only 54% of respondents reported prescribing them. Academic dermatologists were more likely to prescribe OCPs than nonacademic dermatologists, possibly indicating that academic dermatologists are more familiar with the literature on the efficacy and use of OCPs. Nearly half of respondents seeing 25 or more acne patients weekly did not prescribe OCPs, suggesting a notable practice gap. Dermatologists in less dense US regions were more likely to prescribe OCPs, perhaps because dermatologists may be more likely to prescribe OCPs than refer patients in health care access–limited areas, just as primary care providers treat a broader range of conditions in low-density rural areas than urban ones.¹⁷ Exploring all dermatologists’ referral patterns for OCPs is warranted.

A strong knowledge area revealed from this study was hormonal treatment of acne in women, a vital area because appropriate patient selection is key to treatment success.⁸ Weaker knowledge areas included OCP contraindications and differences in VTE risk between formulations containing drospirenone and those not containing drospirenone. Only half the sample identified CDC-defined absolute contraindications, suggesting an education target for dermatologists to ensure patient safety. In contrast, respondents were conservative about relative contraindications, with most identifying deep vein thrombosis or pulmonary embolism, remote breast cancer history, and light smoking at 36 years or older as absolute contraindications. These results could reflect weighing the risk of relative contraindications against the benefit in acne, resulting in appropriately more conservative management than overall guidelines suggest. If so, it may suggest that dermatologists are adapting overall guidelines appropriately for use of OCPs in skin conditions.

Nearly all respondents knew that OCPs are associated with an increased risk for VTE. Approximately half understood that DCOCPs are associated with a greater VTE risk than other OCPs, with no difference between frequent and infrequent prescribers. Comparing these results to the findings on OCP prescribing overall, some dermatologists’ risk-benefit calculation for VTE differs from other specialties because DCOCPs have superior efficacy in acne, whereas DCOCPs have similar contraceptive efficacy to other OCPs.¹⁸ The fact that more dermatologists believed VTE to be an absolute contraindication than hypertension suggests dermatologists have a heightened awareness of VTE risk but prescribe DCOCPs for acne despite it.

Most OCP prescribers felt very comfortable selecting good candidates for OCPs (55.5%) and counseling on treatment initiation (45.8%) and side effects (48.6%). Only 22.2%, by contrast, were very comfortable managing side effects. This finding likely reflects the notion that VTEs are not most appropriately managed by a dermatologist. Exploring if a greater comfort level in managing side effects would make dermatologists more likely to prescribe OCPs is worthwhile. Additionally, exploring why many dermatologists do not prescribe OCPs despite believing they are effective for acne is warranted.

Study limitations included the use of convenience sampling. Additionally, our study did not investigate dermatologists’ reasons for not prescribing OCPs.

Conclusion

This study demonstrates that dermatologists believe OCPs effectively treat acne in women and that most dermatologists prescribing OCPs do so for acne treatment. Academic practice setting was associated with higher odds of prescribing OCPs than a nonacademic setting, but the number of weekly acne patients did not impact the likelihood of prescribing OCPs, which suggests a treatment gap warranting education efforts for dermatologists in nonacademic settings seeing many acne patients. Our study also suggests that awareness of the increased risk for VTE associated with DCOCPs is not associated with lower likelihood of prescribing DCOCPs, suggesting dermatologists may find greater treatment efficacy to be worth the higher risk.

Acknowledgments

We are grateful to the Department of Dermatology at the Weill Cornell College of Medicine (New York, New York) for providing funding to complete this study. We also acknowledge Paul Christos, DrPH, MS (New York, New York), and Xuming Sun, MS (New York, New York), for their assistance with the survey design. We also are indebted to numerous dermatologic professional societies for allowing the survey to be distributed to their membership.

The incidence of acne in adult females is rising,¹ and treatment with combined oral contraceptive pills (OCPs) is becoming an increasingly important therapy for women with acne. Prior reports have indicated that OCPs were as effective as systemic antibiotics in reducing inflammatory, noninflammatory, and total facial acne lesions after 6 months of treatment.^2,3 The acne management guidelines of the American Academy of Dermatology confer OCPs a grade A recommendation based on consistent and good-quality patient-oriented evidence.⁴

The US Food and Drug Administration (FDA) has approved 3 OCPs for the treatment of acne in adult women: norgestimate–ethinyl estradiol in 1997, norethindrone acetate–ethinyl estradiol in 2001, and drospirenone–ethinyl estradiol in 2007.⁵ However, the use of these OCPs is poorly understood by many dermatologists. One study showed that dermatologists prescribed OCPs in only 2% of visits with female patients aged 12 to 55 years who presented for acne treatment, which is less often than obstetrician/gynecologists (36%) and internists (11%),⁶ perhaps due to perceived risks or unfamiliarity with OCP formulations and guidelines among dermatologists.⁷ Adverse effects of OCPs include venous thromboembolism (VTE), myocardial infarction, and hypertension,⁸ but they generally are well tolerated.⁹

Even less is known about dermatologists’ use of drospirenone-containing OCPs (DCOCPs), which contain the only FDA-approved progestin that blocks androgen receptors. In prior studies, treatment with DCOCPs was associated with greater reductions in total lesion count and investigator-graded acne severity compared to early-generation OCPs.^10,11 However, DCOCPs have been associated with a greater risk for VTE (4.0–6.3 times higher than OCP nonuse; 1.0–3.3 times higher than levonorgestrel-containing OCPs),¹² which may explain the decline in DCOCP prescriptions among gynecologists in Germany from 23.8% of OCP prescriptions in 2007 to 11.4% in 2011.¹³

In this study, we surveyed US dermatologists about their knowledge, comfort, and prescribing practices pertaining to the use of OCPs. We compare OCP-prescribing to nonprescribing dermatologists, and those frequently prescribing DCOCPs to those who infrequently prescribe DCOCPs.

Methods

Survey Design

We performed a cross-sectional survey study using convenience sampling. The instrument was designed based on primary literature on OCPs in acne treatment and questionnaires assessing the use of OCPs in other specialties. Topics included prescribing practices, contraindications for OCPs defined by the Centers for Disease Control and Prevention (CDC),¹⁴ VTE risk, patient selection for hormonal acne therapy, comfort with prescribing OCP therapy, and participant demographics.

Skip logic was employed (ie, subsequent questions depended on prior answers). A pilot study surveyed 9 board-certified dermatologists at our home institution (Weill Cornell Medical College, New York, New York).

Data Collection

Eligible participants were board-certified US dermatologists. Data were collected and managed using an electronic data capture tool through the Weill Cornell Medical College Clinical & Translational Science Center. Surveys were distributed electronically to dermatologic society members, university alumni networks, investigators’ professional contacts, and dermatologists whose contact information was purchased from an email marketing company. Chain-referral sampling (ie, participants’ recruitment among their colleagues) was used. Surveys were distributed at a regional dermatology meeting. Responses were collected from November 2014 to April 2015. This study was approved by the institutional review board.

Statistical Analysis

For the descriptive data, all responses including pilot study participants were analyzed regardless of survey completion and were summarized using frequency counts and percentages (N=130).

For the analysis of OCP prescription predictors, the sample included all respondents answering the demographic questions and indicating if they prescribe OCPs (N=116). One respondent was excluded for answering other for current practice setting. Demographic predictors of OCP prescription were physician characteristics, geographic region, practice location population density, practice attributes, time spent on medical versus pediatric dermatology, number of weekly acne patients, and percentage of total patients who are female. Medical school graduation year was a categorical variable and was categorized as prior to 1997 (when norgestimate–ethinyl estradiol was FDA approved for acne⁵) versus 1997 or later. Respondents’ practice states were analyzed according to US regions—Northeast, Midwest, South, West/Pacific—and population density (persons per square mile) using US Census Bureau data.^15,16

Univariate logistic regressions modeling OCP prescribing probability were performed for each demographic variable; a multivariable logistic model was constructed including all variables significant at α=.20 from univariate modeling.

To compare frequent prescribers versus infrequent prescribers of DCOCPs, we included all respondents answering whether they frequently prescribe DCOCPs and whether they believed the risk for VTE associated with DCOCPs differed from other OCPs (n=68). A univariate logistic regression was performed to model the probability of responding “Yes, they pose a greater risk” versus any of the other 3 responses by whether or not the respondent frequently prescribed DCOCPs for acne, and an unadjusted odds ratio was obtained. All P values were 2-tailed with statistical significance evaluated at α=.05. Ninety-five percent confidence intervals were calculated to assess precision of obtained estimates. Analyses were performed using SAS software version 9.4.

Results

Demographics

Participant demographics as predictors of OCP prescription practices are described in Table 1.

Knowledge

Oral contraceptive pills were endorsed as effective in the treatment of acne in women by 95.4% (124/130) of respondents. Among prescribers of OCPs for acne, 94.2% (65/69) believed OCPs were associated with an increased risk for VTE, no respondents thought OCPs were associated with a decreased VTE risk, 2.9% (2/69) believed OCPs did not affect VTE risk, and 2.9% (2/69) were unsure.

Among prescribers of OCPs for acne, 46.4% (32/69) believed DCOCPs posed a greater VTE risk than other OCPs. Odds of this response did not differ with frequent DCOCP prescribers versus infrequent prescribers (odds ratio, 0.731 [95% confidence interval, 0.272-1.964]; P=.5342). Participant responses on VTE risk and DCOCPs are provided in Table 2.

Dermatologists prescribing OCPs for acne endorsed greater likelihood of doing so in cases of cyclical flares with menstrual cycle (94.2% [65/69]), acne unresponsive to conventional therapy (87.0% [60/69]), acne on the lower half of the face (78.3% [54/69]), diagnosis of polycystic ovary syndrome (PCOS)(76.8% [53/69]), clinical suspicion of PCOS (71.0% [49/69]), concomitant hirsutism (71.0% [49/69]), late- or adult-onset acne (66.7% [46/69]), laboratory evidence of hyperandrogenism (60.9% [42/69]), and concomitant androgenetic alopecia (49.3% [34/69]).

Among dermatologists who prescribed OCPs for acne, CDC-defined absolute contraindications identified correctly were blood pressure of 160/100 mm Hg (59.4% [41/69]) and history of migraine with focal neurologic symptoms (49.3% [34/69]). The CDC-defined relative contraindications identified correctly were history of deep vein thrombosis or pulmonary embolism (1.4% [1/69]), breast cancer history with 5 years of no disease (15.9% [11/69]), hyperlipidemia (42.0% [29/69]), and 36 years or older smoking fewer than 15 cigarettes per day (21.7% [15/69]).

Comfort

Dermatologist self-reported comfort levels in prescribing OCPs for acne are shown in Table 3.

Prescribing Practices

Among all respondents, acne medications prescribed often included oral antibiotics (76.9% [100/130]), isotretinoin (41.5% [54/130]), and spironolactone (40.8% [53/130]).

Overall, 55.4% (72/130) of respondents prescribed OCPs for the following uses: acne (95.8% [69/72]), concomitant treatment with teratogenic medication (48.6% [35/72]), PCOS (34.7% [25/72]), hirsutism (26.4% [19/72]), androgenetic alopecia (19.4% [14/72]), SAHA (seborrhea, acne, hirsutism, alopecia) syndrome (12.5% [9/72]), and HAIR-AN (hyperandrogenism, insulin resistance, acanthosis nigricans) syndrome (11.1% [8/72]). For teratogenic medications, dermatologists prescribing OCPs did so with isotretinoin (77.8% [56/72]), spironolactone (73.6% [53/72]), tetracycline antibiotics (37.5% [27/72]), and other (34.7% [25/72]).

Of dermatologists prescribing OCPs for acne, frequency included often (19% [13/69]), sometimes (45% [31/69]), and rarely (36% [25/69]). The most frequently prescribed OCPs included Ortho Tri-Cyclen (Janssen Pharmaceuticals, Inc)(80% [55/69]), Yaz (Bayer)(64% [44/69]), and Estrostep (Warner Chilcott)(19% [13/69]). Fill-in responses included Desogen (Merck & Co, Inc)(3/69 [4%]), Alesse (Wyeth Pharmaceuticals, Inc)(3/69 [4%]), Lutera (Watson Pharma, Inc)(1/69 [1%]), Loestrin (Warner Chilcott)(1/69 [1%]), and Yasmin (Bayer)(1/69 [1%]).

In univariate regressions, graduation from medical school in 1997 or later (P=.0416), academic practice setting (P=.0130), and low-density practice setting (P=.0034) were significant predictors of prescribing OCPs. In multivariable regression, only academic practice setting (P=.0295) and low-density practice setting (P=.0050) remained significant predictors. Demographic predictors are summarized in Table 1.

Comment

Our results suggest that most dermatologists (95.4%) believe OCPs effectively treat acne; however, only 54% of respondents reported prescribing them. Academic dermatologists were more likely to prescribe OCPs than nonacademic dermatologists, possibly indicating that academic dermatologists are more familiar with the literature on the efficacy and use of OCPs. Nearly half of respondents seeing 25 or more acne patients weekly did not prescribe OCPs, suggesting a notable practice gap. Dermatologists in less dense US regions were more likely to prescribe OCPs, perhaps because dermatologists may be more likely to prescribe OCPs than refer patients in health care access–limited areas, just as primary care providers treat a broader range of conditions in low-density rural areas than urban ones.¹⁷ Exploring all dermatologists’ referral patterns for OCPs is warranted.

A strong knowledge area revealed from this study was hormonal treatment of acne in women, a vital area because appropriate patient selection is key to treatment success.⁸ Weaker knowledge areas included OCP contraindications and differences in VTE risk between formulations containing drospirenone and those not containing drospirenone. Only half the sample identified CDC-defined absolute contraindications, suggesting an education target for dermatologists to ensure patient safety. In contrast, respondents were conservative about relative contraindications, with most identifying deep vein thrombosis or pulmonary embolism, remote breast cancer history, and light smoking at 36 years or older as absolute contraindications. These results could reflect weighing the risk of relative contraindications against the benefit in acne, resulting in appropriately more conservative management than overall guidelines suggest. If so, it may suggest that dermatologists are adapting overall guidelines appropriately for use of OCPs in skin conditions.

Nearly all respondents knew that OCPs are associated with an increased risk for VTE. Approximately half understood that DCOCPs are associated with a greater VTE risk than other OCPs, with no difference between frequent and infrequent prescribers. Comparing these results to the findings on OCP prescribing overall, some dermatologists’ risk-benefit calculation for VTE differs from other specialties because DCOCPs have superior efficacy in acne, whereas DCOCPs have similar contraceptive efficacy to other OCPs.¹⁸ The fact that more dermatologists believed VTE to be an absolute contraindication than hypertension suggests dermatologists have a heightened awareness of VTE risk but prescribe DCOCPs for acne despite it.

Most OCP prescribers felt very comfortable selecting good candidates for OCPs (55.5%) and counseling on treatment initiation (45.8%) and side effects (48.6%). Only 22.2%, by contrast, were very comfortable managing side effects. This finding likely reflects the notion that VTEs are not most appropriately managed by a dermatologist. Exploring if a greater comfort level in managing side effects would make dermatologists more likely to prescribe OCPs is worthwhile. Additionally, exploring why many dermatologists do not prescribe OCPs despite believing they are effective for acne is warranted.

Study limitations included the use of convenience sampling. Additionally, our study did not investigate dermatologists’ reasons for not prescribing OCPs.

Conclusion

This study demonstrates that dermatologists believe OCPs effectively treat acne in women and that most dermatologists prescribing OCPs do so for acne treatment. Academic practice setting was associated with higher odds of prescribing OCPs than a nonacademic setting, but the number of weekly acne patients did not impact the likelihood of prescribing OCPs, which suggests a treatment gap warranting education efforts for dermatologists in nonacademic settings seeing many acne patients. Our study also suggests that awareness of the increased risk for VTE associated with DCOCPs is not associated with lower likelihood of prescribing DCOCPs, suggesting dermatologists may find greater treatment efficacy to be worth the higher risk.

Acknowledgments

We are grateful to the Department of Dermatology at the Weill Cornell College of Medicine (New York, New York) for providing funding to complete this study. We also acknowledge Paul Christos, DrPH, MS (New York, New York), and Xuming Sun, MS (New York, New York), for their assistance with the survey design. We also are indebted to numerous dermatologic professional societies for allowing the survey to be distributed to their membership.

The care of hospitalized patients requires practitioners from multiple disciplines to assess and communicate the patient’s status in a dynamic manner during hospitalization. Although optimal teamwork is needed for patient care to be delivered reliably and efficiently, care within hospitals is typically delivered in a fragmented manner.¹ A bedside model for daily interdisciplinary rounds (IDR) has been proposed as a method to provide a structured process and engage all team members in a patient-centered, system-of-care delivery.² Specific advantages of convening rounds in the presence of the patient include the ability to directly assess care (eg, presence of a potentially unnecessary urinary catheter), patient engagement in key aspects of their care and disposition, and an increased opportunity for team members to develop a shared understanding of the patient’s views and needs.

Implementing dramatic changes to the workflow of multiple disciplines will require rigorous evidence to support a concerted effort from leadership and buy-in from stakeholders at the front line of patient care. Despite the urgency for evidence, there has been little investigation of this strategy. A systematic review³ identified 30 studies published between 1998 and 2013 addressing interdisciplinary interventions on medical wards, none of which examined a bedside IDR model. In a study performed after the period assessed by the systematic review, Stein et al⁴ described the restructuring of a medical ward as an accountable care unit (ACU), which included a bedside model for rounds by the interdisciplinary team. The change was associated with decreased mortality and length of stay (LOS), although the study did not isolate the impact of rounds or use a concurrent control group and presented aggregate rather than patient-level outcomes. The lack of convincing data may be a reason bedside rounds are not widely employed by hospitals. To provide high-quality evidence, we performed a large, prospective controlled trial comparing a structured bedside model (mobile interdisciplinary care rounds [MICRO]) with standard rounds.

This study took place at the Mount Sinai Hospital, which is a 1171-bed tertiary care academic medical center in New York City, New York. A nonteaching unit offered the ability to use a prospective controlled design. Patients were assigned to the north and the south wings of the unit in a quasi-randomized manner, rather than based on diagnosis or acuity. We transformed IDR to a bedside model on the north side of the unit (MICRO group), while the south side of the unit continued using standard conference room-based IDR (control group). The north and south sides of the unit contain 17 and 14 beds, respectively. During the study period, nurses and hospitalists cared for patients on both sides of the study unit, although on any given day were assigned only patients on 1 side of the unit. The unit uses a clinical microsystem model, which has been defined as “a group of clinicians and staff working together with a shared clinical purpose to provide care for a population of patients,” and has a defined set of characteristics associated with high performance.^5,6 Our microsystem model has incorporated features as described by Stein’s ACU model,⁴ including co-leadership by a hospitalist and a nurse manager, geographic assignment of patients to teams, and unit-level data reports. One hospitalist is assigned geographically to each area of the unit in a 2- to 4-week rotation. Coverage of the unit does not include house staff; patients are primarily assigned to hospitalists working with nurse practitioners. Patients were enrolled prospectively during their initial IDR by a research coordinator. Patient-level data and outcomes were collected prospectively by a research coordinator who attended IDR on the intervention and the control sides of the study unit daily.

Inclusion Criteria

All patients admitted to the medicine service on the study unit were eligible. Patients were greater than 18 years and admitted for an acute medical condition. Patients admitted to another unit and later transferred to the study unit were enrolled at the time of transfer. Patients could be included more than once if hospitalized on the study unit on more than 1 occasion. Most patients were covered by hospitalists, although patients covered by private physicians were included. Patients from other departments, including family medicine, are uncommonly admitted to the unit and were excluded. Patients were also excluded if they were admitted and discharged over the same weekend, because the MICRO rounds occur during weekdays and there was no opportunity to offer the intervention on Saturdays and Sundays.

MICRO Intervention

Interdisciplinary rounds occurred daily at 10:00 am for the control group and at 10:30 am for the MICRO group, and were attended by the hospitalist caring for the majority of patients on the unit, staff nurses, and the unit medical director, nurse manager, social worker, and case manager. Rounds on the control unit focused on the plan of care and disposition but did not follow any set structure and were typically 25 to 30 minutes in duration.

The MICRO rounds occurred at the bedside and followed a structured script (Appendix 1) that was designed to limit discussion of each patient to 3 minutes or less, and included speaking roles for the hospitalist, nurse, and social worker. For private physicians, the nurse practitioner assigned to the patient performed the role of the hospitalist. Rounds were expected to be approximately 50 minutes in duration. Patients were further engaged by asking for their main goal for the day. A patient safety checklist was reviewed. Initially, this task was performed by the nurse manager, who did not verbalize the items unless a deficiency was noted. After 6 months’ experience, this responsibility was given to the staff nurse, who reviewed the checklist verbally as part of the bedside script. Patients were seen daily, including those being discharged later that same day.

Staff and Clinician Education

We developed and implemented a curriculum based on a modified version of the Agency for Healthcare Research and Quality’s TeamStepps^® program to ensure that all team members were provided with the basic principles of communication within the healthcare setting. The curriculum consisted of interactive didactics on essential elements of teamwork, including team structure, communication, situation monitoring, and mutual support, as well as the purpose and structure of the MICRO model. The curriculum was delivered to nurses at 3 monthly staff meetings on the study unit and to hospitalists during 3 hospital medicine grand rounds over a 3-month period. Nurses and physicians providing care on both geographic areas of the study unit received the education program because no group of practitioners was designated to only 1 geographic area.

Outcomes

Primary and Secondary Outcomes

The primary outcomes were clinical deterioration (CD) and length of stay. Clinical deterioration was a composite outcome defined a priori as death; escalation of care (ie, transfer to an intensive care unit, intermediate care unit, or teaching unit); or a hospital-acquired complication (ie, venous thromboembolism, fall, stage III-IV pressure ulcer, catheter-associated urinary tract infection, central-line associated bloodstream infection, or Clostridium difficile-associated diarrhea). The LOS was calculated as the mean LOS with outliers excluded (outliers defined as having a LOS 100 days or longer or 2.5 or more standard deviations from the expected LOS).

Process metrics on IDR, such as the duration of rounds, attendance by members of the interdisciplinary team, the percentage of patients discussed, or the effectiveness of communication, were not collected. We assessed patient satisfaction based on the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey.

Patient Safety Culture Survey

To assess the impact on the perceptions of patient safety, we administered the Agency for Healthcare Research and Quality (AHRQ) Hospital Survey on Patient Safety Culture to all staff and clinicians working on both sides of the study unit immediately before and 12 months after implementation of the MICRO model. Results are reported for the AHRQ dimensions that were most relevant to the MICRO intervention: “teamwork within units,” “overall perceptions of safety,” “communication,” “openness,” “overall patient safety grade,” and “handoffs and transitions.” The survey represents pre- and post-comparison. All nurses and hospitalists on both the MICRO and control sides of the study unit had received the TeamStepps curriculum and participated in MICRO rounds by the time of the postintervention survey. We added 3 questions specifically assessing the perception of the efficiency and effectiveness of IDR. Postintervention respondents reflected on their overall impression of IDR, which included their experiences on both sides of the unit, because no group of nurses or hospitalists was exposed only to the MICRO side or the control side of the unit. Responses to survey questions were recorded on a 5-point Likert scale (from “strongly disagree” to “strongly agree” for opinion questions; and “never,” rarely,” sometimes,” “most of the time,” and “always” for frequency questions) and given a score from 1 to 5. The question asking for an overall grade for patient safety was scored from 1 to 5 points corresponding to letter grade choices F, D, C, B, A.

Statistical Analysis

The sample size was based on the estimate of the baseline rate of the primary outcome of CD and the projected decrease by the MICRO intervention. A study using the Global Trigger Tool developed by the Institute for Healthcare Improvement provided a best estimate of 16% as the baseline rate for CD.⁷ A total of 2000 hospitalizations were planned to be included to have a power of at least 80% to detect a 25% reduction in the annual incidence of CD with a 2-tailed type I error rate of 0.05. Comparisons of dichotomous event rates were made using chi square tests at a 2-tailed level for significance of 0.05. The LOS was analyzed using the nonparametric median test and multivariable regression analysis. We used a generalized linear model with gamma distribution and log link for all analyses of LOS, where LOS was the outcome variable, and intervention vs. control unit type was the predictor variable. Age, sex, race, payer, case mix, and comorbidities defined with the Elixhauser algorithm were used as covariates.⁸ We used multivariable logistic regression for analysis of CD, where the dependent variable was CD. Predictor variables included intervention, patient age, sex, race, payer, case mix and comorbidities. Patient satisfaction data were compared using the chi square test. The Student t test for dependent means was used to analyze the patient safety culture survey data.

The study protocol was submitted to the Icahn Mount Sinai School of Medicine’s institutional review board and determined to be exempt from full review.

A total of 2005 hospitalizations were included over the 12-month study period, consisting of 1089 hospitalizations in the MICRO group and 916 in the control group. Bedside and standard IDR were completed daily, Monday through Friday without exception. The demographic characteristics and comorbidities were similar for the 2 groups (Table). Hospitalizations of patients who were initially admitted to another unit and subsequently transferred to the study unit accounted for 11.1% of hospitalizations.

Risk-adjusted LOS was similar for the groups (6.6 vs 7.0 days, P = 0.17, for the MICRO and control groups, respectively). On subgroup analysis, a reduction in LOS was noted for patients transferred to the study unit (10.4 vs 14.0 days, P = 0.02, for the MICRO and control groups, respectively). The LOS was unchanged for patients admitted directly to the study unit (6.0 vs 5.8 days, P = 0.93). There was no difference in the incidence of clinical deterioration for the MICRO or control groups (7.7% vs 9.3%, odds ratio, 0.89; 95% confidence interval, 0.61-1.22, P = 0.46).

The finding of a LOS benefit for the MICRO group limited to patients transferred to the study unit prompted a comparison of patients transferred to the study unit and patients directly admitted to the study unit from the emergency department (Appendix 2). Compared to patients admitted directly to the study unit, patients transferred to the study unit were more likely to have Medicaid or no insurance, more likely to be discharged to a facility, had longer LOS, and were more likely to experience CD.

Patient Satisfaction

There were 175 and 140 responses to the HCAHPS survey for the MICRO and the control groups, respectively. Patients in the MICRO group were more likely to report that “doctors, nurses, or other hospital staff talk with you about whether you would have the help you needed when you left the hospital” (88% vs 78%, P = 0.01). Responses for all other HCAHPS items were similar for the 2 groups.

Clinician/Staff Survey

The response rate was 96% (30 nurses and 17 hospitalists) pre-intervention and 100% (30 nurses and 22 hospitalists) postintervention. Hospitalists and nurses gave significantly higher scores for the dimensions “teamwork within units,” “overall perception of patient safety,” and “patient safety grade” on the postintervention survey compared to the pre-intervention survey (Figure 1). Hospitalists and nurses rated the efficiency of IDR and the ability of IDR to identify safety issues higher on the postintervention survey compared to the pre-intervention survey (Figure 2).

We transformed daily IDR from a standard conference room model to a structured bedside model with scripted roles, and performed a rigorous comparison using patient-level data. Our finding that transforming daily IDR from a standard conference room model to a bedside model did not significantly reduce LOS suggests either that the model is ineffective or needs to be incorporated into more comprehensive efforts to improve clinical outcomes. Studies suggest that bedside rounding can improve outcomes when implemented in the context of comprehensive restructuring of patient care.^4,9 Stein et al.⁴ have described the reorganization of a medical ward as an “accountable care unit.” The ACU model included daily IDRs at the bedside, as well as geographic-based teams, co-leadership by a hospitalist and nurse manager, and unit-level reporting. Although no definitive conclusions can be drawn based on their descriptive report, transformation of the unit was associated with reduced LOS and mortality. Similarly, Kara et al.⁹ found that the number of elements of an “accountable care team” model implemented by each unit was associated with greater reductions in LOS and cost. In contrast, our findings of a lack of an effect are consistent with a recent cluster-randomized trial by O’Leary et al,¹⁰ which found that implementation of patient-centered bedside rounds did not improve patient satisfaction or perceptions of shared decision-making compared to units using a model of structured IDRs in a conference room setting. It is notable that the control groups in both the O’Leary trial¹⁰ and this study did not represent usual care, because these groups featured localization of the clinical teams and high-quality IDR. In our trial, it is plausible that the control side of the unit was functioning at a high level, which would have decreased our ability to further improve outcomes. Whether restructuring unit processes, including implementation of bedside IDR, improves care compared to usual care without these processes is unknown.

We found that the MICRO intervention significantly decreased LOS compared to the control group for patients transferred to the study unit. This analysis was exploratory and the finding was unexpected. Patients were transferred to the study unit from units of higher acuity, and were more likely to have Medicaid or no insurance and be discharged to facilities rather than home, suggesting that these patients had substantial disposition challenges. It is plausible that this is the population for which bedside IDRs may have the greatest impact. This was a secondary analysis, however, and should be considered as hypothesis-generating for future investigations.

