Scott A. Flanders, MD

The International Consensus Conference (ICC) for sepsis defines severe sepsis as an infection leading to acute organ dysfunction.[1, 2] Severe sepsis afflicts over 1 million patients each year in Medicare alone, and is substantially more common among older Americans than acute myocardial infarction.[3, 4, 5] Recently, the Agency for Healthcare Research and Quality identified severe sepsis as the single most expensive cause of hospitalization in the United States.[6] The incidence of severe sepsis continues to rise.[4, 5]

Severe sepsis is often mischaracterized as a diagnosis cared for primarily in the intensive care unit (ICU). Yet, studies indicate that only 32% to 50% of patients with severe sepsis require ICU care, leaving the majority on the general care wards.[7, 8] These studies also reveal mortality rates of 26% to 30% among patients with severe sepsis who are not admitted to an ICU compared to 11% to 33% in the ICU.[7, 8]

Although a number of epidemiologic and interventional studies have focused on severe sepsis in the ICU,[3, 9, 10] much less is known about patients cared for on the general medicine wards. Without this information, clinicians cannot make informed choices about important management decisions such as targeted diagnostic testing, empirical antimicrobials, and other therapies. To this end, we sought to further characterize the infectious etiologies and resultant organ system dysfunctions in the subset of patients with severe sepsis admitted to non‐ICU medical services at a tertiary academic medical center.

METHODS

RESULTS

Of 23,288 hospitalizations examined from 2009 through 2010, the ICD‐9based automated screen for severe sepsis was positive for 3,146 (14 %). A random sample of 111 medical records, of which 92 had screened positive for severe sepsis and 19 had screened negative, was reviewed in detail. After review by the hospitalists, 64 of these 111 hospitalizations were judged to have severe sepsis, 61 of the 92 screened positive cases (66%), and 3 of the 19 screened negative cases (16%). The 3 reviewers had a kappa of 0.70, indicating good agreement.

Characteristics of the 64 patients with severe sepsis are shown in Table 2. The mean age was 63 years old (standard deviation [SD]=17.7), and 41% were male. The mean length of stay was 13.7 days (SD=20.8). Thirty‐nine percent (95% CI, 27%‐52%) of patients (25/64) were immunosuppressed. Of patients initially admitted to the general medical ward, 25% (16/64; 95% CI, 15%‐37%) ultimately required ICU care during their admission. The overall in‐hospital mortality rate was 13% (8/64; 95% CI, 6%‐23%). Immunosuppressed patients had a mortality rate of 20% and nonimmunosuppressed patients had a mortality rate of 8%. Only 47% (30/64; 95% CI, 34%‐60%) of the medical records had explicit clinician documentation of severe sepsis.

The most common site of infection was found to be the genitourinary system, occurring in 41% (26/64; 95% CI, 29%‐54%) of patients (Table 3). Pulmonary and intra‐abdominal sites were also common, accounting for 14% (95% CI, 6.6%‐25%) and 13% (95% CI, 5.6%‐23%) of sites, respectively. An infecting organism was identified by culture in 66% (42/64; 95% CI, 53%‐77%) of case patients with specific pathogens listed in Table 4. Among patients with positive culture results, the majority grew Gram‐negative organisms (57%; 95% CI, 41%‐72%). Non‐Clostridium difficile Gram‐positive organisms were also prominent and identified in 48% (95% CI, 32%‐64%) of positive cultures. Candida was less common (12%, 95% CI, 4.0%‐26%). Fourteen cases (22%, 95% CI, 10%‐30%) had 2 or more concomitant infectious pathogens.

All 64 patients had at least 1 organ dysfunction, as required by the ICC definition of severe sepsis. Organ dysfunction in 2 or more organ systems occurred in 77% (95% CI, 64%‐86%) of the cases (49/64). The incidence for each organ system dysfunction is presented in Table 5, as well as its relationship to both mortality and ICU admission. The most common organ system dysfunctions were found to be cardiovascular (hypotension) and renal dysfunction occurring in 66% and 64% of the cases, respectively. In this non‐ICU population, pulmonary dysfunction occurred in 30% of cases, but was frequently associated with transfer to the ICU, as 63% of the patients with pulmonary failure required ICU care. Patients with more organ systems affected were more likely to be transferred to the ICU and to die.

DISCUSSION

Severe sepsis was common among patients admitted to the general medical ward in this tertiary care center. Our patient cohort differed in important ways from previously described typical cases of severe sepsis among ICU populations. Severe sepsis on the general medical wards was more commonly associated with Gram‐negative pathogens in the setting of genitourinary tract infections. This is in contrast to Gram‐positive organisms and respiratory tract infections, which are more common in the ICU.[3, 10] Renal and cardiac dysfunction were commonly observed organ failures, whereas in the ICU, severe sepsis has been reported to more likely involve respiratory failure. These results suggest that hospitalists seeking to provide evidence‐based care to prevent postsepsis morbidity and mortality for their non‐ICU patients need to heighten their index of suspicion when caring for an infected patient and appreciate that many severe sepsis patients may not fit neatly into traditional sepsis treatment algorithms.

Studies characterizing severe sepsis in the ICU setting indicate a predominance of pulmonary infections and respiratory failure with occurrence rates of 74% to 95% and 54% to 61%, respectively.[3, 12, 13] Given that either shock or pulmonary dysfunction is often required for admission to many ICUs, it is perhaps not surprising that these rates are dramatically different on the general medicine ward, with a relative scarcity of pulmonary infections (14%) and respiratory dysfunction (30%). Instead, genitourinary infections were noted in 41% (95% CI, 29%‐54%) of the cases, in contrast to the rates of genitourinary infections in ICU patients with severe sepsis, which have rates of 5.4% to 9.1%.[3, 10] Likely as a result of this, a Gram‐negative predominance is noted in the associated microbiology. Furthermore, our study indicates that C difficile and vancomycin‐resistant Enterococcus (VRE) species appear to represent an emerging cause of severe sepsis on the general medicine wards, as they have not been noted to be causative micro‐organisms in previous studies of sepsis. This is concordant with other studies showing increases in incidence and severity of disease for C difficile as well as VRE.[14, 15]

Previous epidemiologic studies of severe sepsis originating outside the ICU are lacking, but some work has been done. One study on the epidemiology of sepsis both with and without organ dysfunction aggregated all hospitalized patients and included those both admitted to the general medicine wards and directly to the ICU.[7] Similar to our study, this study also found a predominance of Gram‐negative causative organisms, as well as comparable in‐hospital mortality rates (12.8% vs 13%). Additionally, genitourinary infections were noted in 20% of the patients, notably higher than rates reported to have been found in patients with severe sepsis in the ICU, but not the magnitude found in our study, perhaps as a result of the combined ICU‐ward population studied. A similar high prevalence of genitourinary infections was also noted in a recent administrative data‐based study of emergency medical services‐transported patients with severe sepsis, half of whom required intensive care during their hospitalization.[16]

Our study is unique in that it focuses on severe sepsis in patients, commonly cared for by hospitalists, who were admitted to the general medical ward, and uses patient level data to elucidate more characteristics of the defining organ dysfunction. Furthermore, our results suggest that severe sepsis was poorly documented in this setting, indicating a potential impact on billing, coding, case mix index, and hospital mortality statistics that rely on very specific wording, as well as a possible need for increased awareness among hospitalists. Without this awareness, an opportunity may be missed for improved patient care via specific sepsis‐targeted measures,[13, 17, 18] including more aggressive resuscitative measures[19] or intensive physical and occupational therapy interventions aimed at impacting the cognitive and functional debilities[20] that result from severe sepsis. Highlighting this growing need to better assist clinicians assess the severity of septic patients and recognize these complex cases on the general medicine wards, 1 recent study evaluated the fitness of several clinical disease‐severity scoring systems for patients with sepsis in general internal medicine departments.[21] Perhaps with the help of tools such as these, which are being piloted in some hospitals, the care of this growing population can be enhanced.

Our study has a number of limitations that should be kept in mind. First, this is a single center study performed at an academic tertiary care center with a relatively high incidence of immunosuppression, which may influence the spectrum of infecting organisms. Our center also has a relatively large, closed‐model ICU, which often operates at near capacity, potentially affecting the severity of our non‐ICU population. Second, although we screened a large number of patients, as necessitated by our intensive and detailed review of clinical information, our sample size with hospitalist‐validated severe sepsis is relatively small. With this small sample size, less prevalent infections, patient characteristics, and organ dysfunctions may by chance have been under or over‐represented, and one could expect some variance in the occurrence rates of organ system dysfunction and infection rates by sampling error alone. Further larger scale studies are warranted to confirm these data and their generalizability. Third, the data necessary to calculate sequential organ failure assessment or multiple organ dysfunction score were not collected. This may limit the ability to directly compare the organ dysfunction noted in this study with others. Additionally, given the ICC definitions of organ dysfunction, some of the organ dysfunction noted, particularly for neurological dysfunction, was reliant on subjective clinical findings documented in the record. Finally, we relied on the lack of specific terminology to indicate a lack of documentation of sepsis, which does not necessarily indicate a lack of recognition or undertreatment of this condition. However, these limitations are offset by the strengths of this study, including the patient‐level medical record validation of severe sepsis by trained hospitalist physicians, high kappa statistic, and strict application of guideline‐based definitions.

This work has important implications for both clinicians and for future research on severe sepsis. The results suggest that severe sepsis may be quite common outside the ICU, and that patients presenting with this condition who are admitted to general medical wards are not routinely characterized by the profound hypoxemia and refractory shock of iconic cases. Certainly, further study looking at larger numbers of cases is needed to better understand the specifics and nuances of this important topic as well as to further evaluate clinicians' ability to recognize and treat such patients in this setting. Furthermore, future research on the treatment of severe sepsis, including both antimicrobials and disease‐modifying agents (eg, anti‐inflammatories) must continue to include and even focus on this large population of non‐ICU patients with severe sepsis, as the risk/benefit ratios of such potential treatments may vary with severity of illness.

In conclusion, severe sepsis was commonly found in patients admitted on the general medicine wards. The epidemiology of the infections and resultant organ dysfunction appears to differ from that found in the ICU. More studies are needed to provide a deeper understanding of this disease process, as this will enable clinicians to better recognize and treat patients thus afflicted, no matter the setting.

