Reports of cancer date back thousands of years to Egyptian texts. Its existence baffled scientists until the 1950s, when Watson, Crick, and Franklin discovered the structure of DNA, laying the groundwork for identifying the genetic pathways leading to cancer. Currently, cancer is a leading global cause of death and the second leading cause of death in the United States.^1,2

In an effort to curtail cancer and its related morbidity and mortality, population-based screening programs have been implemented with tests that identify precancerous lesions and, preferably, early-stage rather than late-stage cancer.

Screening for cancer can lead to early diagnosis and prevent death from cancer, but the topic continues to provoke controversy.

VALUE OF SCREENING QUESTIONED

In a commentary in the March 2019 Cleveland Clinic Journal of Medicine, Kim et al³ argued that cancer screening is not very effective and that we need to find the balance between the potential benefit and harm.

Using data from the US Preventive Services Task Force (USPSTF) and various studies, the authors showed that although screening can prevent some deaths from breast, colon, prostate, and lung cancer, at least 3 times as many people who are screened still die of those diseases. Given that screening does not eliminate all cancer deaths, has not been definitely shown to decrease the all-cause mortality rate, and has the potential to harm through false-positive results, overdiagnosis, and overtreatment, the authors questioned the utility of screening and encouraged us to discuss the benefits and harms with our patients.

In view of the apparently meager benefit, the USPSTF has relaxed its recommendations for screening for breast and prostate cancer in average-risk populations in recent years, a move that has evoked strong reactions from some clinicians. Proponents of screening argue that preventing late-stage cancers can save money, as the direct and indirect costs of morbidity associated with late-stage cancers are substantial, and that patients prefer screening when a test is available. Current models of screening efficacy do not take these factors into account.⁴

Kim et al, in defending the USPSTF’s position, suggested that the motivation for aggressive testing may be a belief that no harm is greater than the benefit of saving a life. They illustrated this through a Swiftian “modest proposal,” ie, universal prophylactic organectomy to prevent cancer. This hypothetical extreme measure would nearly eliminate the risk of cancer in the removed organs and prevent overdiagnosis and overtreatment of malignancies, but at substantial harm and cost.

In response to this proposal, we would like to point out the alternative extreme: stop all cancer screening programs. The pendulum would swing from what was previously considered a benefit—cancer prevention—to a harm, ie, cancer.

Observational studies, systematic reviews, meta-analyses, and modeling studies show that screening for cervical, colorectal, breast, and prostate cancer decreases disease-specific mortality.^5–11

For example, in lung cancer, the National Lung Screening Trial demonstrated reductions in disease-specific and overall mortality in patients at high risk who underwent low-dose screening computed tomography.¹²

In breast cancer, a systematic review demonstrated decreased disease-specific mortality for women ages 50 through 79 who underwent screening mammography.¹³

In cervical cancer, lower rates of cancer-related death and invasive cancer have also been shown with screening.¹⁴

In colorectal cancer, great strides have been made in reducing both the incidence of and mortality from this disease over the past 30 years through fecal occult blood testing. Early detection shifts the 5-year survival rate—14% for late-stage cancer—to over 90%.¹⁵ Colorectal cancer screening has also been shown to be cost-effective, with savings in excess of $30,000 per life-year gained from screening.¹⁶

Moreover, recent data from the Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) screening trial¹⁷ demonstrated a 2-fold higher overall non-cancer-related mortality rate in participants who did not adhere to screening compared with those who were fully adherent to all sex-specific PLCO screening tests when adjusted for age, sex, and ethnicity. Although a possible explanation is that people who adhere to screening recommendations are also likely to have a healthier lifestyle overall, the association persisted (although it was slightly attenuated) even after adjusting for medical risk and behavioral factors.

ON THIS WE CAN AGREE

Like Kim et al, we also believe an informed discussion of screening should occur with each patient—and challenge Kim et al to design an efficient and practical approach to allow providers to do so in a busy office visit aimed to address and manage other competing diseases.

In addition, medical science needs to improve. Methods to increase the efficacy of screening and decrease risks should be explored; these include improving test and operator performance, reducing nonadherence to screening, investigating novel biomarkers or precursors of cancer and pathways that escape current detection, and devising better risk-stratification tools.

Bodies such as the USPSTF should use models that account for factors not considered previously but important when informing patients of potential benefits and harm. Examples include varying sensitivities and specificities at different rounds of testing and accounting for the variability in risk or efficacy affected by race, ethnicity, sex, and patient preferences.

We practice in the era of evidence-based medicine. Guidelines and recommendations are based on the available evidence. As more studies are published, disease mechanisms are better understood, and the effects of previous recommendations are evaluated, cancer screening programs will be further refined or replaced. The balance between benefit and harm will be further delineated.

Kim et al knocked on the door of personalized medicine, where individual screening will be based on individual risk. Until that door is opened, screening should be personalized through the risk-benefit discussions we have with our patients. Ultimately, the choice to undergo screening is the patient’s.

Reports of cancer date back thousands of years to Egyptian texts. Its existence baffled scientists until the 1950s, when Watson, Crick, and Franklin discovered the structure of DNA, laying the groundwork for identifying the genetic pathways leading to cancer. Currently, cancer is a leading global cause of death and the second leading cause of death in the United States.^1,2

In an effort to curtail cancer and its related morbidity and mortality, population-based screening programs have been implemented with tests that identify precancerous lesions and, preferably, early-stage rather than late-stage cancer.

Screening for cancer can lead to early diagnosis and prevent death from cancer, but the topic continues to provoke controversy.

VALUE OF SCREENING QUESTIONED

In a commentary in the March 2019 Cleveland Clinic Journal of Medicine, Kim et al³ argued that cancer screening is not very effective and that we need to find the balance between the potential benefit and harm.

Using data from the US Preventive Services Task Force (USPSTF) and various studies, the authors showed that although screening can prevent some deaths from breast, colon, prostate, and lung cancer, at least 3 times as many people who are screened still die of those diseases. Given that screening does not eliminate all cancer deaths, has not been definitely shown to decrease the all-cause mortality rate, and has the potential to harm through false-positive results, overdiagnosis, and overtreatment, the authors questioned the utility of screening and encouraged us to discuss the benefits and harms with our patients.

In view of the apparently meager benefit, the USPSTF has relaxed its recommendations for screening for breast and prostate cancer in average-risk populations in recent years, a move that has evoked strong reactions from some clinicians. Proponents of screening argue that preventing late-stage cancers can save money, as the direct and indirect costs of morbidity associated with late-stage cancers are substantial, and that patients prefer screening when a test is available. Current models of screening efficacy do not take these factors into account.⁴

Kim et al, in defending the USPSTF’s position, suggested that the motivation for aggressive testing may be a belief that no harm is greater than the benefit of saving a life. They illustrated this through a Swiftian “modest proposal,” ie, universal prophylactic organectomy to prevent cancer. This hypothetical extreme measure would nearly eliminate the risk of cancer in the removed organs and prevent overdiagnosis and overtreatment of malignancies, but at substantial harm and cost.

In response to this proposal, we would like to point out the alternative extreme: stop all cancer screening programs. The pendulum would swing from what was previously considered a benefit—cancer prevention—to a harm, ie, cancer.

Observational studies, systematic reviews, meta-analyses, and modeling studies show that screening for cervical, colorectal, breast, and prostate cancer decreases disease-specific mortality.^5–11

For example, in lung cancer, the National Lung Screening Trial demonstrated reductions in disease-specific and overall mortality in patients at high risk who underwent low-dose screening computed tomography.¹²

In breast cancer, a systematic review demonstrated decreased disease-specific mortality for women ages 50 through 79 who underwent screening mammography.¹³

In cervical cancer, lower rates of cancer-related death and invasive cancer have also been shown with screening.¹⁴

In colorectal cancer, great strides have been made in reducing both the incidence of and mortality from this disease over the past 30 years through fecal occult blood testing. Early detection shifts the 5-year survival rate—14% for late-stage cancer—to over 90%.¹⁵ Colorectal cancer screening has also been shown to be cost-effective, with savings in excess of $30,000 per life-year gained from screening.¹⁶

Moreover, recent data from the Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) screening trial¹⁷ demonstrated a 2-fold higher overall non-cancer-related mortality rate in participants who did not adhere to screening compared with those who were fully adherent to all sex-specific PLCO screening tests when adjusted for age, sex, and ethnicity. Although a possible explanation is that people who adhere to screening recommendations are also likely to have a healthier lifestyle overall, the association persisted (although it was slightly attenuated) even after adjusting for medical risk and behavioral factors.

ON THIS WE CAN AGREE

Like Kim et al, we also believe an informed discussion of screening should occur with each patient—and challenge Kim et al to design an efficient and practical approach to allow providers to do so in a busy office visit aimed to address and manage other competing diseases.

In addition, medical science needs to improve. Methods to increase the efficacy of screening and decrease risks should be explored; these include improving test and operator performance, reducing nonadherence to screening, investigating novel biomarkers or precursors of cancer and pathways that escape current detection, and devising better risk-stratification tools.

Bodies such as the USPSTF should use models that account for factors not considered previously but important when informing patients of potential benefits and harm. Examples include varying sensitivities and specificities at different rounds of testing and accounting for the variability in risk or efficacy affected by race, ethnicity, sex, and patient preferences.

We practice in the era of evidence-based medicine. Guidelines and recommendations are based on the available evidence. As more studies are published, disease mechanisms are better understood, and the effects of previous recommendations are evaluated, cancer screening programs will be further refined or replaced. The balance between benefit and harm will be further delineated.

Kim et al knocked on the door of personalized medicine, where individual screening will be based on individual risk. Until that door is opened, screening should be personalized through the risk-benefit discussions we have with our patients. Ultimately, the choice to undergo screening is the patient’s.

Reports of cancer date back thousands of years to Egyptian texts. Its existence baffled scientists until the 1950s, when Watson, Crick, and Franklin discovered the structure of DNA, laying the groundwork for identifying the genetic pathways leading to cancer. Currently, cancer is a leading global cause of death and the second leading cause of death in the United States.^1,2

In an effort to curtail cancer and its related morbidity and mortality, population-based screening programs have been implemented with tests that identify precancerous lesions and, preferably, early-stage rather than late-stage cancer.

Screening for cancer can lead to early diagnosis and prevent death from cancer, but the topic continues to provoke controversy.

VALUE OF SCREENING QUESTIONED

In a commentary in the March 2019 Cleveland Clinic Journal of Medicine, Kim et al³ argued that cancer screening is not very effective and that we need to find the balance between the potential benefit and harm.

Using data from the US Preventive Services Task Force (USPSTF) and various studies, the authors showed that although screening can prevent some deaths from breast, colon, prostate, and lung cancer, at least 3 times as many people who are screened still die of those diseases. Given that screening does not eliminate all cancer deaths, has not been definitely shown to decrease the all-cause mortality rate, and has the potential to harm through false-positive results, overdiagnosis, and overtreatment, the authors questioned the utility of screening and encouraged us to discuss the benefits and harms with our patients.

In view of the apparently meager benefit, the USPSTF has relaxed its recommendations for screening for breast and prostate cancer in average-risk populations in recent years, a move that has evoked strong reactions from some clinicians. Proponents of screening argue that preventing late-stage cancers can save money, as the direct and indirect costs of morbidity associated with late-stage cancers are substantial, and that patients prefer screening when a test is available. Current models of screening efficacy do not take these factors into account.⁴

Kim et al, in defending the USPSTF’s position, suggested that the motivation for aggressive testing may be a belief that no harm is greater than the benefit of saving a life. They illustrated this through a Swiftian “modest proposal,” ie, universal prophylactic organectomy to prevent cancer. This hypothetical extreme measure would nearly eliminate the risk of cancer in the removed organs and prevent overdiagnosis and overtreatment of malignancies, but at substantial harm and cost.

In response to this proposal, we would like to point out the alternative extreme: stop all cancer screening programs. The pendulum would swing from what was previously considered a benefit—cancer prevention—to a harm, ie, cancer.

Observational studies, systematic reviews, meta-analyses, and modeling studies show that screening for cervical, colorectal, breast, and prostate cancer decreases disease-specific mortality.^5–11

For example, in lung cancer, the National Lung Screening Trial demonstrated reductions in disease-specific and overall mortality in patients at high risk who underwent low-dose screening computed tomography.¹²

In breast cancer, a systematic review demonstrated decreased disease-specific mortality for women ages 50 through 79 who underwent screening mammography.¹³

In cervical cancer, lower rates of cancer-related death and invasive cancer have also been shown with screening.¹⁴

In colorectal cancer, great strides have been made in reducing both the incidence of and mortality from this disease over the past 30 years through fecal occult blood testing. Early detection shifts the 5-year survival rate—14% for late-stage cancer—to over 90%.¹⁵ Colorectal cancer screening has also been shown to be cost-effective, with savings in excess of $30,000 per life-year gained from screening.¹⁶

Moreover, recent data from the Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) screening trial¹⁷ demonstrated a 2-fold higher overall non-cancer-related mortality rate in participants who did not adhere to screening compared with those who were fully adherent to all sex-specific PLCO screening tests when adjusted for age, sex, and ethnicity. Although a possible explanation is that people who adhere to screening recommendations are also likely to have a healthier lifestyle overall, the association persisted (although it was slightly attenuated) even after adjusting for medical risk and behavioral factors.

ON THIS WE CAN AGREE

Like Kim et al, we also believe an informed discussion of screening should occur with each patient—and challenge Kim et al to design an efficient and practical approach to allow providers to do so in a busy office visit aimed to address and manage other competing diseases.

In addition, medical science needs to improve. Methods to increase the efficacy of screening and decrease risks should be explored; these include improving test and operator performance, reducing nonadherence to screening, investigating novel biomarkers or precursors of cancer and pathways that escape current detection, and devising better risk-stratification tools.

Bodies such as the USPSTF should use models that account for factors not considered previously but important when informing patients of potential benefits and harm. Examples include varying sensitivities and specificities at different rounds of testing and accounting for the variability in risk or efficacy affected by race, ethnicity, sex, and patient preferences.

We practice in the era of evidence-based medicine. Guidelines and recommendations are based on the available evidence. As more studies are published, disease mechanisms are better understood, and the effects of previous recommendations are evaluated, cancer screening programs will be further refined or replaced. The balance between benefit and harm will be further delineated.

Kim et al knocked on the door of personalized medicine, where individual screening will be based on individual risk. Until that door is opened, screening should be personalized through the risk-benefit discussions we have with our patients. Ultimately, the choice to undergo screening is the patient’s.

“Twin berries on one stem, grievous damage has been done to both in regarding the Humanities and Science in any other light than complemental.”
—Sir William Osler¹

The year 2019 marks the 100th anniversary of Sir William Osler’s last public speech. Still reeling from the death of his only son in World War I, he had been asked to give the presidential inaugural address of the Classical Association at Oxford. It was the first time a physician had received the honor, and Osler took the assignment very seriously. He chose to speak about “The old humanities and the new science,” and to call for a reunification of the two fields. “Humanists have not enough Science” he warned, “and Science sadly lacks the Humanities…this unhappy divorce…should never have taken place.”¹  Later, he said that it was the speech to which he had given the greatest thought and preparation. It was in fact Osler’s personal legacy: 2 months later he turned 70, and 7 months later he was dead.

Revisiting the address today, what can Osler teach the high-tech physician of today, when doctors have become “providers” and patients “consumers”? Is Osler’s message still relevant to our craft, or has he simply become an icon of professional nostalgia with little value for our times?

THE NEED FOR THE HUMANITIES IN MEDICINE

Medicine has certainly grown both powerful and successful. Yet it is also confronting hurdles that would have been unimaginable in Osler’s time. Physicians are now the professionals with the highest suicide rate,² a burnout rate as high as 70%,^3,4 rampant depression,⁵ dwindling empathy,⁶ a predominantly negative perception by the public,^7,8 and a disturbing propensity to quit.⁹ These, of course, may just be symptoms of an increasingly meaningless environment wherein doctors have become small cogs in a medical-industrial complex they can’t control or even understand. Still, is it possible that something more personal may have been lost in the way we now select and educate physicians? Could this, in turn, make us less resilient?

In this regard, Osler’s last public speech serves as an enduring reminder of the need for the humanities in medicine. Osler not only believed it, but throughout his life never missed a chance to express in words, writings, and deeds that the humanities are indeed “the hormones” of the profession. In 1919 he warned against the risk of separating our humanistic tradition from the sciences, and urged us “to infect [anyone] with the spirit of the Humanities,” since to him that was “the greatest single gift in education.”¹

Unfortunately, the humanities are slippery, not easily quantifiable, hard to define, and seemingly incompatible with an evidence-based approach. Quite understandably, today’s data-obsessed medicine views them with suspicion. But besides reminding us that in medicine not all that counts can be counted, and not all that can be counted counts, the humanities are in fact a fundamental component of the physician’s skill set.

In a multicenter survey of 5 medical schools,¹⁰ there was indeed a correlation between students’ exposure to the humanities and many of the personal qualities whose absence we lament in today’s medicine: empathy, tolerance for ambiguity, emotional intelligence, and prevention of burnout. Most significant was a strong correlation with wisdom, as measured by the 21-item Brief Wisdom Screening Scale.¹¹ That all these traits may correlate with humanities exposure is intuitive, since the humanities not only teach tolerance and compassion, but also capture the collective experience of those who came before us. Hence, they teach us wisdom. Wisdom is not an ACGME competency, but it’s undoubtedly a prerequisite for the art of healing.¹² In fact, wisdom may very well be the fundamental trait that characterizes a well-rounded physician, since it encompasses empathy, resilience, comfort with ambiguity, and the capacity to learn from the past. Not surprisingly, wisdom in the world was Osler’s closing wish in 1919.

