Background: Multidrug-resistant TB is more difficult to treat than is drug-susceptible TB. The 2011 World Health Organization (WHO) recommendations for the treatment of multidrug-resistant TB, based on very-low-quality and conditional evidence, consists of an intensive treatment phase of 8 months and total treatment duration of 20 months. Although cohort studies have shown promising cure rates among patients with multidrug-resistant TB who received existing drugs in regimens shorter than that recommended by the WHO, data from phase 3 randomized trials were lacking.

Bottom line: Short medication regimen (9-11 months) is noninferior to the traditional WHO-­recommended long regimen (20 months) for treating rifampin-resistant tuberculosis.

Citation: Nunn AJ et al. A trial of a shorter regimen for rifampin-resistant tuberculosis. N Engl J Med. 2019 Mar 28; 380:1201-13.

Dr. Kamath is an assistant professor of medicine at Duke University.

Background: Multidrug-resistant TB is more difficult to treat than is drug-susceptible TB. The 2011 World Health Organization (WHO) recommendations for the treatment of multidrug-resistant TB, based on very-low-quality and conditional evidence, consists of an intensive treatment phase of 8 months and total treatment duration of 20 months. Although cohort studies have shown promising cure rates among patients with multidrug-resistant TB who received existing drugs in regimens shorter than that recommended by the WHO, data from phase 3 randomized trials were lacking.

Bottom line: Short medication regimen (9-11 months) is noninferior to the traditional WHO-­recommended long regimen (20 months) for treating rifampin-resistant tuberculosis.

Citation: Nunn AJ et al. A trial of a shorter regimen for rifampin-resistant tuberculosis. N Engl J Med. 2019 Mar 28; 380:1201-13.

Dr. Kamath is an assistant professor of medicine at Duke University.

Background: Multidrug-resistant TB is more difficult to treat than is drug-susceptible TB. The 2011 World Health Organization (WHO) recommendations for the treatment of multidrug-resistant TB, based on very-low-quality and conditional evidence, consists of an intensive treatment phase of 8 months and total treatment duration of 20 months. Although cohort studies have shown promising cure rates among patients with multidrug-resistant TB who received existing drugs in regimens shorter than that recommended by the WHO, data from phase 3 randomized trials were lacking.

Bottom line: Short medication regimen (9-11 months) is noninferior to the traditional WHO-­recommended long regimen (20 months) for treating rifampin-resistant tuberculosis.

Citation: Nunn AJ et al. A trial of a shorter regimen for rifampin-resistant tuberculosis. N Engl J Med. 2019 Mar 28; 380:1201-13.

Dr. Kamath is an assistant professor of medicine at Duke University.

Prior to the current crisis of COVID-19, I had a critical view of the direction of our psychiatric field. We have given up on complicated psychotherapies in favor of dispensing medications. We have given up on complicated diagnostic assessments in favor of simple self-rated symptoms questionnaires. Many of us even chose to give up on seeing patients face to face in favor of practicing telepsychiatry in the comfort of our homes. Some even promoted a future of psychiatry in which psychiatrists treated patients through large spreadsheets of evidence-based rating tools following evidence-based algorithms without even ever meeting the patients.

I do not view this problem as unique to psychiatry but rather as part of a larger trend in society. For the past couple of years, Vivek Murthy, MD, the former U.S. surgeon general, has popularized the idea that we are in a loneliness epidemic, saying, “We live in the most technologically connected age in the history of civilization, yet rates of loneliness have doubled since the 1980s.” Despite having enumerable means to reach other human beings, so many of us feel distant and out of touch with others. This loneliness has a measurable impact on our well-being with one study that states, “Actual and perceived social isolation are both associated with increased risk for early mortality.”

Then, seemingly out of nowhere, we were confronted with the largest challenge to our sense of connectedness in my lifetime. Throughout the past months, we have been asked to meet each other less frequently, do so through sterile means, and certainly not shake hands, hug, or embrace. The COVID-19 crisis has quickly made us all experts in telepsychiatry, remote work, and doing more with less. The COVID-19 crisis has asked many of us to put aside some of our human rituals like eating together, enjoying artistic experiences as a group, and touching, for the sake of saving lives.

For many, socially distancing has been a considerable added stressor – a stressor that continues to test humanity’s ability to be resilient. I am saddened by prior patients reaching out to seek comfort in these difficult times. I am touched by their desire to reconnect with someone they know, someone who feels familiar. I am surprised by the power of connection through phone and video calls. For some patients, despite the added burden, the current crisis has been an opportunity for their mental health and a reminder of the things that are important, including calling old friends and staying in touch with those who matter the most.

Yet, as the social restrictions continue, the stressors mount and the resilience becomes harder to find. Checking in on others can become a chore. The social norm to partake in fashion, and self-care, become harder to find. In some cases, even hygiene and our health take a side role. The weekly phone visits with a therapist can feel just as mundane and repetitive as life. Sleep becomes harder to find, and food loses its taste. At this point, we realize the humanity that we lost in all this.

In the past couple of months, we have all become much more aware of the fragility of connectedness. However, we should recognize that the impact was well on its way before the COVID-19 crisis. It is my opinion that psychiatry should champion the issue of human relations. I do not think that we need to wait for a new DSM diagnosis, an evidence-based paradigm, or a Food and Drug Administration–approved medication to do so. The COVID-19 crisis has rendered us all cognizant of the importance of relationships.

While it may be that psychiatry continues to foray in electronic means of communication, use of impersonal scales and diagnosis, as well as anonymized algorithmic treatment plans, we should also promote as much humanity as society and public health safety will permit. Getting dressed to see your psychiatrist, face to face, to have an open-ended conversation about the nature of one’s life has clearly become something precious and powerful that should be cherished and protected. My hope is the rules and mandates we are required to use during the pandemic today do not become a continued habit that result in further loneliness and disconnect. If we chose to, the lessons we learn today can, in fact, strengthen our appreciation and pursuit of human connection.
 

Dr. Badre is a forensic psychiatrist in San Diego and an expert in correctional mental health. He holds teaching positions at the University of California, San Diego, and the University of San Diego. He teaches medical education, psychopharmacology, ethics in psychiatry, and correctional care. Among his writings is chapter 7 in the book “Critical Psychiatry: Controversies and Clinical Implications” (Springer, 2019). He has no disclosures.

Prior to the current crisis of COVID-19, I had a critical view of the direction of our psychiatric field. We have given up on complicated psychotherapies in favor of dispensing medications. We have given up on complicated diagnostic assessments in favor of simple self-rated symptoms questionnaires. Many of us even chose to give up on seeing patients face to face in favor of practicing telepsychiatry in the comfort of our homes. Some even promoted a future of psychiatry in which psychiatrists treated patients through large spreadsheets of evidence-based rating tools following evidence-based algorithms without even ever meeting the patients.

I do not view this problem as unique to psychiatry but rather as part of a larger trend in society. For the past couple of years, Vivek Murthy, MD, the former U.S. surgeon general, has popularized the idea that we are in a loneliness epidemic, saying, “We live in the most technologically connected age in the history of civilization, yet rates of loneliness have doubled since the 1980s.” Despite having enumerable means to reach other human beings, so many of us feel distant and out of touch with others. This loneliness has a measurable impact on our well-being with one study that states, “Actual and perceived social isolation are both associated with increased risk for early mortality.”

Then, seemingly out of nowhere, we were confronted with the largest challenge to our sense of connectedness in my lifetime. Throughout the past months, we have been asked to meet each other less frequently, do so through sterile means, and certainly not shake hands, hug, or embrace. The COVID-19 crisis has quickly made us all experts in telepsychiatry, remote work, and doing more with less. The COVID-19 crisis has asked many of us to put aside some of our human rituals like eating together, enjoying artistic experiences as a group, and touching, for the sake of saving lives.

For many, socially distancing has been a considerable added stressor – a stressor that continues to test humanity’s ability to be resilient. I am saddened by prior patients reaching out to seek comfort in these difficult times. I am touched by their desire to reconnect with someone they know, someone who feels familiar. I am surprised by the power of connection through phone and video calls. For some patients, despite the added burden, the current crisis has been an opportunity for their mental health and a reminder of the things that are important, including calling old friends and staying in touch with those who matter the most.

Yet, as the social restrictions continue, the stressors mount and the resilience becomes harder to find. Checking in on others can become a chore. The social norm to partake in fashion, and self-care, become harder to find. In some cases, even hygiene and our health take a side role. The weekly phone visits with a therapist can feel just as mundane and repetitive as life. Sleep becomes harder to find, and food loses its taste. At this point, we realize the humanity that we lost in all this.

In the past couple of months, we have all become much more aware of the fragility of connectedness. However, we should recognize that the impact was well on its way before the COVID-19 crisis. It is my opinion that psychiatry should champion the issue of human relations. I do not think that we need to wait for a new DSM diagnosis, an evidence-based paradigm, or a Food and Drug Administration–approved medication to do so. The COVID-19 crisis has rendered us all cognizant of the importance of relationships.

While it may be that psychiatry continues to foray in electronic means of communication, use of impersonal scales and diagnosis, as well as anonymized algorithmic treatment plans, we should also promote as much humanity as society and public health safety will permit. Getting dressed to see your psychiatrist, face to face, to have an open-ended conversation about the nature of one’s life has clearly become something precious and powerful that should be cherished and protected. My hope is the rules and mandates we are required to use during the pandemic today do not become a continued habit that result in further loneliness and disconnect. If we chose to, the lessons we learn today can, in fact, strengthen our appreciation and pursuit of human connection.
 

Dr. Badre is a forensic psychiatrist in San Diego and an expert in correctional mental health. He holds teaching positions at the University of California, San Diego, and the University of San Diego. He teaches medical education, psychopharmacology, ethics in psychiatry, and correctional care. Among his writings is chapter 7 in the book “Critical Psychiatry: Controversies and Clinical Implications” (Springer, 2019). He has no disclosures.

Prior to the current crisis of COVID-19, I had a critical view of the direction of our psychiatric field. We have given up on complicated psychotherapies in favor of dispensing medications. We have given up on complicated diagnostic assessments in favor of simple self-rated symptoms questionnaires. Many of us even chose to give up on seeing patients face to face in favor of practicing telepsychiatry in the comfort of our homes. Some even promoted a future of psychiatry in which psychiatrists treated patients through large spreadsheets of evidence-based rating tools following evidence-based algorithms without even ever meeting the patients.

I do not view this problem as unique to psychiatry but rather as part of a larger trend in society. For the past couple of years, Vivek Murthy, MD, the former U.S. surgeon general, has popularized the idea that we are in a loneliness epidemic, saying, “We live in the most technologically connected age in the history of civilization, yet rates of loneliness have doubled since the 1980s.” Despite having enumerable means to reach other human beings, so many of us feel distant and out of touch with others. This loneliness has a measurable impact on our well-being with one study that states, “Actual and perceived social isolation are both associated with increased risk for early mortality.”

Then, seemingly out of nowhere, we were confronted with the largest challenge to our sense of connectedness in my lifetime. Throughout the past months, we have been asked to meet each other less frequently, do so through sterile means, and certainly not shake hands, hug, or embrace. The COVID-19 crisis has quickly made us all experts in telepsychiatry, remote work, and doing more with less. The COVID-19 crisis has asked many of us to put aside some of our human rituals like eating together, enjoying artistic experiences as a group, and touching, for the sake of saving lives.

For many, socially distancing has been a considerable added stressor – a stressor that continues to test humanity’s ability to be resilient. I am saddened by prior patients reaching out to seek comfort in these difficult times. I am touched by their desire to reconnect with someone they know, someone who feels familiar. I am surprised by the power of connection through phone and video calls. For some patients, despite the added burden, the current crisis has been an opportunity for their mental health and a reminder of the things that are important, including calling old friends and staying in touch with those who matter the most.

Yet, as the social restrictions continue, the stressors mount and the resilience becomes harder to find. Checking in on others can become a chore. The social norm to partake in fashion, and self-care, become harder to find. In some cases, even hygiene and our health take a side role. The weekly phone visits with a therapist can feel just as mundane and repetitive as life. Sleep becomes harder to find, and food loses its taste. At this point, we realize the humanity that we lost in all this.

In the past couple of months, we have all become much more aware of the fragility of connectedness. However, we should recognize that the impact was well on its way before the COVID-19 crisis. It is my opinion that psychiatry should champion the issue of human relations. I do not think that we need to wait for a new DSM diagnosis, an evidence-based paradigm, or a Food and Drug Administration–approved medication to do so. The COVID-19 crisis has rendered us all cognizant of the importance of relationships.

While it may be that psychiatry continues to foray in electronic means of communication, use of impersonal scales and diagnosis, as well as anonymized algorithmic treatment plans, we should also promote as much humanity as society and public health safety will permit. Getting dressed to see your psychiatrist, face to face, to have an open-ended conversation about the nature of one’s life has clearly become something precious and powerful that should be cherished and protected. My hope is the rules and mandates we are required to use during the pandemic today do not become a continued habit that result in further loneliness and disconnect. If we chose to, the lessons we learn today can, in fact, strengthen our appreciation and pursuit of human connection.
 

Dr. Badre is a forensic psychiatrist in San Diego and an expert in correctional mental health. He holds teaching positions at the University of California, San Diego, and the University of San Diego. He teaches medical education, psychopharmacology, ethics in psychiatry, and correctional care. Among his writings is chapter 7 in the book “Critical Psychiatry: Controversies and Clinical Implications” (Springer, 2019). He has no disclosures.

In patients with newly diagnosed stage IV breast cancer and an intact primary tumor, locoregional therapy after optimal systemic therapy does not improve survival or quality of life, results of the phase 3 E2108 trial suggest.

Among 256 patients with stage IV breast cancer with intact primary tumors who had no disease progression for 4-8 months after the start of optimal systemic therapy, there were no significant differences in overall survival or progression-free survival between patients randomized to receive locoregional therapy and those who did not receive the locoregional treatment.

Although patients who did not receive locoregional treatment had a 150% higher rate of local recurrence/progression, health-related quality of life (HRQOL) was actually worse at 18 months among the patients who underwent locoregional therapy. There were no HRQOL differences at 6 months, 12 months, or 30 months of follow-up.

Seema A. Khan, MD, of Northwestern University, Chicago, reported these results during a plenary session broadcast as a part of the American Society of Clinical Oncology virtual scientific program.

“There is no hint here of an advantage in terms of survival with the use of early locoregional therapy for the primary site,” Dr. Khan said.

Although neither the E2108 trial nor similar trials showed an overall survival advantage for locoregional therapy, as many as 20% of patients who are treated with systemic therapy alone may need locoregional therapy with surgery and/or radiation at some point for palliation or progression, said invited discussant Julia R. White, MD, professor of radiation oncology at the Ohio State University, Columbus.

“Locoregional therapy should be reserved for these patients that become symptomatic or progress locally. There may be a role for routine locoregional therapy for de novo oligometastatic breast cancer in combination with systemic therapy plus ablative therapy” to secure long-term remission or cure, questions that are being addressed in ongoing clinical trials, Dr. White said.
 

Past data

An estimated 6% of newly diagnosed breast cancer patients present with stage IV disease and an intact primary tumor.

The rationale for locoregional therapy of the primary tumor in patients with metastatic disease is based on retrospective data suggesting a survival advantage. However, the studies were biased because of younger patient populations with small tumors, a higher proportion of estrogen receptor–positive disease, and a generally lower metastatic burden than that seen in the E2108 population, according to Dr. Khan.

She went on to cite two randomized trials with differing outcomes. One trial showed no survival advantage with locoregional therapy at 2 years (Lancet Oncol. 2015 Oct;16[13]:1380-8). The other showed an improvement in survival with locoregional therapy at 5 years (Ann Surg Oncol. 2018 Oct;25[11]:3141-9).
 

E2108 details

In the E2108 trial, patients first received optimal systemic therapy based on individual patient and disease features. Patients who had no disease progression or distant disease for at least 4-8 months of therapy were then randomized to additional therapy.

In one randomized arm, patients received continued systemic therapy alone. The other arm received early local therapy, which included complete tumor resection with free surgical margins and postoperative radiotherapy according to the standard of care.

A total of 390 patients were registered, and 256 went on to randomization. Of those subjects, 131 were randomized to the continued systemic therapy arm and 125 to the early local therapy arm. All patients in each arm were included in the efficacy analysis.

In all, 59.6% of randomized patients had hormone receptor–positive/HER2-negative disease, 8.2% had triple-negative disease, and 32.2% had HER2-positive disease. Metastases included bone-only disease in 37.9% of patients, visceral-only disease in 24.2%, and 40.9% in both sites.

Among the patients randomized to early local therapy, 14 did not have surgery for personal, clinical, or insurance reasons. Of the 109 who went on to surgery, 87 had clear surgical margins, and 74 received locoregional radiation therapy.
 

Survival, progression, and HRQOL

At a median follow-up of 53 months, the median overall survival was 54 months in each arm. There was no significant difference in survival between the study arms, with superimposable survival curves (hazard ratio, 1.09; P = .63).

An analysis of overall survival by tumor type showed that, for the 20 women with triple-negative disease, survival was worse with early local therapy (HR, 3.50). There were no differences in survival either for the 79 patients with HER2-positive disease or for the 137 patients with hormone receptor–positive/HER2-negative disease.

Locoregional progression occurred in 25.6% of patients assigned to continued systemic therapy, compared with 10.2% assigned to early local therapy. However, progression-free survival was virtually identical between the study arms (P = .40).

At most time points, there were no significant between-arm differences in HRQOL. The exception was at 18 months of follow-up, when the HRQOL was significantly lower among patients who had undergone early local therapy (P = .001).

“Based on available data, locoregional therapy for the primary tumor should not be offered to women with stage IV breast cancer with the expectation of a survival benefit. When systemic disease is well controlled with systemic therapy but the primary site is progressing, as does happen occasionally, locoregional treatment can be considered,” Dr. Khan concluded.

She noted there is an ongoing trial of similar design in Japan (JCOG-1017), with results expected in 2022.

The current trial was supported by the National Cancer Institute and Canadian Cancer Society. Dr. Khan reported no conflicts of interest. Dr. White reported institutional research funding from Intraop Medical.

SOURCE: Khan SA et al. ASCO 2020, Abstract LBA2.

In patients with newly diagnosed stage IV breast cancer and an intact primary tumor, locoregional therapy after optimal systemic therapy does not improve survival or quality of life, results of the phase 3 E2108 trial suggest.

Among 256 patients with stage IV breast cancer with intact primary tumors who had no disease progression for 4-8 months after the start of optimal systemic therapy, there were no significant differences in overall survival or progression-free survival between patients randomized to receive locoregional therapy and those who did not receive the locoregional treatment.

Although patients who did not receive locoregional treatment had a 150% higher rate of local recurrence/progression, health-related quality of life (HRQOL) was actually worse at 18 months among the patients who underwent locoregional therapy. There were no HRQOL differences at 6 months, 12 months, or 30 months of follow-up.

Seema A. Khan, MD, of Northwestern University, Chicago, reported these results during a plenary session broadcast as a part of the American Society of Clinical Oncology virtual scientific program.

“There is no hint here of an advantage in terms of survival with the use of early locoregional therapy for the primary site,” Dr. Khan said.

Although neither the E2108 trial nor similar trials showed an overall survival advantage for locoregional therapy, as many as 20% of patients who are treated with systemic therapy alone may need locoregional therapy with surgery and/or radiation at some point for palliation or progression, said invited discussant Julia R. White, MD, professor of radiation oncology at the Ohio State University, Columbus.

“Locoregional therapy should be reserved for these patients that become symptomatic or progress locally. There may be a role for routine locoregional therapy for de novo oligometastatic breast cancer in combination with systemic therapy plus ablative therapy” to secure long-term remission or cure, questions that are being addressed in ongoing clinical trials, Dr. White said.
 

Past data

An estimated 6% of newly diagnosed breast cancer patients present with stage IV disease and an intact primary tumor.

The rationale for locoregional therapy of the primary tumor in patients with metastatic disease is based on retrospective data suggesting a survival advantage. However, the studies were biased because of younger patient populations with small tumors, a higher proportion of estrogen receptor–positive disease, and a generally lower metastatic burden than that seen in the E2108 population, according to Dr. Khan.

She went on to cite two randomized trials with differing outcomes. One trial showed no survival advantage with locoregional therapy at 2 years (Lancet Oncol. 2015 Oct;16[13]:1380-8). The other showed an improvement in survival with locoregional therapy at 5 years (Ann Surg Oncol. 2018 Oct;25[11]:3141-9).
 

E2108 details

In the E2108 trial, patients first received optimal systemic therapy based on individual patient and disease features. Patients who had no disease progression or distant disease for at least 4-8 months of therapy were then randomized to additional therapy.

In one randomized arm, patients received continued systemic therapy alone. The other arm received early local therapy, which included complete tumor resection with free surgical margins and postoperative radiotherapy according to the standard of care.

A total of 390 patients were registered, and 256 went on to randomization. Of those subjects, 131 were randomized to the continued systemic therapy arm and 125 to the early local therapy arm. All patients in each arm were included in the efficacy analysis.

In all, 59.6% of randomized patients had hormone receptor–positive/HER2-negative disease, 8.2% had triple-negative disease, and 32.2% had HER2-positive disease. Metastases included bone-only disease in 37.9% of patients, visceral-only disease in 24.2%, and 40.9% in both sites.

Among the patients randomized to early local therapy, 14 did not have surgery for personal, clinical, or insurance reasons. Of the 109 who went on to surgery, 87 had clear surgical margins, and 74 received locoregional radiation therapy.
 

Survival, progression, and HRQOL

At a median follow-up of 53 months, the median overall survival was 54 months in each arm. There was no significant difference in survival between the study arms, with superimposable survival curves (hazard ratio, 1.09; P = .63).

An analysis of overall survival by tumor type showed that, for the 20 women with triple-negative disease, survival was worse with early local therapy (HR, 3.50). There were no differences in survival either for the 79 patients with HER2-positive disease or for the 137 patients with hormone receptor–positive/HER2-negative disease.

Locoregional progression occurred in 25.6% of patients assigned to continued systemic therapy, compared with 10.2% assigned to early local therapy. However, progression-free survival was virtually identical between the study arms (P = .40).

At most time points, there were no significant between-arm differences in HRQOL. The exception was at 18 months of follow-up, when the HRQOL was significantly lower among patients who had undergone early local therapy (P = .001).

“Based on available data, locoregional therapy for the primary tumor should not be offered to women with stage IV breast cancer with the expectation of a survival benefit. When systemic disease is well controlled with systemic therapy but the primary site is progressing, as does happen occasionally, locoregional treatment can be considered,” Dr. Khan concluded.

She noted there is an ongoing trial of similar design in Japan (JCOG-1017), with results expected in 2022.

The current trial was supported by the National Cancer Institute and Canadian Cancer Society. Dr. Khan reported no conflicts of interest. Dr. White reported institutional research funding from Intraop Medical.

SOURCE: Khan SA et al. ASCO 2020, Abstract LBA2.

In patients with newly diagnosed stage IV breast cancer and an intact primary tumor, locoregional therapy after optimal systemic therapy does not improve survival or quality of life, results of the phase 3 E2108 trial suggest.

Among 256 patients with stage IV breast cancer with intact primary tumors who had no disease progression for 4-8 months after the start of optimal systemic therapy, there were no significant differences in overall survival or progression-free survival between patients randomized to receive locoregional therapy and those who did not receive the locoregional treatment.

Although patients who did not receive locoregional treatment had a 150% higher rate of local recurrence/progression, health-related quality of life (HRQOL) was actually worse at 18 months among the patients who underwent locoregional therapy. There were no HRQOL differences at 6 months, 12 months, or 30 months of follow-up.

Seema A. Khan, MD, of Northwestern University, Chicago, reported these results during a plenary session broadcast as a part of the American Society of Clinical Oncology virtual scientific program.

“There is no hint here of an advantage in terms of survival with the use of early locoregional therapy for the primary site,” Dr. Khan said.

Although neither the E2108 trial nor similar trials showed an overall survival advantage for locoregional therapy, as many as 20% of patients who are treated with systemic therapy alone may need locoregional therapy with surgery and/or radiation at some point for palliation or progression, said invited discussant Julia R. White, MD, professor of radiation oncology at the Ohio State University, Columbus.

“Locoregional therapy should be reserved for these patients that become symptomatic or progress locally. There may be a role for routine locoregional therapy for de novo oligometastatic breast cancer in combination with systemic therapy plus ablative therapy” to secure long-term remission or cure, questions that are being addressed in ongoing clinical trials, Dr. White said.
 

Past data

An estimated 6% of newly diagnosed breast cancer patients present with stage IV disease and an intact primary tumor.

The rationale for locoregional therapy of the primary tumor in patients with metastatic disease is based on retrospective data suggesting a survival advantage. However, the studies were biased because of younger patient populations with small tumors, a higher proportion of estrogen receptor–positive disease, and a generally lower metastatic burden than that seen in the E2108 population, according to Dr. Khan.

She went on to cite two randomized trials with differing outcomes. One trial showed no survival advantage with locoregional therapy at 2 years (Lancet Oncol. 2015 Oct;16[13]:1380-8). The other showed an improvement in survival with locoregional therapy at 5 years (Ann Surg Oncol. 2018 Oct;25[11]:3141-9).
 

E2108 details

In the E2108 trial, patients first received optimal systemic therapy based on individual patient and disease features. Patients who had no disease progression or distant disease for at least 4-8 months of therapy were then randomized to additional therapy.

In one randomized arm, patients received continued systemic therapy alone. The other arm received early local therapy, which included complete tumor resection with free surgical margins and postoperative radiotherapy according to the standard of care.

A total of 390 patients were registered, and 256 went on to randomization. Of those subjects, 131 were randomized to the continued systemic therapy arm and 125 to the early local therapy arm. All patients in each arm were included in the efficacy analysis.

In all, 59.6% of randomized patients had hormone receptor–positive/HER2-negative disease, 8.2% had triple-negative disease, and 32.2% had HER2-positive disease. Metastases included bone-only disease in 37.9% of patients, visceral-only disease in 24.2%, and 40.9% in both sites.

Among the patients randomized to early local therapy, 14 did not have surgery for personal, clinical, or insurance reasons. Of the 109 who went on to surgery, 87 had clear surgical margins, and 74 received locoregional radiation therapy.
 

Survival, progression, and HRQOL

At a median follow-up of 53 months, the median overall survival was 54 months in each arm. There was no significant difference in survival between the study arms, with superimposable survival curves (hazard ratio, 1.09; P = .63).

An analysis of overall survival by tumor type showed that, for the 20 women with triple-negative disease, survival was worse with early local therapy (HR, 3.50). There were no differences in survival either for the 79 patients with HER2-positive disease or for the 137 patients with hormone receptor–positive/HER2-negative disease.

Locoregional progression occurred in 25.6% of patients assigned to continued systemic therapy, compared with 10.2% assigned to early local therapy. However, progression-free survival was virtually identical between the study arms (P = .40).

At most time points, there were no significant between-arm differences in HRQOL. The exception was at 18 months of follow-up, when the HRQOL was significantly lower among patients who had undergone early local therapy (P = .001).

“Based on available data, locoregional therapy for the primary tumor should not be offered to women with stage IV breast cancer with the expectation of a survival benefit. When systemic disease is well controlled with systemic therapy but the primary site is progressing, as does happen occasionally, locoregional treatment can be considered,” Dr. Khan concluded.

She noted there is an ongoing trial of similar design in Japan (JCOG-1017), with results expected in 2022.

The current trial was supported by the National Cancer Institute and Canadian Cancer Society. Dr. Khan reported no conflicts of interest. Dr. White reported institutional research funding from Intraop Medical.

SOURCE: Khan SA et al. ASCO 2020, Abstract LBA2.

Patients in the landmark ISCHEMIA trial with intermediate left main disease had a greater extent of coronary artery disease on invasive angiography, indicating greater atherosclerotic burden. They also had worse prognosis with a higher risk of cardiovascular events.

“Many times, we are looking at results as to whether patients have left main disease or not,” Sripal Bangalore, MD, said during the Society for Cardiovascular Angiography & Interventions virtual annual scientific sessions. “Here, we are showing that it’s not black and white; there are shades of gray. If a patient has intermediate left main disease, the prognosis is worse. That’s very important information we need to convey to our referrals also, because many times they may just look at the bottom line and say, ‘there is no left main disease.’ But here, we’re seeing that even having intermediate left main disease has significantly worse prognosis. We need to take that seriously.”

Prior studies show that patients with significant left main disease (LMD; defined as 50% or greater stenosis on coronary CT angiography [CCTA]) have a high risk of cardiovascular events and guidelines recommend revascularization to improve survival, said Dr. Bangalore, an interventional cardiologist at New York University Langone Health. However, the impact of intermediate LMD (defined as 25%-49% stenosis on CCTA) on outcomes is unclear.

Members of the ISCHEMIA (International Study of Comparative Health Effectiveness with Medical and Invasive Approaches) research group randomized 5,179 participants to an initial invasive or conservative strategy. The main results showed that immediate revascularization in patients with stable ischemic heart disease provided no reduction in cardiovascular endpoints through 4 years of follow-up, compared with initial optimal medical therapy alone.
 

‘Discordance’ revealed in imaging modalities

For the current analysis, named the ISCHEMIA Intermediate LM Substudy, those who underwent coronary CCTA comprise the LMD substudy cohort. The objective was to evaluate clinical and quality of life outcomes in patients with and without intermediate left main disease on coronary CT and to evaluate the impact of treatment strategy on those outcomes across subgroups.

At baseline, these patients were categorized into those with and without intermediate LMD as determined by a core lab. Patients with LMD of 50% or greater, those with prior coronary artery bypass graft surgery, and those with nonevaluable or missing data on LM stenosis were excluded.

Among the 3,913 ISCHEMIA participants who underwent CCTA, 3,699 satisfied the inclusion criteria. Of these patients, 962 (26%) had intermediate LMD and 2,737 (74%) did not.

The researchers observed no significant differences in baseline characteristics between patients with and without LMD. However, patients with intermediate LMD tended to be older, and a greater proportion had hypertension and diabetes. Stress test characteristics were also similar between patients with and without LMD. However, patients with intermediate LMD tended toward a greater severity of severe ischemia.

This was also true for anatomic disease on CCTA. A higher proportion of patients with intermediate LMD had triple-vessel disease (61%-62%, compared with 36%-40% along those without intermediate LMD). In addition, a higher proportion of patients with intermediate LMD had stenosis in the proximal left anterior artery descending (LAD) artery (65% vs. 39% among those without intermediate LMD).

On analysis limited to 1,846 patients who underwent invasive angiography treatment in the main ISCHEMIA trial, 7% of those who were categorized into the intermediate LMD group were found to have LMD disease of 50% or greater, compared with 1.4% of patients who were categorized as not having intermediate LMD. “This goes to show this discordance between the two modalities [CCTA and coronary angiography], and I think we have to be careful,” said Dr. Bangalore, who also directs NYU Langone’s Cardiac Catheterization Laboratory. “There may be patients with left main disease, even if the CCTA says it’s not at 25%-29% [stenosis].”

