Gabriel J. Escobar, MD

Hospitalized patients who require transfer from medical wards to the intensive care unit (ICU) have high in‐hospital mortality, in some reports exceeding 55%.14 In a previous report in this journal, we found that while these unplanned ICU transfers occurred in only 4% of hospitalizations, they were present in nearly one‐quarter of fatal hospitalizations and were associated with substantial increases in resource utilization.4 For these reasons, interventions aimed at identifying and treating this high‐risk group have received considerable attention and have been proposed as measures of inpatient safety.2, 49

Notably, mortality among patients with unplanned ICU transfers exceeds mortality among patients admitted to the ICU directly from the emergency department (ED)a group traditionally considered to have the highest risk of death.13, 10 Previous single‐center studies suggest that increased mortality rates are present even among patients transferred within 24 hours of hospital admission, and reinforce the notion that earlier recognition of critical illness may result in improved outcomes.1113 However, these studies have been performed primarily in small cohorts of heterogeneous patients, and may obscure the independent effect of unplanned transfers on mortality and hamper efforts to use unplanned transfer rates as a metric of healthcare quality.1, 2, 4, 9

In this study, we evaluated early unplanned ICU transfers drawn from a cohort of 499,995 hospitalizations in an integrated healthcare delivery system. Using patient data, extracted from the automated electronic medical record, we matched unplanned transfer cases to patients directly admitted to the ICU and described the association between delayed ICU transfers and adverse outcomes.

METHODS

RESULTS

During the study period, 313,797 medical hospitalizations originated in the ED (Table 1). Overall, patients' mean age was 67 18 years; 53.7% were female. Patient characteristics differed significantly based on the need for ICU admission. For example, average LAPS was highest among patients admitted directly to the ICU and lowest among patients who never required ICU care (P < 0.01). Patients with unplanned ICU transfers during hospitalization had longer length of stay and higher hospital mortality than direct ICU admit patients (P < 0.01). Overall, more than 1 in 15 patients experienced an unplanned transfer to the ICU.

The majority of unplanned transfers occurred within the first 48 hours of hospitalization (57.6%, Figure 1); nearly 80% occurred within the first 4 days. The rate of unplanned transfer peaked within 24 hours of hospital admission and decreased gradually as elapsed hospital LOS increased (Figure 1). While most patients experienced a single unplanned ICU transfer, 12.7% required multiple transfers to the ICU throughout their hospitalization.

Figure 1

Multivariable case matching between unplanned transfer cases within 24 hours of admission and direct ICU admit controls resulted in 5839 (92%) case‐control pairs (Table 2). Matched pairs were most frequently admitted with diagnoses in Primary Condition groups that included respiratory infections and pneumonia (15.6%); angina, acute myocardial infarction (AMI), and heart failure (15.6%); or gastrointestinal bleeding (13.8%).

In‐hospital mortality was significantly higher among cases (11.6%) than among ICU controls (8.5%, P < 0.001); mean LOS was also longer among cases (8 12 days) than among controls (6 9 days, P < 0.001). Unplanned transfer cases were at an increased odds of death when compared with ICU controls (adjusted odds ratio [OR], 1.44; 95% confidence interval [CI], 1.26‐1.64; P < 0.001); they also had a significantly higher observed‐to‐expected mortality ratio. When cases and controls were matched by hospital facility, the number of case‐control pairs decreased (2949 pairs; 42% matching frequency) but the odds of death was of similar magnitude (OR, 1.43; 95% CI, 1.21‐1.68; P < 0.001). Multivariable mixed‐effects logistic regression including all early unplanned transfer and direct ICU admit patients produced an effect size of similar magnitude (OR, 1.37; 95% CI, 1.24‐1.50; P < 0.001).

Results were similar when cases were limited to patients with transfers within 12 hours of admission; mortality was 10.9% among cases and 9.1% among controls (P = 0.02). When including patients with unplanned transfers within 48 hours of hospital admission, the difference in mortality between cases and controls increased (13.2% vs 9.1%, P < 0.001). The odds of death among patients with unplanned transfers increased as the elapsed time between admission and ICU transfer lengthened (Figure 2); the adjusted OR was statistically significant at each point between 8 and 48 hours.

Figure 2

When stratified by admitting diagnosis groups, cases with unplanned transfers within the first 48 hours had increased mortality compared with matched controls in some categories (Table 3). For example, for patients in the respiratory infection and pneumonia group, mortality was 16.8% among unplanned transfer cases and 13.0% among early matched ICU controls (P < 0.01). A similar pattern was present in groups including: gastrointestinal bleeding, chronic obstructive pulmonary disease (COPD) exacerbation, and seizure groups (Table 3). However, for patients with AMI alone, mortality was 5.0% among cases and 3.7% among matched controls (P = 0.12). Patients with sepsis had a mortality rate of 15.2% among cases and 20.8% among matched controls (P = 0.07). Similarly, patients with stroke had a mortality rate of 12.4% among unplanned transfer cases and 11.4% in the matched controls (P = 0.54).

DISCUSSION

This study found that unplanned ICU transfers were common among medical patients, occurring in 5% of all hospitalizations originating in the ED. The majority of unplanned transfers occurred within 48 hours of admission; the rate of ICU transfers peaked within 24 hours after hospitalization. Compared with patients admitted directly from the ED to the ICU, those transferred early after admission had significantly increased mortality; for example, patients transferred within 24 hours were at a 44% increased odds of hospital death. The adverse outcomes associated with unplanned transfers varied considerably by admission diagnosis subgroups.

Our findings confirm previous reports of increased mortality among patients with unplanned ICU transfers. Escarce and Kelley reported that patients admitted to the ICU from non‐ED locationsincluding wards, intermediate care units, and other hospitalswere at an increased risk of hospital death.1 Multiple subsequent studies have confirmed the increased mortality among patients with unplanned transfers.24, 10, 13, 22, 23 We previously evaluated patients who required a transfer to any higher level of care and reported an observed‐to‐expected mortality ratio of 2.93.4

Fewer studies, however, have evaluated the association between the timing of unplanned transfers and inpatient outcomes; previous small reports suggest that delays in ICU transfer adversely affect mortality and length of stay.12, 13, 24 Parkhe et al. compared 99 direct ICU admit patients with 23 who experienced early unplanned transfers; mortality at 30 days was significantly higher among patients with unplanned transfers.13 The current multifacility study included considerably more patients and confirmed an in‐hospital mortality gapalbeit a smaller onebetween patients with early transfers and those directly admitted to the ICU.

