Time‐motion studies, introduced by Frederick W. Taylor in the 1880s,1 have been widely implemented across the business world; a Google search of time‐motion study returns approximately 32 million results. Such studies continuously document how workers spend their time and then use this information to identify and eliminate inefficient practices. Work‐sampling is a similar methodology introduced by L.H.C. Tippett in 1935.2 Work‐sampling utilizes a trained observer to document activities at predetermined or random intervals of time. Given a large enough number of observations, this method can be comparable to the continuous observation method used in time‐motion studies.3

Healthcare has begun to utilize these time‐study methodologies to evaluate the activities of physicians and nurses. Researchers have successfully used time‐study methodology in the emergency department, intensive care unit, and ambulatory and surgical settings in the U.S. and around the world to better understand physician activities and to design and assess interventions to improve efficiency.49 Hospitals are also eager to enhance efficiency in the inpatient setting given the current economic environment. Hospitalizations account for over a third of healthcare costs in the United States, making them an attractive target for cost‐cutting measures.10 Acknowledging that healthcare expenditures cannot continue to rise,11 insurers, particularly the Centers for Medicare and Medicaid Services (CMS), increasingly seek to reduce payments to hospitals.12 Compounding these pressures, a major supply of relatively inexpensive labor shrank with the decision by the Accreditation Council for Graduate Medicine Education (ACGME) to restrict the number of hours residents are allowed to work. Efficiency concerns gain new urgency as hospitals scramble to cover their patient loads with reduced physician availability.13

We undertook a systematic review of time‐motion and work‐sampling studies performed in the hospital setting to better understand the available literature describing the activities of physicians caring for hospitalized patients. An additional goal of this review was to determine the extent of available time‐flow literature describing the activity of hospitalists. The hospitalist movement provided one viable solution to the gap between demand for hospital patient care and the reduced supply of available physicianstypically primary care physicians in community hospitals and residents in teaching hospitals.14 Hospital medicine is the fastest‐growing specialty in the history of American medicine.15 More than half of American hospitals now have hospital medicine programs with a total of greater than 25,000 hospitalists in the U.S.15 This popularity has been driven by hospitalists' ability to increase efficiency through decreasing overall cost and length of stay for patients without increasing readmission rates or reducing primary care physician satisfaction.1619 However, exactly how hospitalists accomplish this increase in efficiency is still the subject of debate. One time‐motion study provides a glimpse into the activities of hospitalists at an academic urban hospital,20 but may not be applicable to many other hospitals.

Methods

Results

Our database search yielded 4270 potential articles. We then reviewed the title and abstract of each of these articles to identify studies that evaluated physicians, were performed on a hospital ward, and explicitly used time‐motion or direct‐observation work‐sampling methodology. For articles lacking an abstract but having a relevant title, we obtained the full text to determine eligibility for additional review. Sixty‐eight articles from this original search were selected for full‐text review. Ten of these articles met the selection criteria. Most of the articles excluded in this step were either conducted in an outpatient OR or ER setting, or used self‐report data instead of direct‐observation data. A secondary search using the reference lists of all obtained articles as well as consultation with experts in the field yielded 11 additional articles of interest. Three of these 11 articles were found to meet our criteria, bringing the total to 13 articles for review (Fig. 1).

These 13 articles included several types of physicians in their samples. Eleven included interns,2131 7 included residents,2123, 2628, 31 and 4 included attending physicians20, 23, 26, 32 (Table 1). Six articles included more than 1 type of subject.2123, 26, 28, 31 The main focus of these articles also varied. Nine of the 13 studies were designed to simply describe how residents, physicians and nurses spend their time.20, 2227, 29, 31 Three studies were primarily concerned with comparing groups from different intern programs, residency rotations, hospital types, or shifts.28, 30, 32 The remaining study attempted to quantify the amount of time physicians spent on tasks that could be performed by non‐physician staff.21 Only 2 articles evaluated hospitalists,20, 32 and we found no articles studying hospitalists in a community, non‐teaching setting. The studies were performed as early as 1961 and as recently as 2009. Just 5 of the 13 articles were published within the last 10 years. 0

Figure 1

Methodological quality also varied. Of the 11 time‐motion studies, the total amount of time subjects were observed in the studies ranged from 48 to 720 hours, with a mean of 254 hours. The number of subjects observed varied between 1 and 35, with a mean of 12 subjects. Average time observed per subject ranged from 8 hours to 113.5 hours, with a median of 26 hours. Six of the 11 studies observed subjects continuously for an entire shift.22, 25, 2831 Four studies covered an entire shift over the course of several days, using shorter observation periods.20, 21, 26, 27 One study observed subjects for only part of a shift.32 Ten of the time‐flow articles reported collecting data with a stopwatch and paper‐and‐pencil form2022, 25, 2732 and 1 used a handheld computer system.26 Two studies utilized work‐sampling techniques, both using paper‐and‐pencil forms to collect data during a full shift. Ammenwerth and Spotl23 studied 8 physicians for a total of 40 hours, collecting 5500 observation points. Nerenz et al.24 studied 11 interns for a total of approximately 330 hours, and collected 7858 observations. Both of these studies collected sufficiently large samples to satisfy the power requirements described by Sittig.3

Study sites were relatively uniform. Only one study evaluated physicians at both a teaching community hospital and an academic hospital.32 The remaining 12 observed physicians only in academic hospitals. Two studies were conducted in Australia,25, 26 1 in Austria,23 and the remaining 10 were conducted in the United States.

To provide a rough estimate of the amount of time physicians spend on direct care activities at the patients' bedside vs. indirect care activities, we attempted to calculate these figures for each article using a common definition. For the sake of consistency and to allow us to include as many studies as possible, we used the broadest definition of indirect care found among the articles, which included activities such as professional communication, medication review, documentation, and reviewing test results. Three articles did not provide enough information to calculate these values.24, 27, 29

All 10 articles that did provide sufficient information found that indirect care activities consumed the greater portion of time. Indirect care occupied an average of 50% of physicians' time, ranging from 32% to 69%. Direct care, on the other hand, accounted for an average of 23% of physicians' time, and ranged from 8.5% to 41%. Three articles that included data specific to attending physicians or hospitalists demonstrated an even larger disparity between direct and indirect care.20, 26, 32 In these articles, physicians spent an average of 19% of their time on direct care and 64% on indirect care, suggesting that senior physicians in the academic setting spend less time with patients and more time on care activities away from patients.

Four studies recorded various forms of interruptions of work flow.20, 24, 26, 31 Lurie et al.31 found that interns and residents were interrupted approximately 9 minutes into the performance of every history and physical (H&P). Westbrook et al.26 found that residents were interrupted on average every 21 minutes regardless of the task being performed. Nerenz et al.24 reported that interns received an average of 21 pages over the course of a 30 hour shift. They also noted that, on average, 12 of these pages were merely transient distractions, but 9 pages required some action on the part of the intern.24 Finally, O'leary et al.20 found that hospitalists received an average of 3.5 pages an hour and that 7% of their day was spent returning pages. Two articles recorded events of multitasking. Westbrook et al.26 found that 20% of physicians' time was spent performing more than one activity. Similarly, O'Leary et al.20 reported that 21% of hospitalists' time was spent multitasking. Neither study reported the types of activity performed during multitasking.

One article considered the amount of time physicians spend performing tasks that could be performed by non‐physician staff. Knickman et al.21 reported that in the traditional physician‐centered model of care, approximately 19% of a resident's time is spent on tasks that could be performed by non‐physician staff. They suggested that switching to a mid‐level provider model of care could significantly reduce the impact of resident work hour restrictions.21

Parenti and Lurie28 examined internal medicine residents on both day and night shifts.31 These authors concluded that residents on the night shift have an easier time because they see fewer patients and have more down time than residents on day shifts.28 Additionally, Lurie et al. found that residents got an average of 230 minutes (3.8 hours) of sleep per night and slept, on average, 59 minutes before being awakened by an interruption.31 However, these studies preceded work hour regulations.

Discussion

This systematic review of time studies set in the hospital, the first of which we are aware, revealed a sizable number (13) of articles evaluating physicians. However, the studies almost exclusively focused on academic hospitals (92%) and the majority (69%) analyzed only the activities of physicians in training. The studies were diverse in their methodology, subject populations, and, not surprisingly, their results. Even those studies designed simply to document the activities of physicians in the hospital report widely varying findings. For example, the percentage of time physicians spent on direct‐care activities varied from 8.5% to 41%, while indirect‐care time varied from 32% to 69%. These results likely reflect the heterogeneity of the hospital environment and differences among hospitals, as well as variations in the design and quality of the studies.

Despite this variability, a few observations appear consistent. Physicians perform many tasks that may be readily accomplished by less costly staff. This could partly explain why far more time is spent on activities indirectly related to a patient's care (eg, documentation and coordinating tests), instead of directly interacting with hospitalized patients. Additionally, physicians caring for hospitalized patients experience multiple interruptions and must regularly multitask. Unfortunately, very little research in the hospital setting has evaluated the impact of these interruptions on work efficiency, medical errors, or adverse events.

With the intense national interest in improving the value of healthcare by both enhancing quality and reducing costs, further efforts to optimize the efficiency of hospitalists will be needed.33 As hospitals and hospitalists aim to enhance the efficiency of care delivery to hospitalized patients, and also are increasingly asked to expend time to optimize the hospital discharge process to reduce readmissions,34, 35 time‐motion and work‐sampling studies can provide guidance.

One of the principal difficulties in aggregating data from time studies is the variety of approaches used to analyze activities. Lack of standardization in the approach to assessing physician activities (eg, use of a stopwatch with paper documentation vs. computer) and dissimilar categorizations inhibit efforts to summarize the findings across studies. Categories of activity were generally selected with the specific goals of the study in mind, instead of utilizing a readily available standardized approach. Moreover, the lack of detailed definitions of categories and sub‐categories, along with data for each, produces a significant barrier to comparison. Based on this review of available literature and our own experience conducting time‐motion evaluation of hospitalists, we propose the basic activity categorization in Table 2. Future researchers would be able to more readily compare their findings to other time‐motion studies by utilizing such a standardized approach to categorizing physician activities. Adding custom sub‐categories within this basic set would allow researchers to explore more specific time‐flow questions while maintaining comparability of most data. Electronic data collection tools (eg, handheld or tablet computers) could also facilitate the collection of more detailed and accurate data to increase study reliability.

Our systematic review is limited in its scope, as we focused only on the activities of physicians working in the hospital. Our exclusion criteria also eliminated several more focused time studies that evaluated only one small part of a physician's workflow, such as Amusan et al.'s36 evaluation of EMR and CPOE implementation during morning rounds. The available literature itself is also lacking in several important ways. Much of the literature is now limited by its age. The constant advance of medical technology, changes in work hour regulations, and new reimbursement structures have all affected physician workflow, and likely contributed to the variability of time study findings. Additionally, the available literature focuses almost exclusively on academic hospitals and teaching services. All but 1 of the studies collected data exclusively in academic hospitals, despite the fact that more than 90% of hospital care delivery in the U.S. occurs in a non‐academic hospital setting.20, 37 Just 1 study evaluated the activity of hospitalists directly caring for patients without assistance from residents.20 The significantly different workforce composition in community hospitals could mean that most findings are not relevant to the vast majority of U.S. hospitals. For example, the studies documenting that physicians in training (ie, residents) perform many activities that could be performed by a non‐physician are likely not applicable to the community hospital setting. Thus, additional research is needed to better understand how hospitalists can deliver care more efficiently, particularly in the community hospital setting and in the current technological and structural environment of healthcare.

This systematic review of the literature provides insight into published studies attempting to evaluate physician activities in the hospital through time‐motion and work‐sampling studies. Published research to date appears extremely variable in quality, limiting our ability to draw firm conclusions. However, it appears that hospital‐based physicians spend most of their time not interacting with patients, and non‐physician staff could readily complete a sizable portion of their tasks. Given the necessity for multitasking by hospitalists, better documentation of its frequency and impact is needed, as well as information about the types of tasks performed while multitasking, which has yet to be reported. Additionally, the effect of interruptions (including, but not limited to paging) needs further evaluation.

When properly performed, time‐study methodology represents a powerful approach to understanding the activities of hospitalists and how we might reengineer hospital care delivery to be more efficient. Efforts to standardize healthcare delivery and integrate health information technology could benefit dramatically from detailed information regarding physician activities and empirical testing of quality improvement initiatives. Future research using time‐motion or work‐sampling methodology should be careful to define and report categories of activity with enough detail that comparisons with other studies are possible.

Time‐motion studies, introduced by Frederick W. Taylor in the 1880s,1 have been widely implemented across the business world; a Google search of time‐motion study returns approximately 32 million results. Such studies continuously document how workers spend their time and then use this information to identify and eliminate inefficient practices. Work‐sampling is a similar methodology introduced by L.H.C. Tippett in 1935.2 Work‐sampling utilizes a trained observer to document activities at predetermined or random intervals of time. Given a large enough number of observations, this method can be comparable to the continuous observation method used in time‐motion studies.3

Healthcare has begun to utilize these time‐study methodologies to evaluate the activities of physicians and nurses. Researchers have successfully used time‐study methodology in the emergency department, intensive care unit, and ambulatory and surgical settings in the U.S. and around the world to better understand physician activities and to design and assess interventions to improve efficiency.49 Hospitals are also eager to enhance efficiency in the inpatient setting given the current economic environment. Hospitalizations account for over a third of healthcare costs in the United States, making them an attractive target for cost‐cutting measures.10 Acknowledging that healthcare expenditures cannot continue to rise,11 insurers, particularly the Centers for Medicare and Medicaid Services (CMS), increasingly seek to reduce payments to hospitals.12 Compounding these pressures, a major supply of relatively inexpensive labor shrank with the decision by the Accreditation Council for Graduate Medicine Education (ACGME) to restrict the number of hours residents are allowed to work. Efficiency concerns gain new urgency as hospitals scramble to cover their patient loads with reduced physician availability.13

We undertook a systematic review of time‐motion and work‐sampling studies performed in the hospital setting to better understand the available literature describing the activities of physicians caring for hospitalized patients. An additional goal of this review was to determine the extent of available time‐flow literature describing the activity of hospitalists. The hospitalist movement provided one viable solution to the gap between demand for hospital patient care and the reduced supply of available physicianstypically primary care physicians in community hospitals and residents in teaching hospitals.14 Hospital medicine is the fastest‐growing specialty in the history of American medicine.15 More than half of American hospitals now have hospital medicine programs with a total of greater than 25,000 hospitalists in the U.S.15 This popularity has been driven by hospitalists' ability to increase efficiency through decreasing overall cost and length of stay for patients without increasing readmission rates or reducing primary care physician satisfaction.1619 However, exactly how hospitalists accomplish this increase in efficiency is still the subject of debate. One time‐motion study provides a glimpse into the activities of hospitalists at an academic urban hospital,20 but may not be applicable to many other hospitals.

Methods

Results

Our database search yielded 4270 potential articles. We then reviewed the title and abstract of each of these articles to identify studies that evaluated physicians, were performed on a hospital ward, and explicitly used time‐motion or direct‐observation work‐sampling methodology. For articles lacking an abstract but having a relevant title, we obtained the full text to determine eligibility for additional review. Sixty‐eight articles from this original search were selected for full‐text review. Ten of these articles met the selection criteria. Most of the articles excluded in this step were either conducted in an outpatient OR or ER setting, or used self‐report data instead of direct‐observation data. A secondary search using the reference lists of all obtained articles as well as consultation with experts in the field yielded 11 additional articles of interest. Three of these 11 articles were found to meet our criteria, bringing the total to 13 articles for review (Fig. 1).

These 13 articles included several types of physicians in their samples. Eleven included interns,2131 7 included residents,2123, 2628, 31 and 4 included attending physicians20, 23, 26, 32 (Table 1). Six articles included more than 1 type of subject.2123, 26, 28, 31 The main focus of these articles also varied. Nine of the 13 studies were designed to simply describe how residents, physicians and nurses spend their time.20, 2227, 29, 31 Three studies were primarily concerned with comparing groups from different intern programs, residency rotations, hospital types, or shifts.28, 30, 32 The remaining study attempted to quantify the amount of time physicians spent on tasks that could be performed by non‐physician staff.21 Only 2 articles evaluated hospitalists,20, 32 and we found no articles studying hospitalists in a community, non‐teaching setting. The studies were performed as early as 1961 and as recently as 2009. Just 5 of the 13 articles were published within the last 10 years. 0

Figure 1

Methodological quality also varied. Of the 11 time‐motion studies, the total amount of time subjects were observed in the studies ranged from 48 to 720 hours, with a mean of 254 hours. The number of subjects observed varied between 1 and 35, with a mean of 12 subjects. Average time observed per subject ranged from 8 hours to 113.5 hours, with a median of 26 hours. Six of the 11 studies observed subjects continuously for an entire shift.22, 25, 2831 Four studies covered an entire shift over the course of several days, using shorter observation periods.20, 21, 26, 27 One study observed subjects for only part of a shift.32 Ten of the time‐flow articles reported collecting data with a stopwatch and paper‐and‐pencil form2022, 25, 2732 and 1 used a handheld computer system.26 Two studies utilized work‐sampling techniques, both using paper‐and‐pencil forms to collect data during a full shift. Ammenwerth and Spotl23 studied 8 physicians for a total of 40 hours, collecting 5500 observation points. Nerenz et al.24 studied 11 interns for a total of approximately 330 hours, and collected 7858 observations. Both of these studies collected sufficiently large samples to satisfy the power requirements described by Sittig.3

Study sites were relatively uniform. Only one study evaluated physicians at both a teaching community hospital and an academic hospital.32 The remaining 12 observed physicians only in academic hospitals. Two studies were conducted in Australia,25, 26 1 in Austria,23 and the remaining 10 were conducted in the United States.

To provide a rough estimate of the amount of time physicians spend on direct care activities at the patients' bedside vs. indirect care activities, we attempted to calculate these figures for each article using a common definition. For the sake of consistency and to allow us to include as many studies as possible, we used the broadest definition of indirect care found among the articles, which included activities such as professional communication, medication review, documentation, and reviewing test results. Three articles did not provide enough information to calculate these values.24, 27, 29

All 10 articles that did provide sufficient information found that indirect care activities consumed the greater portion of time. Indirect care occupied an average of 50% of physicians' time, ranging from 32% to 69%. Direct care, on the other hand, accounted for an average of 23% of physicians' time, and ranged from 8.5% to 41%. Three articles that included data specific to attending physicians or hospitalists demonstrated an even larger disparity between direct and indirect care.20, 26, 32 In these articles, physicians spent an average of 19% of their time on direct care and 64% on indirect care, suggesting that senior physicians in the academic setting spend less time with patients and more time on care activities away from patients.