The researchers found that, among patients who underwent invasive angiography, a greater proportion of those who were categorized into the LMD group had proximal LAD disease (43% vs. 33% among those who were categorized into the nonintermediate LMD group), triple-vessel disease (47% vs. 35%), a greater extent of coronary artery disease as denoted by a higher SYNTAX score (21 vs. 15), and a higher proportion underwent coronary artery bypass graft surgery (32% vs. 18%).
 

Intermediate LMD linked to worse outcomes

After the researchers adjusted for baseline differences between the two groups in overall substudy cohort, they found that intermediate LMD severity was an independent predictor of the primary composite endpoint of cardiovascular death, MI, hospitalization for unstable angina, heart failure, and resuscitated cardiac arrest (hazard ratio, 1.31; P = .0123); cardiovascular death/MI/stroke (HR, 1.30; P = .0143); procedural primary MI (HR, 1.64; P = .0487); heart failure (HR, 2.06; P = .0239); and stroke (HR, 1.82, P = .0362).

“We then looked to see if there is a treatment difference, a treatment effect based on whether patients had intermediate LMD,” Dr. Bangalore said. “Most of the P values were not significant. The results are very consistent with what we saw in the main analysis: not a significant difference between invasive and conservative strategy. We do see some differences, though. An invasive strategy was associated with a significantly higher risk of procedural MI [2.9% vs. 1.5%], but a significantly lower risk of nonprocedural MI [–6.4% vs. –2%].”

Dr. Bangalore added that there was significant benefit of the invasive strategy in reducing angina and improving quality of life based on the Seattle Angina Questionnaire-7. “This result was durable up to 48 months of follow-up, whether the patient had intermediate left main disease or not. These results were dependent on baseline angina status. The benefit of invasive strategy was mainly in patients who had daily, weekly, and monthly angina, and no benefit in patients with no angina; there was no interaction based on intermediate left main status.”

Dr. Bangalore emphasized that the original ISCHEMIA trial excluded patients with severe left main disease by design. “But patients with intermediate left main disease in ISCHEMIA tended to have a greater extent of coronary artery disease, indicating greater atherosclerotic burden. I don’t think that’s any surprise. They had a worse prognosis with higher risk of cardiovascular events but similar quality of life, including angina-specific quality of life.”

The key clinical message, he said, is that patients with intermediate LMD face an increased risk of cardiovascular events. “I think we have to be aggressive in trying to reduce their risk with medical therapy, etc.,” he said. “If they are symptomatic, ISCHEMIA tells us that patients have two options. They can choose an invasive strategy, because clearly there is a benefit. You have a significant benefit at making you feel better and potentially reducing the risk of spontaneous MI over a period of time. Or, you can try medical therapy first. If you do see some left main disease, it’s showing the general burden of atherosclerosis disease in those patients. I think that’s the critical message, that we have to be very aggressive with these patients.”
 

A call for more imaging studies

An invited panelist, Timothy D. Henry, MD, said that the results of the ISCHEMIA substudy should stimulate further research. “With an intermediate lesion, clearly the interventional group did better, and it wasn’t symptom related,” said Dr. Henry, medical director of the Carl and Edyth Lindner Center for Research and Education at the Christ Hospital in Cincinnati. “So even if you do medical therapy, you’re not going to really find it out. In my mind, this should stimulate us to do more imaging of the left main that are moderate lesions, and follow this up as an independent study. I think this is a really important finding.”

ISCHEMIA was supported by grants from the National Heart, Lung, and Blood Institute. Dr. Bangalore disclosed that he is a member of the advisory board and/or a board member for Meril, SMT, Pfizer, Amgen, Biotronik, and Abbott. He also is a consultant for Reata Pharmaceuticals.

dbrunk@mdedge.com

SOURCE: Bangalore S et al. SCAI 2020, Abstract 11656.

Patients in the landmark ISCHEMIA trial with intermediate left main disease had a greater extent of coronary artery disease on invasive angiography, indicating greater atherosclerotic burden. They also had worse prognosis with a higher risk of cardiovascular events.

“Many times, we are looking at results as to whether patients have left main disease or not,” Sripal Bangalore, MD, said during the Society for Cardiovascular Angiography & Interventions virtual annual scientific sessions. “Here, we are showing that it’s not black and white; there are shades of gray. If a patient has intermediate left main disease, the prognosis is worse. That’s very important information we need to convey to our referrals also, because many times they may just look at the bottom line and say, ‘there is no left main disease.’ But here, we’re seeing that even having intermediate left main disease has significantly worse prognosis. We need to take that seriously.”

Prior studies show that patients with significant left main disease (LMD; defined as 50% or greater stenosis on coronary CT angiography [CCTA]) have a high risk of cardiovascular events and guidelines recommend revascularization to improve survival, said Dr. Bangalore, an interventional cardiologist at New York University Langone Health. However, the impact of intermediate LMD (defined as 25%-49% stenosis on CCTA) on outcomes is unclear.

Members of the ISCHEMIA (International Study of Comparative Health Effectiveness with Medical and Invasive Approaches) research group randomized 5,179 participants to an initial invasive or conservative strategy. The main results showed that immediate revascularization in patients with stable ischemic heart disease provided no reduction in cardiovascular endpoints through 4 years of follow-up, compared with initial optimal medical therapy alone.
 

‘Discordance’ revealed in imaging modalities

For the current analysis, named the ISCHEMIA Intermediate LM Substudy, those who underwent coronary CCTA comprise the LMD substudy cohort. The objective was to evaluate clinical and quality of life outcomes in patients with and without intermediate left main disease on coronary CT and to evaluate the impact of treatment strategy on those outcomes across subgroups.

At baseline, these patients were categorized into those with and without intermediate LMD as determined by a core lab. Patients with LMD of 50% or greater, those with prior coronary artery bypass graft surgery, and those with nonevaluable or missing data on LM stenosis were excluded.

Among the 3,913 ISCHEMIA participants who underwent CCTA, 3,699 satisfied the inclusion criteria. Of these patients, 962 (26%) had intermediate LMD and 2,737 (74%) did not.

The researchers observed no significant differences in baseline characteristics between patients with and without LMD. However, patients with intermediate LMD tended to be older, and a greater proportion had hypertension and diabetes. Stress test characteristics were also similar between patients with and without LMD. However, patients with intermediate LMD tended toward a greater severity of severe ischemia.

This was also true for anatomic disease on CCTA. A higher proportion of patients with intermediate LMD had triple-vessel disease (61%-62%, compared with 36%-40% along those without intermediate LMD). In addition, a higher proportion of patients with intermediate LMD had stenosis in the proximal left anterior artery descending (LAD) artery (65% vs. 39% among those without intermediate LMD).

On analysis limited to 1,846 patients who underwent invasive angiography treatment in the main ISCHEMIA trial, 7% of those who were categorized into the intermediate LMD group were found to have LMD disease of 50% or greater, compared with 1.4% of patients who were categorized as not having intermediate LMD. “This goes to show this discordance between the two modalities [CCTA and coronary angiography], and I think we have to be careful,” said Dr. Bangalore, who also directs NYU Langone’s Cardiac Catheterization Laboratory. “There may be patients with left main disease, even if the CCTA says it’s not at 25%-29% [stenosis].”

The researchers found that, among patients who underwent invasive angiography, a greater proportion of those who were categorized into the LMD group had proximal LAD disease (43% vs. 33% among those who were categorized into the nonintermediate LMD group), triple-vessel disease (47% vs. 35%), a greater extent of coronary artery disease as denoted by a higher SYNTAX score (21 vs. 15), and a higher proportion underwent coronary artery bypass graft surgery (32% vs. 18%).
 

Intermediate LMD linked to worse outcomes

After the researchers adjusted for baseline differences between the two groups in overall substudy cohort, they found that intermediate LMD severity was an independent predictor of the primary composite endpoint of cardiovascular death, MI, hospitalization for unstable angina, heart failure, and resuscitated cardiac arrest (hazard ratio, 1.31; P = .0123); cardiovascular death/MI/stroke (HR, 1.30; P = .0143); procedural primary MI (HR, 1.64; P = .0487); heart failure (HR, 2.06; P = .0239); and stroke (HR, 1.82, P = .0362).

“We then looked to see if there is a treatment difference, a treatment effect based on whether patients had intermediate LMD,” Dr. Bangalore said. “Most of the P values were not significant. The results are very consistent with what we saw in the main analysis: not a significant difference between invasive and conservative strategy. We do see some differences, though. An invasive strategy was associated with a significantly higher risk of procedural MI [2.9% vs. 1.5%], but a significantly lower risk of nonprocedural MI [–6.4% vs. –2%].”

Dr. Bangalore added that there was significant benefit of the invasive strategy in reducing angina and improving quality of life based on the Seattle Angina Questionnaire-7. “This result was durable up to 48 months of follow-up, whether the patient had intermediate left main disease or not. These results were dependent on baseline angina status. The benefit of invasive strategy was mainly in patients who had daily, weekly, and monthly angina, and no benefit in patients with no angina; there was no interaction based on intermediate left main status.”

Dr. Bangalore emphasized that the original ISCHEMIA trial excluded patients with severe left main disease by design. “But patients with intermediate left main disease in ISCHEMIA tended to have a greater extent of coronary artery disease, indicating greater atherosclerotic burden. I don’t think that’s any surprise. They had a worse prognosis with higher risk of cardiovascular events but similar quality of life, including angina-specific quality of life.”

The key clinical message, he said, is that patients with intermediate LMD face an increased risk of cardiovascular events. “I think we have to be aggressive in trying to reduce their risk with medical therapy, etc.,” he said. “If they are symptomatic, ISCHEMIA tells us that patients have two options. They can choose an invasive strategy, because clearly there is a benefit. You have a significant benefit at making you feel better and potentially reducing the risk of spontaneous MI over a period of time. Or, you can try medical therapy first. If you do see some left main disease, it’s showing the general burden of atherosclerosis disease in those patients. I think that’s the critical message, that we have to be very aggressive with these patients.”
 

A call for more imaging studies

An invited panelist, Timothy D. Henry, MD, said that the results of the ISCHEMIA substudy should stimulate further research. “With an intermediate lesion, clearly the interventional group did better, and it wasn’t symptom related,” said Dr. Henry, medical director of the Carl and Edyth Lindner Center for Research and Education at the Christ Hospital in Cincinnati. “So even if you do medical therapy, you’re not going to really find it out. In my mind, this should stimulate us to do more imaging of the left main that are moderate lesions, and follow this up as an independent study. I think this is a really important finding.”

ISCHEMIA was supported by grants from the National Heart, Lung, and Blood Institute. Dr. Bangalore disclosed that he is a member of the advisory board and/or a board member for Meril, SMT, Pfizer, Amgen, Biotronik, and Abbott. He also is a consultant for Reata Pharmaceuticals.

dbrunk@mdedge.com

SOURCE: Bangalore S et al. SCAI 2020, Abstract 11656.

Patients in the landmark ISCHEMIA trial with intermediate left main disease had a greater extent of coronary artery disease on invasive angiography, indicating greater atherosclerotic burden. They also had worse prognosis with a higher risk of cardiovascular events.

“Many times, we are looking at results as to whether patients have left main disease or not,” Sripal Bangalore, MD, said during the Society for Cardiovascular Angiography & Interventions virtual annual scientific sessions. “Here, we are showing that it’s not black and white; there are shades of gray. If a patient has intermediate left main disease, the prognosis is worse. That’s very important information we need to convey to our referrals also, because many times they may just look at the bottom line and say, ‘there is no left main disease.’ But here, we’re seeing that even having intermediate left main disease has significantly worse prognosis. We need to take that seriously.”

Prior studies show that patients with significant left main disease (LMD; defined as 50% or greater stenosis on coronary CT angiography [CCTA]) have a high risk of cardiovascular events and guidelines recommend revascularization to improve survival, said Dr. Bangalore, an interventional cardiologist at New York University Langone Health. However, the impact of intermediate LMD (defined as 25%-49% stenosis on CCTA) on outcomes is unclear.

Members of the ISCHEMIA (International Study of Comparative Health Effectiveness with Medical and Invasive Approaches) research group randomized 5,179 participants to an initial invasive or conservative strategy. The main results showed that immediate revascularization in patients with stable ischemic heart disease provided no reduction in cardiovascular endpoints through 4 years of follow-up, compared with initial optimal medical therapy alone.
 

‘Discordance’ revealed in imaging modalities

For the current analysis, named the ISCHEMIA Intermediate LM Substudy, those who underwent coronary CCTA comprise the LMD substudy cohort. The objective was to evaluate clinical and quality of life outcomes in patients with and without intermediate left main disease on coronary CT and to evaluate the impact of treatment strategy on those outcomes across subgroups.

At baseline, these patients were categorized into those with and without intermediate LMD as determined by a core lab. Patients with LMD of 50% or greater, those with prior coronary artery bypass graft surgery, and those with nonevaluable or missing data on LM stenosis were excluded.

Among the 3,913 ISCHEMIA participants who underwent CCTA, 3,699 satisfied the inclusion criteria. Of these patients, 962 (26%) had intermediate LMD and 2,737 (74%) did not.

The researchers observed no significant differences in baseline characteristics between patients with and without LMD. However, patients with intermediate LMD tended to be older, and a greater proportion had hypertension and diabetes. Stress test characteristics were also similar between patients with and without LMD. However, patients with intermediate LMD tended toward a greater severity of severe ischemia.

This was also true for anatomic disease on CCTA. A higher proportion of patients with intermediate LMD had triple-vessel disease (61%-62%, compared with 36%-40% along those without intermediate LMD). In addition, a higher proportion of patients with intermediate LMD had stenosis in the proximal left anterior artery descending (LAD) artery (65% vs. 39% among those without intermediate LMD).

On analysis limited to 1,846 patients who underwent invasive angiography treatment in the main ISCHEMIA trial, 7% of those who were categorized into the intermediate LMD group were found to have LMD disease of 50% or greater, compared with 1.4% of patients who were categorized as not having intermediate LMD. “This goes to show this discordance between the two modalities [CCTA and coronary angiography], and I think we have to be careful,” said Dr. Bangalore, who also directs NYU Langone’s Cardiac Catheterization Laboratory. “There may be patients with left main disease, even if the CCTA says it’s not at 25%-29% [stenosis].”

The researchers found that, among patients who underwent invasive angiography, a greater proportion of those who were categorized into the LMD group had proximal LAD disease (43% vs. 33% among those who were categorized into the nonintermediate LMD group), triple-vessel disease (47% vs. 35%), a greater extent of coronary artery disease as denoted by a higher SYNTAX score (21 vs. 15), and a higher proportion underwent coronary artery bypass graft surgery (32% vs. 18%).
 

Intermediate LMD linked to worse outcomes

After the researchers adjusted for baseline differences between the two groups in overall substudy cohort, they found that intermediate LMD severity was an independent predictor of the primary composite endpoint of cardiovascular death, MI, hospitalization for unstable angina, heart failure, and resuscitated cardiac arrest (hazard ratio, 1.31; P = .0123); cardiovascular death/MI/stroke (HR, 1.30; P = .0143); procedural primary MI (HR, 1.64; P = .0487); heart failure (HR, 2.06; P = .0239); and stroke (HR, 1.82, P = .0362).

“We then looked to see if there is a treatment difference, a treatment effect based on whether patients had intermediate LMD,” Dr. Bangalore said. “Most of the P values were not significant. The results are very consistent with what we saw in the main analysis: not a significant difference between invasive and conservative strategy. We do see some differences, though. An invasive strategy was associated with a significantly higher risk of procedural MI [2.9% vs. 1.5%], but a significantly lower risk of nonprocedural MI [–6.4% vs. –2%].”

Dr. Bangalore added that there was significant benefit of the invasive strategy in reducing angina and improving quality of life based on the Seattle Angina Questionnaire-7. “This result was durable up to 48 months of follow-up, whether the patient had intermediate left main disease or not. These results were dependent on baseline angina status. The benefit of invasive strategy was mainly in patients who had daily, weekly, and monthly angina, and no benefit in patients with no angina; there was no interaction based on intermediate left main status.”

Dr. Bangalore emphasized that the original ISCHEMIA trial excluded patients with severe left main disease by design. “But patients with intermediate left main disease in ISCHEMIA tended to have a greater extent of coronary artery disease, indicating greater atherosclerotic burden. I don’t think that’s any surprise. They had a worse prognosis with higher risk of cardiovascular events but similar quality of life, including angina-specific quality of life.”

The key clinical message, he said, is that patients with intermediate LMD face an increased risk of cardiovascular events. “I think we have to be aggressive in trying to reduce their risk with medical therapy, etc.,” he said. “If they are symptomatic, ISCHEMIA tells us that patients have two options. They can choose an invasive strategy, because clearly there is a benefit. You have a significant benefit at making you feel better and potentially reducing the risk of spontaneous MI over a period of time. Or, you can try medical therapy first. If you do see some left main disease, it’s showing the general burden of atherosclerosis disease in those patients. I think that’s the critical message, that we have to be very aggressive with these patients.”
 

A call for more imaging studies

An invited panelist, Timothy D. Henry, MD, said that the results of the ISCHEMIA substudy should stimulate further research. “With an intermediate lesion, clearly the interventional group did better, and it wasn’t symptom related,” said Dr. Henry, medical director of the Carl and Edyth Lindner Center for Research and Education at the Christ Hospital in Cincinnati. “So even if you do medical therapy, you’re not going to really find it out. In my mind, this should stimulate us to do more imaging of the left main that are moderate lesions, and follow this up as an independent study. I think this is a really important finding.”

ISCHEMIA was supported by grants from the National Heart, Lung, and Blood Institute. Dr. Bangalore disclosed that he is a member of the advisory board and/or a board member for Meril, SMT, Pfizer, Amgen, Biotronik, and Abbott. He also is a consultant for Reata Pharmaceuticals.

dbrunk@mdedge.com

SOURCE: Bangalore S et al. SCAI 2020, Abstract 11656.

Pembrolizumab treatment significantly improved progression-free survival versus brentuximab vedotin in a randomized, phase 3 trial including patients with relapsed or refractory classical Hodgkin lymphoma, an investigator has reported.

Median progression-free survival (PFS) was 13.2 versus 8.3 months in favor of pembrolizumab, according to the report on the KEYNOTE-204 trial, which included patients with classical Hodgkin lymphoma who either had relapsed after autologous stem cell transplant (SCT) or were ineligible for autologous SCT.

The PFS improvement was “statistically significant and clinically meaningful,” said investigator John Kuruvilla, MD, of Princess Margaret Cancer Centre in Toronto.

“This PFS benefit extended to key subgroups, including those ineligible for autologous transplant, patients with primary refractory disease, and patients who were brentuximab-vedotin naive,” Dr. Kuruvilla added in his presentation, which was part of the American Society of Clinical Oncology virtual scientific program.

Pneumonitis was more frequent in the pembrolizumab arm, but “appeared in general to be quite well managed” among patients who experienced this adverse event, according to Dr. Kuruvilla, who said that treatment with the programmed death–1 inhibitor should be considered “the preferred treatment option and the new standard of care” for patients with relapsed/refractory classic Hodgkin lymphoma who have relapsed after autologous SCT or are ineligible for it.

Although the pneumonitis findings are important to keep in mind, results of KEYNOTE-204 are indeed “practice defining” and immediately impactful, said Mark J. Roschewski, MD, clinical investigator in the lymphoid malignancies branch at the Center for Cancer Research, part of the National Cancer Institute, Bethesda, Md.

“I would select pembrolizumab over brentuximab for this patient population, particularly those that are refractory to chemotherapy,” he said in a commentary on the study also included in the virtual ASCO proceedings.

“There may be specific patient populations that I’d reconsider, such as those that might be at high risk for lung toxicity,” he added. “They may not be suitable for this, but it’s something to at least to be aware of.”

Although the antibody-drug conjugate brentuximab vedotin has been considered the standard of care for patients with relapse after autologous SCT, there has historically been no standard of care for patients who are ineligible for transplant because of chemorefractory disease, advanced age, or comorbidities, Dr. Kuruvilla said in his presentation.

In the KEYNOTE-204 study, 304 patients with relapsed/refractory classic Hodgkin lymphoma were randomized to receive either pembrolizumab 200 mg or brentuximab at 1.8 mg/kg intravenously every 3 weeks for up to 35 cycles.

The median age of patients was 36 years in the pembrolizumab arm and 35 years in the brentuximab vedotin arm, according to the report. Approximately 37% of the patients had previously undergone autologous SCT. About 40% had been refractory to frontline therapy, while 28% relapsed within 12 months of therapy and 32% relapsed later than 12 months.

Median PFS by blinded independent central review was 13.2 versus 8.3 months in the pembrolizumab and brentuximab arms, respectively (hazard ratio, 0.65; 95% confidence interval, 0.48-0.88; P = .00271), Dr. Kuruvilla reported.

The benefit extended to “key subgroups” in the trial, he added, including those who were ineligible for autologous SCT, those with primary refractory disease, and those who were naive to brentuximab vedotin, with HRs of 0.61, 0.52, and 0.67, respectively.

Pembrolizumab was also associated with more durable responses versus brentuximab vedotin, according to the investigator.

The overall response rate was 65.6% and 54.2%, respectively, for pembrolizumab and brentuximab, although this difference of approximately 11 percentage points did not meet criteria for statistical significance, he said. Duration of response was 20.7 months or pembrolizumab and 13.8 months for brentuximab.

The rate of serious treatment-related adverse events was similar between groups, according to Dr. Kuruvilla, who reported grade 3-5 events occurring in 19.6% and 25.0% of the pembrolizumab and brentuximab arms. Serious treatment-related adverse events were numerically more frequent in the pembrolizumab arm (16.2% vs. 10.5%) and there was one treatment-related death caused by pneumonia, seen in the pembrolizumab arm.

Pneumonitis occurred in 2.6% of the brentuximab-treated patients and in 10.8% of pembrolizumab-treated patients, of which half of cases were grade 3-4, according to the report.

In the pembrolizumab arm, pneumonitis was felt to be drug-related in 15 of 16 cases, according to Dr. Kuruvilla, who added that 15 of 16 patients required corticosteroid therapy. “This has led to the resolution of the pneumonitis in 12 of 16 patients, with ongoing resolution in one further patient.”

Research funding for KEYNOTE-204 came from Merck Sharp & Dohme. Dr. Kuruvilla provided disclosures related to Merck and a variety of other pharmaceutical companies. Dr. Roschewski said he had no relationships to disclose.

SOURCE: Kuruvilla J et al. ASCO 2020, Abstract 8005.

Pembrolizumab treatment significantly improved progression-free survival versus brentuximab vedotin in a randomized, phase 3 trial including patients with relapsed or refractory classical Hodgkin lymphoma, an investigator has reported.

Median progression-free survival (PFS) was 13.2 versus 8.3 months in favor of pembrolizumab, according to the report on the KEYNOTE-204 trial, which included patients with classical Hodgkin lymphoma who either had relapsed after autologous stem cell transplant (SCT) or were ineligible for autologous SCT.

The PFS improvement was “statistically significant and clinically meaningful,” said investigator John Kuruvilla, MD, of Princess Margaret Cancer Centre in Toronto.

“This PFS benefit extended to key subgroups, including those ineligible for autologous transplant, patients with primary refractory disease, and patients who were brentuximab-vedotin naive,” Dr. Kuruvilla added in his presentation, which was part of the American Society of Clinical Oncology virtual scientific program.

Pneumonitis was more frequent in the pembrolizumab arm, but “appeared in general to be quite well managed” among patients who experienced this adverse event, according to Dr. Kuruvilla, who said that treatment with the programmed death–1 inhibitor should be considered “the preferred treatment option and the new standard of care” for patients with relapsed/refractory classic Hodgkin lymphoma who have relapsed after autologous SCT or are ineligible for it.

Although the pneumonitis findings are important to keep in mind, results of KEYNOTE-204 are indeed “practice defining” and immediately impactful, said Mark J. Roschewski, MD, clinical investigator in the lymphoid malignancies branch at the Center for Cancer Research, part of the National Cancer Institute, Bethesda, Md.

“I would select pembrolizumab over brentuximab for this patient population, particularly those that are refractory to chemotherapy,” he said in a commentary on the study also included in the virtual ASCO proceedings.

“There may be specific patient populations that I’d reconsider, such as those that might be at high risk for lung toxicity,” he added. “They may not be suitable for this, but it’s something to at least to be aware of.”

Although the antibody-drug conjugate brentuximab vedotin has been considered the standard of care for patients with relapse after autologous SCT, there has historically been no standard of care for patients who are ineligible for transplant because of chemorefractory disease, advanced age, or comorbidities, Dr. Kuruvilla said in his presentation.

In the KEYNOTE-204 study, 304 patients with relapsed/refractory classic Hodgkin lymphoma were randomized to receive either pembrolizumab 200 mg or brentuximab at 1.8 mg/kg intravenously every 3 weeks for up to 35 cycles.

The median age of patients was 36 years in the pembrolizumab arm and 35 years in the brentuximab vedotin arm, according to the report. Approximately 37% of the patients had previously undergone autologous SCT. About 40% had been refractory to frontline therapy, while 28% relapsed within 12 months of therapy and 32% relapsed later than 12 months.

Median PFS by blinded independent central review was 13.2 versus 8.3 months in the pembrolizumab and brentuximab arms, respectively (hazard ratio, 0.65; 95% confidence interval, 0.48-0.88; P = .00271), Dr. Kuruvilla reported.

The benefit extended to “key subgroups” in the trial, he added, including those who were ineligible for autologous SCT, those with primary refractory disease, and those who were naive to brentuximab vedotin, with HRs of 0.61, 0.52, and 0.67, respectively.

Pembrolizumab was also associated with more durable responses versus brentuximab vedotin, according to the investigator.

The overall response rate was 65.6% and 54.2%, respectively, for pembrolizumab and brentuximab, although this difference of approximately 11 percentage points did not meet criteria for statistical significance, he said. Duration of response was 20.7 months or pembrolizumab and 13.8 months for brentuximab.

The rate of serious treatment-related adverse events was similar between groups, according to Dr. Kuruvilla, who reported grade 3-5 events occurring in 19.6% and 25.0% of the pembrolizumab and brentuximab arms. Serious treatment-related adverse events were numerically more frequent in the pembrolizumab arm (16.2% vs. 10.5%) and there was one treatment-related death caused by pneumonia, seen in the pembrolizumab arm.

Pneumonitis occurred in 2.6% of the brentuximab-treated patients and in 10.8% of pembrolizumab-treated patients, of which half of cases were grade 3-4, according to the report.

In the pembrolizumab arm, pneumonitis was felt to be drug-related in 15 of 16 cases, according to Dr. Kuruvilla, who added that 15 of 16 patients required corticosteroid therapy. “This has led to the resolution of the pneumonitis in 12 of 16 patients, with ongoing resolution in one further patient.”

Research funding for KEYNOTE-204 came from Merck Sharp & Dohme. Dr. Kuruvilla provided disclosures related to Merck and a variety of other pharmaceutical companies. Dr. Roschewski said he had no relationships to disclose.

SOURCE: Kuruvilla J et al. ASCO 2020, Abstract 8005.

Pembrolizumab treatment significantly improved progression-free survival versus brentuximab vedotin in a randomized, phase 3 trial including patients with relapsed or refractory classical Hodgkin lymphoma, an investigator has reported.

Median progression-free survival (PFS) was 13.2 versus 8.3 months in favor of pembrolizumab, according to the report on the KEYNOTE-204 trial, which included patients with classical Hodgkin lymphoma who either had relapsed after autologous stem cell transplant (SCT) or were ineligible for autologous SCT.

The PFS improvement was “statistically significant and clinically meaningful,” said investigator John Kuruvilla, MD, of Princess Margaret Cancer Centre in Toronto.

“This PFS benefit extended to key subgroups, including those ineligible for autologous transplant, patients with primary refractory disease, and patients who were brentuximab-vedotin naive,” Dr. Kuruvilla added in his presentation, which was part of the American Society of Clinical Oncology virtual scientific program.

Pneumonitis was more frequent in the pembrolizumab arm, but “appeared in general to be quite well managed” among patients who experienced this adverse event, according to Dr. Kuruvilla, who said that treatment with the programmed death–1 inhibitor should be considered “the preferred treatment option and the new standard of care” for patients with relapsed/refractory classic Hodgkin lymphoma who have relapsed after autologous SCT or are ineligible for it.

Although the pneumonitis findings are important to keep in mind, results of KEYNOTE-204 are indeed “practice defining” and immediately impactful, said Mark J. Roschewski, MD, clinical investigator in the lymphoid malignancies branch at the Center for Cancer Research, part of the National Cancer Institute, Bethesda, Md.

“I would select pembrolizumab over brentuximab for this patient population, particularly those that are refractory to chemotherapy,” he said in a commentary on the study also included in the virtual ASCO proceedings.

“There may be specific patient populations that I’d reconsider, such as those that might be at high risk for lung toxicity,” he added. “They may not be suitable for this, but it’s something to at least to be aware of.”

Although the antibody-drug conjugate brentuximab vedotin has been considered the standard of care for patients with relapse after autologous SCT, there has historically been no standard of care for patients who are ineligible for transplant because of chemorefractory disease, advanced age, or comorbidities, Dr. Kuruvilla said in his presentation.

In the KEYNOTE-204 study, 304 patients with relapsed/refractory classic Hodgkin lymphoma were randomized to receive either pembrolizumab 200 mg or brentuximab at 1.8 mg/kg intravenously every 3 weeks for up to 35 cycles.

The median age of patients was 36 years in the pembrolizumab arm and 35 years in the brentuximab vedotin arm, according to the report. Approximately 37% of the patients had previously undergone autologous SCT. About 40% had been refractory to frontline therapy, while 28% relapsed within 12 months of therapy and 32% relapsed later than 12 months.

Median PFS by blinded independent central review was 13.2 versus 8.3 months in the pembrolizumab and brentuximab arms, respectively (hazard ratio, 0.65; 95% confidence interval, 0.48-0.88; P = .00271), Dr. Kuruvilla reported.

The benefit extended to “key subgroups” in the trial, he added, including those who were ineligible for autologous SCT, those with primary refractory disease, and those who were naive to brentuximab vedotin, with HRs of 0.61, 0.52, and 0.67, respectively.

Pembrolizumab was also associated with more durable responses versus brentuximab vedotin, according to the investigator.

The overall response rate was 65.6% and 54.2%, respectively, for pembrolizumab and brentuximab, although this difference of approximately 11 percentage points did not meet criteria for statistical significance, he said. Duration of response was 20.7 months or pembrolizumab and 13.8 months for brentuximab.

The rate of serious treatment-related adverse events was similar between groups, according to Dr. Kuruvilla, who reported grade 3-5 events occurring in 19.6% and 25.0% of the pembrolizumab and brentuximab arms. Serious treatment-related adverse events were numerically more frequent in the pembrolizumab arm (16.2% vs. 10.5%) and there was one treatment-related death caused by pneumonia, seen in the pembrolizumab arm.

Pneumonitis occurred in 2.6% of the brentuximab-treated patients and in 10.8% of pembrolizumab-treated patients, of which half of cases were grade 3-4, according to the report.

In the pembrolizumab arm, pneumonitis was felt to be drug-related in 15 of 16 cases, according to Dr. Kuruvilla, who added that 15 of 16 patients required corticosteroid therapy. “This has led to the resolution of the pneumonitis in 12 of 16 patients, with ongoing resolution in one further patient.”