We focused on unplanned transfers during the earliest phase of hospitalization to identify patients who might benefit from improved recognition of, and intervention for, impending critical illness. We found that even patients requiring transfers within 8 hours of hospital admission were at an increased risk of death. Bapoje et al. recently reported that as many as 80% of early unplanned transfers were preventable and that most resulted from inappropriate admission triage.11 Together, these findings suggest that heightened attention to identifying such patients at admission or within the first day of hospitalizationwhen the rates of unplanned transfers peakis critical.

Several important limitations should be recognized in interpreting these results. First, this study was not designed to specifically identify the reasons for unplanned transfers, limiting our ability to characterize episodes in which timely care could have prevented excess mortality. Notably, while previous work suggests that many early unplanned transfers might be prevented with appropriate triage, it is likely that some excess deaths are not preventable even if every patient could be admitted to the ICU directly.

We were able to characterize patient outcomes by admitting diagnoses. Patients admitted for pneumonia and respiratory infection, gastrointestinal bleeding, COPD exacerbation, or seizures demonstrated excess mortality compared with matched ICU controls, while those with AMI, sepsis, and stroke did not. It is possible that differences in diagnosis‐specific excess mortality resulted from increasing adherence to well‐defined practice guidelines for specific high‐risk conditions.2527 For example, international awareness campaigns for the treatment of sepsis, AMI, and strokeSurviving Sepsis, Door‐to‐Balloon, and F.A.S.T.emphasize early interventions to minimize morbidity and mortality.

Second, the data utilized in this study were based on automated variables extracted from the electronic medical record. Mortality prediction models based on automated variables have demonstrated excellent performance among ICU and non‐ICU populations14, 18, 28; however, the inclusion of additional data (eg, vital signs or neurological status) would likely improve baseline risk adjustment.5, 10, 2931 Multiple studies have demonstrated that vital signs and clinician judgment can predict patients at an increased risk of deterioration.5, 10, 2931 Such data might also provide insight into residual factors that influenced clinicians' decisions to triage patients to an ICU versus non‐ICU admissiona focus area of our ongoing research efforts. Utilizing electronically available data, however, facilitated the identification of a cohort of patients far larger than that in prior studies. Where previous work has also been limited by substantial variability in baseline characteristics among study subjects,1, 2, 12, 13 our large sample produced a high percentage of multivariable case matches.

Third, we chose to match patients with a severity of illness index based on variables available at the time of hospital admission. While this mortality prediction model has demonstrated excellent performance in internal and external populations,14, 18 it is calibrated for general inpatient, rather than critically ill, populations. It remains possible that case matching with ICU‐specific severity of illness scores might alter matching characteristics, however, previous studies suggest that severity of illness, as measured by these scores, is comparable between direct ICU admits and early ICU transfers.13 Importantly, our matching procedure avoided the potential confounding known to exist with the use of prediction models based on discharge or intra‐hospitalization data.32, 33

Finally, while we were able to evaluate unplanned transfer timing in a multifacility sample, all patient care occurred within a large integrated healthcare delivery system. The overall observed mortality in our study was lower than that reported in prior studies which considered more limited patient cohorts.1, 2, 12, 13, 22 Thus, differences in patient case‐mix or ICU structure must be considered when applying our results to other healthcare delivery systems.

This hypothesis‐generating study, based on a large, multifacility sample of hospitalizations, suggests several areas of future investigation. Future work should detail specific aspects of care among patients with unplanned transfer, including: evaluating the structures and processes involved in triage decisions, measuring the effects on mortality through implementation of interventions (eg, rapid response teams or diagnosis‐specific treatment protocols), and defining the causes and risk factors for unplanned transfers by elapsed time.

In conclusion, the risk of an unplanned ICU transfera common event among hospitalized patientsis highest within 24 hours of hospitalization. Patients with early unplanned transfers have increased mortality and length of stay compared to those admitted directly to the ICU. Even patients transferred to the ICU within 8 hours of hospital admission are at an increased risk of death when compared with those admitted directly. Substantial variability in unplanned transfer outcomes exists based on admitting diagnoses. Future research should characterize unplanned transfers in greater detail with the goal of identifying patients that would benefit from improved triage and early ICU transfer.

Hospitalized patients who require transfer from medical wards to the intensive care unit (ICU) have high in‐hospital mortality, in some reports exceeding 55%.14 In a previous report in this journal, we found that while these unplanned ICU transfers occurred in only 4% of hospitalizations, they were present in nearly one‐quarter of fatal hospitalizations and were associated with substantial increases in resource utilization.4 For these reasons, interventions aimed at identifying and treating this high‐risk group have received considerable attention and have been proposed as measures of inpatient safety.2, 49

Notably, mortality among patients with unplanned ICU transfers exceeds mortality among patients admitted to the ICU directly from the emergency department (ED)a group traditionally considered to have the highest risk of death.13, 10 Previous single‐center studies suggest that increased mortality rates are present even among patients transferred within 24 hours of hospital admission, and reinforce the notion that earlier recognition of critical illness may result in improved outcomes.1113 However, these studies have been performed primarily in small cohorts of heterogeneous patients, and may obscure the independent effect of unplanned transfers on mortality and hamper efforts to use unplanned transfer rates as a metric of healthcare quality.1, 2, 4, 9

In this study, we evaluated early unplanned ICU transfers drawn from a cohort of 499,995 hospitalizations in an integrated healthcare delivery system. Using patient data, extracted from the automated electronic medical record, we matched unplanned transfer cases to patients directly admitted to the ICU and described the association between delayed ICU transfers and adverse outcomes.

METHODS

RESULTS

During the study period, 313,797 medical hospitalizations originated in the ED (Table 1). Overall, patients' mean age was 67 18 years; 53.7% were female. Patient characteristics differed significantly based on the need for ICU admission. For example, average LAPS was highest among patients admitted directly to the ICU and lowest among patients who never required ICU care (P < 0.01). Patients with unplanned ICU transfers during hospitalization had longer length of stay and higher hospital mortality than direct ICU admit patients (P < 0.01). Overall, more than 1 in 15 patients experienced an unplanned transfer to the ICU.