The humanities can also nurture the very personal qualities we desire in physicians. For example, observing drama fosters empathy,¹³ as does taking an elective in medical humanities.¹⁴ Drawing enhances the reading of faces,¹⁵ and observing art improves the art of clinical observation.¹⁶ Reading good literature prompts better detection of emotions,¹⁷ and reflective writing improves students’ well-being.¹⁸ Playing a musical instrument reduces burnout.¹⁹ And an undergraduate major in the humanities correlates with greater tolerance for ambiguity,²⁰ a highly desirable trait in physicians, since it means openness to new ideas and the capacity to better cope with difficult situations.²¹

In fact, some of the qualities fostered by the humanities even translate into better patient care. For instance, tolerance for ambiguity correlates with more positive attitudes towards patients who have frustrating complaints,²² with lower use of resources,²³ and with a career choice in direct patient care.²⁴ Hence, it has been suggested that it should be a prerequisite for medical school admission.²⁵ Physicians’ empathy is also beneficial, since it correlates with a lower rate of complications and better outcomes in the care of diabetic patients.²⁶ This should not come as a surprise. As Hippocrates put it 2,500 years ago, “some patients, though conscious that their condition is perilous, recover their health simply through their contentment with the goodness of the physician.”²⁷

Lastly, studying the humanities may provide crucial antibodies against the pain and suffering that are unavoidable staples of the human condition. To paraphrase Osler, the humanities might vaccinate us against the difficulties of our profession. Hippocrates himself had suggested that “it is well to superintend the sick to make them well, to care for the healthy to keep them well, but also to care for one’s self…”²⁷ That is why many institutions now require medical students to take humanities courses.²⁸

Yet this effort may amount to a rearguard action that arrives too late and provides too little. The humanities should probably be taught before medical school.²⁹ After all, if it’s possible to make a scientist out of a humanist (Osler was living proof), the experience of the past decades seems to suggest that it’s considerably harder to make a humanist out of a scientist—hence the need to revisit undergraduate curricula and admission criteria to medical school, so that students can receive an adequate foundation in both arenas. Ironically, students express positive attitudes toward a liberal education and think it would actually help them as physicians.³⁰ Yet they also understand that the selection process remains tilted towards the sciences.^30–32

For Osler, scientific evidence was important but not a substitute for a humanistic approach. As he reminded students, “The practice of medicine is an art based on science,”³³ whose main goals are to prevent disease, relieve suffering, and heal the sick. To do so, one ought to care more “for the individual patient than for the special features of the disease.”³⁴ But he warned them, “It is much harder to acquire the art than the science.”³⁵ In fact, “The practice of medicine is a calling in which your heart will be exercised equally with your head.”³³ Hence the need to “cultivate equally well hearts and heads.”³⁴ Almost foreseeing our infatuation with guidelines, he also warned against turning medicine into assembly-line work. There are “two great types of practitioners—the routinist and the rationalist,” he said in 1900, and “into the clutches of the demon routine the majority of us ultimately come.”³⁶

Like most great people, Osler was a man of lights, shadows, and contradictions, probably not quite the saint we wish to believe. Yet he provides insights that are as valid today as they were for his own times, and possibly even more so. His 1919 speech is a paean to the humanities, but also a potential eulogy. As a Victorian physician, Osler was a blend of the new science and the old humanities. He knew that “the old art cannot possibly be replaced by, but must be absorbed in, the new science.”³⁵ Yet he could also see the upcoming split between the two cultures, and he tried to warn us. He could in fact foresee the end of an entire way of life. As he said in his address, “there must be a very different civilization or there will be no civilization at all.”¹

The crisis we face in medicine today may indeed be a symptom of a much larger cultural shift. As Osler himself put it, “The philosophies of one age have become the absurdities of the next, and the foolishness of yesterday has become the wisdom of tomorrow.”³³ Like Osler, we live in times of transition that require us to act. If in 1910 Flexner gave us science,³⁷ Osler in 1919 reminded us that medicine also needs the humanities. We ought to heed his message and reconcile the two fields. The alternative is a future full of tricorders and burned-out technicians, but sorely lacking in healers.

“Twin berries on one stem, grievous damage has been done to both in regarding the Humanities and Science in any other light than complemental.”
—Sir William Osler¹

The year 2019 marks the 100th anniversary of Sir William Osler’s last public speech. Still reeling from the death of his only son in World War I, he had been asked to give the presidential inaugural address of the Classical Association at Oxford. It was the first time a physician had received the honor, and Osler took the assignment very seriously. He chose to speak about “The old humanities and the new science,” and to call for a reunification of the two fields. “Humanists have not enough Science” he warned, “and Science sadly lacks the Humanities…this unhappy divorce…should never have taken place.”¹  Later, he said that it was the speech to which he had given the greatest thought and preparation. It was in fact Osler’s personal legacy: 2 months later he turned 70, and 7 months later he was dead.

Revisiting the address today, what can Osler teach the high-tech physician of today, when doctors have become “providers” and patients “consumers”? Is Osler’s message still relevant to our craft, or has he simply become an icon of professional nostalgia with little value for our times?

THE NEED FOR THE HUMANITIES IN MEDICINE

Medicine has certainly grown both powerful and successful. Yet it is also confronting hurdles that would have been unimaginable in Osler’s time. Physicians are now the professionals with the highest suicide rate,² a burnout rate as high as 70%,^3,4 rampant depression,⁵ dwindling empathy,⁶ a predominantly negative perception by the public,^7,8 and a disturbing propensity to quit.⁹ These, of course, may just be symptoms of an increasingly meaningless environment wherein doctors have become small cogs in a medical-industrial complex they can’t control or even understand. Still, is it possible that something more personal may have been lost in the way we now select and educate physicians? Could this, in turn, make us less resilient?

In this regard, Osler’s last public speech serves as an enduring reminder of the need for the humanities in medicine. Osler not only believed it, but throughout his life never missed a chance to express in words, writings, and deeds that the humanities are indeed “the hormones” of the profession. In 1919 he warned against the risk of separating our humanistic tradition from the sciences, and urged us “to infect [anyone] with the spirit of the Humanities,” since to him that was “the greatest single gift in education.”¹

Unfortunately, the humanities are slippery, not easily quantifiable, hard to define, and seemingly incompatible with an evidence-based approach. Quite understandably, today’s data-obsessed medicine views them with suspicion. But besides reminding us that in medicine not all that counts can be counted, and not all that can be counted counts, the humanities are in fact a fundamental component of the physician’s skill set.

In a multicenter survey of 5 medical schools,¹⁰ there was indeed a correlation between students’ exposure to the humanities and many of the personal qualities whose absence we lament in today’s medicine: empathy, tolerance for ambiguity, emotional intelligence, and prevention of burnout. Most significant was a strong correlation with wisdom, as measured by the 21-item Brief Wisdom Screening Scale.¹¹ That all these traits may correlate with humanities exposure is intuitive, since the humanities not only teach tolerance and compassion, but also capture the collective experience of those who came before us. Hence, they teach us wisdom. Wisdom is not an ACGME competency, but it’s undoubtedly a prerequisite for the art of healing.¹² In fact, wisdom may very well be the fundamental trait that characterizes a well-rounded physician, since it encompasses empathy, resilience, comfort with ambiguity, and the capacity to learn from the past. Not surprisingly, wisdom in the world was Osler’s closing wish in 1919.

The humanities can also nurture the very personal qualities we desire in physicians. For example, observing drama fosters empathy,¹³ as does taking an elective in medical humanities.¹⁴ Drawing enhances the reading of faces,¹⁵ and observing art improves the art of clinical observation.¹⁶ Reading good literature prompts better detection of emotions,¹⁷ and reflective writing improves students’ well-being.¹⁸ Playing a musical instrument reduces burnout.¹⁹ And an undergraduate major in the humanities correlates with greater tolerance for ambiguity,²⁰ a highly desirable trait in physicians, since it means openness to new ideas and the capacity to better cope with difficult situations.²¹

In fact, some of the qualities fostered by the humanities even translate into better patient care. For instance, tolerance for ambiguity correlates with more positive attitudes towards patients who have frustrating complaints,²² with lower use of resources,²³ and with a career choice in direct patient care.²⁴ Hence, it has been suggested that it should be a prerequisite for medical school admission.²⁵ Physicians’ empathy is also beneficial, since it correlates with a lower rate of complications and better outcomes in the care of diabetic patients.²⁶ This should not come as a surprise. As Hippocrates put it 2,500 years ago, “some patients, though conscious that their condition is perilous, recover their health simply through their contentment with the goodness of the physician.”²⁷

Lastly, studying the humanities may provide crucial antibodies against the pain and suffering that are unavoidable staples of the human condition. To paraphrase Osler, the humanities might vaccinate us against the difficulties of our profession. Hippocrates himself had suggested that “it is well to superintend the sick to make them well, to care for the healthy to keep them well, but also to care for one’s self…”²⁷ That is why many institutions now require medical students to take humanities courses.²⁸

Yet this effort may amount to a rearguard action that arrives too late and provides too little. The humanities should probably be taught before medical school.²⁹ After all, if it’s possible to make a scientist out of a humanist (Osler was living proof), the experience of the past decades seems to suggest that it’s considerably harder to make a humanist out of a scientist—hence the need to revisit undergraduate curricula and admission criteria to medical school, so that students can receive an adequate foundation in both arenas. Ironically, students express positive attitudes toward a liberal education and think it would actually help them as physicians.³⁰ Yet they also understand that the selection process remains tilted towards the sciences.^30–32

For Osler, scientific evidence was important but not a substitute for a humanistic approach. As he reminded students, “The practice of medicine is an art based on science,”³³ whose main goals are to prevent disease, relieve suffering, and heal the sick. To do so, one ought to care more “for the individual patient than for the special features of the disease.”³⁴ But he warned them, “It is much harder to acquire the art than the science.”³⁵ In fact, “The practice of medicine is a calling in which your heart will be exercised equally with your head.”³³ Hence the need to “cultivate equally well hearts and heads.”³⁴ Almost foreseeing our infatuation with guidelines, he also warned against turning medicine into assembly-line work. There are “two great types of practitioners—the routinist and the rationalist,” he said in 1900, and “into the clutches of the demon routine the majority of us ultimately come.”³⁶

Like most great people, Osler was a man of lights, shadows, and contradictions, probably not quite the saint we wish to believe. Yet he provides insights that are as valid today as they were for his own times, and possibly even more so. His 1919 speech is a paean to the humanities, but also a potential eulogy. As a Victorian physician, Osler was a blend of the new science and the old humanities. He knew that “the old art cannot possibly be replaced by, but must be absorbed in, the new science.”³⁵ Yet he could also see the upcoming split between the two cultures, and he tried to warn us. He could in fact foresee the end of an entire way of life. As he said in his address, “there must be a very different civilization or there will be no civilization at all.”¹

The crisis we face in medicine today may indeed be a symptom of a much larger cultural shift. As Osler himself put it, “The philosophies of one age have become the absurdities of the next, and the foolishness of yesterday has become the wisdom of tomorrow.”³³ Like Osler, we live in times of transition that require us to act. If in 1910 Flexner gave us science,³⁷ Osler in 1919 reminded us that medicine also needs the humanities. We ought to heed his message and reconcile the two fields. The alternative is a future full of tricorders and burned-out technicians, but sorely lacking in healers.

“Twin berries on one stem, grievous damage has been done to both in regarding the Humanities and Science in any other light than complemental.”
—Sir William Osler¹

The year 2019 marks the 100th anniversary of Sir William Osler’s last public speech. Still reeling from the death of his only son in World War I, he had been asked to give the presidential inaugural address of the Classical Association at Oxford. It was the first time a physician had received the honor, and Osler took the assignment very seriously. He chose to speak about “The old humanities and the new science,” and to call for a reunification of the two fields. “Humanists have not enough Science” he warned, “and Science sadly lacks the Humanities…this unhappy divorce…should never have taken place.”¹  Later, he said that it was the speech to which he had given the greatest thought and preparation. It was in fact Osler’s personal legacy: 2 months later he turned 70, and 7 months later he was dead.

Revisiting the address today, what can Osler teach the high-tech physician of today, when doctors have become “providers” and patients “consumers”? Is Osler’s message still relevant to our craft, or has he simply become an icon of professional nostalgia with little value for our times?

THE NEED FOR THE HUMANITIES IN MEDICINE

Medicine has certainly grown both powerful and successful. Yet it is also confronting hurdles that would have been unimaginable in Osler’s time. Physicians are now the professionals with the highest suicide rate,² a burnout rate as high as 70%,^3,4 rampant depression,⁵ dwindling empathy,⁶ a predominantly negative perception by the public,^7,8 and a disturbing propensity to quit.⁹ These, of course, may just be symptoms of an increasingly meaningless environment wherein doctors have become small cogs in a medical-industrial complex they can’t control or even understand. Still, is it possible that something more personal may have been lost in the way we now select and educate physicians? Could this, in turn, make us less resilient?

In this regard, Osler’s last public speech serves as an enduring reminder of the need for the humanities in medicine. Osler not only believed it, but throughout his life never missed a chance to express in words, writings, and deeds that the humanities are indeed “the hormones” of the profession. In 1919 he warned against the risk of separating our humanistic tradition from the sciences, and urged us “to infect [anyone] with the spirit of the Humanities,” since to him that was “the greatest single gift in education.”¹

Unfortunately, the humanities are slippery, not easily quantifiable, hard to define, and seemingly incompatible with an evidence-based approach. Quite understandably, today’s data-obsessed medicine views them with suspicion. But besides reminding us that in medicine not all that counts can be counted, and not all that can be counted counts, the humanities are in fact a fundamental component of the physician’s skill set.

In a multicenter survey of 5 medical schools,¹⁰ there was indeed a correlation between students’ exposure to the humanities and many of the personal qualities whose absence we lament in today’s medicine: empathy, tolerance for ambiguity, emotional intelligence, and prevention of burnout. Most significant was a strong correlation with wisdom, as measured by the 21-item Brief Wisdom Screening Scale.¹¹ That all these traits may correlate with humanities exposure is intuitive, since the humanities not only teach tolerance and compassion, but also capture the collective experience of those who came before us. Hence, they teach us wisdom. Wisdom is not an ACGME competency, but it’s undoubtedly a prerequisite for the art of healing.¹² In fact, wisdom may very well be the fundamental trait that characterizes a well-rounded physician, since it encompasses empathy, resilience, comfort with ambiguity, and the capacity to learn from the past. Not surprisingly, wisdom in the world was Osler’s closing wish in 1919.

The humanities can also nurture the very personal qualities we desire in physicians. For example, observing drama fosters empathy,¹³ as does taking an elective in medical humanities.¹⁴ Drawing enhances the reading of faces,¹⁵ and observing art improves the art of clinical observation.¹⁶ Reading good literature prompts better detection of emotions,¹⁷ and reflective writing improves students’ well-being.¹⁸ Playing a musical instrument reduces burnout.¹⁹ And an undergraduate major in the humanities correlates with greater tolerance for ambiguity,²⁰ a highly desirable trait in physicians, since it means openness to new ideas and the capacity to better cope with difficult situations.²¹

In fact, some of the qualities fostered by the humanities even translate into better patient care. For instance, tolerance for ambiguity correlates with more positive attitudes towards patients who have frustrating complaints,²² with lower use of resources,²³ and with a career choice in direct patient care.²⁴ Hence, it has been suggested that it should be a prerequisite for medical school admission.²⁵ Physicians’ empathy is also beneficial, since it correlates with a lower rate of complications and better outcomes in the care of diabetic patients.²⁶ This should not come as a surprise. As Hippocrates put it 2,500 years ago, “some patients, though conscious that their condition is perilous, recover their health simply through their contentment with the goodness of the physician.”²⁷

Lastly, studying the humanities may provide crucial antibodies against the pain and suffering that are unavoidable staples of the human condition. To paraphrase Osler, the humanities might vaccinate us against the difficulties of our profession. Hippocrates himself had suggested that “it is well to superintend the sick to make them well, to care for the healthy to keep them well, but also to care for one’s self…”²⁷ That is why many institutions now require medical students to take humanities courses.²⁸

Yet this effort may amount to a rearguard action that arrives too late and provides too little. The humanities should probably be taught before medical school.²⁹ After all, if it’s possible to make a scientist out of a humanist (Osler was living proof), the experience of the past decades seems to suggest that it’s considerably harder to make a humanist out of a scientist—hence the need to revisit undergraduate curricula and admission criteria to medical school, so that students can receive an adequate foundation in both arenas. Ironically, students express positive attitudes toward a liberal education and think it would actually help them as physicians.³⁰ Yet they also understand that the selection process remains tilted towards the sciences.^30–32

For Osler, scientific evidence was important but not a substitute for a humanistic approach. As he reminded students, “The practice of medicine is an art based on science,”³³ whose main goals are to prevent disease, relieve suffering, and heal the sick. To do so, one ought to care more “for the individual patient than for the special features of the disease.”³⁴ But he warned them, “It is much harder to acquire the art than the science.”³⁵ In fact, “The practice of medicine is a calling in which your heart will be exercised equally with your head.”³³ Hence the need to “cultivate equally well hearts and heads.”³⁴ Almost foreseeing our infatuation with guidelines, he also warned against turning medicine into assembly-line work. There are “two great types of practitioners—the routinist and the rationalist,” he said in 1900, and “into the clutches of the demon routine the majority of us ultimately come.”³⁶

Like most great people, Osler was a man of lights, shadows, and contradictions, probably not quite the saint we wish to believe. Yet he provides insights that are as valid today as they were for his own times, and possibly even more so. His 1919 speech is a paean to the humanities, but also a potential eulogy. As a Victorian physician, Osler was a blend of the new science and the old humanities. He knew that “the old art cannot possibly be replaced by, but must be absorbed in, the new science.”³⁵ Yet he could also see the upcoming split between the two cultures, and he tried to warn us. He could in fact foresee the end of an entire way of life. As he said in his address, “there must be a very different civilization or there will be no civilization at all.”¹

The crisis we face in medicine today may indeed be a symptom of a much larger cultural shift. As Osler himself put it, “The philosophies of one age have become the absurdities of the next, and the foolishness of yesterday has become the wisdom of tomorrow.”³³ Like Osler, we live in times of transition that require us to act. If in 1910 Flexner gave us science,³⁷ Osler in 1919 reminded us that medicine also needs the humanities. We ought to heed his message and reconcile the two fields. The alternative is a future full of tricorders and burned-out technicians, but sorely lacking in healers.