Research funding for KEYNOTE-204 came from Merck Sharp & Dohme. Dr. Kuruvilla provided disclosures related to Merck and a variety of other pharmaceutical companies. Dr. Roschewski said he had no relationships to disclose.

SOURCE: Kuruvilla J et al. ASCO 2020, Abstract 8005.

Children hospitalized for bronchiolitis frequently require admission to the intensive care unit (ICU), with estimates as high as 18%^1,2 and 35%³ in two prospective, multicenter studies. The indication for ICU admission is nearly always a need for advanced respiratory support, which historically consisted of continuous or bilevel positive airway pressure (CPAP and BiPAP, respectively) or mechanical ventilation. High-flow nasal cannula (HFNC) is a recent addition to the respiratory support armamentarium, delivering heated and humidified oxygen at rates of up to 60 L/min and allowing for clinicians to titrate both flow rate and fraction of inspired oxygen (FiO₂).⁴

Several studies have demonstrated that HFNC is capable of decreasing a child’s work of breathing,^5-8 and it has the potential advantage of being better tolerated than other forms of advanced respiratory support.^9,10 These case-series physiologic studies informed early ward-based HFNC protocols for bronchiolitis, which were adopted to decrease ICU utilization. Since then, single center observational studies examining the association between ward-based HFNC protocols and subsequent ICU utilization have come to discordant conclusions.^11-14 Studying the effect of employing HFNC outside of the ICU is challenging in the context of a randomized, controlled trial (RCT) because it is difficult to blind healthcare providers to the intervention and because crossover from the control group to HFNC is frequent. Two unblinded RCTs published in 2017 and 2018 found that children randomized to conventional nasal cannula were frequently escalated to HFNC (flow rates of 1-2 L/kg per minute), but neither trial found a difference in ICU admission.^15,16 Sample sizes substantially larger than those present in currently published or registered RCTs would be required to evaluate the impact of ward-based HFNC protocols on the outcome that inspired the protocols in the first place, namely ICU utilization.¹⁷

Children’s hospitals have adopted ward-based HFNC protocols at different time points over the last decade, which allows for a natural experiment—a promising alternative study design that avoids the challenges of blinding, crossover, and modest sample sizes. In order to have sufficient postadoption data for analyses, the present study is limited to ward-based HFNC protocols adopted prior to 2016, which we have termed “early” ward-based HFNC protocols. Among children with bronchiolitis, our objective was to measure the association between hospital-level adoption of a ward-based HFNC protocol and subsequent ICU utilization, using a multicenter network of children’s hospitals.

We conducted a multicenter retrospective cohort study using the Pediatric Health Information System (PHIS) database. The PHIS database is operated by the Children’s Hospital Association (Lenexa, Kansas) and provides deidentified patient-level information for children who receive hospital care at 55 US children’s hospitals. Available data elements include patient demographic data, discharge diagnosis and procedure codes, and detailed billing information, such as laboratory, imaging, pharmacy, and supply charges. At the patient level, the use of HFNC vs standard oxygen therapy circuits cannot be discriminated.

Exposure

The study exposure was a hospital’s first ward-based HFNC protocol, with adoption measured at the hospital level at each PHIS site via direct communication with leaders in hospital medicine. In most cases, first contact was made with the pediatric hospital medicine division chief or fellowship program director, who then, if necessary, connected study investigators to local HFNC champions aware of site-specific historical HFNC protocol details. Contact with a hospital was made only if the hospital had contributed at least 6 consecutive years of data to PHIS. Hospitals were classified as “adopting” hospitals if their HFNC protocol met all of the following criteria: (a) allows initiation of HFNC outside of the ICU (on the floor or in the ED), (b) allows continued care outside of the ICU (on the floor), (c) not limited to a small unit like an intermediate care unit, and (d) adopted during a specific, known respiratory season. Hospitals for which ward-based HFNC protocols were adopted but did not meet these criteria were excluded from further analysis. Our intent was to identify large scale, programmatic protocol launches and exclude hospitals with exceptions that might preclude a sizable portion of our cohort from being eligible for the protocol. Hospitals for which inpatient use of HFNC remains limited to the ICU were defined as “nonadopting” hospitals. Respondents at adopting hospitals were asked to share details about their protocol, including patient eligibility criteria and maximum HFNC rates of flow permitted outside of the ICU.

Patient Characteristics

Patients aged 3 to 24 months who were hospitalized at adopting and nonadopting hospitals were included if an International Classification of Diseases, Ninth Revision (ICD-9) discharge diagnosis code for bronchiolitis (466.XX) was present in any position (not limited to a primary diagnosis). The lower age limit of 3 months was chosen to match the most restrictive age eligibility criteria of provided HFNC protocols (Appendix 1). A crosswalk available from the Centers for Medicare & Medicaid Services¹⁸ was used to convert ICD-10 diagnosis and procedure codes from recent years to ICD-9 diagnosis and procedure codes. Patients were excluded if their encounter contained a diagnosis or procedure code signifying a complex chronic condition,¹⁹ if their hospitalization involved care in the neonatal ICU, or if their admission date occurred outside of the respiratory season. Respiratory season was defined as November 1 through April 30.

Outcomes

Outcomes were measured during three respiratory seasons leading up to adoption and during three respiratory seasons after adoption. The primary outcome was ICU utilization, including the proportion of patients admitted to the ICU and ICU length of stay, expressed as ICU days per 100 patients. Secondary outcomes included mean total length of stay and the proportion of patients who received mechanical ventilation. Lengths of stay were measured in days, the most granular unit of time provided in PHIS over the entire study period. As such, partial days of care are rounded up to 1 full day. A previously published strict definition for mechanical ventilation that limits false positives was used, requiring that patients have a procedure or supply code for mechanical ventilation and a pharmacy charge for a neuromuscular blocking agent.²⁰

Primary Analysis

The primary analysis was restricted to adopting hospitals. An interrupted time series approach was used to measure two possible types of change associated with HFNC protocol adoption: an immediate intervention effect and a change in the slope of an outcome.²¹ The immediate intervention effect represents the change in the level of the outcome that occurs in the period immediately following the introduction of the protocol. The change in slope is the extent to which the outcome changes on a per season basis, attributable to the protocol. Interrupted time series estimates were adjusted for patient age, gender, race, ethnicity, and insurance type; linear regression was used for continuous outcomes and logistic regression for dichotomous outcomes. An ordinary least squares time series model was used to adjust for autocorrelation and Newey-West standard errors were employed.²² Analyses were performed using STATA version 14 (Stata-Corp, College Station, Texas).

Supplementary Analyses

Two preplanned supplementary analyses were conducted. Supplementary analysis 1 was identical to the primary analysis, with the exception that the first season after adoption was censored. The rationale for censoring the first adoption season was to account for a potential learning effect and/or delayed start to full protocol implementation. Supplementary analysis 2 used the nonadopting hospitals as a control group and subtracted the effects measured from an interrupted time series analysis among nonadopting hospitals from the effects measured among adopting hospitals. The rationale for this approach was to control for unmeasured secular (eg, availability of ICU beds) and temporal (eg, severity of a given bronchiolitis season) factors that may have coincidentally occurred with HFNC adoption seasons. The only modification to the interrupted time series approach for supplementary analysis 2 was to provide the nonadopting hospitals with an artificial interruption point because nonadopting hospitals, by definition, did not have an adoption season that could be used in an interrupted time series approach. The interruption point for nonadopting hospitals was set at the median adoption season for adopting hospitals.

Exposure

Leaders at 44 hospitals were contacted regarding their hospital’s use of HFNC outside of the ICU (Figure 1). Responses were obtained for 41 hospitals (93% response rate), 18 of which were classified as nonadopting hospitals. Of the 23 hospitals where the presence of ward-based HFNC protocols were reported, 12 met inclusion criteria and were classified as adopting hospitals. HFNC protocols were adopted at these hospitals in a staggered fashion between the 2010-2011 and 2015-2016 respiratory seasons (Figure 2). The median adoption season was the 2013-14 respiratory season.

Nine adopting hospitals were able to provide details about their first HFNC protocols (Appendix 1). No two protocols were identical, but they shared many similarities. Minimum age requirements ranged from birth to a few months of age. Exclusion criteria were particularly variable, with a history of chronic lung disease or apnea being the most common criteria. Maximum allowed rates of flow ranged from 4 to 10 liters per minute. Criteria for transfer to the ICU were consistently based on an elevated FiO₂ and duration of HFNC exposure.

Patient Characteristics

A total of 32,809 bronchiolitis encounters occurred at adopting hospitals during qualifying respiratory seasons, of which 6,556 (20%) involved patients with a complex chronic condition and were excluded. Of the 26,253 included bronchiolitis encounters, 12,495 encounters occurred prior to ward-based HFNC protocol adoption and 13,758 encounters occurred after adoption. The median age of patients was 8 months (interquartile range, 5-14 months). Most patients were on government insurance (64%), male (58%), of white (56%) or black (18%) race, and of non-Hispanic ethnicity (72%). Pre- and postadoption patient demographics were similar (Appendix 2).

Primary Analysis

Shifts in the level of ICU use and ICU length of stay were observed at the time of adoption of a ward-based HFNC protocol (Figure 3). Specifically, ward-based HFNC protocol adoption was associated with an immediate 3.1% absolute increase (95% CI, 2.8%-3.4%) in the proportion of patients admitted to the ICU and a 9.1 days per 100 patients increase (95% CI, 5.1-13.2) in ICU length of stay (Table). The slope of ICU admissions per season was increasing after HFNC protocol adoption (1.0% increase per season; 95% CI, 0.8%-1.1%). When examined at the individual-hospital level (Appendix 3), seven hospitals were found to have significant increases in ICU admissions (immediate intervention effect or change in slope) after adoption, and one hospital was found to have a significant decrease in ICU admissions (change in slope only). Neither immediate intervention effects nor changes in the slopes of total length of stay and mechanical ventilation were observed, with mean total length of stay approximately 3 days and just over 1% of patients receiving mechanical ventilation (Figure 3).

Supplementary Analyses

Supplementary analyses were largely consistent with the primary analysis. Associations with increased ICU utilization were again observed, although the immediate change in ICU length of stay for supplementary analysis 1 was not significant and the slope for ICU length of stay in supplementary analysis 2 was down trending (Table). Changes in total length of stay and mechanical ventilation were not observed in either supplementary analysis, with the lone exception being an increase in the proportion of patients receiving mechanical ventilation per season (increase in slope) in supplementary analysis 1.

This is the largest multicenter study to date evaluating ICU utilization after adoption of a ward-based HFNC protocol for patients with bronchiolitis. While a principal goal of allowing HFNC use outside of the ICU is to reduce the time that patients with bronchiolitis spend in the ICU, we found that early protocols were, paradoxically, associated with increased ICU utilization. Ward-based HFNC protocols were not associated with changes in hospital length of stay or need for mechanical ventilation. Our findings are particularly relevant given that the majority of children’s hospitals in our sample have adopted ward-based HFNC protocols to care for patients with bronchiolitis.

The increase in ICU utilization measured in our study is a novel finding, seemingly in contradiction to existing literature. Early pilot studies inspired hope that employing HFNC on the general ward might prevent a portion of children from needing ICU care.^11,12 Subsequent larger observational studies did not demonstrate decreases in ICU utilization after adoption of ward-based HFNC protocols.^13,14 The two RCTs comparing low-flow and high-flow nasal cannula use outside of the ICU did not measure a statistically significant effect on ICU utilization, an exploratory outcome in both trials.^15,16 However, the reported point estimates for absolute differences in ICU admission were 2% to 3% higher among patients randomized to HFNC, which is consistent with the 2% to 4% increase in ICU admission measured in the present study.

What might explain this surprising finding? While our observational study cannot speak to mechanism, the protocol details examined in the present study suggest that initial adoption of a ward-based HFNC protocol is often coupled with specific ICU transfer criteria that were unlikely in place prior to protocol initiation. For example, most protocols recommended consideration of ICU transfer for elevated FiO₂ or prolonged duration of HFNC. Transfer to the ICU for prolonged HFNC duration is only possible in the setting of a ward-based HFNC protocol and transfer for elevated FiO₂ was probably unnecessary prior to protocol adoption given that low-flow nasal cannula generally delivers 100% FiO₂. It is also possible that with HFNC comes a perception of increased acuity. For example, medical providers may see patients on HFNC as sicker than patients with the same amount of work of breathing but off HFNC, which makes providers more likely to seek ICU admission for patients on HFNC. The combination of unchanged total length of stay and increased ICU utilization suggests that early ward-based HFNC protocols were an ineffective instrument to improve hospital bed availability during the peak census times that often occur in bronchiolitis season.

The large sample size afforded our study by its multicenter, retrospective design also allowed for a meaningful assessment of the association between a ward-based HFNC protocol and the need for mechanical ventilation. Early indications suggested a lack of substantial association between HFNC use outside of the ICU and rates of mechanical ventilation, but prior studies were limited by small numbers of patients receiving mechanical ventilation (<30 patients in each study).^13,14,16 The present study, in which 783 patients received mechanical ventilation, supports the lack of association between early ward-based HFNC protocols and the need for mechanical ventilation. It should be noted that other studies have measured decreases in mechanical ventilation in association with ICU-based HFNC use.^23-26 In addition to examining HFNC use in a different clinical context, decreases in mechanical ventilation measured after HFNC implementation in the ICU could be explained by preexisting practice trends to limit invasive ventilation and/or selection bias resulting from an increase in less severely ill patients being admitted to the ICU over time. The interrupted time series approach and the staggered adoption of HFNC protocols make the present study less susceptible to biases from preexisting trends and the inclusion of patients cared for both on the ward and within the ICU reduces selection bias.

Our study has several important limitations. First, all hospitals included in the analysis were US children’s hospitals and these findings may not generalize to other practice environments, including community hospitals and other countries. Second, our cohort and outcomes were defined using administrative billing data, which have been incompletely validated, making some degree of misclassification likely. Third, we measured HFNC exposure at the hospital level, but could not examine the extent to which individual patients were exposed to HFNC because such data are not present in PHIS. Even if we had access to patient-level HFNC exposure data, we would have still compared outcomes among all patients with bronchiolitis (not just those who received HFNC), to avoid selection bias. However, knowing HFNC exposure status at the patient level would have allowed for weighting of the effects measured at each hospital according to the extent of HFNC exposure. Fourth, there are likely other, unmeasured secular and temporal factors that could affect study outcomes. To some degree, the interrupted time series approach, observed staggered adoption of protocols, and nonadopting hospital supplementary analysis mitigate this risk of bias. Fifth, while the pre- and postadoption populations appeared demographically similar, it is possible that the populations might have differed by other unmeasured factors. Finally, early ward-based HFNC protocols have likely undergone iterative changes since adoption. We compared pre- and postadoption outcome slopes and censored the first adoption season in a supplementary analysis to attempt to account for this potential limitation.

In conclusion, our findings suggest that initial implementation of ward-based HFNC protocols were not successful at reducing ICU utilization for children with bronchiolitis. Future research should examine whether more evolved HFNC protocols that use higher flow rates, more generous ICU transfer criteria, and more rapid weaning criteria can reduce ICU utilization.

We thank Dr Vineeta Mittal (University of Texas Southwestern Medical Center) for providing feedback regarding the manuscript.

Children hospitalized for bronchiolitis frequently require admission to the intensive care unit (ICU), with estimates as high as 18%^1,2 and 35%³ in two prospective, multicenter studies. The indication for ICU admission is nearly always a need for advanced respiratory support, which historically consisted of continuous or bilevel positive airway pressure (CPAP and BiPAP, respectively) or mechanical ventilation. High-flow nasal cannula (HFNC) is a recent addition to the respiratory support armamentarium, delivering heated and humidified oxygen at rates of up to 60 L/min and allowing for clinicians to titrate both flow rate and fraction of inspired oxygen (FiO₂).⁴

Several studies have demonstrated that HFNC is capable of decreasing a child’s work of breathing,^5-8 and it has the potential advantage of being better tolerated than other forms of advanced respiratory support.^9,10 These case-series physiologic studies informed early ward-based HFNC protocols for bronchiolitis, which were adopted to decrease ICU utilization. Since then, single center observational studies examining the association between ward-based HFNC protocols and subsequent ICU utilization have come to discordant conclusions.^11-14 Studying the effect of employing HFNC outside of the ICU is challenging in the context of a randomized, controlled trial (RCT) because it is difficult to blind healthcare providers to the intervention and because crossover from the control group to HFNC is frequent. Two unblinded RCTs published in 2017 and 2018 found that children randomized to conventional nasal cannula were frequently escalated to HFNC (flow rates of 1-2 L/kg per minute), but neither trial found a difference in ICU admission.^15,16 Sample sizes substantially larger than those present in currently published or registered RCTs would be required to evaluate the impact of ward-based HFNC protocols on the outcome that inspired the protocols in the first place, namely ICU utilization.¹⁷

Children’s hospitals have adopted ward-based HFNC protocols at different time points over the last decade, which allows for a natural experiment—a promising alternative study design that avoids the challenges of blinding, crossover, and modest sample sizes. In order to have sufficient postadoption data for analyses, the present study is limited to ward-based HFNC protocols adopted prior to 2016, which we have termed “early” ward-based HFNC protocols. Among children with bronchiolitis, our objective was to measure the association between hospital-level adoption of a ward-based HFNC protocol and subsequent ICU utilization, using a multicenter network of children’s hospitals.

We conducted a multicenter retrospective cohort study using the Pediatric Health Information System (PHIS) database. The PHIS database is operated by the Children’s Hospital Association (Lenexa, Kansas) and provides deidentified patient-level information for children who receive hospital care at 55 US children’s hospitals. Available data elements include patient demographic data, discharge diagnosis and procedure codes, and detailed billing information, such as laboratory, imaging, pharmacy, and supply charges. At the patient level, the use of HFNC vs standard oxygen therapy circuits cannot be discriminated.

Exposure

The study exposure was a hospital’s first ward-based HFNC protocol, with adoption measured at the hospital level at each PHIS site via direct communication with leaders in hospital medicine. In most cases, first contact was made with the pediatric hospital medicine division chief or fellowship program director, who then, if necessary, connected study investigators to local HFNC champions aware of site-specific historical HFNC protocol details. Contact with a hospital was made only if the hospital had contributed at least 6 consecutive years of data to PHIS. Hospitals were classified as “adopting” hospitals if their HFNC protocol met all of the following criteria: (a) allows initiation of HFNC outside of the ICU (on the floor or in the ED), (b) allows continued care outside of the ICU (on the floor), (c) not limited to a small unit like an intermediate care unit, and (d) adopted during a specific, known respiratory season. Hospitals for which ward-based HFNC protocols were adopted but did not meet these criteria were excluded from further analysis. Our intent was to identify large scale, programmatic protocol launches and exclude hospitals with exceptions that might preclude a sizable portion of our cohort from being eligible for the protocol. Hospitals for which inpatient use of HFNC remains limited to the ICU were defined as “nonadopting” hospitals. Respondents at adopting hospitals were asked to share details about their protocol, including patient eligibility criteria and maximum HFNC rates of flow permitted outside of the ICU.

Patient Characteristics

Patients aged 3 to 24 months who were hospitalized at adopting and nonadopting hospitals were included if an International Classification of Diseases, Ninth Revision (ICD-9) discharge diagnosis code for bronchiolitis (466.XX) was present in any position (not limited to a primary diagnosis). The lower age limit of 3 months was chosen to match the most restrictive age eligibility criteria of provided HFNC protocols (Appendix 1). A crosswalk available from the Centers for Medicare & Medicaid Services¹⁸ was used to convert ICD-10 diagnosis and procedure codes from recent years to ICD-9 diagnosis and procedure codes. Patients were excluded if their encounter contained a diagnosis or procedure code signifying a complex chronic condition,¹⁹ if their hospitalization involved care in the neonatal ICU, or if their admission date occurred outside of the respiratory season. Respiratory season was defined as November 1 through April 30.

Outcomes

Outcomes were measured during three respiratory seasons leading up to adoption and during three respiratory seasons after adoption. The primary outcome was ICU utilization, including the proportion of patients admitted to the ICU and ICU length of stay, expressed as ICU days per 100 patients. Secondary outcomes included mean total length of stay and the proportion of patients who received mechanical ventilation. Lengths of stay were measured in days, the most granular unit of time provided in PHIS over the entire study period. As such, partial days of care are rounded up to 1 full day. A previously published strict definition for mechanical ventilation that limits false positives was used, requiring that patients have a procedure or supply code for mechanical ventilation and a pharmacy charge for a neuromuscular blocking agent.²⁰

Primary Analysis

The primary analysis was restricted to adopting hospitals. An interrupted time series approach was used to measure two possible types of change associated with HFNC protocol adoption: an immediate intervention effect and a change in the slope of an outcome.²¹ The immediate intervention effect represents the change in the level of the outcome that occurs in the period immediately following the introduction of the protocol. The change in slope is the extent to which the outcome changes on a per season basis, attributable to the protocol. Interrupted time series estimates were adjusted for patient age, gender, race, ethnicity, and insurance type; linear regression was used for continuous outcomes and logistic regression for dichotomous outcomes. An ordinary least squares time series model was used to adjust for autocorrelation and Newey-West standard errors were employed.²² Analyses were performed using STATA version 14 (Stata-Corp, College Station, Texas).

Supplementary Analyses

Two preplanned supplementary analyses were conducted. Supplementary analysis 1 was identical to the primary analysis, with the exception that the first season after adoption was censored. The rationale for censoring the first adoption season was to account for a potential learning effect and/or delayed start to full protocol implementation. Supplementary analysis 2 used the nonadopting hospitals as a control group and subtracted the effects measured from an interrupted time series analysis among nonadopting hospitals from the effects measured among adopting hospitals. The rationale for this approach was to control for unmeasured secular (eg, availability of ICU beds) and temporal (eg, severity of a given bronchiolitis season) factors that may have coincidentally occurred with HFNC adoption seasons. The only modification to the interrupted time series approach for supplementary analysis 2 was to provide the nonadopting hospitals with an artificial interruption point because nonadopting hospitals, by definition, did not have an adoption season that could be used in an interrupted time series approach. The interruption point for nonadopting hospitals was set at the median adoption season for adopting hospitals.

Exposure

Leaders at 44 hospitals were contacted regarding their hospital’s use of HFNC outside of the ICU (Figure 1). Responses were obtained for 41 hospitals (93% response rate), 18 of which were classified as nonadopting hospitals. Of the 23 hospitals where the presence of ward-based HFNC protocols were reported, 12 met inclusion criteria and were classified as adopting hospitals. HFNC protocols were adopted at these hospitals in a staggered fashion between the 2010-2011 and 2015-2016 respiratory seasons (Figure 2). The median adoption season was the 2013-14 respiratory season.

Nine adopting hospitals were able to provide details about their first HFNC protocols (Appendix 1). No two protocols were identical, but they shared many similarities. Minimum age requirements ranged from birth to a few months of age. Exclusion criteria were particularly variable, with a history of chronic lung disease or apnea being the most common criteria. Maximum allowed rates of flow ranged from 4 to 10 liters per minute. Criteria for transfer to the ICU were consistently based on an elevated FiO₂ and duration of HFNC exposure.

Patient Characteristics

A total of 32,809 bronchiolitis encounters occurred at adopting hospitals during qualifying respiratory seasons, of which 6,556 (20%) involved patients with a complex chronic condition and were excluded. Of the 26,253 included bronchiolitis encounters, 12,495 encounters occurred prior to ward-based HFNC protocol adoption and 13,758 encounters occurred after adoption. The median age of patients was 8 months (interquartile range, 5-14 months). Most patients were on government insurance (64%), male (58%), of white (56%) or black (18%) race, and of non-Hispanic ethnicity (72%). Pre- and postadoption patient demographics were similar (Appendix 2).

Primary Analysis

Shifts in the level of ICU use and ICU length of stay were observed at the time of adoption of a ward-based HFNC protocol (Figure 3). Specifically, ward-based HFNC protocol adoption was associated with an immediate 3.1% absolute increase (95% CI, 2.8%-3.4%) in the proportion of patients admitted to the ICU and a 9.1 days per 100 patients increase (95% CI, 5.1-13.2) in ICU length of stay (Table). The slope of ICU admissions per season was increasing after HFNC protocol adoption (1.0% increase per season; 95% CI, 0.8%-1.1%). When examined at the individual-hospital level (Appendix 3), seven hospitals were found to have significant increases in ICU admissions (immediate intervention effect or change in slope) after adoption, and one hospital was found to have a significant decrease in ICU admissions (change in slope only). Neither immediate intervention effects nor changes in the slopes of total length of stay and mechanical ventilation were observed, with mean total length of stay approximately 3 days and just over 1% of patients receiving mechanical ventilation (Figure 3).

Supplementary Analyses

Supplementary analyses were largely consistent with the primary analysis. Associations with increased ICU utilization were again observed, although the immediate change in ICU length of stay for supplementary analysis 1 was not significant and the slope for ICU length of stay in supplementary analysis 2 was down trending (Table). Changes in total length of stay and mechanical ventilation were not observed in either supplementary analysis, with the lone exception being an increase in the proportion of patients receiving mechanical ventilation per season (increase in slope) in supplementary analysis 1.

This is the largest multicenter study to date evaluating ICU utilization after adoption of a ward-based HFNC protocol for patients with bronchiolitis. While a principal goal of allowing HFNC use outside of the ICU is to reduce the time that patients with bronchiolitis spend in the ICU, we found that early protocols were, paradoxically, associated with increased ICU utilization. Ward-based HFNC protocols were not associated with changes in hospital length of stay or need for mechanical ventilation. Our findings are particularly relevant given that the majority of children’s hospitals in our sample have adopted ward-based HFNC protocols to care for patients with bronchiolitis.

The increase in ICU utilization measured in our study is a novel finding, seemingly in contradiction to existing literature. Early pilot studies inspired hope that employing HFNC on the general ward might prevent a portion of children from needing ICU care.^11,12 Subsequent larger observational studies did not demonstrate decreases in ICU utilization after adoption of ward-based HFNC protocols.^13,14 The two RCTs comparing low-flow and high-flow nasal cannula use outside of the ICU did not measure a statistically significant effect on ICU utilization, an exploratory outcome in both trials.^15,16 However, the reported point estimates for absolute differences in ICU admission were 2% to 3% higher among patients randomized to HFNC, which is consistent with the 2% to 4% increase in ICU admission measured in the present study.

What might explain this surprising finding? While our observational study cannot speak to mechanism, the protocol details examined in the present study suggest that initial adoption of a ward-based HFNC protocol is often coupled with specific ICU transfer criteria that were unlikely in place prior to protocol initiation. For example, most protocols recommended consideration of ICU transfer for elevated FiO₂ or prolonged duration of HFNC. Transfer to the ICU for prolonged HFNC duration is only possible in the setting of a ward-based HFNC protocol and transfer for elevated FiO₂ was probably unnecessary prior to protocol adoption given that low-flow nasal cannula generally delivers 100% FiO₂. It is also possible that with HFNC comes a perception of increased acuity. For example, medical providers may see patients on HFNC as sicker than patients with the same amount of work of breathing but off HFNC, which makes providers more likely to seek ICU admission for patients on HFNC. The combination of unchanged total length of stay and increased ICU utilization suggests that early ward-based HFNC protocols were an ineffective instrument to improve hospital bed availability during the peak census times that often occur in bronchiolitis season.

The large sample size afforded our study by its multicenter, retrospective design also allowed for a meaningful assessment of the association between a ward-based HFNC protocol and the need for mechanical ventilation. Early indications suggested a lack of substantial association between HFNC use outside of the ICU and rates of mechanical ventilation, but prior studies were limited by small numbers of patients receiving mechanical ventilation (<30 patients in each study).^13,14,16 The present study, in which 783 patients received mechanical ventilation, supports the lack of association between early ward-based HFNC protocols and the need for mechanical ventilation. It should be noted that other studies have measured decreases in mechanical ventilation in association with ICU-based HFNC use.^23-26 In addition to examining HFNC use in a different clinical context, decreases in mechanical ventilation measured after HFNC implementation in the ICU could be explained by preexisting practice trends to limit invasive ventilation and/or selection bias resulting from an increase in less severely ill patients being admitted to the ICU over time. The interrupted time series approach and the staggered adoption of HFNC protocols make the present study less susceptible to biases from preexisting trends and the inclusion of patients cared for both on the ward and within the ICU reduces selection bias.

Our study has several important limitations. First, all hospitals included in the analysis were US children’s hospitals and these findings may not generalize to other practice environments, including community hospitals and other countries. Second, our cohort and outcomes were defined using administrative billing data, which have been incompletely validated, making some degree of misclassification likely. Third, we measured HFNC exposure at the hospital level, but could not examine the extent to which individual patients were exposed to HFNC because such data are not present in PHIS. Even if we had access to patient-level HFNC exposure data, we would have still compared outcomes among all patients with bronchiolitis (not just those who received HFNC), to avoid selection bias. However, knowing HFNC exposure status at the patient level would have allowed for weighting of the effects measured at each hospital according to the extent of HFNC exposure. Fourth, there are likely other, unmeasured secular and temporal factors that could affect study outcomes. To some degree, the interrupted time series approach, observed staggered adoption of protocols, and nonadopting hospital supplementary analysis mitigate this risk of bias. Fifth, while the pre- and postadoption populations appeared demographically similar, it is possible that the populations might have differed by other unmeasured factors. Finally, early ward-based HFNC protocols have likely undergone iterative changes since adoption. We compared pre- and postadoption outcome slopes and censored the first adoption season in a supplementary analysis to attempt to account for this potential limitation.

In conclusion, our findings suggest that initial implementation of ward-based HFNC protocols were not successful at reducing ICU utilization for children with bronchiolitis. Future research should examine whether more evolved HFNC protocols that use higher flow rates, more generous ICU transfer criteria, and more rapid weaning criteria can reduce ICU utilization.

We thank Dr Vineeta Mittal (University of Texas Southwestern Medical Center) for providing feedback regarding the manuscript.

Children hospitalized for bronchiolitis frequently require admission to the intensive care unit (ICU), with estimates as high as 18%^1,2 and 35%³ in two prospective, multicenter studies. The indication for ICU admission is nearly always a need for advanced respiratory support, which historically consisted of continuous or bilevel positive airway pressure (CPAP and BiPAP, respectively) or mechanical ventilation. High-flow nasal cannula (HFNC) is a recent addition to the respiratory support armamentarium, delivering heated and humidified oxygen at rates of up to 60 L/min and allowing for clinicians to titrate both flow rate and fraction of inspired oxygen (FiO₂).⁴

Several studies have demonstrated that HFNC is capable of decreasing a child’s work of breathing,^5-8 and it has the potential advantage of being better tolerated than other forms of advanced respiratory support.^9,10 These case-series physiologic studies informed early ward-based HFNC protocols for bronchiolitis, which were adopted to decrease ICU utilization. Since then, single center observational studies examining the association between ward-based HFNC protocols and subsequent ICU utilization have come to discordant conclusions.^11-14 Studying the effect of employing HFNC outside of the ICU is challenging in the context of a randomized, controlled trial (RCT) because it is difficult to blind healthcare providers to the intervention and because crossover from the control group to HFNC is frequent. Two unblinded RCTs published in 2017 and 2018 found that children randomized to conventional nasal cannula were frequently escalated to HFNC (flow rates of 1-2 L/kg per minute), but neither trial found a difference in ICU admission.^15,16 Sample sizes substantially larger than those present in currently published or registered RCTs would be required to evaluate the impact of ward-based HFNC protocols on the outcome that inspired the protocols in the first place, namely ICU utilization.¹⁷

Children’s hospitals have adopted ward-based HFNC protocols at different time points over the last decade, which allows for a natural experiment—a promising alternative study design that avoids the challenges of blinding, crossover, and modest sample sizes. In order to have sufficient postadoption data for analyses, the present study is limited to ward-based HFNC protocols adopted prior to 2016, which we have termed “early” ward-based HFNC protocols. Among children with bronchiolitis, our objective was to measure the association between hospital-level adoption of a ward-based HFNC protocol and subsequent ICU utilization, using a multicenter network of children’s hospitals.