The majority of unplanned transfers occurred within the first 48 hours of hospitalization (57.6%, Figure 1); nearly 80% occurred within the first 4 days. The rate of unplanned transfer peaked within 24 hours of hospital admission and decreased gradually as elapsed hospital LOS increased (Figure 1). While most patients experienced a single unplanned ICU transfer, 12.7% required multiple transfers to the ICU throughout their hospitalization.

Figure 1

Multivariable case matching between unplanned transfer cases within 24 hours of admission and direct ICU admit controls resulted in 5839 (92%) case‐control pairs (Table 2). Matched pairs were most frequently admitted with diagnoses in Primary Condition groups that included respiratory infections and pneumonia (15.6%); angina, acute myocardial infarction (AMI), and heart failure (15.6%); or gastrointestinal bleeding (13.8%).

In‐hospital mortality was significantly higher among cases (11.6%) than among ICU controls (8.5%, P < 0.001); mean LOS was also longer among cases (8 12 days) than among controls (6 9 days, P < 0.001). Unplanned transfer cases were at an increased odds of death when compared with ICU controls (adjusted odds ratio [OR], 1.44; 95% confidence interval [CI], 1.26‐1.64; P < 0.001); they also had a significantly higher observed‐to‐expected mortality ratio. When cases and controls were matched by hospital facility, the number of case‐control pairs decreased (2949 pairs; 42% matching frequency) but the odds of death was of similar magnitude (OR, 1.43; 95% CI, 1.21‐1.68; P < 0.001). Multivariable mixed‐effects logistic regression including all early unplanned transfer and direct ICU admit patients produced an effect size of similar magnitude (OR, 1.37; 95% CI, 1.24‐1.50; P < 0.001).

Results were similar when cases were limited to patients with transfers within 12 hours of admission; mortality was 10.9% among cases and 9.1% among controls (P = 0.02). When including patients with unplanned transfers within 48 hours of hospital admission, the difference in mortality between cases and controls increased (13.2% vs 9.1%, P < 0.001). The odds of death among patients with unplanned transfers increased as the elapsed time between admission and ICU transfer lengthened (Figure 2); the adjusted OR was statistically significant at each point between 8 and 48 hours.

Figure 2

When stratified by admitting diagnosis groups, cases with unplanned transfers within the first 48 hours had increased mortality compared with matched controls in some categories (Table 3). For example, for patients in the respiratory infection and pneumonia group, mortality was 16.8% among unplanned transfer cases and 13.0% among early matched ICU controls (P < 0.01). A similar pattern was present in groups including: gastrointestinal bleeding, chronic obstructive pulmonary disease (COPD) exacerbation, and seizure groups (Table 3). However, for patients with AMI alone, mortality was 5.0% among cases and 3.7% among matched controls (P = 0.12). Patients with sepsis had a mortality rate of 15.2% among cases and 20.8% among matched controls (P = 0.07). Similarly, patients with stroke had a mortality rate of 12.4% among unplanned transfer cases and 11.4% in the matched controls (P = 0.54).

DISCUSSION

This study found that unplanned ICU transfers were common among medical patients, occurring in 5% of all hospitalizations originating in the ED. The majority of unplanned transfers occurred within 48 hours of admission; the rate of ICU transfers peaked within 24 hours after hospitalization. Compared with patients admitted directly from the ED to the ICU, those transferred early after admission had significantly increased mortality; for example, patients transferred within 24 hours were at a 44% increased odds of hospital death. The adverse outcomes associated with unplanned transfers varied considerably by admission diagnosis subgroups.

Our findings confirm previous reports of increased mortality among patients with unplanned ICU transfers. Escarce and Kelley reported that patients admitted to the ICU from non‐ED locationsincluding wards, intermediate care units, and other hospitalswere at an increased risk of hospital death.1 Multiple subsequent studies have confirmed the increased mortality among patients with unplanned transfers.24, 10, 13, 22, 23 We previously evaluated patients who required a transfer to any higher level of care and reported an observed‐to‐expected mortality ratio of 2.93.4

Fewer studies, however, have evaluated the association between the timing of unplanned transfers and inpatient outcomes; previous small reports suggest that delays in ICU transfer adversely affect mortality and length of stay.12, 13, 24 Parkhe et al. compared 99 direct ICU admit patients with 23 who experienced early unplanned transfers; mortality at 30 days was significantly higher among patients with unplanned transfers.13 The current multifacility study included considerably more patients and confirmed an in‐hospital mortality gapalbeit a smaller onebetween patients with early transfers and those directly admitted to the ICU.

We focused on unplanned transfers during the earliest phase of hospitalization to identify patients who might benefit from improved recognition of, and intervention for, impending critical illness. We found that even patients requiring transfers within 8 hours of hospital admission were at an increased risk of death. Bapoje et al. recently reported that as many as 80% of early unplanned transfers were preventable and that most resulted from inappropriate admission triage.11 Together, these findings suggest that heightened attention to identifying such patients at admission or within the first day of hospitalizationwhen the rates of unplanned transfers peakis critical.

Several important limitations should be recognized in interpreting these results. First, this study was not designed to specifically identify the reasons for unplanned transfers, limiting our ability to characterize episodes in which timely care could have prevented excess mortality. Notably, while previous work suggests that many early unplanned transfers might be prevented with appropriate triage, it is likely that some excess deaths are not preventable even if every patient could be admitted to the ICU directly.

We were able to characterize patient outcomes by admitting diagnoses. Patients admitted for pneumonia and respiratory infection, gastrointestinal bleeding, COPD exacerbation, or seizures demonstrated excess mortality compared with matched ICU controls, while those with AMI, sepsis, and stroke did not. It is possible that differences in diagnosis‐specific excess mortality resulted from increasing adherence to well‐defined practice guidelines for specific high‐risk conditions.2527 For example, international awareness campaigns for the treatment of sepsis, AMI, and strokeSurviving Sepsis, Door‐to‐Balloon, and F.A.S.T.emphasize early interventions to minimize morbidity and mortality.