Four studies recorded various forms of interruptions of work flow.20, 24, 26, 31 Lurie et al.31 found that interns and residents were interrupted approximately 9 minutes into the performance of every history and physical (H&P). Westbrook et al.26 found that residents were interrupted on average every 21 minutes regardless of the task being performed. Nerenz et al.24 reported that interns received an average of 21 pages over the course of a 30 hour shift. They also noted that, on average, 12 of these pages were merely transient distractions, but 9 pages required some action on the part of the intern.24 Finally, O'leary et al.20 found that hospitalists received an average of 3.5 pages an hour and that 7% of their day was spent returning pages. Two articles recorded events of multitasking. Westbrook et al.26 found that 20% of physicians' time was spent performing more than one activity. Similarly, O'Leary et al.20 reported that 21% of hospitalists' time was spent multitasking. Neither study reported the types of activity performed during multitasking.

One article considered the amount of time physicians spend performing tasks that could be performed by non‐physician staff. Knickman et al.21 reported that in the traditional physician‐centered model of care, approximately 19% of a resident's time is spent on tasks that could be performed by non‐physician staff. They suggested that switching to a mid‐level provider model of care could significantly reduce the impact of resident work hour restrictions.21

Parenti and Lurie28 examined internal medicine residents on both day and night shifts.31 These authors concluded that residents on the night shift have an easier time because they see fewer patients and have more down time than residents on day shifts.28 Additionally, Lurie et al. found that residents got an average of 230 minutes (3.8 hours) of sleep per night and slept, on average, 59 minutes before being awakened by an interruption.31 However, these studies preceded work hour regulations.

Discussion

This systematic review of time studies set in the hospital, the first of which we are aware, revealed a sizable number (13) of articles evaluating physicians. However, the studies almost exclusively focused on academic hospitals (92%) and the majority (69%) analyzed only the activities of physicians in training. The studies were diverse in their methodology, subject populations, and, not surprisingly, their results. Even those studies designed simply to document the activities of physicians in the hospital report widely varying findings. For example, the percentage of time physicians spent on direct‐care activities varied from 8.5% to 41%, while indirect‐care time varied from 32% to 69%. These results likely reflect the heterogeneity of the hospital environment and differences among hospitals, as well as variations in the design and quality of the studies.

Despite this variability, a few observations appear consistent. Physicians perform many tasks that may be readily accomplished by less costly staff. This could partly explain why far more time is spent on activities indirectly related to a patient's care (eg, documentation and coordinating tests), instead of directly interacting with hospitalized patients. Additionally, physicians caring for hospitalized patients experience multiple interruptions and must regularly multitask. Unfortunately, very little research in the hospital setting has evaluated the impact of these interruptions on work efficiency, medical errors, or adverse events.

With the intense national interest in improving the value of healthcare by both enhancing quality and reducing costs, further efforts to optimize the efficiency of hospitalists will be needed.33 As hospitals and hospitalists aim to enhance the efficiency of care delivery to hospitalized patients, and also are increasingly asked to expend time to optimize the hospital discharge process to reduce readmissions,34, 35 time‐motion and work‐sampling studies can provide guidance.

One of the principal difficulties in aggregating data from time studies is the variety of approaches used to analyze activities. Lack of standardization in the approach to assessing physician activities (eg, use of a stopwatch with paper documentation vs. computer) and dissimilar categorizations inhibit efforts to summarize the findings across studies. Categories of activity were generally selected with the specific goals of the study in mind, instead of utilizing a readily available standardized approach. Moreover, the lack of detailed definitions of categories and sub‐categories, along with data for each, produces a significant barrier to comparison. Based on this review of available literature and our own experience conducting time‐motion evaluation of hospitalists, we propose the basic activity categorization in Table 2. Future researchers would be able to more readily compare their findings to other time‐motion studies by utilizing such a standardized approach to categorizing physician activities. Adding custom sub‐categories within this basic set would allow researchers to explore more specific time‐flow questions while maintaining comparability of most data. Electronic data collection tools (eg, handheld or tablet computers) could also facilitate the collection of more detailed and accurate data to increase study reliability.

Our systematic review is limited in its scope, as we focused only on the activities of physicians working in the hospital. Our exclusion criteria also eliminated several more focused time studies that evaluated only one small part of a physician's workflow, such as Amusan et al.'s36 evaluation of EMR and CPOE implementation during morning rounds. The available literature itself is also lacking in several important ways. Much of the literature is now limited by its age. The constant advance of medical technology, changes in work hour regulations, and new reimbursement structures have all affected physician workflow, and likely contributed to the variability of time study findings. Additionally, the available literature focuses almost exclusively on academic hospitals and teaching services. All but 1 of the studies collected data exclusively in academic hospitals, despite the fact that more than 90% of hospital care delivery in the U.S. occurs in a non‐academic hospital setting.20, 37 Just 1 study evaluated the activity of hospitalists directly caring for patients without assistance from residents.20 The significantly different workforce composition in community hospitals could mean that most findings are not relevant to the vast majority of U.S. hospitals. For example, the studies documenting that physicians in training (ie, residents) perform many activities that could be performed by a non‐physician are likely not applicable to the community hospital setting. Thus, additional research is needed to better understand how hospitalists can deliver care more efficiently, particularly in the community hospital setting and in the current technological and structural environment of healthcare.

This systematic review of the literature provides insight into published studies attempting to evaluate physician activities in the hospital through time‐motion and work‐sampling studies. Published research to date appears extremely variable in quality, limiting our ability to draw firm conclusions. However, it appears that hospital‐based physicians spend most of their time not interacting with patients, and non‐physician staff could readily complete a sizable portion of their tasks. Given the necessity for multitasking by hospitalists, better documentation of its frequency and impact is needed, as well as information about the types of tasks performed while multitasking, which has yet to be reported. Additionally, the effect of interruptions (including, but not limited to paging) needs further evaluation.

When properly performed, time‐study methodology represents a powerful approach to understanding the activities of hospitalists and how we might reengineer hospital care delivery to be more efficient. Efforts to standardize healthcare delivery and integrate health information technology could benefit dramatically from detailed information regarding physician activities and empirical testing of quality improvement initiatives. Future research using time‐motion or work‐sampling methodology should be careful to define and report categories of activity with enough detail that comparisons with other studies are possible.

Time‐motion studies, introduced by Frederick W. Taylor in the 1880s,1 have been widely implemented across the business world; a Google search of time‐motion study returns approximately 32 million results. Such studies continuously document how workers spend their time and then use this information to identify and eliminate inefficient practices. Work‐sampling is a similar methodology introduced by L.H.C. Tippett in 1935.2 Work‐sampling utilizes a trained observer to document activities at predetermined or random intervals of time. Given a large enough number of observations, this method can be comparable to the continuous observation method used in time‐motion studies.3

Healthcare has begun to utilize these time‐study methodologies to evaluate the activities of physicians and nurses. Researchers have successfully used time‐study methodology in the emergency department, intensive care unit, and ambulatory and surgical settings in the U.S. and around the world to better understand physician activities and to design and assess interventions to improve efficiency.49 Hospitals are also eager to enhance efficiency in the inpatient setting given the current economic environment. Hospitalizations account for over a third of healthcare costs in the United States, making them an attractive target for cost‐cutting measures.10 Acknowledging that healthcare expenditures cannot continue to rise,11 insurers, particularly the Centers for Medicare and Medicaid Services (CMS), increasingly seek to reduce payments to hospitals.12 Compounding these pressures, a major supply of relatively inexpensive labor shrank with the decision by the Accreditation Council for Graduate Medicine Education (ACGME) to restrict the number of hours residents are allowed to work. Efficiency concerns gain new urgency as hospitals scramble to cover their patient loads with reduced physician availability.13

We undertook a systematic review of time‐motion and work‐sampling studies performed in the hospital setting to better understand the available literature describing the activities of physicians caring for hospitalized patients. An additional goal of this review was to determine the extent of available time‐flow literature describing the activity of hospitalists. The hospitalist movement provided one viable solution to the gap between demand for hospital patient care and the reduced supply of available physicianstypically primary care physicians in community hospitals and residents in teaching hospitals.14 Hospital medicine is the fastest‐growing specialty in the history of American medicine.15 More than half of American hospitals now have hospital medicine programs with a total of greater than 25,000 hospitalists in the U.S.15 This popularity has been driven by hospitalists' ability to increase efficiency through decreasing overall cost and length of stay for patients without increasing readmission rates or reducing primary care physician satisfaction.1619 However, exactly how hospitalists accomplish this increase in efficiency is still the subject of debate. One time‐motion study provides a glimpse into the activities of hospitalists at an academic urban hospital,20 but may not be applicable to many other hospitals.

Methods

Results

Our database search yielded 4270 potential articles. We then reviewed the title and abstract of each of these articles to identify studies that evaluated physicians, were performed on a hospital ward, and explicitly used time‐motion or direct‐observation work‐sampling methodology. For articles lacking an abstract but having a relevant title, we obtained the full text to determine eligibility for additional review. Sixty‐eight articles from this original search were selected for full‐text review. Ten of these articles met the selection criteria. Most of the articles excluded in this step were either conducted in an outpatient OR or ER setting, or used self‐report data instead of direct‐observation data. A secondary search using the reference lists of all obtained articles as well as consultation with experts in the field yielded 11 additional articles of interest. Three of these 11 articles were found to meet our criteria, bringing the total to 13 articles for review (Fig. 1).

These 13 articles included several types of physicians in their samples. Eleven included interns,2131 7 included residents,2123, 2628, 31 and 4 included attending physicians20, 23, 26, 32 (Table 1). Six articles included more than 1 type of subject.2123, 26, 28, 31 The main focus of these articles also varied. Nine of the 13 studies were designed to simply describe how residents, physicians and nurses spend their time.20, 2227, 29, 31 Three studies were primarily concerned with comparing groups from different intern programs, residency rotations, hospital types, or shifts.28, 30, 32 The remaining study attempted to quantify the amount of time physicians spent on tasks that could be performed by non‐physician staff.21 Only 2 articles evaluated hospitalists,20, 32 and we found no articles studying hospitalists in a community, non‐teaching setting. The studies were performed as early as 1961 and as recently as 2009. Just 5 of the 13 articles were published within the last 10 years. 0

Figure 1

Methodological quality also varied. Of the 11 time‐motion studies, the total amount of time subjects were observed in the studies ranged from 48 to 720 hours, with a mean of 254 hours. The number of subjects observed varied between 1 and 35, with a mean of 12 subjects. Average time observed per subject ranged from 8 hours to 113.5 hours, with a median of 26 hours. Six of the 11 studies observed subjects continuously for an entire shift.22, 25, 2831 Four studies covered an entire shift over the course of several days, using shorter observation periods.20, 21, 26, 27 One study observed subjects for only part of a shift.32 Ten of the time‐flow articles reported collecting data with a stopwatch and paper‐and‐pencil form2022, 25, 2732 and 1 used a handheld computer system.26 Two studies utilized work‐sampling techniques, both using paper‐and‐pencil forms to collect data during a full shift. Ammenwerth and Spotl23 studied 8 physicians for a total of 40 hours, collecting 5500 observation points. Nerenz et al.24 studied 11 interns for a total of approximately 330 hours, and collected 7858 observations. Both of these studies collected sufficiently large samples to satisfy the power requirements described by Sittig.3

Study sites were relatively uniform. Only one study evaluated physicians at both a teaching community hospital and an academic hospital.32 The remaining 12 observed physicians only in academic hospitals. Two studies were conducted in Australia,25, 26 1 in Austria,23 and the remaining 10 were conducted in the United States.

To provide a rough estimate of the amount of time physicians spend on direct care activities at the patients' bedside vs. indirect care activities, we attempted to calculate these figures for each article using a common definition. For the sake of consistency and to allow us to include as many studies as possible, we used the broadest definition of indirect care found among the articles, which included activities such as professional communication, medication review, documentation, and reviewing test results. Three articles did not provide enough information to calculate these values.24, 27, 29

All 10 articles that did provide sufficient information found that indirect care activities consumed the greater portion of time. Indirect care occupied an average of 50% of physicians' time, ranging from 32% to 69%. Direct care, on the other hand, accounted for an average of 23% of physicians' time, and ranged from 8.5% to 41%. Three articles that included data specific to attending physicians or hospitalists demonstrated an even larger disparity between direct and indirect care.20, 26, 32 In these articles, physicians spent an average of 19% of their time on direct care and 64% on indirect care, suggesting that senior physicians in the academic setting spend less time with patients and more time on care activities away from patients.

Four studies recorded various forms of interruptions of work flow.20, 24, 26, 31 Lurie et al.31 found that interns and residents were interrupted approximately 9 minutes into the performance of every history and physical (H&P). Westbrook et al.26 found that residents were interrupted on average every 21 minutes regardless of the task being performed. Nerenz et al.24 reported that interns received an average of 21 pages over the course of a 30 hour shift. They also noted that, on average, 12 of these pages were merely transient distractions, but 9 pages required some action on the part of the intern.24 Finally, O'leary et al.20 found that hospitalists received an average of 3.5 pages an hour and that 7% of their day was spent returning pages. Two articles recorded events of multitasking. Westbrook et al.26 found that 20% of physicians' time was spent performing more than one activity. Similarly, O'Leary et al.20 reported that 21% of hospitalists' time was spent multitasking. Neither study reported the types of activity performed during multitasking.

One article considered the amount of time physicians spend performing tasks that could be performed by non‐physician staff. Knickman et al.21 reported that in the traditional physician‐centered model of care, approximately 19% of a resident's time is spent on tasks that could be performed by non‐physician staff. They suggested that switching to a mid‐level provider model of care could significantly reduce the impact of resident work hour restrictions.21

Parenti and Lurie28 examined internal medicine residents on both day and night shifts.31 These authors concluded that residents on the night shift have an easier time because they see fewer patients and have more down time than residents on day shifts.28 Additionally, Lurie et al. found that residents got an average of 230 minutes (3.8 hours) of sleep per night and slept, on average, 59 minutes before being awakened by an interruption.31 However, these studies preceded work hour regulations.

Discussion

This systematic review of time studies set in the hospital, the first of which we are aware, revealed a sizable number (13) of articles evaluating physicians. However, the studies almost exclusively focused on academic hospitals (92%) and the majority (69%) analyzed only the activities of physicians in training. The studies were diverse in their methodology, subject populations, and, not surprisingly, their results. Even those studies designed simply to document the activities of physicians in the hospital report widely varying findings. For example, the percentage of time physicians spent on direct‐care activities varied from 8.5% to 41%, while indirect‐care time varied from 32% to 69%. These results likely reflect the heterogeneity of the hospital environment and differences among hospitals, as well as variations in the design and quality of the studies.

Despite this variability, a few observations appear consistent. Physicians perform many tasks that may be readily accomplished by less costly staff. This could partly explain why far more time is spent on activities indirectly related to a patient's care (eg, documentation and coordinating tests), instead of directly interacting with hospitalized patients. Additionally, physicians caring for hospitalized patients experience multiple interruptions and must regularly multitask. Unfortunately, very little research in the hospital setting has evaluated the impact of these interruptions on work efficiency, medical errors, or adverse events.

With the intense national interest in improving the value of healthcare by both enhancing quality and reducing costs, further efforts to optimize the efficiency of hospitalists will be needed.33 As hospitals and hospitalists aim to enhance the efficiency of care delivery to hospitalized patients, and also are increasingly asked to expend time to optimize the hospital discharge process to reduce readmissions,34, 35 time‐motion and work‐sampling studies can provide guidance.

One of the principal difficulties in aggregating data from time studies is the variety of approaches used to analyze activities. Lack of standardization in the approach to assessing physician activities (eg, use of a stopwatch with paper documentation vs. computer) and dissimilar categorizations inhibit efforts to summarize the findings across studies. Categories of activity were generally selected with the specific goals of the study in mind, instead of utilizing a readily available standardized approach. Moreover, the lack of detailed definitions of categories and sub‐categories, along with data for each, produces a significant barrier to comparison. Based on this review of available literature and our own experience conducting time‐motion evaluation of hospitalists, we propose the basic activity categorization in Table 2. Future researchers would be able to more readily compare their findings to other time‐motion studies by utilizing such a standardized approach to categorizing physician activities. Adding custom sub‐categories within this basic set would allow researchers to explore more specific time‐flow questions while maintaining comparability of most data. Electronic data collection tools (eg, handheld or tablet computers) could also facilitate the collection of more detailed and accurate data to increase study reliability.

Our systematic review is limited in its scope, as we focused only on the activities of physicians working in the hospital. Our exclusion criteria also eliminated several more focused time studies that evaluated only one small part of a physician's workflow, such as Amusan et al.'s36 evaluation of EMR and CPOE implementation during morning rounds. The available literature itself is also lacking in several important ways. Much of the literature is now limited by its age. The constant advance of medical technology, changes in work hour regulations, and new reimbursement structures have all affected physician workflow, and likely contributed to the variability of time study findings. Additionally, the available literature focuses almost exclusively on academic hospitals and teaching services. All but 1 of the studies collected data exclusively in academic hospitals, despite the fact that more than 90% of hospital care delivery in the U.S. occurs in a non‐academic hospital setting.20, 37 Just 1 study evaluated the activity of hospitalists directly caring for patients without assistance from residents.20 The significantly different workforce composition in community hospitals could mean that most findings are not relevant to the vast majority of U.S. hospitals. For example, the studies documenting that physicians in training (ie, residents) perform many activities that could be performed by a non‐physician are likely not applicable to the community hospital setting. Thus, additional research is needed to better understand how hospitalists can deliver care more efficiently, particularly in the community hospital setting and in the current technological and structural environment of healthcare.

This systematic review of the literature provides insight into published studies attempting to evaluate physician activities in the hospital through time‐motion and work‐sampling studies. Published research to date appears extremely variable in quality, limiting our ability to draw firm conclusions. However, it appears that hospital‐based physicians spend most of their time not interacting with patients, and non‐physician staff could readily complete a sizable portion of their tasks. Given the necessity for multitasking by hospitalists, better documentation of its frequency and impact is needed, as well as information about the types of tasks performed while multitasking, which has yet to be reported. Additionally, the effect of interruptions (including, but not limited to paging) needs further evaluation.

When properly performed, time‐study methodology represents a powerful approach to understanding the activities of hospitalists and how we might reengineer hospital care delivery to be more efficient. Efforts to standardize healthcare delivery and integrate health information technology could benefit dramatically from detailed information regarding physician activities and empirical testing of quality improvement initiatives. Future research using time‐motion or work‐sampling methodology should be careful to define and report categories of activity with enough detail that comparisons with other studies are possible.