We conducted a multicenter retrospective cohort study using the Pediatric Health Information System (PHIS) database. The PHIS database is operated by the Children’s Hospital Association (Lenexa, Kansas) and provides deidentified patient-level information for children who receive hospital care at 55 US children’s hospitals. Available data elements include patient demographic data, discharge diagnosis and procedure codes, and detailed billing information, such as laboratory, imaging, pharmacy, and supply charges. At the patient level, the use of HFNC vs standard oxygen therapy circuits cannot be discriminated.

Exposure

The study exposure was a hospital’s first ward-based HFNC protocol, with adoption measured at the hospital level at each PHIS site via direct communication with leaders in hospital medicine. In most cases, first contact was made with the pediatric hospital medicine division chief or fellowship program director, who then, if necessary, connected study investigators to local HFNC champions aware of site-specific historical HFNC protocol details. Contact with a hospital was made only if the hospital had contributed at least 6 consecutive years of data to PHIS. Hospitals were classified as “adopting” hospitals if their HFNC protocol met all of the following criteria: (a) allows initiation of HFNC outside of the ICU (on the floor or in the ED), (b) allows continued care outside of the ICU (on the floor), (c) not limited to a small unit like an intermediate care unit, and (d) adopted during a specific, known respiratory season. Hospitals for which ward-based HFNC protocols were adopted but did not meet these criteria were excluded from further analysis. Our intent was to identify large scale, programmatic protocol launches and exclude hospitals with exceptions that might preclude a sizable portion of our cohort from being eligible for the protocol. Hospitals for which inpatient use of HFNC remains limited to the ICU were defined as “nonadopting” hospitals. Respondents at adopting hospitals were asked to share details about their protocol, including patient eligibility criteria and maximum HFNC rates of flow permitted outside of the ICU.

Patient Characteristics

Patients aged 3 to 24 months who were hospitalized at adopting and nonadopting hospitals were included if an International Classification of Diseases, Ninth Revision (ICD-9) discharge diagnosis code for bronchiolitis (466.XX) was present in any position (not limited to a primary diagnosis). The lower age limit of 3 months was chosen to match the most restrictive age eligibility criteria of provided HFNC protocols (Appendix 1). A crosswalk available from the Centers for Medicare & Medicaid Services¹⁸ was used to convert ICD-10 diagnosis and procedure codes from recent years to ICD-9 diagnosis and procedure codes. Patients were excluded if their encounter contained a diagnosis or procedure code signifying a complex chronic condition,¹⁹ if their hospitalization involved care in the neonatal ICU, or if their admission date occurred outside of the respiratory season. Respiratory season was defined as November 1 through April 30.

Outcomes

Outcomes were measured during three respiratory seasons leading up to adoption and during three respiratory seasons after adoption. The primary outcome was ICU utilization, including the proportion of patients admitted to the ICU and ICU length of stay, expressed as ICU days per 100 patients. Secondary outcomes included mean total length of stay and the proportion of patients who received mechanical ventilation. Lengths of stay were measured in days, the most granular unit of time provided in PHIS over the entire study period. As such, partial days of care are rounded up to 1 full day. A previously published strict definition for mechanical ventilation that limits false positives was used, requiring that patients have a procedure or supply code for mechanical ventilation and a pharmacy charge for a neuromuscular blocking agent.²⁰

Primary Analysis

The primary analysis was restricted to adopting hospitals. An interrupted time series approach was used to measure two possible types of change associated with HFNC protocol adoption: an immediate intervention effect and a change in the slope of an outcome.²¹ The immediate intervention effect represents the change in the level of the outcome that occurs in the period immediately following the introduction of the protocol. The change in slope is the extent to which the outcome changes on a per season basis, attributable to the protocol. Interrupted time series estimates were adjusted for patient age, gender, race, ethnicity, and insurance type; linear regression was used for continuous outcomes and logistic regression for dichotomous outcomes. An ordinary least squares time series model was used to adjust for autocorrelation and Newey-West standard errors were employed.²² Analyses were performed using STATA version 14 (Stata-Corp, College Station, Texas).

Supplementary Analyses

Two preplanned supplementary analyses were conducted. Supplementary analysis 1 was identical to the primary analysis, with the exception that the first season after adoption was censored. The rationale for censoring the first adoption season was to account for a potential learning effect and/or delayed start to full protocol implementation. Supplementary analysis 2 used the nonadopting hospitals as a control group and subtracted the effects measured from an interrupted time series analysis among nonadopting hospitals from the effects measured among adopting hospitals. The rationale for this approach was to control for unmeasured secular (eg, availability of ICU beds) and temporal (eg, severity of a given bronchiolitis season) factors that may have coincidentally occurred with HFNC adoption seasons. The only modification to the interrupted time series approach for supplementary analysis 2 was to provide the nonadopting hospitals with an artificial interruption point because nonadopting hospitals, by definition, did not have an adoption season that could be used in an interrupted time series approach. The interruption point for nonadopting hospitals was set at the median adoption season for adopting hospitals.

Exposure

Leaders at 44 hospitals were contacted regarding their hospital’s use of HFNC outside of the ICU (Figure 1). Responses were obtained for 41 hospitals (93% response rate), 18 of which were classified as nonadopting hospitals. Of the 23 hospitals where the presence of ward-based HFNC protocols were reported, 12 met inclusion criteria and were classified as adopting hospitals. HFNC protocols were adopted at these hospitals in a staggered fashion between the 2010-2011 and 2015-2016 respiratory seasons (Figure 2). The median adoption season was the 2013-14 respiratory season.

Nine adopting hospitals were able to provide details about their first HFNC protocols (Appendix 1). No two protocols were identical, but they shared many similarities. Minimum age requirements ranged from birth to a few months of age. Exclusion criteria were particularly variable, with a history of chronic lung disease or apnea being the most common criteria. Maximum allowed rates of flow ranged from 4 to 10 liters per minute. Criteria for transfer to the ICU were consistently based on an elevated FiO₂ and duration of HFNC exposure.

Patient Characteristics

A total of 32,809 bronchiolitis encounters occurred at adopting hospitals during qualifying respiratory seasons, of which 6,556 (20%) involved patients with a complex chronic condition and were excluded. Of the 26,253 included bronchiolitis encounters, 12,495 encounters occurred prior to ward-based HFNC protocol adoption and 13,758 encounters occurred after adoption. The median age of patients was 8 months (interquartile range, 5-14 months). Most patients were on government insurance (64%), male (58%), of white (56%) or black (18%) race, and of non-Hispanic ethnicity (72%). Pre- and postadoption patient demographics were similar (Appendix 2).

Primary Analysis

Shifts in the level of ICU use and ICU length of stay were observed at the time of adoption of a ward-based HFNC protocol (Figure 3). Specifically, ward-based HFNC protocol adoption was associated with an immediate 3.1% absolute increase (95% CI, 2.8%-3.4%) in the proportion of patients admitted to the ICU and a 9.1 days per 100 patients increase (95% CI, 5.1-13.2) in ICU length of stay (Table). The slope of ICU admissions per season was increasing after HFNC protocol adoption (1.0% increase per season; 95% CI, 0.8%-1.1%). When examined at the individual-hospital level (Appendix 3), seven hospitals were found to have significant increases in ICU admissions (immediate intervention effect or change in slope) after adoption, and one hospital was found to have a significant decrease in ICU admissions (change in slope only). Neither immediate intervention effects nor changes in the slopes of total length of stay and mechanical ventilation were observed, with mean total length of stay approximately 3 days and just over 1% of patients receiving mechanical ventilation (Figure 3).

Supplementary Analyses

Supplementary analyses were largely consistent with the primary analysis. Associations with increased ICU utilization were again observed, although the immediate change in ICU length of stay for supplementary analysis 1 was not significant and the slope for ICU length of stay in supplementary analysis 2 was down trending (Table). Changes in total length of stay and mechanical ventilation were not observed in either supplementary analysis, with the lone exception being an increase in the proportion of patients receiving mechanical ventilation per season (increase in slope) in supplementary analysis 1.

This is the largest multicenter study to date evaluating ICU utilization after adoption of a ward-based HFNC protocol for patients with bronchiolitis. While a principal goal of allowing HFNC use outside of the ICU is to reduce the time that patients with bronchiolitis spend in the ICU, we found that early protocols were, paradoxically, associated with increased ICU utilization. Ward-based HFNC protocols were not associated with changes in hospital length of stay or need for mechanical ventilation. Our findings are particularly relevant given that the majority of children’s hospitals in our sample have adopted ward-based HFNC protocols to care for patients with bronchiolitis.

The increase in ICU utilization measured in our study is a novel finding, seemingly in contradiction to existing literature. Early pilot studies inspired hope that employing HFNC on the general ward might prevent a portion of children from needing ICU care.^11,12 Subsequent larger observational studies did not demonstrate decreases in ICU utilization after adoption of ward-based HFNC protocols.^13,14 The two RCTs comparing low-flow and high-flow nasal cannula use outside of the ICU did not measure a statistically significant effect on ICU utilization, an exploratory outcome in both trials.^15,16 However, the reported point estimates for absolute differences in ICU admission were 2% to 3% higher among patients randomized to HFNC, which is consistent with the 2% to 4% increase in ICU admission measured in the present study.

What might explain this surprising finding? While our observational study cannot speak to mechanism, the protocol details examined in the present study suggest that initial adoption of a ward-based HFNC protocol is often coupled with specific ICU transfer criteria that were unlikely in place prior to protocol initiation. For example, most protocols recommended consideration of ICU transfer for elevated FiO₂ or prolonged duration of HFNC. Transfer to the ICU for prolonged HFNC duration is only possible in the setting of a ward-based HFNC protocol and transfer for elevated FiO₂ was probably unnecessary prior to protocol adoption given that low-flow nasal cannula generally delivers 100% FiO₂. It is also possible that with HFNC comes a perception of increased acuity. For example, medical providers may see patients on HFNC as sicker than patients with the same amount of work of breathing but off HFNC, which makes providers more likely to seek ICU admission for patients on HFNC. The combination of unchanged total length of stay and increased ICU utilization suggests that early ward-based HFNC protocols were an ineffective instrument to improve hospital bed availability during the peak census times that often occur in bronchiolitis season.

The large sample size afforded our study by its multicenter, retrospective design also allowed for a meaningful assessment of the association between a ward-based HFNC protocol and the need for mechanical ventilation. Early indications suggested a lack of substantial association between HFNC use outside of the ICU and rates of mechanical ventilation, but prior studies were limited by small numbers of patients receiving mechanical ventilation (<30 patients in each study).^13,14,16 The present study, in which 783 patients received mechanical ventilation, supports the lack of association between early ward-based HFNC protocols and the need for mechanical ventilation. It should be noted that other studies have measured decreases in mechanical ventilation in association with ICU-based HFNC use.^23-26 In addition to examining HFNC use in a different clinical context, decreases in mechanical ventilation measured after HFNC implementation in the ICU could be explained by preexisting practice trends to limit invasive ventilation and/or selection bias resulting from an increase in less severely ill patients being admitted to the ICU over time. The interrupted time series approach and the staggered adoption of HFNC protocols make the present study less susceptible to biases from preexisting trends and the inclusion of patients cared for both on the ward and within the ICU reduces selection bias.

Our study has several important limitations. First, all hospitals included in the analysis were US children’s hospitals and these findings may not generalize to other practice environments, including community hospitals and other countries. Second, our cohort and outcomes were defined using administrative billing data, which have been incompletely validated, making some degree of misclassification likely. Third, we measured HFNC exposure at the hospital level, but could not examine the extent to which individual patients were exposed to HFNC because such data are not present in PHIS. Even if we had access to patient-level HFNC exposure data, we would have still compared outcomes among all patients with bronchiolitis (not just those who received HFNC), to avoid selection bias. However, knowing HFNC exposure status at the patient level would have allowed for weighting of the effects measured at each hospital according to the extent of HFNC exposure. Fourth, there are likely other, unmeasured secular and temporal factors that could affect study outcomes. To some degree, the interrupted time series approach, observed staggered adoption of protocols, and nonadopting hospital supplementary analysis mitigate this risk of bias. Fifth, while the pre- and postadoption populations appeared demographically similar, it is possible that the populations might have differed by other unmeasured factors. Finally, early ward-based HFNC protocols have likely undergone iterative changes since adoption. We compared pre- and postadoption outcome slopes and censored the first adoption season in a supplementary analysis to attempt to account for this potential limitation.

In conclusion, our findings suggest that initial implementation of ward-based HFNC protocols were not successful at reducing ICU utilization for children with bronchiolitis. Future research should examine whether more evolved HFNC protocols that use higher flow rates, more generous ICU transfer criteria, and more rapid weaning criteria can reduce ICU utilization.

We thank Dr Vineeta Mittal (University of Texas Southwestern Medical Center) for providing feedback regarding the manuscript.

Thirty-six million people are hospitalized annually in the United States,¹ and a significant proportion of these patients are rehospitalized within 30 days.² Gaps in hospital care are many and well documented, including high rates of adverse events, hospital-acquired conditions, and suboptimal care transitions.^3-5 Despite significant efforts to improve the care of hospitalized patients and some incremental improvement in the safety of hospital care, hospital care remains suboptimal.^6-9 Importantly, hospitalization remains a challenging and vulnerable time for patients and caregivers.

Despite research efforts to improve hospital care, there remains very little data regarding what patients, caregivers, and other stakeholders believe are the most important priorities for improving hospital care, experiences, and outcomes. Small studies described in brief reports provide limited insights into what aspects of hospital care are most important to patients and to their families.^10-13 These small studies suggest that communication and the comfort of caregivers and of patient family members are important priorities, as are the provision of adequate sleeping arrangements, food choices, and psychosocial support. However, the limited nature of these studies precludes the possibility of larger conclusions regarding patient priorities.^10-13

The evolution of patient-centered care has led to increasing efforts to engage, and partner, with patients, caregivers, and other stakeholders to obtain their input on healthcare, research, and improvement efforts.¹⁴ The guiding principle of this engagement is that patients and their caregivers are uniquely positioned to share their lived experiences of care and that their involvement ensures their voices are represented.^15-17 Therefore to obtain greater insight into priority areas from the perspectives of patients, caregivers, and other healthcare stakeholders, we undertook a systematic engagement process to create a patient-partnered and stakeholder-­partnered research agenda for improving the care of hospitalized adult patients.

Guiding Frameworks for Study Methods

We used two established, validated methods to guide our collaborative, inclusive, and consultative approach to patient and stakeholder engagement and research prioritization:

• The Patient-Centered Outcomes Research Institute (PCORI) standards for formulating patient-centered research questions,¹⁸ which includes methods for stakeholder engagement that ensures the representativeness of engaged groups and dissemination of study results.¹⁸
• The James Lind Alliance (JLA) approach to “priority setting partnerships,” through which patients, caregivers, and clinicians partner to identify and prioritize unanswered questions.¹⁹

The Improving Hospital Outcomes through Patient Engagement (i-HOPE) study included eight stepwise phases to formulate and prioritize a set of patient-centered research questions to improve the care and experiences of hospitalized patients and their families.²⁰ Our process is described below and summarized in Table 1.

Phases of Question Development

Phase 1: Steering Committee Formation

Nine clinical researchers, nine patients and/or caregivers, and two administrators from eight academic and community hospitals from across the United States formed a steering committee to participate in teleconferences every other week to manage all stages of the project including design, implementation, and dissemination. At the time of the project conceptualization, the researchers were a subgroup of the Society of Hospital Medicine Research Committee.²¹ Patient partners on the steering committee were identified from local patient and family advisory councils (PFACs) of the researchers’ institutions. Patients partners had previously participated in research or improvement initiatives with their hospitalist partners. Patient partners received stipends throughout the project in recognition of their participation and expertise. Included in the committee was a representative from the Society of Hospital Medicine (SHM)—our supporting and dissemination partner.

Phase 2: Stakeholder Identification

We created a list of potential stakeholder organizations to participate in the study based on the following:

• Organizations with which SHM has worked on initiatives related to the care of hospitalized adult patients
• Organizations with which steering committee members had worked
• Internet searches of organizations participating in similar PCORI-funded projects and of other professional societies that represented patients or providers who work in hospital or post-acute care settings
• Suggestions from stakeholders identified through the first two approaches as described above

We intended to have a broad representation of stakeholders to ensure diverse perspectives were included in the study. Stakeholder organizations included patient advocacy groups, providers, researchers, payers, policy makers and funding agencies.

Phase 3: Stakeholder Engagement and Awareness Training

Representatives from 39 stakeholder organizations who agreed to participate in the study were further orientated to the study rationale and methods via a series of interactive online webinars. This included reminding organziations that everyone’s input and perspective were valued and that we had a flat organization structure that ensured all stakeholders were equal.

Phase 4: Survey Development and Administration

We chose a survey approach to solicit input on identifying gaps in patient care and to generate research questions. The steering committee developed an online survey collaboratively with stakeholder organization representatives. We used survey pretesting with patient and researcher members from the steering committee. The goal of pretesting was to ensure accessibility and comprehension for all potential respondents, particularly patients and caregivers. The final survey asked respondents to record up to three questions that they thought would improve the care of hospitalized adult patients and their families. The specific wording of the survey is shown in the Figure and the entire survey in Appendix Document 1.

We chose three questions because that is the number of entries per participant that is recommended by JLA; it also minimizes responder burden.¹⁹ We asked respondents to identify the stakeholder group they represented (eg, patient, caregiver, healthcare provider, researcher) and for providers to identify where they primarily worked (eg, acute care hospital, post-acute care, advocacy group).

Survey Administration. We administered the survey electronically using Research Electronic Data Capture (REDCap), a secure web-based application used for collecting research data.²² Stakeholders were asked to disseminate the survey broadly using whatever methods that they felt was appropriate to their leadership or members.

Phase 5: Initial Question Categorization Using Qualitative Content Analysis

Six members of the steering committee independently performed qualitative content analysis to categorize all submitted questions.^23,24 This analytic approach identifies, analyzes, and reports patterns within the data.^23,24 We hypothesized that some of the submitted questions would relate to already-­known problems with hospitalization. Therefore the steering committee developed an a priori codebook of 48 categories using common systems-based issues and diseases related to the care of hospitalized patients based on the hospitalist core competency topics developed by hospitalists and the SHM Education Committee,²⁵ personal and clinical knowledge and experience related to the care of hospitalized adult patients, and published literature on the topic. These a priori categories and their definitions are shown in Appendix Document 2 and were the basis for our initial theory-driven (deductive) approach to data analysis.²³

Once coding began, we identified 32 new and additional categories based on our review of the submitted questions, and these were the basis of our data-driven (inductive) approach to analysis.²³ All proposed new codes and definitions were discussed with and approved by the entire steering committee prior to being added to the codebook (Appendix Document 2).

While coding categories were mutually exclusive, multiple codes could be attributed to a question depending on the content and meaning of a question. To ensure methodological rigor, reviewers met regularly via teleconference or communicated via email throughout the analysis to iteratively refine and define coding categories. All questions were reviewed independently, and then discussed, by at least two members of the analysis team. Any coding disparities were discussed and resolved by negotiated consensus.²⁶ Analysis was conducted using Dedoose V8.0.35 (Sociocultural Research Consultants, Los Angeles, California).

Phase 6: Initial Question Identification Using Quantitative Content Analysis

Following thematic categorization, all steering committee members then reviewed each category to identify and quantify the most commonly submitted questions.²⁷ A question was determined to be a commonly submitted question when it appeared at least 10 times.

Phase 7: Interim Priority Setting

We sent the list of the most commonly submitted questions (Appendix Document 3) to stakeholder organizations and patient partner networks for review and evaluation. Each organization was asked to engage with their constituents and leaders to collectively decide on which of these questions resonated and was most important. These preferences would then be used during the in-person meeting (Phase 8). We did not provide stakeholder organizations with information about how many times each question was submitted by respondents because we felt this could potentially bias their decision-making processes such that true importance and relevance would not obtained.

Phase 8: In-person Meeting for Final Question Prioritization and Refinement

Representatives from all 39 participating stakeholder organizations were invited to participate in a 2-day, in-person meeting to create a final prioritized list of questions to be used to guide patient-centered research seeking to improve the care of hospitalized adult patients and their caregivers. This meeting was attended by 43 stakeholders (26 stakeholder organization representatives and 17 steering committee members) from 37 unique stakeholder organizations. To facilitate the inclusiveness and to ensure a consensus-driven process, we used nominal group technique (NGT) to allow all of the meeting participants to discuss the list of prioritized questions in small groups.²⁸ NGT allows participants to comprehend each other’s point of view to ensure no perpsectives are excluded.²⁸ The NGT was followed by two rounds of individual voting. Stakeholders were then asked to frame their discussions and their votes based on the perspectives of their organizations or PFACs that they represent. The voting process required participants to make choices regarding the relative importance of all of the questions, which therefore makes the resulting list a true prioritized list. In the first round of voting, participants voted for up to five questions for inclusion on the prioritized list. Based on the distribution of votes, where one vote equals one point, each of the 36 questions was then ranked in order of the assigned points. The rank-ordering process resulted in a natural cut point or delineated point, resulting in the 11 questions considered to be the highest prioritized questions. Following this, a second round of voting took place with the same parameters as the first round and allowed us to rank order questions by order of importance and priority. Finally, during small and large group discussions, the original text of each question was edited, refined, and reformatted into questions that could drive a research agenda.

Ethical Oversight

This study was reviewed by the Institutional Review Board of the University of Texas Health Science Center at San Antonio and deemed not to be human subject research (UT Health San Antonio IRB Protocol Number: HSC20170058N).

In total, 499 respondents from 39 unique stakeholder organizations responded to our survey. Respondents self-identified into multiple categorizes resulting in 267 healthcare providers, 244 patients and caregivers, and 63 researchers. Characteristics of respondents to the survey are shown in Table 2.

An overview of study results is shown in Table 1. Respondents submitted a total of 782 questions related to improving the care of hospitalized patients. These questions were categorized during thematic analysis into 70 distinct categories—52 that were health system related and 18 that were disease specific (Appendix 2). The most frequently used health system–related categories were related to discharge care transitions, medications, patient understanding, and patient-family-care team communication (Appendix 2).

From these categories, 36 questions met our criteria for “commonly identified,” ie, submitted at least 10 times (Appendix Document 3). Notably, these 36 questions were derived from 67 different coding categories, of which 24 (36%) were a priori (theory-driven) categories²³ created by the Steering Committee before analysis began and 43 (64%) categories were created as a result of this study’s stakeholder-engaged process and a data-driven approach²³ to analysis (Appendix Document 3). These groups of questions were then presented during the 2-day, in-person meeting and reduced to a final 11 questions that were identified in rank order as top priorities (Table 3). The questions considered highest priority related to ensuring shared treatment and goals of care decision making, improving hospital discharge handoff to other care facilities and providers, and reducing the confusion related to education on medications, conditions, hospital care, and discharge.

Using a dynamic and collaborative stakeholder engagement process, we identified 11 questions prioritized in order of importance by patients, caregivers, and other healthcare stakeholders to improve the care of hospitalized adult patients. While some of the topics identified are already well-known topics in need of research and improvement, our findings frame these topics according to the perspectives of patients, caregivers, and stakeholders. This unique perspective adds a level of richness and nuance that provides insight into how to better address these topics and ultimately inform research and quality improvement efforts.

The question considered to be the highest priority area for future research and improvement surmised how it may be possible to implement interventions that engage patients in shared decision making. Shared decision making involves patients and their care team working together to make decisions about treatment, and other aspects of care based on sound clinical evidence that balances the risks and outcomes with patient preferences and values. Although considered critically important,²⁹ a recent evaluation of shared decision making practices in over 250 inpatient encounters identified significant gaps in physicians’ abilities to perform key elements of a shared decision making approach and reinforced the need to identify what strategies can best promote widespread shared decision making.³⁰ While there has been considerable effort to faciliate shared decision making in practice, there remains mixed evidence regarding the sustainability and impact of tools seeking to support shared decision making, such as decision aids, question prompt lists, and coaches.³¹ This suggests that new approaches to shared decision making may be required and likely explains why this question was rated as a top priority by stakeholders in the current study.

Respondents frequently framed their questions in terms of their lived experiences, providing stories and scenarios to illustrate the importance of the questions they submitted. This personal framing highlighted to us the need to think about improving care delivery from the end-user perspective. For example, respondents framed questions about care transitions not with regard to early appointments, instructions, or medication lists, but rather in terms of whom to call with questions or how best to reach their physician, nurse, or other identified provider. These perspectives suggest that strategies and approaches to improvement that start with patient and caregiver experiences, such as design thinking,³² may be important to continued efforts to improve hospital care. Additionally, the focus on the interpersonal aspects of care delivery highlights the need to focus on the patient-provider relationship and communication.

Questions submitted by respondents demonstrated a stark difference between “patient education” and “patient understanding,” which suggests that being provided with education or education materials regarding care did not necessarily lead to a clear patient understanding. The potential for lack of understanding was particularly prominent in the context of care plan development and during times of care transition—topics that were encompassed in 9 out of 11 of our prioritized research questions. This may suggest that approaches that improve the ability for healthcare providers to deliver information may not be sufficient to meet the needs of patients and caregivers. Rather, partnering to develop a shared understanding—whether about prognosis, medications, hospital, or discharge care plans—is critical. Improved communication practices are not an endpoint for information delivery, but rather a starting point leading to a shared understanding.

Several of the priority areas identified in our study reflect the immensely complex intersections among patients, caregivers, clinicians, and the healthcare delivery system. Addressing these gaps in order to reach the goal of ideal hospital care and an improved patient experience will likely require coordinated approaches and strong involvement and buy-in from multiple stakeholders including the voices of patients and caregivers. Creating patient-centered and stakeholder-driven research has been an increasing priority nationally.³³ Yet to realize this, we must continue to understand the foundations and best practices of authentic stakeholder engagement so that it can be achieved in practice.³⁴ We intend for this prioritized list of questions to galvanize funders, researchers, clinicians, professional societies, and patient and caregiver advocacy groups to work together to address these topics through the creation of new research evidence or the sustainable implementation of existing evidence.

Our findings provide a foundation for stakeholder groups to work in partnership to find research and improvement solutions to the problems identified. Our efforts demonstrate the value and importance of a systematic and broad engagement process to ensure that the voices of patients, caregivers, and other healthcare stakeholders are included in guiding hospital research and quality improvement efforts. This is highlighted by the fact our results of prioritized category areas for research were largely only uncovered following the creation of coding categories during the analysis process and were not captured using a priori catgeories that were expected by the steering committee.

The strengths of this study include our attempts to systematically identify and engage a wide range of perspectives in hospital medicine, including perspectives from patients and their caregivers. There are also acknowledged limitations in our study. While we included patients and PFACs from across the country, the opinions of the people we included may not be representative of all patients. Similarly, the perspectives of the other participants may not have completely represented their stakeholder organizations. While we attempted to include a broad range of organizations, there may be other relevant groups who were not represented in our sample.

In summary, our findings provide direction for the multiple stakeholders involved in improving hospital care. The results will allow the research community to focus on questions that are most important to patients, caregivers, and other stakeholders, reframing them in ways that are more relevant to patients’ lived experiences and that reflect the complexity of the issues. Our findings can also be used by healthcare providers and delivery organizations to target local improvement efforts. We hope that patients and caregivers will use our results to advocate for research and improvement in areas that matter the most to them. We hope that policy makers and funding agencies use our results to promote work in these areas and drive a national conversation about how to most effectively improve hospital care.

The Society of Hospital Medicine (SHM) provided additional administrative, logistical, and technical support.

The authors would like to thank all patients, caregivers, and stakeholders who completed the survey. The authors also would like to acknowledge the organizations and individuals who participated in this study (see Appendix Document 4 for full list). At SHM, the authors would like to specifically thank Claudia Stahl, Jenna Goldstein, Kevin Vuernick, Dr Brad Sharpe, and Dr Larry Wellikson for their support.

The statements presented in this publication are solely the responsibility of the authors and do not necessarily represent the views of the Department of Veterans Affairs, Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors, or Methodology Committee.

Thirty-six million people are hospitalized annually in the United States,¹ and a significant proportion of these patients are rehospitalized within 30 days.² Gaps in hospital care are many and well documented, including high rates of adverse events, hospital-acquired conditions, and suboptimal care transitions.^3-5 Despite significant efforts to improve the care of hospitalized patients and some incremental improvement in the safety of hospital care, hospital care remains suboptimal.^6-9 Importantly, hospitalization remains a challenging and vulnerable time for patients and caregivers.

Despite research efforts to improve hospital care, there remains very little data regarding what patients, caregivers, and other stakeholders believe are the most important priorities for improving hospital care, experiences, and outcomes. Small studies described in brief reports provide limited insights into what aspects of hospital care are most important to patients and to their families.^10-13 These small studies suggest that communication and the comfort of caregivers and of patient family members are important priorities, as are the provision of adequate sleeping arrangements, food choices, and psychosocial support. However, the limited nature of these studies precludes the possibility of larger conclusions regarding patient priorities.^10-13

The evolution of patient-centered care has led to increasing efforts to engage, and partner, with patients, caregivers, and other stakeholders to obtain their input on healthcare, research, and improvement efforts.¹⁴ The guiding principle of this engagement is that patients and their caregivers are uniquely positioned to share their lived experiences of care and that their involvement ensures their voices are represented.^15-17 Therefore to obtain greater insight into priority areas from the perspectives of patients, caregivers, and other healthcare stakeholders, we undertook a systematic engagement process to create a patient-partnered and stakeholder-­partnered research agenda for improving the care of hospitalized adult patients.

Guiding Frameworks for Study Methods

We used two established, validated methods to guide our collaborative, inclusive, and consultative approach to patient and stakeholder engagement and research prioritization:

• The Patient-Centered Outcomes Research Institute (PCORI) standards for formulating patient-centered research questions,¹⁸ which includes methods for stakeholder engagement that ensures the representativeness of engaged groups and dissemination of study results.¹⁸
• The James Lind Alliance (JLA) approach to “priority setting partnerships,” through which patients, caregivers, and clinicians partner to identify and prioritize unanswered questions.¹⁹

The Improving Hospital Outcomes through Patient Engagement (i-HOPE) study included eight stepwise phases to formulate and prioritize a set of patient-centered research questions to improve the care and experiences of hospitalized patients and their families.²⁰ Our process is described below and summarized in Table 1.