Second, the data utilized in this study were based on automated variables extracted from the electronic medical record. Mortality prediction models based on automated variables have demonstrated excellent performance among ICU and non‐ICU populations14, 18, 28; however, the inclusion of additional data (eg, vital signs or neurological status) would likely improve baseline risk adjustment.5, 10, 2931 Multiple studies have demonstrated that vital signs and clinician judgment can predict patients at an increased risk of deterioration.5, 10, 2931 Such data might also provide insight into residual factors that influenced clinicians' decisions to triage patients to an ICU versus non‐ICU admissiona focus area of our ongoing research efforts. Utilizing electronically available data, however, facilitated the identification of a cohort of patients far larger than that in prior studies. Where previous work has also been limited by substantial variability in baseline characteristics among study subjects,1, 2, 12, 13 our large sample produced a high percentage of multivariable case matches.

Third, we chose to match patients with a severity of illness index based on variables available at the time of hospital admission. While this mortality prediction model has demonstrated excellent performance in internal and external populations,14, 18 it is calibrated for general inpatient, rather than critically ill, populations. It remains possible that case matching with ICU‐specific severity of illness scores might alter matching characteristics, however, previous studies suggest that severity of illness, as measured by these scores, is comparable between direct ICU admits and early ICU transfers.13 Importantly, our matching procedure avoided the potential confounding known to exist with the use of prediction models based on discharge or intra‐hospitalization data.32, 33

Finally, while we were able to evaluate unplanned transfer timing in a multifacility sample, all patient care occurred within a large integrated healthcare delivery system. The overall observed mortality in our study was lower than that reported in prior studies which considered more limited patient cohorts.1, 2, 12, 13, 22 Thus, differences in patient case‐mix or ICU structure must be considered when applying our results to other healthcare delivery systems.

This hypothesis‐generating study, based on a large, multifacility sample of hospitalizations, suggests several areas of future investigation. Future work should detail specific aspects of care among patients with unplanned transfer, including: evaluating the structures and processes involved in triage decisions, measuring the effects on mortality through implementation of interventions (eg, rapid response teams or diagnosis‐specific treatment protocols), and defining the causes and risk factors for unplanned transfers by elapsed time.

In conclusion, the risk of an unplanned ICU transfera common event among hospitalized patientsis highest within 24 hours of hospitalization. Patients with early unplanned transfers have increased mortality and length of stay compared to those admitted directly to the ICU. Even patients transferred to the ICU within 8 hours of hospital admission are at an increased risk of death when compared with those admitted directly. Substantial variability in unplanned transfer outcomes exists based on admitting diagnoses. Future research should characterize unplanned transfers in greater detail with the goal of identifying patients that would benefit from improved triage and early ICU transfer.

Hospitalized patients who require transfer from medical wards to the intensive care unit (ICU) have high in‐hospital mortality, in some reports exceeding 55%.14 In a previous report in this journal, we found that while these unplanned ICU transfers occurred in only 4% of hospitalizations, they were present in nearly one‐quarter of fatal hospitalizations and were associated with substantial increases in resource utilization.4 For these reasons, interventions aimed at identifying and treating this high‐risk group have received considerable attention and have been proposed as measures of inpatient safety.2, 49

Notably, mortality among patients with unplanned ICU transfers exceeds mortality among patients admitted to the ICU directly from the emergency department (ED)a group traditionally considered to have the highest risk of death.13, 10 Previous single‐center studies suggest that increased mortality rates are present even among patients transferred within 24 hours of hospital admission, and reinforce the notion that earlier recognition of critical illness may result in improved outcomes.1113 However, these studies have been performed primarily in small cohorts of heterogeneous patients, and may obscure the independent effect of unplanned transfers on mortality and hamper efforts to use unplanned transfer rates as a metric of healthcare quality.1, 2, 4, 9

In this study, we evaluated early unplanned ICU transfers drawn from a cohort of 499,995 hospitalizations in an integrated healthcare delivery system. Using patient data, extracted from the automated electronic medical record, we matched unplanned transfer cases to patients directly admitted to the ICU and described the association between delayed ICU transfers and adverse outcomes.

METHODS

RESULTS

During the study period, 313,797 medical hospitalizations originated in the ED (Table 1). Overall, patients' mean age was 67 18 years; 53.7% were female. Patient characteristics differed significantly based on the need for ICU admission. For example, average LAPS was highest among patients admitted directly to the ICU and lowest among patients who never required ICU care (P < 0.01). Patients with unplanned ICU transfers during hospitalization had longer length of stay and higher hospital mortality than direct ICU admit patients (P < 0.01). Overall, more than 1 in 15 patients experienced an unplanned transfer to the ICU.

The majority of unplanned transfers occurred within the first 48 hours of hospitalization (57.6%, Figure 1); nearly 80% occurred within the first 4 days. The rate of unplanned transfer peaked within 24 hours of hospital admission and decreased gradually as elapsed hospital LOS increased (Figure 1). While most patients experienced a single unplanned ICU transfer, 12.7% required multiple transfers to the ICU throughout their hospitalization.

Figure 1

Multivariable case matching between unplanned transfer cases within 24 hours of admission and direct ICU admit controls resulted in 5839 (92%) case‐control pairs (Table 2). Matched pairs were most frequently admitted with diagnoses in Primary Condition groups that included respiratory infections and pneumonia (15.6%); angina, acute myocardial infarction (AMI), and heart failure (15.6%); or gastrointestinal bleeding (13.8%).

In‐hospital mortality was significantly higher among cases (11.6%) than among ICU controls (8.5%, P < 0.001); mean LOS was also longer among cases (8 12 days) than among controls (6 9 days, P < 0.001). Unplanned transfer cases were at an increased odds of death when compared with ICU controls (adjusted odds ratio [OR], 1.44; 95% confidence interval [CI], 1.26‐1.64; P < 0.001); they also had a significantly higher observed‐to‐expected mortality ratio. When cases and controls were matched by hospital facility, the number of case‐control pairs decreased (2949 pairs; 42% matching frequency) but the odds of death was of similar magnitude (OR, 1.43; 95% CI, 1.21‐1.68; P < 0.001). Multivariable mixed‐effects logistic regression including all early unplanned transfer and direct ICU admit patients produced an effect size of similar magnitude (OR, 1.37; 95% CI, 1.24‐1.50; P < 0.001).