Although the impact on outcomes of bedside IDRs is uncertain, potential benefits and practical barriers have been examined. Gonzalo et al.¹¹ surveyed inpatient physicians and nurses at a hospital employing bedside IDRs and found that the benefits ranked the highest were communication, coordination, and teamwork, and the lowest-ranked benefits were related to efficiency and outcomes. The 6 greatest barriers concerned the time required to complete bedside IDR. These results indicate that the time investiture by staff may be a barrier to widespread adoption. More modest changes, such as increasing the structure of standard conference room rounds, may improve care, although the data are mixed. O’Leary et al.¹² assessed the value of a structured approach in a conference room setting, which primarily entailed implementing a checklist for newly admitted patients, and found no difference in LOS. Follow-up studies by these investigators found mixed results on the ability of structured IDR to decrease the incidence of adverse events.^13,14

The results of our AHRQ survey of patient safety culture found that several important aspects of teamwork and safety were perceived as improved by the intervention, including the “overall grade on patient safety.” Other studies have similarly shown increases in teamwork and safety ratings through redesign of IDR. O’Leary et al.¹² surveyed residents and nurses on a unit that implemented a structured, conference room-based IDR and found that providers on the intervention unit rated the teamwork climate higher than providers on the control unit. Our finding that hospitalists and nurses gave higher ratings for IDR being “efficient” and “a good use of my time” on the postintervention survey than the pre-intervention survey suggests that initial concerns about the additional time commitment may be offset by gains in overall efficiency and in development of an environment of enhanced communication, teamwork, and safety.

This study has several limitations. First, the trial may have been underpowered to find small differences between the groups. The trends for decreased LOS and clinical deterioration in the MICRO group may suggest that bedside IDR can provide a small but clinically significant benefit that would be statistically significant only in a larger trial. Second, patients were not randomized to the 2 groups. The impact is diminished, however, because the routine hospital process for assigning patients to the 2 areas in which the groups were located is random and based solely on bed availability. Third, nurses and hospitalists caring for patients in the control group likely experienced improved communication practices from the unit-wide TeamStepps education and from participating in the MICRO protocol when caring for patients on the intervention side of the unit. Fourth, we did not collect data on the effectiveness of communication and are unable to assess the fidelity with which the structured protocol was followed or whether interprofessional communication was fostered or hindered. Lastly, the study was implemented on a nonteaching unit at a single academic medical center. The protocol and the results may not be generalizable to other hospitals or units that include house staff.

In conclusion, transforming IDR from a conference room model to a bedside model did not reduce overall LOS or clinical deterioration on a unit using features of an ACU structure. Although several beneficial effects were noted, including a reduction in LOS for patients transferred to the study unit and higher ratings of the patient safety climate and efficiency of IDR, implementing bedside IDR in this setting has marginal benefit. Future studies should assess whether a comprehensive transformation of the inpatient model of care, including patient-centered bedside IDR, geographic cohorting of teams, and co-leadership, improves outcomes compared to models without these features.

Disclosures

This trial was funded by Medline’s Prevention Above All Discoveries Grant Program. The authors report no financial conflicts of interest.

The care of hospitalized patients requires practitioners from multiple disciplines to assess and communicate the patient’s status in a dynamic manner during hospitalization. Although optimal teamwork is needed for patient care to be delivered reliably and efficiently, care within hospitals is typically delivered in a fragmented manner.¹ A bedside model for daily interdisciplinary rounds (IDR) has been proposed as a method to provide a structured process and engage all team members in a patient-centered, system-of-care delivery.² Specific advantages of convening rounds in the presence of the patient include the ability to directly assess care (eg, presence of a potentially unnecessary urinary catheter), patient engagement in key aspects of their care and disposition, and an increased opportunity for team members to develop a shared understanding of the patient’s views and needs.

Implementing dramatic changes to the workflow of multiple disciplines will require rigorous evidence to support a concerted effort from leadership and buy-in from stakeholders at the front line of patient care. Despite the urgency for evidence, there has been little investigation of this strategy. A systematic review³ identified 30 studies published between 1998 and 2013 addressing interdisciplinary interventions on medical wards, none of which examined a bedside IDR model. In a study performed after the period assessed by the systematic review, Stein et al⁴ described the restructuring of a medical ward as an accountable care unit (ACU), which included a bedside model for rounds by the interdisciplinary team. The change was associated with decreased mortality and length of stay (LOS), although the study did not isolate the impact of rounds or use a concurrent control group and presented aggregate rather than patient-level outcomes. The lack of convincing data may be a reason bedside rounds are not widely employed by hospitals. To provide high-quality evidence, we performed a large, prospective controlled trial comparing a structured bedside model (mobile interdisciplinary care rounds [MICRO]) with standard rounds.

This study took place at the Mount Sinai Hospital, which is a 1171-bed tertiary care academic medical center in New York City, New York. A nonteaching unit offered the ability to use a prospective controlled design. Patients were assigned to the north and the south wings of the unit in a quasi-randomized manner, rather than based on diagnosis or acuity. We transformed IDR to a bedside model on the north side of the unit (MICRO group), while the south side of the unit continued using standard conference room-based IDR (control group). The north and south sides of the unit contain 17 and 14 beds, respectively. During the study period, nurses and hospitalists cared for patients on both sides of the study unit, although on any given day were assigned only patients on 1 side of the unit. The unit uses a clinical microsystem model, which has been defined as “a group of clinicians and staff working together with a shared clinical purpose to provide care for a population of patients,” and has a defined set of characteristics associated with high performance.^5,6 Our microsystem model has incorporated features as described by Stein’s ACU model,⁴ including co-leadership by a hospitalist and a nurse manager, geographic assignment of patients to teams, and unit-level data reports. One hospitalist is assigned geographically to each area of the unit in a 2- to 4-week rotation. Coverage of the unit does not include house staff; patients are primarily assigned to hospitalists working with nurse practitioners. Patients were enrolled prospectively during their initial IDR by a research coordinator. Patient-level data and outcomes were collected prospectively by a research coordinator who attended IDR on the intervention and the control sides of the study unit daily.

Inclusion Criteria

All patients admitted to the medicine service on the study unit were eligible. Patients were greater than 18 years and admitted for an acute medical condition. Patients admitted to another unit and later transferred to the study unit were enrolled at the time of transfer. Patients could be included more than once if hospitalized on the study unit on more than 1 occasion. Most patients were covered by hospitalists, although patients covered by private physicians were included. Patients from other departments, including family medicine, are uncommonly admitted to the unit and were excluded. Patients were also excluded if they were admitted and discharged over the same weekend, because the MICRO rounds occur during weekdays and there was no opportunity to offer the intervention on Saturdays and Sundays.

MICRO Intervention

Interdisciplinary rounds occurred daily at 10:00 am for the control group and at 10:30 am for the MICRO group, and were attended by the hospitalist caring for the majority of patients on the unit, staff nurses, and the unit medical director, nurse manager, social worker, and case manager. Rounds on the control unit focused on the plan of care and disposition but did not follow any set structure and were typically 25 to 30 minutes in duration.

The MICRO rounds occurred at the bedside and followed a structured script (Appendix 1) that was designed to limit discussion of each patient to 3 minutes or less, and included speaking roles for the hospitalist, nurse, and social worker. For private physicians, the nurse practitioner assigned to the patient performed the role of the hospitalist. Rounds were expected to be approximately 50 minutes in duration. Patients were further engaged by asking for their main goal for the day. A patient safety checklist was reviewed. Initially, this task was performed by the nurse manager, who did not verbalize the items unless a deficiency was noted. After 6 months’ experience, this responsibility was given to the staff nurse, who reviewed the checklist verbally as part of the bedside script. Patients were seen daily, including those being discharged later that same day.

Staff and Clinician Education

We developed and implemented a curriculum based on a modified version of the Agency for Healthcare Research and Quality’s TeamStepps^® program to ensure that all team members were provided with the basic principles of communication within the healthcare setting. The curriculum consisted of interactive didactics on essential elements of teamwork, including team structure, communication, situation monitoring, and mutual support, as well as the purpose and structure of the MICRO model. The curriculum was delivered to nurses at 3 monthly staff meetings on the study unit and to hospitalists during 3 hospital medicine grand rounds over a 3-month period. Nurses and physicians providing care on both geographic areas of the study unit received the education program because no group of practitioners was designated to only 1 geographic area.

Outcomes

Primary and Secondary Outcomes

The primary outcomes were clinical deterioration (CD) and length of stay. Clinical deterioration was a composite outcome defined a priori as death; escalation of care (ie, transfer to an intensive care unit, intermediate care unit, or teaching unit); or a hospital-acquired complication (ie, venous thromboembolism, fall, stage III-IV pressure ulcer, catheter-associated urinary tract infection, central-line associated bloodstream infection, or Clostridium difficile-associated diarrhea). The LOS was calculated as the mean LOS with outliers excluded (outliers defined as having a LOS 100 days or longer or 2.5 or more standard deviations from the expected LOS).

Process metrics on IDR, such as the duration of rounds, attendance by members of the interdisciplinary team, the percentage of patients discussed, or the effectiveness of communication, were not collected. We assessed patient satisfaction based on the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey.

Patient Safety Culture Survey

To assess the impact on the perceptions of patient safety, we administered the Agency for Healthcare Research and Quality (AHRQ) Hospital Survey on Patient Safety Culture to all staff and clinicians working on both sides of the study unit immediately before and 12 months after implementation of the MICRO model. Results are reported for the AHRQ dimensions that were most relevant to the MICRO intervention: “teamwork within units,” “overall perceptions of safety,” “communication,” “openness,” “overall patient safety grade,” and “handoffs and transitions.” The survey represents pre- and post-comparison. All nurses and hospitalists on both the MICRO and control sides of the study unit had received the TeamStepps curriculum and participated in MICRO rounds by the time of the postintervention survey. We added 3 questions specifically assessing the perception of the efficiency and effectiveness of IDR. Postintervention respondents reflected on their overall impression of IDR, which included their experiences on both sides of the unit, because no group of nurses or hospitalists was exposed only to the MICRO side or the control side of the unit. Responses to survey questions were recorded on a 5-point Likert scale (from “strongly disagree” to “strongly agree” for opinion questions; and “never,” rarely,” sometimes,” “most of the time,” and “always” for frequency questions) and given a score from 1 to 5. The question asking for an overall grade for patient safety was scored from 1 to 5 points corresponding to letter grade choices F, D, C, B, A.

Statistical Analysis

The sample size was based on the estimate of the baseline rate of the primary outcome of CD and the projected decrease by the MICRO intervention. A study using the Global Trigger Tool developed by the Institute for Healthcare Improvement provided a best estimate of 16% as the baseline rate for CD.⁷ A total of 2000 hospitalizations were planned to be included to have a power of at least 80% to detect a 25% reduction in the annual incidence of CD with a 2-tailed type I error rate of 0.05. Comparisons of dichotomous event rates were made using chi square tests at a 2-tailed level for significance of 0.05. The LOS was analyzed using the nonparametric median test and multivariable regression analysis. We used a generalized linear model with gamma distribution and log link for all analyses of LOS, where LOS was the outcome variable, and intervention vs. control unit type was the predictor variable. Age, sex, race, payer, case mix, and comorbidities defined with the Elixhauser algorithm were used as covariates.⁸ We used multivariable logistic regression for analysis of CD, where the dependent variable was CD. Predictor variables included intervention, patient age, sex, race, payer, case mix and comorbidities. Patient satisfaction data were compared using the chi square test. The Student t test for dependent means was used to analyze the patient safety culture survey data.

The study protocol was submitted to the Icahn Mount Sinai School of Medicine’s institutional review board and determined to be exempt from full review.

A total of 2005 hospitalizations were included over the 12-month study period, consisting of 1089 hospitalizations in the MICRO group and 916 in the control group. Bedside and standard IDR were completed daily, Monday through Friday without exception. The demographic characteristics and comorbidities were similar for the 2 groups (Table). Hospitalizations of patients who were initially admitted to another unit and subsequently transferred to the study unit accounted for 11.1% of hospitalizations.

Risk-adjusted LOS was similar for the groups (6.6 vs 7.0 days, P = 0.17, for the MICRO and control groups, respectively). On subgroup analysis, a reduction in LOS was noted for patients transferred to the study unit (10.4 vs 14.0 days, P = 0.02, for the MICRO and control groups, respectively). The LOS was unchanged for patients admitted directly to the study unit (6.0 vs 5.8 days, P = 0.93). There was no difference in the incidence of clinical deterioration for the MICRO or control groups (7.7% vs 9.3%, odds ratio, 0.89; 95% confidence interval, 0.61-1.22, P = 0.46).

The finding of a LOS benefit for the MICRO group limited to patients transferred to the study unit prompted a comparison of patients transferred to the study unit and patients directly admitted to the study unit from the emergency department (Appendix 2). Compared to patients admitted directly to the study unit, patients transferred to the study unit were more likely to have Medicaid or no insurance, more likely to be discharged to a facility, had longer LOS, and were more likely to experience CD.

Patient Satisfaction

There were 175 and 140 responses to the HCAHPS survey for the MICRO and the control groups, respectively. Patients in the MICRO group were more likely to report that “doctors, nurses, or other hospital staff talk with you about whether you would have the help you needed when you left the hospital” (88% vs 78%, P = 0.01). Responses for all other HCAHPS items were similar for the 2 groups.

Clinician/Staff Survey

The response rate was 96% (30 nurses and 17 hospitalists) pre-intervention and 100% (30 nurses and 22 hospitalists) postintervention. Hospitalists and nurses gave significantly higher scores for the dimensions “teamwork within units,” “overall perception of patient safety,” and “patient safety grade” on the postintervention survey compared to the pre-intervention survey (Figure 1). Hospitalists and nurses rated the efficiency of IDR and the ability of IDR to identify safety issues higher on the postintervention survey compared to the pre-intervention survey (Figure 2).

We transformed daily IDR from a standard conference room model to a structured bedside model with scripted roles, and performed a rigorous comparison using patient-level data. Our finding that transforming daily IDR from a standard conference room model to a bedside model did not significantly reduce LOS suggests either that the model is ineffective or needs to be incorporated into more comprehensive efforts to improve clinical outcomes. Studies suggest that bedside rounding can improve outcomes when implemented in the context of comprehensive restructuring of patient care.^4,9 Stein et al.⁴ have described the reorganization of a medical ward as an “accountable care unit.” The ACU model included daily IDRs at the bedside, as well as geographic-based teams, co-leadership by a hospitalist and nurse manager, and unit-level reporting. Although no definitive conclusions can be drawn based on their descriptive report, transformation of the unit was associated with reduced LOS and mortality. Similarly, Kara et al.⁹ found that the number of elements of an “accountable care team” model implemented by each unit was associated with greater reductions in LOS and cost. In contrast, our findings of a lack of an effect are consistent with a recent cluster-randomized trial by O’Leary et al,¹⁰ which found that implementation of patient-centered bedside rounds did not improve patient satisfaction or perceptions of shared decision-making compared to units using a model of structured IDRs in a conference room setting. It is notable that the control groups in both the O’Leary trial¹⁰ and this study did not represent usual care, because these groups featured localization of the clinical teams and high-quality IDR. In our trial, it is plausible that the control side of the unit was functioning at a high level, which would have decreased our ability to further improve outcomes. Whether restructuring unit processes, including implementation of bedside IDR, improves care compared to usual care without these processes is unknown.

We found that the MICRO intervention significantly decreased LOS compared to the control group for patients transferred to the study unit. This analysis was exploratory and the finding was unexpected. Patients were transferred to the study unit from units of higher acuity, and were more likely to have Medicaid or no insurance and be discharged to facilities rather than home, suggesting that these patients had substantial disposition challenges. It is plausible that this is the population for which bedside IDRs may have the greatest impact. This was a secondary analysis, however, and should be considered as hypothesis-generating for future investigations.

Although the impact on outcomes of bedside IDRs is uncertain, potential benefits and practical barriers have been examined. Gonzalo et al.¹¹ surveyed inpatient physicians and nurses at a hospital employing bedside IDRs and found that the benefits ranked the highest were communication, coordination, and teamwork, and the lowest-ranked benefits were related to efficiency and outcomes. The 6 greatest barriers concerned the time required to complete bedside IDR. These results indicate that the time investiture by staff may be a barrier to widespread adoption. More modest changes, such as increasing the structure of standard conference room rounds, may improve care, although the data are mixed. O’Leary et al.¹² assessed the value of a structured approach in a conference room setting, which primarily entailed implementing a checklist for newly admitted patients, and found no difference in LOS. Follow-up studies by these investigators found mixed results on the ability of structured IDR to decrease the incidence of adverse events.^13,14

The results of our AHRQ survey of patient safety culture found that several important aspects of teamwork and safety were perceived as improved by the intervention, including the “overall grade on patient safety.” Other studies have similarly shown increases in teamwork and safety ratings through redesign of IDR. O’Leary et al.¹² surveyed residents and nurses on a unit that implemented a structured, conference room-based IDR and found that providers on the intervention unit rated the teamwork climate higher than providers on the control unit. Our finding that hospitalists and nurses gave higher ratings for IDR being “efficient” and “a good use of my time” on the postintervention survey than the pre-intervention survey suggests that initial concerns about the additional time commitment may be offset by gains in overall efficiency and in development of an environment of enhanced communication, teamwork, and safety.

This study has several limitations. First, the trial may have been underpowered to find small differences between the groups. The trends for decreased LOS and clinical deterioration in the MICRO group may suggest that bedside IDR can provide a small but clinically significant benefit that would be statistically significant only in a larger trial. Second, patients were not randomized to the 2 groups. The impact is diminished, however, because the routine hospital process for assigning patients to the 2 areas in which the groups were located is random and based solely on bed availability. Third, nurses and hospitalists caring for patients in the control group likely experienced improved communication practices from the unit-wide TeamStepps education and from participating in the MICRO protocol when caring for patients on the intervention side of the unit. Fourth, we did not collect data on the effectiveness of communication and are unable to assess the fidelity with which the structured protocol was followed or whether interprofessional communication was fostered or hindered. Lastly, the study was implemented on a nonteaching unit at a single academic medical center. The protocol and the results may not be generalizable to other hospitals or units that include house staff.

In conclusion, transforming IDR from a conference room model to a bedside model did not reduce overall LOS or clinical deterioration on a unit using features of an ACU structure. Although several beneficial effects were noted, including a reduction in LOS for patients transferred to the study unit and higher ratings of the patient safety climate and efficiency of IDR, implementing bedside IDR in this setting has marginal benefit. Future studies should assess whether a comprehensive transformation of the inpatient model of care, including patient-centered bedside IDR, geographic cohorting of teams, and co-leadership, improves outcomes compared to models without these features.

Disclosures

This trial was funded by Medline’s Prevention Above All Discoveries Grant Program. The authors report no financial conflicts of interest.

The care of hospitalized patients requires practitioners from multiple disciplines to assess and communicate the patient’s status in a dynamic manner during hospitalization. Although optimal teamwork is needed for patient care to be delivered reliably and efficiently, care within hospitals is typically delivered in a fragmented manner.¹ A bedside model for daily interdisciplinary rounds (IDR) has been proposed as a method to provide a structured process and engage all team members in a patient-centered, system-of-care delivery.² Specific advantages of convening rounds in the presence of the patient include the ability to directly assess care (eg, presence of a potentially unnecessary urinary catheter), patient engagement in key aspects of their care and disposition, and an increased opportunity for team members to develop a shared understanding of the patient’s views and needs.

Implementing dramatic changes to the workflow of multiple disciplines will require rigorous evidence to support a concerted effort from leadership and buy-in from stakeholders at the front line of patient care. Despite the urgency for evidence, there has been little investigation of this strategy. A systematic review³ identified 30 studies published between 1998 and 2013 addressing interdisciplinary interventions on medical wards, none of which examined a bedside IDR model. In a study performed after the period assessed by the systematic review, Stein et al⁴ described the restructuring of a medical ward as an accountable care unit (ACU), which included a bedside model for rounds by the interdisciplinary team. The change was associated with decreased mortality and length of stay (LOS), although the study did not isolate the impact of rounds or use a concurrent control group and presented aggregate rather than patient-level outcomes. The lack of convincing data may be a reason bedside rounds are not widely employed by hospitals. To provide high-quality evidence, we performed a large, prospective controlled trial comparing a structured bedside model (mobile interdisciplinary care rounds [MICRO]) with standard rounds.

This study took place at the Mount Sinai Hospital, which is a 1171-bed tertiary care academic medical center in New York City, New York. A nonteaching unit offered the ability to use a prospective controlled design. Patients were assigned to the north and the south wings of the unit in a quasi-randomized manner, rather than based on diagnosis or acuity. We transformed IDR to a bedside model on the north side of the unit (MICRO group), while the south side of the unit continued using standard conference room-based IDR (control group). The north and south sides of the unit contain 17 and 14 beds, respectively. During the study period, nurses and hospitalists cared for patients on both sides of the study unit, although on any given day were assigned only patients on 1 side of the unit. The unit uses a clinical microsystem model, which has been defined as “a group of clinicians and staff working together with a shared clinical purpose to provide care for a population of patients,” and has a defined set of characteristics associated with high performance.^5,6 Our microsystem model has incorporated features as described by Stein’s ACU model,⁴ including co-leadership by a hospitalist and a nurse manager, geographic assignment of patients to teams, and unit-level data reports. One hospitalist is assigned geographically to each area of the unit in a 2- to 4-week rotation. Coverage of the unit does not include house staff; patients are primarily assigned to hospitalists working with nurse practitioners. Patients were enrolled prospectively during their initial IDR by a research coordinator. Patient-level data and outcomes were collected prospectively by a research coordinator who attended IDR on the intervention and the control sides of the study unit daily.

Inclusion Criteria

All patients admitted to the medicine service on the study unit were eligible. Patients were greater than 18 years and admitted for an acute medical condition. Patients admitted to another unit and later transferred to the study unit were enrolled at the time of transfer. Patients could be included more than once if hospitalized on the study unit on more than 1 occasion. Most patients were covered by hospitalists, although patients covered by private physicians were included. Patients from other departments, including family medicine, are uncommonly admitted to the unit and were excluded. Patients were also excluded if they were admitted and discharged over the same weekend, because the MICRO rounds occur during weekdays and there was no opportunity to offer the intervention on Saturdays and Sundays.

MICRO Intervention

Interdisciplinary rounds occurred daily at 10:00 am for the control group and at 10:30 am for the MICRO group, and were attended by the hospitalist caring for the majority of patients on the unit, staff nurses, and the unit medical director, nurse manager, social worker, and case manager. Rounds on the control unit focused on the plan of care and disposition but did not follow any set structure and were typically 25 to 30 minutes in duration.

The MICRO rounds occurred at the bedside and followed a structured script (Appendix 1) that was designed to limit discussion of each patient to 3 minutes or less, and included speaking roles for the hospitalist, nurse, and social worker. For private physicians, the nurse practitioner assigned to the patient performed the role of the hospitalist. Rounds were expected to be approximately 50 minutes in duration. Patients were further engaged by asking for their main goal for the day. A patient safety checklist was reviewed. Initially, this task was performed by the nurse manager, who did not verbalize the items unless a deficiency was noted. After 6 months’ experience, this responsibility was given to the staff nurse, who reviewed the checklist verbally as part of the bedside script. Patients were seen daily, including those being discharged later that same day.

Staff and Clinician Education

We developed and implemented a curriculum based on a modified version of the Agency for Healthcare Research and Quality’s TeamStepps^® program to ensure that all team members were provided with the basic principles of communication within the healthcare setting. The curriculum consisted of interactive didactics on essential elements of teamwork, including team structure, communication, situation monitoring, and mutual support, as well as the purpose and structure of the MICRO model. The curriculum was delivered to nurses at 3 monthly staff meetings on the study unit and to hospitalists during 3 hospital medicine grand rounds over a 3-month period. Nurses and physicians providing care on both geographic areas of the study unit received the education program because no group of practitioners was designated to only 1 geographic area.

Outcomes

Primary and Secondary Outcomes

The primary outcomes were clinical deterioration (CD) and length of stay. Clinical deterioration was a composite outcome defined a priori as death; escalation of care (ie, transfer to an intensive care unit, intermediate care unit, or teaching unit); or a hospital-acquired complication (ie, venous thromboembolism, fall, stage III-IV pressure ulcer, catheter-associated urinary tract infection, central-line associated bloodstream infection, or Clostridium difficile-associated diarrhea). The LOS was calculated as the mean LOS with outliers excluded (outliers defined as having a LOS 100 days or longer or 2.5 or more standard deviations from the expected LOS).

Process metrics on IDR, such as the duration of rounds, attendance by members of the interdisciplinary team, the percentage of patients discussed, or the effectiveness of communication, were not collected. We assessed patient satisfaction based on the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey.

Patient Safety Culture Survey

To assess the impact on the perceptions of patient safety, we administered the Agency for Healthcare Research and Quality (AHRQ) Hospital Survey on Patient Safety Culture to all staff and clinicians working on both sides of the study unit immediately before and 12 months after implementation of the MICRO model. Results are reported for the AHRQ dimensions that were most relevant to the MICRO intervention: “teamwork within units,” “overall perceptions of safety,” “communication,” “openness,” “overall patient safety grade,” and “handoffs and transitions.” The survey represents pre- and post-comparison. All nurses and hospitalists on both the MICRO and control sides of the study unit had received the TeamStepps curriculum and participated in MICRO rounds by the time of the postintervention survey. We added 3 questions specifically assessing the perception of the efficiency and effectiveness of IDR. Postintervention respondents reflected on their overall impression of IDR, which included their experiences on both sides of the unit, because no group of nurses or hospitalists was exposed only to the MICRO side or the control side of the unit. Responses to survey questions were recorded on a 5-point Likert scale (from “strongly disagree” to “strongly agree” for opinion questions; and “never,” rarely,” sometimes,” “most of the time,” and “always” for frequency questions) and given a score from 1 to 5. The question asking for an overall grade for patient safety was scored from 1 to 5 points corresponding to letter grade choices F, D, C, B, A.

Statistical Analysis

The sample size was based on the estimate of the baseline rate of the primary outcome of CD and the projected decrease by the MICRO intervention. A study using the Global Trigger Tool developed by the Institute for Healthcare Improvement provided a best estimate of 16% as the baseline rate for CD.⁷ A total of 2000 hospitalizations were planned to be included to have a power of at least 80% to detect a 25% reduction in the annual incidence of CD with a 2-tailed type I error rate of 0.05. Comparisons of dichotomous event rates were made using chi square tests at a 2-tailed level for significance of 0.05. The LOS was analyzed using the nonparametric median test and multivariable regression analysis. We used a generalized linear model with gamma distribution and log link for all analyses of LOS, where LOS was the outcome variable, and intervention vs. control unit type was the predictor variable. Age, sex, race, payer, case mix, and comorbidities defined with the Elixhauser algorithm were used as covariates.⁸ We used multivariable logistic regression for analysis of CD, where the dependent variable was CD. Predictor variables included intervention, patient age, sex, race, payer, case mix and comorbidities. Patient satisfaction data were compared using the chi square test. The Student t test for dependent means was used to analyze the patient safety culture survey data.

The study protocol was submitted to the Icahn Mount Sinai School of Medicine’s institutional review board and determined to be exempt from full review.

A total of 2005 hospitalizations were included over the 12-month study period, consisting of 1089 hospitalizations in the MICRO group and 916 in the control group. Bedside and standard IDR were completed daily, Monday through Friday without exception. The demographic characteristics and comorbidities were similar for the 2 groups (Table). Hospitalizations of patients who were initially admitted to another unit and subsequently transferred to the study unit accounted for 11.1% of hospitalizations.

Risk-adjusted LOS was similar for the groups (6.6 vs 7.0 days, P = 0.17, for the MICRO and control groups, respectively). On subgroup analysis, a reduction in LOS was noted for patients transferred to the study unit (10.4 vs 14.0 days, P = 0.02, for the MICRO and control groups, respectively). The LOS was unchanged for patients admitted directly to the study unit (6.0 vs 5.8 days, P = 0.93). There was no difference in the incidence of clinical deterioration for the MICRO or control groups (7.7% vs 9.3%, odds ratio, 0.89; 95% confidence interval, 0.61-1.22, P = 0.46).

The finding of a LOS benefit for the MICRO group limited to patients transferred to the study unit prompted a comparison of patients transferred to the study unit and patients directly admitted to the study unit from the emergency department (Appendix 2). Compared to patients admitted directly to the study unit, patients transferred to the study unit were more likely to have Medicaid or no insurance, more likely to be discharged to a facility, had longer LOS, and were more likely to experience CD.

Patient Satisfaction

There were 175 and 140 responses to the HCAHPS survey for the MICRO and the control groups, respectively. Patients in the MICRO group were more likely to report that “doctors, nurses, or other hospital staff talk with you about whether you would have the help you needed when you left the hospital” (88% vs 78%, P = 0.01). Responses for all other HCAHPS items were similar for the 2 groups.