Many academic medical centers (AMCs) employ hospitalists to provide care for patients on resident services as supervising attendings,1, 2 as well as on nonresident services.3 The number of hospitalists working on nonresident services at AMCs has grown exponentially, as the Accreditation Council for Graduate Medical Education (ACGME) implemented duty‐hour standards for residents.3 According to the latest Society of Hospital Medicine (SHM) estimates, the number of practicing hospitalists is projected to grow to 30,000 by 2010.4 As astonishing as this growth may sound, it is anticipated that more hospitalists will be needed to meet the demand for these physicians.5 Further, as financial realities require AMCs to be increasingly efficient without compromising patient care, and hospitalists provide a greater range of clinical services, it is important to better understand how hospitalists spend their time in the hospital. Understanding the daily work flow of hospitalists can identify how these physicians can be better supported. A previous report by O'Leary et al.6 highlighted how hospitalists spent their time during their usual day shifts at an AMC. It is important to validate their study to determine broadly applicable findings. We performed a time‐motion study where we followed the admitting hospitalists during the day and night shifts. We felt it was important to focus on hospitalists who are admitting patients, as this has potential patient safety and quality implications related to multitasking, triaging, and helping patients navigate through a complex admission process involving multiple clinical services. Our goal was to better understand how the flow of patients impacted these physicians, and determine how our hospitalists spent their time providing direct and indirect patient care‐related activities. In addition, we looked for predictable variations in activities throughout the day that might be associated with the timely care of patients.

Materials and Methods

Results

Overall, time spent on patient care activities comprised the bulk of hospitalists' shifts (82%) (Figure 1). Patient care activities were further categorized as direct patient caredefined as face‐to‐face patient or family time; and indirect patient caredefined as activities related to patient care, but without patient or family contact. Direct and indirect patient care accounted for 15% and 67% of the hospitalists' time, respectively. The other 18% of the hospitalists' time spent in the hospital were broadly categorized into: professional development, education, personal, downtime, and travel. Professional development included activities such as looking up information (eg, literature search); education included times that hospitalists spent with residents or medical students; personal time included only restroom and food breaks; and travel included time spent moving from 1 area to the next during their shift.

Figure 1

The majority of the hospitalists' direct patient care time was spent on evaluating new patients (79%). Significantly smaller amounts of time were spent on other direct care activities: cross‐covering other patients (8%), follow‐up visits (7%), family meetings (4%), and discharge instructions (2%) (Figure 2).

Figure 2

Indirect patient care activities included, 41% of time used to communicate with other healthcare providers, 26% on medical documentation, 20% reviewing medical records and results, and 13% of time writing orders (Figure 3). Communication accounted for a large proportion of a hospitalists' work, and included telephone conversations with Emergency Department (ED) or other admitting providers, handoffs, paging, face‐to‐face conversations with consultants and other support staff, and e‐mail.

Figure 3

Figure 4 shows the hourly distribution of time spent on direct and indirect patient care by a hospitalist throughout the day. The day‐time hospitalists pick up their signout from the nocturnists at 7 _AM to begin their shift. The swing hospitalists arrive at 2 _PM during the weekdays, and their primary duty is to triage and admit patients until 7 _PM. The nocturnists start their shift at 7 _PM, at which time the daytime and swing‐shift hospitalists all sign out for the night.

Figure 4

Discussion

Hospitalists on the nonresident service at our AMC utilize about 15% of their time on face‐to‐face patient care activities, 67% on indirect patient care activities, and 7% of time on moving from 1 part of the hospital to another. Hospitalists are valuable members of the physician work force who address the increasing patient care demands in the face of increasing limitations on residency work‐hours, a growing aging population, and existing inefficiencies in AMCs. The only other work‐flow study of hospitalists of which we are aware provided a single institution's perspective on time utilization by hospitalists. Our study in a different AMC setting revealed strong consistency with the O'Leary et al.6 study in the fraction of time hospitalists spent on direct patient care (15% and 18%, respectively), indirect patient care (67% and 69%); and within indirect patient care the time spent on documentation (26% and 37% of total time) and communications (41% and 35%). While travel in the O'Leary et al.6 study took up only 3% of hospitalists' time, the conclusions in that paper clearly suggest that the authors consider it an area of concern. Our study found that travel accounted for over 7% of hospitalists' time, confirming that intuition. The significant travel time may in part reflect the effects of a non‐geographically‐located hospitalist service. From these 2 studies we can be more confident that in large, tertiary care AMCs the time hospitalists spend on indirect patient care dominates that for direct patient care (by a factor of 4 in these studies), that within indirect patient care documentation and communication are dominant activities, and that travel can take a significant amount of time when patients are dispersed throughout the facility.

Both studies demonstrated that communication accounted for a significant proportion of a hospitalist's time. In our study communication accounted for 28% of their total time in the hospital, and 41% of the indirect patient care portion (Figure 3). A closer look within our communication category revealed that phone calls and handoffs accounted for two‐thirds of all communication time observed. As the hospitalists who carry the admitting pager, they receive the pages to take admission calls, but also take calls from consultants who have recommendations, as well as from nursing and other hospital staff. Depending on the nature of the conversation, the phone calls can last several minutes. While ensuring the communication between health care providers is complete and thorough, there may be opportunities to develop novel approaches to the way hospitalists communicate with other care providers. For example, at the UMHS, alternative communication methods with nursing staff have been proposed such as utilizing a website or a handheld device to help hospitalists prioritize their communications back to the nursing staff7; while standardizing the intake information from the ED or other admitting providers may help reduce the total time spent on phone calls. We will need to further explore the potential benefits of these ideas in future work.

Our data also reveal an interesting cyclicality of daily activities for the hospitalists, as shown in Figure 4. We identified batching behaviors throughout the day, which cause delays in seeing patients and can be deleterious to smooth workflows in support services. Spikes in indirect patient care, followed closely by spikes in direct patient care, occur regularly at shift changes (7 _AM, 2 _PM, and 7 _PM). Also, in the night shift, indirect patient care drops to its lowest levels (in % of time spent) throughout the day, and direct patient care reaches its highest levels. The day‐shift indirect care profile is counter‐cyclical with direct care, as the hospitalist shifts between direct care and indirect care depending on the time of the day. We discuss these phenomena in turn.

It is known that variability in any operation causes congestion and delay, as an unavoidable consequence of the physics of material and information flows.8 Indeed, an entire subindustry based on Lean manufacturing principles has evolved from the Toyota Production System based on the elimination of unnecessary variability in operations.9 Lean processes have been ongoing in manufacturing facilities for decades, and these efforts are just recently being embraced by the service sector in general, and health care specifically.10, 11 Batching is an extreme form of variability, where there is a lull in the amount of work being done and then a burst of work is done over a short period of time. This means that jobs pile up in the queue waiting for the next spike of activity. Our data indicate batching seems to be a common phenomenon for our hospitalists. The majority of the patients admitted to our hospitalist service are unscheduled admissions that arrive primarily through the ED. One potential result of the unscheduled admissions is that patients could be referred to our hospitalist service at a pace that is not well predictable on an hour‐to‐hour basis. This could lead to an unintended result of multiple patients admitted over a short period of time. This means that many patients wait for intake, delaying the onset of their care by the inpatient physician. Also, since an initial exam often results in orders for laboratory tests and studies, batching on the floor will translate into batching of orders going to nursing, pathology, radiology, and other hospital support services. This imposes the cost of variability on these other services in the hospital. From a systems perspective, efficiency will improve if these activities can be smoothed throughout the day. This may suggest opportunities to work with the ED, to help smooth the inflow of patients into the hospital system.

Within the hospital, all of the day‐shift hospitalists can be reached about the needs of their respective patients, however, the physician carrying the admission pager also fields calls for admissions, and acts as the default contact person for the hospitalist group. As this hospitalist receives information on new admissions, he/she is aware of patients ready for intake but cannot evaluate them at the rate they are being referred, so the queue builds. This continues into the swing shift, which also fields referrals faster than they can attend to them. The volatility in indirect care during the swing shift, 2 _PM to 7 _PM, reflects a significant amount of triaging and fielding general calls about hospitalist patients. These activities further reduce the swing shift's ability to clear the intake queue. The night shift finally gets to these patients and, eventually, clears the queue. There may be an opportunity to consider the use of multiple input pagers or other process changes that can smooth this flow and rationalize the recurring tasks of finding patients and the responsible physician.

Another concept in Lean thinking is that variability is costly when it represents a mismatch between demand for a service and the capacity to serve. With regards to admitted patients, when demand outpaces capacity, patients will wait. When capacity outpaces demand, there is excess capacity in the system. The ideal is to match demand and capacity at all times, so nobody waits and the system carries no costly excess capacity. As the intake providers for admitted patients, we can attack this problem from the capacity side. Here, 2 generic Lean tactics are to: (1) reallocate resources to a bottleneck that is holding up the entire system, and (2) relieve workers of time‐consuming but non‐value‐adding work so they have more capacity to devote to serving demand. In our study, carrying multiple input pagers is an example of tactic (1), and efficient communication technologies and practices that reduce indirect time is an example of (2). Systemwide improvements would require further investigation by working with the variability on the input side (eg, ED admissions).

Our study also found that a significant percent of the time observed was spent traveling (7.4%) from room to room between different floors in the hospital. Travel time, which is non‐value‐adding, is one of the major forms of waste Lean thinking.12 Our hospitalists can provide care to patients at any of the general medical‐surgical beds we have available at our health system. These beds are distributed across 14 units on 5 different floors, as well as in the ED if a bed is not available for an admitted patient. In hospitals routinely operating at high occupancy, such as our AMC, patients often get distributed throughout the facility for lack of beds on the appropriate service's ward. One cost for this is a potential mismatch between a patient's needs and floor nurses' training. Our study reveals another cost, and that is its contribution to the significant amount of time hospitalists spent on travel, which is largely driven by the need to see dispersed patients. Reducing this cost requires a systemic, rather than service‐specific, solution. Our AMC is adding observation‐status beds to relieve some of the pressure on licensed beds, and considering bed management (including parts of the admissions and discharge processes) changes designed to promote better collocation of patients with services. Further study on these and other collocation tactics is warranted.

The spike in indirect activities at 4 _PM represents, in part, an early signout by 1 or more of the hospitalists who are not scheduled to hold the admission pager, and have completed their work for the day. This handoff will be replicated at 7 _PM when the nocturnists arrive for their night shift. In addition to a significant indirect load on physicians, multiple handoffs have been associated with decreased quality of care.13 Again, it is worthwhile considering the feasibility of alternative shift schedules that can minimize handoffs.

Finally, our findings revealed that a low percentage of time was dedicated to providing discharge instructions (2.24% of direct patient care time, and 0.34% of total time). Because the task of discharging patients falls primarily on the day‐shift hospitalists, when combined with swing‐shift and night‐shift hospitalists' data, the low percentage measured on discharge instructions may have been diluted. Nonetheless, this may point to the need for further investigation on how hospitalists provide direct patient encounter time during this critical phase of transition out of the hospital.

Our study is not without limitations. The student observers shadowed a representative group of hospitalists, but they were not able to follow everyone in the group. More specifically, their observations were made on the hospitalist who was carrying the primary hospitalist service admitting pager. Although it was the intent of our study to focus on the hospitalists we felt would be the busiest, our results may not be generalizable to all hospitalists. Although our research supports the previous findings by O'Leary et al.,6 a second limitation to our study is that our analysis was done at a single hospitalist group in an AMC, and hence the results may not be generalizable to other hospitalist groups. Another limitation may be that we did not do an evaluation of the hours between 2 _AM to 7 _AM. This period of time is used to catch up on medical documentation and to be available for medical emergencies. As more hospitalist programs are employing the use of nocturnists, it may be informative to have this time period tracked for activities.

Conclusions

Our study supports the broad allocation of hospitalist time found in an earlier study at a different AMC,6 suggesting that these might be generally representative in other AMCs. We found that travel constitutes a significant claim in hospitalists' time, due in part to the inability to collocate hospitalist service patients. Remedies are not likely to be service‐specific, but will require systemwide analyses of admission and discharge processes. Communication takes a significant amount of hospitalist time, with pages and phone calls related to handoffs accounting for most of the total communication time. As hospitalists working at non‐AMC settings may experience different work flow issues, we would like to see time‐motion studies of hospitalists in other types of hospitals. Future studies should also seek to better understand the how hospitals at high occupancy may reduce batching and streamline both the discharge and admission process, determine the factors that account for the significant communication time and how these processes could be streamlined, and evaluate the potential benefits of geographical localization of hospitalists' patients.

Many academic medical centers (AMCs) employ hospitalists to provide care for patients on resident services as supervising attendings,1, 2 as well as on nonresident services.3 The number of hospitalists working on nonresident services at AMCs has grown exponentially, as the Accreditation Council for Graduate Medical Education (ACGME) implemented duty‐hour standards for residents.3 According to the latest Society of Hospital Medicine (SHM) estimates, the number of practicing hospitalists is projected to grow to 30,000 by 2010.4 As astonishing as this growth may sound, it is anticipated that more hospitalists will be needed to meet the demand for these physicians.5 Further, as financial realities require AMCs to be increasingly efficient without compromising patient care, and hospitalists provide a greater range of clinical services, it is important to better understand how hospitalists spend their time in the hospital. Understanding the daily work flow of hospitalists can identify how these physicians can be better supported. A previous report by O'Leary et al.6 highlighted how hospitalists spent their time during their usual day shifts at an AMC. It is important to validate their study to determine broadly applicable findings. We performed a time‐motion study where we followed the admitting hospitalists during the day and night shifts. We felt it was important to focus on hospitalists who are admitting patients, as this has potential patient safety and quality implications related to multitasking, triaging, and helping patients navigate through a complex admission process involving multiple clinical services. Our goal was to better understand how the flow of patients impacted these physicians, and determine how our hospitalists spent their time providing direct and indirect patient care‐related activities. In addition, we looked for predictable variations in activities throughout the day that might be associated with the timely care of patients.

Materials and Methods

Results

Overall, time spent on patient care activities comprised the bulk of hospitalists' shifts (82%) (Figure 1). Patient care activities were further categorized as direct patient caredefined as face‐to‐face patient or family time; and indirect patient caredefined as activities related to patient care, but without patient or family contact. Direct and indirect patient care accounted for 15% and 67% of the hospitalists' time, respectively. The other 18% of the hospitalists' time spent in the hospital were broadly categorized into: professional development, education, personal, downtime, and travel. Professional development included activities such as looking up information (eg, literature search); education included times that hospitalists spent with residents or medical students; personal time included only restroom and food breaks; and travel included time spent moving from 1 area to the next during their shift.

Figure 1

The majority of the hospitalists' direct patient care time was spent on evaluating new patients (79%). Significantly smaller amounts of time were spent on other direct care activities: cross‐covering other patients (8%), follow‐up visits (7%), family meetings (4%), and discharge instructions (2%) (Figure 2).

Figure 2

Indirect patient care activities included, 41% of time used to communicate with other healthcare providers, 26% on medical documentation, 20% reviewing medical records and results, and 13% of time writing orders (Figure 3). Communication accounted for a large proportion of a hospitalists' work, and included telephone conversations with Emergency Department (ED) or other admitting providers, handoffs, paging, face‐to‐face conversations with consultants and other support staff, and e‐mail.

Figure 3

Figure 4 shows the hourly distribution of time spent on direct and indirect patient care by a hospitalist throughout the day. The day‐time hospitalists pick up their signout from the nocturnists at 7 _AM to begin their shift. The swing hospitalists arrive at 2 _PM during the weekdays, and their primary duty is to triage and admit patients until 7 _PM. The nocturnists start their shift at 7 _PM, at which time the daytime and swing‐shift hospitalists all sign out for the night.

Figure 4

Discussion

Hospitalists on the nonresident service at our AMC utilize about 15% of their time on face‐to‐face patient care activities, 67% on indirect patient care activities, and 7% of time on moving from 1 part of the hospital to another. Hospitalists are valuable members of the physician work force who address the increasing patient care demands in the face of increasing limitations on residency work‐hours, a growing aging population, and existing inefficiencies in AMCs. The only other work‐flow study of hospitalists of which we are aware provided a single institution's perspective on time utilization by hospitalists. Our study in a different AMC setting revealed strong consistency with the O'Leary et al.6 study in the fraction of time hospitalists spent on direct patient care (15% and 18%, respectively), indirect patient care (67% and 69%); and within indirect patient care the time spent on documentation (26% and 37% of total time) and communications (41% and 35%). While travel in the O'Leary et al.6 study took up only 3% of hospitalists' time, the conclusions in that paper clearly suggest that the authors consider it an area of concern. Our study found that travel accounted for over 7% of hospitalists' time, confirming that intuition. The significant travel time may in part reflect the effects of a non‐geographically‐located hospitalist service. From these 2 studies we can be more confident that in large, tertiary care AMCs the time hospitalists spend on indirect patient care dominates that for direct patient care (by a factor of 4 in these studies), that within indirect patient care documentation and communication are dominant activities, and that travel can take a significant amount of time when patients are dispersed throughout the facility.

Both studies demonstrated that communication accounted for a significant proportion of a hospitalist's time. In our study communication accounted for 28% of their total time in the hospital, and 41% of the indirect patient care portion (Figure 3). A closer look within our communication category revealed that phone calls and handoffs accounted for two‐thirds of all communication time observed. As the hospitalists who carry the admitting pager, they receive the pages to take admission calls, but also take calls from consultants who have recommendations, as well as from nursing and other hospital staff. Depending on the nature of the conversation, the phone calls can last several minutes. While ensuring the communication between health care providers is complete and thorough, there may be opportunities to develop novel approaches to the way hospitalists communicate with other care providers. For example, at the UMHS, alternative communication methods with nursing staff have been proposed such as utilizing a website or a handheld device to help hospitalists prioritize their communications back to the nursing staff7; while standardizing the intake information from the ED or other admitting providers may help reduce the total time spent on phone calls. We will need to further explore the potential benefits of these ideas in future work.

Our data also reveal an interesting cyclicality of daily activities for the hospitalists, as shown in Figure 4. We identified batching behaviors throughout the day, which cause delays in seeing patients and can be deleterious to smooth workflows in support services. Spikes in indirect patient care, followed closely by spikes in direct patient care, occur regularly at shift changes (7 _AM, 2 _PM, and 7 _PM). Also, in the night shift, indirect patient care drops to its lowest levels (in % of time spent) throughout the day, and direct patient care reaches its highest levels. The day‐shift indirect care profile is counter‐cyclical with direct care, as the hospitalist shifts between direct care and indirect care depending on the time of the day. We discuss these phenomena in turn.