To the Editor: Regarding the article about a man with rapidly progressive pleural effusion by Zoumot et al in the January 2019 issue,¹ there was some inconsistency between the teaching points and the actions taken.

Question 1 asked what was the most likely cause of the patient’s pleuritic chest pain. Pulmonary embolism was an unlikely diagnosis, given the patient’s presentation and his normal D-dimer level, which the text acknowledges, but then proceeds to state that computed tomographic angiography of the chest was done anyway.

After pleural effusion was diagnosed, question 2 asked what was the best management strategy for the patient at that time. The best management strategy was to give oral antibiotics with close follow-up because the patient was at low risk of a poor outcome, but he was advised to be admitted for intravenous antibiotics anyway.

I’m not quite sure of the point of the didactic exercise when actions are not consistent with the analytic rationale for testing and treatment.

To the Editor: Regarding the article about a man with rapidly progressive pleural effusion by Zoumot et al in the January 2019 issue,¹ there was some inconsistency between the teaching points and the actions taken.

Question 1 asked what was the most likely cause of the patient’s pleuritic chest pain. Pulmonary embolism was an unlikely diagnosis, given the patient’s presentation and his normal D-dimer level, which the text acknowledges, but then proceeds to state that computed tomographic angiography of the chest was done anyway.

After pleural effusion was diagnosed, question 2 asked what was the best management strategy for the patient at that time. The best management strategy was to give oral antibiotics with close follow-up because the patient was at low risk of a poor outcome, but he was advised to be admitted for intravenous antibiotics anyway.

I’m not quite sure of the point of the didactic exercise when actions are not consistent with the analytic rationale for testing and treatment.

To the Editor: Regarding the article about a man with rapidly progressive pleural effusion by Zoumot et al in the January 2019 issue,¹ there was some inconsistency between the teaching points and the actions taken.

Question 1 asked what was the most likely cause of the patient’s pleuritic chest pain. Pulmonary embolism was an unlikely diagnosis, given the patient’s presentation and his normal D-dimer level, which the text acknowledges, but then proceeds to state that computed tomographic angiography of the chest was done anyway.

After pleural effusion was diagnosed, question 2 asked what was the best management strategy for the patient at that time. The best management strategy was to give oral antibiotics with close follow-up because the patient was at low risk of a poor outcome, but he was advised to be admitted for intravenous antibiotics anyway.

I’m not quite sure of the point of the didactic exercise when actions are not consistent with the analytic rationale for testing and treatment.

In Reply: We thank Dr. Davidson for his comments. Indeed, the teaching points may appear inconsistent with the actual patient journey in this case. In the real world, physicians from different teams and specialties are involved in the care of a patient, and medical practice may not strictly adhere to guidelines.

In question 1, the emergency department physician decided to proceed with computed tomographic pulmonary angiography to rule out pulmonary embolism. Based on best practice guidelines, pulmonary angiography was not indicated, as the clinical pretest probability of pulmonary embolism was low, supported by the patient’s negative D-dimer test. When we wrote the article, as we already had the scan, we used it to support the learning points in terms of findings on computed tomography at the early stage of a developing empyema, and also to support that the scan was in fact not indicated (not the other way around).

As for question 2, specific data-driven guidelines do not exist on how best to manage patients with bronchopneumonia with an early evolving parapneumonic effusion. In the text that follows question 2, we stated that management as an inpatient or outpatient would have been reasonable. Although we considered the patient at low risk for a poor outcome, we offered inpatient admission at the time for better control of his severe pleuritic pain (this could have been made clearer in the text), as well as close monitoring of his evolving parapneumonic effusion, and we do not believe that this contradicts the teaching points of this case.

In Reply: We thank Dr. Davidson for his comments. Indeed, the teaching points may appear inconsistent with the actual patient journey in this case. In the real world, physicians from different teams and specialties are involved in the care of a patient, and medical practice may not strictly adhere to guidelines.

In question 1, the emergency department physician decided to proceed with computed tomographic pulmonary angiography to rule out pulmonary embolism. Based on best practice guidelines, pulmonary angiography was not indicated, as the clinical pretest probability of pulmonary embolism was low, supported by the patient’s negative D-dimer test. When we wrote the article, as we already had the scan, we used it to support the learning points in terms of findings on computed tomography at the early stage of a developing empyema, and also to support that the scan was in fact not indicated (not the other way around).

As for question 2, specific data-driven guidelines do not exist on how best to manage patients with bronchopneumonia with an early evolving parapneumonic effusion. In the text that follows question 2, we stated that management as an inpatient or outpatient would have been reasonable. Although we considered the patient at low risk for a poor outcome, we offered inpatient admission at the time for better control of his severe pleuritic pain (this could have been made clearer in the text), as well as close monitoring of his evolving parapneumonic effusion, and we do not believe that this contradicts the teaching points of this case.

In Reply: We thank Dr. Davidson for his comments. Indeed, the teaching points may appear inconsistent with the actual patient journey in this case. In the real world, physicians from different teams and specialties are involved in the care of a patient, and medical practice may not strictly adhere to guidelines.

In question 1, the emergency department physician decided to proceed with computed tomographic pulmonary angiography to rule out pulmonary embolism. Based on best practice guidelines, pulmonary angiography was not indicated, as the clinical pretest probability of pulmonary embolism was low, supported by the patient’s negative D-dimer test. When we wrote the article, as we already had the scan, we used it to support the learning points in terms of findings on computed tomography at the early stage of a developing empyema, and also to support that the scan was in fact not indicated (not the other way around).

As for question 2, specific data-driven guidelines do not exist on how best to manage patients with bronchopneumonia with an early evolving parapneumonic effusion. In the text that follows question 2, we stated that management as an inpatient or outpatient would have been reasonable. Although we considered the patient at low risk for a poor outcome, we offered inpatient admission at the time for better control of his severe pleuritic pain (this could have been made clearer in the text), as well as close monitoring of his evolving parapneumonic effusion, and we do not believe that this contradicts the teaching points of this case.

To the Editor: I enjoyed reading “Should metformin be used in every patient with type 2 diabetes” by Makin and Lansang in the January 2019 issue.¹

I just wanted to point out that metformin is a frequent cause of low serum vitamin B₁₂ levels, and serum vitamin B₁₂ levels should be monitored intermittently in patients using metformin.

To the Editor: I enjoyed reading “Should metformin be used in every patient with type 2 diabetes” by Makin and Lansang in the January 2019 issue.¹

I just wanted to point out that metformin is a frequent cause of low serum vitamin B₁₂ levels, and serum vitamin B₁₂ levels should be monitored intermittently in patients using metformin.

To the Editor: I enjoyed reading “Should metformin be used in every patient with type 2 diabetes” by Makin and Lansang in the January 2019 issue.¹

I just wanted to point out that metformin is a frequent cause of low serum vitamin B₁₂ levels, and serum vitamin B₁₂ levels should be monitored intermittently in patients using metformin.

In Reply: We thank Dr. Moskowitz for his kind comments. We agree about the need for assessing vitamin B₁₂ levels during chronic metformin use.

Secondary analysis of patients in the Diabetes Prevention Program Outcomes Study showed a higher incidence of combined low and low-normal vitamin B₁₂ deficiency in users assigned to the metformin group compared with those assigned to the placebo group at the 5-year and 13-year marks after randomization.1 Post hoc analysis of patients in the Hyperinsulinemia: the Outcome of Its Metabolic Effects trial also showed lower levels of vitamin B₁₂ and higher levels of methylmalonic acid associated with significant worsening of a validated neuropathy score in metformin users.²

The mechanism behind the development of vitamin B₁₂ deficiency is not completely understood but could possibly be alterations in intestinal mobility, bacterial overgrowth, or calcium-dependent uptake by ileal cells of the vitamin B₁₂-intrinsic factor complex.³

Our electronic medical record has a built-in tool that suggests checking vitamin B₁₂ whenever a patient requests metformin refills. There are no current guidelines on the need for baseline testing of the vitamin B₁₂ level. The American Diabetes Association recommends periodic measurement of vitamin B₁₂ levels, possibly yearly, in metformin users and more often if there are symptoms indicative of deficiency.⁴

In Reply: We thank Dr. Moskowitz for his kind comments. We agree about the need for assessing vitamin B₁₂ levels during chronic metformin use.

Secondary analysis of patients in the Diabetes Prevention Program Outcomes Study showed a higher incidence of combined low and low-normal vitamin B₁₂ deficiency in users assigned to the metformin group compared with those assigned to the placebo group at the 5-year and 13-year marks after randomization.1 Post hoc analysis of patients in the Hyperinsulinemia: the Outcome of Its Metabolic Effects trial also showed lower levels of vitamin B₁₂ and higher levels of methylmalonic acid associated with significant worsening of a validated neuropathy score in metformin users.²

The mechanism behind the development of vitamin B₁₂ deficiency is not completely understood but could possibly be alterations in intestinal mobility, bacterial overgrowth, or calcium-dependent uptake by ileal cells of the vitamin B₁₂-intrinsic factor complex.³

Our electronic medical record has a built-in tool that suggests checking vitamin B₁₂ whenever a patient requests metformin refills. There are no current guidelines on the need for baseline testing of the vitamin B₁₂ level. The American Diabetes Association recommends periodic measurement of vitamin B₁₂ levels, possibly yearly, in metformin users and more often if there are symptoms indicative of deficiency.⁴

In Reply: We thank Dr. Moskowitz for his kind comments. We agree about the need for assessing vitamin B₁₂ levels during chronic metformin use.

Secondary analysis of patients in the Diabetes Prevention Program Outcomes Study showed a higher incidence of combined low and low-normal vitamin B₁₂ deficiency in users assigned to the metformin group compared with those assigned to the placebo group at the 5-year and 13-year marks after randomization.1 Post hoc analysis of patients in the Hyperinsulinemia: the Outcome of Its Metabolic Effects trial also showed lower levels of vitamin B₁₂ and higher levels of methylmalonic acid associated with significant worsening of a validated neuropathy score in metformin users.²

The mechanism behind the development of vitamin B₁₂ deficiency is not completely understood but could possibly be alterations in intestinal mobility, bacterial overgrowth, or calcium-dependent uptake by ileal cells of the vitamin B₁₂-intrinsic factor complex.³

Our electronic medical record has a built-in tool that suggests checking vitamin B₁₂ whenever a patient requests metformin refills. There are no current guidelines on the need for baseline testing of the vitamin B₁₂ level. The American Diabetes Association recommends periodic measurement of vitamin B₁₂ levels, possibly yearly, in metformin users and more often if there are symptoms indicative of deficiency.⁴

Here are 5 articles from the April issue of Clinician Reviews (individual articles are valid for one year from date of publication—expiration dates below):

1. Back pain persists in one in five patients

To take the posttest, go to: https://bit.ly/2Uiod8N
Expires January 14, 2019

2. COPD linked to higher in-hospital death rates in patients with PAD

To take the posttest, go to: https://bit.ly/2TFCeJC
Expires January 22, 2019

3. Medicaid youth suicides include more females, younger kids, hanging deaths

To take the posttest, go to: https://bit.ly/2Uleyyp
Expires January 17, 2019

4. Potential antidepressant overprescribing found in 24% of elderly cohort

To take the posttest, go to: https://bit.ly/2HWwcSq
Expires January 24, 2019

5. Perceptions of liver transplantation for ALD are evolving

To take the posttest, go to: https://bit.ly/2OCANuA
Expires January 22, 2019

Here are 5 articles from the April issue of Clinician Reviews (individual articles are valid for one year from date of publication—expiration dates below):

1. Back pain persists in one in five patients

To take the posttest, go to: https://bit.ly/2Uiod8N
Expires January 14, 2019

2. COPD linked to higher in-hospital death rates in patients with PAD

To take the posttest, go to: https://bit.ly/2TFCeJC
Expires January 22, 2019

3. Medicaid youth suicides include more females, younger kids, hanging deaths

To take the posttest, go to: https://bit.ly/2Uleyyp
Expires January 17, 2019

4. Potential antidepressant overprescribing found in 24% of elderly cohort

To take the posttest, go to: https://bit.ly/2HWwcSq
Expires January 24, 2019

5. Perceptions of liver transplantation for ALD are evolving

To take the posttest, go to: https://bit.ly/2OCANuA
Expires January 22, 2019

Here are 5 articles from the April issue of Clinician Reviews (individual articles are valid for one year from date of publication—expiration dates below):

1. Back pain persists in one in five patients

To take the posttest, go to: https://bit.ly/2Uiod8N
Expires January 14, 2019

2. COPD linked to higher in-hospital death rates in patients with PAD

To take the posttest, go to: https://bit.ly/2TFCeJC
Expires January 22, 2019

3. Medicaid youth suicides include more females, younger kids, hanging deaths

To take the posttest, go to: https://bit.ly/2Uleyyp
Expires January 17, 2019

4. Potential antidepressant overprescribing found in 24% of elderly cohort

To take the posttest, go to: https://bit.ly/2HWwcSq
Expires January 24, 2019

5. Perceptions of liver transplantation for ALD are evolving

To take the posttest, go to: https://bit.ly/2OCANuA
Expires January 22, 2019

Both CPR rates and survival rates increased after chest compression–only CPR was introduced as an alternative for bystanders witnessing a cardiac arrest, according to a Swedish study of out-of-hospital cardiac arrests and subsequent CPR.

© American Heart Association, Inc.

“These findings support continuous endorsement of chest compression–only CPR as an option in future CPR guidelines because it is associated with higher CPR rates and survival in out-of-hospital cardiac arrests,” wrote Gabriel Riva, MD, of the Karolinska Institutet, Stockholm, and his coauthors. The study was published in Circulation.

To determine changes in the rate and type of CPR performed before emergency medical services (EMS) arrival, the researchers compared all bystander-witnessed out-of-hospital cardiac arrests (OHCAs) reported in Sweden between 2000 and 2017. In all, 30,445 patients were included; the time periods compared were 2000-2005, 2006-2010, and 2011-2017. Patients were categorized as receiving either no CPR (NO-CPR), standard CPR (S-CPR), or chest compression–only CPR (CO-CPR). In 2005, CO-CPR was introduced in national CPR guidelines as an option for bystanders; in 2010, it was recommended for anyone untrained in CPR.

The proportion of patients who received CPR in general increased from 41% in 2000-2005 to 59% in 2006-2010 to 68% in 2011-2017. S-CPR changed from 35% to 45% to 38% over the three periods, while CO-CPR increased from 5% to 14% to 30%. In regard to 30-day survival rates, the S-CPR group saw an increase from 9% to 13% to 16% and the CO-CPR group increased from 8% to 12% to 14%, compared with 4% to 6% to 7% for the NO-CPR group.

The authors noted the limitations of their study, including the results being based on register data and therefore subject to misclassification and missing data. In addition, missing data negated any reporting on the neurological function of survivors; analyzing witnessed OHCAs only also meant the findings could not be validated for nonwitnessed OHCA.

The Swedish Heart and Lung Foundation funded the study. The authors made no disclosures.

SOURCE: Riva G et al. Circulation. 2019 Apr 1. doi: 10.1161/CIRCULATIONAHA.118.038179.

Both CPR rates and survival rates increased after chest compression–only CPR was introduced as an alternative for bystanders witnessing a cardiac arrest, according to a Swedish study of out-of-hospital cardiac arrests and subsequent CPR.

© American Heart Association, Inc.

“These findings support continuous endorsement of chest compression–only CPR as an option in future CPR guidelines because it is associated with higher CPR rates and survival in out-of-hospital cardiac arrests,” wrote Gabriel Riva, MD, of the Karolinska Institutet, Stockholm, and his coauthors. The study was published in Circulation.

To determine changes in the rate and type of CPR performed before emergency medical services (EMS) arrival, the researchers compared all bystander-witnessed out-of-hospital cardiac arrests (OHCAs) reported in Sweden between 2000 and 2017. In all, 30,445 patients were included; the time periods compared were 2000-2005, 2006-2010, and 2011-2017. Patients were categorized as receiving either no CPR (NO-CPR), standard CPR (S-CPR), or chest compression–only CPR (CO-CPR). In 2005, CO-CPR was introduced in national CPR guidelines as an option for bystanders; in 2010, it was recommended for anyone untrained in CPR.

The proportion of patients who received CPR in general increased from 41% in 2000-2005 to 59% in 2006-2010 to 68% in 2011-2017. S-CPR changed from 35% to 45% to 38% over the three periods, while CO-CPR increased from 5% to 14% to 30%. In regard to 30-day survival rates, the S-CPR group saw an increase from 9% to 13% to 16% and the CO-CPR group increased from 8% to 12% to 14%, compared with 4% to 6% to 7% for the NO-CPR group.