Results were similar when cases were limited to patients with transfers within 12 hours of admission; mortality was 10.9% among cases and 9.1% among controls (P = 0.02). When including patients with unplanned transfers within 48 hours of hospital admission, the difference in mortality between cases and controls increased (13.2% vs 9.1%, P < 0.001). The odds of death among patients with unplanned transfers increased as the elapsed time between admission and ICU transfer lengthened (Figure 2); the adjusted OR was statistically significant at each point between 8 and 48 hours.

Figure 2

When stratified by admitting diagnosis groups, cases with unplanned transfers within the first 48 hours had increased mortality compared with matched controls in some categories (Table 3). For example, for patients in the respiratory infection and pneumonia group, mortality was 16.8% among unplanned transfer cases and 13.0% among early matched ICU controls (P < 0.01). A similar pattern was present in groups including: gastrointestinal bleeding, chronic obstructive pulmonary disease (COPD) exacerbation, and seizure groups (Table 3). However, for patients with AMI alone, mortality was 5.0% among cases and 3.7% among matched controls (P = 0.12). Patients with sepsis had a mortality rate of 15.2% among cases and 20.8% among matched controls (P = 0.07). Similarly, patients with stroke had a mortality rate of 12.4% among unplanned transfer cases and 11.4% in the matched controls (P = 0.54).

DISCUSSION

This study found that unplanned ICU transfers were common among medical patients, occurring in 5% of all hospitalizations originating in the ED. The majority of unplanned transfers occurred within 48 hours of admission; the rate of ICU transfers peaked within 24 hours after hospitalization. Compared with patients admitted directly from the ED to the ICU, those transferred early after admission had significantly increased mortality; for example, patients transferred within 24 hours were at a 44% increased odds of hospital death. The adverse outcomes associated with unplanned transfers varied considerably by admission diagnosis subgroups.

Our findings confirm previous reports of increased mortality among patients with unplanned ICU transfers. Escarce and Kelley reported that patients admitted to the ICU from non‐ED locationsincluding wards, intermediate care units, and other hospitalswere at an increased risk of hospital death.1 Multiple subsequent studies have confirmed the increased mortality among patients with unplanned transfers.24, 10, 13, 22, 23 We previously evaluated patients who required a transfer to any higher level of care and reported an observed‐to‐expected mortality ratio of 2.93.4

Fewer studies, however, have evaluated the association between the timing of unplanned transfers and inpatient outcomes; previous small reports suggest that delays in ICU transfer adversely affect mortality and length of stay.12, 13, 24 Parkhe et al. compared 99 direct ICU admit patients with 23 who experienced early unplanned transfers; mortality at 30 days was significantly higher among patients with unplanned transfers.13 The current multifacility study included considerably more patients and confirmed an in‐hospital mortality gapalbeit a smaller onebetween patients with early transfers and those directly admitted to the ICU.

We focused on unplanned transfers during the earliest phase of hospitalization to identify patients who might benefit from improved recognition of, and intervention for, impending critical illness. We found that even patients requiring transfers within 8 hours of hospital admission were at an increased risk of death. Bapoje et al. recently reported that as many as 80% of early unplanned transfers were preventable and that most resulted from inappropriate admission triage.11 Together, these findings suggest that heightened attention to identifying such patients at admission or within the first day of hospitalizationwhen the rates of unplanned transfers peakis critical.

Several important limitations should be recognized in interpreting these results. First, this study was not designed to specifically identify the reasons for unplanned transfers, limiting our ability to characterize episodes in which timely care could have prevented excess mortality. Notably, while previous work suggests that many early unplanned transfers might be prevented with appropriate triage, it is likely that some excess deaths are not preventable even if every patient could be admitted to the ICU directly.

We were able to characterize patient outcomes by admitting diagnoses. Patients admitted for pneumonia and respiratory infection, gastrointestinal bleeding, COPD exacerbation, or seizures demonstrated excess mortality compared with matched ICU controls, while those with AMI, sepsis, and stroke did not. It is possible that differences in diagnosis‐specific excess mortality resulted from increasing adherence to well‐defined practice guidelines for specific high‐risk conditions.2527 For example, international awareness campaigns for the treatment of sepsis, AMI, and strokeSurviving Sepsis, Door‐to‐Balloon, and F.A.S.T.emphasize early interventions to minimize morbidity and mortality.

Second, the data utilized in this study were based on automated variables extracted from the electronic medical record. Mortality prediction models based on automated variables have demonstrated excellent performance among ICU and non‐ICU populations14, 18, 28; however, the inclusion of additional data (eg, vital signs or neurological status) would likely improve baseline risk adjustment.5, 10, 2931 Multiple studies have demonstrated that vital signs and clinician judgment can predict patients at an increased risk of deterioration.5, 10, 2931 Such data might also provide insight into residual factors that influenced clinicians' decisions to triage patients to an ICU versus non‐ICU admissiona focus area of our ongoing research efforts. Utilizing electronically available data, however, facilitated the identification of a cohort of patients far larger than that in prior studies. Where previous work has also been limited by substantial variability in baseline characteristics among study subjects,1, 2, 12, 13 our large sample produced a high percentage of multivariable case matches.

Third, we chose to match patients with a severity of illness index based on variables available at the time of hospital admission. While this mortality prediction model has demonstrated excellent performance in internal and external populations,14, 18 it is calibrated for general inpatient, rather than critically ill, populations. It remains possible that case matching with ICU‐specific severity of illness scores might alter matching characteristics, however, previous studies suggest that severity of illness, as measured by these scores, is comparable between direct ICU admits and early ICU transfers.13 Importantly, our matching procedure avoided the potential confounding known to exist with the use of prediction models based on discharge or intra‐hospitalization data.32, 33

Finally, while we were able to evaluate unplanned transfer timing in a multifacility sample, all patient care occurred within a large integrated healthcare delivery system. The overall observed mortality in our study was lower than that reported in prior studies which considered more limited patient cohorts.1, 2, 12, 13, 22 Thus, differences in patient case‐mix or ICU structure must be considered when applying our results to other healthcare delivery systems.

This hypothesis‐generating study, based on a large, multifacility sample of hospitalizations, suggests several areas of future investigation. Future work should detail specific aspects of care among patients with unplanned transfer, including: evaluating the structures and processes involved in triage decisions, measuring the effects on mortality through implementation of interventions (eg, rapid response teams or diagnosis‐specific treatment protocols), and defining the causes and risk factors for unplanned transfers by elapsed time.