The International Consensus Conference (ICC) for sepsis defines severe sepsis as an infection leading to acute organ dysfunction.[1, 2] Severe sepsis afflicts over 1 million patients each year in Medicare alone, and is substantially more common among older Americans than acute myocardial infarction.[3, 4, 5] Recently, the Agency for Healthcare Research and Quality identified severe sepsis as the single most expensive cause of hospitalization in the United States.[6] The incidence of severe sepsis continues to rise.[4, 5]

Severe sepsis is often mischaracterized as a diagnosis cared for primarily in the intensive care unit (ICU). Yet, studies indicate that only 32% to 50% of patients with severe sepsis require ICU care, leaving the majority on the general care wards.[7, 8] These studies also reveal mortality rates of 26% to 30% among patients with severe sepsis who are not admitted to an ICU compared to 11% to 33% in the ICU.[7, 8]

Although a number of epidemiologic and interventional studies have focused on severe sepsis in the ICU,[3, 9, 10] much less is known about patients cared for on the general medicine wards. Without this information, clinicians cannot make informed choices about important management decisions such as targeted diagnostic testing, empirical antimicrobials, and other therapies. To this end, we sought to further characterize the infectious etiologies and resultant organ system dysfunctions in the subset of patients with severe sepsis admitted to non‐ICU medical services at a tertiary academic medical center.

METHODS

RESULTS

Of 23,288 hospitalizations examined from 2009 through 2010, the ICD‐9based automated screen for severe sepsis was positive for 3,146 (14 %). A random sample of 111 medical records, of which 92 had screened positive for severe sepsis and 19 had screened negative, was reviewed in detail. After review by the hospitalists, 64 of these 111 hospitalizations were judged to have severe sepsis, 61 of the 92 screened positive cases (66%), and 3 of the 19 screened negative cases (16%). The 3 reviewers had a kappa of 0.70, indicating good agreement.

Characteristics of the 64 patients with severe sepsis are shown in Table 2. The mean age was 63 years old (standard deviation [SD]=17.7), and 41% were male. The mean length of stay was 13.7 days (SD=20.8). Thirty‐nine percent (95% CI, 27%‐52%) of patients (25/64) were immunosuppressed. Of patients initially admitted to the general medical ward, 25% (16/64; 95% CI, 15%‐37%) ultimately required ICU care during their admission. The overall in‐hospital mortality rate was 13% (8/64; 95% CI, 6%‐23%). Immunosuppressed patients had a mortality rate of 20% and nonimmunosuppressed patients had a mortality rate of 8%. Only 47% (30/64; 95% CI, 34%‐60%) of the medical records had explicit clinician documentation of severe sepsis.

The most common site of infection was found to be the genitourinary system, occurring in 41% (26/64; 95% CI, 29%‐54%) of patients (Table 3). Pulmonary and intra‐abdominal sites were also common, accounting for 14% (95% CI, 6.6%‐25%) and 13% (95% CI, 5.6%‐23%) of sites, respectively. An infecting organism was identified by culture in 66% (42/64; 95% CI, 53%‐77%) of case patients with specific pathogens listed in Table 4. Among patients with positive culture results, the majority grew Gram‐negative organisms (57%; 95% CI, 41%‐72%). Non‐Clostridium difficile Gram‐positive organisms were also prominent and identified in 48% (95% CI, 32%‐64%) of positive cultures. Candida was less common (12%, 95% CI, 4.0%‐26%). Fourteen cases (22%, 95% CI, 10%‐30%) had 2 or more concomitant infectious pathogens.

All 64 patients had at least 1 organ dysfunction, as required by the ICC definition of severe sepsis. Organ dysfunction in 2 or more organ systems occurred in 77% (95% CI, 64%‐86%) of the cases (49/64). The incidence for each organ system dysfunction is presented in Table 5, as well as its relationship to both mortality and ICU admission. The most common organ system dysfunctions were found to be cardiovascular (hypotension) and renal dysfunction occurring in 66% and 64% of the cases, respectively. In this non‐ICU population, pulmonary dysfunction occurred in 30% of cases, but was frequently associated with transfer to the ICU, as 63% of the patients with pulmonary failure required ICU care. Patients with more organ systems affected were more likely to be transferred to the ICU and to die.

DISCUSSION

Severe sepsis was common among patients admitted to the general medical ward in this tertiary care center. Our patient cohort differed in important ways from previously described typical cases of severe sepsis among ICU populations. Severe sepsis on the general medical wards was more commonly associated with Gram‐negative pathogens in the setting of genitourinary tract infections. This is in contrast to Gram‐positive organisms and respiratory tract infections, which are more common in the ICU.[3, 10] Renal and cardiac dysfunction were commonly observed organ failures, whereas in the ICU, severe sepsis has been reported to more likely involve respiratory failure. These results suggest that hospitalists seeking to provide evidence‐based care to prevent postsepsis morbidity and mortality for their non‐ICU patients need to heighten their index of suspicion when caring for an infected patient and appreciate that many severe sepsis patients may not fit neatly into traditional sepsis treatment algorithms.

Studies characterizing severe sepsis in the ICU setting indicate a predominance of pulmonary infections and respiratory failure with occurrence rates of 74% to 95% and 54% to 61%, respectively.[3, 12, 13] Given that either shock or pulmonary dysfunction is often required for admission to many ICUs, it is perhaps not surprising that these rates are dramatically different on the general medicine ward, with a relative scarcity of pulmonary infections (14%) and respiratory dysfunction (30%). Instead, genitourinary infections were noted in 41% (95% CI, 29%‐54%) of the cases, in contrast to the rates of genitourinary infections in ICU patients with severe sepsis, which have rates of 5.4% to 9.1%.[3, 10] Likely as a result of this, a Gram‐negative predominance is noted in the associated microbiology. Furthermore, our study indicates that C difficile and vancomycin‐resistant Enterococcus (VRE) species appear to represent an emerging cause of severe sepsis on the general medicine wards, as they have not been noted to be causative micro‐organisms in previous studies of sepsis. This is concordant with other studies showing increases in incidence and severity of disease for C difficile as well as VRE.[14, 15]

Previous epidemiologic studies of severe sepsis originating outside the ICU are lacking, but some work has been done. One study on the epidemiology of sepsis both with and without organ dysfunction aggregated all hospitalized patients and included those both admitted to the general medicine wards and directly to the ICU.[7] Similar to our study, this study also found a predominance of Gram‐negative causative organisms, as well as comparable in‐hospital mortality rates (12.8% vs 13%). Additionally, genitourinary infections were noted in 20% of the patients, notably higher than rates reported to have been found in patients with severe sepsis in the ICU, but not the magnitude found in our study, perhaps as a result of the combined ICU‐ward population studied. A similar high prevalence of genitourinary infections was also noted in a recent administrative data‐based study of emergency medical services‐transported patients with severe sepsis, half of whom required intensive care during their hospitalization.[16]

Our study is unique in that it focuses on severe sepsis in patients, commonly cared for by hospitalists, who were admitted to the general medical ward, and uses patient level data to elucidate more characteristics of the defining organ dysfunction. Furthermore, our results suggest that severe sepsis was poorly documented in this setting, indicating a potential impact on billing, coding, case mix index, and hospital mortality statistics that rely on very specific wording, as well as a possible need for increased awareness among hospitalists. Without this awareness, an opportunity may be missed for improved patient care via specific sepsis‐targeted measures,[13, 17, 18] including more aggressive resuscitative measures[19] or intensive physical and occupational therapy interventions aimed at impacting the cognitive and functional debilities[20] that result from severe sepsis. Highlighting this growing need to better assist clinicians assess the severity of septic patients and recognize these complex cases on the general medicine wards, 1 recent study evaluated the fitness of several clinical disease‐severity scoring systems for patients with sepsis in general internal medicine departments.[21] Perhaps with the help of tools such as these, which are being piloted in some hospitals, the care of this growing population can be enhanced.

Our study has a number of limitations that should be kept in mind. First, this is a single center study performed at an academic tertiary care center with a relatively high incidence of immunosuppression, which may influence the spectrum of infecting organisms. Our center also has a relatively large, closed‐model ICU, which often operates at near capacity, potentially affecting the severity of our non‐ICU population. Second, although we screened a large number of patients, as necessitated by our intensive and detailed review of clinical information, our sample size with hospitalist‐validated severe sepsis is relatively small. With this small sample size, less prevalent infections, patient characteristics, and organ dysfunctions may by chance have been under or over‐represented, and one could expect some variance in the occurrence rates of organ system dysfunction and infection rates by sampling error alone. Further larger scale studies are warranted to confirm these data and their generalizability. Third, the data necessary to calculate sequential organ failure assessment or multiple organ dysfunction score were not collected. This may limit the ability to directly compare the organ dysfunction noted in this study with others. Additionally, given the ICC definitions of organ dysfunction, some of the organ dysfunction noted, particularly for neurological dysfunction, was reliant on subjective clinical findings documented in the record. Finally, we relied on the lack of specific terminology to indicate a lack of documentation of sepsis, which does not necessarily indicate a lack of recognition or undertreatment of this condition. However, these limitations are offset by the strengths of this study, including the patient‐level medical record validation of severe sepsis by trained hospitalist physicians, high kappa statistic, and strict application of guideline‐based definitions.

This work has important implications for both clinicians and for future research on severe sepsis. The results suggest that severe sepsis may be quite common outside the ICU, and that patients presenting with this condition who are admitted to general medical wards are not routinely characterized by the profound hypoxemia and refractory shock of iconic cases. Certainly, further study looking at larger numbers of cases is needed to better understand the specifics and nuances of this important topic as well as to further evaluate clinicians' ability to recognize and treat such patients in this setting. Furthermore, future research on the treatment of severe sepsis, including both antimicrobials and disease‐modifying agents (eg, anti‐inflammatories) must continue to include and even focus on this large population of non‐ICU patients with severe sepsis, as the risk/benefit ratios of such potential treatments may vary with severity of illness.

In conclusion, severe sepsis was commonly found in patients admitted on the general medicine wards. The epidemiology of the infections and resultant organ dysfunction appears to differ from that found in the ICU. More studies are needed to provide a deeper understanding of this disease process, as this will enable clinicians to better recognize and treat patients thus afflicted, no matter the setting.