The authors noted the limitations of their study, including the results being based on register data and therefore subject to misclassification and missing data. In addition, missing data negated any reporting on the neurological function of survivors; analyzing witnessed OHCAs only also meant the findings could not be validated for nonwitnessed OHCA.

The Swedish Heart and Lung Foundation funded the study. The authors made no disclosures.

SOURCE: Riva G et al. Circulation. 2019 Apr 1. doi: 10.1161/CIRCULATIONAHA.118.038179.

Both CPR rates and survival rates increased after chest compression–only CPR was introduced as an alternative for bystanders witnessing a cardiac arrest, according to a Swedish study of out-of-hospital cardiac arrests and subsequent CPR.

© American Heart Association, Inc.

“These findings support continuous endorsement of chest compression–only CPR as an option in future CPR guidelines because it is associated with higher CPR rates and survival in out-of-hospital cardiac arrests,” wrote Gabriel Riva, MD, of the Karolinska Institutet, Stockholm, and his coauthors. The study was published in Circulation.

To determine changes in the rate and type of CPR performed before emergency medical services (EMS) arrival, the researchers compared all bystander-witnessed out-of-hospital cardiac arrests (OHCAs) reported in Sweden between 2000 and 2017. In all, 30,445 patients were included; the time periods compared were 2000-2005, 2006-2010, and 2011-2017. Patients were categorized as receiving either no CPR (NO-CPR), standard CPR (S-CPR), or chest compression–only CPR (CO-CPR). In 2005, CO-CPR was introduced in national CPR guidelines as an option for bystanders; in 2010, it was recommended for anyone untrained in CPR.

The proportion of patients who received CPR in general increased from 41% in 2000-2005 to 59% in 2006-2010 to 68% in 2011-2017. S-CPR changed from 35% to 45% to 38% over the three periods, while CO-CPR increased from 5% to 14% to 30%. In regard to 30-day survival rates, the S-CPR group saw an increase from 9% to 13% to 16% and the CO-CPR group increased from 8% to 12% to 14%, compared with 4% to 6% to 7% for the NO-CPR group.

The authors noted the limitations of their study, including the results being based on register data and therefore subject to misclassification and missing data. In addition, missing data negated any reporting on the neurological function of survivors; analyzing witnessed OHCAs only also meant the findings could not be validated for nonwitnessed OHCA.

The Swedish Heart and Lung Foundation funded the study. The authors made no disclosures.

SOURCE: Riva G et al. Circulation. 2019 Apr 1. doi: 10.1161/CIRCULATIONAHA.118.038179.

Delirium is an acute change in mental status, affecting more than seven million hospitalized patients in the United States annually.¹ Several factors increase the risk of developing delirium, including advanced age,² cognitive dysfunction,³ hearing and vision impairment,^4-6 and severe illness or major surgery.⁷ Delirium may be precipitated during hospitalization by common inpatient interventions, such as the use of physical restraints, polypharmacy, or bladder catheters.^4,8 In-hospital delirium impacts an estimated 10%-15% of the general medical admissions and as many as 81% of patients in the intensive care unit (ICU).^9-11 Despite the relative frequency with which delirium is encountered in the hospital, subsequent emergency department (ED) presentations or hospital readmissions for these patients are poorly characterized.

The development of delirium is associated with several negative outcomes during the hospital stay. Delirium is an independent predictor of prolonged hospital stay,^7,9,12,13 prolonged mechanical ventilation,¹⁴ and mortality during admission.^14,15 Inpatient delirium is associated with functional decline at discharge, leading to a new nursing home placement.^16-19 Preexisting dementia is exacerbated by inpatient delirium, and a new diagnosis of cognitive impairment²⁰ or dementia becomes more common after an episode of delirium.²¹

These data suggest that people diagnosed with delirium may be particularly vulnerable in the posthospitalization period. Hospitals with high rates of unplanned readmissions face penalties from the Centers for Medicare and Medicaid Services.^22,23 However, few investigations have focused on postdischarge healthcare utilization, such as readmission rates and ED visits. Studies that address this topic are limited to postoperative patient populations.²⁴

Using a cohort of hospitalized patients, we examined whether those diagnosed with delirium experienced worse outcomes compared with patients with no such condition. We hypothesized that the patients diagnosed with delirium during hospitalization would experience more readmissions and ED visits within 30 days of discharge compared with those without delirium.

Study Design

This single-center retrospective cohort study took place at the Kaiser Permanente San Rafael Medical Center (KP-SRF), a 116-bed general community medical and surgical hospital located in Northern California, from September 6, 2010 to March 31, 2015. The Kaiser Permanente Northern California institutional review board, in accordance with the provisions of the Declaration of the Helsinki and International Conference on Harmonization Guidelines for Good Clinical Practice (CN-15-2491-H), approved this study.

Participants and Eligibility Criteria

This study included Kaiser Permanente members at least 65 years old who were hospitalized at KP-SRF from September 2010 to March 2015. Patient data were obtained from the electronic medical records. Patients with delirium were identified from a delirium registry; all other patients served as controls.

Starting on September 6, 2010, a hospital-wide program was initiated to screen hospitalized medical and surgical patients using the Confusion Assessment Method (CAM).²⁵ As part of this program, nurses completed a four-hour training on delirium; the program included delirium identification and CAM administration. Patients deemed at risk for delirium by their nurse or displaying symptoms of delirium (fluctuation in attention or awareness, disorientation, restlessness, agitation, and psychomotor slowing) were screened by nurses one to two times within a 24-hour period. Physicians were notified by the nurse if their patient screened positive. Nurses were prohibited from performing CAMs in languages that they were not fluent in, thus resulting in screening of primarily English-speaking patients. Psychiatry was consulted at the discretion of the primary team physician to assist with diagnosis and management of delirium. As psychiatry consultation was left up to the discretion of the primary team physician, not all CAM-positive patients were evaluated. The psychiatrists conducted no routine evaluation on the CAM-negative patients unless requested by the primary team physician. The psychiatrist confirmed the delirium diagnosis with a clinical interview and assessment. The patients confirmed with delirium at any point during their hospitalization were prospectively added to a delirium registry. The patients assessed by the psychiatrist as not delirious were excluded from the registry. Only those patients added to the delirium registry during the study period were classified as delirious for this study. All other patients were included as controls. The presence of the nursing screening program using the CAM enriched the cohort, but a positive CAM was unnecessary nor was it sufficient for inclusion in the delirium group (Table 1).

Patient Characteristics

Patient demographics and clinical data were obtained from the electronic medical records. We used several scores to characterize illness severity, including the Charlson comorbidity index,²⁷ Laboratory-Based Acute Physiology, version 2 (LAPS2) score²⁸—an externally validated score for acute severity of illness—and disease categories as defined by the Healthcare Cost and Utilization Project (HCUP).²⁹

Outcomes

The primary outcome was the rate of readmission to the hospital within 30 days of discharge from the hospitalization in which delirium was first diagnosed. Readmissions and ED visits to any Kaiser Permanente hospital and to hospitals outside of the Kaiser Permanente network with Kaiser Permanente insurance were captured. To avoid incorrectly coding patients transferred from the index hospital to another hospital as readmissions, we excluded readmissions that occurred on the day of discharge or the following calendar day. This action was expected to lower the absolute number of readmissions but restrict the analysis to true readmissions. The models of postdischarge outcomes are based on the subset of patients discharged alive. The secondary outcome measures included discharge from the index hospitalization to a skilled nursing facility or hospice rather than to home and emergency room visits within 30 days of discharge. We also quantified rates of mortality during hospitalization and at 30 days postdischarge.

Statistical Analysis

Comparisons between patients with delirium and those without were performed using Pearson’s X² test for categorical variables and student t-test for continuous variables. The estimated odds of our outcome measures for delirious and nondelirious subjects were calculated from multivariable logistic regression models, which controlled for predictors of delirium and additional information obtained during the hospitalization. For inpatient outcomes (in-hospital mortality and discharge to skilled nursing facility or hospice), we adjusted only for admission characteristics: age, race/ethnicity, admission to ICU, Charlson comorbidity index, HCUP category, and admission category. To limit the number of variables in our model, we consolidated the initial 30 HCUP categories (Appendix Table 1) by illness type into 13 categories (Appendix Table 2). For postdischarge outcomes, we adjusted for all the variables, including disposition (Table 2). The average estimated odds were calculated based on the observed marginal distribution of the control variables. The P value indicates how likely the odds on each outcome for delirious subjects differed significantly from those for other subjects. All statistical analyses were performed using SAS 9.3 (SAS Institute Inc., Cary, North Carolina).

Demographics and Clinical Characteristics

A total of 718 patients with delirium and 7,927 patients without delirium were included in this study. The related demographic information is outlined in Table 2. On average, the patients with delirium were older (83 ± 8 years versus 77 ± 8 years, P < .0001) but no difference in gender distribution was observed between groups. A similar racial breakdown was noted between groups, with white patients accounting for 87% of both patients with delirium and those without. The majority of admissions were unplanned medical admissions. The delirium cohort included more emergent surgical admissions compared with patients who did not develop delirium. Patients who developed delirium exhibited higher levels of illness severity on admission, as measured by the Charlson and LAPS2 scores, and were more often admitted to the ICU. Significant differences were also observed between admission illness categories between patients with delirium and those without.

Primary Outcome

Delirium during admission was significantly associated with hospital readmission within 30 days of discharge (adjusted odds ratio [aOR] = 2.60, 95% CI: 1.96–3.44; P < .0001; Table 3).

Secondary Outcomes

Delirium during admission was significantly (P < .0001; Table 3) associated with an ED visit within 30 days of discharge (OR: 2.18; 95% CI: 1.77–2.69) and discharge to a skilled nursing facility or hospice rather than home (OR: 2.52; 95% CI: 2.09–3.01). Delirium was not associated (P > .1) with death during hospitalization nor death 30 days following discharge.

As the delirious patients were much more likely to be discharged to a skilled nursing facility than nondelirious patients, we tested whether discharge disposition influenced readmission rates and ED visits between delirious and nondelirious patients in an unadjusted univariate analysis. The association between delirium and readmission and ED utilization was present regardless of disposition. Among patients discharged to skilled nursing, readmission rates were 4.76% and 13.38% (P < .001), and ED visit rates were 12.29% and 23.24% (P < .001) for nondelirious and delirious patients, respectively. Among patients discharged home, readmission rates were 4.96% and 14.37% (P < .001), and ED visit rates were 11.93% and 29.04% (P < .001) for nondelirious and delirious patients, respectively.

In this study of patients in a community hospital in Northern California, we observed a significant association between inpatient delirium and risk of hospital readmission within 30 days of discharge. We also demonstrated increased skilled nursing facility placement and ED utilization after discharge among hospitalized patients with delirium compared with those without. Patients with delirium in this study were diagnosed by a psychiatrist—a gold standard³⁰—and the study was conducted in a health system database with near comprehensive ascertainment of readmissions. These results suggest that patients with delirium are particularly vulnerable in the posthospitalization period and are a key group to focusing on reducing readmission rates and postdischarge healthcare utilization.

Identifying the risk factors for hospital readmission is important for the benefit of both the patient and the hospital. In an analysis of Medicare claims data from 2003 to 2004, 19.6% of beneficiaries were readmitted within 30 days of discharge.³¹ There is a national effort to reduce unplanned hospital readmissions for both patient safety as hospitals with high readmission rates face penalties from the Centers for Medicare and Medicaid Services.^22,23 Why delirium is associated with readmission remains unclear. Delirium may precipitate aspiration events, reduce oral intake which complicates medication administration and nutrition, or reduced mobility, leading to pulmonary emboli and skin breakdown, any of which could lead to readmission.³² Delirium may also accelerate the progression of cognitive decline and overall loss of functional independence.²⁰ Delirious patients can be difficult to care for at home, and persistent delirium may lead to returns to the ED and readmission. Strategies to reduce readmissions associated with delirium may need to focus on both prevention of hospital-acquired delirium and targeted caregiver and patient support after discharge.

Hospital readmission and ED visits are not mutually exclusive experiences. In the United States, the majority of patients admitted to the hospital are admitted through the ED.³³ Thus, most of the readmissions in this cohort were also likely counted as 30-day ED visits. However, as ED utilization occurs regardless of whether a patient is discharged or admitted from the ED, we reported all ED visits in this analysis, similar to other studies.³⁴ More delirium patients returned to the ED 30 days postdischarge than were ultimately readmitted to the hospital, and delirious patients were more likely to visit the ED or be readmitted than nondelirious patients. These observations point toward the first 30 days after discharge as a crucial period for these patients.

Our study features several strengths. To our knowledge, this study is one of the largest investigations of inpatients with delirium. One distinguishing feature was that all cases of delirium in this study were diagnosed by a psychiatrist, which is considered a gold standard. Many studies rely solely on brief nursing-administered surveys for delirium diagnosis. Using Kaiser Permanente data allowed for more complete follow-up of patients, including vital status. Kaiser Permanente is both a medical system and an insurer, resulting in acquisition of detailed health information from all hospitalizations where Kaiser Permanente insurance was used for each patient. Therefore, patients were only lost to follow-up following discharge in the event of a membership lapse; these patients were excluded from analysis. The obtained data are also more generalizable than those of other studies examining readmission rates in delirious patients as the hospital where these data were collected is a 116-bed general community medical and surgical hospital. Thus, the patients enrolled in this study covered multiple hospital services with a variety of admission diagnoses. This condition contrasts with much of the existing literature on inpatient delirium; these studies mostly center on specific medical conditions or surgeries and are often conducted at academic medical centers. At the same time, Kaiser Permanente is a unique health maintenance organization focused on preventive care, and readmission rates are possibly lower than elsewhere given the universal access to primary care for Kaiser Permanente members. Our results may not generalize to patients hospitalized in other health systems.

The diagnosis of delirium is a clinical diagnosis without biomarkers or radiographic markers and is also underdiagnosed and poorly coded.³² For these reasons, delirium can be challenging to study in large administrative databases or data derived from electronic medical records. We addressed this limitation by classifying the delirium patients only when they had been diagnosed by a staff psychiatrist. However, not all patients who screened positive with the CAM were evaluated by the staff psychiatrist during the study period. Thus, several CAM-positive patients who were not evaluated by psychiatry were included in the control population. This situation may cause bias toward identification of more severe cases of delirium. Although the physicians were encouraged to consult the psychiatry department for any patients who screened positive for delirium with the CAM, the psychiatrist may not have been involved if patients were managed without consultation. These patients may have exhibited less severe delirium or hypoactive delirium. In addition, the CAM fails to detect all delirious patients; interrater variability may occur with CAM administration, and non-English speaking patients are more likely to be excluded.³⁵ These situations are another possible way for our control population to include some delirious patients and those patients with less severe or hypoactive subtypes. While this might bias toward the null hypothesis, it is also possible our results only indicate an association between more clinically apparent delirium and readmission. A major limitation of this study is that we were unable to quantify the number of cohort patients screened with the CAM or the results of screening, thus limiting our ability to quantify the impact of potential biases introduced by the screening program.

This study may have underestimated readmission rates. We defined readmissions as all hospitalizations at any Kaiser Permanente facility, or to an alternate facility where Kaiser Permanente insurance was used, within 30 days of discharge. We excluded the day of discharge or the following calendar day to avoid mischaracterizing transfers from the index hospital to another Kaiser Permanente facility as readmissions. This step was conducted to avoid biasing our comparison, as delirious patients are less frequently discharged home than nondelirious patients. Therefore, while the relative odds of readmission between delirious and nondelirious patients reported in this study should be generalizable to other community hospitals, the absolute readmission rates reported here may not be comparable to those reported in other studies.

Delirium may represent a marker of more severe illness or medical complications accrued during the hospitalization, which could lead to the associations observed in this study due to confounding.³² Patients with delirium are more likely to be admitted emergently, admitted to the ICU, and feature higher acuity conditions than patients without delirium. We attempted to mitigate this possibility by using a multivariable model to control for variables related to illness severity, including the Charlson comorbidity index, HCUP diagnostic categories, and ICU admission. Despite including HCUP diagnostic categories in our model, we were unable to capture the contribution of certain diseases with finer granularity, such as preexistent dementia, which may also affect clinical outcomes.³⁶ Similarly, although we incorporated markers of illness severity into our model, we were unable to adjust for baseline functional status or frailty, which were not reliably recorded in the electronic medical record but are potential confounders when investigating clinical outcomes including hospital readmission.

We also lacked information regarding the duration of delirium in our cohort. Therefore, we were unable to test whether longer episodes of delirium were more predictive of readmission than shorter episodes.

In-hospital delirium is associated with several negative patient outcomes. Our study demonstrates that delirium predicts 30-day readmission and emergency department utilization after hospital discharge. Bearing in mind that a third of hospital-acquired delirium cases may be preventable,³² hospitals should prioritize interventions to reduce postdischarge healthcare utilization and complications in this particularly vulnerable group.

Acknowledgments

The authors would like to acknowledge Dr. Andrew L. Avins for his guidance with the initial development of this project and Julie Fourie for contributing data to the overall study.

Disclosures

Dr. Liu receives funding from NIH K23GM112018 and NIGMS R35128672. Dr. Josephson receives compensation as the JAMA Neurology Editor in Chief and Continuum Audio Associate Editor. The remaining authors have no conflicts of interest.

Funding

This study was funded by Kaiser Permanente Graduate Medical Education, who approved the design, conduct, and reporting of this study.

Delirium is an acute change in mental status, affecting more than seven million hospitalized patients in the United States annually.¹ Several factors increase the risk of developing delirium, including advanced age,² cognitive dysfunction,³ hearing and vision impairment,^4-6 and severe illness or major surgery.⁷ Delirium may be precipitated during hospitalization by common inpatient interventions, such as the use of physical restraints, polypharmacy, or bladder catheters.^4,8 In-hospital delirium impacts an estimated 10%-15% of the general medical admissions and as many as 81% of patients in the intensive care unit (ICU).^9-11 Despite the relative frequency with which delirium is encountered in the hospital, subsequent emergency department (ED) presentations or hospital readmissions for these patients are poorly characterized.