Clinician/Staff Survey

The response rate was 96% (30 nurses and 17 hospitalists) pre-intervention and 100% (30 nurses and 22 hospitalists) postintervention. Hospitalists and nurses gave significantly higher scores for the dimensions “teamwork within units,” “overall perception of patient safety,” and “patient safety grade” on the postintervention survey compared to the pre-intervention survey (Figure 1). Hospitalists and nurses rated the efficiency of IDR and the ability of IDR to identify safety issues higher on the postintervention survey compared to the pre-intervention survey (Figure 2).

We transformed daily IDR from a standard conference room model to a structured bedside model with scripted roles, and performed a rigorous comparison using patient-level data. Our finding that transforming daily IDR from a standard conference room model to a bedside model did not significantly reduce LOS suggests either that the model is ineffective or needs to be incorporated into more comprehensive efforts to improve clinical outcomes. Studies suggest that bedside rounding can improve outcomes when implemented in the context of comprehensive restructuring of patient care.^4,9 Stein et al.⁴ have described the reorganization of a medical ward as an “accountable care unit.” The ACU model included daily IDRs at the bedside, as well as geographic-based teams, co-leadership by a hospitalist and nurse manager, and unit-level reporting. Although no definitive conclusions can be drawn based on their descriptive report, transformation of the unit was associated with reduced LOS and mortality. Similarly, Kara et al.⁹ found that the number of elements of an “accountable care team” model implemented by each unit was associated with greater reductions in LOS and cost. In contrast, our findings of a lack of an effect are consistent with a recent cluster-randomized trial by O’Leary et al,¹⁰ which found that implementation of patient-centered bedside rounds did not improve patient satisfaction or perceptions of shared decision-making compared to units using a model of structured IDRs in a conference room setting. It is notable that the control groups in both the O’Leary trial¹⁰ and this study did not represent usual care, because these groups featured localization of the clinical teams and high-quality IDR. In our trial, it is plausible that the control side of the unit was functioning at a high level, which would have decreased our ability to further improve outcomes. Whether restructuring unit processes, including implementation of bedside IDR, improves care compared to usual care without these processes is unknown.

We found that the MICRO intervention significantly decreased LOS compared to the control group for patients transferred to the study unit. This analysis was exploratory and the finding was unexpected. Patients were transferred to the study unit from units of higher acuity, and were more likely to have Medicaid or no insurance and be discharged to facilities rather than home, suggesting that these patients had substantial disposition challenges. It is plausible that this is the population for which bedside IDRs may have the greatest impact. This was a secondary analysis, however, and should be considered as hypothesis-generating for future investigations.

Although the impact on outcomes of bedside IDRs is uncertain, potential benefits and practical barriers have been examined. Gonzalo et al.¹¹ surveyed inpatient physicians and nurses at a hospital employing bedside IDRs and found that the benefits ranked the highest were communication, coordination, and teamwork, and the lowest-ranked benefits were related to efficiency and outcomes. The 6 greatest barriers concerned the time required to complete bedside IDR. These results indicate that the time investiture by staff may be a barrier to widespread adoption. More modest changes, such as increasing the structure of standard conference room rounds, may improve care, although the data are mixed. O’Leary et al.¹² assessed the value of a structured approach in a conference room setting, which primarily entailed implementing a checklist for newly admitted patients, and found no difference in LOS. Follow-up studies by these investigators found mixed results on the ability of structured IDR to decrease the incidence of adverse events.^13,14

The results of our AHRQ survey of patient safety culture found that several important aspects of teamwork and safety were perceived as improved by the intervention, including the “overall grade on patient safety.” Other studies have similarly shown increases in teamwork and safety ratings through redesign of IDR. O’Leary et al.¹² surveyed residents and nurses on a unit that implemented a structured, conference room-based IDR and found that providers on the intervention unit rated the teamwork climate higher than providers on the control unit. Our finding that hospitalists and nurses gave higher ratings for IDR being “efficient” and “a good use of my time” on the postintervention survey than the pre-intervention survey suggests that initial concerns about the additional time commitment may be offset by gains in overall efficiency and in development of an environment of enhanced communication, teamwork, and safety.

This study has several limitations. First, the trial may have been underpowered to find small differences between the groups. The trends for decreased LOS and clinical deterioration in the MICRO group may suggest that bedside IDR can provide a small but clinically significant benefit that would be statistically significant only in a larger trial. Second, patients were not randomized to the 2 groups. The impact is diminished, however, because the routine hospital process for assigning patients to the 2 areas in which the groups were located is random and based solely on bed availability. Third, nurses and hospitalists caring for patients in the control group likely experienced improved communication practices from the unit-wide TeamStepps education and from participating in the MICRO protocol when caring for patients on the intervention side of the unit. Fourth, we did not collect data on the effectiveness of communication and are unable to assess the fidelity with which the structured protocol was followed or whether interprofessional communication was fostered or hindered. Lastly, the study was implemented on a nonteaching unit at a single academic medical center. The protocol and the results may not be generalizable to other hospitals or units that include house staff.

In conclusion, transforming IDR from a conference room model to a bedside model did not reduce overall LOS or clinical deterioration on a unit using features of an ACU structure. Although several beneficial effects were noted, including a reduction in LOS for patients transferred to the study unit and higher ratings of the patient safety climate and efficiency of IDR, implementing bedside IDR in this setting has marginal benefit. Future studies should assess whether a comprehensive transformation of the inpatient model of care, including patient-centered bedside IDR, geographic cohorting of teams, and co-leadership, improves outcomes compared to models without these features.

Disclosures

This trial was funded by Medline’s Prevention Above All Discoveries Grant Program. The authors report no financial conflicts of interest.

Attending rounds at academic medical centers are often disconnected from patients and non-physician care team members. Time spent bedside is consistently less than one third of total rounding time, with observational studies reporting a range of 9% to 33% over the past several decades.^1-8 Rounds are often conducted outside patient rooms, denying patients, families, and nurses the opportunity to participate and offer valuable insights. Lack of bedside rounds thus limits patient and family engagement, patient input into the care plan, teaching of the physical examination, and communication and collaboration with nurses. In one study, physicians and nurses on rounds engaged in interprofessional communication in only 12% of patient cases.¹ Studies have found interdisciplinary bedside rounds have several benefits, including subjectively improved communication and teamwork between physicians and nurses; increased patient satisfaction, including feeling more cared for by the medical team; and decreased length of stay and costs of care.^2-10

However, there are many barriers to conducting interdisciplinary bedside rounds at large academic medical centers. Patients cared for by a single medical team are often geographically dispersed to several nursing units, and nurses are unable to predict when physicians will round on their patients. This situation limits nursing involvement on rounds and keeps doctors and nurses isolated from each other.² Regionalization of care teams reduces this fragmentation by facilitating more interaction among doctors, patients, families, and nursing staff.

There are few data on how regionalized patients and interdisciplinary bedside rounds affect rounding time and the nature of rounds. This information is needed to understand how these structural changes mediate their effects, whether other steps are required to optimize outcomes, and how to maximize efficiency. We used time-motion analysis (TMA) to investigate how regionalization of medical teams, encouragement of bedside rounding, and systematic inclusion of nurses on ward rounds affect amount of time spent with patients, nursing presence on rounds, and total rounding time.

Setting

This prospective interventional study, approved by the Institutional Review Board of Partners HealthCare, was conducted on the general medical wards at Brigham and Women’s Hospital, an academic 793-bed tertiary-care center in Boston, Massachusetts. Housestaff teams consist of 1 attending, 1 resident, and 2 interns with or without a medical student. Before June 20, 2013, daily rounds on medical inpatients were conducted largely on the patient unit but outside patient rooms. After completing most of a rounding discussion outside a patient’s room, the team might walk in to examine or speak with the patient. A typical medical team had patients dispersed over 7 medical units on average, and over as many as 13. As nurses were unit based, they did not consistently participate in rounds.

Intervention

In June 2013, as part of a general medical service care redesign initiative, the general medical teams were regionalized to specific inpatient units. The goal was to have teams admit patients predominantly to the team’s designated unit and to have all patients on a unit be cared for by the unit’s assigned team as often as possible, with an 85% goal for both. Toward those ends, the admitting structure was changed from a traditional 4-day call cycle to daily admitting for all teams, based on each unit’s bed availability.¹¹

Teams were also expected to conduct rounds with nurses, and a system for facilitating these rounds was established. As physician and nurse care teams were now geographically co-located, it became possible for residents and nurses to check a rounding sheet for the planned patient rounding order, which had been set by the resident and nurse-in-charge before rounds. No more than about 5 minutes was needed to prepare each day’s order. The rounding sheet prioritized sick patients, newly admitted patients, and planned morning discharges, but patients were also always grouped by nurse. For example, the physician team rounded with the first nurse on all 3 of a nurse’s patients, and then proceeded to the next group of 3 patients with the next nurse, until all patients were seen.

Teams were encouraged to conduct patient- and family-centered rounds exclusively at bedside, except when bedside rounding was thought to be detrimental to a patient (eg, one with delirium). After an intern’s bedside presentation, which included a brief summary and details about overnight events and vital signs, the concerns of the patient, family, and nurse were shared, a focused physical examination performed, relevant data (eg, laboratory test results and imaging studies) reviewed, and the day’s plan formulated. The entire team, including the attending, was expected to have read new patients’ admission notes before rounds. Bedside rounds could thus be focused more on patient assessment and patient/family engagement and less on data transfer.

Several actions were taken to facilitate these changes. Residents, attendings, nurses, and other interdisciplinary team members participated in a series of focus groups and conferences to define workflows and share best practices for patient- and family-centered bedside rounds. Tips on bedside rounding were included in a general medicine rotation guidebook made available to residents and attendings. At the beginning of each post-intervention general medicine rotation, attendings and residents attended brief orientation sessions to review the new daily schedule, have interdisciplinary huddles, and share expectations for patient- and family-centered bedside rounds. On the general medicine units, new medical directors were hired to partner with existing nursing directors to support adoption of the workflows. Last, an interdisciplinary leadership team was formed to support the care redesign efforts. This team started meeting every 2 weeks.

Study Design

We used a pre–post analysis to study the effects of care redesign. Analysis was performed at the same time of year for 2 consecutive years to control for the stage of training and experience of the housestaff. TMA was performed by trained medical students using computer tablets linked to a customized Microsoft Access database form (Redmond, Washington). The form and the database were designed with specific buttons that, when pressed, recorded the time of particular events, such as the coming and going of each participant, the location of rounds, and the beginning and the end of rounding encounters with a patient. One research assistant using an Access entry form was able to dynamically track all events in real time, as they occurred. We collected data on 4 teams at baseline and 5 teams after the intervention. Each of the 4 baseline teams was followed for 4 consecutive weekdays—16 rounds total, April-June 2013—to capture the 4-day call cycle. Each of the 5 post-intervention teams was followed for 5 consecutive weekdays—25 rounds total, April–June 2014—to capture the 5-day cycle. (Because of technical difficulties, data from 1 rounding session were not captured.) For inclusion in the statistical analyses, TMA captured 166 on-service patients before the intervention and 304 afterward. Off-service patients, those with an attending other than the team attending, were excluded because their rounds were conducted separately.

We examined 2 primary outcomes, the proportion of time each clinical team member was present on rounds and the proportion of bedside rounding time. Secondary outcomes were round duration, rounding time per patient, and total non-patient time per rounding session (total rounding time minus total patient time).

Statistical Analysis

TMA data were organized in an Access database and analyzed with SAS Version 9.3 (SAS Institute, Cary, North Carolina). We analyzed the data by round session as well as by patient.

Data are presented as means with standard deviations, medians with interquartile ranges, and proportions, as appropriate. For analyses by round session, we used unadjusted linear regression; for patient-level analyses, we used general estimating equations to adjust for clustering of patients within each session; for nurse presence during any part of a round by patient, we used a χ² test. Total non-patient time per round session was compared with use of patient-clustered general estimating equations using a γ distribution to account for the non-normality of the data.

Patient and Care Team Characteristics

Over the first year of the initiative, 85% of a team’s patients were on their assigned unit, and 87% of a unit’s patients were with the assigned team. Census numbers were 10.4 patients per general medicine team in April-June 2013 and 12.7 patients per team in April-June 2014, a 22% increase after care redesign. There were no statistically significant differences in patient characteristics, including age, sex, race, language, admission source, and comorbidity measure (Elixhauser score), between the pre-intervention and post-intervention study periods, except for a slightly higher proportion of patients admitted from home and fewer patients admitted directly from clinic (Table 1).

Primary Outcomes

Mean proportion of time the nurse was present on rounds per round session increased significantly (P < 0.001), from 24.1% to 67.8% (Figure 1A, Table 2). For individual patient encounters, the increased overall nursing presence was attributable to having more nurses on rounds and having nurses present for a larger proportion of individual rounding encounters (Figure 1B, Table 2). Nurses were present for at least some part of rounds for 53% of patients before the intervention and 93% afterward (P < 0.001). Mean proportion of round time by each of the 2 interns on each team decreased from 59.6% to 49.6% (P = 0.007).

Total bedside rounding time increased significantly ( P < 0.001), from 39.9% before the intervention to 55.8% afterward (Table 2). Meanwhile, percentage of rounding time spent on the unit but outside patient rooms decreased significantly ( P = 0.004), from 55.2% to 42.2%, as did rounding time on a unit completely different from the patient’s (4.9% before intervention, 2.0% afterward; P = 0.03). Again, patient-level results were similar (Figure 2, Table 2), but the decreased time spent on the unit, outside the patient rooms, was not significant.

Secondary Outcomes

Total rounding time decreased significantly, from a mean of 182 minutes (3.0 hours) at baseline to a mean of 146 minutes (2.4 hours) after the intervention, despite the higher post-intervention census. (When adjusted for patient census, the difference increased from 35.5 to 53.8 minutes; Table 2.) Mean rounding time per patient decreased significantly, from 14.7 minutes at baseline to 10.5 minutes after the intervention. For newly admitted patients, mean rounding time per patient decreased from 30.0 minutes before implementation to 16.3 minutes afterward. Mean rounding time also decreased, though much less, for subsequent-day patients (Table 2). For both new and existing patients, the decrease in rounding time largely was a reduction in time spent rounding outside patient rooms, with minimal impact on bedside time (Table 2). Mean time nurses were present during a patient’s rounds increased significantly, from 4.5 to 8.0 minutes (Table 2). Total nurse rounding time increased from 45.1 minutes per session to 98.8 minutes. Rounding time not related to patient discussion or evaluation decreased from 22.7 minutes per session to 13.3 minutes ( P = 0.003).

TMA of our care redesign initiative showed that this multipronged intervention, which included team regionalization, encouragement of bedside rounding with nurses, call structure changes, and attendings’ reading of admission notes before rounds, resulted in an increased proportion of rounding time spent with patients and an increased proportion of time nurses were present on rounds. Secondarily, round duration decreased even as patient census increased.

Regionalized teams have been found to improve interdisciplinary communication.¹ The present study elaborates on that finding by demonstrating a dramatic increase in nursing presence on rounds, likely resulting from the unit’s use of rounding schedules and nurses’ prioritization of rounding orders, both of which were made possible by geographic co-localization. Other research has noted that one of the most significant barriers to interdisciplinary rounds is difficulty coordinating the start times of physician/nurse bedside rounding encounters. The system we have studied directly addresses this difficulty.⁹ Of note, nursing presence on rounds is necessary but not sufficient for true physician–nurse collaboration and effective communication,¹ as reflected in a separate study of the intervention showing no significant difference in the concordance of the patient care plan between nurses and physicians before and after regionalization.¹² Additional interventions may be needed to ensure that communication during bedside rounds is effective.

Our regionalized teams spent a significantly higher proportion of rounding time bedside, likely because of a cultural shift in expectations and the increased convenience of seeing patients on the team’s unit. Nevertheless, bedside time was not 100%. Structural barriers (eg, patients off-unit for dialysis) and cultural barriers likely contributed to the less than full adoption of bedside rounding. As described previously, cultural barriers to bedside rounding include trainees’ anxiety about being questioned in front of patients, the desire to freely exchange academic ideas in a conference room, and attendings’ doubts about their bedside teaching ability.^1,9,13 Bedside rounds provide an important opportunity to apply the principles of patient- and family-centered care, including promotion of dignity and respect, information sharing, and collaboration. Thus, overcoming the concerns of housestaff and attendings and helping them feel prepared for bedside rounds can benefit the patient experience. More attention should be given to these practices as these types of interventions are implemented at Brigham and Women’s Hospital and elsewhere.^1,13-15

Another primary concern about interdisciplinary bedside rounding is the perception that it takes more time.⁹ Therefore, it was important for us to measure round duration as a balancing measure to be considered for our intervention. Fortunately, we found round duration decreased with regionalization and encouragement of bedside rounding. This decrease was driven largely by a significant decrease in mean rounding time per new patient, which may be attributable at least in part to setting expectations that attendings and residents will read admission notes before rounds and that interns will summarize rather than recount information from admission notes. However, we also found rounding time decreases for subsequent-day patients, suggesting an underlying time savings. Spending a larger proportion of time bedside may therefore result in more efficient rounds. Bedside presentations can reduce redundancies, such as discussing a patient’s case outside his or her room and subsequently walking in and going over much of the same information with the patient. Our model de-emphasizes data transfer in favor of discussion of care plans. There was also a decrease in non-patient time, likely reflecting reduced transit time for regionalized teams. This decrease aligns with a recent finding that bedside rounding was at least as efficient as rounding outside the room.¹⁶

Of note, though a larger percentage of time was spent bedside after implementation of the care redesign, the absolute amount of bedside time did not change significantly. Our data showed that, even with shorter rounds, the same amount of absolute time can be spent bedside, face to face with the patient, by increasing the proportion of bedside rounding time. In other words, teams on average did not spend more time with patients, though the content and the structure of those encounters may have changed. This finding may be attributable to eliminating redundancy, forgoing the outside-the-room discussion, and thus the largest time reductions were realized there. In addition, teams incompletely adopted beside rounds, as reflected in the data. We expect that, with more complete adoption, an even larger proportion of time will be spent bedside, and absolute time bedside might increase as a result.

An unexpected result of the care redesign was that interns’ proportion of rounding time decreased after the intervention. This decrease most likely is attributable to interns’ being less likely to participate in rounds for a co-intern’s patient, and to their staying outside that patient’s room to give themselves more time to advance the care of their own patients. Before the intervention, when more rounding time was spent outside patient rooms, interns were more likely to join rounds for their co-intern’s patients because they could easily break away, as needed, to continue care of their own patients. The resident is now encouraged to use the morning huddle to identify which patients likely have the most educational value, and both interns are expected to join the bedside rounds for these patients.

This study had a few limitations. First, the pre–post design made it difficult to exclude the possibility that other temporal changes may have affected outcomes, though we did account for time-of-year effects by aligning our data-collection phases. In addition, the authors, including the director of the general medical service, are unaware of any co-interventions during the study period. Second, the multipronged intervention included care team regionalization, encouragement of bedside rounding with nurses, call structure changes (from 4 days to daily admitting), and attendings’ reading of admission notes before rounds. Thus, parsing which component(s) contributed to the results was difficult, though all the changes instituted likely were necessary for system redesign. For example, regionalization of clinicians to unit-based teams was made possible by switching to a daily admitting system.

Time that team members spent preparing for rounds was not recorded before or after the intervention. Thus, the decrease in total rounding time could have been accompanied by an increase in time spent preparing for rounds. However, admission notes were available in our electronic medical record before and after the intervention, and most residents and attendings were already reading them pre-intervention. After the intervention, pre-round note reading was more clearly defined as an expectation, and we were able to set the expectation that interns should use their presentations to summarize rather than recount information. In addition, in the post-intervention period, we did not include time spent preparing rounding orders; as already noted, however, preparation took only 5 minutes per day. Also, we did not analyze the content or the quality of the discussion on rounds, but simply recorded who was present where and when. Regarding the effect of the intervention on patient care, results were mixed. As reported in 2016, we saw no difference in frequency of adverse events with this intervention.¹² However, a more sensitive measure of adverse events—used in a study on handoffs—showed our regionalization efforts had an additive effect on reducing overnight adverse events.¹⁷Researchers should now focus on the effects of care redesign on clinical outcomes, interdisciplinary care team communication, patient engagement and satisfaction, provider opinions of communication, workflow, patient care, and housestaff education. Our methodology can be used as a model to link structure, process, and outcome related to rounds and thereby better understand how best to optimize patient care and efficiency. Additional studies are needed to analyze the content of rounds and their association with patient and educational outcomes. Last, it will be important to conduct a study to see if the effects we have identified can be sustained. Such a study is already under way.

In conclusion, creating regionalized care teams and encouraging focused bedside rounds increased the proportion of bedside time and the presence of nurses on rounds. Rounds were shorter despite higher patient census. TMA revealed that regionalized care teams and bedside rounding at a large academic hospital are feasible, and are useful in establishing the necessary structures for increasing physician–nurse and provider–patient interactions.

Acknowledgments

The authors acknowledge Dr. Stan Ashley, Dr. Jacqueline Somerville, and Sheila Harris for their support of the regionalization initiative.

Disclosures

Dr. Schnipper received funding from Sanofi-aventis to conduct an investigator-initiated study to implement and evaluate a multi-faceted intervention to improve transitions of care in patients discharged home on insulin. The study was also supported by funding from the Marshall A. Wolf Medical Education Fund, Brigham and Women’s Hospital, and Dr. Stan Ashley, Chief Medical Officer, Brigham and Women’s Hospital. Some of the content of this article was orally presented at the annual meeting of the Society of Hospital Medicine; March 29-April 1, 2015; National Harbor, MD.

Attending rounds at academic medical centers are often disconnected from patients and non-physician care team members. Time spent bedside is consistently less than one third of total rounding time, with observational studies reporting a range of 9% to 33% over the past several decades.^1-8 Rounds are often conducted outside patient rooms, denying patients, families, and nurses the opportunity to participate and offer valuable insights. Lack of bedside rounds thus limits patient and family engagement, patient input into the care plan, teaching of the physical examination, and communication and collaboration with nurses. In one study, physicians and nurses on rounds engaged in interprofessional communication in only 12% of patient cases.¹ Studies have found interdisciplinary bedside rounds have several benefits, including subjectively improved communication and teamwork between physicians and nurses; increased patient satisfaction, including feeling more cared for by the medical team; and decreased length of stay and costs of care.^2-10

However, there are many barriers to conducting interdisciplinary bedside rounds at large academic medical centers. Patients cared for by a single medical team are often geographically dispersed to several nursing units, and nurses are unable to predict when physicians will round on their patients. This situation limits nursing involvement on rounds and keeps doctors and nurses isolated from each other.² Regionalization of care teams reduces this fragmentation by facilitating more interaction among doctors, patients, families, and nursing staff.

There are few data on how regionalized patients and interdisciplinary bedside rounds affect rounding time and the nature of rounds. This information is needed to understand how these structural changes mediate their effects, whether other steps are required to optimize outcomes, and how to maximize efficiency. We used time-motion analysis (TMA) to investigate how regionalization of medical teams, encouragement of bedside rounding, and systematic inclusion of nurses on ward rounds affect amount of time spent with patients, nursing presence on rounds, and total rounding time.

Setting

This prospective interventional study, approved by the Institutional Review Board of Partners HealthCare, was conducted on the general medical wards at Brigham and Women’s Hospital, an academic 793-bed tertiary-care center in Boston, Massachusetts. Housestaff teams consist of 1 attending, 1 resident, and 2 interns with or without a medical student. Before June 20, 2013, daily rounds on medical inpatients were conducted largely on the patient unit but outside patient rooms. After completing most of a rounding discussion outside a patient’s room, the team might walk in to examine or speak with the patient. A typical medical team had patients dispersed over 7 medical units on average, and over as many as 13. As nurses were unit based, they did not consistently participate in rounds.

Intervention

In June 2013, as part of a general medical service care redesign initiative, the general medical teams were regionalized to specific inpatient units. The goal was to have teams admit patients predominantly to the team’s designated unit and to have all patients on a unit be cared for by the unit’s assigned team as often as possible, with an 85% goal for both. Toward those ends, the admitting structure was changed from a traditional 4-day call cycle to daily admitting for all teams, based on each unit’s bed availability.¹¹

Teams were also expected to conduct rounds with nurses, and a system for facilitating these rounds was established. As physician and nurse care teams were now geographically co-located, it became possible for residents and nurses to check a rounding sheet for the planned patient rounding order, which had been set by the resident and nurse-in-charge before rounds. No more than about 5 minutes was needed to prepare each day’s order. The rounding sheet prioritized sick patients, newly admitted patients, and planned morning discharges, but patients were also always grouped by nurse. For example, the physician team rounded with the first nurse on all 3 of a nurse’s patients, and then proceeded to the next group of 3 patients with the next nurse, until all patients were seen.

Teams were encouraged to conduct patient- and family-centered rounds exclusively at bedside, except when bedside rounding was thought to be detrimental to a patient (eg, one with delirium). After an intern’s bedside presentation, which included a brief summary and details about overnight events and vital signs, the concerns of the patient, family, and nurse were shared, a focused physical examination performed, relevant data (eg, laboratory test results and imaging studies) reviewed, and the day’s plan formulated. The entire team, including the attending, was expected to have read new patients’ admission notes before rounds. Bedside rounds could thus be focused more on patient assessment and patient/family engagement and less on data transfer.

Several actions were taken to facilitate these changes. Residents, attendings, nurses, and other interdisciplinary team members participated in a series of focus groups and conferences to define workflows and share best practices for patient- and family-centered bedside rounds. Tips on bedside rounding were included in a general medicine rotation guidebook made available to residents and attendings. At the beginning of each post-intervention general medicine rotation, attendings and residents attended brief orientation sessions to review the new daily schedule, have interdisciplinary huddles, and share expectations for patient- and family-centered bedside rounds. On the general medicine units, new medical directors were hired to partner with existing nursing directors to support adoption of the workflows. Last, an interdisciplinary leadership team was formed to support the care redesign efforts. This team started meeting every 2 weeks.

Study Design

We used a pre–post analysis to study the effects of care redesign. Analysis was performed at the same time of year for 2 consecutive years to control for the stage of training and experience of the housestaff. TMA was performed by trained medical students using computer tablets linked to a customized Microsoft Access database form (Redmond, Washington). The form and the database were designed with specific buttons that, when pressed, recorded the time of particular events, such as the coming and going of each participant, the location of rounds, and the beginning and the end of rounding encounters with a patient. One research assistant using an Access entry form was able to dynamically track all events in real time, as they occurred. We collected data on 4 teams at baseline and 5 teams after the intervention. Each of the 4 baseline teams was followed for 4 consecutive weekdays—16 rounds total, April-June 2013—to capture the 4-day call cycle. Each of the 5 post-intervention teams was followed for 5 consecutive weekdays—25 rounds total, April–June 2014—to capture the 5-day cycle. (Because of technical difficulties, data from 1 rounding session were not captured.) For inclusion in the statistical analyses, TMA captured 166 on-service patients before the intervention and 304 afterward. Off-service patients, those with an attending other than the team attending, were excluded because their rounds were conducted separately.

We examined 2 primary outcomes, the proportion of time each clinical team member was present on rounds and the proportion of bedside rounding time. Secondary outcomes were round duration, rounding time per patient, and total non-patient time per rounding session (total rounding time minus total patient time).

Statistical Analysis

TMA data were organized in an Access database and analyzed with SAS Version 9.3 (SAS Institute, Cary, North Carolina). We analyzed the data by round session as well as by patient.

Data are presented as means with standard deviations, medians with interquartile ranges, and proportions, as appropriate. For analyses by round session, we used unadjusted linear regression; for patient-level analyses, we used general estimating equations to adjust for clustering of patients within each session; for nurse presence during any part of a round by patient, we used a χ² test. Total non-patient time per round session was compared with use of patient-clustered general estimating equations using a γ distribution to account for the non-normality of the data.

Patient and Care Team Characteristics

Over the first year of the initiative, 85% of a team’s patients were on their assigned unit, and 87% of a unit’s patients were with the assigned team. Census numbers were 10.4 patients per general medicine team in April-June 2013 and 12.7 patients per team in April-June 2014, a 22% increase after care redesign. There were no statistically significant differences in patient characteristics, including age, sex, race, language, admission source, and comorbidity measure (Elixhauser score), between the pre-intervention and post-intervention study periods, except for a slightly higher proportion of patients admitted from home and fewer patients admitted directly from clinic (Table 1).

Primary Outcomes

Mean proportion of time the nurse was present on rounds per round session increased significantly (P < 0.001), from 24.1% to 67.8% (Figure 1A, Table 2). For individual patient encounters, the increased overall nursing presence was attributable to having more nurses on rounds and having nurses present for a larger proportion of individual rounding encounters (Figure 1B, Table 2). Nurses were present for at least some part of rounds for 53% of patients before the intervention and 93% afterward (P < 0.001). Mean proportion of round time by each of the 2 interns on each team decreased from 59.6% to 49.6% (P = 0.007).