The International Consensus Conference (ICC) for sepsis defines severe sepsis as an infection leading to acute organ dysfunction.[1, 2] Severe sepsis afflicts over 1 million patients each year in Medicare alone, and is substantially more common among older Americans than acute myocardial infarction.[3, 4, 5] Recently, the Agency for Healthcare Research and Quality identified severe sepsis as the single most expensive cause of hospitalization in the United States.[6] The incidence of severe sepsis continues to rise.[4, 5]

Severe sepsis is often mischaracterized as a diagnosis cared for primarily in the intensive care unit (ICU). Yet, studies indicate that only 32% to 50% of patients with severe sepsis require ICU care, leaving the majority on the general care wards.[7, 8] These studies also reveal mortality rates of 26% to 30% among patients with severe sepsis who are not admitted to an ICU compared to 11% to 33% in the ICU.[7, 8]

Although a number of epidemiologic and interventional studies have focused on severe sepsis in the ICU,[3, 9, 10] much less is known about patients cared for on the general medicine wards. Without this information, clinicians cannot make informed choices about important management decisions such as targeted diagnostic testing, empirical antimicrobials, and other therapies. To this end, we sought to further characterize the infectious etiologies and resultant organ system dysfunctions in the subset of patients with severe sepsis admitted to non‐ICU medical services at a tertiary academic medical center.

METHODS

RESULTS

Of 23,288 hospitalizations examined from 2009 through 2010, the ICD‐9based automated screen for severe sepsis was positive for 3,146 (14 %). A random sample of 111 medical records, of which 92 had screened positive for severe sepsis and 19 had screened negative, was reviewed in detail. After review by the hospitalists, 64 of these 111 hospitalizations were judged to have severe sepsis, 61 of the 92 screened positive cases (66%), and 3 of the 19 screened negative cases (16%). The 3 reviewers had a kappa of 0.70, indicating good agreement.

Characteristics of the 64 patients with severe sepsis are shown in Table 2. The mean age was 63 years old (standard deviation [SD]=17.7), and 41% were male. The mean length of stay was 13.7 days (SD=20.8). Thirty‐nine percent (95% CI, 27%‐52%) of patients (25/64) were immunosuppressed. Of patients initially admitted to the general medical ward, 25% (16/64; 95% CI, 15%‐37%) ultimately required ICU care during their admission. The overall in‐hospital mortality rate was 13% (8/64; 95% CI, 6%‐23%). Immunosuppressed patients had a mortality rate of 20% and nonimmunosuppressed patients had a mortality rate of 8%. Only 47% (30/64; 95% CI, 34%‐60%) of the medical records had explicit clinician documentation of severe sepsis.

The most common site of infection was found to be the genitourinary system, occurring in 41% (26/64; 95% CI, 29%‐54%) of patients (Table 3). Pulmonary and intra‐abdominal sites were also common, accounting for 14% (95% CI, 6.6%‐25%) and 13% (95% CI, 5.6%‐23%) of sites, respectively. An infecting organism was identified by culture in 66% (42/64; 95% CI, 53%‐77%) of case patients with specific pathogens listed in Table 4. Among patients with positive culture results, the majority grew Gram‐negative organisms (57%; 95% CI, 41%‐72%). Non‐Clostridium difficile Gram‐positive organisms were also prominent and identified in 48% (95% CI, 32%‐64%) of positive cultures. Candida was less common (12%, 95% CI, 4.0%‐26%). Fourteen cases (22%, 95% CI, 10%‐30%) had 2 or more concomitant infectious pathogens.

All 64 patients had at least 1 organ dysfunction, as required by the ICC definition of severe sepsis. Organ dysfunction in 2 or more organ systems occurred in 77% (95% CI, 64%‐86%) of the cases (49/64). The incidence for each organ system dysfunction is presented in Table 5, as well as its relationship to both mortality and ICU admission. The most common organ system dysfunctions were found to be cardiovascular (hypotension) and renal dysfunction occurring in 66% and 64% of the cases, respectively. In this non‐ICU population, pulmonary dysfunction occurred in 30% of cases, but was frequently associated with transfer to the ICU, as 63% of the patients with pulmonary failure required ICU care. Patients with more organ systems affected were more likely to be transferred to the ICU and to die.

DISCUSSION

Severe sepsis was common among patients admitted to the general medical ward in this tertiary care center. Our patient cohort differed in important ways from previously described typical cases of severe sepsis among ICU populations. Severe sepsis on the general medical wards was more commonly associated with Gram‐negative pathogens in the setting of genitourinary tract infections. This is in contrast to Gram‐positive organisms and respiratory tract infections, which are more common in the ICU.[3, 10] Renal and cardiac dysfunction were commonly observed organ failures, whereas in the ICU, severe sepsis has been reported to more likely involve respiratory failure. These results suggest that hospitalists seeking to provide evidence‐based care to prevent postsepsis morbidity and mortality for their non‐ICU patients need to heighten their index of suspicion when caring for an infected patient and appreciate that many severe sepsis patients may not fit neatly into traditional sepsis treatment algorithms.

Studies characterizing severe sepsis in the ICU setting indicate a predominance of pulmonary infections and respiratory failure with occurrence rates of 74% to 95% and 54% to 61%, respectively.[3, 12, 13] Given that either shock or pulmonary dysfunction is often required for admission to many ICUs, it is perhaps not surprising that these rates are dramatically different on the general medicine ward, with a relative scarcity of pulmonary infections (14%) and respiratory dysfunction (30%). Instead, genitourinary infections were noted in 41% (95% CI, 29%‐54%) of the cases, in contrast to the rates of genitourinary infections in ICU patients with severe sepsis, which have rates of 5.4% to 9.1%.[3, 10] Likely as a result of this, a Gram‐negative predominance is noted in the associated microbiology. Furthermore, our study indicates that C difficile and vancomycin‐resistant Enterococcus (VRE) species appear to represent an emerging cause of severe sepsis on the general medicine wards, as they have not been noted to be causative micro‐organisms in previous studies of sepsis. This is concordant with other studies showing increases in incidence and severity of disease for C difficile as well as VRE.[14, 15]

Previous epidemiologic studies of severe sepsis originating outside the ICU are lacking, but some work has been done. One study on the epidemiology of sepsis both with and without organ dysfunction aggregated all hospitalized patients and included those both admitted to the general medicine wards and directly to the ICU.[7] Similar to our study, this study also found a predominance of Gram‐negative causative organisms, as well as comparable in‐hospital mortality rates (12.8% vs 13%). Additionally, genitourinary infections were noted in 20% of the patients, notably higher than rates reported to have been found in patients with severe sepsis in the ICU, but not the magnitude found in our study, perhaps as a result of the combined ICU‐ward population studied. A similar high prevalence of genitourinary infections was also noted in a recent administrative data‐based study of emergency medical services‐transported patients with severe sepsis, half of whom required intensive care during their hospitalization.[16]

Our study is unique in that it focuses on severe sepsis in patients, commonly cared for by hospitalists, who were admitted to the general medical ward, and uses patient level data to elucidate more characteristics of the defining organ dysfunction. Furthermore, our results suggest that severe sepsis was poorly documented in this setting, indicating a potential impact on billing, coding, case mix index, and hospital mortality statistics that rely on very specific wording, as well as a possible need for increased awareness among hospitalists. Without this awareness, an opportunity may be missed for improved patient care via specific sepsis‐targeted measures,[13, 17, 18] including more aggressive resuscitative measures[19] or intensive physical and occupational therapy interventions aimed at impacting the cognitive and functional debilities[20] that result from severe sepsis. Highlighting this growing need to better assist clinicians assess the severity of septic patients and recognize these complex cases on the general medicine wards, 1 recent study evaluated the fitness of several clinical disease‐severity scoring systems for patients with sepsis in general internal medicine departments.[21] Perhaps with the help of tools such as these, which are being piloted in some hospitals, the care of this growing population can be enhanced.

Our study has a number of limitations that should be kept in mind. First, this is a single center study performed at an academic tertiary care center with a relatively high incidence of immunosuppression, which may influence the spectrum of infecting organisms. Our center also has a relatively large, closed‐model ICU, which often operates at near capacity, potentially affecting the severity of our non‐ICU population. Second, although we screened a large number of patients, as necessitated by our intensive and detailed review of clinical information, our sample size with hospitalist‐validated severe sepsis is relatively small. With this small sample size, less prevalent infections, patient characteristics, and organ dysfunctions may by chance have been under or over‐represented, and one could expect some variance in the occurrence rates of organ system dysfunction and infection rates by sampling error alone. Further larger scale studies are warranted to confirm these data and their generalizability. Third, the data necessary to calculate sequential organ failure assessment or multiple organ dysfunction score were not collected. This may limit the ability to directly compare the organ dysfunction noted in this study with others. Additionally, given the ICC definitions of organ dysfunction, some of the organ dysfunction noted, particularly for neurological dysfunction, was reliant on subjective clinical findings documented in the record. Finally, we relied on the lack of specific terminology to indicate a lack of documentation of sepsis, which does not necessarily indicate a lack of recognition or undertreatment of this condition. However, these limitations are offset by the strengths of this study, including the patient‐level medical record validation of severe sepsis by trained hospitalist physicians, high kappa statistic, and strict application of guideline‐based definitions.

This work has important implications for both clinicians and for future research on severe sepsis. The results suggest that severe sepsis may be quite common outside the ICU, and that patients presenting with this condition who are admitted to general medical wards are not routinely characterized by the profound hypoxemia and refractory shock of iconic cases. Certainly, further study looking at larger numbers of cases is needed to better understand the specifics and nuances of this important topic as well as to further evaluate clinicians' ability to recognize and treat such patients in this setting. Furthermore, future research on the treatment of severe sepsis, including both antimicrobials and disease‐modifying agents (eg, anti‐inflammatories) must continue to include and even focus on this large population of non‐ICU patients with severe sepsis, as the risk/benefit ratios of such potential treatments may vary with severity of illness.

In conclusion, severe sepsis was commonly found in patients admitted on the general medicine wards. The epidemiology of the infections and resultant organ dysfunction appears to differ from that found in the ICU. More studies are needed to provide a deeper understanding of this disease process, as this will enable clinicians to better recognize and treat patients thus afflicted, no matter the setting.

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.