The development of delirium is associated with several negative outcomes during the hospital stay. Delirium is an independent predictor of prolonged hospital stay,^7,9,12,13 prolonged mechanical ventilation,¹⁴ and mortality during admission.^14,15 Inpatient delirium is associated with functional decline at discharge, leading to a new nursing home placement.^16-19 Preexisting dementia is exacerbated by inpatient delirium, and a new diagnosis of cognitive impairment²⁰ or dementia becomes more common after an episode of delirium.²¹

These data suggest that people diagnosed with delirium may be particularly vulnerable in the posthospitalization period. Hospitals with high rates of unplanned readmissions face penalties from the Centers for Medicare and Medicaid Services.^22,23 However, few investigations have focused on postdischarge healthcare utilization, such as readmission rates and ED visits. Studies that address this topic are limited to postoperative patient populations.²⁴

Using a cohort of hospitalized patients, we examined whether those diagnosed with delirium experienced worse outcomes compared with patients with no such condition. We hypothesized that the patients diagnosed with delirium during hospitalization would experience more readmissions and ED visits within 30 days of discharge compared with those without delirium.

Study Design

This single-center retrospective cohort study took place at the Kaiser Permanente San Rafael Medical Center (KP-SRF), a 116-bed general community medical and surgical hospital located in Northern California, from September 6, 2010 to March 31, 2015. The Kaiser Permanente Northern California institutional review board, in accordance with the provisions of the Declaration of the Helsinki and International Conference on Harmonization Guidelines for Good Clinical Practice (CN-15-2491-H), approved this study.

Participants and Eligibility Criteria

This study included Kaiser Permanente members at least 65 years old who were hospitalized at KP-SRF from September 2010 to March 2015. Patient data were obtained from the electronic medical records. Patients with delirium were identified from a delirium registry; all other patients served as controls.

Starting on September 6, 2010, a hospital-wide program was initiated to screen hospitalized medical and surgical patients using the Confusion Assessment Method (CAM).²⁵ As part of this program, nurses completed a four-hour training on delirium; the program included delirium identification and CAM administration. Patients deemed at risk for delirium by their nurse or displaying symptoms of delirium (fluctuation in attention or awareness, disorientation, restlessness, agitation, and psychomotor slowing) were screened by nurses one to two times within a 24-hour period. Physicians were notified by the nurse if their patient screened positive. Nurses were prohibited from performing CAMs in languages that they were not fluent in, thus resulting in screening of primarily English-speaking patients. Psychiatry was consulted at the discretion of the primary team physician to assist with diagnosis and management of delirium. As psychiatry consultation was left up to the discretion of the primary team physician, not all CAM-positive patients were evaluated. The psychiatrists conducted no routine evaluation on the CAM-negative patients unless requested by the primary team physician. The psychiatrist confirmed the delirium diagnosis with a clinical interview and assessment. The patients confirmed with delirium at any point during their hospitalization were prospectively added to a delirium registry. The patients assessed by the psychiatrist as not delirious were excluded from the registry. Only those patients added to the delirium registry during the study period were classified as delirious for this study. All other patients were included as controls. The presence of the nursing screening program using the CAM enriched the cohort, but a positive CAM was unnecessary nor was it sufficient for inclusion in the delirium group (Table 1).

Patient Characteristics

Patient demographics and clinical data were obtained from the electronic medical records. We used several scores to characterize illness severity, including the Charlson comorbidity index,²⁷ Laboratory-Based Acute Physiology, version 2 (LAPS2) score²⁸—an externally validated score for acute severity of illness—and disease categories as defined by the Healthcare Cost and Utilization Project (HCUP).²⁹

Outcomes

The primary outcome was the rate of readmission to the hospital within 30 days of discharge from the hospitalization in which delirium was first diagnosed. Readmissions and ED visits to any Kaiser Permanente hospital and to hospitals outside of the Kaiser Permanente network with Kaiser Permanente insurance were captured. To avoid incorrectly coding patients transferred from the index hospital to another hospital as readmissions, we excluded readmissions that occurred on the day of discharge or the following calendar day. This action was expected to lower the absolute number of readmissions but restrict the analysis to true readmissions. The models of postdischarge outcomes are based on the subset of patients discharged alive. The secondary outcome measures included discharge from the index hospitalization to a skilled nursing facility or hospice rather than to home and emergency room visits within 30 days of discharge. We also quantified rates of mortality during hospitalization and at 30 days postdischarge.

Statistical Analysis

Comparisons between patients with delirium and those without were performed using Pearson’s X² test for categorical variables and student t-test for continuous variables. The estimated odds of our outcome measures for delirious and nondelirious subjects were calculated from multivariable logistic regression models, which controlled for predictors of delirium and additional information obtained during the hospitalization. For inpatient outcomes (in-hospital mortality and discharge to skilled nursing facility or hospice), we adjusted only for admission characteristics: age, race/ethnicity, admission to ICU, Charlson comorbidity index, HCUP category, and admission category. To limit the number of variables in our model, we consolidated the initial 30 HCUP categories (Appendix Table 1) by illness type into 13 categories (Appendix Table 2). For postdischarge outcomes, we adjusted for all the variables, including disposition (Table 2). The average estimated odds were calculated based on the observed marginal distribution of the control variables. The P value indicates how likely the odds on each outcome for delirious subjects differed significantly from those for other subjects. All statistical analyses were performed using SAS 9.3 (SAS Institute Inc., Cary, North Carolina).

Demographics and Clinical Characteristics

A total of 718 patients with delirium and 7,927 patients without delirium were included in this study. The related demographic information is outlined in Table 2. On average, the patients with delirium were older (83 ± 8 years versus 77 ± 8 years, P < .0001) but no difference in gender distribution was observed between groups. A similar racial breakdown was noted between groups, with white patients accounting for 87% of both patients with delirium and those without. The majority of admissions were unplanned medical admissions. The delirium cohort included more emergent surgical admissions compared with patients who did not develop delirium. Patients who developed delirium exhibited higher levels of illness severity on admission, as measured by the Charlson and LAPS2 scores, and were more often admitted to the ICU. Significant differences were also observed between admission illness categories between patients with delirium and those without.

Primary Outcome

Delirium during admission was significantly associated with hospital readmission within 30 days of discharge (adjusted odds ratio [aOR] = 2.60, 95% CI: 1.96–3.44; P < .0001; Table 3).

Secondary Outcomes

Delirium during admission was significantly (P < .0001; Table 3) associated with an ED visit within 30 days of discharge (OR: 2.18; 95% CI: 1.77–2.69) and discharge to a skilled nursing facility or hospice rather than home (OR: 2.52; 95% CI: 2.09–3.01). Delirium was not associated (P > .1) with death during hospitalization nor death 30 days following discharge.

As the delirious patients were much more likely to be discharged to a skilled nursing facility than nondelirious patients, we tested whether discharge disposition influenced readmission rates and ED visits between delirious and nondelirious patients in an unadjusted univariate analysis. The association between delirium and readmission and ED utilization was present regardless of disposition. Among patients discharged to skilled nursing, readmission rates were 4.76% and 13.38% (P < .001), and ED visit rates were 12.29% and 23.24% (P < .001) for nondelirious and delirious patients, respectively. Among patients discharged home, readmission rates were 4.96% and 14.37% (P < .001), and ED visit rates were 11.93% and 29.04% (P < .001) for nondelirious and delirious patients, respectively.

In this study of patients in a community hospital in Northern California, we observed a significant association between inpatient delirium and risk of hospital readmission within 30 days of discharge. We also demonstrated increased skilled nursing facility placement and ED utilization after discharge among hospitalized patients with delirium compared with those without. Patients with delirium in this study were diagnosed by a psychiatrist—a gold standard³⁰—and the study was conducted in a health system database with near comprehensive ascertainment of readmissions. These results suggest that patients with delirium are particularly vulnerable in the posthospitalization period and are a key group to focusing on reducing readmission rates and postdischarge healthcare utilization.

Identifying the risk factors for hospital readmission is important for the benefit of both the patient and the hospital. In an analysis of Medicare claims data from 2003 to 2004, 19.6% of beneficiaries were readmitted within 30 days of discharge.³¹ There is a national effort to reduce unplanned hospital readmissions for both patient safety as hospitals with high readmission rates face penalties from the Centers for Medicare and Medicaid Services.^22,23 Why delirium is associated with readmission remains unclear. Delirium may precipitate aspiration events, reduce oral intake which complicates medication administration and nutrition, or reduced mobility, leading to pulmonary emboli and skin breakdown, any of which could lead to readmission.³² Delirium may also accelerate the progression of cognitive decline and overall loss of functional independence.²⁰ Delirious patients can be difficult to care for at home, and persistent delirium may lead to returns to the ED and readmission. Strategies to reduce readmissions associated with delirium may need to focus on both prevention of hospital-acquired delirium and targeted caregiver and patient support after discharge.

Hospital readmission and ED visits are not mutually exclusive experiences. In the United States, the majority of patients admitted to the hospital are admitted through the ED.³³ Thus, most of the readmissions in this cohort were also likely counted as 30-day ED visits. However, as ED utilization occurs regardless of whether a patient is discharged or admitted from the ED, we reported all ED visits in this analysis, similar to other studies.³⁴ More delirium patients returned to the ED 30 days postdischarge than were ultimately readmitted to the hospital, and delirious patients were more likely to visit the ED or be readmitted than nondelirious patients. These observations point toward the first 30 days after discharge as a crucial period for these patients.

Our study features several strengths. To our knowledge, this study is one of the largest investigations of inpatients with delirium. One distinguishing feature was that all cases of delirium in this study were diagnosed by a psychiatrist, which is considered a gold standard. Many studies rely solely on brief nursing-administered surveys for delirium diagnosis. Using Kaiser Permanente data allowed for more complete follow-up of patients, including vital status. Kaiser Permanente is both a medical system and an insurer, resulting in acquisition of detailed health information from all hospitalizations where Kaiser Permanente insurance was used for each patient. Therefore, patients were only lost to follow-up following discharge in the event of a membership lapse; these patients were excluded from analysis. The obtained data are also more generalizable than those of other studies examining readmission rates in delirious patients as the hospital where these data were collected is a 116-bed general community medical and surgical hospital. Thus, the patients enrolled in this study covered multiple hospital services with a variety of admission diagnoses. This condition contrasts with much of the existing literature on inpatient delirium; these studies mostly center on specific medical conditions or surgeries and are often conducted at academic medical centers. At the same time, Kaiser Permanente is a unique health maintenance organization focused on preventive care, and readmission rates are possibly lower than elsewhere given the universal access to primary care for Kaiser Permanente members. Our results may not generalize to patients hospitalized in other health systems.

The diagnosis of delirium is a clinical diagnosis without biomarkers or radiographic markers and is also underdiagnosed and poorly coded.³² For these reasons, delirium can be challenging to study in large administrative databases or data derived from electronic medical records. We addressed this limitation by classifying the delirium patients only when they had been diagnosed by a staff psychiatrist. However, not all patients who screened positive with the CAM were evaluated by the staff psychiatrist during the study period. Thus, several CAM-positive patients who were not evaluated by psychiatry were included in the control population. This situation may cause bias toward identification of more severe cases of delirium. Although the physicians were encouraged to consult the psychiatry department for any patients who screened positive for delirium with the CAM, the psychiatrist may not have been involved if patients were managed without consultation. These patients may have exhibited less severe delirium or hypoactive delirium. In addition, the CAM fails to detect all delirious patients; interrater variability may occur with CAM administration, and non-English speaking patients are more likely to be excluded.³⁵ These situations are another possible way for our control population to include some delirious patients and those patients with less severe or hypoactive subtypes. While this might bias toward the null hypothesis, it is also possible our results only indicate an association between more clinically apparent delirium and readmission. A major limitation of this study is that we were unable to quantify the number of cohort patients screened with the CAM or the results of screening, thus limiting our ability to quantify the impact of potential biases introduced by the screening program.

This study may have underestimated readmission rates. We defined readmissions as all hospitalizations at any Kaiser Permanente facility, or to an alternate facility where Kaiser Permanente insurance was used, within 30 days of discharge. We excluded the day of discharge or the following calendar day to avoid mischaracterizing transfers from the index hospital to another Kaiser Permanente facility as readmissions. This step was conducted to avoid biasing our comparison, as delirious patients are less frequently discharged home than nondelirious patients. Therefore, while the relative odds of readmission between delirious and nondelirious patients reported in this study should be generalizable to other community hospitals, the absolute readmission rates reported here may not be comparable to those reported in other studies.

Delirium may represent a marker of more severe illness or medical complications accrued during the hospitalization, which could lead to the associations observed in this study due to confounding.³² Patients with delirium are more likely to be admitted emergently, admitted to the ICU, and feature higher acuity conditions than patients without delirium. We attempted to mitigate this possibility by using a multivariable model to control for variables related to illness severity, including the Charlson comorbidity index, HCUP diagnostic categories, and ICU admission. Despite including HCUP diagnostic categories in our model, we were unable to capture the contribution of certain diseases with finer granularity, such as preexistent dementia, which may also affect clinical outcomes.³⁶ Similarly, although we incorporated markers of illness severity into our model, we were unable to adjust for baseline functional status or frailty, which were not reliably recorded in the electronic medical record but are potential confounders when investigating clinical outcomes including hospital readmission.

We also lacked information regarding the duration of delirium in our cohort. Therefore, we were unable to test whether longer episodes of delirium were more predictive of readmission than shorter episodes.

In-hospital delirium is associated with several negative patient outcomes. Our study demonstrates that delirium predicts 30-day readmission and emergency department utilization after hospital discharge. Bearing in mind that a third of hospital-acquired delirium cases may be preventable,³² hospitals should prioritize interventions to reduce postdischarge healthcare utilization and complications in this particularly vulnerable group.

Acknowledgments

The authors would like to acknowledge Dr. Andrew L. Avins for his guidance with the initial development of this project and Julie Fourie for contributing data to the overall study.

Disclosures

Dr. Liu receives funding from NIH K23GM112018 and NIGMS R35128672. Dr. Josephson receives compensation as the JAMA Neurology Editor in Chief and Continuum Audio Associate Editor. The remaining authors have no conflicts of interest.

Funding

This study was funded by Kaiser Permanente Graduate Medical Education, who approved the design, conduct, and reporting of this study.

Delirium is an acute change in mental status, affecting more than seven million hospitalized patients in the United States annually.¹ Several factors increase the risk of developing delirium, including advanced age,² cognitive dysfunction,³ hearing and vision impairment,^4-6 and severe illness or major surgery.⁷ Delirium may be precipitated during hospitalization by common inpatient interventions, such as the use of physical restraints, polypharmacy, or bladder catheters.^4,8 In-hospital delirium impacts an estimated 10%-15% of the general medical admissions and as many as 81% of patients in the intensive care unit (ICU).^9-11 Despite the relative frequency with which delirium is encountered in the hospital, subsequent emergency department (ED) presentations or hospital readmissions for these patients are poorly characterized.

The development of delirium is associated with several negative outcomes during the hospital stay. Delirium is an independent predictor of prolonged hospital stay,^7,9,12,13 prolonged mechanical ventilation,¹⁴ and mortality during admission.^14,15 Inpatient delirium is associated with functional decline at discharge, leading to a new nursing home placement.^16-19 Preexisting dementia is exacerbated by inpatient delirium, and a new diagnosis of cognitive impairment²⁰ or dementia becomes more common after an episode of delirium.²¹

These data suggest that people diagnosed with delirium may be particularly vulnerable in the posthospitalization period. Hospitals with high rates of unplanned readmissions face penalties from the Centers for Medicare and Medicaid Services.^22,23 However, few investigations have focused on postdischarge healthcare utilization, such as readmission rates and ED visits. Studies that address this topic are limited to postoperative patient populations.²⁴

Using a cohort of hospitalized patients, we examined whether those diagnosed with delirium experienced worse outcomes compared with patients with no such condition. We hypothesized that the patients diagnosed with delirium during hospitalization would experience more readmissions and ED visits within 30 days of discharge compared with those without delirium.

Study Design

This single-center retrospective cohort study took place at the Kaiser Permanente San Rafael Medical Center (KP-SRF), a 116-bed general community medical and surgical hospital located in Northern California, from September 6, 2010 to March 31, 2015. The Kaiser Permanente Northern California institutional review board, in accordance with the provisions of the Declaration of the Helsinki and International Conference on Harmonization Guidelines for Good Clinical Practice (CN-15-2491-H), approved this study.

Participants and Eligibility Criteria

This study included Kaiser Permanente members at least 65 years old who were hospitalized at KP-SRF from September 2010 to March 2015. Patient data were obtained from the electronic medical records. Patients with delirium were identified from a delirium registry; all other patients served as controls.