Phases of Question Development

Phase 1: Steering Committee Formation

Nine clinical researchers, nine patients and/or caregivers, and two administrators from eight academic and community hospitals from across the United States formed a steering committee to participate in teleconferences every other week to manage all stages of the project including design, implementation, and dissemination. At the time of the project conceptualization, the researchers were a subgroup of the Society of Hospital Medicine Research Committee.²¹ Patient partners on the steering committee were identified from local patient and family advisory councils (PFACs) of the researchers’ institutions. Patients partners had previously participated in research or improvement initiatives with their hospitalist partners. Patient partners received stipends throughout the project in recognition of their participation and expertise. Included in the committee was a representative from the Society of Hospital Medicine (SHM)—our supporting and dissemination partner.

Phase 2: Stakeholder Identification

We created a list of potential stakeholder organizations to participate in the study based on the following:

• Organizations with which SHM has worked on initiatives related to the care of hospitalized adult patients
• Organizations with which steering committee members had worked
• Internet searches of organizations participating in similar PCORI-funded projects and of other professional societies that represented patients or providers who work in hospital or post-acute care settings
• Suggestions from stakeholders identified through the first two approaches as described above

We intended to have a broad representation of stakeholders to ensure diverse perspectives were included in the study. Stakeholder organizations included patient advocacy groups, providers, researchers, payers, policy makers and funding agencies.

Phase 3: Stakeholder Engagement and Awareness Training

Representatives from 39 stakeholder organizations who agreed to participate in the study were further orientated to the study rationale and methods via a series of interactive online webinars. This included reminding organziations that everyone’s input and perspective were valued and that we had a flat organization structure that ensured all stakeholders were equal.

Phase 4: Survey Development and Administration

We chose a survey approach to solicit input on identifying gaps in patient care and to generate research questions. The steering committee developed an online survey collaboratively with stakeholder organization representatives. We used survey pretesting with patient and researcher members from the steering committee. The goal of pretesting was to ensure accessibility and comprehension for all potential respondents, particularly patients and caregivers. The final survey asked respondents to record up to three questions that they thought would improve the care of hospitalized adult patients and their families. The specific wording of the survey is shown in the Figure and the entire survey in Appendix Document 1.

We chose three questions because that is the number of entries per participant that is recommended by JLA; it also minimizes responder burden.¹⁹ We asked respondents to identify the stakeholder group they represented (eg, patient, caregiver, healthcare provider, researcher) and for providers to identify where they primarily worked (eg, acute care hospital, post-acute care, advocacy group).

Survey Administration. We administered the survey electronically using Research Electronic Data Capture (REDCap), a secure web-based application used for collecting research data.²² Stakeholders were asked to disseminate the survey broadly using whatever methods that they felt was appropriate to their leadership or members.

Phase 5: Initial Question Categorization Using Qualitative Content Analysis

Six members of the steering committee independently performed qualitative content analysis to categorize all submitted questions.^23,24 This analytic approach identifies, analyzes, and reports patterns within the data.^23,24 We hypothesized that some of the submitted questions would relate to already-­known problems with hospitalization. Therefore the steering committee developed an a priori codebook of 48 categories using common systems-based issues and diseases related to the care of hospitalized patients based on the hospitalist core competency topics developed by hospitalists and the SHM Education Committee,²⁵ personal and clinical knowledge and experience related to the care of hospitalized adult patients, and published literature on the topic. These a priori categories and their definitions are shown in Appendix Document 2 and were the basis for our initial theory-driven (deductive) approach to data analysis.²³

Once coding began, we identified 32 new and additional categories based on our review of the submitted questions, and these were the basis of our data-driven (inductive) approach to analysis.²³ All proposed new codes and definitions were discussed with and approved by the entire steering committee prior to being added to the codebook (Appendix Document 2).

While coding categories were mutually exclusive, multiple codes could be attributed to a question depending on the content and meaning of a question. To ensure methodological rigor, reviewers met regularly via teleconference or communicated via email throughout the analysis to iteratively refine and define coding categories. All questions were reviewed independently, and then discussed, by at least two members of the analysis team. Any coding disparities were discussed and resolved by negotiated consensus.²⁶ Analysis was conducted using Dedoose V8.0.35 (Sociocultural Research Consultants, Los Angeles, California).

Phase 6: Initial Question Identification Using Quantitative Content Analysis

Following thematic categorization, all steering committee members then reviewed each category to identify and quantify the most commonly submitted questions.²⁷ A question was determined to be a commonly submitted question when it appeared at least 10 times.

Phase 7: Interim Priority Setting

We sent the list of the most commonly submitted questions (Appendix Document 3) to stakeholder organizations and patient partner networks for review and evaluation. Each organization was asked to engage with their constituents and leaders to collectively decide on which of these questions resonated and was most important. These preferences would then be used during the in-person meeting (Phase 8). We did not provide stakeholder organizations with information about how many times each question was submitted by respondents because we felt this could potentially bias their decision-making processes such that true importance and relevance would not obtained.

Phase 8: In-person Meeting for Final Question Prioritization and Refinement

Representatives from all 39 participating stakeholder organizations were invited to participate in a 2-day, in-person meeting to create a final prioritized list of questions to be used to guide patient-centered research seeking to improve the care of hospitalized adult patients and their caregivers. This meeting was attended by 43 stakeholders (26 stakeholder organization representatives and 17 steering committee members) from 37 unique stakeholder organizations. To facilitate the inclusiveness and to ensure a consensus-driven process, we used nominal group technique (NGT) to allow all of the meeting participants to discuss the list of prioritized questions in small groups.²⁸ NGT allows participants to comprehend each other’s point of view to ensure no perpsectives are excluded.²⁸ The NGT was followed by two rounds of individual voting. Stakeholders were then asked to frame their discussions and their votes based on the perspectives of their organizations or PFACs that they represent. The voting process required participants to make choices regarding the relative importance of all of the questions, which therefore makes the resulting list a true prioritized list. In the first round of voting, participants voted for up to five questions for inclusion on the prioritized list. Based on the distribution of votes, where one vote equals one point, each of the 36 questions was then ranked in order of the assigned points. The rank-ordering process resulted in a natural cut point or delineated point, resulting in the 11 questions considered to be the highest prioritized questions. Following this, a second round of voting took place with the same parameters as the first round and allowed us to rank order questions by order of importance and priority. Finally, during small and large group discussions, the original text of each question was edited, refined, and reformatted into questions that could drive a research agenda.

Ethical Oversight

This study was reviewed by the Institutional Review Board of the University of Texas Health Science Center at San Antonio and deemed not to be human subject research (UT Health San Antonio IRB Protocol Number: HSC20170058N).

In total, 499 respondents from 39 unique stakeholder organizations responded to our survey. Respondents self-identified into multiple categorizes resulting in 267 healthcare providers, 244 patients and caregivers, and 63 researchers. Characteristics of respondents to the survey are shown in Table 2.

An overview of study results is shown in Table 1. Respondents submitted a total of 782 questions related to improving the care of hospitalized patients. These questions were categorized during thematic analysis into 70 distinct categories—52 that were health system related and 18 that were disease specific (Appendix 2). The most frequently used health system–related categories were related to discharge care transitions, medications, patient understanding, and patient-family-care team communication (Appendix 2).

From these categories, 36 questions met our criteria for “commonly identified,” ie, submitted at least 10 times (Appendix Document 3). Notably, these 36 questions were derived from 67 different coding categories, of which 24 (36%) were a priori (theory-driven) categories²³ created by the Steering Committee before analysis began and 43 (64%) categories were created as a result of this study’s stakeholder-engaged process and a data-driven approach²³ to analysis (Appendix Document 3). These groups of questions were then presented during the 2-day, in-person meeting and reduced to a final 11 questions that were identified in rank order as top priorities (Table 3). The questions considered highest priority related to ensuring shared treatment and goals of care decision making, improving hospital discharge handoff to other care facilities and providers, and reducing the confusion related to education on medications, conditions, hospital care, and discharge.

Using a dynamic and collaborative stakeholder engagement process, we identified 11 questions prioritized in order of importance by patients, caregivers, and other healthcare stakeholders to improve the care of hospitalized adult patients. While some of the topics identified are already well-known topics in need of research and improvement, our findings frame these topics according to the perspectives of patients, caregivers, and stakeholders. This unique perspective adds a level of richness and nuance that provides insight into how to better address these topics and ultimately inform research and quality improvement efforts.

The question considered to be the highest priority area for future research and improvement surmised how it may be possible to implement interventions that engage patients in shared decision making. Shared decision making involves patients and their care team working together to make decisions about treatment, and other aspects of care based on sound clinical evidence that balances the risks and outcomes with patient preferences and values. Although considered critically important,²⁹ a recent evaluation of shared decision making practices in over 250 inpatient encounters identified significant gaps in physicians’ abilities to perform key elements of a shared decision making approach and reinforced the need to identify what strategies can best promote widespread shared decision making.³⁰ While there has been considerable effort to faciliate shared decision making in practice, there remains mixed evidence regarding the sustainability and impact of tools seeking to support shared decision making, such as decision aids, question prompt lists, and coaches.³¹ This suggests that new approaches to shared decision making may be required and likely explains why this question was rated as a top priority by stakeholders in the current study.

Respondents frequently framed their questions in terms of their lived experiences, providing stories and scenarios to illustrate the importance of the questions they submitted. This personal framing highlighted to us the need to think about improving care delivery from the end-user perspective. For example, respondents framed questions about care transitions not with regard to early appointments, instructions, or medication lists, but rather in terms of whom to call with questions or how best to reach their physician, nurse, or other identified provider. These perspectives suggest that strategies and approaches to improvement that start with patient and caregiver experiences, such as design thinking,³² may be important to continued efforts to improve hospital care. Additionally, the focus on the interpersonal aspects of care delivery highlights the need to focus on the patient-provider relationship and communication.

Questions submitted by respondents demonstrated a stark difference between “patient education” and “patient understanding,” which suggests that being provided with education or education materials regarding care did not necessarily lead to a clear patient understanding. The potential for lack of understanding was particularly prominent in the context of care plan development and during times of care transition—topics that were encompassed in 9 out of 11 of our prioritized research questions. This may suggest that approaches that improve the ability for healthcare providers to deliver information may not be sufficient to meet the needs of patients and caregivers. Rather, partnering to develop a shared understanding—whether about prognosis, medications, hospital, or discharge care plans—is critical. Improved communication practices are not an endpoint for information delivery, but rather a starting point leading to a shared understanding.

Several of the priority areas identified in our study reflect the immensely complex intersections among patients, caregivers, clinicians, and the healthcare delivery system. Addressing these gaps in order to reach the goal of ideal hospital care and an improved patient experience will likely require coordinated approaches and strong involvement and buy-in from multiple stakeholders including the voices of patients and caregivers. Creating patient-centered and stakeholder-driven research has been an increasing priority nationally.³³ Yet to realize this, we must continue to understand the foundations and best practices of authentic stakeholder engagement so that it can be achieved in practice.³⁴ We intend for this prioritized list of questions to galvanize funders, researchers, clinicians, professional societies, and patient and caregiver advocacy groups to work together to address these topics through the creation of new research evidence or the sustainable implementation of existing evidence.

Our findings provide a foundation for stakeholder groups to work in partnership to find research and improvement solutions to the problems identified. Our efforts demonstrate the value and importance of a systematic and broad engagement process to ensure that the voices of patients, caregivers, and other healthcare stakeholders are included in guiding hospital research and quality improvement efforts. This is highlighted by the fact our results of prioritized category areas for research were largely only uncovered following the creation of coding categories during the analysis process and were not captured using a priori catgeories that were expected by the steering committee.

The strengths of this study include our attempts to systematically identify and engage a wide range of perspectives in hospital medicine, including perspectives from patients and their caregivers. There are also acknowledged limitations in our study. While we included patients and PFACs from across the country, the opinions of the people we included may not be representative of all patients. Similarly, the perspectives of the other participants may not have completely represented their stakeholder organizations. While we attempted to include a broad range of organizations, there may be other relevant groups who were not represented in our sample.

In summary, our findings provide direction for the multiple stakeholders involved in improving hospital care. The results will allow the research community to focus on questions that are most important to patients, caregivers, and other stakeholders, reframing them in ways that are more relevant to patients’ lived experiences and that reflect the complexity of the issues. Our findings can also be used by healthcare providers and delivery organizations to target local improvement efforts. We hope that patients and caregivers will use our results to advocate for research and improvement in areas that matter the most to them. We hope that policy makers and funding agencies use our results to promote work in these areas and drive a national conversation about how to most effectively improve hospital care.

The Society of Hospital Medicine (SHM) provided additional administrative, logistical, and technical support.

The authors would like to thank all patients, caregivers, and stakeholders who completed the survey. The authors also would like to acknowledge the organizations and individuals who participated in this study (see Appendix Document 4 for full list). At SHM, the authors would like to specifically thank Claudia Stahl, Jenna Goldstein, Kevin Vuernick, Dr Brad Sharpe, and Dr Larry Wellikson for their support.

The statements presented in this publication are solely the responsibility of the authors and do not necessarily represent the views of the Department of Veterans Affairs, Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors, or Methodology Committee.

Thirty-six million people are hospitalized annually in the United States,¹ and a significant proportion of these patients are rehospitalized within 30 days.² Gaps in hospital care are many and well documented, including high rates of adverse events, hospital-acquired conditions, and suboptimal care transitions.^3-5 Despite significant efforts to improve the care of hospitalized patients and some incremental improvement in the safety of hospital care, hospital care remains suboptimal.^6-9 Importantly, hospitalization remains a challenging and vulnerable time for patients and caregivers.

Despite research efforts to improve hospital care, there remains very little data regarding what patients, caregivers, and other stakeholders believe are the most important priorities for improving hospital care, experiences, and outcomes. Small studies described in brief reports provide limited insights into what aspects of hospital care are most important to patients and to their families.^10-13 These small studies suggest that communication and the comfort of caregivers and of patient family members are important priorities, as are the provision of adequate sleeping arrangements, food choices, and psychosocial support. However, the limited nature of these studies precludes the possibility of larger conclusions regarding patient priorities.^10-13

The evolution of patient-centered care has led to increasing efforts to engage, and partner, with patients, caregivers, and other stakeholders to obtain their input on healthcare, research, and improvement efforts.¹⁴ The guiding principle of this engagement is that patients and their caregivers are uniquely positioned to share their lived experiences of care and that their involvement ensures their voices are represented.^15-17 Therefore to obtain greater insight into priority areas from the perspectives of patients, caregivers, and other healthcare stakeholders, we undertook a systematic engagement process to create a patient-partnered and stakeholder-­partnered research agenda for improving the care of hospitalized adult patients.

Guiding Frameworks for Study Methods

We used two established, validated methods to guide our collaborative, inclusive, and consultative approach to patient and stakeholder engagement and research prioritization:

• The Patient-Centered Outcomes Research Institute (PCORI) standards for formulating patient-centered research questions,¹⁸ which includes methods for stakeholder engagement that ensures the representativeness of engaged groups and dissemination of study results.¹⁸
• The James Lind Alliance (JLA) approach to “priority setting partnerships,” through which patients, caregivers, and clinicians partner to identify and prioritize unanswered questions.¹⁹

The Improving Hospital Outcomes through Patient Engagement (i-HOPE) study included eight stepwise phases to formulate and prioritize a set of patient-centered research questions to improve the care and experiences of hospitalized patients and their families.²⁰ Our process is described below and summarized in Table 1.

Phases of Question Development

Phase 1: Steering Committee Formation

Nine clinical researchers, nine patients and/or caregivers, and two administrators from eight academic and community hospitals from across the United States formed a steering committee to participate in teleconferences every other week to manage all stages of the project including design, implementation, and dissemination. At the time of the project conceptualization, the researchers were a subgroup of the Society of Hospital Medicine Research Committee.²¹ Patient partners on the steering committee were identified from local patient and family advisory councils (PFACs) of the researchers’ institutions. Patients partners had previously participated in research or improvement initiatives with their hospitalist partners. Patient partners received stipends throughout the project in recognition of their participation and expertise. Included in the committee was a representative from the Society of Hospital Medicine (SHM)—our supporting and dissemination partner.

Phase 2: Stakeholder Identification

We created a list of potential stakeholder organizations to participate in the study based on the following:

• Organizations with which SHM has worked on initiatives related to the care of hospitalized adult patients
• Organizations with which steering committee members had worked
• Internet searches of organizations participating in similar PCORI-funded projects and of other professional societies that represented patients or providers who work in hospital or post-acute care settings
• Suggestions from stakeholders identified through the first two approaches as described above

We intended to have a broad representation of stakeholders to ensure diverse perspectives were included in the study. Stakeholder organizations included patient advocacy groups, providers, researchers, payers, policy makers and funding agencies.

Phase 3: Stakeholder Engagement and Awareness Training

Representatives from 39 stakeholder organizations who agreed to participate in the study were further orientated to the study rationale and methods via a series of interactive online webinars. This included reminding organziations that everyone’s input and perspective were valued and that we had a flat organization structure that ensured all stakeholders were equal.

Phase 4: Survey Development and Administration

We chose a survey approach to solicit input on identifying gaps in patient care and to generate research questions. The steering committee developed an online survey collaboratively with stakeholder organization representatives. We used survey pretesting with patient and researcher members from the steering committee. The goal of pretesting was to ensure accessibility and comprehension for all potential respondents, particularly patients and caregivers. The final survey asked respondents to record up to three questions that they thought would improve the care of hospitalized adult patients and their families. The specific wording of the survey is shown in the Figure and the entire survey in Appendix Document 1.

We chose three questions because that is the number of entries per participant that is recommended by JLA; it also minimizes responder burden.¹⁹ We asked respondents to identify the stakeholder group they represented (eg, patient, caregiver, healthcare provider, researcher) and for providers to identify where they primarily worked (eg, acute care hospital, post-acute care, advocacy group).

Survey Administration. We administered the survey electronically using Research Electronic Data Capture (REDCap), a secure web-based application used for collecting research data.²² Stakeholders were asked to disseminate the survey broadly using whatever methods that they felt was appropriate to their leadership or members.

Phase 5: Initial Question Categorization Using Qualitative Content Analysis

Six members of the steering committee independently performed qualitative content analysis to categorize all submitted questions.^23,24 This analytic approach identifies, analyzes, and reports patterns within the data.^23,24 We hypothesized that some of the submitted questions would relate to already-­known problems with hospitalization. Therefore the steering committee developed an a priori codebook of 48 categories using common systems-based issues and diseases related to the care of hospitalized patients based on the hospitalist core competency topics developed by hospitalists and the SHM Education Committee,²⁵ personal and clinical knowledge and experience related to the care of hospitalized adult patients, and published literature on the topic. These a priori categories and their definitions are shown in Appendix Document 2 and were the basis for our initial theory-driven (deductive) approach to data analysis.²³

Once coding began, we identified 32 new and additional categories based on our review of the submitted questions, and these were the basis of our data-driven (inductive) approach to analysis.²³ All proposed new codes and definitions were discussed with and approved by the entire steering committee prior to being added to the codebook (Appendix Document 2).

While coding categories were mutually exclusive, multiple codes could be attributed to a question depending on the content and meaning of a question. To ensure methodological rigor, reviewers met regularly via teleconference or communicated via email throughout the analysis to iteratively refine and define coding categories. All questions were reviewed independently, and then discussed, by at least two members of the analysis team. Any coding disparities were discussed and resolved by negotiated consensus.²⁶ Analysis was conducted using Dedoose V8.0.35 (Sociocultural Research Consultants, Los Angeles, California).

Phase 6: Initial Question Identification Using Quantitative Content Analysis

Following thematic categorization, all steering committee members then reviewed each category to identify and quantify the most commonly submitted questions.²⁷ A question was determined to be a commonly submitted question when it appeared at least 10 times.

Phase 7: Interim Priority Setting

We sent the list of the most commonly submitted questions (Appendix Document 3) to stakeholder organizations and patient partner networks for review and evaluation. Each organization was asked to engage with their constituents and leaders to collectively decide on which of these questions resonated and was most important. These preferences would then be used during the in-person meeting (Phase 8). We did not provide stakeholder organizations with information about how many times each question was submitted by respondents because we felt this could potentially bias their decision-making processes such that true importance and relevance would not obtained.

Phase 8: In-person Meeting for Final Question Prioritization and Refinement

Representatives from all 39 participating stakeholder organizations were invited to participate in a 2-day, in-person meeting to create a final prioritized list of questions to be used to guide patient-centered research seeking to improve the care of hospitalized adult patients and their caregivers. This meeting was attended by 43 stakeholders (26 stakeholder organization representatives and 17 steering committee members) from 37 unique stakeholder organizations. To facilitate the inclusiveness and to ensure a consensus-driven process, we used nominal group technique (NGT) to allow all of the meeting participants to discuss the list of prioritized questions in small groups.²⁸ NGT allows participants to comprehend each other’s point of view to ensure no perpsectives are excluded.²⁸ The NGT was followed by two rounds of individual voting. Stakeholders were then asked to frame their discussions and their votes based on the perspectives of their organizations or PFACs that they represent. The voting process required participants to make choices regarding the relative importance of all of the questions, which therefore makes the resulting list a true prioritized list. In the first round of voting, participants voted for up to five questions for inclusion on the prioritized list. Based on the distribution of votes, where one vote equals one point, each of the 36 questions was then ranked in order of the assigned points. The rank-ordering process resulted in a natural cut point or delineated point, resulting in the 11 questions considered to be the highest prioritized questions. Following this, a second round of voting took place with the same parameters as the first round and allowed us to rank order questions by order of importance and priority. Finally, during small and large group discussions, the original text of each question was edited, refined, and reformatted into questions that could drive a research agenda.

Ethical Oversight

This study was reviewed by the Institutional Review Board of the University of Texas Health Science Center at San Antonio and deemed not to be human subject research (UT Health San Antonio IRB Protocol Number: HSC20170058N).

In total, 499 respondents from 39 unique stakeholder organizations responded to our survey. Respondents self-identified into multiple categorizes resulting in 267 healthcare providers, 244 patients and caregivers, and 63 researchers. Characteristics of respondents to the survey are shown in Table 2.

An overview of study results is shown in Table 1. Respondents submitted a total of 782 questions related to improving the care of hospitalized patients. These questions were categorized during thematic analysis into 70 distinct categories—52 that were health system related and 18 that were disease specific (Appendix 2). The most frequently used health system–related categories were related to discharge care transitions, medications, patient understanding, and patient-family-care team communication (Appendix 2).

From these categories, 36 questions met our criteria for “commonly identified,” ie, submitted at least 10 times (Appendix Document 3). Notably, these 36 questions were derived from 67 different coding categories, of which 24 (36%) were a priori (theory-driven) categories²³ created by the Steering Committee before analysis began and 43 (64%) categories were created as a result of this study’s stakeholder-engaged process and a data-driven approach²³ to analysis (Appendix Document 3). These groups of questions were then presented during the 2-day, in-person meeting and reduced to a final 11 questions that were identified in rank order as top priorities (Table 3). The questions considered highest priority related to ensuring shared treatment and goals of care decision making, improving hospital discharge handoff to other care facilities and providers, and reducing the confusion related to education on medications, conditions, hospital care, and discharge.

Using a dynamic and collaborative stakeholder engagement process, we identified 11 questions prioritized in order of importance by patients, caregivers, and other healthcare stakeholders to improve the care of hospitalized adult patients. While some of the topics identified are already well-known topics in need of research and improvement, our findings frame these topics according to the perspectives of patients, caregivers, and stakeholders. This unique perspective adds a level of richness and nuance that provides insight into how to better address these topics and ultimately inform research and quality improvement efforts.

The question considered to be the highest priority area for future research and improvement surmised how it may be possible to implement interventions that engage patients in shared decision making. Shared decision making involves patients and their care team working together to make decisions about treatment, and other aspects of care based on sound clinical evidence that balances the risks and outcomes with patient preferences and values. Although considered critically important,²⁹ a recent evaluation of shared decision making practices in over 250 inpatient encounters identified significant gaps in physicians’ abilities to perform key elements of a shared decision making approach and reinforced the need to identify what strategies can best promote widespread shared decision making.³⁰ While there has been considerable effort to faciliate shared decision making in practice, there remains mixed evidence regarding the sustainability and impact of tools seeking to support shared decision making, such as decision aids, question prompt lists, and coaches.³¹ This suggests that new approaches to shared decision making may be required and likely explains why this question was rated as a top priority by stakeholders in the current study.

Respondents frequently framed their questions in terms of their lived experiences, providing stories and scenarios to illustrate the importance of the questions they submitted. This personal framing highlighted to us the need to think about improving care delivery from the end-user perspective. For example, respondents framed questions about care transitions not with regard to early appointments, instructions, or medication lists, but rather in terms of whom to call with questions or how best to reach their physician, nurse, or other identified provider. These perspectives suggest that strategies and approaches to improvement that start with patient and caregiver experiences, such as design thinking,³² may be important to continued efforts to improve hospital care. Additionally, the focus on the interpersonal aspects of care delivery highlights the need to focus on the patient-provider relationship and communication.

Questions submitted by respondents demonstrated a stark difference between “patient education” and “patient understanding,” which suggests that being provided with education or education materials regarding care did not necessarily lead to a clear patient understanding. The potential for lack of understanding was particularly prominent in the context of care plan development and during times of care transition—topics that were encompassed in 9 out of 11 of our prioritized research questions. This may suggest that approaches that improve the ability for healthcare providers to deliver information may not be sufficient to meet the needs of patients and caregivers. Rather, partnering to develop a shared understanding—whether about prognosis, medications, hospital, or discharge care plans—is critical. Improved communication practices are not an endpoint for information delivery, but rather a starting point leading to a shared understanding.

Several of the priority areas identified in our study reflect the immensely complex intersections among patients, caregivers, clinicians, and the healthcare delivery system. Addressing these gaps in order to reach the goal of ideal hospital care and an improved patient experience will likely require coordinated approaches and strong involvement and buy-in from multiple stakeholders including the voices of patients and caregivers. Creating patient-centered and stakeholder-driven research has been an increasing priority nationally.³³ Yet to realize this, we must continue to understand the foundations and best practices of authentic stakeholder engagement so that it can be achieved in practice.³⁴ We intend for this prioritized list of questions to galvanize funders, researchers, clinicians, professional societies, and patient and caregiver advocacy groups to work together to address these topics through the creation of new research evidence or the sustainable implementation of existing evidence.

Our findings provide a foundation for stakeholder groups to work in partnership to find research and improvement solutions to the problems identified. Our efforts demonstrate the value and importance of a systematic and broad engagement process to ensure that the voices of patients, caregivers, and other healthcare stakeholders are included in guiding hospital research and quality improvement efforts. This is highlighted by the fact our results of prioritized category areas for research were largely only uncovered following the creation of coding categories during the analysis process and were not captured using a priori catgeories that were expected by the steering committee.

The strengths of this study include our attempts to systematically identify and engage a wide range of perspectives in hospital medicine, including perspectives from patients and their caregivers. There are also acknowledged limitations in our study. While we included patients and PFACs from across the country, the opinions of the people we included may not be representative of all patients. Similarly, the perspectives of the other participants may not have completely represented their stakeholder organizations. While we attempted to include a broad range of organizations, there may be other relevant groups who were not represented in our sample.

In summary, our findings provide direction for the multiple stakeholders involved in improving hospital care. The results will allow the research community to focus on questions that are most important to patients, caregivers, and other stakeholders, reframing them in ways that are more relevant to patients’ lived experiences and that reflect the complexity of the issues. Our findings can also be used by healthcare providers and delivery organizations to target local improvement efforts. We hope that patients and caregivers will use our results to advocate for research and improvement in areas that matter the most to them. We hope that policy makers and funding agencies use our results to promote work in these areas and drive a national conversation about how to most effectively improve hospital care.

The Society of Hospital Medicine (SHM) provided additional administrative, logistical, and technical support.

The authors would like to thank all patients, caregivers, and stakeholders who completed the survey. The authors also would like to acknowledge the organizations and individuals who participated in this study (see Appendix Document 4 for full list). At SHM, the authors would like to specifically thank Claudia Stahl, Jenna Goldstein, Kevin Vuernick, Dr Brad Sharpe, and Dr Larry Wellikson for their support.

The statements presented in this publication are solely the responsibility of the authors and do not necessarily represent the views of the Department of Veterans Affairs, Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors, or Methodology Committee.

Geographic cohorting (GCh, also known as “localization” or “regionalization”) refers to the practice wherein hospitalists are assigned to a single inpatient unit. Its adoption is increasing and in 2017, 30% of surveyed United States hospital medicine group leaders reported that their clinicians rounded on 1-2 units daily.¹ As a component of intervention bundles, GCh is associated with reductions in mortality, length of stay, and costs.^2,3

However, details on how GCh affects the hospitalist workday are unknown. Most time-motion studies of inpatient clinicians have reported the experiences of physicians in training with few specifically evaluating the workflow of attending hospitalists.⁴ Three studies of the attending hospitalist’s workday that were performed a decade ago excluded teams with learners, had patient loads as low as 9.4 per day, and did not differentiate between GCh and non-GCh models.^5-7

The objective of this observational study was to describe and compare the workday of GCh and non-GCh hospitalists by using automated geographical-tracking methods supplemented by in-person observations.

Setting and Participants

This work was conducted at a large academic center in the Midwestern US which adopted GCh in 2012. During the study, hospitalists staffed 11 GCh and four non-GCh teams. GCh teams aim to maintain ≥80% of their patients on their assigned unit and conduct interprofessional huddles on weekdays.³ Some units specialize in the care of specific populations (eg, patients with oncologic diagnoses), while others serve as general medical or surgical units. Non-GCh teams are assigned patients without regard to location. Resident housestaff are assigned only to GCh teams and residents and advanced practice providers (APPs) are never assigned to the same team. Based on team members, this yielded five distinct team types: GCh-hospitalist, GCh-hospitalist with APP, GCh-hospitalist with resident, non-GCh-hospitalist, and non-GCh-hospitalist with APP. Hospitalists provided verbal consent to participate. The protocol was reviewed and approved by the Indiana University Institutional Review Board. Two complementary observation modalities were used. Locator badges were used to quantify direct and indirect time unobtrusively over long periods. In-person observations were conducted to examine the workday in greater detail. Data were collected between October 2017 and May 2018.

Observations by Locator Badges

Our institution uses a system designed by Hill-Rom^® (Cary, North Carolina) to facilitate staff communication. Staff wear the I-Badge^® Locator Badge, which emits an infra-red signal.⁸ Centrally located receivers tabulate time spent by the badge wearer in each location (Appendix Figure 1). Each hospitalist was given a badge to wear at work for a minimum of six weeks, after which the I-Badge^® data were downloaded.

Schedules detailing each team’s members and assigned units (if cohorted) were retrieved. For each observed day, the hospitalist was linked to his or her team type and unit. Team lists were retrieved to ascertain patient load at the start of the day. Data sources were merged to categorize observations.