It is known that variability in any operation causes congestion and delay, as an unavoidable consequence of the physics of material and information flows.8 Indeed, an entire subindustry based on Lean manufacturing principles has evolved from the Toyota Production System based on the elimination of unnecessary variability in operations.9 Lean processes have been ongoing in manufacturing facilities for decades, and these efforts are just recently being embraced by the service sector in general, and health care specifically.10, 11 Batching is an extreme form of variability, where there is a lull in the amount of work being done and then a burst of work is done over a short period of time. This means that jobs pile up in the queue waiting for the next spike of activity. Our data indicate batching seems to be a common phenomenon for our hospitalists. The majority of the patients admitted to our hospitalist service are unscheduled admissions that arrive primarily through the ED. One potential result of the unscheduled admissions is that patients could be referred to our hospitalist service at a pace that is not well predictable on an hour‐to‐hour basis. This could lead to an unintended result of multiple patients admitted over a short period of time. This means that many patients wait for intake, delaying the onset of their care by the inpatient physician. Also, since an initial exam often results in orders for laboratory tests and studies, batching on the floor will translate into batching of orders going to nursing, pathology, radiology, and other hospital support services. This imposes the cost of variability on these other services in the hospital. From a systems perspective, efficiency will improve if these activities can be smoothed throughout the day. This may suggest opportunities to work with the ED, to help smooth the inflow of patients into the hospital system.

Within the hospital, all of the day‐shift hospitalists can be reached about the needs of their respective patients, however, the physician carrying the admission pager also fields calls for admissions, and acts as the default contact person for the hospitalist group. As this hospitalist receives information on new admissions, he/she is aware of patients ready for intake but cannot evaluate them at the rate they are being referred, so the queue builds. This continues into the swing shift, which also fields referrals faster than they can attend to them. The volatility in indirect care during the swing shift, 2 _PM to 7 _PM, reflects a significant amount of triaging and fielding general calls about hospitalist patients. These activities further reduce the swing shift's ability to clear the intake queue. The night shift finally gets to these patients and, eventually, clears the queue. There may be an opportunity to consider the use of multiple input pagers or other process changes that can smooth this flow and rationalize the recurring tasks of finding patients and the responsible physician.

Another concept in Lean thinking is that variability is costly when it represents a mismatch between demand for a service and the capacity to serve. With regards to admitted patients, when demand outpaces capacity, patients will wait. When capacity outpaces demand, there is excess capacity in the system. The ideal is to match demand and capacity at all times, so nobody waits and the system carries no costly excess capacity. As the intake providers for admitted patients, we can attack this problem from the capacity side. Here, 2 generic Lean tactics are to: (1) reallocate resources to a bottleneck that is holding up the entire system, and (2) relieve workers of time‐consuming but non‐value‐adding work so they have more capacity to devote to serving demand. In our study, carrying multiple input pagers is an example of tactic (1), and efficient communication technologies and practices that reduce indirect time is an example of (2). Systemwide improvements would require further investigation by working with the variability on the input side (eg, ED admissions).

Our study also found that a significant percent of the time observed was spent traveling (7.4%) from room to room between different floors in the hospital. Travel time, which is non‐value‐adding, is one of the major forms of waste Lean thinking.12 Our hospitalists can provide care to patients at any of the general medical‐surgical beds we have available at our health system. These beds are distributed across 14 units on 5 different floors, as well as in the ED if a bed is not available for an admitted patient. In hospitals routinely operating at high occupancy, such as our AMC, patients often get distributed throughout the facility for lack of beds on the appropriate service's ward. One cost for this is a potential mismatch between a patient's needs and floor nurses' training. Our study reveals another cost, and that is its contribution to the significant amount of time hospitalists spent on travel, which is largely driven by the need to see dispersed patients. Reducing this cost requires a systemic, rather than service‐specific, solution. Our AMC is adding observation‐status beds to relieve some of the pressure on licensed beds, and considering bed management (including parts of the admissions and discharge processes) changes designed to promote better collocation of patients with services. Further study on these and other collocation tactics is warranted.

The spike in indirect activities at 4 _PM represents, in part, an early signout by 1 or more of the hospitalists who are not scheduled to hold the admission pager, and have completed their work for the day. This handoff will be replicated at 7 _PM when the nocturnists arrive for their night shift. In addition to a significant indirect load on physicians, multiple handoffs have been associated with decreased quality of care.13 Again, it is worthwhile considering the feasibility of alternative shift schedules that can minimize handoffs.

Finally, our findings revealed that a low percentage of time was dedicated to providing discharge instructions (2.24% of direct patient care time, and 0.34% of total time). Because the task of discharging patients falls primarily on the day‐shift hospitalists, when combined with swing‐shift and night‐shift hospitalists' data, the low percentage measured on discharge instructions may have been diluted. Nonetheless, this may point to the need for further investigation on how hospitalists provide direct patient encounter time during this critical phase of transition out of the hospital.

Our study is not without limitations. The student observers shadowed a representative group of hospitalists, but they were not able to follow everyone in the group. More specifically, their observations were made on the hospitalist who was carrying the primary hospitalist service admitting pager. Although it was the intent of our study to focus on the hospitalists we felt would be the busiest, our results may not be generalizable to all hospitalists. Although our research supports the previous findings by O'Leary et al.,6 a second limitation to our study is that our analysis was done at a single hospitalist group in an AMC, and hence the results may not be generalizable to other hospitalist groups. Another limitation may be that we did not do an evaluation of the hours between 2 _AM to 7 _AM. This period of time is used to catch up on medical documentation and to be available for medical emergencies. As more hospitalist programs are employing the use of nocturnists, it may be informative to have this time period tracked for activities.

Conclusions

Our study supports the broad allocation of hospitalist time found in an earlier study at a different AMC,6 suggesting that these might be generally representative in other AMCs. We found that travel constitutes a significant claim in hospitalists' time, due in part to the inability to collocate hospitalist service patients. Remedies are not likely to be service‐specific, but will require systemwide analyses of admission and discharge processes. Communication takes a significant amount of hospitalist time, with pages and phone calls related to handoffs accounting for most of the total communication time. As hospitalists working at non‐AMC settings may experience different work flow issues, we would like to see time‐motion studies of hospitalists in other types of hospitals. Future studies should also seek to better understand the how hospitals at high occupancy may reduce batching and streamline both the discharge and admission process, determine the factors that account for the significant communication time and how these processes could be streamlined, and evaluate the potential benefits of geographical localization of hospitalists' patients.

Many academic medical centers (AMCs) employ hospitalists to provide care for patients on resident services as supervising attendings,1, 2 as well as on nonresident services.3 The number of hospitalists working on nonresident services at AMCs has grown exponentially, as the Accreditation Council for Graduate Medical Education (ACGME) implemented duty‐hour standards for residents.3 According to the latest Society of Hospital Medicine (SHM) estimates, the number of practicing hospitalists is projected to grow to 30,000 by 2010.4 As astonishing as this growth may sound, it is anticipated that more hospitalists will be needed to meet the demand for these physicians.5 Further, as financial realities require AMCs to be increasingly efficient without compromising patient care, and hospitalists provide a greater range of clinical services, it is important to better understand how hospitalists spend their time in the hospital. Understanding the daily work flow of hospitalists can identify how these physicians can be better supported. A previous report by O'Leary et al.6 highlighted how hospitalists spent their time during their usual day shifts at an AMC. It is important to validate their study to determine broadly applicable findings. We performed a time‐motion study where we followed the admitting hospitalists during the day and night shifts. We felt it was important to focus on hospitalists who are admitting patients, as this has potential patient safety and quality implications related to multitasking, triaging, and helping patients navigate through a complex admission process involving multiple clinical services. Our goal was to better understand how the flow of patients impacted these physicians, and determine how our hospitalists spent their time providing direct and indirect patient care‐related activities. In addition, we looked for predictable variations in activities throughout the day that might be associated with the timely care of patients.

Materials and Methods

Results

Overall, time spent on patient care activities comprised the bulk of hospitalists' shifts (82%) (Figure 1). Patient care activities were further categorized as direct patient caredefined as face‐to‐face patient or family time; and indirect patient caredefined as activities related to patient care, but without patient or family contact. Direct and indirect patient care accounted for 15% and 67% of the hospitalists' time, respectively. The other 18% of the hospitalists' time spent in the hospital were broadly categorized into: professional development, education, personal, downtime, and travel. Professional development included activities such as looking up information (eg, literature search); education included times that hospitalists spent with residents or medical students; personal time included only restroom and food breaks; and travel included time spent moving from 1 area to the next during their shift.

Figure 1

The majority of the hospitalists' direct patient care time was spent on evaluating new patients (79%). Significantly smaller amounts of time were spent on other direct care activities: cross‐covering other patients (8%), follow‐up visits (7%), family meetings (4%), and discharge instructions (2%) (Figure 2).

Figure 2

Indirect patient care activities included, 41% of time used to communicate with other healthcare providers, 26% on medical documentation, 20% reviewing medical records and results, and 13% of time writing orders (Figure 3). Communication accounted for a large proportion of a hospitalists' work, and included telephone conversations with Emergency Department (ED) or other admitting providers, handoffs, paging, face‐to‐face conversations with consultants and other support staff, and e‐mail.

Figure 3

Figure 4 shows the hourly distribution of time spent on direct and indirect patient care by a hospitalist throughout the day. The day‐time hospitalists pick up their signout from the nocturnists at 7 _AM to begin their shift. The swing hospitalists arrive at 2 _PM during the weekdays, and their primary duty is to triage and admit patients until 7 _PM. The nocturnists start their shift at 7 _PM, at which time the daytime and swing‐shift hospitalists all sign out for the night.

Figure 4

Discussion

Hospitalists on the nonresident service at our AMC utilize about 15% of their time on face‐to‐face patient care activities, 67% on indirect patient care activities, and 7% of time on moving from 1 part of the hospital to another. Hospitalists are valuable members of the physician work force who address the increasing patient care demands in the face of increasing limitations on residency work‐hours, a growing aging population, and existing inefficiencies in AMCs. The only other work‐flow study of hospitalists of which we are aware provided a single institution's perspective on time utilization by hospitalists. Our study in a different AMC setting revealed strong consistency with the O'Leary et al.6 study in the fraction of time hospitalists spent on direct patient care (15% and 18%, respectively), indirect patient care (67% and 69%); and within indirect patient care the time spent on documentation (26% and 37% of total time) and communications (41% and 35%). While travel in the O'Leary et al.6 study took up only 3% of hospitalists' time, the conclusions in that paper clearly suggest that the authors consider it an area of concern. Our study found that travel accounted for over 7% of hospitalists' time, confirming that intuition. The significant travel time may in part reflect the effects of a non‐geographically‐located hospitalist service. From these 2 studies we can be more confident that in large, tertiary care AMCs the time hospitalists spend on indirect patient care dominates that for direct patient care (by a factor of 4 in these studies), that within indirect patient care documentation and communication are dominant activities, and that travel can take a significant amount of time when patients are dispersed throughout the facility.

Both studies demonstrated that communication accounted for a significant proportion of a hospitalist's time. In our study communication accounted for 28% of their total time in the hospital, and 41% of the indirect patient care portion (Figure 3). A closer look within our communication category revealed that phone calls and handoffs accounted for two‐thirds of all communication time observed. As the hospitalists who carry the admitting pager, they receive the pages to take admission calls, but also take calls from consultants who have recommendations, as well as from nursing and other hospital staff. Depending on the nature of the conversation, the phone calls can last several minutes. While ensuring the communication between health care providers is complete and thorough, there may be opportunities to develop novel approaches to the way hospitalists communicate with other care providers. For example, at the UMHS, alternative communication methods with nursing staff have been proposed such as utilizing a website or a handheld device to help hospitalists prioritize their communications back to the nursing staff7; while standardizing the intake information from the ED or other admitting providers may help reduce the total time spent on phone calls. We will need to further explore the potential benefits of these ideas in future work.

Our data also reveal an interesting cyclicality of daily activities for the hospitalists, as shown in Figure 4. We identified batching behaviors throughout the day, which cause delays in seeing patients and can be deleterious to smooth workflows in support services. Spikes in indirect patient care, followed closely by spikes in direct patient care, occur regularly at shift changes (7 _AM, 2 _PM, and 7 _PM). Also, in the night shift, indirect patient care drops to its lowest levels (in % of time spent) throughout the day, and direct patient care reaches its highest levels. The day‐shift indirect care profile is counter‐cyclical with direct care, as the hospitalist shifts between direct care and indirect care depending on the time of the day. We discuss these phenomena in turn.

It is known that variability in any operation causes congestion and delay, as an unavoidable consequence of the physics of material and information flows.8 Indeed, an entire subindustry based on Lean manufacturing principles has evolved from the Toyota Production System based on the elimination of unnecessary variability in operations.9 Lean processes have been ongoing in manufacturing facilities for decades, and these efforts are just recently being embraced by the service sector in general, and health care specifically.10, 11 Batching is an extreme form of variability, where there is a lull in the amount of work being done and then a burst of work is done over a short period of time. This means that jobs pile up in the queue waiting for the next spike of activity. Our data indicate batching seems to be a common phenomenon for our hospitalists. The majority of the patients admitted to our hospitalist service are unscheduled admissions that arrive primarily through the ED. One potential result of the unscheduled admissions is that patients could be referred to our hospitalist service at a pace that is not well predictable on an hour‐to‐hour basis. This could lead to an unintended result of multiple patients admitted over a short period of time. This means that many patients wait for intake, delaying the onset of their care by the inpatient physician. Also, since an initial exam often results in orders for laboratory tests and studies, batching on the floor will translate into batching of orders going to nursing, pathology, radiology, and other hospital support services. This imposes the cost of variability on these other services in the hospital. From a systems perspective, efficiency will improve if these activities can be smoothed throughout the day. This may suggest opportunities to work with the ED, to help smooth the inflow of patients into the hospital system.

Within the hospital, all of the day‐shift hospitalists can be reached about the needs of their respective patients, however, the physician carrying the admission pager also fields calls for admissions, and acts as the default contact person for the hospitalist group. As this hospitalist receives information on new admissions, he/she is aware of patients ready for intake but cannot evaluate them at the rate they are being referred, so the queue builds. This continues into the swing shift, which also fields referrals faster than they can attend to them. The volatility in indirect care during the swing shift, 2 _PM to 7 _PM, reflects a significant amount of triaging and fielding general calls about hospitalist patients. These activities further reduce the swing shift's ability to clear the intake queue. The night shift finally gets to these patients and, eventually, clears the queue. There may be an opportunity to consider the use of multiple input pagers or other process changes that can smooth this flow and rationalize the recurring tasks of finding patients and the responsible physician.

Another concept in Lean thinking is that variability is costly when it represents a mismatch between demand for a service and the capacity to serve. With regards to admitted patients, when demand outpaces capacity, patients will wait. When capacity outpaces demand, there is excess capacity in the system. The ideal is to match demand and capacity at all times, so nobody waits and the system carries no costly excess capacity. As the intake providers for admitted patients, we can attack this problem from the capacity side. Here, 2 generic Lean tactics are to: (1) reallocate resources to a bottleneck that is holding up the entire system, and (2) relieve workers of time‐consuming but non‐value‐adding work so they have more capacity to devote to serving demand. In our study, carrying multiple input pagers is an example of tactic (1), and efficient communication technologies and practices that reduce indirect time is an example of (2). Systemwide improvements would require further investigation by working with the variability on the input side (eg, ED admissions).

Our study also found that a significant percent of the time observed was spent traveling (7.4%) from room to room between different floors in the hospital. Travel time, which is non‐value‐adding, is one of the major forms of waste Lean thinking.12 Our hospitalists can provide care to patients at any of the general medical‐surgical beds we have available at our health system. These beds are distributed across 14 units on 5 different floors, as well as in the ED if a bed is not available for an admitted patient. In hospitals routinely operating at high occupancy, such as our AMC, patients often get distributed throughout the facility for lack of beds on the appropriate service's ward. One cost for this is a potential mismatch between a patient's needs and floor nurses' training. Our study reveals another cost, and that is its contribution to the significant amount of time hospitalists spent on travel, which is largely driven by the need to see dispersed patients. Reducing this cost requires a systemic, rather than service‐specific, solution. Our AMC is adding observation‐status beds to relieve some of the pressure on licensed beds, and considering bed management (including parts of the admissions and discharge processes) changes designed to promote better collocation of patients with services. Further study on these and other collocation tactics is warranted.

The spike in indirect activities at 4 _PM represents, in part, an early signout by 1 or more of the hospitalists who are not scheduled to hold the admission pager, and have completed their work for the day. This handoff will be replicated at 7 _PM when the nocturnists arrive for their night shift. In addition to a significant indirect load on physicians, multiple handoffs have been associated with decreased quality of care.13 Again, it is worthwhile considering the feasibility of alternative shift schedules that can minimize handoffs.

Finally, our findings revealed that a low percentage of time was dedicated to providing discharge instructions (2.24% of direct patient care time, and 0.34% of total time). Because the task of discharging patients falls primarily on the day‐shift hospitalists, when combined with swing‐shift and night‐shift hospitalists' data, the low percentage measured on discharge instructions may have been diluted. Nonetheless, this may point to the need for further investigation on how hospitalists provide direct patient encounter time during this critical phase of transition out of the hospital.

Our study is not without limitations. The student observers shadowed a representative group of hospitalists, but they were not able to follow everyone in the group. More specifically, their observations were made on the hospitalist who was carrying the primary hospitalist service admitting pager. Although it was the intent of our study to focus on the hospitalists we felt would be the busiest, our results may not be generalizable to all hospitalists. Although our research supports the previous findings by O'Leary et al.,6 a second limitation to our study is that our analysis was done at a single hospitalist group in an AMC, and hence the results may not be generalizable to other hospitalist groups. Another limitation may be that we did not do an evaluation of the hours between 2 _AM to 7 _AM. This period of time is used to catch up on medical documentation and to be available for medical emergencies. As more hospitalist programs are employing the use of nocturnists, it may be informative to have this time period tracked for activities.

Conclusions

Our study supports the broad allocation of hospitalist time found in an earlier study at a different AMC,6 suggesting that these might be generally representative in other AMCs. We found that travel constitutes a significant claim in hospitalists' time, due in part to the inability to collocate hospitalist service patients. Remedies are not likely to be service‐specific, but will require systemwide analyses of admission and discharge processes. Communication takes a significant amount of hospitalist time, with pages and phone calls related to handoffs accounting for most of the total communication time. As hospitalists working at non‐AMC settings may experience different work flow issues, we would like to see time‐motion studies of hospitalists in other types of hospitals. Future studies should also seek to better understand the how hospitals at high occupancy may reduce batching and streamline both the discharge and admission process, determine the factors that account for the significant communication time and how these processes could be streamlined, and evaluate the potential benefits of geographical localization of hospitalists' patients.

A 35‐year‐old African American female presented to her primary care provider with a 4‐day history of progressive nausea, vomiting, and generalized malaise. The patient had been in her usual state of health prior to the onset of these symptoms and had no history of prior hospitalization. She denied any fevers, chills, abdominal pain, or change in diet prior to the onset of her symptoms. She also had no recent exposure to sick contacts, human immunodeficiency virus (HIV) risk factors, or history of recent travel. One week prior to her presentation, the patient had been prescribed rifampin for treatment of chronic hidradenitis suppurativa. She had been taking rifampin for 5 days until she developed her current symptoms. The patient was not taking any other medications and had no other medical problems.