Total bedside rounding time increased significantly ( P < 0.001), from 39.9% before the intervention to 55.8% afterward (Table 2). Meanwhile, percentage of rounding time spent on the unit but outside patient rooms decreased significantly ( P = 0.004), from 55.2% to 42.2%, as did rounding time on a unit completely different from the patient’s (4.9% before intervention, 2.0% afterward; P = 0.03). Again, patient-level results were similar (Figure 2, Table 2), but the decreased time spent on the unit, outside the patient rooms, was not significant.

Secondary Outcomes

Total rounding time decreased significantly, from a mean of 182 minutes (3.0 hours) at baseline to a mean of 146 minutes (2.4 hours) after the intervention, despite the higher post-intervention census. (When adjusted for patient census, the difference increased from 35.5 to 53.8 minutes; Table 2.) Mean rounding time per patient decreased significantly, from 14.7 minutes at baseline to 10.5 minutes after the intervention. For newly admitted patients, mean rounding time per patient decreased from 30.0 minutes before implementation to 16.3 minutes afterward. Mean rounding time also decreased, though much less, for subsequent-day patients (Table 2). For both new and existing patients, the decrease in rounding time largely was a reduction in time spent rounding outside patient rooms, with minimal impact on bedside time (Table 2). Mean time nurses were present during a patient’s rounds increased significantly, from 4.5 to 8.0 minutes (Table 2). Total nurse rounding time increased from 45.1 minutes per session to 98.8 minutes. Rounding time not related to patient discussion or evaluation decreased from 22.7 minutes per session to 13.3 minutes ( P = 0.003).

TMA of our care redesign initiative showed that this multipronged intervention, which included team regionalization, encouragement of bedside rounding with nurses, call structure changes, and attendings’ reading of admission notes before rounds, resulted in an increased proportion of rounding time spent with patients and an increased proportion of time nurses were present on rounds. Secondarily, round duration decreased even as patient census increased.

Regionalized teams have been found to improve interdisciplinary communication.¹ The present study elaborates on that finding by demonstrating a dramatic increase in nursing presence on rounds, likely resulting from the unit’s use of rounding schedules and nurses’ prioritization of rounding orders, both of which were made possible by geographic co-localization. Other research has noted that one of the most significant barriers to interdisciplinary rounds is difficulty coordinating the start times of physician/nurse bedside rounding encounters. The system we have studied directly addresses this difficulty.⁹ Of note, nursing presence on rounds is necessary but not sufficient for true physician–nurse collaboration and effective communication,¹ as reflected in a separate study of the intervention showing no significant difference in the concordance of the patient care plan between nurses and physicians before and after regionalization.¹² Additional interventions may be needed to ensure that communication during bedside rounds is effective.

Our regionalized teams spent a significantly higher proportion of rounding time bedside, likely because of a cultural shift in expectations and the increased convenience of seeing patients on the team’s unit. Nevertheless, bedside time was not 100%. Structural barriers (eg, patients off-unit for dialysis) and cultural barriers likely contributed to the less than full adoption of bedside rounding. As described previously, cultural barriers to bedside rounding include trainees’ anxiety about being questioned in front of patients, the desire to freely exchange academic ideas in a conference room, and attendings’ doubts about their bedside teaching ability.^1,9,13 Bedside rounds provide an important opportunity to apply the principles of patient- and family-centered care, including promotion of dignity and respect, information sharing, and collaboration. Thus, overcoming the concerns of housestaff and attendings and helping them feel prepared for bedside rounds can benefit the patient experience. More attention should be given to these practices as these types of interventions are implemented at Brigham and Women’s Hospital and elsewhere.^1,13-15

Another primary concern about interdisciplinary bedside rounding is the perception that it takes more time.⁹ Therefore, it was important for us to measure round duration as a balancing measure to be considered for our intervention. Fortunately, we found round duration decreased with regionalization and encouragement of bedside rounding. This decrease was driven largely by a significant decrease in mean rounding time per new patient, which may be attributable at least in part to setting expectations that attendings and residents will read admission notes before rounds and that interns will summarize rather than recount information from admission notes. However, we also found rounding time decreases for subsequent-day patients, suggesting an underlying time savings. Spending a larger proportion of time bedside may therefore result in more efficient rounds. Bedside presentations can reduce redundancies, such as discussing a patient’s case outside his or her room and subsequently walking in and going over much of the same information with the patient. Our model de-emphasizes data transfer in favor of discussion of care plans. There was also a decrease in non-patient time, likely reflecting reduced transit time for regionalized teams. This decrease aligns with a recent finding that bedside rounding was at least as efficient as rounding outside the room.¹⁶

Of note, though a larger percentage of time was spent bedside after implementation of the care redesign, the absolute amount of bedside time did not change significantly. Our data showed that, even with shorter rounds, the same amount of absolute time can be spent bedside, face to face with the patient, by increasing the proportion of bedside rounding time. In other words, teams on average did not spend more time with patients, though the content and the structure of those encounters may have changed. This finding may be attributable to eliminating redundancy, forgoing the outside-the-room discussion, and thus the largest time reductions were realized there. In addition, teams incompletely adopted beside rounds, as reflected in the data. We expect that, with more complete adoption, an even larger proportion of time will be spent bedside, and absolute time bedside might increase as a result.

An unexpected result of the care redesign was that interns’ proportion of rounding time decreased after the intervention. This decrease most likely is attributable to interns’ being less likely to participate in rounds for a co-intern’s patient, and to their staying outside that patient’s room to give themselves more time to advance the care of their own patients. Before the intervention, when more rounding time was spent outside patient rooms, interns were more likely to join rounds for their co-intern’s patients because they could easily break away, as needed, to continue care of their own patients. The resident is now encouraged to use the morning huddle to identify which patients likely have the most educational value, and both interns are expected to join the bedside rounds for these patients.

This study had a few limitations. First, the pre–post design made it difficult to exclude the possibility that other temporal changes may have affected outcomes, though we did account for time-of-year effects by aligning our data-collection phases. In addition, the authors, including the director of the general medical service, are unaware of any co-interventions during the study period. Second, the multipronged intervention included care team regionalization, encouragement of bedside rounding with nurses, call structure changes (from 4 days to daily admitting), and attendings’ reading of admission notes before rounds. Thus, parsing which component(s) contributed to the results was difficult, though all the changes instituted likely were necessary for system redesign. For example, regionalization of clinicians to unit-based teams was made possible by switching to a daily admitting system.

Time that team members spent preparing for rounds was not recorded before or after the intervention. Thus, the decrease in total rounding time could have been accompanied by an increase in time spent preparing for rounds. However, admission notes were available in our electronic medical record before and after the intervention, and most residents and attendings were already reading them pre-intervention. After the intervention, pre-round note reading was more clearly defined as an expectation, and we were able to set the expectation that interns should use their presentations to summarize rather than recount information. In addition, in the post-intervention period, we did not include time spent preparing rounding orders; as already noted, however, preparation took only 5 minutes per day. Also, we did not analyze the content or the quality of the discussion on rounds, but simply recorded who was present where and when. Regarding the effect of the intervention on patient care, results were mixed. As reported in 2016, we saw no difference in frequency of adverse events with this intervention.¹² However, a more sensitive measure of adverse events—used in a study on handoffs—showed our regionalization efforts had an additive effect on reducing overnight adverse events.¹⁷Researchers should now focus on the effects of care redesign on clinical outcomes, interdisciplinary care team communication, patient engagement and satisfaction, provider opinions of communication, workflow, patient care, and housestaff education. Our methodology can be used as a model to link structure, process, and outcome related to rounds and thereby better understand how best to optimize patient care and efficiency. Additional studies are needed to analyze the content of rounds and their association with patient and educational outcomes. Last, it will be important to conduct a study to see if the effects we have identified can be sustained. Such a study is already under way.

In conclusion, creating regionalized care teams and encouraging focused bedside rounds increased the proportion of bedside time and the presence of nurses on rounds. Rounds were shorter despite higher patient census. TMA revealed that regionalized care teams and bedside rounding at a large academic hospital are feasible, and are useful in establishing the necessary structures for increasing physician–nurse and provider–patient interactions.

Acknowledgments

The authors acknowledge Dr. Stan Ashley, Dr. Jacqueline Somerville, and Sheila Harris for their support of the regionalization initiative.

Disclosures

Dr. Schnipper received funding from Sanofi-aventis to conduct an investigator-initiated study to implement and evaluate a multi-faceted intervention to improve transitions of care in patients discharged home on insulin. The study was also supported by funding from the Marshall A. Wolf Medical Education Fund, Brigham and Women’s Hospital, and Dr. Stan Ashley, Chief Medical Officer, Brigham and Women’s Hospital. Some of the content of this article was orally presented at the annual meeting of the Society of Hospital Medicine; March 29-April 1, 2015; National Harbor, MD.

Attending rounds at academic medical centers are often disconnected from patients and non-physician care team members. Time spent bedside is consistently less than one third of total rounding time, with observational studies reporting a range of 9% to 33% over the past several decades.^1-8 Rounds are often conducted outside patient rooms, denying patients, families, and nurses the opportunity to participate and offer valuable insights. Lack of bedside rounds thus limits patient and family engagement, patient input into the care plan, teaching of the physical examination, and communication and collaboration with nurses. In one study, physicians and nurses on rounds engaged in interprofessional communication in only 12% of patient cases.¹ Studies have found interdisciplinary bedside rounds have several benefits, including subjectively improved communication and teamwork between physicians and nurses; increased patient satisfaction, including feeling more cared for by the medical team; and decreased length of stay and costs of care.^2-10

However, there are many barriers to conducting interdisciplinary bedside rounds at large academic medical centers. Patients cared for by a single medical team are often geographically dispersed to several nursing units, and nurses are unable to predict when physicians will round on their patients. This situation limits nursing involvement on rounds and keeps doctors and nurses isolated from each other.² Regionalization of care teams reduces this fragmentation by facilitating more interaction among doctors, patients, families, and nursing staff.

There are few data on how regionalized patients and interdisciplinary bedside rounds affect rounding time and the nature of rounds. This information is needed to understand how these structural changes mediate their effects, whether other steps are required to optimize outcomes, and how to maximize efficiency. We used time-motion analysis (TMA) to investigate how regionalization of medical teams, encouragement of bedside rounding, and systematic inclusion of nurses on ward rounds affect amount of time spent with patients, nursing presence on rounds, and total rounding time.

Setting

This prospective interventional study, approved by the Institutional Review Board of Partners HealthCare, was conducted on the general medical wards at Brigham and Women’s Hospital, an academic 793-bed tertiary-care center in Boston, Massachusetts. Housestaff teams consist of 1 attending, 1 resident, and 2 interns with or without a medical student. Before June 20, 2013, daily rounds on medical inpatients were conducted largely on the patient unit but outside patient rooms. After completing most of a rounding discussion outside a patient’s room, the team might walk in to examine or speak with the patient. A typical medical team had patients dispersed over 7 medical units on average, and over as many as 13. As nurses were unit based, they did not consistently participate in rounds.

Intervention

In June 2013, as part of a general medical service care redesign initiative, the general medical teams were regionalized to specific inpatient units. The goal was to have teams admit patients predominantly to the team’s designated unit and to have all patients on a unit be cared for by the unit’s assigned team as often as possible, with an 85% goal for both. Toward those ends, the admitting structure was changed from a traditional 4-day call cycle to daily admitting for all teams, based on each unit’s bed availability.¹¹

Teams were also expected to conduct rounds with nurses, and a system for facilitating these rounds was established. As physician and nurse care teams were now geographically co-located, it became possible for residents and nurses to check a rounding sheet for the planned patient rounding order, which had been set by the resident and nurse-in-charge before rounds. No more than about 5 minutes was needed to prepare each day’s order. The rounding sheet prioritized sick patients, newly admitted patients, and planned morning discharges, but patients were also always grouped by nurse. For example, the physician team rounded with the first nurse on all 3 of a nurse’s patients, and then proceeded to the next group of 3 patients with the next nurse, until all patients were seen.

Teams were encouraged to conduct patient- and family-centered rounds exclusively at bedside, except when bedside rounding was thought to be detrimental to a patient (eg, one with delirium). After an intern’s bedside presentation, which included a brief summary and details about overnight events and vital signs, the concerns of the patient, family, and nurse were shared, a focused physical examination performed, relevant data (eg, laboratory test results and imaging studies) reviewed, and the day’s plan formulated. The entire team, including the attending, was expected to have read new patients’ admission notes before rounds. Bedside rounds could thus be focused more on patient assessment and patient/family engagement and less on data transfer.

Several actions were taken to facilitate these changes. Residents, attendings, nurses, and other interdisciplinary team members participated in a series of focus groups and conferences to define workflows and share best practices for patient- and family-centered bedside rounds. Tips on bedside rounding were included in a general medicine rotation guidebook made available to residents and attendings. At the beginning of each post-intervention general medicine rotation, attendings and residents attended brief orientation sessions to review the new daily schedule, have interdisciplinary huddles, and share expectations for patient- and family-centered bedside rounds. On the general medicine units, new medical directors were hired to partner with existing nursing directors to support adoption of the workflows. Last, an interdisciplinary leadership team was formed to support the care redesign efforts. This team started meeting every 2 weeks.

Study Design

We used a pre–post analysis to study the effects of care redesign. Analysis was performed at the same time of year for 2 consecutive years to control for the stage of training and experience of the housestaff. TMA was performed by trained medical students using computer tablets linked to a customized Microsoft Access database form (Redmond, Washington). The form and the database were designed with specific buttons that, when pressed, recorded the time of particular events, such as the coming and going of each participant, the location of rounds, and the beginning and the end of rounding encounters with a patient. One research assistant using an Access entry form was able to dynamically track all events in real time, as they occurred. We collected data on 4 teams at baseline and 5 teams after the intervention. Each of the 4 baseline teams was followed for 4 consecutive weekdays—16 rounds total, April-June 2013—to capture the 4-day call cycle. Each of the 5 post-intervention teams was followed for 5 consecutive weekdays—25 rounds total, April–June 2014—to capture the 5-day cycle. (Because of technical difficulties, data from 1 rounding session were not captured.) For inclusion in the statistical analyses, TMA captured 166 on-service patients before the intervention and 304 afterward. Off-service patients, those with an attending other than the team attending, were excluded because their rounds were conducted separately.

We examined 2 primary outcomes, the proportion of time each clinical team member was present on rounds and the proportion of bedside rounding time. Secondary outcomes were round duration, rounding time per patient, and total non-patient time per rounding session (total rounding time minus total patient time).

Statistical Analysis

TMA data were organized in an Access database and analyzed with SAS Version 9.3 (SAS Institute, Cary, North Carolina). We analyzed the data by round session as well as by patient.

Data are presented as means with standard deviations, medians with interquartile ranges, and proportions, as appropriate. For analyses by round session, we used unadjusted linear regression; for patient-level analyses, we used general estimating equations to adjust for clustering of patients within each session; for nurse presence during any part of a round by patient, we used a χ² test. Total non-patient time per round session was compared with use of patient-clustered general estimating equations using a γ distribution to account for the non-normality of the data.

Patient and Care Team Characteristics

Over the first year of the initiative, 85% of a team’s patients were on their assigned unit, and 87% of a unit’s patients were with the assigned team. Census numbers were 10.4 patients per general medicine team in April-June 2013 and 12.7 patients per team in April-June 2014, a 22% increase after care redesign. There were no statistically significant differences in patient characteristics, including age, sex, race, language, admission source, and comorbidity measure (Elixhauser score), between the pre-intervention and post-intervention study periods, except for a slightly higher proportion of patients admitted from home and fewer patients admitted directly from clinic (Table 1).

Primary Outcomes

Mean proportion of time the nurse was present on rounds per round session increased significantly (P < 0.001), from 24.1% to 67.8% (Figure 1A, Table 2). For individual patient encounters, the increased overall nursing presence was attributable to having more nurses on rounds and having nurses present for a larger proportion of individual rounding encounters (Figure 1B, Table 2). Nurses were present for at least some part of rounds for 53% of patients before the intervention and 93% afterward (P < 0.001). Mean proportion of round time by each of the 2 interns on each team decreased from 59.6% to 49.6% (P = 0.007).

Total bedside rounding time increased significantly ( P < 0.001), from 39.9% before the intervention to 55.8% afterward (Table 2). Meanwhile, percentage of rounding time spent on the unit but outside patient rooms decreased significantly ( P = 0.004), from 55.2% to 42.2%, as did rounding time on a unit completely different from the patient’s (4.9% before intervention, 2.0% afterward; P = 0.03). Again, patient-level results were similar (Figure 2, Table 2), but the decreased time spent on the unit, outside the patient rooms, was not significant.

Secondary Outcomes

Total rounding time decreased significantly, from a mean of 182 minutes (3.0 hours) at baseline to a mean of 146 minutes (2.4 hours) after the intervention, despite the higher post-intervention census. (When adjusted for patient census, the difference increased from 35.5 to 53.8 minutes; Table 2.) Mean rounding time per patient decreased significantly, from 14.7 minutes at baseline to 10.5 minutes after the intervention. For newly admitted patients, mean rounding time per patient decreased from 30.0 minutes before implementation to 16.3 minutes afterward. Mean rounding time also decreased, though much less, for subsequent-day patients (Table 2). For both new and existing patients, the decrease in rounding time largely was a reduction in time spent rounding outside patient rooms, with minimal impact on bedside time (Table 2). Mean time nurses were present during a patient’s rounds increased significantly, from 4.5 to 8.0 minutes (Table 2). Total nurse rounding time increased from 45.1 minutes per session to 98.8 minutes. Rounding time not related to patient discussion or evaluation decreased from 22.7 minutes per session to 13.3 minutes ( P = 0.003).

TMA of our care redesign initiative showed that this multipronged intervention, which included team regionalization, encouragement of bedside rounding with nurses, call structure changes, and attendings’ reading of admission notes before rounds, resulted in an increased proportion of rounding time spent with patients and an increased proportion of time nurses were present on rounds. Secondarily, round duration decreased even as patient census increased.

Regionalized teams have been found to improve interdisciplinary communication.¹ The present study elaborates on that finding by demonstrating a dramatic increase in nursing presence on rounds, likely resulting from the unit’s use of rounding schedules and nurses’ prioritization of rounding orders, both of which were made possible by geographic co-localization. Other research has noted that one of the most significant barriers to interdisciplinary rounds is difficulty coordinating the start times of physician/nurse bedside rounding encounters. The system we have studied directly addresses this difficulty.⁹ Of note, nursing presence on rounds is necessary but not sufficient for true physician–nurse collaboration and effective communication,¹ as reflected in a separate study of the intervention showing no significant difference in the concordance of the patient care plan between nurses and physicians before and after regionalization.¹² Additional interventions may be needed to ensure that communication during bedside rounds is effective.

Our regionalized teams spent a significantly higher proportion of rounding time bedside, likely because of a cultural shift in expectations and the increased convenience of seeing patients on the team’s unit. Nevertheless, bedside time was not 100%. Structural barriers (eg, patients off-unit for dialysis) and cultural barriers likely contributed to the less than full adoption of bedside rounding. As described previously, cultural barriers to bedside rounding include trainees’ anxiety about being questioned in front of patients, the desire to freely exchange academic ideas in a conference room, and attendings’ doubts about their bedside teaching ability.^1,9,13 Bedside rounds provide an important opportunity to apply the principles of patient- and family-centered care, including promotion of dignity and respect, information sharing, and collaboration. Thus, overcoming the concerns of housestaff and attendings and helping them feel prepared for bedside rounds can benefit the patient experience. More attention should be given to these practices as these types of interventions are implemented at Brigham and Women’s Hospital and elsewhere.^1,13-15

Another primary concern about interdisciplinary bedside rounding is the perception that it takes more time.⁹ Therefore, it was important for us to measure round duration as a balancing measure to be considered for our intervention. Fortunately, we found round duration decreased with regionalization and encouragement of bedside rounding. This decrease was driven largely by a significant decrease in mean rounding time per new patient, which may be attributable at least in part to setting expectations that attendings and residents will read admission notes before rounds and that interns will summarize rather than recount information from admission notes. However, we also found rounding time decreases for subsequent-day patients, suggesting an underlying time savings. Spending a larger proportion of time bedside may therefore result in more efficient rounds. Bedside presentations can reduce redundancies, such as discussing a patient’s case outside his or her room and subsequently walking in and going over much of the same information with the patient. Our model de-emphasizes data transfer in favor of discussion of care plans. There was also a decrease in non-patient time, likely reflecting reduced transit time for regionalized teams. This decrease aligns with a recent finding that bedside rounding was at least as efficient as rounding outside the room.¹⁶

Of note, though a larger percentage of time was spent bedside after implementation of the care redesign, the absolute amount of bedside time did not change significantly. Our data showed that, even with shorter rounds, the same amount of absolute time can be spent bedside, face to face with the patient, by increasing the proportion of bedside rounding time. In other words, teams on average did not spend more time with patients, though the content and the structure of those encounters may have changed. This finding may be attributable to eliminating redundancy, forgoing the outside-the-room discussion, and thus the largest time reductions were realized there. In addition, teams incompletely adopted beside rounds, as reflected in the data. We expect that, with more complete adoption, an even larger proportion of time will be spent bedside, and absolute time bedside might increase as a result.

An unexpected result of the care redesign was that interns’ proportion of rounding time decreased after the intervention. This decrease most likely is attributable to interns’ being less likely to participate in rounds for a co-intern’s patient, and to their staying outside that patient’s room to give themselves more time to advance the care of their own patients. Before the intervention, when more rounding time was spent outside patient rooms, interns were more likely to join rounds for their co-intern’s patients because they could easily break away, as needed, to continue care of their own patients. The resident is now encouraged to use the morning huddle to identify which patients likely have the most educational value, and both interns are expected to join the bedside rounds for these patients.

This study had a few limitations. First, the pre–post design made it difficult to exclude the possibility that other temporal changes may have affected outcomes, though we did account for time-of-year effects by aligning our data-collection phases. In addition, the authors, including the director of the general medical service, are unaware of any co-interventions during the study period. Second, the multipronged intervention included care team regionalization, encouragement of bedside rounding with nurses, call structure changes (from 4 days to daily admitting), and attendings’ reading of admission notes before rounds. Thus, parsing which component(s) contributed to the results was difficult, though all the changes instituted likely were necessary for system redesign. For example, regionalization of clinicians to unit-based teams was made possible by switching to a daily admitting system.

Time that team members spent preparing for rounds was not recorded before or after the intervention. Thus, the decrease in total rounding time could have been accompanied by an increase in time spent preparing for rounds. However, admission notes were available in our electronic medical record before and after the intervention, and most residents and attendings were already reading them pre-intervention. After the intervention, pre-round note reading was more clearly defined as an expectation, and we were able to set the expectation that interns should use their presentations to summarize rather than recount information. In addition, in the post-intervention period, we did not include time spent preparing rounding orders; as already noted, however, preparation took only 5 minutes per day. Also, we did not analyze the content or the quality of the discussion on rounds, but simply recorded who was present where and when. Regarding the effect of the intervention on patient care, results were mixed. As reported in 2016, we saw no difference in frequency of adverse events with this intervention.¹² However, a more sensitive measure of adverse events—used in a study on handoffs—showed our regionalization efforts had an additive effect on reducing overnight adverse events.¹⁷Researchers should now focus on the effects of care redesign on clinical outcomes, interdisciplinary care team communication, patient engagement and satisfaction, provider opinions of communication, workflow, patient care, and housestaff education. Our methodology can be used as a model to link structure, process, and outcome related to rounds and thereby better understand how best to optimize patient care and efficiency. Additional studies are needed to analyze the content of rounds and their association with patient and educational outcomes. Last, it will be important to conduct a study to see if the effects we have identified can be sustained. Such a study is already under way.

In conclusion, creating regionalized care teams and encouraging focused bedside rounds increased the proportion of bedside time and the presence of nurses on rounds. Rounds were shorter despite higher patient census. TMA revealed that regionalized care teams and bedside rounding at a large academic hospital are feasible, and are useful in establishing the necessary structures for increasing physician–nurse and provider–patient interactions.

Acknowledgments

The authors acknowledge Dr. Stan Ashley, Dr. Jacqueline Somerville, and Sheila Harris for their support of the regionalization initiative.

Disclosures

Dr. Schnipper received funding from Sanofi-aventis to conduct an investigator-initiated study to implement and evaluate a multi-faceted intervention to improve transitions of care in patients discharged home on insulin. The study was also supported by funding from the Marshall A. Wolf Medical Education Fund, Brigham and Women’s Hospital, and Dr. Stan Ashley, Chief Medical Officer, Brigham and Women’s Hospital. Some of the content of this article was orally presented at the annual meeting of the Society of Hospital Medicine; March 29-April 1, 2015; National Harbor, MD.

In recent years, rapid response teams (RRTs) have been widely implemented to improve patient safety and quality of care. RRTs traditionally are activated by providers to address a clinically deteriorating patient; trained nurses, respiratory care specialists, and physicians are brought bedside to assist in triage and management. After the Joint Commission¹ endorsed patient engagement as a strategy for enhancing patient safety, new initiatives were developed to meet the challenge. Programs designed to enhance patient engagement have taken a variety of forms, including educational campaigns encouraging patients to report adverse events, requests for handwashing by providers, and the institution of patient- and family-activated RRTs.² Patient involvement is viewed favorably and has been shown to increase patients’ perception of health care quality.³ Although these initiatives are presumed helpful in encouraging communication, there is limited evidence that more communication leads to safety improvements. Despite the increasing prevalence of patient-activated RRTs in the United States, they have gone largely unevaluated in the adult population, and their efficacy remains unclear.

Condition Help (CH) is a patient- and family-initiated RRT designed to prevent medical errors and communication problems and improve patient safety. Patients and families are encouraged to call the CH hotline if they believe that there has been a breakdown in care or that their health is in imminent danger. This RRT was inspired by the case of Josie King, an 18-month-old girl who died of preventable causes at a large children’s hospital.⁴ After her daughter’s death, Sorrel King started the Josie King Foundation, an organization committed to preventing medical errors and creating a culture of patient safety. With the support of this foundation, CH was launched in 2005 at the Children’s Hospital of Pittsburgh at the University of Pittsburgh Medical Center (UPMC). Later it was implemented at the UPMC adult tertiary-care center, and now it is available in all UPMC facilities.

On admission, patients receive a brochure that details the purpose of CH and provides examples of when and how to call the CH hotline. In this brochure, patients are instructed to call CH in 3 situations: “1) There is an emergency and you cannot get the attention of hospital staff, 2) You see a change in the patient’s condition and the health care team is not recognizing the concern, or 3) There is breakdown in how care is given or uncertainty over what needs to be done.” These instructions are printed on bulletins placed in elevators and hallways throughout the hospital. Patients and families may activate the system at any time and can even do so from home.

When a patient or family member calls the hotline, an operator notifies the CH team. This team, which consists of a patient care liaison (or an on-duty administrator) and the unit charge nurse, convenes bedside to address the patient’s concern. The team was designed without a physician to ensure that the primary team remains in charge of the care plan. CH is kept separate from our traditional RRT and does not compete for resources (personnel, equipment, time) with the RRT, which is designed to address a clinically deteriorating patient.

In this article, we describe the characteristics of patients for whom CH was activated at our adult hospital. We also describe reasons for calls, whether changes in care were implemented, and outcomes, including traditional RRT activation, transfer to intensive care unit (ICU), and inpatient mortality. As CH was designed with patient safety as a goal, we tracked 2 types of calls, those involving safety issues and those involving nonsafety issues.

This study was approved by the quality improvement committee at the University of Pittsburgh and was considered exempt from review by the university’s Institutional Review Board.

Our integrated health system consists of more than 20 hospitals serving a tristate region. UPMC Presbyterian and UPMC Montefiore are adult tertiary-care referral hospitals with more than 750 medical/surgical beds and 150 critical care beds and more than 30,000 annual inpatient admissions. These hospitals are physically connected and function as a single large medical center. We reviewed all CH events that occurred at this combined hospital during the period January 2012 through June 2015. The dates coincided with CH data acquisition.

CH was available 24 hours a day 7 days a week. A patient care liaison (or an on-duty administrator) and the unit charge nurse responded to CH calls. Data from all calls included date and time of call, day of week, primary service, patient location, unique patient identifiers, call initiator (patient or family), whether a call led to changes in care, and primary reason for call. Each call reason was sorted into 1 of 10 categories: pain control, staff problem, lack of communication between patient/family and care team, questions about patient management, care delays, delays in a particular service, questions about discharge, administrative issues, acute psychiatric needs, and unknown/other. In addition, after a call, we reviewed all charts to determine if a safety issue was involved; Dr. Eden and Dr. Bump independently reviewed calls for safety issues and discussed any differences until they reached consensus. We also recorded outcomes, including activation of a traditional RRT or transfer to ICU within 24 hours of CH call, inpatient mortality, and against medical advice (AMA) discharges. Given that many calls were made by patients who called more than once (during a single admission or over multiple admissions), we also sorted patients into one-time callers and repeat callers for comparison. Patient satisfaction data were unavailable for review.