Deep venous thrombosis (DVT) and pulmonary embolism (PE), collectively referred to as venous thromboembolism (VTE), are common events in hospitalized patients and result in significant morbidity and mortality. Often silent and frequently unexpected, VTE is preventable. Accordingly, the American College of Chest Physicians recommends that pharmacologic prophylaxis be given to acutely ill medical patients admitted to the hospital with congestive heart failure or severe respiratory disease, or to patients who are confined to bed who have additional risk factors, such as cancer or previous VTE.1 Three recent meta‐analyses24 demonstrated significant reductions in VTE in general medicine patients with pharmacologic prophylaxis. Recently the National Quality Forum advocated that hospitals evaluate each patient upon admission and regularly thereafter, for the risk of developing DVT/VTE and utilize clinically appropriate methods to prevent DVT/VTE.5

Despite recommendations for prophylaxis, multiple studies demonstrate utilization in <50% of at‐risk general medical patients.68 Physicians' lack of awareness may partially explain this underutilization, but other likely factors include physicians' questions about the clinical importance of the outcome (eg, some studies have shown reductions primarily in asymptomatic distal DVT), doubt regarding the best form of prophylaxis (ie, unfractionated heparin [UFH] vs. low molecular weight heparin [LMWH]), uncertainty regarding optimal dosing regimens, and comparable uncertainty regarding which patients have sufficiently high risk for VTE to outweigh the risks of anticoagulation.

We undertook the current meta‐analysis to address questions about thromboembolism prevention in general medicine patients. Does pharmacologic prophylaxis prevent clinically relevant events? Is LMWH or UFH preferable in terms of either efficacy or safety?

MATERIALS AND METHODS

RESULTS

Study Identification and Selection

The computerized literature search resulted in 5284 articles. Three additional citations were found by review of bibliographies. No additional trials were identified from reviews of abstracts from national meetings. Representatives from the 3 pharmaceutical companies reported no knowledge of additional published or unpublished data. Of the 5287 studies identified by the search, 14 studies met all eligibility criteria (Figure 1).

Figure 1

UFH or LMWH vs. Control

DVT

Across 7 trials comparing either UFH or LMWH to control, heparin products significantly decreased the risk of all DVT (RR = 0.55; 95% CI: 0.36‐0.83) (Figure 2A). When stratified by methodological quality, 5 trials16, 2225 with Jadad scores of 3 or higher showed an RR reduction of 0.53 (95% CI: 0.38‐0.72) in reducing all DVT. All of the higher‐quality trials compared LMWH to placebo. Across 4 trials that reported data for symptomatic DVT there was a nonsignificant reduction in RR compared with placebo (RR = 0.73; 95% CI: 0.45‐1.16) (Figure 2B). Only 2 trials24, 25 (both LMWH trials) reported results for proximal DVT and demonstrated significant benefit of prophylaxis with a pooled RR of 0.46 (95% CI: 0.31‐0.69) (Figure 2C).

Figure 2

PE

Across 7 trials comparing either UFH or LMWH to control, heparin products significantly decreased the risk of PE (RR = 0.70; 95% CI: 0.53‐0.93) (Figure 3A). The 5 trials16, 2225 with Jadad scores of 3 or greater showed a similar relative risk reduction, but the result was no longer statistically significant (RR = 0.56; 95% CI: 0.31‐1.02). Two of the trials16, 21 relied solely on the results of autopsy to diagnose PE, which may have given rise to chance differences in detection due to generally low autopsy rates. Eliminating these 2 studies from the analysis resulted in loss of statistical significance for the reduction in risk for PE (RR = 0.48; 95% CI: 0.20‐1.15).

Figure 3

Death

Seven trials16, 2025 comparing either UFH or LMWH to control examined the impact of pharmacologic prophylaxis on death and found no significant difference between treated and untreated patients across all trials (RR = 0.92; 95% CI: 0.82‐1.03) and those limited to studies with Jadad scores of 3 or higher (RR = 0.97; 95% CI: 0.80‐1.17).

Complications

We evaluated adverse events of heparin products used for prophylaxis and whether there were differences between UFH and LMWH. Reporting of complications was not uniform from study to study, making pooling more difficult. However, we were able to abstract data on any bleeding, major bleeding, and thrombocytopenia from several studies. In 5 studies15, 16, 2325 of either UFH or LMWH vs. control, a significantly increased risk of any bleeding (RR = 1.54; 95% CI: 1.15‐2.06) (Figure 4A) was found. When only major bleeding was evaluated, no statistically significant difference was noted (RR = 1.20; 95% CI: 0.55‐2.58) (Figure 4B). In 4 trials16, 22, 24, 25 the occurrence of thrombocytopenia was not significantly different when comparing UFH or LMWH to control (RR = 0.92; 95% CI: 0.46‐1.86).

Figure 4

When LMWH was compared to UFH in 4 trials,14, 17, 18, 27 a nonsignificant trend toward a decrease in any bleeding was found in the LMWH group (RR = 0.72; 95% CI: 0.44‐1.16) (Figure 5A). A similar trend was seen favoring LMWH in rates of major bleeding (RR = 0.57; 95% CI: 0.25‐1.32) (Figure 5B). Neither trend was statistically significant. Three trials comparing LMWH to UFH reported on thrombocytopenia17, 18, 27 with no significant difference noted (RR = 0.52; 95% CI: 0.06‐4.18).

Figure 5

DISCUSSION

When compared to control, LMWH or UFH decreased the risk of all DVT by 45% (RR = 0.55; 95% CI: 0.36‐0.83) and proximal DVT by 54% (RR = 0.46; 95% CI: 0.31‐0.69). PE was also decreased by 30% (RR = 0.70; 95% CI: 0.53‐0.93). Of note, when prophylaxis was compared with placebo all of the high‐quality studies showing a benefit were done using LMWH. The benefits of prophylaxis occurred at the cost of a 54% increased overall risk of bleeding (RR = 1.54; 95% CI 1.15‐2.06). However, the risk of major bleeding was not significantly increased. We did not find a mortality benefit to pharmacologic thromboembolism prophylaxis.

When comparing UFH to LMWH, we noted no difference in all DVT, symptomatic DVT, proximal DVT, PE, or death. While there was a trend toward less bleeding with LMWH, this was not statistically significant.

Taken in aggregate, our findings are in agreement with previous published meta‐analyses reporting net benefit for thromboembolism prophylaxis in medical patients.24, 22, 28, 29 Our meta‐analysis has several methodological strengths over the prior studies, including a comprehensive search of both the published and unpublished literature and assessment of the relationship between methodological quality of included trials and reported benefit. In contrast to previous reviews, our analysis highlights several limitations of the current evidence.

First, many of the studies are older, with predicted lengths of stay of greater than 1 week. The 8‐13‐day range of treatment duration we found in this study is longer than the average length of stay in today's hospitals. Second, there is variability in the diagnostic tests used to diagnose DVT, as well as variation in the definition of DVT among studies. Studies using fibrinogen uptake scanning reported rates of DVT as high as 26%15 while studies using venography reported DVT rates of almost 15% in the placebo arm.24 These rates are higher than most physicians' routine practice. One reason for this discrepancy is most studies did not distinguish below‐the‐knee DVT from more clinically relevant above‐the‐knee DVT. Systematic reviews of medical and surgical patients have found rates of proximal propagation from 0% to 29% in untreated patients.30, 31 Though controversial, below‐the‐knee DVT is believed less morbid than proximal DVT or symptomatic DVT. We addressed this by focusing specifically on clinically relevant endpoints of proximal and symptomatic DVT. When we restricted our analysis to proximal DVT we found a 54% RR reduction in 2 pooled trials of LMWH compared to placebo. In pooled analyses symptomatic DVT was not affected by prophylaxis. When compared head‐to‐head there were no differences between LMWH and UFH for proximal DVT or symptomatic DVT.

When considering PE, the utilization of autopsy as the sole diagnostic method in 2 large trials16, 21 is particularly problematic. In the trial by Garlund,21 the mortality rate was 5.4%, with an autopsy rate of 60.1%. Similarly, in the trial by Mahe et al.,16 the mortality rate was 10%, with an autopsy rate of 49%. Given the low absolute number of deaths and substantial proportion of decedents without autopsy, the potential for chance to produce an imbalance in detection of PE is high in these studies. When we excluded these 2 trials, we found that PE was no longer reduced to a statistically significant degree by prophylaxis. Loss of significance for PE in 2 sensitivity analyses (when excluding studies of lower quality, or using autopsy as a sole diagnostic study) is problematic and calls into question the true benefit of prophylaxis for prevention of PE.

Another limitation of the current literature centers on the variability of dosing used. We pooled trials of UFH whether given BID or TID. Given the small number of trials we did not do sensitivity analyses by dosage. A recent meta‐analysis3 found both doses are efficacious, while a recent review article32 suggested superiority of TID dosing. We believe the available literature does not clearly address this issue. Regarding comparisons of LMWH to UFH, dosing variability was also noted. The trial by Bergmann and Neuhart27 used enoxaparin 20 mg per day and found similar efficacy to UFH BID, while the Samama et al.24 trial found enoxaparin 20 mg per day no more efficacious than placebo. While the literature does not clearly define a best dose, we believe enoxaparin doses lower than 40 mg daily do not reflect the standard of care.

An additional limitation of the literature is publication bias. We assessed the possibility of publication bias by a variety of means. We did find statistical evidence of publication bias for the outcome of PE when prophylaxis was compared to control. Importantly, two meta‐analyses2, 4 on thromboembolism prophylaxis for general medicine patients suggested publication bias is present and our finding supports this conclusion. While no test for publication bias is foolproof, the best protection against publication bias, which we pursued in our study, consists of a thorough search for unpublished studies, including a search of conference proceedings, contact with experts in the field, and manufacturers of LMWH.

A final limitation of the current literature centers on risk assessment. All of the trials in this meta‐analysis included patients with an elevated level of risk. Unfortunately, risk was not clearly defined in many studies, and there was no minimum level of risk between trials. While immobility, age, and length of stay were reported for most studies, other risk factors such as personal history of thromboembolism and malignancy were not uniformly reported. Based on our analysis we are not confident our results can be extrapolated to all general medicine patients.

In conclusion, we found good evidence that pharmacologic prophylaxis significantly decreases the risk of all DVT and proximal DVT in at‐risk general medical patients. However, only LMWH was shown to prevent proximal DVT. We found inconclusive evidence that prophylaxis prevents PE. When compared directly we did not find clear superiority between UFH and LMWH, though several limitations of the current literature hamper decision‐making. Given the lower cost, it may seem justified to use UFH. However, there are other practical issues, such as the fact that LMWH is given once daily, and so potentially preferred by patients and more efficient for nurses. All of these results pertain to patients with elevated risk. While we did not find significant safety concerns with prophylaxis we do not know if these results can be extrapolated to lower‐risk patients. We believe that recommending widespread prophylaxis of all general medicine patients requires additional evidence about appropriate patient selection.