On presentation, the patient was afebrile and her vital signs were within normal limits. She was alert and oriented with no scleral icterus. Cardiopulmonary exam was within normal limits. Her abdomen was nondistended with diffuse nonlocalizing tenderness, normal bowel sounds, and no signs of acute abdomen. No hepatomegaly was noted, and stool was negative for occult blood. No rashes or joint abnormalities were noted on exam, but multiple nodulocystic lesions were noted bilaterally in her axillae. Laboratory findings on presentation were most notable for a blood urea nitrogen level of 38 mg/dL, a creatinine of 5.3 mg/dL, and a calculated fractional excretion of sodium of 2.6%. Urine analysis revealed no significant hematuria, proteinuria, or red blood cell casts, but did demonstrate white blood cells, white blood cell casts, and eosinophils. Blood cultures drawn on admission were negative and the patient had a normal leukocyte count.

The patient was admitted to the general medicine service and the causes of her acute renal failure were explored. She was treated with intravenous fluids because a component of prerenal azotemia was initially suspected. Rifampin was discontinued. Despite significant hydration, the patient remained oliguric. She was challenged with high‐dose loop diuretics for 3 days but still remained oliguric. Renal ultrasound showed moderately echogenic, large 16‐cm kidneys bilaterally, with no evidence of hydronephrosis or renal calculi. Laboratory evaluation for diabetes and infiltrative disease of the kidneys such as HIV, amyloidosis, and nonspecific gammopathies were negative. The patient's creatinine level steadily increased and eventually peaked at 14.2 mg/dL. When the patient began to develop shortness of breath, lower extremity edema, and abdominal distension on hospital day 4, hemodialysis was initiated. On hospital day 6, the patient underwent a renal biopsy (Figure 1) that demonstrated patchy inflammatory infiltrates with scattered eosinophils and evidence of interstitial edema and tubulitis. Congo red staining was negative for amyloid and no immune deposits were noted. A diagnosis of acute interstitial nephritis (AIN) was made and the patient was started on high‐dose prednisone.

Figure 1

Over the 48 hours following initiation of prednisone therapy, the patient's urine output gradually began to improve and the patient was producing over 2 liters of urine per day. In addition, the patient's axillary cystic lesions became less inflamed and painful. The patient was discharged home with plans to continue hemodialysis as an outpatient. Three days after discharge, when the patient presented for hemodialysis, her creatinine was noted to be 1.2 mg/dL. Due to her improved creatinine and maintenance of good urine output, hemodialysis was discontinued. The patient was slowly tapered off her prednisone over the next several weeks. One month later her creatinine was 0.9 mg/dL. She had required no further hemodialysis since her hospitalization.

DISCUSSION

AIN is an uncommon but significant cause of acute renal failure, and accounts for 2% to 3% of all renal biopsies performed.1 AIN is thought to be an immune‐mediated process, and drug‐induced hypersensitivity is the most common cause of AIN. Nonsteroidal antiinflammatory drug (NSAID) use, antibiotics, proton pump inhibitors, and several other medications have been implicated in the pathogenesis of AIN. Rifampin is a medication that has a known association with AIN, with most cases being described in regions where treatment of endemic tuberculosis is common. The majority of cases of rifampin‐induced AIN occur in the setting of drug reexposure, due to an immunologically‐mediated process that causes tubulointerstitial injury.2

Patients with drug‐induced AIN typically present with oliguria secondary to an acute decline in renal function. The classic clinical triad of fever, rash, and arthralgias is uncommon, and all 3 occur in only 30% of all cases.3 More commonly, patients typically present with vague flu‐like and gastrointestinal symptoms, including fever, abdominal pain, nausea, and vomiting. Urinalysis may be helpful, but hematuria occurs in less than one‐half of all cases, and sterile pyuria is common but not always present. It has been suggested that the presence of eosinophiluria may lead to high suspicion of AIN, but the sensitivity and specificity of eosinophiluria are low, at 40% and 72%, respectively.3 Thus, renal biopsy is often performed to make a confirmatory diagnosis of AIN in the appropriate clinical setting. Histopathologically, the presence of inflammatory infiltrates in the renal tubules and interstitium with conservation of the glomerular structures is visualized.2, 3

A large number of patients who present with AIN may require temporary renal replacement therapy; however, most patients have been observed to recover full renal function. Despite this, review of the literature shows that many patients may have persistent elevations in their serum creatinine. Corticosteroid therapy, although controversial, has commonly been initiated in patients whose renal function does not improve with conservative therapy. To date there are no prospective randomized clinical trials, and data guiding optimal management in AIN is sparse. Some studies have demonstrated no benefit in corticosteroid therapy in lowering serum creatinine levels in patients with AIN,4 but others have observed a significantly increased risk of interstitial fibrosis and failure to return to baseline creatinine in those patients that received delayed treatment with corticosteroids more than 1 week after the withdrawal of the offending agent.5

The patient described in our case did not present with the classic symptoms noted in AIN. Yet she had evidence of eosinophiluria, which increased our suspicion for AIN. Although other potential etiologies of this patient's acute renal failure were considered, given her negative serologic studies and the results of her renal biopsy, AIN was considered the leading diagnosis. Since AIN was recognized early in this patient, the offending medication was discontinued promptly, prednisone therapy was initiated appropriately, and the renal failure that had developed quickly vanished.

A 35‐year‐old African American female presented to her primary care provider with a 4‐day history of progressive nausea, vomiting, and generalized malaise. The patient had been in her usual state of health prior to the onset of these symptoms and had no history of prior hospitalization. She denied any fevers, chills, abdominal pain, or change in diet prior to the onset of her symptoms. She also had no recent exposure to sick contacts, human immunodeficiency virus (HIV) risk factors, or history of recent travel. One week prior to her presentation, the patient had been prescribed rifampin for treatment of chronic hidradenitis suppurativa. She had been taking rifampin for 5 days until she developed her current symptoms. The patient was not taking any other medications and had no other medical problems.

On presentation, the patient was afebrile and her vital signs were within normal limits. She was alert and oriented with no scleral icterus. Cardiopulmonary exam was within normal limits. Her abdomen was nondistended with diffuse nonlocalizing tenderness, normal bowel sounds, and no signs of acute abdomen. No hepatomegaly was noted, and stool was negative for occult blood. No rashes or joint abnormalities were noted on exam, but multiple nodulocystic lesions were noted bilaterally in her axillae. Laboratory findings on presentation were most notable for a blood urea nitrogen level of 38 mg/dL, a creatinine of 5.3 mg/dL, and a calculated fractional excretion of sodium of 2.6%. Urine analysis revealed no significant hematuria, proteinuria, or red blood cell casts, but did demonstrate white blood cells, white blood cell casts, and eosinophils. Blood cultures drawn on admission were negative and the patient had a normal leukocyte count.

The patient was admitted to the general medicine service and the causes of her acute renal failure were explored. She was treated with intravenous fluids because a component of prerenal azotemia was initially suspected. Rifampin was discontinued. Despite significant hydration, the patient remained oliguric. She was challenged with high‐dose loop diuretics for 3 days but still remained oliguric. Renal ultrasound showed moderately echogenic, large 16‐cm kidneys bilaterally, with no evidence of hydronephrosis or renal calculi. Laboratory evaluation for diabetes and infiltrative disease of the kidneys such as HIV, amyloidosis, and nonspecific gammopathies were negative. The patient's creatinine level steadily increased and eventually peaked at 14.2 mg/dL. When the patient began to develop shortness of breath, lower extremity edema, and abdominal distension on hospital day 4, hemodialysis was initiated. On hospital day 6, the patient underwent a renal biopsy (Figure 1) that demonstrated patchy inflammatory infiltrates with scattered eosinophils and evidence of interstitial edema and tubulitis. Congo red staining was negative for amyloid and no immune deposits were noted. A diagnosis of acute interstitial nephritis (AIN) was made and the patient was started on high‐dose prednisone.

Figure 1

Over the 48 hours following initiation of prednisone therapy, the patient's urine output gradually began to improve and the patient was producing over 2 liters of urine per day. In addition, the patient's axillary cystic lesions became less inflamed and painful. The patient was discharged home with plans to continue hemodialysis as an outpatient. Three days after discharge, when the patient presented for hemodialysis, her creatinine was noted to be 1.2 mg/dL. Due to her improved creatinine and maintenance of good urine output, hemodialysis was discontinued. The patient was slowly tapered off her prednisone over the next several weeks. One month later her creatinine was 0.9 mg/dL. She had required no further hemodialysis since her hospitalization.

DISCUSSION

AIN is an uncommon but significant cause of acute renal failure, and accounts for 2% to 3% of all renal biopsies performed.1 AIN is thought to be an immune‐mediated process, and drug‐induced hypersensitivity is the most common cause of AIN. Nonsteroidal antiinflammatory drug (NSAID) use, antibiotics, proton pump inhibitors, and several other medications have been implicated in the pathogenesis of AIN. Rifampin is a medication that has a known association with AIN, with most cases being described in regions where treatment of endemic tuberculosis is common. The majority of cases of rifampin‐induced AIN occur in the setting of drug reexposure, due to an immunologically‐mediated process that causes tubulointerstitial injury.2

Patients with drug‐induced AIN typically present with oliguria secondary to an acute decline in renal function. The classic clinical triad of fever, rash, and arthralgias is uncommon, and all 3 occur in only 30% of all cases.3 More commonly, patients typically present with vague flu‐like and gastrointestinal symptoms, including fever, abdominal pain, nausea, and vomiting. Urinalysis may be helpful, but hematuria occurs in less than one‐half of all cases, and sterile pyuria is common but not always present. It has been suggested that the presence of eosinophiluria may lead to high suspicion of AIN, but the sensitivity and specificity of eosinophiluria are low, at 40% and 72%, respectively.3 Thus, renal biopsy is often performed to make a confirmatory diagnosis of AIN in the appropriate clinical setting. Histopathologically, the presence of inflammatory infiltrates in the renal tubules and interstitium with conservation of the glomerular structures is visualized.2, 3

A large number of patients who present with AIN may require temporary renal replacement therapy; however, most patients have been observed to recover full renal function. Despite this, review of the literature shows that many patients may have persistent elevations in their serum creatinine. Corticosteroid therapy, although controversial, has commonly been initiated in patients whose renal function does not improve with conservative therapy. To date there are no prospective randomized clinical trials, and data guiding optimal management in AIN is sparse. Some studies have demonstrated no benefit in corticosteroid therapy in lowering serum creatinine levels in patients with AIN,4 but others have observed a significantly increased risk of interstitial fibrosis and failure to return to baseline creatinine in those patients that received delayed treatment with corticosteroids more than 1 week after the withdrawal of the offending agent.5

The patient described in our case did not present with the classic symptoms noted in AIN. Yet she had evidence of eosinophiluria, which increased our suspicion for AIN. Although other potential etiologies of this patient's acute renal failure were considered, given her negative serologic studies and the results of her renal biopsy, AIN was considered the leading diagnosis. Since AIN was recognized early in this patient, the offending medication was discontinued promptly, prednisone therapy was initiated appropriately, and the renal failure that had developed quickly vanished.

A 35‐year‐old African American female presented to her primary care provider with a 4‐day history of progressive nausea, vomiting, and generalized malaise. The patient had been in her usual state of health prior to the onset of these symptoms and had no history of prior hospitalization. She denied any fevers, chills, abdominal pain, or change in diet prior to the onset of her symptoms. She also had no recent exposure to sick contacts, human immunodeficiency virus (HIV) risk factors, or history of recent travel. One week prior to her presentation, the patient had been prescribed rifampin for treatment of chronic hidradenitis suppurativa. She had been taking rifampin for 5 days until she developed her current symptoms. The patient was not taking any other medications and had no other medical problems.

On presentation, the patient was afebrile and her vital signs were within normal limits. She was alert and oriented with no scleral icterus. Cardiopulmonary exam was within normal limits. Her abdomen was nondistended with diffuse nonlocalizing tenderness, normal bowel sounds, and no signs of acute abdomen. No hepatomegaly was noted, and stool was negative for occult blood. No rashes or joint abnormalities were noted on exam, but multiple nodulocystic lesions were noted bilaterally in her axillae. Laboratory findings on presentation were most notable for a blood urea nitrogen level of 38 mg/dL, a creatinine of 5.3 mg/dL, and a calculated fractional excretion of sodium of 2.6%. Urine analysis revealed no significant hematuria, proteinuria, or red blood cell casts, but did demonstrate white blood cells, white blood cell casts, and eosinophils. Blood cultures drawn on admission were negative and the patient had a normal leukocyte count.

The patient was admitted to the general medicine service and the causes of her acute renal failure were explored. She was treated with intravenous fluids because a component of prerenal azotemia was initially suspected. Rifampin was discontinued. Despite significant hydration, the patient remained oliguric. She was challenged with high‐dose loop diuretics for 3 days but still remained oliguric. Renal ultrasound showed moderately echogenic, large 16‐cm kidneys bilaterally, with no evidence of hydronephrosis or renal calculi. Laboratory evaluation for diabetes and infiltrative disease of the kidneys such as HIV, amyloidosis, and nonspecific gammopathies were negative. The patient's creatinine level steadily increased and eventually peaked at 14.2 mg/dL. When the patient began to develop shortness of breath, lower extremity edema, and abdominal distension on hospital day 4, hemodialysis was initiated. On hospital day 6, the patient underwent a renal biopsy (Figure 1) that demonstrated patchy inflammatory infiltrates with scattered eosinophils and evidence of interstitial edema and tubulitis. Congo red staining was negative for amyloid and no immune deposits were noted. A diagnosis of acute interstitial nephritis (AIN) was made and the patient was started on high‐dose prednisone.

Figure 1

Over the 48 hours following initiation of prednisone therapy, the patient's urine output gradually began to improve and the patient was producing over 2 liters of urine per day. In addition, the patient's axillary cystic lesions became less inflamed and painful. The patient was discharged home with plans to continue hemodialysis as an outpatient. Three days after discharge, when the patient presented for hemodialysis, her creatinine was noted to be 1.2 mg/dL. Due to her improved creatinine and maintenance of good urine output, hemodialysis was discontinued. The patient was slowly tapered off her prednisone over the next several weeks. One month later her creatinine was 0.9 mg/dL. She had required no further hemodialysis since her hospitalization.

DISCUSSION

AIN is an uncommon but significant cause of acute renal failure, and accounts for 2% to 3% of all renal biopsies performed.1 AIN is thought to be an immune‐mediated process, and drug‐induced hypersensitivity is the most common cause of AIN. Nonsteroidal antiinflammatory drug (NSAID) use, antibiotics, proton pump inhibitors, and several other medications have been implicated in the pathogenesis of AIN. Rifampin is a medication that has a known association with AIN, with most cases being described in regions where treatment of endemic tuberculosis is common. The majority of cases of rifampin‐induced AIN occur in the setting of drug reexposure, due to an immunologically‐mediated process that causes tubulointerstitial injury.2

Patients with drug‐induced AIN typically present with oliguria secondary to an acute decline in renal function. The classic clinical triad of fever, rash, and arthralgias is uncommon, and all 3 occur in only 30% of all cases.3 More commonly, patients typically present with vague flu‐like and gastrointestinal symptoms, including fever, abdominal pain, nausea, and vomiting. Urinalysis may be helpful, but hematuria occurs in less than one‐half of all cases, and sterile pyuria is common but not always present. It has been suggested that the presence of eosinophiluria may lead to high suspicion of AIN, but the sensitivity and specificity of eosinophiluria are low, at 40% and 72%, respectively.3 Thus, renal biopsy is often performed to make a confirmatory diagnosis of AIN in the appropriate clinical setting. Histopathologically, the presence of inflammatory infiltrates in the renal tubules and interstitium with conservation of the glomerular structures is visualized.2, 3

A large number of patients who present with AIN may require temporary renal replacement therapy; however, most patients have been observed to recover full renal function. Despite this, review of the literature shows that many patients may have persistent elevations in their serum creatinine. Corticosteroid therapy, although controversial, has commonly been initiated in patients whose renal function does not improve with conservative therapy. To date there are no prospective randomized clinical trials, and data guiding optimal management in AIN is sparse. Some studies have demonstrated no benefit in corticosteroid therapy in lowering serum creatinine levels in patients with AIN,4 but others have observed a significantly increased risk of interstitial fibrosis and failure to return to baseline creatinine in those patients that received delayed treatment with corticosteroids more than 1 week after the withdrawal of the offending agent.5

The patient described in our case did not present with the classic symptoms noted in AIN. Yet she had evidence of eosinophiluria, which increased our suspicion for AIN. Although other potential etiologies of this patient's acute renal failure were considered, given her negative serologic studies and the results of her renal biopsy, AIN was considered the leading diagnosis. Since AIN was recognized early in this patient, the offending medication was discontinued promptly, prednisone therapy was initiated appropriately, and the renal failure that had developed quickly vanished.

A healthy 44‐year‐old man presented to the emergency room with buttock pain, 2 days after falling from a 4‐foot table onto a cement floor, striking his right buttock. On presentation he was in moderate distress, with heart rate = 130 beats per minute (bpm) and blood pressure = 80/40 mm Hg. There was a 2‐cm area of erythema on the right flank, and mild tenderness of the right buttock. There was no evidence of skin anesthesia or crepitance. Initial laboratory results were notable for creatinine = 1.5 mg/dl, creatine phosphokinase = 11,500 U/L, lactate = 6.1 mmo/L, and white blood cell count = 10 k/UL, with 93% neutrophils. Plain radiography of the hip and spine were negative for fractures and soft tissue gas. Despite receiving multiple doses of narcotic analgesia, the patient continued to complain of severe pain. Within 2 hours of presentation, the right flank erythema extended proximally up the back and distally to the right thigh (Figure 1). The patient was taken to the operating room for surgical exploration and was diagnosed with necrotizing fasciitis extending from his posterior neck to the right popliteal fossa (Figures 2 and 3). Intraoperative cultures grew Group A streptococcus, confirming a diagnosis of Type II necrotizing fasciitis. The patient required multiple debridements and subsequent reconstructive procedures. After more than 3 months in the hospital and acute rehabilitation, he returned home.

Figure 1

Figure 2

Figure 3

Necrotizing fasciitis is a rapidly progressive infection that spreads along fascial planes, causing necrosis of subcutaneous tissues. Type II necrotizing fasciitis is a monomicrobial infection and occurs in healthy patients, with blunt trauma being a known precipitant. The classic finding in necrotizing fasciitis is pain out of proportion to the physical examination. Additionally, patients will typically present with septic shock and end‐organ dysfunction. Diagnosis requires a strong index of suspicion and surgical exploration is necessary for diagnosis and treatment.