Patient demographic data are presented as means, standard deviations, and percentages, and call characteristics as percentages. Chi-square tests and t tests were used for analyses except for comparisons having few observations. For those, Fisher exact test was used. All analyses were performed with SAS Version 9.4 (SAS Institute, Cary, North Carolina).

From January 2012 through June 2015, 367 CH calls were made, about 105 annually. During this period, there were about 33,000 admissions, 800 combined grievances and complaints, 170 AMA discharges, 155 cardiac arrests, 2300 traditional RRT activations, and 1200 inpatient deaths per year. The 367 CH calls were made by 240 patients (Table 1). Of these 240 patients, 43 (18%) activated the CH team with multiple calls; their calls accounted for (46.3%) of all calls (170/367). The majority of calls were made by patients (76.8%) rather than family members (21.8%). Mean (SD) patient age was 45.8 (17.4) years. Mean (SD) number of admissions per patient per year was 2.7 (3.5). More events were activated for patients admitted to medical services (66%) than surgical services (34%). Calls were evenly distributed between time of day and day of week.

The most common reason for CH calls was inadequate pain control (48.2%), followed by dissatisfaction with staff (12.5%); the remaining calls were evenly distributed among the other categories. The majority of calls involved nonsafety issues (83.4%) rather than safety issues (11.4%); in 5.2% of calls, the distinction could not be made because of lack of information (Table 2). In 152 (41.4%) of the 367 total calls, a change in care or alteration in management was made. Of these 152 calls, 99 (65.1%) involved distinct changes in the care plan, such as medication changes, imaging or additional testing, or consultation with other physicians; the other 53 calls (34.9%) involved additional patient counseling or nonmedical changes. Our traditional RRT was activated within 24 hours of CH in 19 cases (5.2%); of the 19 patients, 6 were transferred to ICU. Seven patients (2.9%) died during admission. Twelve (3.3%) were discharged AMA. We compared outcomes of patients who made safety-issue calls with those of patients who made nonsafety-issue calls. The composite outcome of RRT activation, ICU transfer, and mortality was found for 6 (14.3%) of the 42 safety-issue calls and 15 (4.9%) of the 306 nonsafety-issue calls (P = 0.0291).

Patient- and family-activated RRTs provide unique opportunities for patient and family engagement during inpatient hospital stays. Our study described the results obtained with use of a well-established patient-activated RRT over several years, one of the longer observation periods reported in the literature. We found that, with use of patient-activated RRTs, patient safety issues were identified, though these were far outnumbered by nonsafety issues.

Almost half of all CH events were related to pain. Pain as the primary driver for RRT activation may be attributable to several factors, including degree of illness, poor communication about pain management expectations, positive reinforcement of narcotic-seeking behavior as a result of CH activation, and high rate of opiate use in the catchment area. A striking finding of our analysis was repeat calling; only 43 (18%) of the 240 callers were repeat callers, but they made almost half of all the calls. In some cases, during a single admission, multiple calls were made because the first had no effect on care or management; more typically, though, multiple calls were made over several admissions. Repeat callers were admitted more often per year, and they used hospital services more. They should be further studied with a goal of designing programs that better meet their needs and that prospectively address expectations of pain control.

Our study was unique in describing several outcomes related to CH events. We found that traditional RRTs were seldom activated, level of care was seldom escalated, and mortality was rare, though these outcomes occurred more often for safety-issue calls than nonsafety-issue calls. We also found that activation of CH teams often led to changes in medical management, though we could not determine whether these changes in care led to different patient outcomes.

Patient-initiated RRTs are described in a limited number of pediatric and adult studies, all with findings differing from ours. In the pediatric models, most calls were initiated by family members, were less frequent, and tended to signal higher patient acuity.^5,6 For example, in a pediatric RRT model,⁵ family members activated the RRT only twice within the study year, but both calls resulted in ICU transfer. Most descriptions of patient-activated RRTs in adult hospitals are from pilot studies, which similarly identified infrequent RRT calls but often did not identify call reasons or specific outcomes.⁷ A single-center study concluded that, after implementation of a mixed-model RRT⁸—a traditional practitioner-activated RRT later enhanced with a patient/family activation mechanism—non-ICU codes decreased, and there was a statistically significant drop in hospital-wide mortality rates. However, this RRT was patient-activated only 25 times over 2 years, and the specific outcomes of those events were not described.

Other initiatives have been designed to enhance patient care and communication. Purposeful rounding systems⁹ involve hourly rounding by bedside nurses and daily rounding by nurse leaders to improve timely patient care and provide proactive service. Such systems ideally preempt calls involving dissatisfaction and nonsafety issues. Although they would reduce the number of patient-dissatisfaction calls made in the CH system, they may not be any better than the CH system is in its main purpose, identifying safety issues. In addition, whether patient-activated RRTs or purposeful rounding systems are better at addressing patient dissatisfaction is unclear.

This study had its limitations. First, like other studies, it was a single-center observational study without a concurrent control group. Second, because CH was first implemented 10 years ago, we could not compare patient outcomes or traditional RRT use before and after program initiation. Third, our study cohort consisted of patients hospitalized at one academic tertiary-care center in one region, and the hospital is a training site for multiple residencies and fellowships. These factors likely affect the generalizability of our data to smaller or community-based centers. Fourth, some determinations were subjective (eg, whether calls involved safety or nonsafety issues). We tried to minimize bias by having 2 authors independently review cases, but the process did not reflect patient experience or perspective. Fifth, our hospital adopted its traditional RRT years before its CH system. The criteria used by hospital personnel for traditional RRT activation are designed to encourage staff to call for help at early signs of patient deterioration. Consequently, traditional RRT activations substantially outnumber CH calls. Whether this resulted in fewer CH safety calls is unclear. Sixth, we did not capture the financial implications of using CH teams.

Although patient-activated RRTs identified patient safety issues, questions about the utility or necessity of these RRTs remain. In our era of limited hospital resources, the case has not been definitively made that these teams are practical, based on patient outcomes, though other studies have found improved patient satisfaction.⁷ Most of the RRT calls in our study involved patient dissatisfaction and communication issues. CH may not be the ideal approach for managing these issues, but it represents the last line of patient advocacy once other systems have failed.

We think patient-activated RRTs have the potential to effect patient engagement in safe care. Given the importance of establishing a culture of patient safety and engagement, and increased detection of safety-related events, CH remains active throughout our hospital system. Newer iterations of CH may benefit from stricter language in defining appropriate occasions for calling RRTs, and from descriptions of other resources for patient advocacy within the hospital. These modifications could end up restricting RRT activations to patient complaints and preserving CH resources for patients with safety concerns. Our study lays the groundwork for other institutions that are considering similar interventions. Studies should now start evaluating how well patient- and family-activated RRTs improve patient satisfaction, staff satisfaction, and patient outcomes.

Patient- and family-activated RRTs were designed to engage patients and families in safe care. Although CH detects patient safety issues, these are far outnumbered by nonsafety issues. CH demonstrates a commitment to patient engagement and a culture that emphasizes patient safety.

Acknowledgements

This work was presented as a poster at the annual meeting of the Society of Hospital Medicine; March 6-9, 2016; San Diego, CA.

Disclosure

Nothing to report.

In recent years, rapid response teams (RRTs) have been widely implemented to improve patient safety and quality of care. RRTs traditionally are activated by providers to address a clinically deteriorating patient; trained nurses, respiratory care specialists, and physicians are brought bedside to assist in triage and management. After the Joint Commission¹ endorsed patient engagement as a strategy for enhancing patient safety, new initiatives were developed to meet the challenge. Programs designed to enhance patient engagement have taken a variety of forms, including educational campaigns encouraging patients to report adverse events, requests for handwashing by providers, and the institution of patient- and family-activated RRTs.² Patient involvement is viewed favorably and has been shown to increase patients’ perception of health care quality.³ Although these initiatives are presumed helpful in encouraging communication, there is limited evidence that more communication leads to safety improvements. Despite the increasing prevalence of patient-activated RRTs in the United States, they have gone largely unevaluated in the adult population, and their efficacy remains unclear.

Condition Help (CH) is a patient- and family-initiated RRT designed to prevent medical errors and communication problems and improve patient safety. Patients and families are encouraged to call the CH hotline if they believe that there has been a breakdown in care or that their health is in imminent danger. This RRT was inspired by the case of Josie King, an 18-month-old girl who died of preventable causes at a large children’s hospital.⁴ After her daughter’s death, Sorrel King started the Josie King Foundation, an organization committed to preventing medical errors and creating a culture of patient safety. With the support of this foundation, CH was launched in 2005 at the Children’s Hospital of Pittsburgh at the University of Pittsburgh Medical Center (UPMC). Later it was implemented at the UPMC adult tertiary-care center, and now it is available in all UPMC facilities.

On admission, patients receive a brochure that details the purpose of CH and provides examples of when and how to call the CH hotline. In this brochure, patients are instructed to call CH in 3 situations: “1) There is an emergency and you cannot get the attention of hospital staff, 2) You see a change in the patient’s condition and the health care team is not recognizing the concern, or 3) There is breakdown in how care is given or uncertainty over what needs to be done.” These instructions are printed on bulletins placed in elevators and hallways throughout the hospital. Patients and families may activate the system at any time and can even do so from home.

When a patient or family member calls the hotline, an operator notifies the CH team. This team, which consists of a patient care liaison (or an on-duty administrator) and the unit charge nurse, convenes bedside to address the patient’s concern. The team was designed without a physician to ensure that the primary team remains in charge of the care plan. CH is kept separate from our traditional RRT and does not compete for resources (personnel, equipment, time) with the RRT, which is designed to address a clinically deteriorating patient.

In this article, we describe the characteristics of patients for whom CH was activated at our adult hospital. We also describe reasons for calls, whether changes in care were implemented, and outcomes, including traditional RRT activation, transfer to intensive care unit (ICU), and inpatient mortality. As CH was designed with patient safety as a goal, we tracked 2 types of calls, those involving safety issues and those involving nonsafety issues.

This study was approved by the quality improvement committee at the University of Pittsburgh and was considered exempt from review by the university’s Institutional Review Board.

Our integrated health system consists of more than 20 hospitals serving a tristate region. UPMC Presbyterian and UPMC Montefiore are adult tertiary-care referral hospitals with more than 750 medical/surgical beds and 150 critical care beds and more than 30,000 annual inpatient admissions. These hospitals are physically connected and function as a single large medical center. We reviewed all CH events that occurred at this combined hospital during the period January 2012 through June 2015. The dates coincided with CH data acquisition.

CH was available 24 hours a day 7 days a week. A patient care liaison (or an on-duty administrator) and the unit charge nurse responded to CH calls. Data from all calls included date and time of call, day of week, primary service, patient location, unique patient identifiers, call initiator (patient or family), whether a call led to changes in care, and primary reason for call. Each call reason was sorted into 1 of 10 categories: pain control, staff problem, lack of communication between patient/family and care team, questions about patient management, care delays, delays in a particular service, questions about discharge, administrative issues, acute psychiatric needs, and unknown/other. In addition, after a call, we reviewed all charts to determine if a safety issue was involved; Dr. Eden and Dr. Bump independently reviewed calls for safety issues and discussed any differences until they reached consensus. We also recorded outcomes, including activation of a traditional RRT or transfer to ICU within 24 hours of CH call, inpatient mortality, and against medical advice (AMA) discharges. Given that many calls were made by patients who called more than once (during a single admission or over multiple admissions), we also sorted patients into one-time callers and repeat callers for comparison. Patient satisfaction data were unavailable for review.

Patient demographic data are presented as means, standard deviations, and percentages, and call characteristics as percentages. Chi-square tests and t tests were used for analyses except for comparisons having few observations. For those, Fisher exact test was used. All analyses were performed with SAS Version 9.4 (SAS Institute, Cary, North Carolina).

From January 2012 through June 2015, 367 CH calls were made, about 105 annually. During this period, there were about 33,000 admissions, 800 combined grievances and complaints, 170 AMA discharges, 155 cardiac arrests, 2300 traditional RRT activations, and 1200 inpatient deaths per year. The 367 CH calls were made by 240 patients (Table 1). Of these 240 patients, 43 (18%) activated the CH team with multiple calls; their calls accounted for (46.3%) of all calls (170/367). The majority of calls were made by patients (76.8%) rather than family members (21.8%). Mean (SD) patient age was 45.8 (17.4) years. Mean (SD) number of admissions per patient per year was 2.7 (3.5). More events were activated for patients admitted to medical services (66%) than surgical services (34%). Calls were evenly distributed between time of day and day of week.

The most common reason for CH calls was inadequate pain control (48.2%), followed by dissatisfaction with staff (12.5%); the remaining calls were evenly distributed among the other categories. The majority of calls involved nonsafety issues (83.4%) rather than safety issues (11.4%); in 5.2% of calls, the distinction could not be made because of lack of information (Table 2). In 152 (41.4%) of the 367 total calls, a change in care or alteration in management was made. Of these 152 calls, 99 (65.1%) involved distinct changes in the care plan, such as medication changes, imaging or additional testing, or consultation with other physicians; the other 53 calls (34.9%) involved additional patient counseling or nonmedical changes. Our traditional RRT was activated within 24 hours of CH in 19 cases (5.2%); of the 19 patients, 6 were transferred to ICU. Seven patients (2.9%) died during admission. Twelve (3.3%) were discharged AMA. We compared outcomes of patients who made safety-issue calls with those of patients who made nonsafety-issue calls. The composite outcome of RRT activation, ICU transfer, and mortality was found for 6 (14.3%) of the 42 safety-issue calls and 15 (4.9%) of the 306 nonsafety-issue calls (P = 0.0291).

Patient- and family-activated RRTs provide unique opportunities for patient and family engagement during inpatient hospital stays. Our study described the results obtained with use of a well-established patient-activated RRT over several years, one of the longer observation periods reported in the literature. We found that, with use of patient-activated RRTs, patient safety issues were identified, though these were far outnumbered by nonsafety issues.

Almost half of all CH events were related to pain. Pain as the primary driver for RRT activation may be attributable to several factors, including degree of illness, poor communication about pain management expectations, positive reinforcement of narcotic-seeking behavior as a result of CH activation, and high rate of opiate use in the catchment area. A striking finding of our analysis was repeat calling; only 43 (18%) of the 240 callers were repeat callers, but they made almost half of all the calls. In some cases, during a single admission, multiple calls were made because the first had no effect on care or management; more typically, though, multiple calls were made over several admissions. Repeat callers were admitted more often per year, and they used hospital services more. They should be further studied with a goal of designing programs that better meet their needs and that prospectively address expectations of pain control.

Our study was unique in describing several outcomes related to CH events. We found that traditional RRTs were seldom activated, level of care was seldom escalated, and mortality was rare, though these outcomes occurred more often for safety-issue calls than nonsafety-issue calls. We also found that activation of CH teams often led to changes in medical management, though we could not determine whether these changes in care led to different patient outcomes.

Patient-initiated RRTs are described in a limited number of pediatric and adult studies, all with findings differing from ours. In the pediatric models, most calls were initiated by family members, were less frequent, and tended to signal higher patient acuity.^5,6 For example, in a pediatric RRT model,⁵ family members activated the RRT only twice within the study year, but both calls resulted in ICU transfer. Most descriptions of patient-activated RRTs in adult hospitals are from pilot studies, which similarly identified infrequent RRT calls but often did not identify call reasons or specific outcomes.⁷ A single-center study concluded that, after implementation of a mixed-model RRT⁸—a traditional practitioner-activated RRT later enhanced with a patient/family activation mechanism—non-ICU codes decreased, and there was a statistically significant drop in hospital-wide mortality rates. However, this RRT was patient-activated only 25 times over 2 years, and the specific outcomes of those events were not described.

Other initiatives have been designed to enhance patient care and communication. Purposeful rounding systems⁹ involve hourly rounding by bedside nurses and daily rounding by nurse leaders to improve timely patient care and provide proactive service. Such systems ideally preempt calls involving dissatisfaction and nonsafety issues. Although they would reduce the number of patient-dissatisfaction calls made in the CH system, they may not be any better than the CH system is in its main purpose, identifying safety issues. In addition, whether patient-activated RRTs or purposeful rounding systems are better at addressing patient dissatisfaction is unclear.

This study had its limitations. First, like other studies, it was a single-center observational study without a concurrent control group. Second, because CH was first implemented 10 years ago, we could not compare patient outcomes or traditional RRT use before and after program initiation. Third, our study cohort consisted of patients hospitalized at one academic tertiary-care center in one region, and the hospital is a training site for multiple residencies and fellowships. These factors likely affect the generalizability of our data to smaller or community-based centers. Fourth, some determinations were subjective (eg, whether calls involved safety or nonsafety issues). We tried to minimize bias by having 2 authors independently review cases, but the process did not reflect patient experience or perspective. Fifth, our hospital adopted its traditional RRT years before its CH system. The criteria used by hospital personnel for traditional RRT activation are designed to encourage staff to call for help at early signs of patient deterioration. Consequently, traditional RRT activations substantially outnumber CH calls. Whether this resulted in fewer CH safety calls is unclear. Sixth, we did not capture the financial implications of using CH teams.

Although patient-activated RRTs identified patient safety issues, questions about the utility or necessity of these RRTs remain. In our era of limited hospital resources, the case has not been definitively made that these teams are practical, based on patient outcomes, though other studies have found improved patient satisfaction.⁷ Most of the RRT calls in our study involved patient dissatisfaction and communication issues. CH may not be the ideal approach for managing these issues, but it represents the last line of patient advocacy once other systems have failed.

We think patient-activated RRTs have the potential to effect patient engagement in safe care. Given the importance of establishing a culture of patient safety and engagement, and increased detection of safety-related events, CH remains active throughout our hospital system. Newer iterations of CH may benefit from stricter language in defining appropriate occasions for calling RRTs, and from descriptions of other resources for patient advocacy within the hospital. These modifications could end up restricting RRT activations to patient complaints and preserving CH resources for patients with safety concerns. Our study lays the groundwork for other institutions that are considering similar interventions. Studies should now start evaluating how well patient- and family-activated RRTs improve patient satisfaction, staff satisfaction, and patient outcomes.

Patient- and family-activated RRTs were designed to engage patients and families in safe care. Although CH detects patient safety issues, these are far outnumbered by nonsafety issues. CH demonstrates a commitment to patient engagement and a culture that emphasizes patient safety.

Acknowledgements

This work was presented as a poster at the annual meeting of the Society of Hospital Medicine; March 6-9, 2016; San Diego, CA.

Disclosure

Nothing to report.

In recent years, rapid response teams (RRTs) have been widely implemented to improve patient safety and quality of care. RRTs traditionally are activated by providers to address a clinically deteriorating patient; trained nurses, respiratory care specialists, and physicians are brought bedside to assist in triage and management. After the Joint Commission¹ endorsed patient engagement as a strategy for enhancing patient safety, new initiatives were developed to meet the challenge. Programs designed to enhance patient engagement have taken a variety of forms, including educational campaigns encouraging patients to report adverse events, requests for handwashing by providers, and the institution of patient- and family-activated RRTs.² Patient involvement is viewed favorably and has been shown to increase patients’ perception of health care quality.³ Although these initiatives are presumed helpful in encouraging communication, there is limited evidence that more communication leads to safety improvements. Despite the increasing prevalence of patient-activated RRTs in the United States, they have gone largely unevaluated in the adult population, and their efficacy remains unclear.

Condition Help (CH) is a patient- and family-initiated RRT designed to prevent medical errors and communication problems and improve patient safety. Patients and families are encouraged to call the CH hotline if they believe that there has been a breakdown in care or that their health is in imminent danger. This RRT was inspired by the case of Josie King, an 18-month-old girl who died of preventable causes at a large children’s hospital.⁴ After her daughter’s death, Sorrel King started the Josie King Foundation, an organization committed to preventing medical errors and creating a culture of patient safety. With the support of this foundation, CH was launched in 2005 at the Children’s Hospital of Pittsburgh at the University of Pittsburgh Medical Center (UPMC). Later it was implemented at the UPMC adult tertiary-care center, and now it is available in all UPMC facilities.

On admission, patients receive a brochure that details the purpose of CH and provides examples of when and how to call the CH hotline. In this brochure, patients are instructed to call CH in 3 situations: “1) There is an emergency and you cannot get the attention of hospital staff, 2) You see a change in the patient’s condition and the health care team is not recognizing the concern, or 3) There is breakdown in how care is given or uncertainty over what needs to be done.” These instructions are printed on bulletins placed in elevators and hallways throughout the hospital. Patients and families may activate the system at any time and can even do so from home.

When a patient or family member calls the hotline, an operator notifies the CH team. This team, which consists of a patient care liaison (or an on-duty administrator) and the unit charge nurse, convenes bedside to address the patient’s concern. The team was designed without a physician to ensure that the primary team remains in charge of the care plan. CH is kept separate from our traditional RRT and does not compete for resources (personnel, equipment, time) with the RRT, which is designed to address a clinically deteriorating patient.

In this article, we describe the characteristics of patients for whom CH was activated at our adult hospital. We also describe reasons for calls, whether changes in care were implemented, and outcomes, including traditional RRT activation, transfer to intensive care unit (ICU), and inpatient mortality. As CH was designed with patient safety as a goal, we tracked 2 types of calls, those involving safety issues and those involving nonsafety issues.

This study was approved by the quality improvement committee at the University of Pittsburgh and was considered exempt from review by the university’s Institutional Review Board.

Our integrated health system consists of more than 20 hospitals serving a tristate region. UPMC Presbyterian and UPMC Montefiore are adult tertiary-care referral hospitals with more than 750 medical/surgical beds and 150 critical care beds and more than 30,000 annual inpatient admissions. These hospitals are physically connected and function as a single large medical center. We reviewed all CH events that occurred at this combined hospital during the period January 2012 through June 2015. The dates coincided with CH data acquisition.

CH was available 24 hours a day 7 days a week. A patient care liaison (or an on-duty administrator) and the unit charge nurse responded to CH calls. Data from all calls included date and time of call, day of week, primary service, patient location, unique patient identifiers, call initiator (patient or family), whether a call led to changes in care, and primary reason for call. Each call reason was sorted into 1 of 10 categories: pain control, staff problem, lack of communication between patient/family and care team, questions about patient management, care delays, delays in a particular service, questions about discharge, administrative issues, acute psychiatric needs, and unknown/other. In addition, after a call, we reviewed all charts to determine if a safety issue was involved; Dr. Eden and Dr. Bump independently reviewed calls for safety issues and discussed any differences until they reached consensus. We also recorded outcomes, including activation of a traditional RRT or transfer to ICU within 24 hours of CH call, inpatient mortality, and against medical advice (AMA) discharges. Given that many calls were made by patients who called more than once (during a single admission or over multiple admissions), we also sorted patients into one-time callers and repeat callers for comparison. Patient satisfaction data were unavailable for review.

Patient demographic data are presented as means, standard deviations, and percentages, and call characteristics as percentages. Chi-square tests and t tests were used for analyses except for comparisons having few observations. For those, Fisher exact test was used. All analyses were performed with SAS Version 9.4 (SAS Institute, Cary, North Carolina).

From January 2012 through June 2015, 367 CH calls were made, about 105 annually. During this period, there were about 33,000 admissions, 800 combined grievances and complaints, 170 AMA discharges, 155 cardiac arrests, 2300 traditional RRT activations, and 1200 inpatient deaths per year. The 367 CH calls were made by 240 patients (Table 1). Of these 240 patients, 43 (18%) activated the CH team with multiple calls; their calls accounted for (46.3%) of all calls (170/367). The majority of calls were made by patients (76.8%) rather than family members (21.8%). Mean (SD) patient age was 45.8 (17.4) years. Mean (SD) number of admissions per patient per year was 2.7 (3.5). More events were activated for patients admitted to medical services (66%) than surgical services (34%). Calls were evenly distributed between time of day and day of week.

The most common reason for CH calls was inadequate pain control (48.2%), followed by dissatisfaction with staff (12.5%); the remaining calls were evenly distributed among the other categories. The majority of calls involved nonsafety issues (83.4%) rather than safety issues (11.4%); in 5.2% of calls, the distinction could not be made because of lack of information (Table 2). In 152 (41.4%) of the 367 total calls, a change in care or alteration in management was made. Of these 152 calls, 99 (65.1%) involved distinct changes in the care plan, such as medication changes, imaging or additional testing, or consultation with other physicians; the other 53 calls (34.9%) involved additional patient counseling or nonmedical changes. Our traditional RRT was activated within 24 hours of CH in 19 cases (5.2%); of the 19 patients, 6 were transferred to ICU. Seven patients (2.9%) died during admission. Twelve (3.3%) were discharged AMA. We compared outcomes of patients who made safety-issue calls with those of patients who made nonsafety-issue calls. The composite outcome of RRT activation, ICU transfer, and mortality was found for 6 (14.3%) of the 42 safety-issue calls and 15 (4.9%) of the 306 nonsafety-issue calls (P = 0.0291).

Patient- and family-activated RRTs provide unique opportunities for patient and family engagement during inpatient hospital stays. Our study described the results obtained with use of a well-established patient-activated RRT over several years, one of the longer observation periods reported in the literature. We found that, with use of patient-activated RRTs, patient safety issues were identified, though these were far outnumbered by nonsafety issues.

Almost half of all CH events were related to pain. Pain as the primary driver for RRT activation may be attributable to several factors, including degree of illness, poor communication about pain management expectations, positive reinforcement of narcotic-seeking behavior as a result of CH activation, and high rate of opiate use in the catchment area. A striking finding of our analysis was repeat calling; only 43 (18%) of the 240 callers were repeat callers, but they made almost half of all the calls. In some cases, during a single admission, multiple calls were made because the first had no effect on care or management; more typically, though, multiple calls were made over several admissions. Repeat callers were admitted more often per year, and they used hospital services more. They should be further studied with a goal of designing programs that better meet their needs and that prospectively address expectations of pain control.

Our study was unique in describing several outcomes related to CH events. We found that traditional RRTs were seldom activated, level of care was seldom escalated, and mortality was rare, though these outcomes occurred more often for safety-issue calls than nonsafety-issue calls. We also found that activation of CH teams often led to changes in medical management, though we could not determine whether these changes in care led to different patient outcomes.

Patient-initiated RRTs are described in a limited number of pediatric and adult studies, all with findings differing from ours. In the pediatric models, most calls were initiated by family members, were less frequent, and tended to signal higher patient acuity.^5,6 For example, in a pediatric RRT model,⁵ family members activated the RRT only twice within the study year, but both calls resulted in ICU transfer. Most descriptions of patient-activated RRTs in adult hospitals are from pilot studies, which similarly identified infrequent RRT calls but often did not identify call reasons or specific outcomes.⁷ A single-center study concluded that, after implementation of a mixed-model RRT⁸—a traditional practitioner-activated RRT later enhanced with a patient/family activation mechanism—non-ICU codes decreased, and there was a statistically significant drop in hospital-wide mortality rates. However, this RRT was patient-activated only 25 times over 2 years, and the specific outcomes of those events were not described.

Other initiatives have been designed to enhance patient care and communication. Purposeful rounding systems⁹ involve hourly rounding by bedside nurses and daily rounding by nurse leaders to improve timely patient care and provide proactive service. Such systems ideally preempt calls involving dissatisfaction and nonsafety issues. Although they would reduce the number of patient-dissatisfaction calls made in the CH system, they may not be any better than the CH system is in its main purpose, identifying safety issues. In addition, whether patient-activated RRTs or purposeful rounding systems are better at addressing patient dissatisfaction is unclear.

This study had its limitations. First, like other studies, it was a single-center observational study without a concurrent control group. Second, because CH was first implemented 10 years ago, we could not compare patient outcomes or traditional RRT use before and after program initiation. Third, our study cohort consisted of patients hospitalized at one academic tertiary-care center in one region, and the hospital is a training site for multiple residencies and fellowships. These factors likely affect the generalizability of our data to smaller or community-based centers. Fourth, some determinations were subjective (eg, whether calls involved safety or nonsafety issues). We tried to minimize bias by having 2 authors independently review cases, but the process did not reflect patient experience or perspective. Fifth, our hospital adopted its traditional RRT years before its CH system. The criteria used by hospital personnel for traditional RRT activation are designed to encourage staff to call for help at early signs of patient deterioration. Consequently, traditional RRT activations substantially outnumber CH calls. Whether this resulted in fewer CH safety calls is unclear. Sixth, we did not capture the financial implications of using CH teams.