Deep venous thrombosis (DVT) and pulmonary embolism (PE), collectively referred to as venous thromboembolism (VTE), are common events in hospitalized patients and result in significant morbidity and mortality. Often silent and frequently unexpected, VTE is preventable. Accordingly, the American College of Chest Physicians recommends that pharmacologic prophylaxis be given to acutely ill medical patients admitted to the hospital with congestive heart failure or severe respiratory disease, or to patients who are confined to bed who have additional risk factors, such as cancer or previous VTE.1 Three recent meta‐analyses24 demonstrated significant reductions in VTE in general medicine patients with pharmacologic prophylaxis. Recently the National Quality Forum advocated that hospitals evaluate each patient upon admission and regularly thereafter, for the risk of developing DVT/VTE and utilize clinically appropriate methods to prevent DVT/VTE.5

Despite recommendations for prophylaxis, multiple studies demonstrate utilization in <50% of at‐risk general medical patients.68 Physicians' lack of awareness may partially explain this underutilization, but other likely factors include physicians' questions about the clinical importance of the outcome (eg, some studies have shown reductions primarily in asymptomatic distal DVT), doubt regarding the best form of prophylaxis (ie, unfractionated heparin [UFH] vs. low molecular weight heparin [LMWH]), uncertainty regarding optimal dosing regimens, and comparable uncertainty regarding which patients have sufficiently high risk for VTE to outweigh the risks of anticoagulation.

We undertook the current meta‐analysis to address questions about thromboembolism prevention in general medicine patients. Does pharmacologic prophylaxis prevent clinically relevant events? Is LMWH or UFH preferable in terms of either efficacy or safety?

MATERIALS AND METHODS

RESULTS

Study Identification and Selection

The computerized literature search resulted in 5284 articles. Three additional citations were found by review of bibliographies. No additional trials were identified from reviews of abstracts from national meetings. Representatives from the 3 pharmaceutical companies reported no knowledge of additional published or unpublished data. Of the 5287 studies identified by the search, 14 studies met all eligibility criteria (Figure 1).

Figure 1

UFH or LMWH vs. Control

DVT

Across 7 trials comparing either UFH or LMWH to control, heparin products significantly decreased the risk of all DVT (RR = 0.55; 95% CI: 0.36‐0.83) (Figure 2A). When stratified by methodological quality, 5 trials16, 2225 with Jadad scores of 3 or higher showed an RR reduction of 0.53 (95% CI: 0.38‐0.72) in reducing all DVT. All of the higher‐quality trials compared LMWH to placebo. Across 4 trials that reported data for symptomatic DVT there was a nonsignificant reduction in RR compared with placebo (RR = 0.73; 95% CI: 0.45‐1.16) (Figure 2B). Only 2 trials24, 25 (both LMWH trials) reported results for proximal DVT and demonstrated significant benefit of prophylaxis with a pooled RR of 0.46 (95% CI: 0.31‐0.69) (Figure 2C).

Figure 2

PE

Across 7 trials comparing either UFH or LMWH to control, heparin products significantly decreased the risk of PE (RR = 0.70; 95% CI: 0.53‐0.93) (Figure 3A). The 5 trials16, 2225 with Jadad scores of 3 or greater showed a similar relative risk reduction, but the result was no longer statistically significant (RR = 0.56; 95% CI: 0.31‐1.02). Two of the trials16, 21 relied solely on the results of autopsy to diagnose PE, which may have given rise to chance differences in detection due to generally low autopsy rates. Eliminating these 2 studies from the analysis resulted in loss of statistical significance for the reduction in risk for PE (RR = 0.48; 95% CI: 0.20‐1.15).

Figure 3

Death

Seven trials16, 2025 comparing either UFH or LMWH to control examined the impact of pharmacologic prophylaxis on death and found no significant difference between treated and untreated patients across all trials (RR = 0.92; 95% CI: 0.82‐1.03) and those limited to studies with Jadad scores of 3 or higher (RR = 0.97; 95% CI: 0.80‐1.17).

Complications

We evaluated adverse events of heparin products used for prophylaxis and whether there were differences between UFH and LMWH. Reporting of complications was not uniform from study to study, making pooling more difficult. However, we were able to abstract data on any bleeding, major bleeding, and thrombocytopenia from several studies. In 5 studies15, 16, 2325 of either UFH or LMWH vs. control, a significantly increased risk of any bleeding (RR = 1.54; 95% CI: 1.15‐2.06) (Figure 4A) was found. When only major bleeding was evaluated, no statistically significant difference was noted (RR = 1.20; 95% CI: 0.55‐2.58) (Figure 4B). In 4 trials16, 22, 24, 25 the occurrence of thrombocytopenia was not significantly different when comparing UFH or LMWH to control (RR = 0.92; 95% CI: 0.46‐1.86).

Figure 4

When LMWH was compared to UFH in 4 trials,14, 17, 18, 27 a nonsignificant trend toward a decrease in any bleeding was found in the LMWH group (RR = 0.72; 95% CI: 0.44‐1.16) (Figure 5A). A similar trend was seen favoring LMWH in rates of major bleeding (RR = 0.57; 95% CI: 0.25‐1.32) (Figure 5B). Neither trend was statistically significant. Three trials comparing LMWH to UFH reported on thrombocytopenia17, 18, 27 with no significant difference noted (RR = 0.52; 95% CI: 0.06‐4.18).

Figure 5

DISCUSSION

When compared to control, LMWH or UFH decreased the risk of all DVT by 45% (RR = 0.55; 95% CI: 0.36‐0.83) and proximal DVT by 54% (RR = 0.46; 95% CI: 0.31‐0.69). PE was also decreased by 30% (RR = 0.70; 95% CI: 0.53‐0.93). Of note, when prophylaxis was compared with placebo all of the high‐quality studies showing a benefit were done using LMWH. The benefits of prophylaxis occurred at the cost of a 54% increased overall risk of bleeding (RR = 1.54; 95% CI 1.15‐2.06). However, the risk of major bleeding was not significantly increased. We did not find a mortality benefit to pharmacologic thromboembolism prophylaxis.

When comparing UFH to LMWH, we noted no difference in all DVT, symptomatic DVT, proximal DVT, PE, or death. While there was a trend toward less bleeding with LMWH, this was not statistically significant.

Taken in aggregate, our findings are in agreement with previous published meta‐analyses reporting net benefit for thromboembolism prophylaxis in medical patients.24, 22, 28, 29 Our meta‐analysis has several methodological strengths over the prior studies, including a comprehensive search of both the published and unpublished literature and assessment of the relationship between methodological quality of included trials and reported benefit. In contrast to previous reviews, our analysis highlights several limitations of the current evidence.

First, many of the studies are older, with predicted lengths of stay of greater than 1 week. The 8‐13‐day range of treatment duration we found in this study is longer than the average length of stay in today's hospitals. Second, there is variability in the diagnostic tests used to diagnose DVT, as well as variation in the definition of DVT among studies. Studies using fibrinogen uptake scanning reported rates of DVT as high as 26%15 while studies using venography reported DVT rates of almost 15% in the placebo arm.24 These rates are higher than most physicians' routine practice. One reason for this discrepancy is most studies did not distinguish below‐the‐knee DVT from more clinically relevant above‐the‐knee DVT. Systematic reviews of medical and surgical patients have found rates of proximal propagation from 0% to 29% in untreated patients.30, 31 Though controversial, below‐the‐knee DVT is believed less morbid than proximal DVT or symptomatic DVT. We addressed this by focusing specifically on clinically relevant endpoints of proximal and symptomatic DVT. When we restricted our analysis to proximal DVT we found a 54% RR reduction in 2 pooled trials of LMWH compared to placebo. In pooled analyses symptomatic DVT was not affected by prophylaxis. When compared head‐to‐head there were no differences between LMWH and UFH for proximal DVT or symptomatic DVT.

When considering PE, the utilization of autopsy as the sole diagnostic method in 2 large trials16, 21 is particularly problematic. In the trial by Garlund,21 the mortality rate was 5.4%, with an autopsy rate of 60.1%. Similarly, in the trial by Mahe et al.,16 the mortality rate was 10%, with an autopsy rate of 49%. Given the low absolute number of deaths and substantial proportion of decedents without autopsy, the potential for chance to produce an imbalance in detection of PE is high in these studies. When we excluded these 2 trials, we found that PE was no longer reduced to a statistically significant degree by prophylaxis. Loss of significance for PE in 2 sensitivity analyses (when excluding studies of lower quality, or using autopsy as a sole diagnostic study) is problematic and calls into question the true benefit of prophylaxis for prevention of PE.

Another limitation of the current literature centers on the variability of dosing used. We pooled trials of UFH whether given BID or TID. Given the small number of trials we did not do sensitivity analyses by dosage. A recent meta‐analysis3 found both doses are efficacious, while a recent review article32 suggested superiority of TID dosing. We believe the available literature does not clearly address this issue. Regarding comparisons of LMWH to UFH, dosing variability was also noted. The trial by Bergmann and Neuhart27 used enoxaparin 20 mg per day and found similar efficacy to UFH BID, while the Samama et al.24 trial found enoxaparin 20 mg per day no more efficacious than placebo. While the literature does not clearly define a best dose, we believe enoxaparin doses lower than 40 mg daily do not reflect the standard of care.

An additional limitation of the literature is publication bias. We assessed the possibility of publication bias by a variety of means. We did find statistical evidence of publication bias for the outcome of PE when prophylaxis was compared to control. Importantly, two meta‐analyses2, 4 on thromboembolism prophylaxis for general medicine patients suggested publication bias is present and our finding supports this conclusion. While no test for publication bias is foolproof, the best protection against publication bias, which we pursued in our study, consists of a thorough search for unpublished studies, including a search of conference proceedings, contact with experts in the field, and manufacturers of LMWH.

A final limitation of the current literature centers on risk assessment. All of the trials in this meta‐analysis included patients with an elevated level of risk. Unfortunately, risk was not clearly defined in many studies, and there was no minimum level of risk between trials. While immobility, age, and length of stay were reported for most studies, other risk factors such as personal history of thromboembolism and malignancy were not uniformly reported. Based on our analysis we are not confident our results can be extrapolated to all general medicine patients.