Starting on September 6, 2010, a hospital-wide program was initiated to screen hospitalized medical and surgical patients using the Confusion Assessment Method (CAM).²⁵ As part of this program, nurses completed a four-hour training on delirium; the program included delirium identification and CAM administration. Patients deemed at risk for delirium by their nurse or displaying symptoms of delirium (fluctuation in attention or awareness, disorientation, restlessness, agitation, and psychomotor slowing) were screened by nurses one to two times within a 24-hour period. Physicians were notified by the nurse if their patient screened positive. Nurses were prohibited from performing CAMs in languages that they were not fluent in, thus resulting in screening of primarily English-speaking patients. Psychiatry was consulted at the discretion of the primary team physician to assist with diagnosis and management of delirium. As psychiatry consultation was left up to the discretion of the primary team physician, not all CAM-positive patients were evaluated. The psychiatrists conducted no routine evaluation on the CAM-negative patients unless requested by the primary team physician. The psychiatrist confirmed the delirium diagnosis with a clinical interview and assessment. The patients confirmed with delirium at any point during their hospitalization were prospectively added to a delirium registry. The patients assessed by the psychiatrist as not delirious were excluded from the registry. Only those patients added to the delirium registry during the study period were classified as delirious for this study. All other patients were included as controls. The presence of the nursing screening program using the CAM enriched the cohort, but a positive CAM was unnecessary nor was it sufficient for inclusion in the delirium group (Table 1).

Patient Characteristics

Patient demographics and clinical data were obtained from the electronic medical records. We used several scores to characterize illness severity, including the Charlson comorbidity index,²⁷ Laboratory-Based Acute Physiology, version 2 (LAPS2) score²⁸—an externally validated score for acute severity of illness—and disease categories as defined by the Healthcare Cost and Utilization Project (HCUP).²⁹

Outcomes

The primary outcome was the rate of readmission to the hospital within 30 days of discharge from the hospitalization in which delirium was first diagnosed. Readmissions and ED visits to any Kaiser Permanente hospital and to hospitals outside of the Kaiser Permanente network with Kaiser Permanente insurance were captured. To avoid incorrectly coding patients transferred from the index hospital to another hospital as readmissions, we excluded readmissions that occurred on the day of discharge or the following calendar day. This action was expected to lower the absolute number of readmissions but restrict the analysis to true readmissions. The models of postdischarge outcomes are based on the subset of patients discharged alive. The secondary outcome measures included discharge from the index hospitalization to a skilled nursing facility or hospice rather than to home and emergency room visits within 30 days of discharge. We also quantified rates of mortality during hospitalization and at 30 days postdischarge.

Statistical Analysis

Comparisons between patients with delirium and those without were performed using Pearson’s X² test for categorical variables and student t-test for continuous variables. The estimated odds of our outcome measures for delirious and nondelirious subjects were calculated from multivariable logistic regression models, which controlled for predictors of delirium and additional information obtained during the hospitalization. For inpatient outcomes (in-hospital mortality and discharge to skilled nursing facility or hospice), we adjusted only for admission characteristics: age, race/ethnicity, admission to ICU, Charlson comorbidity index, HCUP category, and admission category. To limit the number of variables in our model, we consolidated the initial 30 HCUP categories (Appendix Table 1) by illness type into 13 categories (Appendix Table 2). For postdischarge outcomes, we adjusted for all the variables, including disposition (Table 2). The average estimated odds were calculated based on the observed marginal distribution of the control variables. The P value indicates how likely the odds on each outcome for delirious subjects differed significantly from those for other subjects. All statistical analyses were performed using SAS 9.3 (SAS Institute Inc., Cary, North Carolina).

Demographics and Clinical Characteristics

A total of 718 patients with delirium and 7,927 patients without delirium were included in this study. The related demographic information is outlined in Table 2. On average, the patients with delirium were older (83 ± 8 years versus 77 ± 8 years, P < .0001) but no difference in gender distribution was observed between groups. A similar racial breakdown was noted between groups, with white patients accounting for 87% of both patients with delirium and those without. The majority of admissions were unplanned medical admissions. The delirium cohort included more emergent surgical admissions compared with patients who did not develop delirium. Patients who developed delirium exhibited higher levels of illness severity on admission, as measured by the Charlson and LAPS2 scores, and were more often admitted to the ICU. Significant differences were also observed between admission illness categories between patients with delirium and those without.

Primary Outcome

Delirium during admission was significantly associated with hospital readmission within 30 days of discharge (adjusted odds ratio [aOR] = 2.60, 95% CI: 1.96–3.44; P < .0001; Table 3).

Secondary Outcomes

Delirium during admission was significantly (P < .0001; Table 3) associated with an ED visit within 30 days of discharge (OR: 2.18; 95% CI: 1.77–2.69) and discharge to a skilled nursing facility or hospice rather than home (OR: 2.52; 95% CI: 2.09–3.01). Delirium was not associated (P > .1) with death during hospitalization nor death 30 days following discharge.

As the delirious patients were much more likely to be discharged to a skilled nursing facility than nondelirious patients, we tested whether discharge disposition influenced readmission rates and ED visits between delirious and nondelirious patients in an unadjusted univariate analysis. The association between delirium and readmission and ED utilization was present regardless of disposition. Among patients discharged to skilled nursing, readmission rates were 4.76% and 13.38% (P < .001), and ED visit rates were 12.29% and 23.24% (P < .001) for nondelirious and delirious patients, respectively. Among patients discharged home, readmission rates were 4.96% and 14.37% (P < .001), and ED visit rates were 11.93% and 29.04% (P < .001) for nondelirious and delirious patients, respectively.

In this study of patients in a community hospital in Northern California, we observed a significant association between inpatient delirium and risk of hospital readmission within 30 days of discharge. We also demonstrated increased skilled nursing facility placement and ED utilization after discharge among hospitalized patients with delirium compared with those without. Patients with delirium in this study were diagnosed by a psychiatrist—a gold standard³⁰—and the study was conducted in a health system database with near comprehensive ascertainment of readmissions. These results suggest that patients with delirium are particularly vulnerable in the posthospitalization period and are a key group to focusing on reducing readmission rates and postdischarge healthcare utilization.

Identifying the risk factors for hospital readmission is important for the benefit of both the patient and the hospital. In an analysis of Medicare claims data from 2003 to 2004, 19.6% of beneficiaries were readmitted within 30 days of discharge.³¹ There is a national effort to reduce unplanned hospital readmissions for both patient safety as hospitals with high readmission rates face penalties from the Centers for Medicare and Medicaid Services.^22,23 Why delirium is associated with readmission remains unclear. Delirium may precipitate aspiration events, reduce oral intake which complicates medication administration and nutrition, or reduced mobility, leading to pulmonary emboli and skin breakdown, any of which could lead to readmission.³² Delirium may also accelerate the progression of cognitive decline and overall loss of functional independence.²⁰ Delirious patients can be difficult to care for at home, and persistent delirium may lead to returns to the ED and readmission. Strategies to reduce readmissions associated with delirium may need to focus on both prevention of hospital-acquired delirium and targeted caregiver and patient support after discharge.

Hospital readmission and ED visits are not mutually exclusive experiences. In the United States, the majority of patients admitted to the hospital are admitted through the ED.³³ Thus, most of the readmissions in this cohort were also likely counted as 30-day ED visits. However, as ED utilization occurs regardless of whether a patient is discharged or admitted from the ED, we reported all ED visits in this analysis, similar to other studies.³⁴ More delirium patients returned to the ED 30 days postdischarge than were ultimately readmitted to the hospital, and delirious patients were more likely to visit the ED or be readmitted than nondelirious patients. These observations point toward the first 30 days after discharge as a crucial period for these patients.

Our study features several strengths. To our knowledge, this study is one of the largest investigations of inpatients with delirium. One distinguishing feature was that all cases of delirium in this study were diagnosed by a psychiatrist, which is considered a gold standard. Many studies rely solely on brief nursing-administered surveys for delirium diagnosis. Using Kaiser Permanente data allowed for more complete follow-up of patients, including vital status. Kaiser Permanente is both a medical system and an insurer, resulting in acquisition of detailed health information from all hospitalizations where Kaiser Permanente insurance was used for each patient. Therefore, patients were only lost to follow-up following discharge in the event of a membership lapse; these patients were excluded from analysis. The obtained data are also more generalizable than those of other studies examining readmission rates in delirious patients as the hospital where these data were collected is a 116-bed general community medical and surgical hospital. Thus, the patients enrolled in this study covered multiple hospital services with a variety of admission diagnoses. This condition contrasts with much of the existing literature on inpatient delirium; these studies mostly center on specific medical conditions or surgeries and are often conducted at academic medical centers. At the same time, Kaiser Permanente is a unique health maintenance organization focused on preventive care, and readmission rates are possibly lower than elsewhere given the universal access to primary care for Kaiser Permanente members. Our results may not generalize to patients hospitalized in other health systems.

The diagnosis of delirium is a clinical diagnosis without biomarkers or radiographic markers and is also underdiagnosed and poorly coded.³² For these reasons, delirium can be challenging to study in large administrative databases or data derived from electronic medical records. We addressed this limitation by classifying the delirium patients only when they had been diagnosed by a staff psychiatrist. However, not all patients who screened positive with the CAM were evaluated by the staff psychiatrist during the study period. Thus, several CAM-positive patients who were not evaluated by psychiatry were included in the control population. This situation may cause bias toward identification of more severe cases of delirium. Although the physicians were encouraged to consult the psychiatry department for any patients who screened positive for delirium with the CAM, the psychiatrist may not have been involved if patients were managed without consultation. These patients may have exhibited less severe delirium or hypoactive delirium. In addition, the CAM fails to detect all delirious patients; interrater variability may occur with CAM administration, and non-English speaking patients are more likely to be excluded.³⁵ These situations are another possible way for our control population to include some delirious patients and those patients with less severe or hypoactive subtypes. While this might bias toward the null hypothesis, it is also possible our results only indicate an association between more clinically apparent delirium and readmission. A major limitation of this study is that we were unable to quantify the number of cohort patients screened with the CAM or the results of screening, thus limiting our ability to quantify the impact of potential biases introduced by the screening program.

This study may have underestimated readmission rates. We defined readmissions as all hospitalizations at any Kaiser Permanente facility, or to an alternate facility where Kaiser Permanente insurance was used, within 30 days of discharge. We excluded the day of discharge or the following calendar day to avoid mischaracterizing transfers from the index hospital to another Kaiser Permanente facility as readmissions. This step was conducted to avoid biasing our comparison, as delirious patients are less frequently discharged home than nondelirious patients. Therefore, while the relative odds of readmission between delirious and nondelirious patients reported in this study should be generalizable to other community hospitals, the absolute readmission rates reported here may not be comparable to those reported in other studies.

Delirium may represent a marker of more severe illness or medical complications accrued during the hospitalization, which could lead to the associations observed in this study due to confounding.³² Patients with delirium are more likely to be admitted emergently, admitted to the ICU, and feature higher acuity conditions than patients without delirium. We attempted to mitigate this possibility by using a multivariable model to control for variables related to illness severity, including the Charlson comorbidity index, HCUP diagnostic categories, and ICU admission. Despite including HCUP diagnostic categories in our model, we were unable to capture the contribution of certain diseases with finer granularity, such as preexistent dementia, which may also affect clinical outcomes.³⁶ Similarly, although we incorporated markers of illness severity into our model, we were unable to adjust for baseline functional status or frailty, which were not reliably recorded in the electronic medical record but are potential confounders when investigating clinical outcomes including hospital readmission.

We also lacked information regarding the duration of delirium in our cohort. Therefore, we were unable to test whether longer episodes of delirium were more predictive of readmission than shorter episodes.

In-hospital delirium is associated with several negative patient outcomes. Our study demonstrates that delirium predicts 30-day readmission and emergency department utilization after hospital discharge. Bearing in mind that a third of hospital-acquired delirium cases may be preventable,³² hospitals should prioritize interventions to reduce postdischarge healthcare utilization and complications in this particularly vulnerable group.

Acknowledgments

The authors would like to acknowledge Dr. Andrew L. Avins for his guidance with the initial development of this project and Julie Fourie for contributing data to the overall study.

Disclosures

Dr. Liu receives funding from NIH K23GM112018 and NIGMS R35128672. Dr. Josephson receives compensation as the JAMA Neurology Editor in Chief and Continuum Audio Associate Editor. The remaining authors have no conflicts of interest.

Funding

This study was funded by Kaiser Permanente Graduate Medical Education, who approved the design, conduct, and reporting of this study.

Almost all specialties in internal medicine have a sound scientific research base through which clinical practice is informed.¹ For the field of Hospital Medicine (HM), this evidence has largely comprised research generated from fields outside of the specialty. The need to develop, invest, and grow investigators in hospital-based medicine remains unmet as HM and its footprint in hospital systems continue to grow.^2,3

Despite this fact, little is known about the current state of research in HM. A 2014 survey of the members of the Society of Hospital Medicine (SHM) found that research output across the field of HM, as measured on the basis of peer-reviewed publications, was growing.⁴ Since then, however, the numbers of individuals engaged in research activities, their background and training, publication output, or funding sources have not been quantified. Similarly, little is known about which institutions support the development of junior investigators (ie, HM research fellowships), how these programs are funded, and whether or not matriculants enter the field as investigators. These gaps must be measured, evaluated, and ideally addressed through strategic policy and funding initiatives to advance the state of science within HM.

Members of the SHM Research Committee developed, designed, and deployed a survey to improve the understanding of the state of research in HM. In this study, we aimed to establish the baseline of research in HM to enable the measurement of progress through periodic waves of data collection. Specifically, we sought to quantify and describe the characteristics of existing research programs, the sources and types of funding, the number and background of faculty, and the availability of resources for training researchers in HM.

Study Setting and Participants

Given that no defined list, database, or external resource that identifies research programs and contacts in HM exists, we began by creating a strategy to identify and sample adult HM programs and their leaders engaged in research activity. We iteratively developed a two-step approach to maximize inclusivity. First, we partnered with SHM to identify programs and leaders actively engaging in research activities. SHM is the largest professional organization within HM and maintains an extensive membership database that includes the titles, e-mail addresses, and affiliations of hospitalists in the United States, including academic and nonacademic sites. This list was manually scanned, and the leaders of academic and research programs in adult HM were identified by examining their titles (eg, Division Chief, Research Lead, etc.) and academic affiliations. During this step, members of the committee noticed that certain key individuals were either missing, no longer occupying their role/title, or had been replaced by others. Therefore, we performed a second step and asked the members of the SHM Research Committee to identify academic and research leaders by using current personal contacts, publication history, and social networks. We asked members to identify individuals and programs that had received grant funding, were actively presenting research at SHM (or other major national venues), and/or were producing peer-reviewed publications related to HM. These programs were purposefully chosen (ie, over HM programs known for clinical activities) to create an enriched sample of those engaged in research in HM. The research committee performed the “second pass” to ensure that established investigators who may not be accurately captured within the SHM database were included to maximize yield for the survey. Finally, these two sources were merged to ensure the absence of duplicate contacts and the identification of a primary respondent for each affiliate. As a result, a convenience sample of 100 programs and corresponding individuals was compiled for the purposes of this survey.

Survey Development

A workgroup within the SHM Research Committee was tasked to create a survey that would achieve four distinct goals: (1) identify institutions currently engaging in hospital-based research; (2) define the characteristics, including sources of research funding, training opportunities, criteria for promotion, and grant support, of research programs within institutions; (3) understand the prevalence of research fellowship programs, including size, training curricula, and funding sources; and (4) evaluate the productivity and funding sources of HM investigators at each site.

Survey questions that target each of these domains were drafted by the workgroup. Questions were pretested with colleagues outside the workgroup focused on this project (ie, from the main research committee). The instrument was refined and edited to improve the readability and clarity of questions on the basis of the feedback obtained through the iterative process. The revised instrument was then programmed into an online survey administration tool (SurveyMonkey^®) to facilitate electronic dissemination. Finally, the members of the workgroup tested the online survey to ensure functionality. No identifiable information was collected from respondents, and no monetary incentive was offered for the completion of the survey. An invitation to participate in the survey was sent via e-mail to each of the program contacts identified.

Statistical Analysis

Descriptive statistics, including proportions, means, and percentages, were used to tabulate results. All analyses were conducted using Stata 13 MP/SE (StataCorp, College Station, Texas).

Ethical and Regulatory Considerations

The study was reviewed and deemed exempt from regulation by the University of Michigan Institutional Review Board (HUM000138628).

General Characteristics of Research Programs and Faculty

Out of 100 program contacts, 28 (representing 1,586 faculty members) responded and were included in the survey (program response rate = 28%). When comparing programs that did respond with those that did not, a greater proportion of programs in university settings were noted among respondents (79% vs 21%). Respondents represented programs from all regions of the United States, with most representing university-based (79%), university-affiliated (14%) or Veterans Health Administration (VHA; 11%) programs. Most respondents were in leadership roles, including division chiefs (32%), research directors/leads (21%), section chiefs (18%), and related titles, such as program director. Respondents indicated that the total number of faculty members in their programs (including nonclinicians and advance practice providers) varied from eight to 152 (mean [SD] = 57 [36]) members, with physicians representing the majority of faculty members (Table 1).

Among the 1,586 faculty members within the 28 programs, respondents identified 192 faculty members (12%) as currently receiving extra- or intramural support for research activities. Of these faculty, over half (58%) received <25% of effort from intra or extramural sources, and 28 (15%) and 52 (27%) faculty members received 25%-50% or >50% of support for their effort, respectively. The number of investigators who received funding across programs ranged from 0 to 28 faculty members. Compared with the 192 funded investigators, respondents indicated that a larger number of faculty in their programs (n = 656 or 41%) were involved in local quality improvement (QI) efforts. Of the 656 faculty members involved in QI efforts, 241 individuals (37%) were internally funded and received protected time/effort for their work.

Key Attributes of Research Programs

In the evaluation of the amount of total grant funding, respondents from 17 programs indicated that they received $500,000 in annual extra and intramural funding, and those from three programs stated that they received $500,000 to $999,999 in funding. Five respondents indicated that their programs currently received $1 million to $5 million in grant funding, and three reported >$5 million in research support. The sources of research funding included several divisions within the National Institute of Health (NIH, 12 programs), Agency for Healthcare Research and Quality (AHRQ, four programs), foundations (four programs), and internal grants (six programs). Additionally, six programs indicated “other” sources of funding that included the VHA, Patient-Centered Outcomes Research Institute (PCORI), Centers for Medicare and Medicaid Services, Centers for Disease Control (CDC), and industry sources.