Although patient-activated RRTs identified patient safety issues, questions about the utility or necessity of these RRTs remain. In our era of limited hospital resources, the case has not been definitively made that these teams are practical, based on patient outcomes, though other studies have found improved patient satisfaction.⁷ Most of the RRT calls in our study involved patient dissatisfaction and communication issues. CH may not be the ideal approach for managing these issues, but it represents the last line of patient advocacy once other systems have failed.

We think patient-activated RRTs have the potential to effect patient engagement in safe care. Given the importance of establishing a culture of patient safety and engagement, and increased detection of safety-related events, CH remains active throughout our hospital system. Newer iterations of CH may benefit from stricter language in defining appropriate occasions for calling RRTs, and from descriptions of other resources for patient advocacy within the hospital. These modifications could end up restricting RRT activations to patient complaints and preserving CH resources for patients with safety concerns. Our study lays the groundwork for other institutions that are considering similar interventions. Studies should now start evaluating how well patient- and family-activated RRTs improve patient satisfaction, staff satisfaction, and patient outcomes.

Patient- and family-activated RRTs were designed to engage patients and families in safe care. Although CH detects patient safety issues, these are far outnumbered by nonsafety issues. CH demonstrates a commitment to patient engagement and a culture that emphasizes patient safety.

Acknowledgements

This work was presented as a poster at the annual meeting of the Society of Hospital Medicine; March 6-9, 2016; San Diego, CA.

Disclosure

Nothing to report.

Millions of individuals with chronic diseases are hospitalized annually in the United States. More than 90% of hospitalized adults have at least 1 chronic disease,¹ and almost half of Medicare beneficiaries in the hospital have 4 or more chronic conditions.² While many patients are admitted for worsening of a single chronic disease, patients are hospitalized more commonly for other causes. For instance, although acute heart failure is among the most frequent causes of hospitalizations among older adults, three-fourths of hospitalizations of patients with heart failure are for reasons other than acute heart failure.³

When a patient with a chronic disease is hospitalized, the inpatient provider must consider whether to actively or passively manage the chronic disease. Studies have suggested that intervening in chronic diseases during hospitalizations can lead to long-term improvement in treatment;^4-6 for instance, stroke patients who were started on antihypertensive therapy at discharge were more likely to have their blood pressure controlled in the next year.⁵ However, some authors have argued that aggressive hypertension management by inpatient providers may result in patient harm.⁷ One case-based survey suggested that hospitalists were mixed in their interest in participating in chronic disease management in the hospital.⁸ This study found that providers were less likely to participate in chronic disease management if it was unrelated to the reason for hospitalization.⁸ However, to our knowledge, no studies have broadly evaluated inpatient provider attitudes, motivating factors, or barriers to participation in chronic disease management.

The purpose of this study was to understand provider attitudes towards chronic disease management for patients who are hospitalized for other causes. We were particularly interested in perceptions of barriers and facilitators to delivery of inpatient chronic disease management. Ultimately, such findings can inform future interventions to improve inpatient care of chronic disease.

In this qualitative study, we conducted in-depth interviews with providers to understand attitudes, barriers, and facilitators towards inpatient management of chronic disease; this study was part of a larger study to implement an electronic health record-based clinical decision-support system intervention to improve quality of care for hospitalized patients with heart failure.

We included providers who care for and can write medication orders for hospitalized adult patients at New York University (NYU) Langone Medical Center, an urban academic medical center. As patients with chronic conditions are commonly hospitalized for many reasons, we sought to interview providers from a range of clinical services without consideration of factors such as frequency of caring for patients with heart failure. We used a purposive sampling framework: we invited participants to ensure a range of services, including medicine, surgery, and neurology, and provider types, including attending physicians, resident physicians, nurse practitioners, and physician assistants. Potential participants, therefore, included all providers for adult hospitalized patients.

We identified potential participants through study team members, referrals from department heads and prior interviewees, and e-mails to department list serves. We did not formally track declinations to being interviewed, although we estimate them as fewer than 20% of providers directly approached. While we focused on inpatient providers at New York University Langone Medical Center, many of the attending physicians and residents spend a portion of their time at the Manhattan Veterans Affairs Hospital and Bellevue Hospital, a safety-net city hospital; providers could have outpatient responsibilities as well.

All participants provided verbal consent to participate. The study was approved by the New York University Institutional Review Board, which granted a waiver of documentation of consent. Participants received a $25 gift card following the interview.

We used a semi-structured interview guide (Appendix) to elicit in-depth accounts of provider attitudes, experiences with, and barriers and facilitators towards chronic disease management in the hospital. The interview began by asking about chronic disease in general and then asked more specific questions about heart failure; we included responses to both groups of questions in the current study. The interview also included questions related to the clinical decision-support system being developed as part of the larger implementation study, although we do not report on these results in the current study. The semi-structured interview guide was informed by the consolidated framework for advancing implementation science (CFIR), which offers an overarching typology for delineating factors that influence guideline implementation;⁹ we also used CFIR constructs in theme development. We conducted in-depth interviews with providers.

A priori, we estimated 25 interviews would be sufficient to include the purposive sample and achieve data saturation,¹⁰ which was reached after 31 interviews. Interviews were held in person or by telephone, at the convenience of the subject. All interviews were transcribed by a professional service. Transcriptions were reviewed against recordings with any mistakes corrected. Prior to each interview, we conducted a brief demographic survey.

Qualitative data were analyzed using a constant comparative analytic technique.¹¹ The investigative team met after reviewing the first 10 interviews and discussed emergent themes from these early transcripts, which led to the initial code list. Two investigators coded the transcripts. Reliability was evaluated by independent coding of a 20% subset of interviews. Differences were reviewed and discussed until consensus was reached. Final intercoder reliability was determined to be greater than 95%.¹² All investigators reviewed and refined the code list during the analysis phase. Codes were clustered into themes based on CFIR constructs.⁹ Analyses were performed using Atlas.ti v. 7 (ATLAS.ti Scientific Software Development GmbH, Berlin, Germany).

We conducted interviews with 31 providers. Of these, 12 were on the medicine service, 12 were on the surgery or a surgical subspecialty service, and 7 were on other services; 11 were attending physicians, 12 were resident physicians, 5 were NPs, and 3 were PAs. Only 2 providers—an attending in medicine and a resident in surgery—had a specialty focus that was cardiac-related. Median time in current position was 4 years (Table 1). Seventeen of the interviews were in person, and 14 were conducted by telephone. The mean interview time was 20 minutes and ranged from 11 to 41 minutes.

We identified 5 main themes with 29 supporting codes (Table 2) describing provider attitudes towards the management of chronic disease for hospitalized patients. These themes, with related CFIR constructs, were: 1) perceived impact on patient outcomes (CFIR construct: intervention characteristics, relative advantage); 2) hospital structural characteristics (inner setting, structural characteristics); 3) provider knowledge and self-efficacy (characteristic of individual, knowledge and beliefs about the intervention and self-efficacy); 4) hospital priorities (inner setting, implementation climate, relative priority); and 5) continuity and communication (inner setting, networks and communications). For most themes, subjects described both positive and negative aspects of chronic disease management, as well as related facilitators and barriers to delivery of chronic disease care for hospitalized patients. Illustrative quotes for each theme are shown in Table 3.

Perceived Impact on Patient Outcomes

Perceived impact on patient outcomes was mixed. Most providers believed the management of chronic diseases could lead to improvement in important patient outcomes, including decreased length of stay (LOS), prevention of hospital complication, and decreased readmissions. Surgical providers focused particularly on the benefits of preventing surgical complications and noted that they were more likely to manage chronic conditions—primarily through use of specialist consultation—when they perceived a benefit to prevention of surgical outcomes or a fear that surgery may worsen a stable chronic condition:

“Most of the surgery I do is pretty stressful on the body and is very likely to induce acute on chronic exacerbations of heart failure. For someone with Class II or higher heart failure, I’m definitely gonna have cardiology on board or at least internal medicine on board right from the beginning.”

Hospital Structural Characteristics

For many providers, the hospital setting provides a unique opportunity for care of patients with chronic disease. First, a hospitalization is a time for a patient’s management to be reviewed by a new care team. The hospital team reviews the management plan for patients at admission, which is a time to reevaluate whether patients are on evidence-based therapies: “It’s helpful to have a new set of eyes on somebody, like fresh information.” According to providers, this reevaluation can overcome instances of therapeutic inertia by the outpatient physician. Second, the hospital has many resources, including readily available specialist services and diagnostic tests, which can allow a patient-centered approach that coordinates care in 1 place, as a surgery NP described: “I think the advantage for the patient is that they wind up stopping in for 1 thing but we wind up taking care of a few without requiring the need for him or her to go to all these different specialists on the outside. They’re mostly elderly and not able to get around.” Third, the high availability of services and frequent monitoring allows rapid titration of evidence-based medicines, as discussed by a medicine resident: “It’s easier and faster to titrate medication—they’re in a monitored setting; you can ensure compliance.”

Patients may also differ from their usual state while hospitalized, creating both risks and benefits. The hospital setting can provide an opportunity to educate patients on their chronic disease(s) because they are motivated: “They’re in an office visit and their sugars are out of whack or something, they may take it a little bit more seriously if they were just in the hospital even though it was on an unrelated issue. I think it probably just changes their perspective on their disease.” However, in the hospital, patients are in an unusual environment with a restricted diet and forced medication compliance. Furthermore, the acute condition can lead to changes in their chronic disease, as described by 1 medicine attending: “their sugar is high because they’re acutely ill.” Providers expressed concern that changing medications in this setting may lead to adverse events (AEs) when patients return to their usual environment.

Provider Knowledge and Self-Efficacy

Insufficient knowledge of treatments for chronic conditions was cited as a barrier to some providers’ ability to actively manage chronic disease for hospitalized patients. Some providers described management of conditions outside their area as less satisfying than their primary focus. For example, an orthopedic surgeon explained: “…it’s very simple. You see your bone is broken, you fix it, that’s it…it’s intellectually satisfying…managing chronic diseases is less like that.” Reliance on consultants was 1 approach to deal with knowledge gaps in areas outside a provider’s expertise.

For a number of providers, management of stable chronic disease is the responsibility of the outpatient provider. Providers expressed concern that inpatient management was a reach into the domain of the primary care provider (PCP) and might take “away from the primary focus” of the hospitalization. Nonetheless, some providers noted an “ethical responsibility to manage [a] patient correctly,” and some providers believed that engaging in chronic disease management in the hospital would present an opportunity to expand their own expertise.

A few providers were worried about legal risk related to chronic disease management: “we don’t typically deal too much with managing some of these other medical issues for medical and legal reasons.” Providers again suggested that consults can help overcome this concern for risk, as discussed by 1 surgical attending: “We’re all not wanting to be sued, and we want to do the right thing. It costs me nothing to have a cardiologist on board, so like—why not.”

Hospital Priorities

Providers explained that the hospital has strong interests in early discharge and minimizing LOS. These priorities are based on goals of improving patient outcomes, increasing bed availability and hospital volume, and reducing costs. Providers perceive these hospital priorities as potential barriers to chronic disease management, which can increase LOS and costs through additional testing and treatment. As a medicine resident described: “The DBN philosophy, ‘discharge before noon’ philosophy, which is part of the hospital efficiency to get people in and out of the hospital as quickly as [is] safe, or maybe faster. And I think that there’s a culture where you’re encouraged to only focus on the acute issue and tend to defer everything else.”

Continuity and Communication

According to many providers, care continuity between the outpatient setting and the hospital played a major role in management of chronic disease. One barrier to starting a new evidence-based medication was lack of knowledge of patient history. As noted, providers expressed concern that a patient may not be on a given therapy because of an adverse reaction that was not documented in the hospital chart. This is particularly true because, as discussed by a surgery resident, patients with “PCPs outside the system [in which providers] don’t have access to the electronic medical record.” To overcome this barrier, providers attempt to communicate with the outpatient provider to confirm a lack of contraindications to therapies prior to any changes; notably, communication is easier if the inpatient provider has a relationship with the outpatient PCP.

Some providers were more likely to start chronic disease therapies if the patient had no prior outpatient care, because the provider was reassured that there was no rationale for missing therapies. One neurology attending noted that if a patient had newly documented “hypertension even if they were in for something else, I might start them on an antihypertensive, but then arrange for a close follow-up with a new PCP.”

Following hospitalization, providers wanted assurance that any changes to chronic disease management would be followed up by an outpatient physician. Any changes are relayed to the outpatient provider and the “level of communication…with the outpatient provider who’s gonna inherit” these changes can influence how aggressively the inpatient provider manages chronic diseases. Providers may be reluctant to start therapy for patients if they are concerned about outpatient follow up: “they have diabetes and they should really technically be on an ACE [angiotensin converting enzyme]inhibitor and aspirin, but they’re not. I might send them out on the aspirin but I might either start ACE inhibitor and have them follow up with their PCP in 2 weeks if I’m confident that they’ll do it or if I’m really confident that they’ll not follow up, I will help them get the appointment and then the discharge instruction is to the PCP is ‘Please start this patient on ACE inhibitor if they show up.’”

Providers frequently perceive benefit to chronic disease management in the hospital, including improvements in clinical outcomes. Notably, providers see opportunities to improve compliance with evidence-based care to overcome potential barriers to managing chronic disease in the outpatient setting, which can be limited by pressure for brief encounters,¹³ clinical inertia,¹⁴ difficulty with close monitoring of patients,¹⁵ and care fragmentation.¹⁶ Concurrently, inpatient providers are concerned about potential for patient harm related to chronic disease management, primarily related to AEs from medications. Similar to a case study about a patient with outpatient hypotension following aggressive inpatient hypertension management,⁷ providers fear that changing a patient’s chronic disease management in a hospital setting may cause harm when the patient returns home.

Although some clinicians have argued against aggressive in-hospital chronic disease management because of concerns for risk of AEs,⁷ our study and others⁸ have suggested that many clinicians perceive benefit. In some cases, such as smoking cessation counseling for all current smokers and prescribing an angiotensin converting enzyme inhibitor for patients with systolic heart failure, the perceived importance is so great that chronic disease management has been used as a national quality metric for hospitals. While these hospital metrics may be justified for short-term benefits after hospitalization, studies have demonstrated only weak improvement in short-term postdischarge outcomes related to chronic disease management.¹⁷ The true benefit is likely from improved processes of care in the short term that lead to long-term improvement in outcomes.^4,5,18 Thus, the advantage of starting a patient hospitalized for a stroke on blood pressure medication is the increased likelihood that the patient will continue the medication as an outpatient, which may reduce long-term mortality.

For hospital delivery systems that are concerned with such care process improvement through in-hospital chronic disease management, we identified a number of barriers and facilitators to delivering this care. One significant barrier was poor transitions between the inpatient and the outpatient settings. When a patient transitions into the hospital, providers need to understand prior management choices. Facilitators to help inpatient providers understand prior management included either knowing the outpatient provider, or understanding that there was a lack of regular outpatient care; in both these cases, inpatient providers felt more comfortable managing chronic diseases because they had insight into the outpatient plan, or lack thereof. However, these facilitators may not be practical to incorporate in interventions to improve chronic disease care, which should consider overcoming these communication barriers. Use of shared electronic health records or standardized telephone calls with well-documented care plans obtained through health information exchanges may facilitate an inpatient provider to manage appropriately chronic disease. Similarly, discontinuity between the inpatient provider and the outpatient provider is a barrier that must be overcome to ease concerns that any chronic disease management changes do not result in harm in the postdischarge period. These findings again point to the need for improved documentation and communication between inpatient and outpatient providers. Of course, the transitional care period is one of high risk, and improving communication between providers has been an area of ongoing work.¹⁹

Lack of comfort among inpatient providers with managing chronic diseases is another important barrier, which appears to be largely overcome through the use of consultation services. Ready availability of specialists, common in academic medical centers, can facilitate delivery of chronic disease management. Inpatient interventions designed to improve evidence-based care for a chronic disease may benefit from involvement or at least availability of specialists in the effort. Another major barrier relates to hospital priorities, which in our study were closely aligned with external factors such as payment models. As hospitalizations are typically paid based on the discharge diagnosis, hospitals have incentives to discharge quickly and not order extra diagnostic tests. As a result, there are disincentives for chronic disease management that may require additional testing or monitoring in the hospital. Conversely, as hospitals accept postdischarge financial risks through readmission penalties or postdischarge cost savings, hospitals may perceive that long-term benefits of chronic disease management may outweigh short-term costs.

The study findings should be interpreted in the context of its limitations. Findings of our study of providers from a single academic medical center may not be generalizable. Nearly half of our interviews were conducted by telephone, which limits our ability to capture nonverbal cues in communication. Providers may have had social desirability bias towards positive aspects of chronic disease management. We did not have the power to determine differences in response by provider characteristic because this was an exploratory qualitative study. Future studies with representative sampling, a larger sample size, and measures for constructs such as provider self-efficacy are needed to examine differences by specialty, provider type, and experience level.

In conclusion, inpatient providers believe that hospital chronic disease management has the potential to be beneficial for both process of care and clinical outcomes; providers also express concern about potential adverse consequences of managing chronic disease during acute hospitalizations. To maximize both quality of care and patient safety, overcoming communication barriers between inpatient and outpatient providers is needed. Both a supportive hospital environment and availability of specialty support can facilitate in-hospital chronic disease management. Interventions that incorporate these factors may be well-suited to improve chronic disease care and long-term outcomes.

Disclosures

This work was supported by the Agency for Healthcare Research and Quality (AHRQ) grant K08HS23683. The authors report no financial conflicts of interest.

Millions of individuals with chronic diseases are hospitalized annually in the United States. More than 90% of hospitalized adults have at least 1 chronic disease,¹ and almost half of Medicare beneficiaries in the hospital have 4 or more chronic conditions.² While many patients are admitted for worsening of a single chronic disease, patients are hospitalized more commonly for other causes. For instance, although acute heart failure is among the most frequent causes of hospitalizations among older adults, three-fourths of hospitalizations of patients with heart failure are for reasons other than acute heart failure.³

When a patient with a chronic disease is hospitalized, the inpatient provider must consider whether to actively or passively manage the chronic disease. Studies have suggested that intervening in chronic diseases during hospitalizations can lead to long-term improvement in treatment;^4-6 for instance, stroke patients who were started on antihypertensive therapy at discharge were more likely to have their blood pressure controlled in the next year.⁵ However, some authors have argued that aggressive hypertension management by inpatient providers may result in patient harm.⁷ One case-based survey suggested that hospitalists were mixed in their interest in participating in chronic disease management in the hospital.⁸ This study found that providers were less likely to participate in chronic disease management if it was unrelated to the reason for hospitalization.⁸ However, to our knowledge, no studies have broadly evaluated inpatient provider attitudes, motivating factors, or barriers to participation in chronic disease management.

The purpose of this study was to understand provider attitudes towards chronic disease management for patients who are hospitalized for other causes. We were particularly interested in perceptions of barriers and facilitators to delivery of inpatient chronic disease management. Ultimately, such findings can inform future interventions to improve inpatient care of chronic disease.

In this qualitative study, we conducted in-depth interviews with providers to understand attitudes, barriers, and facilitators towards inpatient management of chronic disease; this study was part of a larger study to implement an electronic health record-based clinical decision-support system intervention to improve quality of care for hospitalized patients with heart failure.

We included providers who care for and can write medication orders for hospitalized adult patients at New York University (NYU) Langone Medical Center, an urban academic medical center. As patients with chronic conditions are commonly hospitalized for many reasons, we sought to interview providers from a range of clinical services without consideration of factors such as frequency of caring for patients with heart failure. We used a purposive sampling framework: we invited participants to ensure a range of services, including medicine, surgery, and neurology, and provider types, including attending physicians, resident physicians, nurse practitioners, and physician assistants. Potential participants, therefore, included all providers for adult hospitalized patients.

We identified potential participants through study team members, referrals from department heads and prior interviewees, and e-mails to department list serves. We did not formally track declinations to being interviewed, although we estimate them as fewer than 20% of providers directly approached. While we focused on inpatient providers at New York University Langone Medical Center, many of the attending physicians and residents spend a portion of their time at the Manhattan Veterans Affairs Hospital and Bellevue Hospital, a safety-net city hospital; providers could have outpatient responsibilities as well.

All participants provided verbal consent to participate. The study was approved by the New York University Institutional Review Board, which granted a waiver of documentation of consent. Participants received a $25 gift card following the interview.

We used a semi-structured interview guide (Appendix) to elicit in-depth accounts of provider attitudes, experiences with, and barriers and facilitators towards chronic disease management in the hospital. The interview began by asking about chronic disease in general and then asked more specific questions about heart failure; we included responses to both groups of questions in the current study. The interview also included questions related to the clinical decision-support system being developed as part of the larger implementation study, although we do not report on these results in the current study. The semi-structured interview guide was informed by the consolidated framework for advancing implementation science (CFIR), which offers an overarching typology for delineating factors that influence guideline implementation;⁹ we also used CFIR constructs in theme development. We conducted in-depth interviews with providers.

A priori, we estimated 25 interviews would be sufficient to include the purposive sample and achieve data saturation,¹⁰ which was reached after 31 interviews. Interviews were held in person or by telephone, at the convenience of the subject. All interviews were transcribed by a professional service. Transcriptions were reviewed against recordings with any mistakes corrected. Prior to each interview, we conducted a brief demographic survey.

Qualitative data were analyzed using a constant comparative analytic technique.¹¹ The investigative team met after reviewing the first 10 interviews and discussed emergent themes from these early transcripts, which led to the initial code list. Two investigators coded the transcripts. Reliability was evaluated by independent coding of a 20% subset of interviews. Differences were reviewed and discussed until consensus was reached. Final intercoder reliability was determined to be greater than 95%.¹² All investigators reviewed and refined the code list during the analysis phase. Codes were clustered into themes based on CFIR constructs.⁹ Analyses were performed using Atlas.ti v. 7 (ATLAS.ti Scientific Software Development GmbH, Berlin, Germany).

We conducted interviews with 31 providers. Of these, 12 were on the medicine service, 12 were on the surgery or a surgical subspecialty service, and 7 were on other services; 11 were attending physicians, 12 were resident physicians, 5 were NPs, and 3 were PAs. Only 2 providers—an attending in medicine and a resident in surgery—had a specialty focus that was cardiac-related. Median time in current position was 4 years (Table 1). Seventeen of the interviews were in person, and 14 were conducted by telephone. The mean interview time was 20 minutes and ranged from 11 to 41 minutes.

We identified 5 main themes with 29 supporting codes (Table 2) describing provider attitudes towards the management of chronic disease for hospitalized patients. These themes, with related CFIR constructs, were: 1) perceived impact on patient outcomes (CFIR construct: intervention characteristics, relative advantage); 2) hospital structural characteristics (inner setting, structural characteristics); 3) provider knowledge and self-efficacy (characteristic of individual, knowledge and beliefs about the intervention and self-efficacy); 4) hospital priorities (inner setting, implementation climate, relative priority); and 5) continuity and communication (inner setting, networks and communications). For most themes, subjects described both positive and negative aspects of chronic disease management, as well as related facilitators and barriers to delivery of chronic disease care for hospitalized patients. Illustrative quotes for each theme are shown in Table 3.

Perceived Impact on Patient Outcomes

Perceived impact on patient outcomes was mixed. Most providers believed the management of chronic diseases could lead to improvement in important patient outcomes, including decreased length of stay (LOS), prevention of hospital complication, and decreased readmissions. Surgical providers focused particularly on the benefits of preventing surgical complications and noted that they were more likely to manage chronic conditions—primarily through use of specialist consultation—when they perceived a benefit to prevention of surgical outcomes or a fear that surgery may worsen a stable chronic condition:

“Most of the surgery I do is pretty stressful on the body and is very likely to induce acute on chronic exacerbations of heart failure. For someone with Class II or higher heart failure, I’m definitely gonna have cardiology on board or at least internal medicine on board right from the beginning.”

Hospital Structural Characteristics

For many providers, the hospital setting provides a unique opportunity for care of patients with chronic disease. First, a hospitalization is a time for a patient’s management to be reviewed by a new care team. The hospital team reviews the management plan for patients at admission, which is a time to reevaluate whether patients are on evidence-based therapies: “It’s helpful to have a new set of eyes on somebody, like fresh information.” According to providers, this reevaluation can overcome instances of therapeutic inertia by the outpatient physician. Second, the hospital has many resources, including readily available specialist services and diagnostic tests, which can allow a patient-centered approach that coordinates care in 1 place, as a surgery NP described: “I think the advantage for the patient is that they wind up stopping in for 1 thing but we wind up taking care of a few without requiring the need for him or her to go to all these different specialists on the outside. They’re mostly elderly and not able to get around.” Third, the high availability of services and frequent monitoring allows rapid titration of evidence-based medicines, as discussed by a medicine resident: “It’s easier and faster to titrate medication—they’re in a monitored setting; you can ensure compliance.”

Patients may also differ from their usual state while hospitalized, creating both risks and benefits. The hospital setting can provide an opportunity to educate patients on their chronic disease(s) because they are motivated: “They’re in an office visit and their sugars are out of whack or something, they may take it a little bit more seriously if they were just in the hospital even though it was on an unrelated issue. I think it probably just changes their perspective on their disease.” However, in the hospital, patients are in an unusual environment with a restricted diet and forced medication compliance. Furthermore, the acute condition can lead to changes in their chronic disease, as described by 1 medicine attending: “their sugar is high because they’re acutely ill.” Providers expressed concern that changing medications in this setting may lead to adverse events (AEs) when patients return to their usual environment.

Provider Knowledge and Self-Efficacy

Insufficient knowledge of treatments for chronic conditions was cited as a barrier to some providers’ ability to actively manage chronic disease for hospitalized patients. Some providers described management of conditions outside their area as less satisfying than their primary focus. For example, an orthopedic surgeon explained: “…it’s very simple. You see your bone is broken, you fix it, that’s it…it’s intellectually satisfying…managing chronic diseases is less like that.” Reliance on consultants was 1 approach to deal with knowledge gaps in areas outside a provider’s expertise.

For a number of providers, management of stable chronic disease is the responsibility of the outpatient provider. Providers expressed concern that inpatient management was a reach into the domain of the primary care provider (PCP) and might take “away from the primary focus” of the hospitalization. Nonetheless, some providers noted an “ethical responsibility to manage [a] patient correctly,” and some providers believed that engaging in chronic disease management in the hospital would present an opportunity to expand their own expertise.

A few providers were worried about legal risk related to chronic disease management: “we don’t typically deal too much with managing some of these other medical issues for medical and legal reasons.” Providers again suggested that consults can help overcome this concern for risk, as discussed by 1 surgical attending: “We’re all not wanting to be sued, and we want to do the right thing. It costs me nothing to have a cardiologist on board, so like—why not.”

Hospital Priorities

Providers explained that the hospital has strong interests in early discharge and minimizing LOS. These priorities are based on goals of improving patient outcomes, increasing bed availability and hospital volume, and reducing costs. Providers perceive these hospital priorities as potential barriers to chronic disease management, which can increase LOS and costs through additional testing and treatment. As a medicine resident described: “The DBN philosophy, ‘discharge before noon’ philosophy, which is part of the hospital efficiency to get people in and out of the hospital as quickly as [is] safe, or maybe faster. And I think that there’s a culture where you’re encouraged to only focus on the acute issue and tend to defer everything else.”

Continuity and Communication

According to many providers, care continuity between the outpatient setting and the hospital played a major role in management of chronic disease. One barrier to starting a new evidence-based medication was lack of knowledge of patient history. As noted, providers expressed concern that a patient may not be on a given therapy because of an adverse reaction that was not documented in the hospital chart. This is particularly true because, as discussed by a surgery resident, patients with “PCPs outside the system [in which providers] don’t have access to the electronic medical record.” To overcome this barrier, providers attempt to communicate with the outpatient provider to confirm a lack of contraindications to therapies prior to any changes; notably, communication is easier if the inpatient provider has a relationship with the outpatient PCP.

Some providers were more likely to start chronic disease therapies if the patient had no prior outpatient care, because the provider was reassured that there was no rationale for missing therapies. One neurology attending noted that if a patient had newly documented “hypertension even if they were in for something else, I might start them on an antihypertensive, but then arrange for a close follow-up with a new PCP.”

Following hospitalization, providers wanted assurance that any changes to chronic disease management would be followed up by an outpatient physician. Any changes are relayed to the outpatient provider and the “level of communication…with the outpatient provider who’s gonna inherit” these changes can influence how aggressively the inpatient provider manages chronic diseases. Providers may be reluctant to start therapy for patients if they are concerned about outpatient follow up: “they have diabetes and they should really technically be on an ACE [angiotensin converting enzyme]inhibitor and aspirin, but they’re not. I might send them out on the aspirin but I might either start ACE inhibitor and have them follow up with their PCP in 2 weeks if I’m confident that they’ll do it or if I’m really confident that they’ll not follow up, I will help them get the appointment and then the discharge instruction is to the PCP is ‘Please start this patient on ACE inhibitor if they show up.’”