In conclusion, we found good evidence that pharmacologic prophylaxis significantly decreases the risk of all DVT and proximal DVT in at‐risk general medical patients. However, only LMWH was shown to prevent proximal DVT. We found inconclusive evidence that prophylaxis prevents PE. When compared directly we did not find clear superiority between UFH and LMWH, though several limitations of the current literature hamper decision‐making. Given the lower cost, it may seem justified to use UFH. However, there are other practical issues, such as the fact that LMWH is given once daily, and so potentially preferred by patients and more efficient for nurses. All of these results pertain to patients with elevated risk. While we did not find significant safety concerns with prophylaxis we do not know if these results can be extrapolated to lower‐risk patients. We believe that recommending widespread prophylaxis of all general medicine patients requires additional evidence about appropriate patient selection.

Deep venous thrombosis (DVT) and pulmonary embolism (PE), collectively referred to as venous thromboembolism (VTE), are common events in hospitalized patients and result in significant morbidity and mortality. Often silent and frequently unexpected, VTE is preventable. Accordingly, the American College of Chest Physicians recommends that pharmacologic prophylaxis be given to acutely ill medical patients admitted to the hospital with congestive heart failure or severe respiratory disease, or to patients who are confined to bed who have additional risk factors, such as cancer or previous VTE.1 Three recent meta‐analyses24 demonstrated significant reductions in VTE in general medicine patients with pharmacologic prophylaxis. Recently the National Quality Forum advocated that hospitals evaluate each patient upon admission and regularly thereafter, for the risk of developing DVT/VTE and utilize clinically appropriate methods to prevent DVT/VTE.5

Despite recommendations for prophylaxis, multiple studies demonstrate utilization in <50% of at‐risk general medical patients.68 Physicians' lack of awareness may partially explain this underutilization, but other likely factors include physicians' questions about the clinical importance of the outcome (eg, some studies have shown reductions primarily in asymptomatic distal DVT), doubt regarding the best form of prophylaxis (ie, unfractionated heparin [UFH] vs. low molecular weight heparin [LMWH]), uncertainty regarding optimal dosing regimens, and comparable uncertainty regarding which patients have sufficiently high risk for VTE to outweigh the risks of anticoagulation.

We undertook the current meta‐analysis to address questions about thromboembolism prevention in general medicine patients. Does pharmacologic prophylaxis prevent clinically relevant events? Is LMWH or UFH preferable in terms of either efficacy or safety?

MATERIALS AND METHODS

RESULTS

Study Identification and Selection

The computerized literature search resulted in 5284 articles. Three additional citations were found by review of bibliographies. No additional trials were identified from reviews of abstracts from national meetings. Representatives from the 3 pharmaceutical companies reported no knowledge of additional published or unpublished data. Of the 5287 studies identified by the search, 14 studies met all eligibility criteria (Figure 1).

Figure 1

UFH or LMWH vs. Control

DVT

Across 7 trials comparing either UFH or LMWH to control, heparin products significantly decreased the risk of all DVT (RR = 0.55; 95% CI: 0.36‐0.83) (Figure 2A). When stratified by methodological quality, 5 trials16, 2225 with Jadad scores of 3 or higher showed an RR reduction of 0.53 (95% CI: 0.38‐0.72) in reducing all DVT. All of the higher‐quality trials compared LMWH to placebo. Across 4 trials that reported data for symptomatic DVT there was a nonsignificant reduction in RR compared with placebo (RR = 0.73; 95% CI: 0.45‐1.16) (Figure 2B). Only 2 trials24, 25 (both LMWH trials) reported results for proximal DVT and demonstrated significant benefit of prophylaxis with a pooled RR of 0.46 (95% CI: 0.31‐0.69) (Figure 2C).

Figure 2

PE

Across 7 trials comparing either UFH or LMWH to control, heparin products significantly decreased the risk of PE (RR = 0.70; 95% CI: 0.53‐0.93) (Figure 3A). The 5 trials16, 2225 with Jadad scores of 3 or greater showed a similar relative risk reduction, but the result was no longer statistically significant (RR = 0.56; 95% CI: 0.31‐1.02). Two of the trials16, 21 relied solely on the results of autopsy to diagnose PE, which may have given rise to chance differences in detection due to generally low autopsy rates. Eliminating these 2 studies from the analysis resulted in loss of statistical significance for the reduction in risk for PE (RR = 0.48; 95% CI: 0.20‐1.15).

Figure 3

Death

Seven trials16, 2025 comparing either UFH or LMWH to control examined the impact of pharmacologic prophylaxis on death and found no significant difference between treated and untreated patients across all trials (RR = 0.92; 95% CI: 0.82‐1.03) and those limited to studies with Jadad scores of 3 or higher (RR = 0.97; 95% CI: 0.80‐1.17).

Complications

We evaluated adverse events of heparin products used for prophylaxis and whether there were differences between UFH and LMWH. Reporting of complications was not uniform from study to study, making pooling more difficult. However, we were able to abstract data on any bleeding, major bleeding, and thrombocytopenia from several studies. In 5 studies15, 16, 2325 of either UFH or LMWH vs. control, a significantly increased risk of any bleeding (RR = 1.54; 95% CI: 1.15‐2.06) (Figure 4A) was found. When only major bleeding was evaluated, no statistically significant difference was noted (RR = 1.20; 95% CI: 0.55‐2.58) (Figure 4B). In 4 trials16, 22, 24, 25 the occurrence of thrombocytopenia was not significantly different when comparing UFH or LMWH to control (RR = 0.92; 95% CI: 0.46‐1.86).

Figure 4

When LMWH was compared to UFH in 4 trials,14, 17, 18, 27 a nonsignificant trend toward a decrease in any bleeding was found in the LMWH group (RR = 0.72; 95% CI: 0.44‐1.16) (Figure 5A). A similar trend was seen favoring LMWH in rates of major bleeding (RR = 0.57; 95% CI: 0.25‐1.32) (Figure 5B). Neither trend was statistically significant. Three trials comparing LMWH to UFH reported on thrombocytopenia17, 18, 27 with no significant difference noted (RR = 0.52; 95% CI: 0.06‐4.18).

Figure 5

DISCUSSION

When compared to control, LMWH or UFH decreased the risk of all DVT by 45% (RR = 0.55; 95% CI: 0.36‐0.83) and proximal DVT by 54% (RR = 0.46; 95% CI: 0.31‐0.69). PE was also decreased by 30% (RR = 0.70; 95% CI: 0.53‐0.93). Of note, when prophylaxis was compared with placebo all of the high‐quality studies showing a benefit were done using LMWH. The benefits of prophylaxis occurred at the cost of a 54% increased overall risk of bleeding (RR = 1.54; 95% CI 1.15‐2.06). However, the risk of major bleeding was not significantly increased. We did not find a mortality benefit to pharmacologic thromboembolism prophylaxis.

When comparing UFH to LMWH, we noted no difference in all DVT, symptomatic DVT, proximal DVT, PE, or death. While there was a trend toward less bleeding with LMWH, this was not statistically significant.

Taken in aggregate, our findings are in agreement with previous published meta‐analyses reporting net benefit for thromboembolism prophylaxis in medical patients.24, 22, 28, 29 Our meta‐analysis has several methodological strengths over the prior studies, including a comprehensive search of both the published and unpublished literature and assessment of the relationship between methodological quality of included trials and reported benefit. In contrast to previous reviews, our analysis highlights several limitations of the current evidence.

First, many of the studies are older, with predicted lengths of stay of greater than 1 week. The 8‐13‐day range of treatment duration we found in this study is longer than the average length of stay in today's hospitals. Second, there is variability in the diagnostic tests used to diagnose DVT, as well as variation in the definition of DVT among studies. Studies using fibrinogen uptake scanning reported rates of DVT as high as 26%15 while studies using venography reported DVT rates of almost 15% in the placebo arm.24 These rates are higher than most physicians' routine practice. One reason for this discrepancy is most studies did not distinguish below‐the‐knee DVT from more clinically relevant above‐the‐knee DVT. Systematic reviews of medical and surgical patients have found rates of proximal propagation from 0% to 29% in untreated patients.30, 31 Though controversial, below‐the‐knee DVT is believed less morbid than proximal DVT or symptomatic DVT. We addressed this by focusing specifically on clinically relevant endpoints of proximal and symptomatic DVT. When we restricted our analysis to proximal DVT we found a 54% RR reduction in 2 pooled trials of LMWH compared to placebo. In pooled analyses symptomatic DVT was not affected by prophylaxis. When compared head‐to‐head there were no differences between LMWH and UFH for proximal DVT or symptomatic DVT.

When considering PE, the utilization of autopsy as the sole diagnostic method in 2 large trials16, 21 is particularly problematic. In the trial by Garlund,21 the mortality rate was 5.4%, with an autopsy rate of 60.1%. Similarly, in the trial by Mahe et al.,16 the mortality rate was 10%, with an autopsy rate of 49%. Given the low absolute number of deaths and substantial proportion of decedents without autopsy, the potential for chance to produce an imbalance in detection of PE is high in these studies. When we excluded these 2 trials, we found that PE was no longer reduced to a statistically significant degree by prophylaxis. Loss of significance for PE in 2 sensitivity analyses (when excluding studies of lower quality, or using autopsy as a sole diagnostic study) is problematic and calls into question the true benefit of prophylaxis for prevention of PE.

Another limitation of the current literature centers on the variability of dosing used. We pooled trials of UFH whether given BID or TID. Given the small number of trials we did not do sensitivity analyses by dosage. A recent meta‐analysis3 found both doses are efficacious, while a recent review article32 suggested superiority of TID dosing. We believe the available literature does not clearly address this issue. Regarding comparisons of LMWH to UFH, dosing variability was also noted. The trial by Bergmann and Neuhart27 used enoxaparin 20 mg per day and found similar efficacy to UFH BID, while the Samama et al.24 trial found enoxaparin 20 mg per day no more efficacious than placebo. While the literature does not clearly define a best dose, we believe enoxaparin doses lower than 40 mg daily do not reflect the standard of care.