Observation Categories for Locator Badge Data

The I-Badge^® data provided details of how much time the hospitalist spent in each location (eg, nursing station, hallways, patient rooms). All observations in patient rooms were considered “direct care” while all other locations were categorized as “Indirect Care”. Observations were also categorized by the intensity of care provided on that unit, which included the Emergency Department (ED), Progressive Care Units (PCU), Medical-Surgical + PCU units (for units having a mixture of Medical-Surgical and PCU beds), and Medical-Surgical units.

In-person Observations

Four research assistants (RAs) were trained until interrater reliability using task times achieved an intraclass correlation coefficient of 0.98. Task categories included direct care (all time with patients), indirect care (computer interactions, communication), professional development, and travel and personal time. Interruptions were defined as “an unplanned and unscheduled task, causing a discontinuation, a noticeable break, or task switch behavior”.⁹ “Electronic interruptions” were caused by pagers or phones whereas in-person interruptions were “face-to-face” interruptions. When at least two tasks were performed simultaneously, it was considered multitasking. A data collection form created in REDCap was accessed on computer tablets or smartphones¹⁰ (Appendix Table 1). To limit each observation period to five hours, two RAs were scheduled each day. Observations were continued until the hospitalist reported that work activities were complete or until 5 pm.

Statistical Analysis

Due to the nested structure of the locator badge data, multilevel models that permit predictors to vary at more than one level were used.¹¹ The distribution of the duration of direct care observations was log normal for which the parameters were estimated using generalized linear mixed models (GLMM). The GLMM estimates were converted using a nonlinear transformation to predict the mean duration of interactions. The GLMM estimates were then used to predict time allocations for hospitalists with various workloads and contexts. The five team types were captured in a single categorical variable.

Univariate three-level models predicting minutes spent in direct care were tested for each predictor. Predictors, described below, were selected due to their hypothesized relation to time spent in direct patient care, or to account statistically for differences among teams due to the observational nature of the study.¹² Predictors were: Level 3, hospitalist characteristics (years since medical school, age, gender, international graduate, years at current hospital); Level 2, work day characteristics (number of units visited, number of patients visited, team type, weekday); and Level 1, individual observation characteristics (intensity of care on unit, number of visits to the same patient room per day). Predictors that were significantly related to the duration of direct care at P value <.05 and whose inclusion resulted in better model fit based on likelihood ratio tests were retained in a multivariate model. Additionally, a logistic regression model with random effects was tested to determine whether hospitalists working in GCh vs non-GCh teams (including teams with APPs and residents) made more than one visit to the same patient in a day. For duration of direct care encounters, the amount of variation explained (intraclass correlation) at the hospitalist level was .05, and at the day level was .03.

For total daily indirect care, a similar modeling process was used. A log normal distribution was used because the data was right-skewed and contained positive values. The restricted maximum likelihood method was used to calculate final estimates for models. Least square mean values for independent variables were subjected to backward transformation for interpretation. Post hoc pairwise comparisons between team types were conducted using Tukey–Kramer tests for direct and indirect care time. Analyses were conducted using SAS software version 9.4 (Cary, North Carolina).

The in-person observations were summarized using descriptive statistics. Exploratory analyses were performed using t-tests and Fisher’s exact tests to compare continuous and categorical variables respectively.

Locator Badge Observations

Participants

The 17 hospitalists had a mean (SD) age of 38 years (6.4); 10 (59%) were male, 7 (41%) were international medical graduates, and 10 (59%) had worked at the hospital ≥5 years. The duration of observation was <45 days for 7 hospitalists, 46-55 days for 4, and >55 days for 6, yielding observations for 666 hospitalist workdays. The mean time since medical school graduation was 13 years. Seven hospitalists were observed only in the GCh model, one was observed only in the non-GCh model, and nine were observed in both.

Team Characteristics

On average, non-GCh teams visited more units per day than GCh teams. Teams with APPs had higher patient loads (Table 1).

Time Observed in Direct and Indirect Care

In total, 10,522 observations were recorded in providing direct care. The average duration of a direct care encounter ranged from 4.1 to 5.8 minutes. The ratio of indirect to direct time ranged from 2.7 to 3.7 (Table 2).

The number of times that a hospitalist visited the same patient room in one day ranged from 1 to 9. Most (84%) of the patient rooms were visited once per day. The odds that a GCh hospitalist would visit a patient more than once per day were 1.8 times higher (95% CI: 1.37, 2.34; P < .0001) than for a non-GCh hospitalist (data not shown).

Predictors Associated with Time Expenditure

Predictors significantly associated with both the duration of direct care encounters and total daily indirect care time included team type and patient count. Predicted time in direct care encounters was highest for the GCh-hospitalist team (9.5 minutes) and lowest for the GCh-hospitalist with residents team (7 minutes). Predicted total indirect care time was highest for the GCh-hospitalist with APP team (160 minutes) while the lowest expenditure in indirect care time was predicted for the non-GCh-hospitalist team (102 minutes). Increasing patient load from 10 to 20 was predicted to decrease the duration of a direct care encounter by one minute (14%) and increase the total indirect care time by a larger amount (39 min, 24%).

The duration of direct care encounters was also inversely related with years since medical school and number of visits made to same patient room. Finally, acuity of care was associated with the duration of direct care encounters with the longest predicted encounters in the ED (9.4 minutes). Physician gender and age, international graduation, years at current hospital, weekday, and the number of units visited in a day were neither associated with direct care time at P value < .05 nor improved model fit and therefore were not retained in the final model (Table 3).

In-person Observations

Four hospitalists cohorted to general medical units and four non-GCh hospitalists were observed for one day each, yielding a total of 3,032 minutes of data. These hospitalists were on teams without residents or APPs. On average, GCh hospitalists had 78% of their patients on their assigned unit, rounded on fewer units (3 vs 6) and had two more patients at the start of the day than non-GCh hospitalists (14 vs 12). Age and gender distribution of the GCh and non-GCh hospitalists were similar.

As a percentage of total observed time, GCh hospitalists were noted to spend a larger proportion of the workday in computer interactions vs non-GCh hospitalists (56% vs 39%; P = .005). The proportion of time in other activities or locations was not statistically different between GCh and non-GCh hospitalists, including face-to-face communication (21% vs 15%), multitasking (18% vs 14%), time spent at the nursing station (58% vs 34%), direct care (15% vs 20%), and time traveling (4% vs 11%). The most frequently observed combination of multitasking was computer and phone use (59% of all multitasking) followed by computer use and face-to-face communication (17%; Appendix Figure 2).

The mean duration of an interruption was 1.3 minutes. More interruptions were observed in the GCh group than the non-GCh group (139 vs 102). Interruptions in the GCh group were face-to-face in 62% of instances and electronic in 25%. The remaining 13% were instances in which electronic and face-to-face interruptions occurred simultaneously. In the non-GCh group, 51% of interruptions were face-to-face; 47% were electronic; and 2% were simultaneous. GCh hospitalists were interrupted once every 14 minutes in the morning, with interruption frequency increasing to once every eight minutes in the afternoon. Non-GCh hospitalists were interrupted once every 13 minutes in the morning and saw interruption frequency decrease to once every 17 minutes in the afternoon. The task most frequently interrupted was computer use.

DISCUSSION

Previous investigations have studied the impact of cohorting on outcomes, including the facilitation of bedside rounding, adverse events, agreement between nurses and physicians on the plan of care, productivity, and the number of pages received.^13-16 Cohorting’s benefits are theorized to include increased hospitalist time with patients, while its downsides are perceived to include increased interruptions.^17,18 Neither has previously been evaluated by direct observation.

Our findings support cohorting’s association with increased hospitalist–patient time. While GCh hospitalists were observed spending 5% less time in direct care than non-GCh hospitalists by in-person observations, this difference did not achieve statistical significance and was unadjusted for hospitalist, patient load, team or patient characteristics. Using the larger badge dataset, the predicted values for time spent in direct care encounters were higher in cohorted teams. Pairwise comparisons consistently trended toward longer durations in cohorted vs noncohorted teams. The notable exception was in cohorted teams with residents, which had the shortest predicted patient visits; however, we did not have noncohorted teams with residents in our study, limiting interpretation. Additionally, the odds of repeat visits to a patient in a single day were almost twice as high in the cohorted vs noncohorted group. The magnitude of this gain, however, is estimated to be a modest 1.2 minutes for a hospitalist only team and 1.7 minutes for a hospitalist with APP team and may be insufficient to provide compassionate, patient-centered care.¹⁹

Furthermore, these gains may be eroded if patient loads are high: similar to a previous study, we found that the duration of each patient visit decreased by 14% when the load increased from 10 to 20 patients.⁶ The expected gains in efficiency from cohorting leads to an expectation that hospitalists can manage more patients, but such reflexive increases should be carefully considered.¹⁸

Similar to earlier investigations where hospitalists were found to spend 60 to 69% of the day in indirect care activities,^5,6 hospitalists in both cohorted and noncohorted models spent approximately three times more time in indirect than direct care. Cohorting was associated with increased indirect care time. This association was expected as interdisciplinary huddles and increased nursing and physician communication are both related to cohorting.^3,14 However, similar to previous reports, in-person observations revealed that the bulk of this indirect time was spent in computer interactions, rather than in interprofessional communication. Interactions with the electronic health record (EHR) consume between one-third to one-half of the day in inpatient settings.^20,21 While EHRs are intended to enhance safety, they also fulfill multiple, nonclinical purposes and increase time spent on documentation.^22,23 Nonclinical tasks may contribute to clinician burnout and detract from patient centeredness.²² Our findings suggest that cohorting may not offset the burden of these time-intensive EHR tasks. The larger expenditure of time spent in computer interactions observed in the GCh group may be partially explained both by the higher number of patients and the higher frequency of interruptions observed in this group; computer use was the task most frequently observed to be interrupted. While longer tasks are more likely to be interrupted, the interruption in turn further increases the time taken to complete the task.²⁴

The interruption rates we observed are concerning. The hospitalist workday emerges as cognitively intense. GCh hospitalists were noted to be interrupted as frequently as once every eight minutes, a rate more than double that of an earlier investigation and approaching that of ED physicians.^5,25,26 Interruptions and multitasking contribute to errors and a perception of increased workload and frustration for clinicians.^9,27-29 Although interruptions were pervasive, GCh hospitalists were interrupted more frequently, corroborating a national survey in which hospitalists perceived that cohorting increased face-to-face interruptions.³⁰ The prolonged availability of the cohorted hospitalist on the unit may require different strategies for promoting timely interactions while preserving uninterrupted work time. Our work, however, does not allow us to quantify appropriate and urgent interruptions that reflect improved teamwork and patient safety. Interruptions increase as patient loads increase.²⁵ The contribution to interruptions by the higher patient census on the GCh teams cannot be quantified in this work, but without attention to these details, potential benefits from GCh may be attenuated.

Previous work has delineated variables important in determining hospitalist workload,³¹ and our work contributes additional considerations. Hospitalist experience and resident presence on cohorted teams was associated with shorter patient visits, while ED encounters were predicted to be the most time intensive. Increasing numbers of units visited in a day was associated with more indirect time, while weekends were associated with a lower burden of indirect care. As expected, APP presence was associated with more time in indirect care as the hospitalist spends time in providing oversight. As noted, cohorting was associated with increases in both direct and indirect care time. These findings may help inform hospital medicine groups. Additionally, attention should be paid to the fact that while support for cohorting stems from investigations in which it was used as part of a bundle of interventions,^2,3 in practice, it is often implemented incompletely, with cohorted hospitalists dispersed over several units, or in isolation from other interventions.¹

Our work has several limitations. As a single-center investigation, our findings may not be generalizable to other institutions. Second, we did not evaluate clinical outcomes, clinician, patient or nursing satisfaction to assess the effect of cohorting. Third, we cannot comment on whether the observed interruptions were beneficial or detrimental. Finally, while we used statistical control for the measured imbalanced variables between groups, unmeasured confounding factors between team types including differences in patient populations, pathologies and severity of illness, or the unit’s work environment and processes may have affected results.

Our work underscores the importance of paying careful attention to specific components and monitoring for unintended consequences in a complex intervention such as cohorting to allow subsequent refinement. Further studies to assess the interplay between models of care, their impact on interruptions, multitasking, errors and clinician burnout may be necessary. Such investigations will be critical to support the evolution of hospital medicine that enables it to be the driver of excellence in care.

Acknowledgments

The authors thank the participating hospitalists, research assistants, Shelly Harrison, Joni Godfrey, Mark Luetkemeyer, Deanne Kashiwagi, Tammy Kemlage, Dustin Hertel and Adeel Zaidi for their enthusiasm and support. The authors also thank Ann Cottingham, Rich Frankel and Greg Sachs from the ASPIRE program for their guidance and vision. Dr. Weiner is Chief of Health Services Research and Development at the Richard L. Roudebush Veterans Affairs Medical Center in Indianapolis, Indiana.

Disclaimer

The views expressed in this article are those of the authors and do not necessarily represent the views of the U.S. Department of Veterans Affairs.

Geographic cohorting (GCh, also known as “localization” or “regionalization”) refers to the practice wherein hospitalists are assigned to a single inpatient unit. Its adoption is increasing and in 2017, 30% of surveyed United States hospital medicine group leaders reported that their clinicians rounded on 1-2 units daily.¹ As a component of intervention bundles, GCh is associated with reductions in mortality, length of stay, and costs.^2,3

However, details on how GCh affects the hospitalist workday are unknown. Most time-motion studies of inpatient clinicians have reported the experiences of physicians in training with few specifically evaluating the workflow of attending hospitalists.⁴ Three studies of the attending hospitalist’s workday that were performed a decade ago excluded teams with learners, had patient loads as low as 9.4 per day, and did not differentiate between GCh and non-GCh models.^5-7

The objective of this observational study was to describe and compare the workday of GCh and non-GCh hospitalists by using automated geographical-tracking methods supplemented by in-person observations.

Setting and Participants

This work was conducted at a large academic center in the Midwestern US which adopted GCh in 2012. During the study, hospitalists staffed 11 GCh and four non-GCh teams. GCh teams aim to maintain ≥80% of their patients on their assigned unit and conduct interprofessional huddles on weekdays.³ Some units specialize in the care of specific populations (eg, patients with oncologic diagnoses), while others serve as general medical or surgical units. Non-GCh teams are assigned patients without regard to location. Resident housestaff are assigned only to GCh teams and residents and advanced practice providers (APPs) are never assigned to the same team. Based on team members, this yielded five distinct team types: GCh-hospitalist, GCh-hospitalist with APP, GCh-hospitalist with resident, non-GCh-hospitalist, and non-GCh-hospitalist with APP. Hospitalists provided verbal consent to participate. The protocol was reviewed and approved by the Indiana University Institutional Review Board. Two complementary observation modalities were used. Locator badges were used to quantify direct and indirect time unobtrusively over long periods. In-person observations were conducted to examine the workday in greater detail. Data were collected between October 2017 and May 2018.

Observations by Locator Badges

Our institution uses a system designed by Hill-Rom^® (Cary, North Carolina) to facilitate staff communication. Staff wear the I-Badge^® Locator Badge, which emits an infra-red signal.⁸ Centrally located receivers tabulate time spent by the badge wearer in each location (Appendix Figure 1). Each hospitalist was given a badge to wear at work for a minimum of six weeks, after which the I-Badge^® data were downloaded.

Schedules detailing each team’s members and assigned units (if cohorted) were retrieved. For each observed day, the hospitalist was linked to his or her team type and unit. Team lists were retrieved to ascertain patient load at the start of the day. Data sources were merged to categorize observations.

Observation Categories for Locator Badge Data

The I-Badge^® data provided details of how much time the hospitalist spent in each location (eg, nursing station, hallways, patient rooms). All observations in patient rooms were considered “direct care” while all other locations were categorized as “Indirect Care”. Observations were also categorized by the intensity of care provided on that unit, which included the Emergency Department (ED), Progressive Care Units (PCU), Medical-Surgical + PCU units (for units having a mixture of Medical-Surgical and PCU beds), and Medical-Surgical units.

In-person Observations

Four research assistants (RAs) were trained until interrater reliability using task times achieved an intraclass correlation coefficient of 0.98. Task categories included direct care (all time with patients), indirect care (computer interactions, communication), professional development, and travel and personal time. Interruptions were defined as “an unplanned and unscheduled task, causing a discontinuation, a noticeable break, or task switch behavior”.⁹ “Electronic interruptions” were caused by pagers or phones whereas in-person interruptions were “face-to-face” interruptions. When at least two tasks were performed simultaneously, it was considered multitasking. A data collection form created in REDCap was accessed on computer tablets or smartphones¹⁰ (Appendix Table 1). To limit each observation period to five hours, two RAs were scheduled each day. Observations were continued until the hospitalist reported that work activities were complete or until 5 pm.

Statistical Analysis

Due to the nested structure of the locator badge data, multilevel models that permit predictors to vary at more than one level were used.¹¹ The distribution of the duration of direct care observations was log normal for which the parameters were estimated using generalized linear mixed models (GLMM). The GLMM estimates were converted using a nonlinear transformation to predict the mean duration of interactions. The GLMM estimates were then used to predict time allocations for hospitalists with various workloads and contexts. The five team types were captured in a single categorical variable.

Univariate three-level models predicting minutes spent in direct care were tested for each predictor. Predictors, described below, were selected due to their hypothesized relation to time spent in direct patient care, or to account statistically for differences among teams due to the observational nature of the study.¹² Predictors were: Level 3, hospitalist characteristics (years since medical school, age, gender, international graduate, years at current hospital); Level 2, work day characteristics (number of units visited, number of patients visited, team type, weekday); and Level 1, individual observation characteristics (intensity of care on unit, number of visits to the same patient room per day). Predictors that were significantly related to the duration of direct care at P value <.05 and whose inclusion resulted in better model fit based on likelihood ratio tests were retained in a multivariate model. Additionally, a logistic regression model with random effects was tested to determine whether hospitalists working in GCh vs non-GCh teams (including teams with APPs and residents) made more than one visit to the same patient in a day. For duration of direct care encounters, the amount of variation explained (intraclass correlation) at the hospitalist level was .05, and at the day level was .03.

For total daily indirect care, a similar modeling process was used. A log normal distribution was used because the data was right-skewed and contained positive values. The restricted maximum likelihood method was used to calculate final estimates for models. Least square mean values for independent variables were subjected to backward transformation for interpretation. Post hoc pairwise comparisons between team types were conducted using Tukey–Kramer tests for direct and indirect care time. Analyses were conducted using SAS software version 9.4 (Cary, North Carolina).

The in-person observations were summarized using descriptive statistics. Exploratory analyses were performed using t-tests and Fisher’s exact tests to compare continuous and categorical variables respectively.

Locator Badge Observations

Participants

The 17 hospitalists had a mean (SD) age of 38 years (6.4); 10 (59%) were male, 7 (41%) were international medical graduates, and 10 (59%) had worked at the hospital ≥5 years. The duration of observation was <45 days for 7 hospitalists, 46-55 days for 4, and >55 days for 6, yielding observations for 666 hospitalist workdays. The mean time since medical school graduation was 13 years. Seven hospitalists were observed only in the GCh model, one was observed only in the non-GCh model, and nine were observed in both.

Team Characteristics

On average, non-GCh teams visited more units per day than GCh teams. Teams with APPs had higher patient loads (Table 1).

Time Observed in Direct and Indirect Care

In total, 10,522 observations were recorded in providing direct care. The average duration of a direct care encounter ranged from 4.1 to 5.8 minutes. The ratio of indirect to direct time ranged from 2.7 to 3.7 (Table 2).

The number of times that a hospitalist visited the same patient room in one day ranged from 1 to 9. Most (84%) of the patient rooms were visited once per day. The odds that a GCh hospitalist would visit a patient more than once per day were 1.8 times higher (95% CI: 1.37, 2.34; P < .0001) than for a non-GCh hospitalist (data not shown).

Predictors Associated with Time Expenditure

Predictors significantly associated with both the duration of direct care encounters and total daily indirect care time included team type and patient count. Predicted time in direct care encounters was highest for the GCh-hospitalist team (9.5 minutes) and lowest for the GCh-hospitalist with residents team (7 minutes). Predicted total indirect care time was highest for the GCh-hospitalist with APP team (160 minutes) while the lowest expenditure in indirect care time was predicted for the non-GCh-hospitalist team (102 minutes). Increasing patient load from 10 to 20 was predicted to decrease the duration of a direct care encounter by one minute (14%) and increase the total indirect care time by a larger amount (39 min, 24%).

The duration of direct care encounters was also inversely related with years since medical school and number of visits made to same patient room. Finally, acuity of care was associated with the duration of direct care encounters with the longest predicted encounters in the ED (9.4 minutes). Physician gender and age, international graduation, years at current hospital, weekday, and the number of units visited in a day were neither associated with direct care time at P value < .05 nor improved model fit and therefore were not retained in the final model (Table 3).

In-person Observations

Four hospitalists cohorted to general medical units and four non-GCh hospitalists were observed for one day each, yielding a total of 3,032 minutes of data. These hospitalists were on teams without residents or APPs. On average, GCh hospitalists had 78% of their patients on their assigned unit, rounded on fewer units (3 vs 6) and had two more patients at the start of the day than non-GCh hospitalists (14 vs 12). Age and gender distribution of the GCh and non-GCh hospitalists were similar.

As a percentage of total observed time, GCh hospitalists were noted to spend a larger proportion of the workday in computer interactions vs non-GCh hospitalists (56% vs 39%; P = .005). The proportion of time in other activities or locations was not statistically different between GCh and non-GCh hospitalists, including face-to-face communication (21% vs 15%), multitasking (18% vs 14%), time spent at the nursing station (58% vs 34%), direct care (15% vs 20%), and time traveling (4% vs 11%). The most frequently observed combination of multitasking was computer and phone use (59% of all multitasking) followed by computer use and face-to-face communication (17%; Appendix Figure 2).

The mean duration of an interruption was 1.3 minutes. More interruptions were observed in the GCh group than the non-GCh group (139 vs 102). Interruptions in the GCh group were face-to-face in 62% of instances and electronic in 25%. The remaining 13% were instances in which electronic and face-to-face interruptions occurred simultaneously. In the non-GCh group, 51% of interruptions were face-to-face; 47% were electronic; and 2% were simultaneous. GCh hospitalists were interrupted once every 14 minutes in the morning, with interruption frequency increasing to once every eight minutes in the afternoon. Non-GCh hospitalists were interrupted once every 13 minutes in the morning and saw interruption frequency decrease to once every 17 minutes in the afternoon. The task most frequently interrupted was computer use.

DISCUSSION

Previous investigations have studied the impact of cohorting on outcomes, including the facilitation of bedside rounding, adverse events, agreement between nurses and physicians on the plan of care, productivity, and the number of pages received.^13-16 Cohorting’s benefits are theorized to include increased hospitalist time with patients, while its downsides are perceived to include increased interruptions.^17,18 Neither has previously been evaluated by direct observation.

Our findings support cohorting’s association with increased hospitalist–patient time. While GCh hospitalists were observed spending 5% less time in direct care than non-GCh hospitalists by in-person observations, this difference did not achieve statistical significance and was unadjusted for hospitalist, patient load, team or patient characteristics. Using the larger badge dataset, the predicted values for time spent in direct care encounters were higher in cohorted teams. Pairwise comparisons consistently trended toward longer durations in cohorted vs noncohorted teams. The notable exception was in cohorted teams with residents, which had the shortest predicted patient visits; however, we did not have noncohorted teams with residents in our study, limiting interpretation. Additionally, the odds of repeat visits to a patient in a single day were almost twice as high in the cohorted vs noncohorted group. The magnitude of this gain, however, is estimated to be a modest 1.2 minutes for a hospitalist only team and 1.7 minutes for a hospitalist with APP team and may be insufficient to provide compassionate, patient-centered care.¹⁹

Furthermore, these gains may be eroded if patient loads are high: similar to a previous study, we found that the duration of each patient visit decreased by 14% when the load increased from 10 to 20 patients.⁶ The expected gains in efficiency from cohorting leads to an expectation that hospitalists can manage more patients, but such reflexive increases should be carefully considered.¹⁸

Similar to earlier investigations where hospitalists were found to spend 60 to 69% of the day in indirect care activities,^5,6 hospitalists in both cohorted and noncohorted models spent approximately three times more time in indirect than direct care. Cohorting was associated with increased indirect care time. This association was expected as interdisciplinary huddles and increased nursing and physician communication are both related to cohorting.^3,14 However, similar to previous reports, in-person observations revealed that the bulk of this indirect time was spent in computer interactions, rather than in interprofessional communication. Interactions with the electronic health record (EHR) consume between one-third to one-half of the day in inpatient settings.^20,21 While EHRs are intended to enhance safety, they also fulfill multiple, nonclinical purposes and increase time spent on documentation.^22,23 Nonclinical tasks may contribute to clinician burnout and detract from patient centeredness.²² Our findings suggest that cohorting may not offset the burden of these time-intensive EHR tasks. The larger expenditure of time spent in computer interactions observed in the GCh group may be partially explained both by the higher number of patients and the higher frequency of interruptions observed in this group; computer use was the task most frequently observed to be interrupted. While longer tasks are more likely to be interrupted, the interruption in turn further increases the time taken to complete the task.²⁴

The interruption rates we observed are concerning. The hospitalist workday emerges as cognitively intense. GCh hospitalists were noted to be interrupted as frequently as once every eight minutes, a rate more than double that of an earlier investigation and approaching that of ED physicians.^5,25,26 Interruptions and multitasking contribute to errors and a perception of increased workload and frustration for clinicians.^9,27-29 Although interruptions were pervasive, GCh hospitalists were interrupted more frequently, corroborating a national survey in which hospitalists perceived that cohorting increased face-to-face interruptions.³⁰ The prolonged availability of the cohorted hospitalist on the unit may require different strategies for promoting timely interactions while preserving uninterrupted work time. Our work, however, does not allow us to quantify appropriate and urgent interruptions that reflect improved teamwork and patient safety. Interruptions increase as patient loads increase.²⁵ The contribution to interruptions by the higher patient census on the GCh teams cannot be quantified in this work, but without attention to these details, potential benefits from GCh may be attenuated.

Previous work has delineated variables important in determining hospitalist workload,³¹ and our work contributes additional considerations. Hospitalist experience and resident presence on cohorted teams was associated with shorter patient visits, while ED encounters were predicted to be the most time intensive. Increasing numbers of units visited in a day was associated with more indirect time, while weekends were associated with a lower burden of indirect care. As expected, APP presence was associated with more time in indirect care as the hospitalist spends time in providing oversight. As noted, cohorting was associated with increases in both direct and indirect care time. These findings may help inform hospital medicine groups. Additionally, attention should be paid to the fact that while support for cohorting stems from investigations in which it was used as part of a bundle of interventions,^2,3 in practice, it is often implemented incompletely, with cohorted hospitalists dispersed over several units, or in isolation from other interventions.¹

Our work has several limitations. As a single-center investigation, our findings may not be generalizable to other institutions. Second, we did not evaluate clinical outcomes, clinician, patient or nursing satisfaction to assess the effect of cohorting. Third, we cannot comment on whether the observed interruptions were beneficial or detrimental. Finally, while we used statistical control for the measured imbalanced variables between groups, unmeasured confounding factors between team types including differences in patient populations, pathologies and severity of illness, or the unit’s work environment and processes may have affected results.

Our work underscores the importance of paying careful attention to specific components and monitoring for unintended consequences in a complex intervention such as cohorting to allow subsequent refinement. Further studies to assess the interplay between models of care, their impact on interruptions, multitasking, errors and clinician burnout may be necessary. Such investigations will be critical to support the evolution of hospital medicine that enables it to be the driver of excellence in care.

Acknowledgments

The authors thank the participating hospitalists, research assistants, Shelly Harrison, Joni Godfrey, Mark Luetkemeyer, Deanne Kashiwagi, Tammy Kemlage, Dustin Hertel and Adeel Zaidi for their enthusiasm and support. The authors also thank Ann Cottingham, Rich Frankel and Greg Sachs from the ASPIRE program for their guidance and vision. Dr. Weiner is Chief of Health Services Research and Development at the Richard L. Roudebush Veterans Affairs Medical Center in Indianapolis, Indiana.

Disclaimer

The views expressed in this article are those of the authors and do not necessarily represent the views of the U.S. Department of Veterans Affairs.

Geographic cohorting (GCh, also known as “localization” or “regionalization”) refers to the practice wherein hospitalists are assigned to a single inpatient unit. Its adoption is increasing and in 2017, 30% of surveyed United States hospital medicine group leaders reported that their clinicians rounded on 1-2 units daily.¹ As a component of intervention bundles, GCh is associated with reductions in mortality, length of stay, and costs.^2,3

However, details on how GCh affects the hospitalist workday are unknown. Most time-motion studies of inpatient clinicians have reported the experiences of physicians in training with few specifically evaluating the workflow of attending hospitalists.⁴ Three studies of the attending hospitalist’s workday that were performed a decade ago excluded teams with learners, had patient loads as low as 9.4 per day, and did not differentiate between GCh and non-GCh models.^5-7

The objective of this observational study was to describe and compare the workday of GCh and non-GCh hospitalists by using automated geographical-tracking methods supplemented by in-person observations.

Setting and Participants

This work was conducted at a large academic center in the Midwestern US which adopted GCh in 2012. During the study, hospitalists staffed 11 GCh and four non-GCh teams. GCh teams aim to maintain ≥80% of their patients on their assigned unit and conduct interprofessional huddles on weekdays.³ Some units specialize in the care of specific populations (eg, patients with oncologic diagnoses), while others serve as general medical or surgical units. Non-GCh teams are assigned patients without regard to location. Resident housestaff are assigned only to GCh teams and residents and advanced practice providers (APPs) are never assigned to the same team. Based on team members, this yielded five distinct team types: GCh-hospitalist, GCh-hospitalist with APP, GCh-hospitalist with resident, non-GCh-hospitalist, and non-GCh-hospitalist with APP. Hospitalists provided verbal consent to participate. The protocol was reviewed and approved by the Indiana University Institutional Review Board. Two complementary observation modalities were used. Locator badges were used to quantify direct and indirect time unobtrusively over long periods. In-person observations were conducted to examine the workday in greater detail. Data were collected between October 2017 and May 2018.

Observations by Locator Badges

Our institution uses a system designed by Hill-Rom^® (Cary, North Carolina) to facilitate staff communication. Staff wear the I-Badge^® Locator Badge, which emits an infra-red signal.⁸ Centrally located receivers tabulate time spent by the badge wearer in each location (Appendix Figure 1). Each hospitalist was given a badge to wear at work for a minimum of six weeks, after which the I-Badge^® data were downloaded.

Schedules detailing each team’s members and assigned units (if cohorted) were retrieved. For each observed day, the hospitalist was linked to his or her team type and unit. Team lists were retrieved to ascertain patient load at the start of the day. Data sources were merged to categorize observations.

Observation Categories for Locator Badge Data

The I-Badge^® data provided details of how much time the hospitalist spent in each location (eg, nursing station, hallways, patient rooms). All observations in patient rooms were considered “direct care” while all other locations were categorized as “Indirect Care”. Observations were also categorized by the intensity of care provided on that unit, which included the Emergency Department (ED), Progressive Care Units (PCU), Medical-Surgical + PCU units (for units having a mixture of Medical-Surgical and PCU beds), and Medical-Surgical units.