Providers frequently perceive benefit to chronic disease management in the hospital, including improvements in clinical outcomes. Notably, providers see opportunities to improve compliance with evidence-based care to overcome potential barriers to managing chronic disease in the outpatient setting, which can be limited by pressure for brief encounters,¹³ clinical inertia,¹⁴ difficulty with close monitoring of patients,¹⁵ and care fragmentation.¹⁶ Concurrently, inpatient providers are concerned about potential for patient harm related to chronic disease management, primarily related to AEs from medications. Similar to a case study about a patient with outpatient hypotension following aggressive inpatient hypertension management,⁷ providers fear that changing a patient’s chronic disease management in a hospital setting may cause harm when the patient returns home.

Although some clinicians have argued against aggressive in-hospital chronic disease management because of concerns for risk of AEs,⁷ our study and others⁸ have suggested that many clinicians perceive benefit. In some cases, such as smoking cessation counseling for all current smokers and prescribing an angiotensin converting enzyme inhibitor for patients with systolic heart failure, the perceived importance is so great that chronic disease management has been used as a national quality metric for hospitals. While these hospital metrics may be justified for short-term benefits after hospitalization, studies have demonstrated only weak improvement in short-term postdischarge outcomes related to chronic disease management.¹⁷ The true benefit is likely from improved processes of care in the short term that lead to long-term improvement in outcomes.^4,5,18 Thus, the advantage of starting a patient hospitalized for a stroke on blood pressure medication is the increased likelihood that the patient will continue the medication as an outpatient, which may reduce long-term mortality.

For hospital delivery systems that are concerned with such care process improvement through in-hospital chronic disease management, we identified a number of barriers and facilitators to delivering this care. One significant barrier was poor transitions between the inpatient and the outpatient settings. When a patient transitions into the hospital, providers need to understand prior management choices. Facilitators to help inpatient providers understand prior management included either knowing the outpatient provider, or understanding that there was a lack of regular outpatient care; in both these cases, inpatient providers felt more comfortable managing chronic diseases because they had insight into the outpatient plan, or lack thereof. However, these facilitators may not be practical to incorporate in interventions to improve chronic disease care, which should consider overcoming these communication barriers. Use of shared electronic health records or standardized telephone calls with well-documented care plans obtained through health information exchanges may facilitate an inpatient provider to manage appropriately chronic disease. Similarly, discontinuity between the inpatient provider and the outpatient provider is a barrier that must be overcome to ease concerns that any chronic disease management changes do not result in harm in the postdischarge period. These findings again point to the need for improved documentation and communication between inpatient and outpatient providers. Of course, the transitional care period is one of high risk, and improving communication between providers has been an area of ongoing work.¹⁹

Lack of comfort among inpatient providers with managing chronic diseases is another important barrier, which appears to be largely overcome through the use of consultation services. Ready availability of specialists, common in academic medical centers, can facilitate delivery of chronic disease management. Inpatient interventions designed to improve evidence-based care for a chronic disease may benefit from involvement or at least availability of specialists in the effort. Another major barrier relates to hospital priorities, which in our study were closely aligned with external factors such as payment models. As hospitalizations are typically paid based on the discharge diagnosis, hospitals have incentives to discharge quickly and not order extra diagnostic tests. As a result, there are disincentives for chronic disease management that may require additional testing or monitoring in the hospital. Conversely, as hospitals accept postdischarge financial risks through readmission penalties or postdischarge cost savings, hospitals may perceive that long-term benefits of chronic disease management may outweigh short-term costs.

The study findings should be interpreted in the context of its limitations. Findings of our study of providers from a single academic medical center may not be generalizable. Nearly half of our interviews were conducted by telephone, which limits our ability to capture nonverbal cues in communication. Providers may have had social desirability bias towards positive aspects of chronic disease management. We did not have the power to determine differences in response by provider characteristic because this was an exploratory qualitative study. Future studies with representative sampling, a larger sample size, and measures for constructs such as provider self-efficacy are needed to examine differences by specialty, provider type, and experience level.

In conclusion, inpatient providers believe that hospital chronic disease management has the potential to be beneficial for both process of care and clinical outcomes; providers also express concern about potential adverse consequences of managing chronic disease during acute hospitalizations. To maximize both quality of care and patient safety, overcoming communication barriers between inpatient and outpatient providers is needed. Both a supportive hospital environment and availability of specialty support can facilitate in-hospital chronic disease management. Interventions that incorporate these factors may be well-suited to improve chronic disease care and long-term outcomes.

Disclosures

This work was supported by the Agency for Healthcare Research and Quality (AHRQ) grant K08HS23683. The authors report no financial conflicts of interest.

Millions of individuals with chronic diseases are hospitalized annually in the United States. More than 90% of hospitalized adults have at least 1 chronic disease,¹ and almost half of Medicare beneficiaries in the hospital have 4 or more chronic conditions.² While many patients are admitted for worsening of a single chronic disease, patients are hospitalized more commonly for other causes. For instance, although acute heart failure is among the most frequent causes of hospitalizations among older adults, three-fourths of hospitalizations of patients with heart failure are for reasons other than acute heart failure.³

When a patient with a chronic disease is hospitalized, the inpatient provider must consider whether to actively or passively manage the chronic disease. Studies have suggested that intervening in chronic diseases during hospitalizations can lead to long-term improvement in treatment;^4-6 for instance, stroke patients who were started on antihypertensive therapy at discharge were more likely to have their blood pressure controlled in the next year.⁵ However, some authors have argued that aggressive hypertension management by inpatient providers may result in patient harm.⁷ One case-based survey suggested that hospitalists were mixed in their interest in participating in chronic disease management in the hospital.⁸ This study found that providers were less likely to participate in chronic disease management if it was unrelated to the reason for hospitalization.⁸ However, to our knowledge, no studies have broadly evaluated inpatient provider attitudes, motivating factors, or barriers to participation in chronic disease management.

The purpose of this study was to understand provider attitudes towards chronic disease management for patients who are hospitalized for other causes. We were particularly interested in perceptions of barriers and facilitators to delivery of inpatient chronic disease management. Ultimately, such findings can inform future interventions to improve inpatient care of chronic disease.

In this qualitative study, we conducted in-depth interviews with providers to understand attitudes, barriers, and facilitators towards inpatient management of chronic disease; this study was part of a larger study to implement an electronic health record-based clinical decision-support system intervention to improve quality of care for hospitalized patients with heart failure.

We included providers who care for and can write medication orders for hospitalized adult patients at New York University (NYU) Langone Medical Center, an urban academic medical center. As patients with chronic conditions are commonly hospitalized for many reasons, we sought to interview providers from a range of clinical services without consideration of factors such as frequency of caring for patients with heart failure. We used a purposive sampling framework: we invited participants to ensure a range of services, including medicine, surgery, and neurology, and provider types, including attending physicians, resident physicians, nurse practitioners, and physician assistants. Potential participants, therefore, included all providers for adult hospitalized patients.

We identified potential participants through study team members, referrals from department heads and prior interviewees, and e-mails to department list serves. We did not formally track declinations to being interviewed, although we estimate them as fewer than 20% of providers directly approached. While we focused on inpatient providers at New York University Langone Medical Center, many of the attending physicians and residents spend a portion of their time at the Manhattan Veterans Affairs Hospital and Bellevue Hospital, a safety-net city hospital; providers could have outpatient responsibilities as well.

All participants provided verbal consent to participate. The study was approved by the New York University Institutional Review Board, which granted a waiver of documentation of consent. Participants received a $25 gift card following the interview.

We used a semi-structured interview guide (Appendix) to elicit in-depth accounts of provider attitudes, experiences with, and barriers and facilitators towards chronic disease management in the hospital. The interview began by asking about chronic disease in general and then asked more specific questions about heart failure; we included responses to both groups of questions in the current study. The interview also included questions related to the clinical decision-support system being developed as part of the larger implementation study, although we do not report on these results in the current study. The semi-structured interview guide was informed by the consolidated framework for advancing implementation science (CFIR), which offers an overarching typology for delineating factors that influence guideline implementation;⁹ we also used CFIR constructs in theme development. We conducted in-depth interviews with providers.

A priori, we estimated 25 interviews would be sufficient to include the purposive sample and achieve data saturation,¹⁰ which was reached after 31 interviews. Interviews were held in person or by telephone, at the convenience of the subject. All interviews were transcribed by a professional service. Transcriptions were reviewed against recordings with any mistakes corrected. Prior to each interview, we conducted a brief demographic survey.

Qualitative data were analyzed using a constant comparative analytic technique.¹¹ The investigative team met after reviewing the first 10 interviews and discussed emergent themes from these early transcripts, which led to the initial code list. Two investigators coded the transcripts. Reliability was evaluated by independent coding of a 20% subset of interviews. Differences were reviewed and discussed until consensus was reached. Final intercoder reliability was determined to be greater than 95%.¹² All investigators reviewed and refined the code list during the analysis phase. Codes were clustered into themes based on CFIR constructs.⁹ Analyses were performed using Atlas.ti v. 7 (ATLAS.ti Scientific Software Development GmbH, Berlin, Germany).

We conducted interviews with 31 providers. Of these, 12 were on the medicine service, 12 were on the surgery or a surgical subspecialty service, and 7 were on other services; 11 were attending physicians, 12 were resident physicians, 5 were NPs, and 3 were PAs. Only 2 providers—an attending in medicine and a resident in surgery—had a specialty focus that was cardiac-related. Median time in current position was 4 years (Table 1). Seventeen of the interviews were in person, and 14 were conducted by telephone. The mean interview time was 20 minutes and ranged from 11 to 41 minutes.

We identified 5 main themes with 29 supporting codes (Table 2) describing provider attitudes towards the management of chronic disease for hospitalized patients. These themes, with related CFIR constructs, were: 1) perceived impact on patient outcomes (CFIR construct: intervention characteristics, relative advantage); 2) hospital structural characteristics (inner setting, structural characteristics); 3) provider knowledge and self-efficacy (characteristic of individual, knowledge and beliefs about the intervention and self-efficacy); 4) hospital priorities (inner setting, implementation climate, relative priority); and 5) continuity and communication (inner setting, networks and communications). For most themes, subjects described both positive and negative aspects of chronic disease management, as well as related facilitators and barriers to delivery of chronic disease care for hospitalized patients. Illustrative quotes for each theme are shown in Table 3.

Perceived Impact on Patient Outcomes

Perceived impact on patient outcomes was mixed. Most providers believed the management of chronic diseases could lead to improvement in important patient outcomes, including decreased length of stay (LOS), prevention of hospital complication, and decreased readmissions. Surgical providers focused particularly on the benefits of preventing surgical complications and noted that they were more likely to manage chronic conditions—primarily through use of specialist consultation—when they perceived a benefit to prevention of surgical outcomes or a fear that surgery may worsen a stable chronic condition:

“Most of the surgery I do is pretty stressful on the body and is very likely to induce acute on chronic exacerbations of heart failure. For someone with Class II or higher heart failure, I’m definitely gonna have cardiology on board or at least internal medicine on board right from the beginning.”

Hospital Structural Characteristics

For many providers, the hospital setting provides a unique opportunity for care of patients with chronic disease. First, a hospitalization is a time for a patient’s management to be reviewed by a new care team. The hospital team reviews the management plan for patients at admission, which is a time to reevaluate whether patients are on evidence-based therapies: “It’s helpful to have a new set of eyes on somebody, like fresh information.” According to providers, this reevaluation can overcome instances of therapeutic inertia by the outpatient physician. Second, the hospital has many resources, including readily available specialist services and diagnostic tests, which can allow a patient-centered approach that coordinates care in 1 place, as a surgery NP described: “I think the advantage for the patient is that they wind up stopping in for 1 thing but we wind up taking care of a few without requiring the need for him or her to go to all these different specialists on the outside. They’re mostly elderly and not able to get around.” Third, the high availability of services and frequent monitoring allows rapid titration of evidence-based medicines, as discussed by a medicine resident: “It’s easier and faster to titrate medication—they’re in a monitored setting; you can ensure compliance.”

Patients may also differ from their usual state while hospitalized, creating both risks and benefits. The hospital setting can provide an opportunity to educate patients on their chronic disease(s) because they are motivated: “They’re in an office visit and their sugars are out of whack or something, they may take it a little bit more seriously if they were just in the hospital even though it was on an unrelated issue. I think it probably just changes their perspective on their disease.” However, in the hospital, patients are in an unusual environment with a restricted diet and forced medication compliance. Furthermore, the acute condition can lead to changes in their chronic disease, as described by 1 medicine attending: “their sugar is high because they’re acutely ill.” Providers expressed concern that changing medications in this setting may lead to adverse events (AEs) when patients return to their usual environment.

Provider Knowledge and Self-Efficacy

Insufficient knowledge of treatments for chronic conditions was cited as a barrier to some providers’ ability to actively manage chronic disease for hospitalized patients. Some providers described management of conditions outside their area as less satisfying than their primary focus. For example, an orthopedic surgeon explained: “…it’s very simple. You see your bone is broken, you fix it, that’s it…it’s intellectually satisfying…managing chronic diseases is less like that.” Reliance on consultants was 1 approach to deal with knowledge gaps in areas outside a provider’s expertise.

For a number of providers, management of stable chronic disease is the responsibility of the outpatient provider. Providers expressed concern that inpatient management was a reach into the domain of the primary care provider (PCP) and might take “away from the primary focus” of the hospitalization. Nonetheless, some providers noted an “ethical responsibility to manage [a] patient correctly,” and some providers believed that engaging in chronic disease management in the hospital would present an opportunity to expand their own expertise.

A few providers were worried about legal risk related to chronic disease management: “we don’t typically deal too much with managing some of these other medical issues for medical and legal reasons.” Providers again suggested that consults can help overcome this concern for risk, as discussed by 1 surgical attending: “We’re all not wanting to be sued, and we want to do the right thing. It costs me nothing to have a cardiologist on board, so like—why not.”

Hospital Priorities

Providers explained that the hospital has strong interests in early discharge and minimizing LOS. These priorities are based on goals of improving patient outcomes, increasing bed availability and hospital volume, and reducing costs. Providers perceive these hospital priorities as potential barriers to chronic disease management, which can increase LOS and costs through additional testing and treatment. As a medicine resident described: “The DBN philosophy, ‘discharge before noon’ philosophy, which is part of the hospital efficiency to get people in and out of the hospital as quickly as [is] safe, or maybe faster. And I think that there’s a culture where you’re encouraged to only focus on the acute issue and tend to defer everything else.”

Continuity and Communication

According to many providers, care continuity between the outpatient setting and the hospital played a major role in management of chronic disease. One barrier to starting a new evidence-based medication was lack of knowledge of patient history. As noted, providers expressed concern that a patient may not be on a given therapy because of an adverse reaction that was not documented in the hospital chart. This is particularly true because, as discussed by a surgery resident, patients with “PCPs outside the system [in which providers] don’t have access to the electronic medical record.” To overcome this barrier, providers attempt to communicate with the outpatient provider to confirm a lack of contraindications to therapies prior to any changes; notably, communication is easier if the inpatient provider has a relationship with the outpatient PCP.

Some providers were more likely to start chronic disease therapies if the patient had no prior outpatient care, because the provider was reassured that there was no rationale for missing therapies. One neurology attending noted that if a patient had newly documented “hypertension even if they were in for something else, I might start them on an antihypertensive, but then arrange for a close follow-up with a new PCP.”

Following hospitalization, providers wanted assurance that any changes to chronic disease management would be followed up by an outpatient physician. Any changes are relayed to the outpatient provider and the “level of communication…with the outpatient provider who’s gonna inherit” these changes can influence how aggressively the inpatient provider manages chronic diseases. Providers may be reluctant to start therapy for patients if they are concerned about outpatient follow up: “they have diabetes and they should really technically be on an ACE [angiotensin converting enzyme]inhibitor and aspirin, but they’re not. I might send them out on the aspirin but I might either start ACE inhibitor and have them follow up with their PCP in 2 weeks if I’m confident that they’ll do it or if I’m really confident that they’ll not follow up, I will help them get the appointment and then the discharge instruction is to the PCP is ‘Please start this patient on ACE inhibitor if they show up.’”

Providers frequently perceive benefit to chronic disease management in the hospital, including improvements in clinical outcomes. Notably, providers see opportunities to improve compliance with evidence-based care to overcome potential barriers to managing chronic disease in the outpatient setting, which can be limited by pressure for brief encounters,¹³ clinical inertia,¹⁴ difficulty with close monitoring of patients,¹⁵ and care fragmentation.¹⁶ Concurrently, inpatient providers are concerned about potential for patient harm related to chronic disease management, primarily related to AEs from medications. Similar to a case study about a patient with outpatient hypotension following aggressive inpatient hypertension management,⁷ providers fear that changing a patient’s chronic disease management in a hospital setting may cause harm when the patient returns home.

Although some clinicians have argued against aggressive in-hospital chronic disease management because of concerns for risk of AEs,⁷ our study and others⁸ have suggested that many clinicians perceive benefit. In some cases, such as smoking cessation counseling for all current smokers and prescribing an angiotensin converting enzyme inhibitor for patients with systolic heart failure, the perceived importance is so great that chronic disease management has been used as a national quality metric for hospitals. While these hospital metrics may be justified for short-term benefits after hospitalization, studies have demonstrated only weak improvement in short-term postdischarge outcomes related to chronic disease management.¹⁷ The true benefit is likely from improved processes of care in the short term that lead to long-term improvement in outcomes.^4,5,18 Thus, the advantage of starting a patient hospitalized for a stroke on blood pressure medication is the increased likelihood that the patient will continue the medication as an outpatient, which may reduce long-term mortality.

For hospital delivery systems that are concerned with such care process improvement through in-hospital chronic disease management, we identified a number of barriers and facilitators to delivering this care. One significant barrier was poor transitions between the inpatient and the outpatient settings. When a patient transitions into the hospital, providers need to understand prior management choices. Facilitators to help inpatient providers understand prior management included either knowing the outpatient provider, or understanding that there was a lack of regular outpatient care; in both these cases, inpatient providers felt more comfortable managing chronic diseases because they had insight into the outpatient plan, or lack thereof. However, these facilitators may not be practical to incorporate in interventions to improve chronic disease care, which should consider overcoming these communication barriers. Use of shared electronic health records or standardized telephone calls with well-documented care plans obtained through health information exchanges may facilitate an inpatient provider to manage appropriately chronic disease. Similarly, discontinuity between the inpatient provider and the outpatient provider is a barrier that must be overcome to ease concerns that any chronic disease management changes do not result in harm in the postdischarge period. These findings again point to the need for improved documentation and communication between inpatient and outpatient providers. Of course, the transitional care period is one of high risk, and improving communication between providers has been an area of ongoing work.¹⁹

Lack of comfort among inpatient providers with managing chronic diseases is another important barrier, which appears to be largely overcome through the use of consultation services. Ready availability of specialists, common in academic medical centers, can facilitate delivery of chronic disease management. Inpatient interventions designed to improve evidence-based care for a chronic disease may benefit from involvement or at least availability of specialists in the effort. Another major barrier relates to hospital priorities, which in our study were closely aligned with external factors such as payment models. As hospitalizations are typically paid based on the discharge diagnosis, hospitals have incentives to discharge quickly and not order extra diagnostic tests. As a result, there are disincentives for chronic disease management that may require additional testing or monitoring in the hospital. Conversely, as hospitals accept postdischarge financial risks through readmission penalties or postdischarge cost savings, hospitals may perceive that long-term benefits of chronic disease management may outweigh short-term costs.

The study findings should be interpreted in the context of its limitations. Findings of our study of providers from a single academic medical center may not be generalizable. Nearly half of our interviews were conducted by telephone, which limits our ability to capture nonverbal cues in communication. Providers may have had social desirability bias towards positive aspects of chronic disease management. We did not have the power to determine differences in response by provider characteristic because this was an exploratory qualitative study. Future studies with representative sampling, a larger sample size, and measures for constructs such as provider self-efficacy are needed to examine differences by specialty, provider type, and experience level.

In conclusion, inpatient providers believe that hospital chronic disease management has the potential to be beneficial for both process of care and clinical outcomes; providers also express concern about potential adverse consequences of managing chronic disease during acute hospitalizations. To maximize both quality of care and patient safety, overcoming communication barriers between inpatient and outpatient providers is needed. Both a supportive hospital environment and availability of specialty support can facilitate in-hospital chronic disease management. Interventions that incorporate these factors may be well-suited to improve chronic disease care and long-term outcomes.

Disclosures

This work was supported by the Agency for Healthcare Research and Quality (AHRQ) grant K08HS23683. The authors report no financial conflicts of interest.

As the US population ages and becomes increasingly frail, the need for rehabilitation rises. By 2030, an estimated 20% of the population will be 65 years old or older, and almost 10% will be over 75.¹ About 20% of hospitalized Medicare patients receive subsequent care in postacute inpatient rehabilitation (PAIR) facilities, accounting for $31 billion in Medicare expenditures in 2014.² Although the need for rehabilitation will continue to rise, Medicare policy restricts access to it.

Under Medicare policy, PAIR services are covered for certain hospitalized patients but not others. Hospitalized patients are either inpatients, who are billed under Medicare Part A, or outpatients, billed under Part B. When hospital length of stay (LOS) is anticipated to be less than 2 midnights, patients are admitted as outpatients under the term observation status; when longer stays are expected, patients are admitted as inpatients.³ This recently implemented time-based distinction has been criticized as arbitrary, and as potentially shifting many patients from inpatient to outpatient (observation) status.⁴

The distinction between inpatient and observation status has significant consequences for posthospital care. Medicare Part A covers care in skilled nursing facilities (SNFs) and acute inpatient rehabilitation facilities (IRFs); after hospitalization, inpatients have access to either, without copay. As observation patients are covered under Medicare Part B, they are technically not covered for either service after their hospital stay. IRFs sometimes accept patients from ambulatory and nonacute settings; observation patients may be accepted in rare circumstances, but they pay the Part A deductible ($1288 in 2016) to have the services covered by Medicare. SNF services are never covered for observation patients, and access to this care requires an average out-of-pocket payment of more than $10,503 per beneficiary for a typical SNF stay.⁵ Given that about 70% of Medicare patients fall below 300% of the federal poverty line,⁶ the out-of-pocket costs for PAIR services for observation patients can be prohibitive.

Although only 0.75% of community-dwelling Medicare observation patients are discharged to PAIR facilities,⁷ it is unclear if the need for this care is higher but remains unmet secondary to cost concerns of Medicare beneficiaries. Also unclear is whether observation patients who would benefit from this care but do not receive it end up with poorer health outcomes and therefore use more healthcare services.

The purpose of this study was to estimate the proportion of Medicare observation patients who are admitted from home and receive a recommendation for placement in a PAIR facility, and to determine the ultimate disposition of such patients. We also sought to evaluate the association between recommendation for PAIR placement, LOS, and 30-day hospital revisit rate.

The Institutional Review Board of Christiana Care Health System (CCHS) approved this study.

Sample and Design

This was an observational study of community-dwelling Medicare patients admitted under observation status to Delaware’s CCHS, which consists of a 907-bed regional tertiary-care facility in Newark and a 241-bed community hospital in Wilmington. The study period was January 1 to December 31, 2013. We limited our sample to patients treated by hospitalists on hospital wards, as this care constitutes 80% of the care provided to observation patients at CCHS and the majority of care nationally.⁸ As neither SNF care nor IRF care is covered under Medicare Part B, and both would result in high out-of-pocket costs for Medicare observation patients, we combined them into a single variable, PAIR.

All data were obtained from institutional electronic medical record and administrative data systems. Study inclusion criteria were Medicare as primary insurance, admission to hospital from home, and care received at either CCHS facility. Exclusion criteria were admission from PAIR facility, long-term care facility, assisted-living facility, or inpatient psychiatric facility; death; discharge against medical advice (AMA) or to hospice, non-SNF, or inpatient psychiatric facility; and discovery (during review of case management [CM] notes) of erroneous listing of Medicare as primary insurance, or of inpatient admission (within 30 days before index observation stay) that qualified for PAIR coverage under Medicare Part A.

We reviewed the medical charts of a representative (~30%) sample of the cohort and examined physical therapy (PT) and CM notes to determine the proportions of patients with recommendations for home with no services, home-based PT, possible PAIR, and PAIR. Charts were sorted by medical record number and were reviewed in consecutive order. We coded a patient as having a recommendation for possible PAIR if the PT notes indicated the patient may benefit from PAIR but could have home PT if PAIR placement was not possible. CM notes were also reviewed for evidence of patient or family preference regarding PAIR placement. All questions about PT and CM recommendations were resolved by consensus.

Measures

For the total study sample, we calculated descriptive statistics and frequencies for demographic and administrative variables, including age, sex, race (Caucasian, African American, other), ethnicity (Hispanic/non-Hispanic), ICD-9 (International Classification of Diseases, Ninth Revision) primary diagnosis code, LOS (in hours) for index observation admission, discharge disposition (home with no services, home PT, possible PAIR, PAIR), and 30-day hospital revisit (emergency department, observation, inpatient admission). We used χ² test, Student t test, and analysis of variance (ANOVA) to test for statistically significant differences in characteristics between the chart review subgroup and the rest of the sample and between the groups with different disposition recommendations from PT notes.

For the chart review subgroup, we used ANOVA to calculate the unadjusted association between PT recommendation and LOS. We then adjusted for potential confounders, using multivariable linear regression with PT recommendation as a predictor and LOS as the outcome, controlling for variables previously associated with increased LOS among observation patients (primary diagnosis category, age, sex).⁶ We also adjusted for hospitalist group to account for potential variability in care delivery. As LOS was not normally distributed, we calculated the fourth root of LOS, which resulted in a more normal distribution, and used the transformed values in the regression model. We then calculated predicted values from the regression and back-transformed these to obtain adjusted mean values for LOS.

Of the 1417 unique patients who had Medicare as primary insurance and were admitted under observation status to a hospitalist service during the study period (2013), 94 were excluded (Figure). Of the remaining 1323 patients, the majority were 65 years old or older, female, white, and non-Hispanic. The most common ICD-9 diagnoses were syncope and chest pain. Mean LOS was 46.7 hours (range, 0-519 h). Less than 1% of patients were discharged to PAIR. Almost 25% of patients returned to the hospital, either for an emergency department visit or for observation or inpatient stay, within 30 days (Table).

Of the 419 charts reviewed to determine the proportion of patients evaluated by PT, and their subsequent recommendations, 33 were excluded, leaving 386 (92%) for analysis (Figure). There were no significant demographic differences between the patients in the chart review subgroup and the rest of the patients (Appendix). Of the 386 patients whose charts were analyzed, 181 (46.9%) had a PT evaluation, and 17 (4.4%) received a PAIR recommendation (Figure). Of the 17 patients recommended for PAIR, 12 (70.5%) were 65 years old or older, and 1 was discharged to a PAIR facility. Of the 46 patients recommended for home PT, 29 (63%) were discharged home with no services (Table).

PT-evaluated patients had unadjusted mean LOS of 52.2 hours (discharged home with no services), 64.1 hours (home PT or possible PAIR), and 83.1 hours (PAIR) (P = 0.001). With adjustment made for variables previously associated with increased LOS for observation patients, mean LOS for patients recommended for PAIR remained higher than that for patients in the other 2 categories (Table). Patients recommended for PAIR were more likely to return to hospital within 30 days than patients recommended for home PT or possible PAIR and patients discharged home with no services (Table).

Review of CM notes revealed that, of the 17 patients recommended for PAIR, 7 would have accepted PAIR services had they been covered by Medicare, 4 preferred discharge with home health services, and 6 did not provide clear details of patient or family preference.

To our knowledge, this is the first study to use chart review to examine the proportion of observation patients who would benefit from PAIR and the relationships among these patients’ rehabilitation needs, dispositions, and outcomes. We tried to be conservative in our estimates by limiting the study population to patients admitted from home. Nevertheless, the potential need for PAIR significantly outweighed the actual use of PAIR on discharge. The study sample was consistent with nationally representative samples of observation patients in terms of proportion of patients admitted from and discharged to facilities⁷ and the most common ICD-9 diagnoses.⁹

Physical Therapy Consultations and Observation

Of the 386 patients whose charts were reviewed and analyzed, 17 (4.4%) were evaluated as medically qualifying for and potentially benefiting from PAIR. Although the rate represents a minority of patients, it is 5- to 6-fold higher than the rate of discharge to PAIR, both in our study population and in previous national samples that used administrative data.⁷ In some cases, the decision not to discharge the patient to PAIR reflected patient and family preference. However, in other cases, patients clearly could have benefited from PAIR and would have gone had it been covered by Medicare. The gap suggests an unmet need for PAIR among a substantial proportion of Medicare beneficiaries for whom the therapy is recommended and wanted.