An additional limitation of the literature is publication bias. We assessed the possibility of publication bias by a variety of means. We did find statistical evidence of publication bias for the outcome of PE when prophylaxis was compared to control. Importantly, two meta‐analyses2, 4 on thromboembolism prophylaxis for general medicine patients suggested publication bias is present and our finding supports this conclusion. While no test for publication bias is foolproof, the best protection against publication bias, which we pursued in our study, consists of a thorough search for unpublished studies, including a search of conference proceedings, contact with experts in the field, and manufacturers of LMWH.

A final limitation of the current literature centers on risk assessment. All of the trials in this meta‐analysis included patients with an elevated level of risk. Unfortunately, risk was not clearly defined in many studies, and there was no minimum level of risk between trials. While immobility, age, and length of stay were reported for most studies, other risk factors such as personal history of thromboembolism and malignancy were not uniformly reported. Based on our analysis we are not confident our results can be extrapolated to all general medicine patients.

In conclusion, we found good evidence that pharmacologic prophylaxis significantly decreases the risk of all DVT and proximal DVT in at‐risk general medical patients. However, only LMWH was shown to prevent proximal DVT. We found inconclusive evidence that prophylaxis prevents PE. When compared directly we did not find clear superiority between UFH and LMWH, though several limitations of the current literature hamper decision‐making. Given the lower cost, it may seem justified to use UFH. However, there are other practical issues, such as the fact that LMWH is given once daily, and so potentially preferred by patients and more efficient for nurses. All of these results pertain to patients with elevated risk. While we did not find significant safety concerns with prophylaxis we do not know if these results can be extrapolated to lower‐risk patients. We believe that recommending widespread prophylaxis of all general medicine patients requires additional evidence about appropriate patient selection.

The past decade has seen hospital medicine grow from fewer than 1000 hospitalists nationwide to more than 20,000.1 In fact, survey data suggest that hospital medicine is the fastest growing field of internal medicine in the history of the US, and the growth of hospital medicine has produced a net increase in the number of generalists in the US.2

Although few direct estimates exist, academic hospital medicine (AHM) is also growing rapidly.1 Fueled by potential efficiency gains, a need for increased educational oversight of teaching services, and new residency work hour limitations, many academic medical centers and teaching hospitals have developed large hospital medicine programs. Internal medicine residency graduates interested in general medicine are finding hospital medicine an increasingly popular career choice. As a result, AHM groups have many recent residency graduates with an average age that is generally younger than 40.3

Over 85% of hospitalists are generalists and should find natural alliances with the nonhospitalist side of general internal medicine by collaborating in the course of clinical care, by teaching residents and students, or by designing quality improvement or research projects. In many academic centers, hospitalists are part of the division of general internal medicine, whereas in a few centers, hospitalists either have a separate division or lie outside the internal medicine department (employed by their hospitals).

Despite sharing a common training background and generalist mindset, many new academic hospitalists face different challenges than those faced by pure outpatient‐based academic generalists. First, at many centers, the financial arrangements between the AHM group and the hospital discourage hospitalists from traditional academic pursuits and draw them into clinical, operational, or administrative duties (such as responsibility for utilization review) that, although locally valuable, may not count as academic products in themselves or may take time away from more academic activities. Close alignment between hospitals and AHM may result in hospital administrators dictating hospitalists' practice in a way that further impedes academic viability. Reductions in resident training hours and an increasing need to provide 24‐hour coverage have facilitated growth in AHM into roles beyond those of the traditional academic generalist, such as medical comanagement of surgical patients and coverage of nonteaching services.4, 5 The youth of the field may exacerbate these problems. Most academic hospitalist groups have few senior leaders, whether they are clinical‐, education‐, or research‐focused. Young faculty need senior leaders as mentors to buffer them from relentless clinical demands that would compromise their hopes for academic success.

In order to better characterize these concerns and develop a shared work plan for future activities in support of AHM, the Society of Hospital Medicine (SHM) and the Society of General Internal Medicine (SGIM) convened an AHM consensus conference, a collaborative meeting developed and attended by representatives from SHM, SGIM, the Association of Chiefs of General Internal Medicine (ACGIM), the Association of Professors of Medicine, the Association of Program Directors in Internal Medicine, and the Association of Administrators in Internal Medicine. Using a structured consensus‐building format, we identified key barriers and challenges to AHM, then developed potential solutions.

Consensus Conference Format

Consensus Findings 1: Current Challenges in AHM (Table 1)

Consensus Findings 2: Overcoming Challenges to the Development of AHM (Table 2)

Summit attendees spent considerable time developing and refining solutions to the challenges described previously. Addressing the challenges resulted in a diverse group of proposed products that included educating key stakeholders, designing meetings, courses, or workshops, and gathering and disseminating data. There was considerable overlap among the solutions (Table 2).

Conclusions

AHM is at a crossroads. Unparalleled growth has created a large cadre of hospitalists who are struggling to meet the clinical demands of practice and the requirements for academic promotion; this situation will likely lead to, at a minimum, worsening problems with faculty turnover, and even greater losses of talented and passionate clinicians from the field of academic General Internal Medicine.

The challenges are numerous but not insurmountable, and our process identified issues and potential solutions which address clinical, educational, and research aspects of academic hospitalists' lives. We acknowledge that our findings are most relevant to hospitalists at academic medical centers or large academically oriented community teaching hospitals rather than hospitalists at community hospitals whose work is predominantly clinical with smaller teaching roles. However, we feel the academic hospitalists we targeted are in greater need of assistance. We believe that the most important issues are unsustainable, nonacademic positions, poor job preparation and training, inadequate prioritization of academic roles, and insufficient leadership and mentoring within the field.

It is the hope of all the consensus conference attendees that efforts focusing on academic hospitalists in the short term are not viewed as effort diverted from general internal medicine; in fact, the group felt that while many of the products of the consensus conference were probably most needed by AHM in the short term, these same solutions would likely be useful to outpatient‐based generalists as well. Despite the concerns and challenges outlined, the consensus conference group was also very hopeful that, in the setting where resources and collaboration are appropriately marshaled, that AHM will flourish quickly. In doing so, academic hospitalists will become better role models for residents and students, attracting the next generation of generalists needed to provide care to an increasingly complex patient population, and further advance the mission of General Internal Medicine.

The past decade has seen hospital medicine grow from fewer than 1000 hospitalists nationwide to more than 20,000.1 In fact, survey data suggest that hospital medicine is the fastest growing field of internal medicine in the history of the US, and the growth of hospital medicine has produced a net increase in the number of generalists in the US.2

Although few direct estimates exist, academic hospital medicine (AHM) is also growing rapidly.1 Fueled by potential efficiency gains, a need for increased educational oversight of teaching services, and new residency work hour limitations, many academic medical centers and teaching hospitals have developed large hospital medicine programs. Internal medicine residency graduates interested in general medicine are finding hospital medicine an increasingly popular career choice. As a result, AHM groups have many recent residency graduates with an average age that is generally younger than 40.3

Over 85% of hospitalists are generalists and should find natural alliances with the nonhospitalist side of general internal medicine by collaborating in the course of clinical care, by teaching residents and students, or by designing quality improvement or research projects. In many academic centers, hospitalists are part of the division of general internal medicine, whereas in a few centers, hospitalists either have a separate division or lie outside the internal medicine department (employed by their hospitals).

Despite sharing a common training background and generalist mindset, many new academic hospitalists face different challenges than those faced by pure outpatient‐based academic generalists. First, at many centers, the financial arrangements between the AHM group and the hospital discourage hospitalists from traditional academic pursuits and draw them into clinical, operational, or administrative duties (such as responsibility for utilization review) that, although locally valuable, may not count as academic products in themselves or may take time away from more academic activities. Close alignment between hospitals and AHM may result in hospital administrators dictating hospitalists' practice in a way that further impedes academic viability. Reductions in resident training hours and an increasing need to provide 24‐hour coverage have facilitated growth in AHM into roles beyond those of the traditional academic generalist, such as medical comanagement of surgical patients and coverage of nonteaching services.4, 5 The youth of the field may exacerbate these problems. Most academic hospitalist groups have few senior leaders, whether they are clinical‐, education‐, or research‐focused. Young faculty need senior leaders as mentors to buffer them from relentless clinical demands that would compromise their hopes for academic success.

In order to better characterize these concerns and develop a shared work plan for future activities in support of AHM, the Society of Hospital Medicine (SHM) and the Society of General Internal Medicine (SGIM) convened an AHM consensus conference, a collaborative meeting developed and attended by representatives from SHM, SGIM, the Association of Chiefs of General Internal Medicine (ACGIM), the Association of Professors of Medicine, the Association of Program Directors in Internal Medicine, and the Association of Administrators in Internal Medicine. Using a structured consensus‐building format, we identified key barriers and challenges to AHM, then developed potential solutions.

Consensus Conference Format

Consensus Findings 1: Current Challenges in AHM (Table 1)

Consensus Findings 2: Overcoming Challenges to the Development of AHM (Table 2)

Summit attendees spent considerable time developing and refining solutions to the challenges described previously. Addressing the challenges resulted in a diverse group of proposed products that included educating key stakeholders, designing meetings, courses, or workshops, and gathering and disseminating data. There was considerable overlap among the solutions (Table 2).

Conclusions

AHM is at a crossroads. Unparalleled growth has created a large cadre of hospitalists who are struggling to meet the clinical demands of practice and the requirements for academic promotion; this situation will likely lead to, at a minimum, worsening problems with faculty turnover, and even greater losses of talented and passionate clinicians from the field of academic General Internal Medicine.

The challenges are numerous but not insurmountable, and our process identified issues and potential solutions which address clinical, educational, and research aspects of academic hospitalists' lives. We acknowledge that our findings are most relevant to hospitalists at academic medical centers or large academically oriented community teaching hospitals rather than hospitalists at community hospitals whose work is predominantly clinical with smaller teaching roles. However, we feel the academic hospitalists we targeted are in greater need of assistance. We believe that the most important issues are unsustainable, nonacademic positions, poor job preparation and training, inadequate prioritization of academic roles, and insufficient leadership and mentoring within the field.

It is the hope of all the consensus conference attendees that efforts focusing on academic hospitalists in the short term are not viewed as effort diverted from general internal medicine; in fact, the group felt that while many of the products of the consensus conference were probably most needed by AHM in the short term, these same solutions would likely be useful to outpatient‐based generalists as well. Despite the concerns and challenges outlined, the consensus conference group was also very hopeful that, in the setting where resources and collaboration are appropriately marshaled, that AHM will flourish quickly. In doing so, academic hospitalists will become better role models for residents and students, attracting the next generation of generalists needed to provide care to an increasingly complex patient population, and further advance the mission of General Internal Medicine.