In-person Observations

Four research assistants (RAs) were trained until interrater reliability using task times achieved an intraclass correlation coefficient of 0.98. Task categories included direct care (all time with patients), indirect care (computer interactions, communication), professional development, and travel and personal time. Interruptions were defined as “an unplanned and unscheduled task, causing a discontinuation, a noticeable break, or task switch behavior”.⁹ “Electronic interruptions” were caused by pagers or phones whereas in-person interruptions were “face-to-face” interruptions. When at least two tasks were performed simultaneously, it was considered multitasking. A data collection form created in REDCap was accessed on computer tablets or smartphones¹⁰ (Appendix Table 1). To limit each observation period to five hours, two RAs were scheduled each day. Observations were continued until the hospitalist reported that work activities were complete or until 5 pm.

Statistical Analysis

Due to the nested structure of the locator badge data, multilevel models that permit predictors to vary at more than one level were used.¹¹ The distribution of the duration of direct care observations was log normal for which the parameters were estimated using generalized linear mixed models (GLMM). The GLMM estimates were converted using a nonlinear transformation to predict the mean duration of interactions. The GLMM estimates were then used to predict time allocations for hospitalists with various workloads and contexts. The five team types were captured in a single categorical variable.

Univariate three-level models predicting minutes spent in direct care were tested for each predictor. Predictors, described below, were selected due to their hypothesized relation to time spent in direct patient care, or to account statistically for differences among teams due to the observational nature of the study.¹² Predictors were: Level 3, hospitalist characteristics (years since medical school, age, gender, international graduate, years at current hospital); Level 2, work day characteristics (number of units visited, number of patients visited, team type, weekday); and Level 1, individual observation characteristics (intensity of care on unit, number of visits to the same patient room per day). Predictors that were significantly related to the duration of direct care at P value <.05 and whose inclusion resulted in better model fit based on likelihood ratio tests were retained in a multivariate model. Additionally, a logistic regression model with random effects was tested to determine whether hospitalists working in GCh vs non-GCh teams (including teams with APPs and residents) made more than one visit to the same patient in a day. For duration of direct care encounters, the amount of variation explained (intraclass correlation) at the hospitalist level was .05, and at the day level was .03.

For total daily indirect care, a similar modeling process was used. A log normal distribution was used because the data was right-skewed and contained positive values. The restricted maximum likelihood method was used to calculate final estimates for models. Least square mean values for independent variables were subjected to backward transformation for interpretation. Post hoc pairwise comparisons between team types were conducted using Tukey–Kramer tests for direct and indirect care time. Analyses were conducted using SAS software version 9.4 (Cary, North Carolina).

The in-person observations were summarized using descriptive statistics. Exploratory analyses were performed using t-tests and Fisher’s exact tests to compare continuous and categorical variables respectively.

Locator Badge Observations

Participants

The 17 hospitalists had a mean (SD) age of 38 years (6.4); 10 (59%) were male, 7 (41%) were international medical graduates, and 10 (59%) had worked at the hospital ≥5 years. The duration of observation was <45 days for 7 hospitalists, 46-55 days for 4, and >55 days for 6, yielding observations for 666 hospitalist workdays. The mean time since medical school graduation was 13 years. Seven hospitalists were observed only in the GCh model, one was observed only in the non-GCh model, and nine were observed in both.

Team Characteristics

On average, non-GCh teams visited more units per day than GCh teams. Teams with APPs had higher patient loads (Table 1).

Time Observed in Direct and Indirect Care

In total, 10,522 observations were recorded in providing direct care. The average duration of a direct care encounter ranged from 4.1 to 5.8 minutes. The ratio of indirect to direct time ranged from 2.7 to 3.7 (Table 2).

The number of times that a hospitalist visited the same patient room in one day ranged from 1 to 9. Most (84%) of the patient rooms were visited once per day. The odds that a GCh hospitalist would visit a patient more than once per day were 1.8 times higher (95% CI: 1.37, 2.34; P < .0001) than for a non-GCh hospitalist (data not shown).

Predictors Associated with Time Expenditure

Predictors significantly associated with both the duration of direct care encounters and total daily indirect care time included team type and patient count. Predicted time in direct care encounters was highest for the GCh-hospitalist team (9.5 minutes) and lowest for the GCh-hospitalist with residents team (7 minutes). Predicted total indirect care time was highest for the GCh-hospitalist with APP team (160 minutes) while the lowest expenditure in indirect care time was predicted for the non-GCh-hospitalist team (102 minutes). Increasing patient load from 10 to 20 was predicted to decrease the duration of a direct care encounter by one minute (14%) and increase the total indirect care time by a larger amount (39 min, 24%).

The duration of direct care encounters was also inversely related with years since medical school and number of visits made to same patient room. Finally, acuity of care was associated with the duration of direct care encounters with the longest predicted encounters in the ED (9.4 minutes). Physician gender and age, international graduation, years at current hospital, weekday, and the number of units visited in a day were neither associated with direct care time at P value < .05 nor improved model fit and therefore were not retained in the final model (Table 3).

In-person Observations

Four hospitalists cohorted to general medical units and four non-GCh hospitalists were observed for one day each, yielding a total of 3,032 minutes of data. These hospitalists were on teams without residents or APPs. On average, GCh hospitalists had 78% of their patients on their assigned unit, rounded on fewer units (3 vs 6) and had two more patients at the start of the day than non-GCh hospitalists (14 vs 12). Age and gender distribution of the GCh and non-GCh hospitalists were similar.

As a percentage of total observed time, GCh hospitalists were noted to spend a larger proportion of the workday in computer interactions vs non-GCh hospitalists (56% vs 39%; P = .005). The proportion of time in other activities or locations was not statistically different between GCh and non-GCh hospitalists, including face-to-face communication (21% vs 15%), multitasking (18% vs 14%), time spent at the nursing station (58% vs 34%), direct care (15% vs 20%), and time traveling (4% vs 11%). The most frequently observed combination of multitasking was computer and phone use (59% of all multitasking) followed by computer use and face-to-face communication (17%; Appendix Figure 2).

The mean duration of an interruption was 1.3 minutes. More interruptions were observed in the GCh group than the non-GCh group (139 vs 102). Interruptions in the GCh group were face-to-face in 62% of instances and electronic in 25%. The remaining 13% were instances in which electronic and face-to-face interruptions occurred simultaneously. In the non-GCh group, 51% of interruptions were face-to-face; 47% were electronic; and 2% were simultaneous. GCh hospitalists were interrupted once every 14 minutes in the morning, with interruption frequency increasing to once every eight minutes in the afternoon. Non-GCh hospitalists were interrupted once every 13 minutes in the morning and saw interruption frequency decrease to once every 17 minutes in the afternoon. The task most frequently interrupted was computer use.

DISCUSSION

Previous investigations have studied the impact of cohorting on outcomes, including the facilitation of bedside rounding, adverse events, agreement between nurses and physicians on the plan of care, productivity, and the number of pages received.^13-16 Cohorting’s benefits are theorized to include increased hospitalist time with patients, while its downsides are perceived to include increased interruptions.^17,18 Neither has previously been evaluated by direct observation.

Our findings support cohorting’s association with increased hospitalist–patient time. While GCh hospitalists were observed spending 5% less time in direct care than non-GCh hospitalists by in-person observations, this difference did not achieve statistical significance and was unadjusted for hospitalist, patient load, team or patient characteristics. Using the larger badge dataset, the predicted values for time spent in direct care encounters were higher in cohorted teams. Pairwise comparisons consistently trended toward longer durations in cohorted vs noncohorted teams. The notable exception was in cohorted teams with residents, which had the shortest predicted patient visits; however, we did not have noncohorted teams with residents in our study, limiting interpretation. Additionally, the odds of repeat visits to a patient in a single day were almost twice as high in the cohorted vs noncohorted group. The magnitude of this gain, however, is estimated to be a modest 1.2 minutes for a hospitalist only team and 1.7 minutes for a hospitalist with APP team and may be insufficient to provide compassionate, patient-centered care.¹⁹

Furthermore, these gains may be eroded if patient loads are high: similar to a previous study, we found that the duration of each patient visit decreased by 14% when the load increased from 10 to 20 patients.⁶ The expected gains in efficiency from cohorting leads to an expectation that hospitalists can manage more patients, but such reflexive increases should be carefully considered.¹⁸

Similar to earlier investigations where hospitalists were found to spend 60 to 69% of the day in indirect care activities,^5,6 hospitalists in both cohorted and noncohorted models spent approximately three times more time in indirect than direct care. Cohorting was associated with increased indirect care time. This association was expected as interdisciplinary huddles and increased nursing and physician communication are both related to cohorting.^3,14 However, similar to previous reports, in-person observations revealed that the bulk of this indirect time was spent in computer interactions, rather than in interprofessional communication. Interactions with the electronic health record (EHR) consume between one-third to one-half of the day in inpatient settings.^20,21 While EHRs are intended to enhance safety, they also fulfill multiple, nonclinical purposes and increase time spent on documentation.^22,23 Nonclinical tasks may contribute to clinician burnout and detract from patient centeredness.²² Our findings suggest that cohorting may not offset the burden of these time-intensive EHR tasks. The larger expenditure of time spent in computer interactions observed in the GCh group may be partially explained both by the higher number of patients and the higher frequency of interruptions observed in this group; computer use was the task most frequently observed to be interrupted. While longer tasks are more likely to be interrupted, the interruption in turn further increases the time taken to complete the task.²⁴

The interruption rates we observed are concerning. The hospitalist workday emerges as cognitively intense. GCh hospitalists were noted to be interrupted as frequently as once every eight minutes, a rate more than double that of an earlier investigation and approaching that of ED physicians.^5,25,26 Interruptions and multitasking contribute to errors and a perception of increased workload and frustration for clinicians.^9,27-29 Although interruptions were pervasive, GCh hospitalists were interrupted more frequently, corroborating a national survey in which hospitalists perceived that cohorting increased face-to-face interruptions.³⁰ The prolonged availability of the cohorted hospitalist on the unit may require different strategies for promoting timely interactions while preserving uninterrupted work time. Our work, however, does not allow us to quantify appropriate and urgent interruptions that reflect improved teamwork and patient safety. Interruptions increase as patient loads increase.²⁵ The contribution to interruptions by the higher patient census on the GCh teams cannot be quantified in this work, but without attention to these details, potential benefits from GCh may be attenuated.

Previous work has delineated variables important in determining hospitalist workload,³¹ and our work contributes additional considerations. Hospitalist experience and resident presence on cohorted teams was associated with shorter patient visits, while ED encounters were predicted to be the most time intensive. Increasing numbers of units visited in a day was associated with more indirect time, while weekends were associated with a lower burden of indirect care. As expected, APP presence was associated with more time in indirect care as the hospitalist spends time in providing oversight. As noted, cohorting was associated with increases in both direct and indirect care time. These findings may help inform hospital medicine groups. Additionally, attention should be paid to the fact that while support for cohorting stems from investigations in which it was used as part of a bundle of interventions,^2,3 in practice, it is often implemented incompletely, with cohorted hospitalists dispersed over several units, or in isolation from other interventions.¹

Our work has several limitations. As a single-center investigation, our findings may not be generalizable to other institutions. Second, we did not evaluate clinical outcomes, clinician, patient or nursing satisfaction to assess the effect of cohorting. Third, we cannot comment on whether the observed interruptions were beneficial or detrimental. Finally, while we used statistical control for the measured imbalanced variables between groups, unmeasured confounding factors between team types including differences in patient populations, pathologies and severity of illness, or the unit’s work environment and processes may have affected results.

Our work underscores the importance of paying careful attention to specific components and monitoring for unintended consequences in a complex intervention such as cohorting to allow subsequent refinement. Further studies to assess the interplay between models of care, their impact on interruptions, multitasking, errors and clinician burnout may be necessary. Such investigations will be critical to support the evolution of hospital medicine that enables it to be the driver of excellence in care.

Acknowledgments

The authors thank the participating hospitalists, research assistants, Shelly Harrison, Joni Godfrey, Mark Luetkemeyer, Deanne Kashiwagi, Tammy Kemlage, Dustin Hertel and Adeel Zaidi for their enthusiasm and support. The authors also thank Ann Cottingham, Rich Frankel and Greg Sachs from the ASPIRE program for their guidance and vision. Dr. Weiner is Chief of Health Services Research and Development at the Richard L. Roudebush Veterans Affairs Medical Center in Indianapolis, Indiana.

Disclaimer

The views expressed in this article are those of the authors and do not necessarily represent the views of the U.S. Department of Veterans Affairs.

Healthcare delivery in rural America faces unique, growing challenges related to health and emergency care access.¹ Telemedicine approaches have the potential to increase rural hospitals’ ability to deliver efficient emergency care and reduce clinician shortages.² While initial evidence of telemedicine success exists, more quality research is needed to understand telemedicine patient and care team experiences,³ especially with real-time, clinician-initiated video conferencing in critical access hospital (CAH) emergency departments (ED). Some experience studies exist,⁴ but results are primarily quantitative⁵ and lack the nuanced qualitative depth needed to understand topics such as satisfaction and communication.⁶ Additionally, few explore combined patient and care team perspectives.⁵ The lack of breadth and depth makes it difficult to provide actionable recommendations for improvements and affects the feasibility of continuing this work and improving telemedicine care quality. To address these gaps, we evaluated a real-time, clinician-­initiated video conferencing program with overnight clinicians servicing ED patients in three Midwestern care system CAHs. This evaluation assessed patient and care team (nurse and clinician) experience with telemedicine using quantitative and qualitative survey data analysis.

Because this evaluation was designed to measure and improve program quality in a single healthcare system, it was deemed non-human subjects research by the organization’s institutional review board. This brief report follows telemedicine reporting guidelines.⁷

Setting and Telemedicine Program

This program, designed to reduce the need for on-call hospitalist clinicians to be onsite at CAHs overnight, was implemented in a large Midwestern nonprofit integrated healthcare system with three rural CAHs (combined capacity for 75 inpatient admissions, with full-time onsite ED clinicians and nurses, as well as on-call hospitalist clinicians) and a large metropolitan tertiary-care hospital. All adult patients presenting to CAH EDs between 6 pm and 8 am were evaluated, as usual, by an onsite ED clinician. If the admitting ED clinician and charge nurse determined that admission was appropriate, patients were signed out to remote hospitalist clinicians and roomed by onsite nurses. Nurses facilitated live audio-video telemedicine “history and physical” visits with remote clinicians via telemedicine carts (AmericanWell C750, Boston, Massachusetts, and ThinkLabs One Electronic Stethoscope, Centennial, Colorado). Already-­hospitalized patients, as well as patients admitted to a remote clinician, were cared for by the remote clinician and onsite nurse for the remainder of the night, which eliminated the need for local on-call clinicians. The onsite ED clinician responded to emergencies of already-hospitalized patients, but often consulted with remote clinicians to assist virtually with necessary orders and documentation. Remote clinicians were located at the metropolitan tertiary care hospital or home work stations.

Following a pilot period, the full-scale program was implemented in September 2017 and included 14 remote clinicians and 60 onsite nurses.

Survey Administration and Design

A postimplementation survey was designed to explore patient and care team experience with telemedicine. Patients who received a telemedicine visit between September 2017 and April 2018 were mailed a paper survey. Nonresponders were called by professional interviewers affiliated with the healthcare system. All participating clinicians (N = 14, all MDs) and nurses (N = 60, all RNs) were emailed an online care team survey with phone-in option. Care team nonresponders were sent up to two reminder emails.

Surveys captured the following five constructs: communication, workflow integration, telemedicine technology, quality of care, and general satisfaction. Existing questionnaires were used where possible; additional items were designed with clinical experts following survey design best practices.⁸ Patient-perceived communication was assessed via three Consumer Assessment of Healthcare Providers and Systems Outpatient and Ambulatory Surgery Survey items.⁹ Five additional program-developed patient survey items included satisfaction with clinician-nurse communication, satisfaction with technology, telemedicine quality of care overall and in comparison with traditional care, and whether or not patients would recommend telemedicine (Table). Four open-ended questions asked patients about improvement opportunities and general satisfaction.

Care team surveys included two items regarding ability to effectively communicate, two about satisfaction with workflow integration, one about technical problems, two about quality of care, and one about general satisfaction. Open-ended questions gathered further information and recommendations to improve communication, workflow integration, technology issues, and general satisfaction.

Analysis

Closed-ended items were dichotomized (satisfied yes/no); descriptive statistics (frequencies/percents) are presented to quantify patient and care team experience. Quantitative analyses were conducted in SAS software version 9.4 (SAS Institute, Cary, North Carolina). Open-ended responses were coded separately for patient and care team experience, following qualitative content analysis best practices.¹⁰ A lead coder read all responses, created a coding framework of identified themes, and coded individual responses. A second coder independently coded responses using the same framework. Interrater reliability was calculated for each major theme using percent agreement and prevalence- and bias-adjusted k (PABAK) statistic. A single representative quote was selected and lightly edited for each subtheme to deepen understanding and provide respondent voice.¹¹

Of eligible patients mailed a survey (N = 408), 3% self-reported as ineligible, and 54% completed the survey. This is a maximum response rate (response rate 6) according to the American Association for Public Opinion Research.¹² Patients were 67 years old on average (SD = 15), they were primarily white (97%), and 54% were female. All clinicians and 63% of nurses completed the survey.¹² Clinicians and nurses were 29% and 95% female, respectively.

Quantitative results (Table) show generally positive experience across patient and care team respondents. Over 90% were satisfied with all measures of communication. Care teams had high satisfaction with admissions processes and reported telemedicine improved cross-coverage. Patient-reported technology experience was positive but was less positive from the care team perspective. Care teams reported lower absolute quality of care than did patients but were more likely to perceive telemedicine as high quality, compared with traditional care. Most patients, clinicians, and nurses would recommend telemedicine.

Qualitatively, four major themes were identified in open-­ended responses with high interrater reliability (PABAK ranging from 0.92 to 0.98 in patient responses and 0.88 to 0.95 in care team responses) and aligned with the quantitative survey constructs: clinician-nurse communication, clinician-patient communication, workflow integration, and telemedicine technology. Patients reported satisfaction with communication with remote clinicians:

“[The clinician] was extremely attentive to me and what was going on. She was articulate and clear. I understood what was going to happen.” –Patient

Care teams suggested concrete improvement opportunities:

“I’d prefer to have some time with nursing staff both before and (sometimes) after the patient encounter.” –Clinician

“Since we cannot hear what [the clinicians] are hearing with the stethoscope, it’s nice when they tell us when to move it to the next spot.” –Nurse

Clinicians and nurses gave favorable responses regarding workflow integration, though time (both admissions wait time and session duration) was a reported opportunity:

“It would be helpful if we could speed up the time from admit request to screen time.” –Clinician

“When the [clinicians] get swamped, they’re hard to get a hold of, and admissions can take a long time. They may have too much on their plates dealing with several locations.” –Nurse

Technology issues—internet connection, stethoscope, sound, and screen or camera—were mentioned by patients and care teams, though technology was reviewed favorably overall by most patients:

“I was fascinated by the technology. Visiting someone over a television was impressive. ... The picture, the sound clarity, and the connection itself was flawless.” –Patient

Some patients commented that telemedicine was the best option given the situation, but still preferred an in-person doctor:

“If a doctor wasn’t available, telemedicine is better than nothing.” –Patient

Nurses who would not recommend telemedicine noted the need for personal connection:

“[I] still prefer [an] in-person MD for more personal contact. The older patients often state they wish the doctor would come and see them.” –Nurse

Patients who would not recommend telemedicine also desired personal connection:

“I would sooner talk to a person than a machine.” –Patient

A few clinicians noted the connection with patients would be improved if they knew about others in the room:

“It’d be nice if everyone in the room was introduced. Sometimes people are sitting out of view of the camera and I don’t realize they’re there until later.” –Clinician

These results make important contributions to understanding and improving the telemedicine experience in rural emergency hospital medicine. While the predominantly white patient respondent population limits generalizability, these demographics are representative of the overall population of the participating hospitals. A strength of this evaluation is its contemporaneous consideration of patient and care team experience with both quantitative and rich, qualitative analysis. Patients and care teams alike thought overnight telemedicine was better than the status quo. While our quality of care findings align with some previous literature,¹³ care teams in the current analysis overwhelmingly would recommend telemedicine, whereas some clinicians in prior work would not recommend telemedicine.¹⁴

In terms of communication, in line with existing literature, some patients still preferred in-person visits,¹⁵ a view also shared by some care team members. Workflow and technology barriers were raised, corroborating existing work,¹³ but actionable solutions (eg, adding care team–only time before visits or verbalizing when to move stethoscopes) were also identified.

Embedding patient and care team experience surveys and sharing results is critical in advancing telemedicine. Findings from this evaluation strengthen the case for payer reimbursement of telemedicine in rural acute care. Continued work to improve, test, and publish findings on patient and care team experience with telemedicine is critical to providing quality services in often-underserved communities.

The authors would like to acknowledge the contributions of Ann Werner in identifying the patient survey sample, Brian Barklind in identifying source data for the analysis, and both Brian Barklind and Rachael Rivard for conducting the analyses and summarizing results. We would also like to thank Kelly Logue for her involvement in conceptualizing the telemedicine evaluation described here, as well as Larisa Polynskaya for her help preparing the manuscript for publication, and the care teams and patients who provided valuable input.

Healthcare delivery in rural America faces unique, growing challenges related to health and emergency care access.¹ Telemedicine approaches have the potential to increase rural hospitals’ ability to deliver efficient emergency care and reduce clinician shortages.² While initial evidence of telemedicine success exists, more quality research is needed to understand telemedicine patient and care team experiences,³ especially with real-time, clinician-initiated video conferencing in critical access hospital (CAH) emergency departments (ED). Some experience studies exist,⁴ but results are primarily quantitative⁵ and lack the nuanced qualitative depth needed to understand topics such as satisfaction and communication.⁶ Additionally, few explore combined patient and care team perspectives.⁵ The lack of breadth and depth makes it difficult to provide actionable recommendations for improvements and affects the feasibility of continuing this work and improving telemedicine care quality. To address these gaps, we evaluated a real-time, clinician-­initiated video conferencing program with overnight clinicians servicing ED patients in three Midwestern care system CAHs. This evaluation assessed patient and care team (nurse and clinician) experience with telemedicine using quantitative and qualitative survey data analysis.

Because this evaluation was designed to measure and improve program quality in a single healthcare system, it was deemed non-human subjects research by the organization’s institutional review board. This brief report follows telemedicine reporting guidelines.⁷

Setting and Telemedicine Program

This program, designed to reduce the need for on-call hospitalist clinicians to be onsite at CAHs overnight, was implemented in a large Midwestern nonprofit integrated healthcare system with three rural CAHs (combined capacity for 75 inpatient admissions, with full-time onsite ED clinicians and nurses, as well as on-call hospitalist clinicians) and a large metropolitan tertiary-care hospital. All adult patients presenting to CAH EDs between 6 pm and 8 am were evaluated, as usual, by an onsite ED clinician. If the admitting ED clinician and charge nurse determined that admission was appropriate, patients were signed out to remote hospitalist clinicians and roomed by onsite nurses. Nurses facilitated live audio-video telemedicine “history and physical” visits with remote clinicians via telemedicine carts (AmericanWell C750, Boston, Massachusetts, and ThinkLabs One Electronic Stethoscope, Centennial, Colorado). Already-­hospitalized patients, as well as patients admitted to a remote clinician, were cared for by the remote clinician and onsite nurse for the remainder of the night, which eliminated the need for local on-call clinicians. The onsite ED clinician responded to emergencies of already-hospitalized patients, but often consulted with remote clinicians to assist virtually with necessary orders and documentation. Remote clinicians were located at the metropolitan tertiary care hospital or home work stations.

Following a pilot period, the full-scale program was implemented in September 2017 and included 14 remote clinicians and 60 onsite nurses.

Survey Administration and Design

A postimplementation survey was designed to explore patient and care team experience with telemedicine. Patients who received a telemedicine visit between September 2017 and April 2018 were mailed a paper survey. Nonresponders were called by professional interviewers affiliated with the healthcare system. All participating clinicians (N = 14, all MDs) and nurses (N = 60, all RNs) were emailed an online care team survey with phone-in option. Care team nonresponders were sent up to two reminder emails.

Surveys captured the following five constructs: communication, workflow integration, telemedicine technology, quality of care, and general satisfaction. Existing questionnaires were used where possible; additional items were designed with clinical experts following survey design best practices.⁸ Patient-perceived communication was assessed via three Consumer Assessment of Healthcare Providers and Systems Outpatient and Ambulatory Surgery Survey items.⁹ Five additional program-developed patient survey items included satisfaction with clinician-nurse communication, satisfaction with technology, telemedicine quality of care overall and in comparison with traditional care, and whether or not patients would recommend telemedicine (Table). Four open-ended questions asked patients about improvement opportunities and general satisfaction.

Care team surveys included two items regarding ability to effectively communicate, two about satisfaction with workflow integration, one about technical problems, two about quality of care, and one about general satisfaction. Open-ended questions gathered further information and recommendations to improve communication, workflow integration, technology issues, and general satisfaction.

Analysis

Closed-ended items were dichotomized (satisfied yes/no); descriptive statistics (frequencies/percents) are presented to quantify patient and care team experience. Quantitative analyses were conducted in SAS software version 9.4 (SAS Institute, Cary, North Carolina). Open-ended responses were coded separately for patient and care team experience, following qualitative content analysis best practices.¹⁰ A lead coder read all responses, created a coding framework of identified themes, and coded individual responses. A second coder independently coded responses using the same framework. Interrater reliability was calculated for each major theme using percent agreement and prevalence- and bias-adjusted k (PABAK) statistic. A single representative quote was selected and lightly edited for each subtheme to deepen understanding and provide respondent voice.¹¹

Of eligible patients mailed a survey (N = 408), 3% self-reported as ineligible, and 54% completed the survey. This is a maximum response rate (response rate 6) according to the American Association for Public Opinion Research.¹² Patients were 67 years old on average (SD = 15), they were primarily white (97%), and 54% were female. All clinicians and 63% of nurses completed the survey.¹² Clinicians and nurses were 29% and 95% female, respectively.

Quantitative results (Table) show generally positive experience across patient and care team respondents. Over 90% were satisfied with all measures of communication. Care teams had high satisfaction with admissions processes and reported telemedicine improved cross-coverage. Patient-reported technology experience was positive but was less positive from the care team perspective. Care teams reported lower absolute quality of care than did patients but were more likely to perceive telemedicine as high quality, compared with traditional care. Most patients, clinicians, and nurses would recommend telemedicine.

Qualitatively, four major themes were identified in open-­ended responses with high interrater reliability (PABAK ranging from 0.92 to 0.98 in patient responses and 0.88 to 0.95 in care team responses) and aligned with the quantitative survey constructs: clinician-nurse communication, clinician-patient communication, workflow integration, and telemedicine technology. Patients reported satisfaction with communication with remote clinicians:

“[The clinician] was extremely attentive to me and what was going on. She was articulate and clear. I understood what was going to happen.” –Patient

Care teams suggested concrete improvement opportunities:

“I’d prefer to have some time with nursing staff both before and (sometimes) after the patient encounter.” –Clinician

“Since we cannot hear what [the clinicians] are hearing with the stethoscope, it’s nice when they tell us when to move it to the next spot.” –Nurse

Clinicians and nurses gave favorable responses regarding workflow integration, though time (both admissions wait time and session duration) was a reported opportunity:

“It would be helpful if we could speed up the time from admit request to screen time.” –Clinician

“When the [clinicians] get swamped, they’re hard to get a hold of, and admissions can take a long time. They may have too much on their plates dealing with several locations.” –Nurse

Technology issues—internet connection, stethoscope, sound, and screen or camera—were mentioned by patients and care teams, though technology was reviewed favorably overall by most patients:

“I was fascinated by the technology. Visiting someone over a television was impressive. ... The picture, the sound clarity, and the connection itself was flawless.” –Patient

Some patients commented that telemedicine was the best option given the situation, but still preferred an in-person doctor:

“If a doctor wasn’t available, telemedicine is better than nothing.” –Patient

Nurses who would not recommend telemedicine noted the need for personal connection:

“[I] still prefer [an] in-person MD for more personal contact. The older patients often state they wish the doctor would come and see them.” –Nurse

Patients who would not recommend telemedicine also desired personal connection:

“I would sooner talk to a person than a machine.” –Patient

A few clinicians noted the connection with patients would be improved if they knew about others in the room:

“It’d be nice if everyone in the room was introduced. Sometimes people are sitting out of view of the camera and I don’t realize they’re there until later.” –Clinician

These results make important contributions to understanding and improving the telemedicine experience in rural emergency hospital medicine. While the predominantly white patient respondent population limits generalizability, these demographics are representative of the overall population of the participating hospitals. A strength of this evaluation is its contemporaneous consideration of patient and care team experience with both quantitative and rich, qualitative analysis. Patients and care teams alike thought overnight telemedicine was better than the status quo. While our quality of care findings align with some previous literature,¹³ care teams in the current analysis overwhelmingly would recommend telemedicine, whereas some clinicians in prior work would not recommend telemedicine.¹⁴

In terms of communication, in line with existing literature, some patients still preferred in-person visits,¹⁵ a view also shared by some care team members. Workflow and technology barriers were raised, corroborating existing work,¹³ but actionable solutions (eg, adding care team–only time before visits or verbalizing when to move stethoscopes) were also identified.

Embedding patient and care team experience surveys and sharing results is critical in advancing telemedicine. Findings from this evaluation strengthen the case for payer reimbursement of telemedicine in rural acute care. Continued work to improve, test, and publish findings on patient and care team experience with telemedicine is critical to providing quality services in often-underserved communities.

The authors would like to acknowledge the contributions of Ann Werner in identifying the patient survey sample, Brian Barklind in identifying source data for the analysis, and both Brian Barklind and Rachael Rivard for conducting the analyses and summarizing results. We would also like to thank Kelly Logue for her involvement in conceptualizing the telemedicine evaluation described here, as well as Larisa Polynskaya for her help preparing the manuscript for publication, and the care teams and patients who provided valuable input.

Healthcare delivery in rural America faces unique, growing challenges related to health and emergency care access.¹ Telemedicine approaches have the potential to increase rural hospitals’ ability to deliver efficient emergency care and reduce clinician shortages.² While initial evidence of telemedicine success exists, more quality research is needed to understand telemedicine patient and care team experiences,³ especially with real-time, clinician-initiated video conferencing in critical access hospital (CAH) emergency departments (ED). Some experience studies exist,⁴ but results are primarily quantitative⁵ and lack the nuanced qualitative depth needed to understand topics such as satisfaction and communication.⁶ Additionally, few explore combined patient and care team perspectives.⁵ The lack of breadth and depth makes it difficult to provide actionable recommendations for improvements and affects the feasibility of continuing this work and improving telemedicine care quality. To address these gaps, we evaluated a real-time, clinician-­initiated video conferencing program with overnight clinicians servicing ED patients in three Midwestern care system CAHs. This evaluation assessed patient and care team (nurse and clinician) experience with telemedicine using quantitative and qualitative survey data analysis.