Efforts to expand coverage for PAIR have been resisted. According to Medicare regulations, beneficiaries qualify for PAIR coverage if they are hospitalized as inpatients for 3 midnights or longer. Days under observation status do not count toward this requirement, even if this status is changed to inpatient.¹⁰ The Medicare Payment Advisory Commission (MedPAC) recommendation that time under observation status count toward the Medicare requirement¹¹ has not been accepted,¹² in large part because further expansion of PAIR services likely would be unaffordable to Medicare under its payment structure.¹³ Given our finding that the need for PAIR likely is much higher than previously anticipated, Medicare policy makers should consider broadening access to PAIR while efforts are made to rein in expenditures through payment reform.

One potential area of cost savings is more judicious use of PT evaluation for observation patients, particularly given our finding that the majority of PT consultations resulted in no further recommendations. Efforts to triage PT consultations for appropriateness have had some success, though the literature is scant.¹⁴ To improve value for Medicare, healthcare systems, and patients, researchers should rigorously evaluate approaches that maximize appropriate use of PT services.

Hospital Length of Stay

Our cohort’s mean hospital stay was longer than averages reported elsewhere,⁹ likely reflecting our selection of Medicare patients rather than a general medicine population.⁶ However, our cohort’s adjusted mean hospital stay was significantly longer for patients recommended for PAIR than for patients without PT needs. That out-of-pocket costs for observation patients increase dramatically as LOS goes past 48 hours⁶ could have significant financial implications for Medicare beneficiaries.

Return Visits

Almost 25% of our observation patients returned to hospital within 30 days. There was a significant trend toward increased rehospitalization among patients recommended for PAIR than among patients with no PT needs.

Policies related to PAIR for observation patients are rooted in the concern that expanded access to services will contribute to overuse of services and higher healthcare costs.¹⁵ However, patients who could have benefited from PAIR but were not covered also were at risk for increased healthcare use and costs. A recent study found that more than one fourth of observation patients with repeat observation stays accrued excessive financial liability.¹⁶ Researchers should determine more precisely how the cost of coverage for PAIR placement on an index observation admission compares with the cost of subsequent healthcare use potentially related to insufficient supportive care at home.

Study Limitations

Our results must be interpreted within the context of study limitations. First is the small sample size, particularly the subset of patients selected for detailed manual chart review. We were limited in our ability to calculate sample size prospectively because we were unaware of prior work that described the association between PT recommendation and outcomes among observation patients. However, post hoc analysis estimated that a sample size of 181 patients would have been needed to determine a statistically significant difference in 30-day hospital revisit between patients recommended for PAIR and patients with no PT needs with 80% power, which we achieved. Although there are significant limitations to post hoc sample size estimation, we consider our work hypothesis-generating and hope it will lead to larger studies.

We could not account for the potential bias of the physical therapists, whose evaluations could have been influenced by knowledge of patients’ observation status. Our findings could have underestimated the proportion of patients who otherwise would have been recommended for PAIR. Alternatively, therapists could have inaccurately assessed and overstated the need for PAIR. Although we could not account for the therapists’ accuracy and biases, their assessments provided crucial information beyond what was previously obtained from administrative data alone.^7,9

Hospital revisits were only accounted for within our hospital system—another potential source of underestimated findings. A significant proportion of patients recommended for home PT were discharged without services, which is counterintuitive, as Medicare covers home nursing services for observation patients. This finding most likely reflects administrative error but probably merits further evaluation.

Last, causality cannot be inferred from the results of a retrospective observational study.

As our study results suggest, there is an unmet need for PAIR services for Medicare observation patients, and LOS and subsequent use may be increased among patients recommended for PAIR. Our estimates are conservative and may underestimate the true need for services within this population. Our findings bolster MedPAC recommendations to amend the policies for Medicare coverage of PAIR services for observation patients.

Acknowledgment

The authors thank Paul Kolm, PhD, for statistical support.

Disclosures

Dr. Schwartz reports receiving personal fees from the Agency for Health Research and Quality, Bayer, the Blue Cross Blue Shield Association, Pfizer, and Takeda, all outside the submitted work. Dr. Hicks is supported by an Institutional Development Award from the National Institute of General Medical Sciences of the National Institutes of Health (grant U54-GM104941; principal investigator Stuart Binder-Macleod, PT, PhD, FAPTA). The other authors have nothing to report.

As the US population ages and becomes increasingly frail, the need for rehabilitation rises. By 2030, an estimated 20% of the population will be 65 years old or older, and almost 10% will be over 75.¹ About 20% of hospitalized Medicare patients receive subsequent care in postacute inpatient rehabilitation (PAIR) facilities, accounting for $31 billion in Medicare expenditures in 2014.² Although the need for rehabilitation will continue to rise, Medicare policy restricts access to it.

Under Medicare policy, PAIR services are covered for certain hospitalized patients but not others. Hospitalized patients are either inpatients, who are billed under Medicare Part A, or outpatients, billed under Part B. When hospital length of stay (LOS) is anticipated to be less than 2 midnights, patients are admitted as outpatients under the term observation status; when longer stays are expected, patients are admitted as inpatients.³ This recently implemented time-based distinction has been criticized as arbitrary, and as potentially shifting many patients from inpatient to outpatient (observation) status.⁴

The distinction between inpatient and observation status has significant consequences for posthospital care. Medicare Part A covers care in skilled nursing facilities (SNFs) and acute inpatient rehabilitation facilities (IRFs); after hospitalization, inpatients have access to either, without copay. As observation patients are covered under Medicare Part B, they are technically not covered for either service after their hospital stay. IRFs sometimes accept patients from ambulatory and nonacute settings; observation patients may be accepted in rare circumstances, but they pay the Part A deductible ($1288 in 2016) to have the services covered by Medicare. SNF services are never covered for observation patients, and access to this care requires an average out-of-pocket payment of more than $10,503 per beneficiary for a typical SNF stay.⁵ Given that about 70% of Medicare patients fall below 300% of the federal poverty line,⁶ the out-of-pocket costs for PAIR services for observation patients can be prohibitive.

Although only 0.75% of community-dwelling Medicare observation patients are discharged to PAIR facilities,⁷ it is unclear if the need for this care is higher but remains unmet secondary to cost concerns of Medicare beneficiaries. Also unclear is whether observation patients who would benefit from this care but do not receive it end up with poorer health outcomes and therefore use more healthcare services.

The purpose of this study was to estimate the proportion of Medicare observation patients who are admitted from home and receive a recommendation for placement in a PAIR facility, and to determine the ultimate disposition of such patients. We also sought to evaluate the association between recommendation for PAIR placement, LOS, and 30-day hospital revisit rate.

The Institutional Review Board of Christiana Care Health System (CCHS) approved this study.

Sample and Design

This was an observational study of community-dwelling Medicare patients admitted under observation status to Delaware’s CCHS, which consists of a 907-bed regional tertiary-care facility in Newark and a 241-bed community hospital in Wilmington. The study period was January 1 to December 31, 2013. We limited our sample to patients treated by hospitalists on hospital wards, as this care constitutes 80% of the care provided to observation patients at CCHS and the majority of care nationally.⁸ As neither SNF care nor IRF care is covered under Medicare Part B, and both would result in high out-of-pocket costs for Medicare observation patients, we combined them into a single variable, PAIR.

All data were obtained from institutional electronic medical record and administrative data systems. Study inclusion criteria were Medicare as primary insurance, admission to hospital from home, and care received at either CCHS facility. Exclusion criteria were admission from PAIR facility, long-term care facility, assisted-living facility, or inpatient psychiatric facility; death; discharge against medical advice (AMA) or to hospice, non-SNF, or inpatient psychiatric facility; and discovery (during review of case management [CM] notes) of erroneous listing of Medicare as primary insurance, or of inpatient admission (within 30 days before index observation stay) that qualified for PAIR coverage under Medicare Part A.

We reviewed the medical charts of a representative (~30%) sample of the cohort and examined physical therapy (PT) and CM notes to determine the proportions of patients with recommendations for home with no services, home-based PT, possible PAIR, and PAIR. Charts were sorted by medical record number and were reviewed in consecutive order. We coded a patient as having a recommendation for possible PAIR if the PT notes indicated the patient may benefit from PAIR but could have home PT if PAIR placement was not possible. CM notes were also reviewed for evidence of patient or family preference regarding PAIR placement. All questions about PT and CM recommendations were resolved by consensus.

Measures

For the total study sample, we calculated descriptive statistics and frequencies for demographic and administrative variables, including age, sex, race (Caucasian, African American, other), ethnicity (Hispanic/non-Hispanic), ICD-9 (International Classification of Diseases, Ninth Revision) primary diagnosis code, LOS (in hours) for index observation admission, discharge disposition (home with no services, home PT, possible PAIR, PAIR), and 30-day hospital revisit (emergency department, observation, inpatient admission). We used χ² test, Student t test, and analysis of variance (ANOVA) to test for statistically significant differences in characteristics between the chart review subgroup and the rest of the sample and between the groups with different disposition recommendations from PT notes.

For the chart review subgroup, we used ANOVA to calculate the unadjusted association between PT recommendation and LOS. We then adjusted for potential confounders, using multivariable linear regression with PT recommendation as a predictor and LOS as the outcome, controlling for variables previously associated with increased LOS among observation patients (primary diagnosis category, age, sex).⁶ We also adjusted for hospitalist group to account for potential variability in care delivery. As LOS was not normally distributed, we calculated the fourth root of LOS, which resulted in a more normal distribution, and used the transformed values in the regression model. We then calculated predicted values from the regression and back-transformed these to obtain adjusted mean values for LOS.

Of the 1417 unique patients who had Medicare as primary insurance and were admitted under observation status to a hospitalist service during the study period (2013), 94 were excluded (Figure). Of the remaining 1323 patients, the majority were 65 years old or older, female, white, and non-Hispanic. The most common ICD-9 diagnoses were syncope and chest pain. Mean LOS was 46.7 hours (range, 0-519 h). Less than 1% of patients were discharged to PAIR. Almost 25% of patients returned to the hospital, either for an emergency department visit or for observation or inpatient stay, within 30 days (Table).

Of the 419 charts reviewed to determine the proportion of patients evaluated by PT, and their subsequent recommendations, 33 were excluded, leaving 386 (92%) for analysis (Figure). There were no significant demographic differences between the patients in the chart review subgroup and the rest of the patients (Appendix). Of the 386 patients whose charts were analyzed, 181 (46.9%) had a PT evaluation, and 17 (4.4%) received a PAIR recommendation (Figure). Of the 17 patients recommended for PAIR, 12 (70.5%) were 65 years old or older, and 1 was discharged to a PAIR facility. Of the 46 patients recommended for home PT, 29 (63%) were discharged home with no services (Table).

PT-evaluated patients had unadjusted mean LOS of 52.2 hours (discharged home with no services), 64.1 hours (home PT or possible PAIR), and 83.1 hours (PAIR) (P = 0.001). With adjustment made for variables previously associated with increased LOS for observation patients, mean LOS for patients recommended for PAIR remained higher than that for patients in the other 2 categories (Table). Patients recommended for PAIR were more likely to return to hospital within 30 days than patients recommended for home PT or possible PAIR and patients discharged home with no services (Table).

Review of CM notes revealed that, of the 17 patients recommended for PAIR, 7 would have accepted PAIR services had they been covered by Medicare, 4 preferred discharge with home health services, and 6 did not provide clear details of patient or family preference.

To our knowledge, this is the first study to use chart review to examine the proportion of observation patients who would benefit from PAIR and the relationships among these patients’ rehabilitation needs, dispositions, and outcomes. We tried to be conservative in our estimates by limiting the study population to patients admitted from home. Nevertheless, the potential need for PAIR significantly outweighed the actual use of PAIR on discharge. The study sample was consistent with nationally representative samples of observation patients in terms of proportion of patients admitted from and discharged to facilities⁷ and the most common ICD-9 diagnoses.⁹

Physical Therapy Consultations and Observation

Of the 386 patients whose charts were reviewed and analyzed, 17 (4.4%) were evaluated as medically qualifying for and potentially benefiting from PAIR. Although the rate represents a minority of patients, it is 5- to 6-fold higher than the rate of discharge to PAIR, both in our study population and in previous national samples that used administrative data.⁷ In some cases, the decision not to discharge the patient to PAIR reflected patient and family preference. However, in other cases, patients clearly could have benefited from PAIR and would have gone had it been covered by Medicare. The gap suggests an unmet need for PAIR among a substantial proportion of Medicare beneficiaries for whom the therapy is recommended and wanted.

Efforts to expand coverage for PAIR have been resisted. According to Medicare regulations, beneficiaries qualify for PAIR coverage if they are hospitalized as inpatients for 3 midnights or longer. Days under observation status do not count toward this requirement, even if this status is changed to inpatient.¹⁰ The Medicare Payment Advisory Commission (MedPAC) recommendation that time under observation status count toward the Medicare requirement¹¹ has not been accepted,¹² in large part because further expansion of PAIR services likely would be unaffordable to Medicare under its payment structure.¹³ Given our finding that the need for PAIR likely is much higher than previously anticipated, Medicare policy makers should consider broadening access to PAIR while efforts are made to rein in expenditures through payment reform.

One potential area of cost savings is more judicious use of PT evaluation for observation patients, particularly given our finding that the majority of PT consultations resulted in no further recommendations. Efforts to triage PT consultations for appropriateness have had some success, though the literature is scant.¹⁴ To improve value for Medicare, healthcare systems, and patients, researchers should rigorously evaluate approaches that maximize appropriate use of PT services.

Hospital Length of Stay

Our cohort’s mean hospital stay was longer than averages reported elsewhere,⁹ likely reflecting our selection of Medicare patients rather than a general medicine population.⁶ However, our cohort’s adjusted mean hospital stay was significantly longer for patients recommended for PAIR than for patients without PT needs. That out-of-pocket costs for observation patients increase dramatically as LOS goes past 48 hours⁶ could have significant financial implications for Medicare beneficiaries.

Return Visits

Almost 25% of our observation patients returned to hospital within 30 days. There was a significant trend toward increased rehospitalization among patients recommended for PAIR than among patients with no PT needs.

Policies related to PAIR for observation patients are rooted in the concern that expanded access to services will contribute to overuse of services and higher healthcare costs.¹⁵ However, patients who could have benefited from PAIR but were not covered also were at risk for increased healthcare use and costs. A recent study found that more than one fourth of observation patients with repeat observation stays accrued excessive financial liability.¹⁶ Researchers should determine more precisely how the cost of coverage for PAIR placement on an index observation admission compares with the cost of subsequent healthcare use potentially related to insufficient supportive care at home.

Study Limitations

Our results must be interpreted within the context of study limitations. First is the small sample size, particularly the subset of patients selected for detailed manual chart review. We were limited in our ability to calculate sample size prospectively because we were unaware of prior work that described the association between PT recommendation and outcomes among observation patients. However, post hoc analysis estimated that a sample size of 181 patients would have been needed to determine a statistically significant difference in 30-day hospital revisit between patients recommended for PAIR and patients with no PT needs with 80% power, which we achieved. Although there are significant limitations to post hoc sample size estimation, we consider our work hypothesis-generating and hope it will lead to larger studies.

We could not account for the potential bias of the physical therapists, whose evaluations could have been influenced by knowledge of patients’ observation status. Our findings could have underestimated the proportion of patients who otherwise would have been recommended for PAIR. Alternatively, therapists could have inaccurately assessed and overstated the need for PAIR. Although we could not account for the therapists’ accuracy and biases, their assessments provided crucial information beyond what was previously obtained from administrative data alone.^7,9

Hospital revisits were only accounted for within our hospital system—another potential source of underestimated findings. A significant proportion of patients recommended for home PT were discharged without services, which is counterintuitive, as Medicare covers home nursing services for observation patients. This finding most likely reflects administrative error but probably merits further evaluation.

Last, causality cannot be inferred from the results of a retrospective observational study.

As our study results suggest, there is an unmet need for PAIR services for Medicare observation patients, and LOS and subsequent use may be increased among patients recommended for PAIR. Our estimates are conservative and may underestimate the true need for services within this population. Our findings bolster MedPAC recommendations to amend the policies for Medicare coverage of PAIR services for observation patients.

Acknowledgment

The authors thank Paul Kolm, PhD, for statistical support.

Disclosures

Dr. Schwartz reports receiving personal fees from the Agency for Health Research and Quality, Bayer, the Blue Cross Blue Shield Association, Pfizer, and Takeda, all outside the submitted work. Dr. Hicks is supported by an Institutional Development Award from the National Institute of General Medical Sciences of the National Institutes of Health (grant U54-GM104941; principal investigator Stuart Binder-Macleod, PT, PhD, FAPTA). The other authors have nothing to report.

As the US population ages and becomes increasingly frail, the need for rehabilitation rises. By 2030, an estimated 20% of the population will be 65 years old or older, and almost 10% will be over 75.¹ About 20% of hospitalized Medicare patients receive subsequent care in postacute inpatient rehabilitation (PAIR) facilities, accounting for $31 billion in Medicare expenditures in 2014.² Although the need for rehabilitation will continue to rise, Medicare policy restricts access to it.

Under Medicare policy, PAIR services are covered for certain hospitalized patients but not others. Hospitalized patients are either inpatients, who are billed under Medicare Part A, or outpatients, billed under Part B. When hospital length of stay (LOS) is anticipated to be less than 2 midnights, patients are admitted as outpatients under the term observation status; when longer stays are expected, patients are admitted as inpatients.³ This recently implemented time-based distinction has been criticized as arbitrary, and as potentially shifting many patients from inpatient to outpatient (observation) status.⁴

The distinction between inpatient and observation status has significant consequences for posthospital care. Medicare Part A covers care in skilled nursing facilities (SNFs) and acute inpatient rehabilitation facilities (IRFs); after hospitalization, inpatients have access to either, without copay. As observation patients are covered under Medicare Part B, they are technically not covered for either service after their hospital stay. IRFs sometimes accept patients from ambulatory and nonacute settings; observation patients may be accepted in rare circumstances, but they pay the Part A deductible ($1288 in 2016) to have the services covered by Medicare. SNF services are never covered for observation patients, and access to this care requires an average out-of-pocket payment of more than $10,503 per beneficiary for a typical SNF stay.⁵ Given that about 70% of Medicare patients fall below 300% of the federal poverty line,⁶ the out-of-pocket costs for PAIR services for observation patients can be prohibitive.

Although only 0.75% of community-dwelling Medicare observation patients are discharged to PAIR facilities,⁷ it is unclear if the need for this care is higher but remains unmet secondary to cost concerns of Medicare beneficiaries. Also unclear is whether observation patients who would benefit from this care but do not receive it end up with poorer health outcomes and therefore use more healthcare services.

The purpose of this study was to estimate the proportion of Medicare observation patients who are admitted from home and receive a recommendation for placement in a PAIR facility, and to determine the ultimate disposition of such patients. We also sought to evaluate the association between recommendation for PAIR placement, LOS, and 30-day hospital revisit rate.

The Institutional Review Board of Christiana Care Health System (CCHS) approved this study.

Sample and Design

This was an observational study of community-dwelling Medicare patients admitted under observation status to Delaware’s CCHS, which consists of a 907-bed regional tertiary-care facility in Newark and a 241-bed community hospital in Wilmington. The study period was January 1 to December 31, 2013. We limited our sample to patients treated by hospitalists on hospital wards, as this care constitutes 80% of the care provided to observation patients at CCHS and the majority of care nationally.⁸ As neither SNF care nor IRF care is covered under Medicare Part B, and both would result in high out-of-pocket costs for Medicare observation patients, we combined them into a single variable, PAIR.

All data were obtained from institutional electronic medical record and administrative data systems. Study inclusion criteria were Medicare as primary insurance, admission to hospital from home, and care received at either CCHS facility. Exclusion criteria were admission from PAIR facility, long-term care facility, assisted-living facility, or inpatient psychiatric facility; death; discharge against medical advice (AMA) or to hospice, non-SNF, or inpatient psychiatric facility; and discovery (during review of case management [CM] notes) of erroneous listing of Medicare as primary insurance, or of inpatient admission (within 30 days before index observation stay) that qualified for PAIR coverage under Medicare Part A.

We reviewed the medical charts of a representative (~30%) sample of the cohort and examined physical therapy (PT) and CM notes to determine the proportions of patients with recommendations for home with no services, home-based PT, possible PAIR, and PAIR. Charts were sorted by medical record number and were reviewed in consecutive order. We coded a patient as having a recommendation for possible PAIR if the PT notes indicated the patient may benefit from PAIR but could have home PT if PAIR placement was not possible. CM notes were also reviewed for evidence of patient or family preference regarding PAIR placement. All questions about PT and CM recommendations were resolved by consensus.

Measures

For the total study sample, we calculated descriptive statistics and frequencies for demographic and administrative variables, including age, sex, race (Caucasian, African American, other), ethnicity (Hispanic/non-Hispanic), ICD-9 (International Classification of Diseases, Ninth Revision) primary diagnosis code, LOS (in hours) for index observation admission, discharge disposition (home with no services, home PT, possible PAIR, PAIR), and 30-day hospital revisit (emergency department, observation, inpatient admission). We used χ² test, Student t test, and analysis of variance (ANOVA) to test for statistically significant differences in characteristics between the chart review subgroup and the rest of the sample and between the groups with different disposition recommendations from PT notes.

For the chart review subgroup, we used ANOVA to calculate the unadjusted association between PT recommendation and LOS. We then adjusted for potential confounders, using multivariable linear regression with PT recommendation as a predictor and LOS as the outcome, controlling for variables previously associated with increased LOS among observation patients (primary diagnosis category, age, sex).⁶ We also adjusted for hospitalist group to account for potential variability in care delivery. As LOS was not normally distributed, we calculated the fourth root of LOS, which resulted in a more normal distribution, and used the transformed values in the regression model. We then calculated predicted values from the regression and back-transformed these to obtain adjusted mean values for LOS.

Of the 1417 unique patients who had Medicare as primary insurance and were admitted under observation status to a hospitalist service during the study period (2013), 94 were excluded (Figure). Of the remaining 1323 patients, the majority were 65 years old or older, female, white, and non-Hispanic. The most common ICD-9 diagnoses were syncope and chest pain. Mean LOS was 46.7 hours (range, 0-519 h). Less than 1% of patients were discharged to PAIR. Almost 25% of patients returned to the hospital, either for an emergency department visit or for observation or inpatient stay, within 30 days (Table).

Of the 419 charts reviewed to determine the proportion of patients evaluated by PT, and their subsequent recommendations, 33 were excluded, leaving 386 (92%) for analysis (Figure). There were no significant demographic differences between the patients in the chart review subgroup and the rest of the patients (Appendix). Of the 386 patients whose charts were analyzed, 181 (46.9%) had a PT evaluation, and 17 (4.4%) received a PAIR recommendation (Figure). Of the 17 patients recommended for PAIR, 12 (70.5%) were 65 years old or older, and 1 was discharged to a PAIR facility. Of the 46 patients recommended for home PT, 29 (63%) were discharged home with no services (Table).

PT-evaluated patients had unadjusted mean LOS of 52.2 hours (discharged home with no services), 64.1 hours (home PT or possible PAIR), and 83.1 hours (PAIR) (P = 0.001). With adjustment made for variables previously associated with increased LOS for observation patients, mean LOS for patients recommended for PAIR remained higher than that for patients in the other 2 categories (Table). Patients recommended for PAIR were more likely to return to hospital within 30 days than patients recommended for home PT or possible PAIR and patients discharged home with no services (Table).

Review of CM notes revealed that, of the 17 patients recommended for PAIR, 7 would have accepted PAIR services had they been covered by Medicare, 4 preferred discharge with home health services, and 6 did not provide clear details of patient or family preference.

To our knowledge, this is the first study to use chart review to examine the proportion of observation patients who would benefit from PAIR and the relationships among these patients’ rehabilitation needs, dispositions, and outcomes. We tried to be conservative in our estimates by limiting the study population to patients admitted from home. Nevertheless, the potential need for PAIR significantly outweighed the actual use of PAIR on discharge. The study sample was consistent with nationally representative samples of observation patients in terms of proportion of patients admitted from and discharged to facilities⁷ and the most common ICD-9 diagnoses.⁹

Physical Therapy Consultations and Observation

Of the 386 patients whose charts were reviewed and analyzed, 17 (4.4%) were evaluated as medically qualifying for and potentially benefiting from PAIR. Although the rate represents a minority of patients, it is 5- to 6-fold higher than the rate of discharge to PAIR, both in our study population and in previous national samples that used administrative data.⁷ In some cases, the decision not to discharge the patient to PAIR reflected patient and family preference. However, in other cases, patients clearly could have benefited from PAIR and would have gone had it been covered by Medicare. The gap suggests an unmet need for PAIR among a substantial proportion of Medicare beneficiaries for whom the therapy is recommended and wanted.

Efforts to expand coverage for PAIR have been resisted. According to Medicare regulations, beneficiaries qualify for PAIR coverage if they are hospitalized as inpatients for 3 midnights or longer. Days under observation status do not count toward this requirement, even if this status is changed to inpatient.¹⁰ The Medicare Payment Advisory Commission (MedPAC) recommendation that time under observation status count toward the Medicare requirement¹¹ has not been accepted,¹² in large part because further expansion of PAIR services likely would be unaffordable to Medicare under its payment structure.¹³ Given our finding that the need for PAIR likely is much higher than previously anticipated, Medicare policy makers should consider broadening access to PAIR while efforts are made to rein in expenditures through payment reform.

One potential area of cost savings is more judicious use of PT evaluation for observation patients, particularly given our finding that the majority of PT consultations resulted in no further recommendations. Efforts to triage PT consultations for appropriateness have had some success, though the literature is scant.¹⁴ To improve value for Medicare, healthcare systems, and patients, researchers should rigorously evaluate approaches that maximize appropriate use of PT services.

Hospital Length of Stay

Our cohort’s mean hospital stay was longer than averages reported elsewhere,⁹ likely reflecting our selection of Medicare patients rather than a general medicine population.⁶ However, our cohort’s adjusted mean hospital stay was significantly longer for patients recommended for PAIR than for patients without PT needs. That out-of-pocket costs for observation patients increase dramatically as LOS goes past 48 hours⁶ could have significant financial implications for Medicare beneficiaries.

Return Visits

Almost 25% of our observation patients returned to hospital within 30 days. There was a significant trend toward increased rehospitalization among patients recommended for PAIR than among patients with no PT needs.

Policies related to PAIR for observation patients are rooted in the concern that expanded access to services will contribute to overuse of services and higher healthcare costs.¹⁵ However, patients who could have benefited from PAIR but were not covered also were at risk for increased healthcare use and costs. A recent study found that more than one fourth of observation patients with repeat observation stays accrued excessive financial liability.¹⁶ Researchers should determine more precisely how the cost of coverage for PAIR placement on an index observation admission compares with the cost of subsequent healthcare use potentially related to insufficient supportive care at home.

Study Limitations

Our results must be interpreted within the context of study limitations. First is the small sample size, particularly the subset of patients selected for detailed manual chart review. We were limited in our ability to calculate sample size prospectively because we were unaware of prior work that described the association between PT recommendation and outcomes among observation patients. However, post hoc analysis estimated that a sample size of 181 patients would have been needed to determine a statistically significant difference in 30-day hospital revisit between patients recommended for PAIR and patients with no PT needs with 80% power, which we achieved. Although there are significant limitations to post hoc sample size estimation, we consider our work hypothesis-generating and hope it will lead to larger studies.

We could not account for the potential bias of the physical therapists, whose evaluations could have been influenced by knowledge of patients’ observation status. Our findings could have underestimated the proportion of patients who otherwise would have been recommended for PAIR. Alternatively, therapists could have inaccurately assessed and overstated the need for PAIR. Although we could not account for the therapists’ accuracy and biases, their assessments provided crucial information beyond what was previously obtained from administrative data alone.^7,9

Hospital revisits were only accounted for within our hospital system—another potential source of underestimated findings. A significant proportion of patients recommended for home PT were discharged without services, which is counterintuitive, as Medicare covers home nursing services for observation patients. This finding most likely reflects administrative error but probably merits further evaluation.

Last, causality cannot be inferred from the results of a retrospective observational study.

As our study results suggest, there is an unmet need for PAIR services for Medicare observation patients, and LOS and subsequent use may be increased among patients recommended for PAIR. Our estimates are conservative and may underestimate the true need for services within this population. Our findings bolster MedPAC recommendations to amend the policies for Medicare coverage of PAIR services for observation patients.

Acknowledgment

The authors thank Paul Kolm, PhD, for statistical support.

Disclosures

Dr. Schwartz reports receiving personal fees from the Agency for Health Research and Quality, Bayer, the Blue Cross Blue Shield Association, Pfizer, and Takeda, all outside the submitted work. Dr. Hicks is supported by an Institutional Development Award from the National Institute of General Medical Sciences of the National Institutes of Health (grant U54-GM104941; principal investigator Stuart Binder-Macleod, PT, PhD, FAPTA). The other authors have nothing to report.