A healthy 44‐year‐old man presented to the emergency room with buttock pain, 2 days after falling from a 4‐foot table onto a cement floor, striking his right buttock. On presentation he was in moderate distress, with heart rate = 130 beats per minute (bpm) and blood pressure = 80/40 mm Hg. There was a 2‐cm area of erythema on the right flank, and mild tenderness of the right buttock. There was no evidence of skin anesthesia or crepitance. Initial laboratory results were notable for creatinine = 1.5 mg/dl, creatine phosphokinase = 11,500 U/L, lactate = 6.1 mmo/L, and white blood cell count = 10 k/UL, with 93% neutrophils. Plain radiography of the hip and spine were negative for fractures and soft tissue gas. Despite receiving multiple doses of narcotic analgesia, the patient continued to complain of severe pain. Within 2 hours of presentation, the right flank erythema extended proximally up the back and distally to the right thigh (Figure 1). The patient was taken to the operating room for surgical exploration and was diagnosed with necrotizing fasciitis extending from his posterior neck to the right popliteal fossa (Figures 2 and 3). Intraoperative cultures grew Group A streptococcus, confirming a diagnosis of Type II necrotizing fasciitis. The patient required multiple debridements and subsequent reconstructive procedures. After more than 3 months in the hospital and acute rehabilitation, he returned home.

Figure 1

Figure 2

Figure 3

Necrotizing fasciitis is a rapidly progressive infection that spreads along fascial planes, causing necrosis of subcutaneous tissues. Type II necrotizing fasciitis is a monomicrobial infection and occurs in healthy patients, with blunt trauma being a known precipitant. The classic finding in necrotizing fasciitis is pain out of proportion to the physical examination. Additionally, patients will typically present with septic shock and end‐organ dysfunction. Diagnosis requires a strong index of suspicion and surgical exploration is necessary for diagnosis and treatment.

A healthy 44‐year‐old man presented to the emergency room with buttock pain, 2 days after falling from a 4‐foot table onto a cement floor, striking his right buttock. On presentation he was in moderate distress, with heart rate = 130 beats per minute (bpm) and blood pressure = 80/40 mm Hg. There was a 2‐cm area of erythema on the right flank, and mild tenderness of the right buttock. There was no evidence of skin anesthesia or crepitance. Initial laboratory results were notable for creatinine = 1.5 mg/dl, creatine phosphokinase = 11,500 U/L, lactate = 6.1 mmo/L, and white blood cell count = 10 k/UL, with 93% neutrophils. Plain radiography of the hip and spine were negative for fractures and soft tissue gas. Despite receiving multiple doses of narcotic analgesia, the patient continued to complain of severe pain. Within 2 hours of presentation, the right flank erythema extended proximally up the back and distally to the right thigh (Figure 1). The patient was taken to the operating room for surgical exploration and was diagnosed with necrotizing fasciitis extending from his posterior neck to the right popliteal fossa (Figures 2 and 3). Intraoperative cultures grew Group A streptococcus, confirming a diagnosis of Type II necrotizing fasciitis. The patient required multiple debridements and subsequent reconstructive procedures. After more than 3 months in the hospital and acute rehabilitation, he returned home.

Figure 1

Figure 2

Figure 3

Necrotizing fasciitis is a rapidly progressive infection that spreads along fascial planes, causing necrosis of subcutaneous tissues. Type II necrotizing fasciitis is a monomicrobial infection and occurs in healthy patients, with blunt trauma being a known precipitant. The classic finding in necrotizing fasciitis is pain out of proportion to the physical examination. Additionally, patients will typically present with septic shock and end‐organ dysfunction. Diagnosis requires a strong index of suspicion and surgical exploration is necessary for diagnosis and treatment.

Introduction

The Society of Hospital Medicine (SHM) defines hospitalists as physicians whose primary professional focus is the comprehensive general medical care of hospitalized patients. Their activities include patient care, teaching, research, and leadership related to Hospital Medicine.1 It is estimated that there are up to 2500 pediatric hospitalists in the United States, with continued growth due to the converging needs for a dedicated focus on patient safety, quality improvement, hospital throughput, and inpatient teaching.2‐9 (Pediatric Hospital Medicine (PHM), as defined today, has been practiced in the United States for at least 30 years10 and continues to evolve as an area of specialization, with the refinement of a distinct knowledgebase and skill set focused on the provision of high quality general pediatric care in the inpatient setting. PHM is the latest site‐specific specialty to emerge from the field of general pediatrics it's development analogous to the evolution of critical care or emergency medicine during previous decades.11 Adult hospital medicine has defined itself within the field of general internal medicine12 and has recently received approval to provide a recognized focus of practice exam in 2010 for those re‐certifying with the American Board of Internal Medicine,13 PHM is creating an identity as a subspecialty practice with distinct focus on inpatient care for children within the larger context of general pediatric care.8, 14

The Pediatric Hospital Medicine Core Competencies were created to help define the roles and expectations for pediatric hospitalists, regardless of practice setting. The intent is to provide a unified approach toward identifying the specific body of knowledge and measurable skills needed to assure delivery of the highest quality of care for all hospitalized pediatric patients. Most children requiring hospitalization in the United States are hospitalized in community settings where subspecialty support is more limited and many pediatric services may be unavailable. Children with complex, chronic medical problems, however, are more likely to be hospitalized at a tertiary care or academic institutions. In order to unify pediatric hospitalists who work in different practice environments, the PHM Core Competencies were constructed to represent the knowledge, skills, attitudes, and systems improvements that all pediatric hospitalists can be expected to acquire and maintain.

Furthermore, the content of the PHM Core Competencies reflect the fact that children are a vulnerable population. Their care requires attention to many elements which distinguishes it from that given to the majority of the adult population: dependency, differences in developmental physiology and behavior, occurrence of congenital genetic disorders and age‐based clinical conditions, impact of chronic disease states on whole child development, and weight‐based medication dosing often with limited guidance from pediatric studies, to name a few. Awareness of these needs must be heightened when a child enters the hospital where diagnoses, procedures, and treatments often include use of high‐risk modalities and require coordination of care across multiple providers.

Pediatric hospitalists commonly work to improve the systems of care in which they operate and therefore both clinical and non‐clinical topics are included. The 54 chapters address the fundamental and most common components of inpatient care but are not an extensive review of all aspects of inpatient medicine encountered by those caring for hospitalized children. Finally, the PHM Core Competencies are not intended for use in assessing proficiency immediately post‐residency, but do provide a framework for the education and evaluation of both physicians‐in‐training and practicing hospitalists. Meeting these competencies is anticipated to take from one to three years of active practice in pediatric hospital medicine, and may be reached through a combination of practice experience, course work, self‐directed work, and/or formalized training.

Methods

Areas of Focused Practice

The PHM Core Competencies were conceptualized similarly to the SHM adult core competencies. Initial sections were divided into clinical conditions, procedures, and systems. However as content developed and reviewer comments were addressed, the four final sections were modified to those noted in Table 2. For the Common Clinical Diagnoses and Conditions, the goal was to select conditions most commonly encountered by pediatric hospitalists. Non‐surgical diagnosis‐related group (DRG) conditions were selected from the following sources: The Joint Commission's (TJC) Oryx Performance Measures Report15‐16 (asthma, abdominal pain, acute gastroenteritis, simple pneumonia); Child Health Corporation of America's Pediatric Health Information System Dataset (CHCA PHIS, Shawnee Mission, KS), and relevant publications on common pediatric hospitalizations.17 These data were compared to billing data from randomly‐selected practicing hospitalists representing free‐standing children's and community hospitals, teaching and non‐teaching settings, and urban and rural locations. The 22 clinical conditions chosen by the CCTF were those most relevant to the practice of pediatric hospital medicine.

The Specialized Clinical Servicessection addresses important components of care that are not DRG‐based and reflect the unique needs of hospitalized children, as assessed by the CCTF, editors, and contributors. Core Skillswere chosen based on the HCUP Factbook 2 Procedures,18 billing data from randomly‐selected practicing hospitalists representing the same settings listed above, and critical input from reviewers. Depending on the individual setting, pediatric hospitalists may require skills in areas not found in these 11 chapters, such as chest tube placement or ventilator management. The list is therefore not exhaustive, but rather representative of skills most pediatric hospitalists should maintain.

The Healthcare Systems: Supporting and Advancing Child Healthchapters are likely the most dissimilar to any core content taught in traditional residency programs. While residency graduates are versed in some components listed in these chapters, comprehensive education in most of these competencies is currently lacking. Improvement of healthcare systems is an essential element of pediatric hospital medicine, and unifies all pediatric hospitalists regardless of practice environment or patient population. Therefore, this section includes chapters that not only focus on systems of care, but also on advancing child health through advocacy, research, education, evidence‐based medicine, and ethical practice. These chapters were drawn from a combination of several sources: expectations of external agencies (TJC, Center for Medicaid and Medicare) related to the specific nonclinical work in which pediatric hospitalists are integrally involved; expectations for advocacy as best defined by the AAP and the National Association of Children's Hospitals and Related Institutions (NACHRI); the six core competency domains mandated by the Accrediting Council on Graduate Medical Education (ACGME), the American Board of Pediatrics (ABP), and hospital medical staff offices as part of Focused Professional Practice Evaluation (FPPE) and Ongoing Professional Practice Evaluation (OPPE)16; and assessment of responsibilities and leadership roles fulfilled by pediatric hospitalists in all venues. In keeping with the intent of the competencies to be timeless, the competency elements call out the need to attend to the changing goals of these groups as well as those of the Institute of Healthcare Improvement (IHI), the Alliance for Pediatric Quality (which consists of ABP, AAP, TJC, CHCA, NACHRI), and local hospital systems leaders.

Contributors and Review

The CCTF selected section (associate) editors from SHM based on established expertise in each area, with input from the SHM Pediatric and Education Committees and the SHM Board. As a collaborative effort, authors for various chapters were solicited in consultation with experts from the AAP, APA, and SHM, and included non‐hospitalists with reputations as experts in various fields. Numerous SHM Pediatric Committee and CCTF conference calls were held to review hospital and academic appointments, presentations given, and affiliations relevant to the practice of pediatric hospital medicine. This vetting process resulted in a robust author list representing diverse geographic and practice settings. Contributors were provided with structure (Knowledge, Skills, Attitudes, and Systems subsections) and content (timeless, competency based) guidelines.

The review process was rigorous, and included both internal and external reviewers. The APA review in 2007 included the PHM Special Interest Group as well as the PHM Fellowship Directors (Table 1). After return to SHM and further editing, the internal review commenced which focused on content and scope. The editors addressed the resulting suggestions and worked to standardize formatting and use of Bloom's taxonomy.19 A list of common terms and phrases were created to add consistency between chapters. External reviewers were first mailed a letter requesting interest, which was followed up by emails, letters, and phone calls to encourage feedback. External review included 29 solicited agencies and societies (Table 3), with overall response rate of 66% (41% for Groups I and II). Individual contributors then reviewed comments specific to their chapters, with associate editor overview of their respective sections. The editors reviewed each chapter individually multiple times throughout the 2007‐2009 years, contacting individual contributors and reviewers by email and phone. Editors concluded a final comprehensive review of all chapters in late 2009.

Chapter Content

Each of the 54 chapters within the four sections of these competencies is presented in the educational theory of learning domains: Knowledge, Skills, Attitudes, with a final Systems domain added to reflect the emphasis of hospitalist practice on improving healthcare systems. Each chapter is designed to stand alone, which may assist with development of curriculum at individual practice locations. Certain key phrases are apparent throughout, such as lead, coordinate, or participate in and work with hospital and community leaders to which were designed to note the varied roles in different practice settings. Some chapters specifically comment on the application of competency bullets given the unique and differing roles and expectations of pediatric hospitalists, such as research and education. Chapters state specific proficiencies expected wherever possible, with phrases and wording selected to help guide learning activities to achieve the competency.

Application and Future Directions

Although pediatric hospitalists care for children in many settings, these core competencies address the common expectations for any venue. Pediatric hospital medicine requires skills in acute care clinical medicine that attend to the changing needs of hospitalized children. The core of pediatric hospital medicine is dedicated to the care of children in the geographic hospital environment between emergency medicine and tertiary pediatric and neonatal intensive care units. Pediatric hospitalists provide care in related clinical service programs that are linked to hospital systems. In performing these activities, pediatric hospitalists consistently partner with ambulatory providers and subspecialists to render coordinated care across the continuum for a given child. Pediatric hospital medicine is an interdisciplinary practice, with focus on processes of care and clinical quality outcomes based in evidence. Engagement in local, state, and national initiatives to improve child health outcomes is a cornerstone of pediatric hospitalists' practice. These competencies provide the framework for creation of curricula that can reflect local issues and react to changing evidence.

As providers of systems‐based care, pediatric hospitalists are called upon more and more to render care and provide leadership in clinical arenas that are integral to healthcare organizations, such as home health care, sub‐acute care facilities, and hospice and palliative care programs. The practice of pediatric hospital medicine has evolved to its current state through efforts of many represented in the competencies as contributors, associate editors, editors, and reviewers. Pediatric hospitalists are committed to leading change in healthcare for hospitalized children, and are positioned well to address the interests and needs of community and urban, teaching and non‐teaching facilities, and the children and families they serve. These competencies reflect the areas of focused practice which, similar to pediatric emergency medicine, will no doubt be refined but not fundamentally changed in future years. The intent, we hope, is clear: to provide excellence in clinical care, accountability for practice, and lead improvements in healthcare for hospitalized children.

Introduction

The Society of Hospital Medicine (SHM) defines hospitalists as physicians whose primary professional focus is the comprehensive general medical care of hospitalized patients. Their activities include patient care, teaching, research, and leadership related to Hospital Medicine.1 It is estimated that there are up to 2500 pediatric hospitalists in the United States, with continued growth due to the converging needs for a dedicated focus on patient safety, quality improvement, hospital throughput, and inpatient teaching.2‐9 (Pediatric Hospital Medicine (PHM), as defined today, has been practiced in the United States for at least 30 years10 and continues to evolve as an area of specialization, with the refinement of a distinct knowledgebase and skill set focused on the provision of high quality general pediatric care in the inpatient setting. PHM is the latest site‐specific specialty to emerge from the field of general pediatrics it's development analogous to the evolution of critical care or emergency medicine during previous decades.11 Adult hospital medicine has defined itself within the field of general internal medicine12 and has recently received approval to provide a recognized focus of practice exam in 2010 for those re‐certifying with the American Board of Internal Medicine,13 PHM is creating an identity as a subspecialty practice with distinct focus on inpatient care for children within the larger context of general pediatric care.8, 14

The Pediatric Hospital Medicine Core Competencies were created to help define the roles and expectations for pediatric hospitalists, regardless of practice setting. The intent is to provide a unified approach toward identifying the specific body of knowledge and measurable skills needed to assure delivery of the highest quality of care for all hospitalized pediatric patients. Most children requiring hospitalization in the United States are hospitalized in community settings where subspecialty support is more limited and many pediatric services may be unavailable. Children with complex, chronic medical problems, however, are more likely to be hospitalized at a tertiary care or academic institutions. In order to unify pediatric hospitalists who work in different practice environments, the PHM Core Competencies were constructed to represent the knowledge, skills, attitudes, and systems improvements that all pediatric hospitalists can be expected to acquire and maintain.

Furthermore, the content of the PHM Core Competencies reflect the fact that children are a vulnerable population. Their care requires attention to many elements which distinguishes it from that given to the majority of the adult population: dependency, differences in developmental physiology and behavior, occurrence of congenital genetic disorders and age‐based clinical conditions, impact of chronic disease states on whole child development, and weight‐based medication dosing often with limited guidance from pediatric studies, to name a few. Awareness of these needs must be heightened when a child enters the hospital where diagnoses, procedures, and treatments often include use of high‐risk modalities and require coordination of care across multiple providers.

Pediatric hospitalists commonly work to improve the systems of care in which they operate and therefore both clinical and non‐clinical topics are included. The 54 chapters address the fundamental and most common components of inpatient care but are not an extensive review of all aspects of inpatient medicine encountered by those caring for hospitalized children. Finally, the PHM Core Competencies are not intended for use in assessing proficiency immediately post‐residency, but do provide a framework for the education and evaluation of both physicians‐in‐training and practicing hospitalists. Meeting these competencies is anticipated to take from one to three years of active practice in pediatric hospital medicine, and may be reached through a combination of practice experience, course work, self‐directed work, and/or formalized training.

Methods

Areas of Focused Practice

The PHM Core Competencies were conceptualized similarly to the SHM adult core competencies. Initial sections were divided into clinical conditions, procedures, and systems. However as content developed and reviewer comments were addressed, the four final sections were modified to those noted in Table 2. For the Common Clinical Diagnoses and Conditions, the goal was to select conditions most commonly encountered by pediatric hospitalists. Non‐surgical diagnosis‐related group (DRG) conditions were selected from the following sources: The Joint Commission's (TJC) Oryx Performance Measures Report15‐16 (asthma, abdominal pain, acute gastroenteritis, simple pneumonia); Child Health Corporation of America's Pediatric Health Information System Dataset (CHCA PHIS, Shawnee Mission, KS), and relevant publications on common pediatric hospitalizations.17 These data were compared to billing data from randomly‐selected practicing hospitalists representing free‐standing children's and community hospitals, teaching and non‐teaching settings, and urban and rural locations. The 22 clinical conditions chosen by the CCTF were those most relevant to the practice of pediatric hospital medicine.

The Specialized Clinical Servicessection addresses important components of care that are not DRG‐based and reflect the unique needs of hospitalized children, as assessed by the CCTF, editors, and contributors. Core Skillswere chosen based on the HCUP Factbook 2 Procedures,18 billing data from randomly‐selected practicing hospitalists representing the same settings listed above, and critical input from reviewers. Depending on the individual setting, pediatric hospitalists may require skills in areas not found in these 11 chapters, such as chest tube placement or ventilator management. The list is therefore not exhaustive, but rather representative of skills most pediatric hospitalists should maintain.

The Healthcare Systems: Supporting and Advancing Child Healthchapters are likely the most dissimilar to any core content taught in traditional residency programs. While residency graduates are versed in some components listed in these chapters, comprehensive education in most of these competencies is currently lacking. Improvement of healthcare systems is an essential element of pediatric hospital medicine, and unifies all pediatric hospitalists regardless of practice environment or patient population. Therefore, this section includes chapters that not only focus on systems of care, but also on advancing child health through advocacy, research, education, evidence‐based medicine, and ethical practice. These chapters were drawn from a combination of several sources: expectations of external agencies (TJC, Center for Medicaid and Medicare) related to the specific nonclinical work in which pediatric hospitalists are integrally involved; expectations for advocacy as best defined by the AAP and the National Association of Children's Hospitals and Related Institutions (NACHRI); the six core competency domains mandated by the Accrediting Council on Graduate Medical Education (ACGME), the American Board of Pediatrics (ABP), and hospital medical staff offices as part of Focused Professional Practice Evaluation (FPPE) and Ongoing Professional Practice Evaluation (OPPE)16; and assessment of responsibilities and leadership roles fulfilled by pediatric hospitalists in all venues. In keeping with the intent of the competencies to be timeless, the competency elements call out the need to attend to the changing goals of these groups as well as those of the Institute of Healthcare Improvement (IHI), the Alliance for Pediatric Quality (which consists of ABP, AAP, TJC, CHCA, NACHRI), and local hospital systems leaders.