Because this evaluation was designed to measure and improve program quality in a single healthcare system, it was deemed non-human subjects research by the organization’s institutional review board. This brief report follows telemedicine reporting guidelines.⁷

Setting and Telemedicine Program

This program, designed to reduce the need for on-call hospitalist clinicians to be onsite at CAHs overnight, was implemented in a large Midwestern nonprofit integrated healthcare system with three rural CAHs (combined capacity for 75 inpatient admissions, with full-time onsite ED clinicians and nurses, as well as on-call hospitalist clinicians) and a large metropolitan tertiary-care hospital. All adult patients presenting to CAH EDs between 6 pm and 8 am were evaluated, as usual, by an onsite ED clinician. If the admitting ED clinician and charge nurse determined that admission was appropriate, patients were signed out to remote hospitalist clinicians and roomed by onsite nurses. Nurses facilitated live audio-video telemedicine “history and physical” visits with remote clinicians via telemedicine carts (AmericanWell C750, Boston, Massachusetts, and ThinkLabs One Electronic Stethoscope, Centennial, Colorado). Already-­hospitalized patients, as well as patients admitted to a remote clinician, were cared for by the remote clinician and onsite nurse for the remainder of the night, which eliminated the need for local on-call clinicians. The onsite ED clinician responded to emergencies of already-hospitalized patients, but often consulted with remote clinicians to assist virtually with necessary orders and documentation. Remote clinicians were located at the metropolitan tertiary care hospital or home work stations.

Following a pilot period, the full-scale program was implemented in September 2017 and included 14 remote clinicians and 60 onsite nurses.

Survey Administration and Design

A postimplementation survey was designed to explore patient and care team experience with telemedicine. Patients who received a telemedicine visit between September 2017 and April 2018 were mailed a paper survey. Nonresponders were called by professional interviewers affiliated with the healthcare system. All participating clinicians (N = 14, all MDs) and nurses (N = 60, all RNs) were emailed an online care team survey with phone-in option. Care team nonresponders were sent up to two reminder emails.

Surveys captured the following five constructs: communication, workflow integration, telemedicine technology, quality of care, and general satisfaction. Existing questionnaires were used where possible; additional items were designed with clinical experts following survey design best practices.⁸ Patient-perceived communication was assessed via three Consumer Assessment of Healthcare Providers and Systems Outpatient and Ambulatory Surgery Survey items.⁹ Five additional program-developed patient survey items included satisfaction with clinician-nurse communication, satisfaction with technology, telemedicine quality of care overall and in comparison with traditional care, and whether or not patients would recommend telemedicine (Table). Four open-ended questions asked patients about improvement opportunities and general satisfaction.

Care team surveys included two items regarding ability to effectively communicate, two about satisfaction with workflow integration, one about technical problems, two about quality of care, and one about general satisfaction. Open-ended questions gathered further information and recommendations to improve communication, workflow integration, technology issues, and general satisfaction.

Analysis

Closed-ended items were dichotomized (satisfied yes/no); descriptive statistics (frequencies/percents) are presented to quantify patient and care team experience. Quantitative analyses were conducted in SAS software version 9.4 (SAS Institute, Cary, North Carolina). Open-ended responses were coded separately for patient and care team experience, following qualitative content analysis best practices.¹⁰ A lead coder read all responses, created a coding framework of identified themes, and coded individual responses. A second coder independently coded responses using the same framework. Interrater reliability was calculated for each major theme using percent agreement and prevalence- and bias-adjusted k (PABAK) statistic. A single representative quote was selected and lightly edited for each subtheme to deepen understanding and provide respondent voice.¹¹

Of eligible patients mailed a survey (N = 408), 3% self-reported as ineligible, and 54% completed the survey. This is a maximum response rate (response rate 6) according to the American Association for Public Opinion Research.¹² Patients were 67 years old on average (SD = 15), they were primarily white (97%), and 54% were female. All clinicians and 63% of nurses completed the survey.¹² Clinicians and nurses were 29% and 95% female, respectively.

Quantitative results (Table) show generally positive experience across patient and care team respondents. Over 90% were satisfied with all measures of communication. Care teams had high satisfaction with admissions processes and reported telemedicine improved cross-coverage. Patient-reported technology experience was positive but was less positive from the care team perspective. Care teams reported lower absolute quality of care than did patients but were more likely to perceive telemedicine as high quality, compared with traditional care. Most patients, clinicians, and nurses would recommend telemedicine.

Qualitatively, four major themes were identified in open-­ended responses with high interrater reliability (PABAK ranging from 0.92 to 0.98 in patient responses and 0.88 to 0.95 in care team responses) and aligned with the quantitative survey constructs: clinician-nurse communication, clinician-patient communication, workflow integration, and telemedicine technology. Patients reported satisfaction with communication with remote clinicians:

“[The clinician] was extremely attentive to me and what was going on. She was articulate and clear. I understood what was going to happen.” –Patient

Care teams suggested concrete improvement opportunities:

“I’d prefer to have some time with nursing staff both before and (sometimes) after the patient encounter.” –Clinician

“Since we cannot hear what [the clinicians] are hearing with the stethoscope, it’s nice when they tell us when to move it to the next spot.” –Nurse

Clinicians and nurses gave favorable responses regarding workflow integration, though time (both admissions wait time and session duration) was a reported opportunity:

“It would be helpful if we could speed up the time from admit request to screen time.” –Clinician

“When the [clinicians] get swamped, they’re hard to get a hold of, and admissions can take a long time. They may have too much on their plates dealing with several locations.” –Nurse

Technology issues—internet connection, stethoscope, sound, and screen or camera—were mentioned by patients and care teams, though technology was reviewed favorably overall by most patients:

“I was fascinated by the technology. Visiting someone over a television was impressive. ... The picture, the sound clarity, and the connection itself was flawless.” –Patient

Some patients commented that telemedicine was the best option given the situation, but still preferred an in-person doctor:

“If a doctor wasn’t available, telemedicine is better than nothing.” –Patient

Nurses who would not recommend telemedicine noted the need for personal connection:

“[I] still prefer [an] in-person MD for more personal contact. The older patients often state they wish the doctor would come and see them.” –Nurse

Patients who would not recommend telemedicine also desired personal connection:

“I would sooner talk to a person than a machine.” –Patient

A few clinicians noted the connection with patients would be improved if they knew about others in the room:

“It’d be nice if everyone in the room was introduced. Sometimes people are sitting out of view of the camera and I don’t realize they’re there until later.” –Clinician

These results make important contributions to understanding and improving the telemedicine experience in rural emergency hospital medicine. While the predominantly white patient respondent population limits generalizability, these demographics are representative of the overall population of the participating hospitals. A strength of this evaluation is its contemporaneous consideration of patient and care team experience with both quantitative and rich, qualitative analysis. Patients and care teams alike thought overnight telemedicine was better than the status quo. While our quality of care findings align with some previous literature,¹³ care teams in the current analysis overwhelmingly would recommend telemedicine, whereas some clinicians in prior work would not recommend telemedicine.¹⁴

In terms of communication, in line with existing literature, some patients still preferred in-person visits,¹⁵ a view also shared by some care team members. Workflow and technology barriers were raised, corroborating existing work,¹³ but actionable solutions (eg, adding care team–only time before visits or verbalizing when to move stethoscopes) were also identified.

Embedding patient and care team experience surveys and sharing results is critical in advancing telemedicine. Findings from this evaluation strengthen the case for payer reimbursement of telemedicine in rural acute care. Continued work to improve, test, and publish findings on patient and care team experience with telemedicine is critical to providing quality services in often-underserved communities.

The authors would like to acknowledge the contributions of Ann Werner in identifying the patient survey sample, Brian Barklind in identifying source data for the analysis, and both Brian Barklind and Rachael Rivard for conducting the analyses and summarizing results. We would also like to thank Kelly Logue for her involvement in conceptualizing the telemedicine evaluation described here, as well as Larisa Polynskaya for her help preparing the manuscript for publication, and the care teams and patients who provided valuable input.

Sleep disturbance is common in hospitals, and both the quality and quantity of sleep are negatively affected in hospitalized patients.¹ Sleep disturbances in hospitals are associated with hyperglycemia,² delirium,³ lower patient satisfaction,⁴ and increased risk of readmission.⁵

A significant proportion of hospitalized patients receive sleep medications (ie, hypnotic medication) despite limited evidence.^6,7 Sleep medications have adverse effects including falls, fractures, cognitive impairment, and delirium.⁸ Commonly used nonbenzodiazepine sleep medications (eg, zopiclone) are perceived as safer but may have similar risks.⁹

Melatonin is increasingly used to treat insomnia, although evidence for its efficacy and safety is lacking.¹⁰ While melatonin use doubled between 2007 and 2012 in the United States,¹¹ previous hospital-based studies have not included melatonin.⁷ It is not known if increased melatonin use in the hospital mitigates use of higher-risk medications. Melatonin preparations can also have quality issues, including deviations from labelled dosage and contamination with compounds such as serotonin,¹² and patients continuing melatonin after discharge could have adverse effects if switched to different preparations.

In this study, we aimed to determine temporal trends in melatonin use in hospitalized patients, and compare them with trends in use of other sleep medications.

We conducted the study at two urban academic hospitals with a total of 706 acute care beds in the same network in Toronto, Canada. This study was approved by the University Health Network’s research ethics board.

We abstracted pharmacy dispensing data on melatonin, zopiclone, and lorazepam from January 1, 2013, to December 31, 2018. We included oral medications dispensed to inpatient units or admitted patients in the emergency department (ED). Zopiclone is the most commonly used nonbenzodiazepine sleep medication in Canada.¹³ Lorazepam is the most commonly used benzodiazepine at our institution (97% of all oral benzodiazepine doses). While lorazepam is prescribed for many nonsleep indications, we included it to assess the impact of melatonin. We did not include antipsychotics or trazodone, which are rarely newly initiated for insomnia at our institution.

We abstracted the monthly number of doses dispensed by unit and hospital. We categorized units based on the primary patient population as either internal medicine, critical care, or other. Admitted patients in the ED were counted as “other” regardless of service. As the focus of our study was on internal medicine and critical care, we did not analyze by type of unit in the “other” group, which is heterogeneous.

Each medication-dispensing event was counted as one dose, regardless of the number or strength of tablets (eg, a patient dispensed two 3-mg tablets of melatonin, for a total of 6 mg, would be counted as a single dose). Most unused doses are credited back (ie, if a medication was refused and returned to pharmacy, it was not counted). Lorazepam and zopiclone are on hospital formulary, while melatonin is not. To order melatonin, clinicians must select “Nonformulary medication” in the electronic health record and manually enter medication name, dose, route, and frequency, as well as select a justification for use. The hospital supplies nonformulary medications such as melatonin to patients.

To account for changes in patient volumes, we standardized medication dispensing rates per 1,000 inpatient days. We discovered rare instances in which the monthly number of doses was a negative number because of pharmacy inventory accounting. This issue affected only 0.13% of observations and the magnitude was small (8 doses or fewer); in these cases, we assumed the number of doses was zero.

We used line charts to visualize changes in medication dispensing over time by medication and hospital. We compared rates of medications use between unit type and hospital with use of relative difference and rate difference. Statistical analysis was performed with R (The R Foundation for Statistical Computing, 2018) using lubridate (2011), dplyr (2018), ggplot2 (2016), fmsb (2019), and forcats (2018).

A total of 1,542,225 inpatient days were analyzed, of which 60.4% were at hospital A. Internal medicine accounted for 23.5% of inpatient days, critical care for 11.7%, and other units for 64.8%.

Overall Trends in Sleep Medication Use

There were 351,131 dispensed doses of study medications (13% melatonin, 43% lorazepam, and 44% zopiclone). Overall use of the three study medications per 1,000 inpatient days increased by 25.7% during the study.

Melatonin use increased by 71.3 doses per 1,000 inpatient days during 2013-2018, while zopiclone use decreased by 20.4 doses per 1,000 inpatient days (Table). Lorazepam use increased slightly by 3.5 doses per 1,000 inpatient days. All rate differences reported in the results are statistically significant (Appendix Table).

Unit Type Comparison

Melatonin use was highest in critical care and internal medicine (50.9 and 48.4 doses per 1,000 inpatient days, respectively), compared with that in other units (19.3 doses per 1,000 inpatient days). Among critical care units, melatonin use was highest in medical-surgical units (67.4 doses per 1,000 inpatient days) and lower in cardiac and cardiovascular surgery units (24.6 and 18.3 doses per 1,000 inpatient days respectively). Zopiclone use was highest in critical care and other units (117.9 and 112.2 doses per 1,000 inpatient days, respectively) and lowest in internal medicine (57.0 doses per 1,000 inpatient days).

Hospital Site Comparison

Overall melatonin use was 65.4% higher at hospital B than at hospital A (42.4 vs 21.5 doses per 1,000 inpatient days; Figure). Zopiclone use was 81.7% lower at hospital B (54.6 vs 130.0 doses per 1,000 inpatient days).

When similar units were compared between hospitals, the trends were similar. For example, among internal medicine units, melatonin use was 66.7% higher at hospital B than at hospital A (64.4 vs 32.3 doses per 1,000 inpatient days).

During this 6-year study period of sleep medication use at two academic hospitals, overall use of melatonin, zopiclone, and lorazepam increased by 25.7%. Melatonin increased from almost no use to more than 70 doses per 1,000 inpatient days. The increase in melatonin was not accompanied by a proportional decline in zopiclone, which only decreased by 20.4 doses per 1,000 inpatient days. Lorazepam use increased slightly. This suggests that melatonin is not simply being substituted for higher-risk sleep medications and is instead being given to patients who might not have received sleep medications otherwise.

There are a few potential explanations for the disproportionate increase in melatonin. Providers may be more liberal in prescribing melatonin for insomnia because of perceived greater safety, compared with other medications. Melatonin may also be prescribed for delirium, despite a lack of high-quality evidence.¹⁴ Interestingly, melatonin use has increased despite a paucity of evidence for its efficacy or safety in hospital.⁶ Considering the additional barriers that exist to ordering melatonin, a nonformulary medication at our institution, the magnitude of increase is even more striking.

Melatonin use was highest on internal medicine and critical care units. This may reflect patient differences (eg, older patients with more comorbid conditions might leave prescribers reluctant to use benzodiazepines), differences in the physical environment (eg, noise/lighting), differences in nursing practices (eg, intensity of monitoring or medication administration), or differences in prescribing.

Melatonin use was almost twice as high at hospital B as it was at hospital A. While the services differ at each hospital, the results were similar when comparing the same unit type (eg, internal medicine). Internal medicine units have similar (though not identical) patient populations and team structures at both hospitals, and residents rotate between hospitals. Attendings and nurses are based primarily at one hospital and their practice patterns might differ. Geriatricians have a stronger presence at hospital B. Higher zopiclone use at hospital A could explain lower melatonin use. Lastly, improvement initiatives may have contributed (eg, one unit at hospital B promoted melatonin in 2017).

Our study has potential limitations. We studied dispensed rather than administered medications; however, numbers of doses dispensed but not administered are expected to be low because most unused doses are accounted for. By studying dispensing data, we might have underestimated the number of prescriptions (eg, if a patient was prescribed but refused a medication, this would not be captured). Our study did not examine medications after hospital discharge, although medications started in a hospital are often continued at discharge.^7,15 Our study could not determine indications for medication prescribing, and melatonin and lorazepam are both used for nonsleep indications. Our study could not differentiate between continuation of home medications and new prescriptions. Finally, the results may not be generalizable to other settings.

In this 6-year study of sleep medication use at two academic hospitals, we found that overall use of melatonin, zopiclone, and lorazepam increased by 25%, predominantly because of markedly increased melatonin use. Given the current lack of high-quality evidence, further research on the use of melatonin in hospitalized patients is needed.

Sleep disturbance is common in hospitals, and both the quality and quantity of sleep are negatively affected in hospitalized patients.¹ Sleep disturbances in hospitals are associated with hyperglycemia,² delirium,³ lower patient satisfaction,⁴ and increased risk of readmission.⁵

A significant proportion of hospitalized patients receive sleep medications (ie, hypnotic medication) despite limited evidence.^6,7 Sleep medications have adverse effects including falls, fractures, cognitive impairment, and delirium.⁸ Commonly used nonbenzodiazepine sleep medications (eg, zopiclone) are perceived as safer but may have similar risks.⁹

Melatonin is increasingly used to treat insomnia, although evidence for its efficacy and safety is lacking.¹⁰ While melatonin use doubled between 2007 and 2012 in the United States,¹¹ previous hospital-based studies have not included melatonin.⁷ It is not known if increased melatonin use in the hospital mitigates use of higher-risk medications. Melatonin preparations can also have quality issues, including deviations from labelled dosage and contamination with compounds such as serotonin,¹² and patients continuing melatonin after discharge could have adverse effects if switched to different preparations.

In this study, we aimed to determine temporal trends in melatonin use in hospitalized patients, and compare them with trends in use of other sleep medications.

We conducted the study at two urban academic hospitals with a total of 706 acute care beds in the same network in Toronto, Canada. This study was approved by the University Health Network’s research ethics board.

We abstracted pharmacy dispensing data on melatonin, zopiclone, and lorazepam from January 1, 2013, to December 31, 2018. We included oral medications dispensed to inpatient units or admitted patients in the emergency department (ED). Zopiclone is the most commonly used nonbenzodiazepine sleep medication in Canada.¹³ Lorazepam is the most commonly used benzodiazepine at our institution (97% of all oral benzodiazepine doses). While lorazepam is prescribed for many nonsleep indications, we included it to assess the impact of melatonin. We did not include antipsychotics or trazodone, which are rarely newly initiated for insomnia at our institution.

We abstracted the monthly number of doses dispensed by unit and hospital. We categorized units based on the primary patient population as either internal medicine, critical care, or other. Admitted patients in the ED were counted as “other” regardless of service. As the focus of our study was on internal medicine and critical care, we did not analyze by type of unit in the “other” group, which is heterogeneous.

Each medication-dispensing event was counted as one dose, regardless of the number or strength of tablets (eg, a patient dispensed two 3-mg tablets of melatonin, for a total of 6 mg, would be counted as a single dose). Most unused doses are credited back (ie, if a medication was refused and returned to pharmacy, it was not counted). Lorazepam and zopiclone are on hospital formulary, while melatonin is not. To order melatonin, clinicians must select “Nonformulary medication” in the electronic health record and manually enter medication name, dose, route, and frequency, as well as select a justification for use. The hospital supplies nonformulary medications such as melatonin to patients.

To account for changes in patient volumes, we standardized medication dispensing rates per 1,000 inpatient days. We discovered rare instances in which the monthly number of doses was a negative number because of pharmacy inventory accounting. This issue affected only 0.13% of observations and the magnitude was small (8 doses or fewer); in these cases, we assumed the number of doses was zero.

We used line charts to visualize changes in medication dispensing over time by medication and hospital. We compared rates of medications use between unit type and hospital with use of relative difference and rate difference. Statistical analysis was performed with R (The R Foundation for Statistical Computing, 2018) using lubridate (2011), dplyr (2018), ggplot2 (2016), fmsb (2019), and forcats (2018).

A total of 1,542,225 inpatient days were analyzed, of which 60.4% were at hospital A. Internal medicine accounted for 23.5% of inpatient days, critical care for 11.7%, and other units for 64.8%.

Overall Trends in Sleep Medication Use

There were 351,131 dispensed doses of study medications (13% melatonin, 43% lorazepam, and 44% zopiclone). Overall use of the three study medications per 1,000 inpatient days increased by 25.7% during the study.

Melatonin use increased by 71.3 doses per 1,000 inpatient days during 2013-2018, while zopiclone use decreased by 20.4 doses per 1,000 inpatient days (Table). Lorazepam use increased slightly by 3.5 doses per 1,000 inpatient days. All rate differences reported in the results are statistically significant (Appendix Table).

Unit Type Comparison

Melatonin use was highest in critical care and internal medicine (50.9 and 48.4 doses per 1,000 inpatient days, respectively), compared with that in other units (19.3 doses per 1,000 inpatient days). Among critical care units, melatonin use was highest in medical-surgical units (67.4 doses per 1,000 inpatient days) and lower in cardiac and cardiovascular surgery units (24.6 and 18.3 doses per 1,000 inpatient days respectively). Zopiclone use was highest in critical care and other units (117.9 and 112.2 doses per 1,000 inpatient days, respectively) and lowest in internal medicine (57.0 doses per 1,000 inpatient days).

Hospital Site Comparison

Overall melatonin use was 65.4% higher at hospital B than at hospital A (42.4 vs 21.5 doses per 1,000 inpatient days; Figure). Zopiclone use was 81.7% lower at hospital B (54.6 vs 130.0 doses per 1,000 inpatient days).

When similar units were compared between hospitals, the trends were similar. For example, among internal medicine units, melatonin use was 66.7% higher at hospital B than at hospital A (64.4 vs 32.3 doses per 1,000 inpatient days).

During this 6-year study period of sleep medication use at two academic hospitals, overall use of melatonin, zopiclone, and lorazepam increased by 25.7%. Melatonin increased from almost no use to more than 70 doses per 1,000 inpatient days. The increase in melatonin was not accompanied by a proportional decline in zopiclone, which only decreased by 20.4 doses per 1,000 inpatient days. Lorazepam use increased slightly. This suggests that melatonin is not simply being substituted for higher-risk sleep medications and is instead being given to patients who might not have received sleep medications otherwise.

There are a few potential explanations for the disproportionate increase in melatonin. Providers may be more liberal in prescribing melatonin for insomnia because of perceived greater safety, compared with other medications. Melatonin may also be prescribed for delirium, despite a lack of high-quality evidence.¹⁴ Interestingly, melatonin use has increased despite a paucity of evidence for its efficacy or safety in hospital.⁶ Considering the additional barriers that exist to ordering melatonin, a nonformulary medication at our institution, the magnitude of increase is even more striking.

Melatonin use was highest on internal medicine and critical care units. This may reflect patient differences (eg, older patients with more comorbid conditions might leave prescribers reluctant to use benzodiazepines), differences in the physical environment (eg, noise/lighting), differences in nursing practices (eg, intensity of monitoring or medication administration), or differences in prescribing.

Melatonin use was almost twice as high at hospital B as it was at hospital A. While the services differ at each hospital, the results were similar when comparing the same unit type (eg, internal medicine). Internal medicine units have similar (though not identical) patient populations and team structures at both hospitals, and residents rotate between hospitals. Attendings and nurses are based primarily at one hospital and their practice patterns might differ. Geriatricians have a stronger presence at hospital B. Higher zopiclone use at hospital A could explain lower melatonin use. Lastly, improvement initiatives may have contributed (eg, one unit at hospital B promoted melatonin in 2017).

Our study has potential limitations. We studied dispensed rather than administered medications; however, numbers of doses dispensed but not administered are expected to be low because most unused doses are accounted for. By studying dispensing data, we might have underestimated the number of prescriptions (eg, if a patient was prescribed but refused a medication, this would not be captured). Our study did not examine medications after hospital discharge, although medications started in a hospital are often continued at discharge.^7,15 Our study could not determine indications for medication prescribing, and melatonin and lorazepam are both used for nonsleep indications. Our study could not differentiate between continuation of home medications and new prescriptions. Finally, the results may not be generalizable to other settings.

In this 6-year study of sleep medication use at two academic hospitals, we found that overall use of melatonin, zopiclone, and lorazepam increased by 25%, predominantly because of markedly increased melatonin use. Given the current lack of high-quality evidence, further research on the use of melatonin in hospitalized patients is needed.

Sleep disturbance is common in hospitals, and both the quality and quantity of sleep are negatively affected in hospitalized patients.¹ Sleep disturbances in hospitals are associated with hyperglycemia,² delirium,³ lower patient satisfaction,⁴ and increased risk of readmission.⁵

A significant proportion of hospitalized patients receive sleep medications (ie, hypnotic medication) despite limited evidence.^6,7 Sleep medications have adverse effects including falls, fractures, cognitive impairment, and delirium.⁸ Commonly used nonbenzodiazepine sleep medications (eg, zopiclone) are perceived as safer but may have similar risks.⁹

Melatonin is increasingly used to treat insomnia, although evidence for its efficacy and safety is lacking.¹⁰ While melatonin use doubled between 2007 and 2012 in the United States,¹¹ previous hospital-based studies have not included melatonin.⁷ It is not known if increased melatonin use in the hospital mitigates use of higher-risk medications. Melatonin preparations can also have quality issues, including deviations from labelled dosage and contamination with compounds such as serotonin,¹² and patients continuing melatonin after discharge could have adverse effects if switched to different preparations.

In this study, we aimed to determine temporal trends in melatonin use in hospitalized patients, and compare them with trends in use of other sleep medications.

We conducted the study at two urban academic hospitals with a total of 706 acute care beds in the same network in Toronto, Canada. This study was approved by the University Health Network’s research ethics board.

We abstracted pharmacy dispensing data on melatonin, zopiclone, and lorazepam from January 1, 2013, to December 31, 2018. We included oral medications dispensed to inpatient units or admitted patients in the emergency department (ED). Zopiclone is the most commonly used nonbenzodiazepine sleep medication in Canada.¹³ Lorazepam is the most commonly used benzodiazepine at our institution (97% of all oral benzodiazepine doses). While lorazepam is prescribed for many nonsleep indications, we included it to assess the impact of melatonin. We did not include antipsychotics or trazodone, which are rarely newly initiated for insomnia at our institution.

We abstracted the monthly number of doses dispensed by unit and hospital. We categorized units based on the primary patient population as either internal medicine, critical care, or other. Admitted patients in the ED were counted as “other” regardless of service. As the focus of our study was on internal medicine and critical care, we did not analyze by type of unit in the “other” group, which is heterogeneous.

Each medication-dispensing event was counted as one dose, regardless of the number or strength of tablets (eg, a patient dispensed two 3-mg tablets of melatonin, for a total of 6 mg, would be counted as a single dose). Most unused doses are credited back (ie, if a medication was refused and returned to pharmacy, it was not counted). Lorazepam and zopiclone are on hospital formulary, while melatonin is not. To order melatonin, clinicians must select “Nonformulary medication” in the electronic health record and manually enter medication name, dose, route, and frequency, as well as select a justification for use. The hospital supplies nonformulary medications such as melatonin to patients.

To account for changes in patient volumes, we standardized medication dispensing rates per 1,000 inpatient days. We discovered rare instances in which the monthly number of doses was a negative number because of pharmacy inventory accounting. This issue affected only 0.13% of observations and the magnitude was small (8 doses or fewer); in these cases, we assumed the number of doses was zero.

We used line charts to visualize changes in medication dispensing over time by medication and hospital. We compared rates of medications use between unit type and hospital with use of relative difference and rate difference. Statistical analysis was performed with R (The R Foundation for Statistical Computing, 2018) using lubridate (2011), dplyr (2018), ggplot2 (2016), fmsb (2019), and forcats (2018).

A total of 1,542,225 inpatient days were analyzed, of which 60.4% were at hospital A. Internal medicine accounted for 23.5% of inpatient days, critical care for 11.7%, and other units for 64.8%.

Overall Trends in Sleep Medication Use

There were 351,131 dispensed doses of study medications (13% melatonin, 43% lorazepam, and 44% zopiclone). Overall use of the three study medications per 1,000 inpatient days increased by 25.7% during the study.

Melatonin use increased by 71.3 doses per 1,000 inpatient days during 2013-2018, while zopiclone use decreased by 20.4 doses per 1,000 inpatient days (Table). Lorazepam use increased slightly by 3.5 doses per 1,000 inpatient days. All rate differences reported in the results are statistically significant (Appendix Table).

Unit Type Comparison

Melatonin use was highest in critical care and internal medicine (50.9 and 48.4 doses per 1,000 inpatient days, respectively), compared with that in other units (19.3 doses per 1,000 inpatient days). Among critical care units, melatonin use was highest in medical-surgical units (67.4 doses per 1,000 inpatient days) and lower in cardiac and cardiovascular surgery units (24.6 and 18.3 doses per 1,000 inpatient days respectively). Zopiclone use was highest in critical care and other units (117.9 and 112.2 doses per 1,000 inpatient days, respectively) and lowest in internal medicine (57.0 doses per 1,000 inpatient days).

Hospital Site Comparison

Overall melatonin use was 65.4% higher at hospital B than at hospital A (42.4 vs 21.5 doses per 1,000 inpatient days; Figure). Zopiclone use was 81.7% lower at hospital B (54.6 vs 130.0 doses per 1,000 inpatient days).

When similar units were compared between hospitals, the trends were similar. For example, among internal medicine units, melatonin use was 66.7% higher at hospital B than at hospital A (64.4 vs 32.3 doses per 1,000 inpatient days).

During this 6-year study period of sleep medication use at two academic hospitals, overall use of melatonin, zopiclone, and lorazepam increased by 25.7%. Melatonin increased from almost no use to more than 70 doses per 1,000 inpatient days. The increase in melatonin was not accompanied by a proportional decline in zopiclone, which only decreased by 20.4 doses per 1,000 inpatient days. Lorazepam use increased slightly. This suggests that melatonin is not simply being substituted for higher-risk sleep medications and is instead being given to patients who might not have received sleep medications otherwise.

There are a few potential explanations for the disproportionate increase in melatonin. Providers may be more liberal in prescribing melatonin for insomnia because of perceived greater safety, compared with other medications. Melatonin may also be prescribed for delirium, despite a lack of high-quality evidence.¹⁴ Interestingly, melatonin use has increased despite a paucity of evidence for its efficacy or safety in hospital.⁶ Considering the additional barriers that exist to ordering melatonin, a nonformulary medication at our institution, the magnitude of increase is even more striking.

Melatonin use was highest on internal medicine and critical care units. This may reflect patient differences (eg, older patients with more comorbid conditions might leave prescribers reluctant to use benzodiazepines), differences in the physical environment (eg, noise/lighting), differences in nursing practices (eg, intensity of monitoring or medication administration), or differences in prescribing.

Melatonin use was almost twice as high at hospital B as it was at hospital A. While the services differ at each hospital, the results were similar when comparing the same unit type (eg, internal medicine). Internal medicine units have similar (though not identical) patient populations and team structures at both hospitals, and residents rotate between hospitals. Attendings and nurses are based primarily at one hospital and their practice patterns might differ. Geriatricians have a stronger presence at hospital B. Higher zopiclone use at hospital A could explain lower melatonin use. Lastly, improvement initiatives may have contributed (eg, one unit at hospital B promoted melatonin in 2017).

Our study has potential limitations. We studied dispensed rather than administered medications; however, numbers of doses dispensed but not administered are expected to be low because most unused doses are accounted for. By studying dispensing data, we might have underestimated the number of prescriptions (eg, if a patient was prescribed but refused a medication, this would not be captured). Our study did not examine medications after hospital discharge, although medications started in a hospital are often continued at discharge.^7,15 Our study could not determine indications for medication prescribing, and melatonin and lorazepam are both used for nonsleep indications. Our study could not differentiate between continuation of home medications and new prescriptions. Finally, the results may not be generalizable to other settings.

In this 6-year study of sleep medication use at two academic hospitals, we found that overall use of melatonin, zopiclone, and lorazepam increased by 25%, predominantly because of markedly increased melatonin use. Given the current lack of high-quality evidence, further research on the use of melatonin in hospitalized patients is needed.