A range of grants, including career development awards (11 programs); small grants, such as R21 and R03s (eight programs); R-level grants, including VA merit awards (five programs); program series grants, such as P and U grants (five programs), and foundation grants (eight programs), were reported as types of awards. Respondents from 16 programs indicated that they provided internal pilot grants. Amounts for such grants ranged from <$50,000 (14 programs) to $50,000-$100,000 (two programs).

Research Fellowship Programs/Training Programs

Only five of the 28 surveyed programs indicated that they currently had a research training or fellowship program for developing hospitalist investigators. The age of these programs varied from <1 year to 10 years. Three of the five programs stated that they had two fellows per year, and two stated they had spots for one trainee annually. All respondents indicated that fellows received training on study design, research methods, quantitative (eg, large database and secondary analyses) and qualitative data analysis. In addition, two programs included training in systematic review and meta-analyses, and three included focused courses on healthcare policy. Four of the five programs included training in QI tools, such as LEAN and Six Sigma. Funding for four of the five fellowship programs came from internal sources (eg, department and CTSA). However, two programs added they received some support from extramural funding and philanthropy. Following training, respondents from programs indicated that the majority of their graduates (60%) went on to hybrid research/QI roles (50/50 research/clinical effort), whereas 40% obtained dedicated research investigator (80/20) positions (Table 2).

The 23 institutions without research training programs cited that the most important barrier for establishing such programs was lack of funding (12 programs) and the lack of a pipeline of hospitalists seeking such training (six programs). However, 15 programs indicated that opportunities for hospitalists to gain research training in the form of courses were available internally (eg, courses in the department or medical school) or externally (eg, School of Public Health). Seven programs indicated that they were planning to start a HM research fellowship within the next five years.

Research Faculty

Among the 28 respondents, 15 stated that they have faculty members who conduct research as their main professional activity (ie, >50% effort). The number of faculty members in each program in such roles varied from one to 10. Respondents indicated that faculty members in this category were most often midcareer assistant or associate professors with few full professors. All programs indicated that scholarship in the form of peer-reviewed publications was required for the promotion of faculty. Faculty members who performed research as their main activity had all received formal fellowship training and consequently had dual degrees (MD with MPH or MD, with MSc being the two most common combinations). With respect to clinical activities, most respondents indicated that research faculty spent 10% to 49% of their effort on clinical work. However, five respondents indicated that research faculty had <10% effort on clinical duties (Table 3).

Eleven respondents (39%) identified the main focus of faculty as health service research, where four (14%) identified their main focus as clinical trials. Regardless of funding status, all respondents stated that their faculty were interested in studying quality and process improvement efforts (eg, transitions or readmissions, n = 19), patient safety initiatives (eg, hospital-acquired complications, n = 17), and disease-specific areas (eg, thrombosis, n = 15).

In terms of research output, 12 respondents stated that their research/QI faculty collectively published 11-50 peer-reviewed papers during the academic year, and 10 programs indicated that their faculty published 0-10 papers per year. Only three programs reported that their faculty collectively published 50-99 peer-reviewed papers per year. With respect to abstract presentations at national conferences, 13 programs indicated that they presented 0-10 abstracts, and 12 indicated that they presented 11-50.

In this first survey quantifying research activities in HM, respondents from 28 programs shared important insights into research activities at their institutions. Although our sample size was small, substantial variation in the size, composition, and structure of research programs in HM among respondents was observed. For example, few respondents indicated the availability of training programs for research in HM at their institutions. Similarly, among faculty who focused mainly on research, variation in funding streams and effort protection was observed. A preponderance of midcareer faculty with a range of funding sources, including NIH, AHRQ, VHA, CMS, and CDC was reported. Collectively, these data not only provide a unique glimpse into the state of research in HM but also help establish a baseline of the status of the field at large.

Some findings of our study are intuitive given our sampling strategy and the types of programs that responded. For example, the fact that most respondents for research programs represented university-based or affiliated institutions is expected given the tripartite academic mission. However, even within our sample of highly motivated programs, some findings are surprising and merit further exploration. For example, the observation that some respondents identified HM investigators within their program with <25% in intra- or extramural funding was unexpected. On the other extreme, we were surprised to find that three programs reported >$5 million in research funding. Understanding whether specific factors, such as the availability of experienced mentors within and outside departments or assistance from support staff (eg, statisticians and project managers), are associated with success and funding within these programs are important questions to answer. By focusing on these issues, we will be well poised as a field to understand what works, what does not work, and why.

Likewise, the finding that few programs within our sample offer formal training in the form of fellowships to research investigators represents an improvement opportunity. A pipeline for growing investigators is critical for the specialty that is HM. Notably, this call is not new; rather, previous investigators have highlighted the importance of developing academically oriented hospitalists for the future of the field.⁵ The implementation of faculty scholarship development programs has improved the scholarly output, mentoring activities, and succession planning of academics within HM.^6,7 Conversely, lack of adequate mentorship and support for academic activities remains a challenge and as a factor associated with the failure to produce academic work.⁸ Without a cadre of investigators asking critical questions related to care delivery, the legitimacy of our field may be threatened.

While extrapolating to the field is difficult given the small number of our respondents, highlighting the progress that has been made is important. For example, while misalignment between funding and clinical and research mission persists, our survey found that several programs have been successful in securing extramural funding for their investigators. Additionally, internal funding for QI work appears to be increasing, with hospitalists receiving dedicated effort for much of this work. Innovation in how best to support and develop these types of efforts have also emerged. For example, the University of Michigan Specialist Hospitalist Allied Research Program offers dedicated effort and funding for hospitalists tackling projects germane to HM (eg, ordering of blood cultures for febrile inpatients) that overlap with subspecialists (eg, infectious diseases).⁹ Thus, hospitalists are linked with other specialties in the development of research agendas and academic products. Similarly, the launch of the HOMERUN network, a coalition of investigators who bridge health systems to study problems central to HM, has helped usher in a new era of research opportunities in the specialty.¹⁰ Fundamentally, the culture of HM has begun to place an emphasis on academic and scholarly productivity in addition to clinical prowess.^11-13 Increased support and funding for training programs geared toward innovation and research in HM is needed to continue this mission. The Society for General Internal Medicine, American College of Physicians, and SHM have important roles to play as the largest professional organizations for generalists in this respect. Support for research, QI, and investigators in HM remains an urgent and largely unmet need.

Our study has limitations. First, our response rate was low at 28% but is consistent with the response rates of other surveys of physician groups.¹⁴ Caution in making inferences to the field at large is necessary given the potential for selection and nonresponse bias. However, we expect that respondents are likely biased toward programs actively conducting research and engaged in QI, thus better reflecting the state of these activities in HM. Second, given that we did not ask for any identifying information, we have no way of establishing the accuracy of the data provided by respondents. However, we have no reason to believe that responses would be altered in a systematic fashion. Future studies that link our findings to publicly available data (eg, databases of active grants and funding) might be useful. Third, while our survey instrument was created and internally validated by hospitalist researchers, its lack of external validation could limit findings. Finally, our results vary on the basis of how respondents answered questions related to effort and time allocation given that these measures differ across programs.

In summary, the findings from this study highlight substantial variations in the number, training, and funding of research faculty across HM programs. Understanding the factors behind the success of some programs and the failures of others appears important in informing and growing the research in the field. Future studies that aim to expand survey participation, raise the awareness of the state of research in HM, and identify barriers and facilitators to academic success in HM are needed.

Disclosures

Dr. Chopra discloses grant funding from the Agency for Healthcare Research and Quality (AHRQ), VA Health Services and Research Department, and Centers for Disease Control. Dr. Jones discloses grant funding from AHRQ. All other authors disclose no conflicts of interest.

Almost all specialties in internal medicine have a sound scientific research base through which clinical practice is informed.¹ For the field of Hospital Medicine (HM), this evidence has largely comprised research generated from fields outside of the specialty. The need to develop, invest, and grow investigators in hospital-based medicine remains unmet as HM and its footprint in hospital systems continue to grow.^2,3

Despite this fact, little is known about the current state of research in HM. A 2014 survey of the members of the Society of Hospital Medicine (SHM) found that research output across the field of HM, as measured on the basis of peer-reviewed publications, was growing.⁴ Since then, however, the numbers of individuals engaged in research activities, their background and training, publication output, or funding sources have not been quantified. Similarly, little is known about which institutions support the development of junior investigators (ie, HM research fellowships), how these programs are funded, and whether or not matriculants enter the field as investigators. These gaps must be measured, evaluated, and ideally addressed through strategic policy and funding initiatives to advance the state of science within HM.

Members of the SHM Research Committee developed, designed, and deployed a survey to improve the understanding of the state of research in HM. In this study, we aimed to establish the baseline of research in HM to enable the measurement of progress through periodic waves of data collection. Specifically, we sought to quantify and describe the characteristics of existing research programs, the sources and types of funding, the number and background of faculty, and the availability of resources for training researchers in HM.

Study Setting and Participants

Given that no defined list, database, or external resource that identifies research programs and contacts in HM exists, we began by creating a strategy to identify and sample adult HM programs and their leaders engaged in research activity. We iteratively developed a two-step approach to maximize inclusivity. First, we partnered with SHM to identify programs and leaders actively engaging in research activities. SHM is the largest professional organization within HM and maintains an extensive membership database that includes the titles, e-mail addresses, and affiliations of hospitalists in the United States, including academic and nonacademic sites. This list was manually scanned, and the leaders of academic and research programs in adult HM were identified by examining their titles (eg, Division Chief, Research Lead, etc.) and academic affiliations. During this step, members of the committee noticed that certain key individuals were either missing, no longer occupying their role/title, or had been replaced by others. Therefore, we performed a second step and asked the members of the SHM Research Committee to identify academic and research leaders by using current personal contacts, publication history, and social networks. We asked members to identify individuals and programs that had received grant funding, were actively presenting research at SHM (or other major national venues), and/or were producing peer-reviewed publications related to HM. These programs were purposefully chosen (ie, over HM programs known for clinical activities) to create an enriched sample of those engaged in research in HM. The research committee performed the “second pass” to ensure that established investigators who may not be accurately captured within the SHM database were included to maximize yield for the survey. Finally, these two sources were merged to ensure the absence of duplicate contacts and the identification of a primary respondent for each affiliate. As a result, a convenience sample of 100 programs and corresponding individuals was compiled for the purposes of this survey.

Survey Development

A workgroup within the SHM Research Committee was tasked to create a survey that would achieve four distinct goals: (1) identify institutions currently engaging in hospital-based research; (2) define the characteristics, including sources of research funding, training opportunities, criteria for promotion, and grant support, of research programs within institutions; (3) understand the prevalence of research fellowship programs, including size, training curricula, and funding sources; and (4) evaluate the productivity and funding sources of HM investigators at each site.

Survey questions that target each of these domains were drafted by the workgroup. Questions were pretested with colleagues outside the workgroup focused on this project (ie, from the main research committee). The instrument was refined and edited to improve the readability and clarity of questions on the basis of the feedback obtained through the iterative process. The revised instrument was then programmed into an online survey administration tool (SurveyMonkey^®) to facilitate electronic dissemination. Finally, the members of the workgroup tested the online survey to ensure functionality. No identifiable information was collected from respondents, and no monetary incentive was offered for the completion of the survey. An invitation to participate in the survey was sent via e-mail to each of the program contacts identified.

Statistical Analysis

Descriptive statistics, including proportions, means, and percentages, were used to tabulate results. All analyses were conducted using Stata 13 MP/SE (StataCorp, College Station, Texas).

Ethical and Regulatory Considerations

The study was reviewed and deemed exempt from regulation by the University of Michigan Institutional Review Board (HUM000138628).

General Characteristics of Research Programs and Faculty

Out of 100 program contacts, 28 (representing 1,586 faculty members) responded and were included in the survey (program response rate = 28%). When comparing programs that did respond with those that did not, a greater proportion of programs in university settings were noted among respondents (79% vs 21%). Respondents represented programs from all regions of the United States, with most representing university-based (79%), university-affiliated (14%) or Veterans Health Administration (VHA; 11%) programs. Most respondents were in leadership roles, including division chiefs (32%), research directors/leads (21%), section chiefs (18%), and related titles, such as program director. Respondents indicated that the total number of faculty members in their programs (including nonclinicians and advance practice providers) varied from eight to 152 (mean [SD] = 57 [36]) members, with physicians representing the majority of faculty members (Table 1).

Among the 1,586 faculty members within the 28 programs, respondents identified 192 faculty members (12%) as currently receiving extra- or intramural support for research activities. Of these faculty, over half (58%) received <25% of effort from intra or extramural sources, and 28 (15%) and 52 (27%) faculty members received 25%-50% or >50% of support for their effort, respectively. The number of investigators who received funding across programs ranged from 0 to 28 faculty members. Compared with the 192 funded investigators, respondents indicated that a larger number of faculty in their programs (n = 656 or 41%) were involved in local quality improvement (QI) efforts. Of the 656 faculty members involved in QI efforts, 241 individuals (37%) were internally funded and received protected time/effort for their work.

Key Attributes of Research Programs

In the evaluation of the amount of total grant funding, respondents from 17 programs indicated that they received $500,000 in annual extra and intramural funding, and those from three programs stated that they received $500,000 to $999,999 in funding. Five respondents indicated that their programs currently received $1 million to $5 million in grant funding, and three reported >$5 million in research support. The sources of research funding included several divisions within the National Institute of Health (NIH, 12 programs), Agency for Healthcare Research and Quality (AHRQ, four programs), foundations (four programs), and internal grants (six programs). Additionally, six programs indicated “other” sources of funding that included the VHA, Patient-Centered Outcomes Research Institute (PCORI), Centers for Medicare and Medicaid Services, Centers for Disease Control (CDC), and industry sources.

A range of grants, including career development awards (11 programs); small grants, such as R21 and R03s (eight programs); R-level grants, including VA merit awards (five programs); program series grants, such as P and U grants (five programs), and foundation grants (eight programs), were reported as types of awards. Respondents from 16 programs indicated that they provided internal pilot grants. Amounts for such grants ranged from <$50,000 (14 programs) to $50,000-$100,000 (two programs).

Research Fellowship Programs/Training Programs

Only five of the 28 surveyed programs indicated that they currently had a research training or fellowship program for developing hospitalist investigators. The age of these programs varied from <1 year to 10 years. Three of the five programs stated that they had two fellows per year, and two stated they had spots for one trainee annually. All respondents indicated that fellows received training on study design, research methods, quantitative (eg, large database and secondary analyses) and qualitative data analysis. In addition, two programs included training in systematic review and meta-analyses, and three included focused courses on healthcare policy. Four of the five programs included training in QI tools, such as LEAN and Six Sigma. Funding for four of the five fellowship programs came from internal sources (eg, department and CTSA). However, two programs added they received some support from extramural funding and philanthropy. Following training, respondents from programs indicated that the majority of their graduates (60%) went on to hybrid research/QI roles (50/50 research/clinical effort), whereas 40% obtained dedicated research investigator (80/20) positions (Table 2).

The 23 institutions without research training programs cited that the most important barrier for establishing such programs was lack of funding (12 programs) and the lack of a pipeline of hospitalists seeking such training (six programs). However, 15 programs indicated that opportunities for hospitalists to gain research training in the form of courses were available internally (eg, courses in the department or medical school) or externally (eg, School of Public Health). Seven programs indicated that they were planning to start a HM research fellowship within the next five years.

Research Faculty

Among the 28 respondents, 15 stated that they have faculty members who conduct research as their main professional activity (ie, >50% effort). The number of faculty members in each program in such roles varied from one to 10. Respondents indicated that faculty members in this category were most often midcareer assistant or associate professors with few full professors. All programs indicated that scholarship in the form of peer-reviewed publications was required for the promotion of faculty. Faculty members who performed research as their main activity had all received formal fellowship training and consequently had dual degrees (MD with MPH or MD, with MSc being the two most common combinations). With respect to clinical activities, most respondents indicated that research faculty spent 10% to 49% of their effort on clinical work. However, five respondents indicated that research faculty had <10% effort on clinical duties (Table 3).

Eleven respondents (39%) identified the main focus of faculty as health service research, where four (14%) identified their main focus as clinical trials. Regardless of funding status, all respondents stated that their faculty were interested in studying quality and process improvement efforts (eg, transitions or readmissions, n = 19), patient safety initiatives (eg, hospital-acquired complications, n = 17), and disease-specific areas (eg, thrombosis, n = 15).

In terms of research output, 12 respondents stated that their research/QI faculty collectively published 11-50 peer-reviewed papers during the academic year, and 10 programs indicated that their faculty published 0-10 papers per year. Only three programs reported that their faculty collectively published 50-99 peer-reviewed papers per year. With respect to abstract presentations at national conferences, 13 programs indicated that they presented 0-10 abstracts, and 12 indicated that they presented 11-50.

In this first survey quantifying research activities in HM, respondents from 28 programs shared important insights into research activities at their institutions. Although our sample size was small, substantial variation in the size, composition, and structure of research programs in HM among respondents was observed. For example, few respondents indicated the availability of training programs for research in HM at their institutions. Similarly, among faculty who focused mainly on research, variation in funding streams and effort protection was observed. A preponderance of midcareer faculty with a range of funding sources, including NIH, AHRQ, VHA, CMS, and CDC was reported. Collectively, these data not only provide a unique glimpse into the state of research in HM but also help establish a baseline of the status of the field at large.