Contributors and Review

The CCTF selected section (associate) editors from SHM based on established expertise in each area, with input from the SHM Pediatric and Education Committees and the SHM Board. As a collaborative effort, authors for various chapters were solicited in consultation with experts from the AAP, APA, and SHM, and included non‐hospitalists with reputations as experts in various fields. Numerous SHM Pediatric Committee and CCTF conference calls were held to review hospital and academic appointments, presentations given, and affiliations relevant to the practice of pediatric hospital medicine. This vetting process resulted in a robust author list representing diverse geographic and practice settings. Contributors were provided with structure (Knowledge, Skills, Attitudes, and Systems subsections) and content (timeless, competency based) guidelines.

The review process was rigorous, and included both internal and external reviewers. The APA review in 2007 included the PHM Special Interest Group as well as the PHM Fellowship Directors (Table 1). After return to SHM and further editing, the internal review commenced which focused on content and scope. The editors addressed the resulting suggestions and worked to standardize formatting and use of Bloom's taxonomy.19 A list of common terms and phrases were created to add consistency between chapters. External reviewers were first mailed a letter requesting interest, which was followed up by emails, letters, and phone calls to encourage feedback. External review included 29 solicited agencies and societies (Table 3), with overall response rate of 66% (41% for Groups I and II). Individual contributors then reviewed comments specific to their chapters, with associate editor overview of their respective sections. The editors reviewed each chapter individually multiple times throughout the 2007‐2009 years, contacting individual contributors and reviewers by email and phone. Editors concluded a final comprehensive review of all chapters in late 2009.

Chapter Content

Each of the 54 chapters within the four sections of these competencies is presented in the educational theory of learning domains: Knowledge, Skills, Attitudes, with a final Systems domain added to reflect the emphasis of hospitalist practice on improving healthcare systems. Each chapter is designed to stand alone, which may assist with development of curriculum at individual practice locations. Certain key phrases are apparent throughout, such as lead, coordinate, or participate in and work with hospital and community leaders to which were designed to note the varied roles in different practice settings. Some chapters specifically comment on the application of competency bullets given the unique and differing roles and expectations of pediatric hospitalists, such as research and education. Chapters state specific proficiencies expected wherever possible, with phrases and wording selected to help guide learning activities to achieve the competency.

Application and Future Directions

Although pediatric hospitalists care for children in many settings, these core competencies address the common expectations for any venue. Pediatric hospital medicine requires skills in acute care clinical medicine that attend to the changing needs of hospitalized children. The core of pediatric hospital medicine is dedicated to the care of children in the geographic hospital environment between emergency medicine and tertiary pediatric and neonatal intensive care units. Pediatric hospitalists provide care in related clinical service programs that are linked to hospital systems. In performing these activities, pediatric hospitalists consistently partner with ambulatory providers and subspecialists to render coordinated care across the continuum for a given child. Pediatric hospital medicine is an interdisciplinary practice, with focus on processes of care and clinical quality outcomes based in evidence. Engagement in local, state, and national initiatives to improve child health outcomes is a cornerstone of pediatric hospitalists' practice. These competencies provide the framework for creation of curricula that can reflect local issues and react to changing evidence.

As providers of systems‐based care, pediatric hospitalists are called upon more and more to render care and provide leadership in clinical arenas that are integral to healthcare organizations, such as home health care, sub‐acute care facilities, and hospice and palliative care programs. The practice of pediatric hospital medicine has evolved to its current state through efforts of many represented in the competencies as contributors, associate editors, editors, and reviewers. Pediatric hospitalists are committed to leading change in healthcare for hospitalized children, and are positioned well to address the interests and needs of community and urban, teaching and non‐teaching facilities, and the children and families they serve. These competencies reflect the areas of focused practice which, similar to pediatric emergency medicine, will no doubt be refined but not fundamentally changed in future years. The intent, we hope, is clear: to provide excellence in clinical care, accountability for practice, and lead improvements in healthcare for hospitalized children.

Introduction

The Society of Hospital Medicine (SHM) defines hospitalists as physicians whose primary professional focus is the comprehensive general medical care of hospitalized patients. Their activities include patient care, teaching, research, and leadership related to Hospital Medicine.1 It is estimated that there are up to 2500 pediatric hospitalists in the United States, with continued growth due to the converging needs for a dedicated focus on patient safety, quality improvement, hospital throughput, and inpatient teaching.2‐9 (Pediatric Hospital Medicine (PHM), as defined today, has been practiced in the United States for at least 30 years10 and continues to evolve as an area of specialization, with the refinement of a distinct knowledgebase and skill set focused on the provision of high quality general pediatric care in the inpatient setting. PHM is the latest site‐specific specialty to emerge from the field of general pediatrics it's development analogous to the evolution of critical care or emergency medicine during previous decades.11 Adult hospital medicine has defined itself within the field of general internal medicine12 and has recently received approval to provide a recognized focus of practice exam in 2010 for those re‐certifying with the American Board of Internal Medicine,13 PHM is creating an identity as a subspecialty practice with distinct focus on inpatient care for children within the larger context of general pediatric care.8, 14

The Pediatric Hospital Medicine Core Competencies were created to help define the roles and expectations for pediatric hospitalists, regardless of practice setting. The intent is to provide a unified approach toward identifying the specific body of knowledge and measurable skills needed to assure delivery of the highest quality of care for all hospitalized pediatric patients. Most children requiring hospitalization in the United States are hospitalized in community settings where subspecialty support is more limited and many pediatric services may be unavailable. Children with complex, chronic medical problems, however, are more likely to be hospitalized at a tertiary care or academic institutions. In order to unify pediatric hospitalists who work in different practice environments, the PHM Core Competencies were constructed to represent the knowledge, skills, attitudes, and systems improvements that all pediatric hospitalists can be expected to acquire and maintain.

Furthermore, the content of the PHM Core Competencies reflect the fact that children are a vulnerable population. Their care requires attention to many elements which distinguishes it from that given to the majority of the adult population: dependency, differences in developmental physiology and behavior, occurrence of congenital genetic disorders and age‐based clinical conditions, impact of chronic disease states on whole child development, and weight‐based medication dosing often with limited guidance from pediatric studies, to name a few. Awareness of these needs must be heightened when a child enters the hospital where diagnoses, procedures, and treatments often include use of high‐risk modalities and require coordination of care across multiple providers.

Pediatric hospitalists commonly work to improve the systems of care in which they operate and therefore both clinical and non‐clinical topics are included. The 54 chapters address the fundamental and most common components of inpatient care but are not an extensive review of all aspects of inpatient medicine encountered by those caring for hospitalized children. Finally, the PHM Core Competencies are not intended for use in assessing proficiency immediately post‐residency, but do provide a framework for the education and evaluation of both physicians‐in‐training and practicing hospitalists. Meeting these competencies is anticipated to take from one to three years of active practice in pediatric hospital medicine, and may be reached through a combination of practice experience, course work, self‐directed work, and/or formalized training.

Methods

Areas of Focused Practice

The PHM Core Competencies were conceptualized similarly to the SHM adult core competencies. Initial sections were divided into clinical conditions, procedures, and systems. However as content developed and reviewer comments were addressed, the four final sections were modified to those noted in Table 2. For the Common Clinical Diagnoses and Conditions, the goal was to select conditions most commonly encountered by pediatric hospitalists. Non‐surgical diagnosis‐related group (DRG) conditions were selected from the following sources: The Joint Commission's (TJC) Oryx Performance Measures Report15‐16 (asthma, abdominal pain, acute gastroenteritis, simple pneumonia); Child Health Corporation of America's Pediatric Health Information System Dataset (CHCA PHIS, Shawnee Mission, KS), and relevant publications on common pediatric hospitalizations.17 These data were compared to billing data from randomly‐selected practicing hospitalists representing free‐standing children's and community hospitals, teaching and non‐teaching settings, and urban and rural locations. The 22 clinical conditions chosen by the CCTF were those most relevant to the practice of pediatric hospital medicine.

The Specialized Clinical Servicessection addresses important components of care that are not DRG‐based and reflect the unique needs of hospitalized children, as assessed by the CCTF, editors, and contributors. Core Skillswere chosen based on the HCUP Factbook 2 Procedures,18 billing data from randomly‐selected practicing hospitalists representing the same settings listed above, and critical input from reviewers. Depending on the individual setting, pediatric hospitalists may require skills in areas not found in these 11 chapters, such as chest tube placement or ventilator management. The list is therefore not exhaustive, but rather representative of skills most pediatric hospitalists should maintain.

The Healthcare Systems: Supporting and Advancing Child Healthchapters are likely the most dissimilar to any core content taught in traditional residency programs. While residency graduates are versed in some components listed in these chapters, comprehensive education in most of these competencies is currently lacking. Improvement of healthcare systems is an essential element of pediatric hospital medicine, and unifies all pediatric hospitalists regardless of practice environment or patient population. Therefore, this section includes chapters that not only focus on systems of care, but also on advancing child health through advocacy, research, education, evidence‐based medicine, and ethical practice. These chapters were drawn from a combination of several sources: expectations of external agencies (TJC, Center for Medicaid and Medicare) related to the specific nonclinical work in which pediatric hospitalists are integrally involved; expectations for advocacy as best defined by the AAP and the National Association of Children's Hospitals and Related Institutions (NACHRI); the six core competency domains mandated by the Accrediting Council on Graduate Medical Education (ACGME), the American Board of Pediatrics (ABP), and hospital medical staff offices as part of Focused Professional Practice Evaluation (FPPE) and Ongoing Professional Practice Evaluation (OPPE)16; and assessment of responsibilities and leadership roles fulfilled by pediatric hospitalists in all venues. In keeping with the intent of the competencies to be timeless, the competency elements call out the need to attend to the changing goals of these groups as well as those of the Institute of Healthcare Improvement (IHI), the Alliance for Pediatric Quality (which consists of ABP, AAP, TJC, CHCA, NACHRI), and local hospital systems leaders.

Contributors and Review

The CCTF selected section (associate) editors from SHM based on established expertise in each area, with input from the SHM Pediatric and Education Committees and the SHM Board. As a collaborative effort, authors for various chapters were solicited in consultation with experts from the AAP, APA, and SHM, and included non‐hospitalists with reputations as experts in various fields. Numerous SHM Pediatric Committee and CCTF conference calls were held to review hospital and academic appointments, presentations given, and affiliations relevant to the practice of pediatric hospital medicine. This vetting process resulted in a robust author list representing diverse geographic and practice settings. Contributors were provided with structure (Knowledge, Skills, Attitudes, and Systems subsections) and content (timeless, competency based) guidelines.

The review process was rigorous, and included both internal and external reviewers. The APA review in 2007 included the PHM Special Interest Group as well as the PHM Fellowship Directors (Table 1). After return to SHM and further editing, the internal review commenced which focused on content and scope. The editors addressed the resulting suggestions and worked to standardize formatting and use of Bloom's taxonomy.19 A list of common terms and phrases were created to add consistency between chapters. External reviewers were first mailed a letter requesting interest, which was followed up by emails, letters, and phone calls to encourage feedback. External review included 29 solicited agencies and societies (Table 3), with overall response rate of 66% (41% for Groups I and II). Individual contributors then reviewed comments specific to their chapters, with associate editor overview of their respective sections. The editors reviewed each chapter individually multiple times throughout the 2007‐2009 years, contacting individual contributors and reviewers by email and phone. Editors concluded a final comprehensive review of all chapters in late 2009.

Chapter Content

Each of the 54 chapters within the four sections of these competencies is presented in the educational theory of learning domains: Knowledge, Skills, Attitudes, with a final Systems domain added to reflect the emphasis of hospitalist practice on improving healthcare systems. Each chapter is designed to stand alone, which may assist with development of curriculum at individual practice locations. Certain key phrases are apparent throughout, such as lead, coordinate, or participate in and work with hospital and community leaders to which were designed to note the varied roles in different practice settings. Some chapters specifically comment on the application of competency bullets given the unique and differing roles and expectations of pediatric hospitalists, such as research and education. Chapters state specific proficiencies expected wherever possible, with phrases and wording selected to help guide learning activities to achieve the competency.

Application and Future Directions

Although pediatric hospitalists care for children in many settings, these core competencies address the common expectations for any venue. Pediatric hospital medicine requires skills in acute care clinical medicine that attend to the changing needs of hospitalized children. The core of pediatric hospital medicine is dedicated to the care of children in the geographic hospital environment between emergency medicine and tertiary pediatric and neonatal intensive care units. Pediatric hospitalists provide care in related clinical service programs that are linked to hospital systems. In performing these activities, pediatric hospitalists consistently partner with ambulatory providers and subspecialists to render coordinated care across the continuum for a given child. Pediatric hospital medicine is an interdisciplinary practice, with focus on processes of care and clinical quality outcomes based in evidence. Engagement in local, state, and national initiatives to improve child health outcomes is a cornerstone of pediatric hospitalists' practice. These competencies provide the framework for creation of curricula that can reflect local issues and react to changing evidence.

As providers of systems‐based care, pediatric hospitalists are called upon more and more to render care and provide leadership in clinical arenas that are integral to healthcare organizations, such as home health care, sub‐acute care facilities, and hospice and palliative care programs. The practice of pediatric hospital medicine has evolved to its current state through efforts of many represented in the competencies as contributors, associate editors, editors, and reviewers. Pediatric hospitalists are committed to leading change in healthcare for hospitalized children, and are positioned well to address the interests and needs of community and urban, teaching and non‐teaching facilities, and the children and families they serve. These competencies reflect the areas of focused practice which, similar to pediatric emergency medicine, will no doubt be refined but not fundamentally changed in future years. The intent, we hope, is clear: to provide excellence in clinical care, accountability for practice, and lead improvements in healthcare for hospitalized children.

In 1992, the Joint Commission on Accreditation of Healthcare Organizations (Joint Commission) introduced standards to make hospital buildings smoke‐free, resulting in the nation's first industry‐wide ban on smoking in the workplace. This hospital smoking ban has led to increased smoking cessation among employees.1 Since 2003, core measures from the Joint Commission and quality indicators from the Centers for Medicare and Medicaid Services have included inpatient smoking cessation counseling for acute myocardial infarction, pneumonia, and heart failure, as national guidelines strongly recommend smoking cessation counseling for patients with these diseases who smoke.25

The Department of Health and Human Services (DHHS) 2008 update on Clinical Practice Guidelines for Treating Tobacco Use and Dependence6 recommends that clinicians use hospitalization as an opportunity to promote smoking cessation and to prescribe medications to alleviate withdrawal symptoms. Hospitalization is an opportune time for smoking cessation because patients are restricted to a smoke‐free environment in the hospital and, increasingly, on hospital campuses.6 The illness leading to hospitalization may be attributable, at least in part, to tobacco use, thereby increasing the patient's receptivity to cessation counseling. Last, medications used in‐hospital to treat nicotine withdrawal symptoms may lead to continued or future use of these medications that, in turn, may ultimately lead to a successful quit attempt.

We report on the outcomes of our hospital's attempt to do this in the context of implementation of a smoke‐free medical campus.7 This study was designed to measure whether an inpatient smoking cessation intervention increases the likelihood of smoking cessation 6 months post‐hospital discharge. Because effectiveness studies are the next step to improving translation of research into health promotion practice,8 we set out to measure what the impact of this intervention would be in routine clinical practice as opposed to a carefully structured efficacy trial.

Methods

Results

From January 1, 2007 to May 30, 2008, 660 inpatients who were current smokers were recruited into the study. Figure 1 summarizes patient flow through the study and explains the final sample size of 607. Exclusions include 52 inpatients from the study who completed the 6‐month interview but who did not return a written HIPAA release. Without a HIPAA release to access the EMR, baseline comparison of the study groups and adjustment for comorbidity could not be completed for these patients. At 6 months posthospital discharge, 53 subjects refused the interview when contacted by telephone.

Figure 1

As might be expected in a quasiexperimental design, the study groups were not equivalent at baseline (Table 1). The intervention and comparison groups differed with regard to age, length of stay (LOS), proportion of acute admissions, and the Elixhauser comorbidity index. These differences suggest that the intervention group was older, had a longer LOS, higher acuity at the time of admission, and more comorbidities. In addition, as a result of the intervention, the intervention group was more likely to receive NRT or bupropion in hospital and a Fax‐to‐Quit referral to the NYS Smokers' Quitline. In the intervention group, most patients received 1 visit from the SCS, only 2% received 2 visits. Family members were included in smoking cessation counseling for 58% of the intervention group.

As shown in Table 2, there was a significant amount of diagnostic heterogeneity in the discharge diagnoses codes of patients included in the study. However, there were significantly more patients in the intervention group with a first discharge diagnosis of cardiovascular disease (25%) compared to the comparison group (12%; P = 0.00).

Readmission outcomes based on EMR and administrative data were available for 607 inpatients with signed HIPAA releases. The readmission rate was higher for the intervention (41%) than the comparison group (20%). Despite a higher readmission rate, the crude mortality within the 6 months posthospital discharge was lower for the intervention group, ie, 0.02 (6/276), than the comparison group, which had a crude mortality of 0.04 (16/384) during this period.

A multivariate survival model, controlling for age, sex, Elixhauser comorbidity index, LOS, and cardiovascular diagnosis, showed a significantly reduced mortality in the intervention group (hazard ratio [HR] = 0.37; P = 0.04). Although cardiac status (P = 0.09) and LOS (P = 0.15) were not significant in this model, they were retained because both of these variables showed significantly higher levels (along with the Elixhauser Index) at baseline in the intervention group, implying that the intervention group was sicker than the comparison group. The Elixhauser comorbidity index (HR = 1.42; P < 0.00) and age (HR = 1.07; P < 0.00) were the only other significant predictors of mortality in this model.

Among those responding to the interview at 6 months post‐hospital discharge (n = 326), there were no significant differences between the study groups with regard to age first started smoking, gender, educational level, employment status, ethnicity, or physical health status (data not shown). Table 3 summarizes the outcomes by the intervention and comparison groups at 6 months post‐hospital discharge. The point prevalence for abstinence was 27% in the intervention group compared to 19% in the comparison group (P = 0.09). Using the intent to treat analysis, the point prevalence for abstinence was 16% in the intervention group compared to 9.8% in the comparison group (P = 0.02). Self‐reported quit status was 63% in the intervention group vs. 48% in the comparison group (P = 0.00). Using the intent to treat analysis, quit status was 44% in the intervention group vs. 30% in the comparison group (P = 0.00). Exclusion of the 52 patients without signed HIPAA releases (Figure 1) did not significantly alter these outcomes.