In conclusion, the risk of an unplanned ICU transfera common event among hospitalized patientsis highest within 24 hours of hospitalization. Patients with early unplanned transfers have increased mortality and length of stay compared to those admitted directly to the ICU. Even patients transferred to the ICU within 8 hours of hospital admission are at an increased risk of death when compared with those admitted directly. Substantial variability in unplanned transfer outcomes exists based on admitting diagnoses. Future research should characterize unplanned transfers in greater detail with the goal of identifying patients that would benefit from improved triage and early ICU transfer.

If you wish to receive credit for this activity, which begins on the next page, please refer to the website: www.blackwellpublishing.com/cme.

Accreditation and Designation Statement

Blackwell Futura Media Services designates this educational activity for a 1 AMA PRA Category 1 Credit. Physicians should only claim credit commensurate with the extent of their participation in the activity.

Blackwell Futura Media Services is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.

Educational Objectives

Upon completion of this educational activity, participants will be better able to employ automated bed history data to examine outcomes of intra‐hospital transfers using all hospital admissions as the denominator.

Continuous participation in the Journal of Hospital Medicine CME program will enable learners to be better able to:

• Interpret clinical guidelines and their applications for higher quality and more efficient care for all hospitalized patients.

• Describe the standard of care for common illnesses and conditions treated in the hospital; such as pneumonia, COPD exacerbation, acute coronary syndrome, HF exacerbation, glycemic control, venous thromboembolic disease, stroke, etc.

• Discuss evidence‐based recommendations involving transitions of care, including the hospital discharge process.

• Gain insights into the roles of hospitalists as medical educators, researchers, medical ethicists, palliative care providers, and hospital‐based geriatricians.

• Incorporate best practices for hospitalist administration, including quality improvement, patient safety, practice management, leadership, and demonstrating hospitalist value.

• Identify evidence‐based best practices and trends for both adult and pediatric hospital medicine.

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For information on applicability and acceptance of continuing medical education credit for this activity, please consult your professional licensing board.

This activity is designed to be completed within the time designated on the title page; physicians should claim only those credits that reflect the time actually spent in the activity. To successfully earn credit, participants must complete the activity during the valid credit period that is noted on the title page.

Follow these steps to earn credit:

• Log on to www.blackwellpublishing.com/cme.

• Read the target audience, learning objectives, and author disclosures.

• Read the article in print or online format.

• Reflect on the article.

• Access the CME Exam, and choose the best answer to each question.

• Complete the required evaluation component of the activity.

If you wish to receive credit for this activity, which begins on the next page, please refer to the website: www.blackwellpublishing.com/cme.

Accreditation and Designation Statement

Blackwell Futura Media Services designates this educational activity for a 1 AMA PRA Category 1 Credit. Physicians should only claim credit commensurate with the extent of their participation in the activity.

Blackwell Futura Media Services is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.

Educational Objectives

Upon completion of this educational activity, participants will be better able to employ automated bed history data to examine outcomes of intra‐hospital transfers using all hospital admissions as the denominator.

Continuous participation in the Journal of Hospital Medicine CME program will enable learners to be better able to:

• Interpret clinical guidelines and their applications for higher quality and more efficient care for all hospitalized patients.

• Describe the standard of care for common illnesses and conditions treated in the hospital; such as pneumonia, COPD exacerbation, acute coronary syndrome, HF exacerbation, glycemic control, venous thromboembolic disease, stroke, etc.

• Discuss evidence‐based recommendations involving transitions of care, including the hospital discharge process.

• Gain insights into the roles of hospitalists as medical educators, researchers, medical ethicists, palliative care providers, and hospital‐based geriatricians.

• Incorporate best practices for hospitalist administration, including quality improvement, patient safety, practice management, leadership, and demonstrating hospitalist value.

• Identify evidence‐based best practices and trends for both adult and pediatric hospital medicine.

Instructions on Receiving Credit

For information on applicability and acceptance of continuing medical education credit for this activity, please consult your professional licensing board.

This activity is designed to be completed within the time designated on the title page; physicians should claim only those credits that reflect the time actually spent in the activity. To successfully earn credit, participants must complete the activity during the valid credit period that is noted on the title page.

Follow these steps to earn credit:

• Log on to www.blackwellpublishing.com/cme.

• Read the target audience, learning objectives, and author disclosures.

• Read the article in print or online format.

• Reflect on the article.

• Access the CME Exam, and choose the best answer to each question.

• Complete the required evaluation component of the activity.

If you wish to receive credit for this activity, which begins on the next page, please refer to the website: www.blackwellpublishing.com/cme.

Accreditation and Designation Statement

Blackwell Futura Media Services designates this educational activity for a 1 AMA PRA Category 1 Credit. Physicians should only claim credit commensurate with the extent of their participation in the activity.

Blackwell Futura Media Services is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.

Educational Objectives

Upon completion of this educational activity, participants will be better able to employ automated bed history data to examine outcomes of intra‐hospital transfers using all hospital admissions as the denominator.

Continuous participation in the Journal of Hospital Medicine CME program will enable learners to be better able to:

• Interpret clinical guidelines and their applications for higher quality and more efficient care for all hospitalized patients.

• Describe the standard of care for common illnesses and conditions treated in the hospital; such as pneumonia, COPD exacerbation, acute coronary syndrome, HF exacerbation, glycemic control, venous thromboembolic disease, stroke, etc.

• Discuss evidence‐based recommendations involving transitions of care, including the hospital discharge process.

• Gain insights into the roles of hospitalists as medical educators, researchers, medical ethicists, palliative care providers, and hospital‐based geriatricians.

• Incorporate best practices for hospitalist administration, including quality improvement, patient safety, practice management, leadership, and demonstrating hospitalist value.

• Identify evidence‐based best practices and trends for both adult and pediatric hospital medicine.

Instructions on Receiving Credit

For information on applicability and acceptance of continuing medical education credit for this activity, please consult your professional licensing board.

This activity is designed to be completed within the time designated on the title page; physicians should claim only those credits that reflect the time actually spent in the activity. To successfully earn credit, participants must complete the activity during the valid credit period that is noted on the title page.

Follow these steps to earn credit:

• Log on to www.blackwellpublishing.com/cme.

• Read the target audience, learning objectives, and author disclosures.

• Read the article in print or online format.

• Reflect on the article.