The past decade has seen hospital medicine grow from fewer than 1000 hospitalists nationwide to more than 20,000.1 In fact, survey data suggest that hospital medicine is the fastest growing field of internal medicine in the history of the US, and the growth of hospital medicine has produced a net increase in the number of generalists in the US.2

Although few direct estimates exist, academic hospital medicine (AHM) is also growing rapidly.1 Fueled by potential efficiency gains, a need for increased educational oversight of teaching services, and new residency work hour limitations, many academic medical centers and teaching hospitals have developed large hospital medicine programs. Internal medicine residency graduates interested in general medicine are finding hospital medicine an increasingly popular career choice. As a result, AHM groups have many recent residency graduates with an average age that is generally younger than 40.3

Over 85% of hospitalists are generalists and should find natural alliances with the nonhospitalist side of general internal medicine by collaborating in the course of clinical care, by teaching residents and students, or by designing quality improvement or research projects. In many academic centers, hospitalists are part of the division of general internal medicine, whereas in a few centers, hospitalists either have a separate division or lie outside the internal medicine department (employed by their hospitals).

Despite sharing a common training background and generalist mindset, many new academic hospitalists face different challenges than those faced by pure outpatient‐based academic generalists. First, at many centers, the financial arrangements between the AHM group and the hospital discourage hospitalists from traditional academic pursuits and draw them into clinical, operational, or administrative duties (such as responsibility for utilization review) that, although locally valuable, may not count as academic products in themselves or may take time away from more academic activities. Close alignment between hospitals and AHM may result in hospital administrators dictating hospitalists' practice in a way that further impedes academic viability. Reductions in resident training hours and an increasing need to provide 24‐hour coverage have facilitated growth in AHM into roles beyond those of the traditional academic generalist, such as medical comanagement of surgical patients and coverage of nonteaching services.4, 5 The youth of the field may exacerbate these problems. Most academic hospitalist groups have few senior leaders, whether they are clinical‐, education‐, or research‐focused. Young faculty need senior leaders as mentors to buffer them from relentless clinical demands that would compromise their hopes for academic success.

In order to better characterize these concerns and develop a shared work plan for future activities in support of AHM, the Society of Hospital Medicine (SHM) and the Society of General Internal Medicine (SGIM) convened an AHM consensus conference, a collaborative meeting developed and attended by representatives from SHM, SGIM, the Association of Chiefs of General Internal Medicine (ACGIM), the Association of Professors of Medicine, the Association of Program Directors in Internal Medicine, and the Association of Administrators in Internal Medicine. Using a structured consensus‐building format, we identified key barriers and challenges to AHM, then developed potential solutions.

Consensus Conference Format

Consensus Findings 1: Current Challenges in AHM (Table 1)

Consensus Findings 2: Overcoming Challenges to the Development of AHM (Table 2)

Summit attendees spent considerable time developing and refining solutions to the challenges described previously. Addressing the challenges resulted in a diverse group of proposed products that included educating key stakeholders, designing meetings, courses, or workshops, and gathering and disseminating data. There was considerable overlap among the solutions (Table 2).

Conclusions

AHM is at a crossroads. Unparalleled growth has created a large cadre of hospitalists who are struggling to meet the clinical demands of practice and the requirements for academic promotion; this situation will likely lead to, at a minimum, worsening problems with faculty turnover, and even greater losses of talented and passionate clinicians from the field of academic General Internal Medicine.

The challenges are numerous but not insurmountable, and our process identified issues and potential solutions which address clinical, educational, and research aspects of academic hospitalists' lives. We acknowledge that our findings are most relevant to hospitalists at academic medical centers or large academically oriented community teaching hospitals rather than hospitalists at community hospitals whose work is predominantly clinical with smaller teaching roles. However, we feel the academic hospitalists we targeted are in greater need of assistance. We believe that the most important issues are unsustainable, nonacademic positions, poor job preparation and training, inadequate prioritization of academic roles, and insufficient leadership and mentoring within the field.

It is the hope of all the consensus conference attendees that efforts focusing on academic hospitalists in the short term are not viewed as effort diverted from general internal medicine; in fact, the group felt that while many of the products of the consensus conference were probably most needed by AHM in the short term, these same solutions would likely be useful to outpatient‐based generalists as well. Despite the concerns and challenges outlined, the consensus conference group was also very hopeful that, in the setting where resources and collaboration are appropriately marshaled, that AHM will flourish quickly. In doing so, academic hospitalists will become better role models for residents and students, attracting the next generation of generalists needed to provide care to an increasingly complex patient population, and further advance the mission of General Internal Medicine.

Dramatic changes in the organization, financing, and delivery of hospital care that began a decade ago continue to accelerate. One of the most important changes has been the emergence of hospitalists as providers of inpatient care.1 Hospitalists are physicians, usually general internists, whose clinical focus is the hospitalized patient. As patient illnesses have become more severe and complex, physicians have found it difficult to balance inpatient and outpatient care and have focused on one of the two.25 It is estimated that there are currently 15,000 practicing hospitalists nationally, and projections suggest that this number may exceed 30,000 by 2010, which is equal to the number of cardiologists currently practicing in the United States.6 A 2003 survey from the American Hospital Association showed that more than 30% of the nation's 4900 community hospitals have hospital medicine groups.7 Furthermore, more than 70% of the nation's largest hospitals (>500 beds) and 66% of major teaching hospitals use hospitalists.7

The transition to a hospitalist model generates multiple new research questions about the best approach to caring for the hospitalized patient. Additionally, hospitalists may spawn new areas of clinical research by tackling clinical issues that formerly lacked a large number of specialist investigators. Examples include implementation‐based studies,8, 9 inpatient safety practices,1012 quasi‐experimental studies focusing on common inpatient issues,13, 14 and the evaluation of new methods for reducing resource utilization within various inpatient care delivery structures.15, 16

Similarly, if future clinical trials are to be carried out in real‐world settings, by necessity these will require the participation of hospitalists. Clinical research performed by hospitalists and hospital medicine programs, however, remains underdeveloped. Although this has been attributed to several variables, including the youth of the field, a paucity of fellowship‐trained hospitalist researchers, and a lack of a hospitalist‐oriented national funding source, we also believe that additional barriers exist which could be overcome if hospitalists actively partnered with specialists to perform hospital‐based clinical and translational research.

Hospitalists lack clinical expertise in many clinical issues. In both academic and nonacademic settings, the diagnostic approach, individual treatment decisions, and follow‐up of complex patients occur with frequent consultation of specialists. Specialists often provide a deeper understanding of both the pathophysiologic concepts and scientific principles underlying important clinical questions and are more likely to have had fellowship training that included clinical research experience. Specialists also have more access to extramural funding for disease‐based investigation, and thus their involvement in hospital‐based clinical research would likely enhance funding opportunities, improve project feasibility, and increase dissemination of the results. A successful clinical research program will therefore be one that combines specialists and hospitalists working collaboratively to determine the best way to care for inpatients. With that in mind, we created the University of Michigan SpecialistHospitalist Allied Research Program (SHARP).

METHODS

DISCUSSION AND NEXT STEPS

SHARP is a novel clinical research program partnering hospitalists with specialists. Its current focus targets single‐institution studies that generate pilot data leading to larger projects. The ultimate goal is to develop the ability to do larger multicenter investigator‐initiated projects. The SHARP program will also have the ability to perform observational studies to identify predictors and risk factors and the ability to carry out implementation studies that show how best to translate results from published articles to direct patient care.

A specialisthospitalist collaboration overcomes barriers that we feel may impede hospital medicine research at an academic medical center. For a similar program to succeed at other institutions, key components from our program will have to be replicated. First, senior, fellowship‐trained researchers are required to mentor junior investigators (who may or may not have additional fellowship training), help guide project selection, oversee grant and manuscript submissions, and troubleshoot problems that arise in the course of any clinical research project. In our institution, this comes from within our hospitalist program and from our specialist collaborators. In institutions lacking hospitalists with research experience, this guidance could come from within a division of general medicine, internal medicine specialty divisions, internal medicine department leadership, or even noninternal medicine departments (eg, emergency medicine, neurology, and surgery) that have traditionally been involved in clinical research programs.

A second key component that must be considered is funding. An initial investment is necessary to fund key personnel dedicated to getting projects started on the right track, collecting pilot data, and ensuring project completion and dissemination of the results. The positive margin generated by our hospitalist program facilitated the initial investment. In the absence of a positive margin, resources could come directly from the hospital, the medical school, the department of internal medicine, or perhaps a foundation. The case would need to be made that an initial short‐term investment would enhance the academic standing of the institution, enhance the careers of young investigators, and over time lead to a self‐sustaining program through investigator‐initiated grants and extramural funding. In addition to experienced leadership and funding, we created an oversight committee, but we feel that this is not a critical component. A potential concern with a program that partners with specialists might be that research topics become too disease‐specific or specialty‐oriented. We specifically created the oversight committee to protect against this possibility, and other institutions might need similar safeguards.

Our next step includes leveraging existing hospitalist collaboratives that reach beyond academic medical centers to expand further the reach of SHARP. Ultimately, any new therapy, clinical tool, diagnostic paradigm, or implementation strategy that is developed or evaluated bythe SHARP program would need to be tested in a real‐world setting to assess external validity. With support from the Blue Cross Blue Shield of Michigan Foundation, we have created a multihospital patient safety consortium, the Hospitalists as Emerging Leaders in Patient Safety Consortium, which includes academic, government, urban, rural, teaching, and nonteaching hospitals.23 Although the initial focus is patient safety, our goal for the consortium is to develop it into a multihospital clinical research program that could take pilot projects developed by SHARP and test them in real‐world settings. We believe that full‐scale multihospital studies based on SHARP pilot data will be very attractive to external funding agencies and will help SHARP become financially self‐sufficient after the initial 3‐year start‐up.

Hospital medicine research is desperately needed.24, 25 Unfortunately, the clinical research capabilities of most hospital medicine programs are quite underdeveloped. We believe that partnering hospitalists with specialists can facilitate collaborative research to identify the best way to care for inpatients. If successful, we believe that variations of this model can be replicated at other institutions and will be a critical factor in jumpstarting hospital medicine clinical research.