Patients who received the inpatient smoking cessation counseling were more likely to be called by or use the NYS Smokers' Quitline; however, these differences were not statistically significant. There was no difference between the study groups in awareness of the Quitline but the intervention group was more aware that free NRT was offered by the NYS Quitline. In terms of quit methods used during the 6‐month period (Table 4), NRT or bupropion use was higher in the intervention group. There were no other significant differences between the study groups, except for the use of acupuncture.

Multivariate analysis predicting quit status at 6 months post‐hospital discharge included covariates controlling for age, sex, LOS, study group, and comorbidity. This analysis showed that patients with a cardiovascular discharge diagnosis were more likely to quit than patients who had other discharge diagnoses (odds ratio [OR], 3.02; 95% confidence interval [CI], 1.65.7; P = 0.00). Another statistically significant covariate in this model included sex (men were more likely to quit: OR, 0.61; 95% CI, 0.390.97; P = 0.04). Participating in the inpatient intervention group was marginally significant when controlling for these other variables (OR, 1.54; 95% CI, 0.982.45; P = 0.06). Hospital LOS, age, receipt of NRT in hospital, and the Elixhauser comorbidity index were not predictive of quit status at 6 months.

Discussion

This study demonstrates how effective an inpatient smoking cessation program can be for increasing the success of quitting smoking after hospital discharge. At 6 months posthospital discharge, the intervention group had significantly higher intent to treat outcomes for point prevalence abstinence and quit status as well as lower crude and adjusted mortality than the comparison group.

Although at baseline the intervention group was older, had a longer LOS, more cardiovascular diagnoses, and higher comorbidity index, crude post‐hospital discharge mortality was significantly less in the intervention group (0.02) than in the comparison group (0.04). This finding is more significant in light of the higher comorbidity and acuity of the intervention group at baseline. Our multivariate survival model that controlled for these imbalances at baseline demonstrated that the intervention group had significantly less mortality than the comparison group (HR = 0.37; P = 0.04). Reduction in mortality, as soon as 30 days after inpatient smoking cessation counseling, has been demonstrated postmyocardial infarction.17, 18 Intensive smoking cessation quit services were also linked with lower all cause mortality among cardiovascular disease patients 2 years posthospitalization.19 Our study, despite its relatively small sample size, demonstrates that the intervention retains its impact on mortality in real‐world settings.

Following participation in an inpatient smoking cessation program, self‐reported quit status at 6 months post‐hospital discharge in the intervention group was significantly higher in the intervention group (63%) than the comparison group (48%; P = 0.00). Using the intent to treat method, the differences between the study groups was still significant (44% in the intervention group, 30% in the comparison group; P = 0.00). Given the limitations of self‐report and responder bias, the actual outcomes fall somewhere between these 2 estimates. In an effectiveness study of inpatient smoking cessation involving 6 hospitals in California, self‐reported quit rates of 26% at 6 months were reported; however, different methods were used so the results are not strictly comparable.20

Our multivariate analysis suggests that patients with cardiovascular discharge diagnosis were more likely to quit than patients who had other discharge diagnoses (OR, 3.02; 95% CI, 1.65.7; P = 0.0007). This study extends findings of other studies that show that the success of smoking cessation may vary by diagnosis, particularly for smokers admitted for cardiovascular disease.21, 22 Other studies have shown that smoking cessation rates among patients post‐myocardial infarction were higher in admitting facilities that had hospital‐based smoking cessation programs and for those patients referred to cardiac rehabilitation.23 Thus, the availability of a hospital‐based smoking cessation program may be considered a structural measure of health care quality as suggested by Dawood et al.23

The use of NRT greatly increased in our hospital, coincident with the start of the inpatient cessation program7 and, in this study, NRT use appears to continue after hospital discharge. Some studies show an additive effect of NRT combined with cessation counseling.24, 25 Although a Cochrane review did not find a statistically significant difference, there was a trend toward higher quit rates with the addition of NRT.21, 22

Because hospitalized smokers may be more motivated to stop smoking, the updated 2008 DHHS clinical practice guidelines for Treating Tobacco Use and Dependence now recommend that all inpatients who currently smoke be given medications, advised, counseled, and receive follow‐up after discharge.26 Although our inpatient cessation program was started before these clinical practice guidelines were updated, we have had the opportunity to evaluate the recommended practice of inpatient tobacco cessation counseling. Compared to effects shown in efficacy studies, clinical interventions often lose effect size in daily practice and real‐world settings.27, 28 It is reassuring that, in this effectiveness study, the impact of this intervention is still demonstrable.

Provision of inpatient smoking cessation has been shown to be an effective smoking cessation intervention if combined with outpatient follow‐up.29 Reviews by Rigotti et al.21, 22 recommend that inpatient high‐intensity behavioral interventions should be followed by at least 1 month of supportive contact after discharge to promote smoking cessation among hospitalized patients. In our study, specific cessation‐related outpatient follow‐up was not provided by our program. Although letters were sent to primary care providers describing the cessation service provided during the inpatient stay, our study could not ascertain what specific cessation service was offered by either primary‐care or specialty‐care providers during posthospitalization follow‐up visits. An efficient alternative to outpatient visits may be follow‐up delivered via a quitline. Follow‐up in our study included referrals to the NYS Smokers' Quitline; however, only about 10% of inpatient reported using this service. While feasible, the effectiveness of quitline follow‐up is as yet unknown.30

Conclusions

This quasiexperimental effectiveness study showed that inpatient smoking cessation intervention improved smoking cessation outcomes, use of NRT, and was associated with a decreased mortality 6 months post‐hospital discharge. The effectiveness of this inpatient intervention is maintained in real world settings but may be improved with posthospital discharge follow‐up.

In 1992, the Joint Commission on Accreditation of Healthcare Organizations (Joint Commission) introduced standards to make hospital buildings smoke‐free, resulting in the nation's first industry‐wide ban on smoking in the workplace. This hospital smoking ban has led to increased smoking cessation among employees.1 Since 2003, core measures from the Joint Commission and quality indicators from the Centers for Medicare and Medicaid Services have included inpatient smoking cessation counseling for acute myocardial infarction, pneumonia, and heart failure, as national guidelines strongly recommend smoking cessation counseling for patients with these diseases who smoke.25

The Department of Health and Human Services (DHHS) 2008 update on Clinical Practice Guidelines for Treating Tobacco Use and Dependence6 recommends that clinicians use hospitalization as an opportunity to promote smoking cessation and to prescribe medications to alleviate withdrawal symptoms. Hospitalization is an opportune time for smoking cessation because patients are restricted to a smoke‐free environment in the hospital and, increasingly, on hospital campuses.6 The illness leading to hospitalization may be attributable, at least in part, to tobacco use, thereby increasing the patient's receptivity to cessation counseling. Last, medications used in‐hospital to treat nicotine withdrawal symptoms may lead to continued or future use of these medications that, in turn, may ultimately lead to a successful quit attempt.

We report on the outcomes of our hospital's attempt to do this in the context of implementation of a smoke‐free medical campus.7 This study was designed to measure whether an inpatient smoking cessation intervention increases the likelihood of smoking cessation 6 months post‐hospital discharge. Because effectiveness studies are the next step to improving translation of research into health promotion practice,8 we set out to measure what the impact of this intervention would be in routine clinical practice as opposed to a carefully structured efficacy trial.

Methods

Results

From January 1, 2007 to May 30, 2008, 660 inpatients who were current smokers were recruited into the study. Figure 1 summarizes patient flow through the study and explains the final sample size of 607. Exclusions include 52 inpatients from the study who completed the 6‐month interview but who did not return a written HIPAA release. Without a HIPAA release to access the EMR, baseline comparison of the study groups and adjustment for comorbidity could not be completed for these patients. At 6 months posthospital discharge, 53 subjects refused the interview when contacted by telephone.

Figure 1

As might be expected in a quasiexperimental design, the study groups were not equivalent at baseline (Table 1). The intervention and comparison groups differed with regard to age, length of stay (LOS), proportion of acute admissions, and the Elixhauser comorbidity index. These differences suggest that the intervention group was older, had a longer LOS, higher acuity at the time of admission, and more comorbidities. In addition, as a result of the intervention, the intervention group was more likely to receive NRT or bupropion in hospital and a Fax‐to‐Quit referral to the NYS Smokers' Quitline. In the intervention group, most patients received 1 visit from the SCS, only 2% received 2 visits. Family members were included in smoking cessation counseling for 58% of the intervention group.

As shown in Table 2, there was a significant amount of diagnostic heterogeneity in the discharge diagnoses codes of patients included in the study. However, there were significantly more patients in the intervention group with a first discharge diagnosis of cardiovascular disease (25%) compared to the comparison group (12%; P = 0.00).

Readmission outcomes based on EMR and administrative data were available for 607 inpatients with signed HIPAA releases. The readmission rate was higher for the intervention (41%) than the comparison group (20%). Despite a higher readmission rate, the crude mortality within the 6 months posthospital discharge was lower for the intervention group, ie, 0.02 (6/276), than the comparison group, which had a crude mortality of 0.04 (16/384) during this period.

A multivariate survival model, controlling for age, sex, Elixhauser comorbidity index, LOS, and cardiovascular diagnosis, showed a significantly reduced mortality in the intervention group (hazard ratio [HR] = 0.37; P = 0.04). Although cardiac status (P = 0.09) and LOS (P = 0.15) were not significant in this model, they were retained because both of these variables showed significantly higher levels (along with the Elixhauser Index) at baseline in the intervention group, implying that the intervention group was sicker than the comparison group. The Elixhauser comorbidity index (HR = 1.42; P < 0.00) and age (HR = 1.07; P < 0.00) were the only other significant predictors of mortality in this model.

Among those responding to the interview at 6 months post‐hospital discharge (n = 326), there were no significant differences between the study groups with regard to age first started smoking, gender, educational level, employment status, ethnicity, or physical health status (data not shown). Table 3 summarizes the outcomes by the intervention and comparison groups at 6 months post‐hospital discharge. The point prevalence for abstinence was 27% in the intervention group compared to 19% in the comparison group (P = 0.09). Using the intent to treat analysis, the point prevalence for abstinence was 16% in the intervention group compared to 9.8% in the comparison group (P = 0.02). Self‐reported quit status was 63% in the intervention group vs. 48% in the comparison group (P = 0.00). Using the intent to treat analysis, quit status was 44% in the intervention group vs. 30% in the comparison group (P = 0.00). Exclusion of the 52 patients without signed HIPAA releases (Figure 1) did not significantly alter these outcomes.

Patients who received the inpatient smoking cessation counseling were more likely to be called by or use the NYS Smokers' Quitline; however, these differences were not statistically significant. There was no difference between the study groups in awareness of the Quitline but the intervention group was more aware that free NRT was offered by the NYS Quitline. In terms of quit methods used during the 6‐month period (Table 4), NRT or bupropion use was higher in the intervention group. There were no other significant differences between the study groups, except for the use of acupuncture.

Multivariate analysis predicting quit status at 6 months post‐hospital discharge included covariates controlling for age, sex, LOS, study group, and comorbidity. This analysis showed that patients with a cardiovascular discharge diagnosis were more likely to quit than patients who had other discharge diagnoses (odds ratio [OR], 3.02; 95% confidence interval [CI], 1.65.7; P = 0.00). Another statistically significant covariate in this model included sex (men were more likely to quit: OR, 0.61; 95% CI, 0.390.97; P = 0.04). Participating in the inpatient intervention group was marginally significant when controlling for these other variables (OR, 1.54; 95% CI, 0.982.45; P = 0.06). Hospital LOS, age, receipt of NRT in hospital, and the Elixhauser comorbidity index were not predictive of quit status at 6 months.

Discussion

This study demonstrates how effective an inpatient smoking cessation program can be for increasing the success of quitting smoking after hospital discharge. At 6 months posthospital discharge, the intervention group had significantly higher intent to treat outcomes for point prevalence abstinence and quit status as well as lower crude and adjusted mortality than the comparison group.

Although at baseline the intervention group was older, had a longer LOS, more cardiovascular diagnoses, and higher comorbidity index, crude post‐hospital discharge mortality was significantly less in the intervention group (0.02) than in the comparison group (0.04). This finding is more significant in light of the higher comorbidity and acuity of the intervention group at baseline. Our multivariate survival model that controlled for these imbalances at baseline demonstrated that the intervention group had significantly less mortality than the comparison group (HR = 0.37; P = 0.04). Reduction in mortality, as soon as 30 days after inpatient smoking cessation counseling, has been demonstrated postmyocardial infarction.17, 18 Intensive smoking cessation quit services were also linked with lower all cause mortality among cardiovascular disease patients 2 years posthospitalization.19 Our study, despite its relatively small sample size, demonstrates that the intervention retains its impact on mortality in real‐world settings.

Following participation in an inpatient smoking cessation program, self‐reported quit status at 6 months post‐hospital discharge in the intervention group was significantly higher in the intervention group (63%) than the comparison group (48%; P = 0.00). Using the intent to treat method, the differences between the study groups was still significant (44% in the intervention group, 30% in the comparison group; P = 0.00). Given the limitations of self‐report and responder bias, the actual outcomes fall somewhere between these 2 estimates. In an effectiveness study of inpatient smoking cessation involving 6 hospitals in California, self‐reported quit rates of 26% at 6 months were reported; however, different methods were used so the results are not strictly comparable.20

Our multivariate analysis suggests that patients with cardiovascular discharge diagnosis were more likely to quit than patients who had other discharge diagnoses (OR, 3.02; 95% CI, 1.65.7; P = 0.0007). This study extends findings of other studies that show that the success of smoking cessation may vary by diagnosis, particularly for smokers admitted for cardiovascular disease.21, 22 Other studies have shown that smoking cessation rates among patients post‐myocardial infarction were higher in admitting facilities that had hospital‐based smoking cessation programs and for those patients referred to cardiac rehabilitation.23 Thus, the availability of a hospital‐based smoking cessation program may be considered a structural measure of health care quality as suggested by Dawood et al.23

The use of NRT greatly increased in our hospital, coincident with the start of the inpatient cessation program7 and, in this study, NRT use appears to continue after hospital discharge. Some studies show an additive effect of NRT combined with cessation counseling.24, 25 Although a Cochrane review did not find a statistically significant difference, there was a trend toward higher quit rates with the addition of NRT.21, 22

Because hospitalized smokers may be more motivated to stop smoking, the updated 2008 DHHS clinical practice guidelines for Treating Tobacco Use and Dependence now recommend that all inpatients who currently smoke be given medications, advised, counseled, and receive follow‐up after discharge.26 Although our inpatient cessation program was started before these clinical practice guidelines were updated, we have had the opportunity to evaluate the recommended practice of inpatient tobacco cessation counseling. Compared to effects shown in efficacy studies, clinical interventions often lose effect size in daily practice and real‐world settings.27, 28 It is reassuring that, in this effectiveness study, the impact of this intervention is still demonstrable.

Provision of inpatient smoking cessation has been shown to be an effective smoking cessation intervention if combined with outpatient follow‐up.29 Reviews by Rigotti et al.21, 22 recommend that inpatient high‐intensity behavioral interventions should be followed by at least 1 month of supportive contact after discharge to promote smoking cessation among hospitalized patients. In our study, specific cessation‐related outpatient follow‐up was not provided by our program. Although letters were sent to primary care providers describing the cessation service provided during the inpatient stay, our study could not ascertain what specific cessation service was offered by either primary‐care or specialty‐care providers during posthospitalization follow‐up visits. An efficient alternative to outpatient visits may be follow‐up delivered via a quitline. Follow‐up in our study included referrals to the NYS Smokers' Quitline; however, only about 10% of inpatient reported using this service. While feasible, the effectiveness of quitline follow‐up is as yet unknown.30

Conclusions

This quasiexperimental effectiveness study showed that inpatient smoking cessation intervention improved smoking cessation outcomes, use of NRT, and was associated with a decreased mortality 6 months post‐hospital discharge. The effectiveness of this inpatient intervention is maintained in real world settings but may be improved with posthospital discharge follow‐up.

In 1992, the Joint Commission on Accreditation of Healthcare Organizations (Joint Commission) introduced standards to make hospital buildings smoke‐free, resulting in the nation's first industry‐wide ban on smoking in the workplace. This hospital smoking ban has led to increased smoking cessation among employees.1 Since 2003, core measures from the Joint Commission and quality indicators from the Centers for Medicare and Medicaid Services have included inpatient smoking cessation counseling for acute myocardial infarction, pneumonia, and heart failure, as national guidelines strongly recommend smoking cessation counseling for patients with these diseases who smoke.25

The Department of Health and Human Services (DHHS) 2008 update on Clinical Practice Guidelines for Treating Tobacco Use and Dependence6 recommends that clinicians use hospitalization as an opportunity to promote smoking cessation and to prescribe medications to alleviate withdrawal symptoms. Hospitalization is an opportune time for smoking cessation because patients are restricted to a smoke‐free environment in the hospital and, increasingly, on hospital campuses.6 The illness leading to hospitalization may be attributable, at least in part, to tobacco use, thereby increasing the patient's receptivity to cessation counseling. Last, medications used in‐hospital to treat nicotine withdrawal symptoms may lead to continued or future use of these medications that, in turn, may ultimately lead to a successful quit attempt.

We report on the outcomes of our hospital's attempt to do this in the context of implementation of a smoke‐free medical campus.7 This study was designed to measure whether an inpatient smoking cessation intervention increases the likelihood of smoking cessation 6 months post‐hospital discharge. Because effectiveness studies are the next step to improving translation of research into health promotion practice,8 we set out to measure what the impact of this intervention would be in routine clinical practice as opposed to a carefully structured efficacy trial.

Methods

Results

From January 1, 2007 to May 30, 2008, 660 inpatients who were current smokers were recruited into the study. Figure 1 summarizes patient flow through the study and explains the final sample size of 607. Exclusions include 52 inpatients from the study who completed the 6‐month interview but who did not return a written HIPAA release. Without a HIPAA release to access the EMR, baseline comparison of the study groups and adjustment for comorbidity could not be completed for these patients. At 6 months posthospital discharge, 53 subjects refused the interview when contacted by telephone.

Figure 1

As might be expected in a quasiexperimental design, the study groups were not equivalent at baseline (Table 1). The intervention and comparison groups differed with regard to age, length of stay (LOS), proportion of acute admissions, and the Elixhauser comorbidity index. These differences suggest that the intervention group was older, had a longer LOS, higher acuity at the time of admission, and more comorbidities. In addition, as a result of the intervention, the intervention group was more likely to receive NRT or bupropion in hospital and a Fax‐to‐Quit referral to the NYS Smokers' Quitline. In the intervention group, most patients received 1 visit from the SCS, only 2% received 2 visits. Family members were included in smoking cessation counseling for 58% of the intervention group.