• Access the CME Exam, and choose the best answer to each question.

• Complete the required evaluation component of the activity.

Considerable research and public attention is being paid to the quantification, risk adjustment, and reporting of inpatient mortality.15 Inpatient mortality is reported as aggregate mortality (for all hospitalized patients or those with a specific diagnosis3, 6) or intensive care unit (ICU) mortality.7, 8 While reporting aggregate hospital or aggregate ICU mortality rates is useful, it is also important to develop reporting strategies that go beyond simply using data elements found in administrative databases (eg, diagnosis and procedure codes) to quantify practice variation. Ideally, such strategies would permit delineating processes of careparticularly those potentially under the control of hospitalists, not only intensiviststo identify improvement opportunities. One such process, which can be tracked using the bed history component of a patient's electronic medical record, is the transfer of patients between different units within the same hospital.

Several studies have documented that risk of ICU death is highest among patients transferred from general medical‐surgical wards, intermediate among direct admissions from the emergency department, and lowest among surgical admissions.911 Opportunities to reduce subsequent ICU mortality have been studied among ward patients who develop sepsis and are then transferred to the ICU,12 among patients who experience cardiac arrest,13, 14 as well as among patients with any physiological deterioration (eg, through the use of rapid response teams).1517 Most of these studies have been single‐center studies and/or studies reporting only an ICU denominator. While useful in some respects, such studies are less helpful to hospitalists, who would benefit from better understanding of the types of patients transferred and the total impact that transfers to a higher level of care make on general medical‐surgical wards. In addition, entities such as the Institute for Healthcare Improvement recommend the manual review of records of patients who were transferred from the ward to the ICU18 to identify performance improvement opportunities. While laudable, such approaches do not lend themselves to automated reporting strategies.

We recently described a new risk adjustment methodology for inpatient mortality based entirely on automated data preceding hospital admission and not restricted to ICU patients. This methodology, which has been externally validated in Ottawa, Canada, after development in the Kaiser Permanente Medical Care Program (KPMCP), permits quantification of a patient's pre‐existing comorbidity burden, physiologic derangement at the time of admission, and overall inpatient mortality risk.19, 20 The primary purpose of this study was to combine this methodology with bed history analysis to quantify the in‐hospital mortality and length of stay (LOS) of patients who experienced intra‐hospital transfers in a large, multihospital system. As a secondary goal, we also wanted to assess the degree to which these transfers could be predicted based on information available prior to a patient's admission.

Materials and Methods

This project was approved by the Northern California KPMCP Institutional Review Board for the Protection of Human Subjects.

The Northern California KPMCP serves a total population of approximately 3.3 million members. Under a mutual exclusivity arrangement, physicians of The Permanente Medical Group, Inc., care for Kaiser Foundation Health Plan, Inc. members at facilities owned by Kaiser Foundation Hospitals, Inc. All Northern California KPMCP hospitals and clinics employ the same information systems with a common medical record number and can track care covered by the plan but delivered elsewhere. Databases maintained by the KPMCP capture admission and discharge times, admission and discharge diagnoses and procedures (assigned by professional coders), bed histories, inter‐hospital transfers, as well as the results of all inpatient and outpatient laboratory tests. The use of these databases for research has been described in multiple reports.2124

Our setting consisted of all 19 hospitals owned and operated by the KPMCP, whose characteristics are summarized in the Supporting Information Appendix available to interested readers. These include the 17 described in our previous report19 as well as 2 new hospitals (Antioch and Manteca) which are similar in size and type of population served. Our study population consisted of all patients admitted to these 19 hospitals who met these criteria: 1) hospitalization began from November 1st, 2006 through January 31st, 2008; 2) initial hospitalization occurred at a Northern California KPMCP hospital (ie, for inter‐hospital transfers, the first hospital stay occurred within the KPMCP); 3) age 15 years; and 4) hospitalization was not for childbirth.

We defined a linked hospitalization as the time period that began with a patient's admission to the hospital and ended with the patient's discharge (home, to a nursing home, or death). Linked hospitalizations can thus involve more than 1 hospital stay and could include a patient transfer from one hospital to another prior to definitive discharge. For linked hospitalizations, mortality was attributed to the admitting KPMCP hospital (ie, if a patient was admitted to hospital A, transferred to B, and died at hospital B, mortality was attributed to hospital A). We defined total LOS as the exact time in hours from when a patient was first admitted to the hospital until death or final discharge home or to a nursing home, while total ICU or transitional care unit (TCU, referred to as stepdown unit in some hospitals) LOS was calculated for all individual ICU or TCU stays during the hospital stay.

Results

During the study period, a total of 249,129 individual hospital stays involving 170,151 patients occurred at these 19 hospitals. After concatenation of inter‐hospital transfers, we were left with 237,208 linked hospitalizations. We excluded 26,738 linked hospitalizations that began at a non‐KPMCP hospital (ie, they were transported in), leaving a total of 210,470 linked hospitalizations involving 150,495 patients. The overall linked hospitalization mortality rate was 3.30%.

Table 1 summarizes cohort characteristics based on initial hospital location. On admission, ICU patients had the highest degree of physiologic derangement as well as the highest predicted mortality. Considerable inter‐hospital variation was present in both predictors and outcomes; details on these variations are provided in the Supporting Information Appendix.

Table 2 summarizes data from 3 groups of patients: patients initially admitted to the ward, or TCU, who did not experience a transfer to a higher level of care and patients admitted to these 2 units who did experience such a transfer. Patients who experienced a transfer constituted 5.3% (6,484/121,237) of ward patients and 6.7% (1,384/20,556) of TCU patients. Transferred patients tended to be older, have more acute physiologic derangement (higher LAPS), a greater pre‐existing illness burden (higher COPS), and a higher predicted mortality risk. Among ward patients, those with the following admission diagnoses were most likely to experience a transfer to a higher level of care: gastrointestinal bleeding (10.8% of all transfers), pneumonia (8.7%), and other infections (8.2%). The diagnoses most likely to be associated with death following transfer were cancer (death rate among transferred patients, 48%), renal disease (death rate, 36%), and liver disease (33%). Similar distributions were observed for TCU patients.