Some findings of our study are intuitive given our sampling strategy and the types of programs that responded. For example, the fact that most respondents for research programs represented university-based or affiliated institutions is expected given the tripartite academic mission. However, even within our sample of highly motivated programs, some findings are surprising and merit further exploration. For example, the observation that some respondents identified HM investigators within their program with <25% in intra- or extramural funding was unexpected. On the other extreme, we were surprised to find that three programs reported >$5 million in research funding. Understanding whether specific factors, such as the availability of experienced mentors within and outside departments or assistance from support staff (eg, statisticians and project managers), are associated with success and funding within these programs are important questions to answer. By focusing on these issues, we will be well poised as a field to understand what works, what does not work, and why.

Likewise, the finding that few programs within our sample offer formal training in the form of fellowships to research investigators represents an improvement opportunity. A pipeline for growing investigators is critical for the specialty that is HM. Notably, this call is not new; rather, previous investigators have highlighted the importance of developing academically oriented hospitalists for the future of the field.⁵ The implementation of faculty scholarship development programs has improved the scholarly output, mentoring activities, and succession planning of academics within HM.^6,7 Conversely, lack of adequate mentorship and support for academic activities remains a challenge and as a factor associated with the failure to produce academic work.⁸ Without a cadre of investigators asking critical questions related to care delivery, the legitimacy of our field may be threatened.

While extrapolating to the field is difficult given the small number of our respondents, highlighting the progress that has been made is important. For example, while misalignment between funding and clinical and research mission persists, our survey found that several programs have been successful in securing extramural funding for their investigators. Additionally, internal funding for QI work appears to be increasing, with hospitalists receiving dedicated effort for much of this work. Innovation in how best to support and develop these types of efforts have also emerged. For example, the University of Michigan Specialist Hospitalist Allied Research Program offers dedicated effort and funding for hospitalists tackling projects germane to HM (eg, ordering of blood cultures for febrile inpatients) that overlap with subspecialists (eg, infectious diseases).⁹ Thus, hospitalists are linked with other specialties in the development of research agendas and academic products. Similarly, the launch of the HOMERUN network, a coalition of investigators who bridge health systems to study problems central to HM, has helped usher in a new era of research opportunities in the specialty.¹⁰ Fundamentally, the culture of HM has begun to place an emphasis on academic and scholarly productivity in addition to clinical prowess.^11-13 Increased support and funding for training programs geared toward innovation and research in HM is needed to continue this mission. The Society for General Internal Medicine, American College of Physicians, and SHM have important roles to play as the largest professional organizations for generalists in this respect. Support for research, QI, and investigators in HM remains an urgent and largely unmet need.

Our study has limitations. First, our response rate was low at 28% but is consistent with the response rates of other surveys of physician groups.¹⁴ Caution in making inferences to the field at large is necessary given the potential for selection and nonresponse bias. However, we expect that respondents are likely biased toward programs actively conducting research and engaged in QI, thus better reflecting the state of these activities in HM. Second, given that we did not ask for any identifying information, we have no way of establishing the accuracy of the data provided by respondents. However, we have no reason to believe that responses would be altered in a systematic fashion. Future studies that link our findings to publicly available data (eg, databases of active grants and funding) might be useful. Third, while our survey instrument was created and internally validated by hospitalist researchers, its lack of external validation could limit findings. Finally, our results vary on the basis of how respondents answered questions related to effort and time allocation given that these measures differ across programs.

In summary, the findings from this study highlight substantial variations in the number, training, and funding of research faculty across HM programs. Understanding the factors behind the success of some programs and the failures of others appears important in informing and growing the research in the field. Future studies that aim to expand survey participation, raise the awareness of the state of research in HM, and identify barriers and facilitators to academic success in HM are needed.

Disclosures

Dr. Chopra discloses grant funding from the Agency for Healthcare Research and Quality (AHRQ), VA Health Services and Research Department, and Centers for Disease Control. Dr. Jones discloses grant funding from AHRQ. All other authors disclose no conflicts of interest.

Almost all specialties in internal medicine have a sound scientific research base through which clinical practice is informed.¹ For the field of Hospital Medicine (HM), this evidence has largely comprised research generated from fields outside of the specialty. The need to develop, invest, and grow investigators in hospital-based medicine remains unmet as HM and its footprint in hospital systems continue to grow.^2,3

Despite this fact, little is known about the current state of research in HM. A 2014 survey of the members of the Society of Hospital Medicine (SHM) found that research output across the field of HM, as measured on the basis of peer-reviewed publications, was growing.⁴ Since then, however, the numbers of individuals engaged in research activities, their background and training, publication output, or funding sources have not been quantified. Similarly, little is known about which institutions support the development of junior investigators (ie, HM research fellowships), how these programs are funded, and whether or not matriculants enter the field as investigators. These gaps must be measured, evaluated, and ideally addressed through strategic policy and funding initiatives to advance the state of science within HM.

Members of the SHM Research Committee developed, designed, and deployed a survey to improve the understanding of the state of research in HM. In this study, we aimed to establish the baseline of research in HM to enable the measurement of progress through periodic waves of data collection. Specifically, we sought to quantify and describe the characteristics of existing research programs, the sources and types of funding, the number and background of faculty, and the availability of resources for training researchers in HM.

Study Setting and Participants

Given that no defined list, database, or external resource that identifies research programs and contacts in HM exists, we began by creating a strategy to identify and sample adult HM programs and their leaders engaged in research activity. We iteratively developed a two-step approach to maximize inclusivity. First, we partnered with SHM to identify programs and leaders actively engaging in research activities. SHM is the largest professional organization within HM and maintains an extensive membership database that includes the titles, e-mail addresses, and affiliations of hospitalists in the United States, including academic and nonacademic sites. This list was manually scanned, and the leaders of academic and research programs in adult HM were identified by examining their titles (eg, Division Chief, Research Lead, etc.) and academic affiliations. During this step, members of the committee noticed that certain key individuals were either missing, no longer occupying their role/title, or had been replaced by others. Therefore, we performed a second step and asked the members of the SHM Research Committee to identify academic and research leaders by using current personal contacts, publication history, and social networks. We asked members to identify individuals and programs that had received grant funding, were actively presenting research at SHM (or other major national venues), and/or were producing peer-reviewed publications related to HM. These programs were purposefully chosen (ie, over HM programs known for clinical activities) to create an enriched sample of those engaged in research in HM. The research committee performed the “second pass” to ensure that established investigators who may not be accurately captured within the SHM database were included to maximize yield for the survey. Finally, these two sources were merged to ensure the absence of duplicate contacts and the identification of a primary respondent for each affiliate. As a result, a convenience sample of 100 programs and corresponding individuals was compiled for the purposes of this survey.

Survey Development

A workgroup within the SHM Research Committee was tasked to create a survey that would achieve four distinct goals: (1) identify institutions currently engaging in hospital-based research; (2) define the characteristics, including sources of research funding, training opportunities, criteria for promotion, and grant support, of research programs within institutions; (3) understand the prevalence of research fellowship programs, including size, training curricula, and funding sources; and (4) evaluate the productivity and funding sources of HM investigators at each site.

Survey questions that target each of these domains were drafted by the workgroup. Questions were pretested with colleagues outside the workgroup focused on this project (ie, from the main research committee). The instrument was refined and edited to improve the readability and clarity of questions on the basis of the feedback obtained through the iterative process. The revised instrument was then programmed into an online survey administration tool (SurveyMonkey^®) to facilitate electronic dissemination. Finally, the members of the workgroup tested the online survey to ensure functionality. No identifiable information was collected from respondents, and no monetary incentive was offered for the completion of the survey. An invitation to participate in the survey was sent via e-mail to each of the program contacts identified.

Statistical Analysis

Descriptive statistics, including proportions, means, and percentages, were used to tabulate results. All analyses were conducted using Stata 13 MP/SE (StataCorp, College Station, Texas).

Ethical and Regulatory Considerations

The study was reviewed and deemed exempt from regulation by the University of Michigan Institutional Review Board (HUM000138628).

General Characteristics of Research Programs and Faculty

Out of 100 program contacts, 28 (representing 1,586 faculty members) responded and were included in the survey (program response rate = 28%). When comparing programs that did respond with those that did not, a greater proportion of programs in university settings were noted among respondents (79% vs 21%). Respondents represented programs from all regions of the United States, with most representing university-based (79%), university-affiliated (14%) or Veterans Health Administration (VHA; 11%) programs. Most respondents were in leadership roles, including division chiefs (32%), research directors/leads (21%), section chiefs (18%), and related titles, such as program director. Respondents indicated that the total number of faculty members in their programs (including nonclinicians and advance practice providers) varied from eight to 152 (mean [SD] = 57 [36]) members, with physicians representing the majority of faculty members (Table 1).

Among the 1,586 faculty members within the 28 programs, respondents identified 192 faculty members (12%) as currently receiving extra- or intramural support for research activities. Of these faculty, over half (58%) received <25% of effort from intra or extramural sources, and 28 (15%) and 52 (27%) faculty members received 25%-50% or >50% of support for their effort, respectively. The number of investigators who received funding across programs ranged from 0 to 28 faculty members. Compared with the 192 funded investigators, respondents indicated that a larger number of faculty in their programs (n = 656 or 41%) were involved in local quality improvement (QI) efforts. Of the 656 faculty members involved in QI efforts, 241 individuals (37%) were internally funded and received protected time/effort for their work.

Key Attributes of Research Programs

In the evaluation of the amount of total grant funding, respondents from 17 programs indicated that they received $500,000 in annual extra and intramural funding, and those from three programs stated that they received $500,000 to $999,999 in funding. Five respondents indicated that their programs currently received $1 million to $5 million in grant funding, and three reported >$5 million in research support. The sources of research funding included several divisions within the National Institute of Health (NIH, 12 programs), Agency for Healthcare Research and Quality (AHRQ, four programs), foundations (four programs), and internal grants (six programs). Additionally, six programs indicated “other” sources of funding that included the VHA, Patient-Centered Outcomes Research Institute (PCORI), Centers for Medicare and Medicaid Services, Centers for Disease Control (CDC), and industry sources.

A range of grants, including career development awards (11 programs); small grants, such as R21 and R03s (eight programs); R-level grants, including VA merit awards (five programs); program series grants, such as P and U grants (five programs), and foundation grants (eight programs), were reported as types of awards. Respondents from 16 programs indicated that they provided internal pilot grants. Amounts for such grants ranged from <$50,000 (14 programs) to $50,000-$100,000 (two programs).

Research Fellowship Programs/Training Programs

Only five of the 28 surveyed programs indicated that they currently had a research training or fellowship program for developing hospitalist investigators. The age of these programs varied from <1 year to 10 years. Three of the five programs stated that they had two fellows per year, and two stated they had spots for one trainee annually. All respondents indicated that fellows received training on study design, research methods, quantitative (eg, large database and secondary analyses) and qualitative data analysis. In addition, two programs included training in systematic review and meta-analyses, and three included focused courses on healthcare policy. Four of the five programs included training in QI tools, such as LEAN and Six Sigma. Funding for four of the five fellowship programs came from internal sources (eg, department and CTSA). However, two programs added they received some support from extramural funding and philanthropy. Following training, respondents from programs indicated that the majority of their graduates (60%) went on to hybrid research/QI roles (50/50 research/clinical effort), whereas 40% obtained dedicated research investigator (80/20) positions (Table 2).

The 23 institutions without research training programs cited that the most important barrier for establishing such programs was lack of funding (12 programs) and the lack of a pipeline of hospitalists seeking such training (six programs). However, 15 programs indicated that opportunities for hospitalists to gain research training in the form of courses were available internally (eg, courses in the department or medical school) or externally (eg, School of Public Health). Seven programs indicated that they were planning to start a HM research fellowship within the next five years.

Research Faculty

Among the 28 respondents, 15 stated that they have faculty members who conduct research as their main professional activity (ie, >50% effort). The number of faculty members in each program in such roles varied from one to 10. Respondents indicated that faculty members in this category were most often midcareer assistant or associate professors with few full professors. All programs indicated that scholarship in the form of peer-reviewed publications was required for the promotion of faculty. Faculty members who performed research as their main activity had all received formal fellowship training and consequently had dual degrees (MD with MPH or MD, with MSc being the two most common combinations). With respect to clinical activities, most respondents indicated that research faculty spent 10% to 49% of their effort on clinical work. However, five respondents indicated that research faculty had <10% effort on clinical duties (Table 3).

Eleven respondents (39%) identified the main focus of faculty as health service research, where four (14%) identified their main focus as clinical trials. Regardless of funding status, all respondents stated that their faculty were interested in studying quality and process improvement efforts (eg, transitions or readmissions, n = 19), patient safety initiatives (eg, hospital-acquired complications, n = 17), and disease-specific areas (eg, thrombosis, n = 15).

In terms of research output, 12 respondents stated that their research/QI faculty collectively published 11-50 peer-reviewed papers during the academic year, and 10 programs indicated that their faculty published 0-10 papers per year. Only three programs reported that their faculty collectively published 50-99 peer-reviewed papers per year. With respect to abstract presentations at national conferences, 13 programs indicated that they presented 0-10 abstracts, and 12 indicated that they presented 11-50.

In this first survey quantifying research activities in HM, respondents from 28 programs shared important insights into research activities at their institutions. Although our sample size was small, substantial variation in the size, composition, and structure of research programs in HM among respondents was observed. For example, few respondents indicated the availability of training programs for research in HM at their institutions. Similarly, among faculty who focused mainly on research, variation in funding streams and effort protection was observed. A preponderance of midcareer faculty with a range of funding sources, including NIH, AHRQ, VHA, CMS, and CDC was reported. Collectively, these data not only provide a unique glimpse into the state of research in HM but also help establish a baseline of the status of the field at large.

Some findings of our study are intuitive given our sampling strategy and the types of programs that responded. For example, the fact that most respondents for research programs represented university-based or affiliated institutions is expected given the tripartite academic mission. However, even within our sample of highly motivated programs, some findings are surprising and merit further exploration. For example, the observation that some respondents identified HM investigators within their program with <25% in intra- or extramural funding was unexpected. On the other extreme, we were surprised to find that three programs reported >$5 million in research funding. Understanding whether specific factors, such as the availability of experienced mentors within and outside departments or assistance from support staff (eg, statisticians and project managers), are associated with success and funding within these programs are important questions to answer. By focusing on these issues, we will be well poised as a field to understand what works, what does not work, and why.

Likewise, the finding that few programs within our sample offer formal training in the form of fellowships to research investigators represents an improvement opportunity. A pipeline for growing investigators is critical for the specialty that is HM. Notably, this call is not new; rather, previous investigators have highlighted the importance of developing academically oriented hospitalists for the future of the field.⁵ The implementation of faculty scholarship development programs has improved the scholarly output, mentoring activities, and succession planning of academics within HM.^6,7 Conversely, lack of adequate mentorship and support for academic activities remains a challenge and as a factor associated with the failure to produce academic work.⁸ Without a cadre of investigators asking critical questions related to care delivery, the legitimacy of our field may be threatened.

While extrapolating to the field is difficult given the small number of our respondents, highlighting the progress that has been made is important. For example, while misalignment between funding and clinical and research mission persists, our survey found that several programs have been successful in securing extramural funding for their investigators. Additionally, internal funding for QI work appears to be increasing, with hospitalists receiving dedicated effort for much of this work. Innovation in how best to support and develop these types of efforts have also emerged. For example, the University of Michigan Specialist Hospitalist Allied Research Program offers dedicated effort and funding for hospitalists tackling projects germane to HM (eg, ordering of blood cultures for febrile inpatients) that overlap with subspecialists (eg, infectious diseases).⁹ Thus, hospitalists are linked with other specialties in the development of research agendas and academic products. Similarly, the launch of the HOMERUN network, a coalition of investigators who bridge health systems to study problems central to HM, has helped usher in a new era of research opportunities in the specialty.¹⁰ Fundamentally, the culture of HM has begun to place an emphasis on academic and scholarly productivity in addition to clinical prowess.^11-13 Increased support and funding for training programs geared toward innovation and research in HM is needed to continue this mission. The Society for General Internal Medicine, American College of Physicians, and SHM have important roles to play as the largest professional organizations for generalists in this respect. Support for research, QI, and investigators in HM remains an urgent and largely unmet need.

Our study has limitations. First, our response rate was low at 28% but is consistent with the response rates of other surveys of physician groups.¹⁴ Caution in making inferences to the field at large is necessary given the potential for selection and nonresponse bias. However, we expect that respondents are likely biased toward programs actively conducting research and engaged in QI, thus better reflecting the state of these activities in HM. Second, given that we did not ask for any identifying information, we have no way of establishing the accuracy of the data provided by respondents. However, we have no reason to believe that responses would be altered in a systematic fashion. Future studies that link our findings to publicly available data (eg, databases of active grants and funding) might be useful. Third, while our survey instrument was created and internally validated by hospitalist researchers, its lack of external validation could limit findings. Finally, our results vary on the basis of how respondents answered questions related to effort and time allocation given that these measures differ across programs.

In summary, the findings from this study highlight substantial variations in the number, training, and funding of research faculty across HM programs. Understanding the factors behind the success of some programs and the failures of others appears important in informing and growing the research in the field. Future studies that aim to expand survey participation, raise the awareness of the state of research in HM, and identify barriers and facilitators to academic success in HM are needed.

Disclosures

Dr. Chopra discloses grant funding from the Agency for Healthcare Research and Quality (AHRQ), VA Health Services and Research Department, and Centers for Disease Control. Dr. Jones discloses grant funding from AHRQ. All other authors disclose no conflicts of interest.