Table 3 compares outcomes among ward and TCU patients who did and did not experience a transfer to a higher level of care. The table shows that transferred patients were almost 3 times as likely to die, even after controlling for severity of illness, and that their hospital LOS was 9 days higher than expected. This increased risk was seen in all hospitals and among all transfer types (ward to ICU, ward to TCU, and TCU to ICU).

Table 3 also shows that, among decedent patients, those who never left the ward or TCU died much sooner than those who died following transfer. Among direct admits to the ICU, the median LOS at time of death was 3.9 days, with a mean of 9.4 standard deviation of 19.9 days, while the corresponding times for TCU direct admits were a median and mean LOS of 6.5 and 11.7 19.5 days.

Table 4 summarizes outcomes among different patient subgroups that did and did not experience a transfer to a higher level of care. Based on location, patients who experienced a transfer from the TCU to the ICU had the highest crude death rate, but patients transferred from the ward to the ICU had the highest OEMR. On the other hand, if one divides patients by the degree of physiologic derangement, patients with low LAPS who experienced a transfer had the highest OEMR. With respect to LOS, patients transferred from the TCU to the ICU had the highest OMELOS (13.4 extra days).

Transfers to a higher level of care at a different hospital, which in the KPMCP are usually planned, experienced lower mortality than transfers within the same hospital. For ward to TCU transfers, intra‐hospital transfers had a mortality of 12.1% while inter‐hospital transfers had a mortality of 5.7%. Corresponding rates for ward to ICU transfers were 21.7% and 11.2%, and for TCU to ICU transfers the rates were 25.9% and 12.5%, respectively.

Among patients initially admitted to the ward, a model to predict the occurrence of a transfer to a higher level of care (within 48 hours after admission) that included age, sex, admission type, primary condition, LAPS, COPS, and interaction terms had poor discrimination, with an area under the receiver operator characteristic (c statistic) of only 0.64. The c statistic for a model to predict transfer after 48 hours was 0.66. The corresponding models for TCU admits had c statistics of 0.67 and 0.68. All four models had poor calibration.

Discussion

Using automated bed history data permits characterizing a patient population with disproportionate mortality and LOS: intra‐hospital transfers to special care units (ICUs or TCUs). Indeed, the largest subset of these patients (those initially admitted to the ward or TCU) constituted only 3.7% of all admissions, but accounted for 24.2% of all ICU admissions, 21.7% of all hospital deaths, and 13.2% of all hospital days. These patients also had very elevated OEMRs and OMELOS. Models based on age, sex, preadmission laboratory test results, and comorbidities did not predict the occurrence of these transfers.

We performed multivariate analyses to explore the degree to which electronically assigned preadmission severity scores could predict these transfers. These analyses found that, compared to our ability to predict inpatient or 30‐day mortality at the time of admission, which is excellent, our ability to predict the occurrence of transfer after admission is much more limited. These results highlight the limitations of severity scores that rely on automated data, which may not have adequate discrimination when it comes to determining the risk of an adverse outcome within a narrow time frame. For example, among the 121,237 patients initially admitted to the ward who did not experience an intra‐hospital transfer, the mean LAPS was 18.9, while the mean LAPS among the 6,484 ward patients who did experience a transfer was 25.5. Differences between the mean and median LAPS, COPS, and predicted mortality risk among transferred and non‐transferred patients were significant (P < 0.0001 for all comparisons). However, examination of the distribution of LAPS, COPS, and predicted mortality risk between these two groups of patients showed considerable overlap.

Our methodology resembles Silber et al.'s27, 28 concept of failure to rescue in that it focuses on events occurring after hospitalization. Silber et al. argue that a hospital's quality can be measured by quantifying the degree to which patients who experience new problems are successfully rescued. Furthermore, quantification of those situations where rescue attempts are unsuccessful is felt to be superior to simply comparing raw or adjusted mortality rates because these are primarily determined by underlying case mix. The primary difference between Silber et al.'s approach and ours is at the level of detailthey specified a specific set of complications, whereas our measure is more generic and would include patients with many of the complications specified by Silber et al.27, 28

Most of the patients transferred to a higher level of care in our cohort survived (ie, were rescued), indicating that intensive care is beneficial. However, the fact that these patients had elevated OEMRs and OMELOS indicates that the real challenge facing hospitalists involves the timing of provision of a beneficial intervention. In theory, improved timing could result from earlier detection of problems, which is the underlying rationale for employing rapid response teams. However, the fact that our electronic tools (LAPS, COPS) cannot predict patient deteriorations within a narrow time frame suggests that early detection will remain a major challenge. Manually assigned vital signs scores designed for this purpose do not have good discrimination either.29, 30 This raises the possibility that, though patient groups may differ in terms of overall illness severity and mortality risk, differences at the individual patient level may be too subtle for clinicians to detect. Future research may thus need to focus on scores that combine laboratory data, vital signs, trends in data,31, 32 and newer proteomic markers (eg, procalcitonin).33 We also found that most transfers occurred early (within <72 hours), raising the possibility that at least some of these transfers may involve issues around triage rather than sudden deterioration.

Our study has important limitations. Due to resource constraints and limited data availability, we could not characterize the patients as well as might be desirable; in particular, we could not make full determinations of the actual reasons for patients' transfer for all patients. Broadly speaking, transfer to a higher level of care could be due to inappropriate triage, appropriate (preventive) transfer (which could include transfer to a more richly staffed unit for a specific procedure), relentless progression of disease despite maximal therapy, the occurrence of management errors, patient and family uncertainty about goals of care or inadequate understanding of treatment options and prognoses, or a combination of these factors. We could not make these distinctions with currently available electronic data. This is also true of postsurgical patients, in whom it is difficult to determine which transfers to intensive care might be planned (eg, in the case of surgical procedures where ICU care is anticipated) as opposed to the occurrence of a deterioration during or following surgery. Another major limitation of this study is our inability to identify code or no code status electronically. The elapsed LOS at time of death among patients who experienced a transfer to a higher level of care (as compared to patients who died in the ward without ever experiencing intra‐hospital transfer) suggests, but does not prove, that prolonged efforts were being made to keep them alive. We were also limited in terms of having access to other process data (eg, physician staffing levels, provision and timing of palliative care). Having ICU severity of illness scores would have permitted us to compare our cohort to those of other recent studies showing elevated mortality rates among transfer patients,911 but we have not yet developed that capability.

Consideration of our study findings suggests a possible research agenda that could be implemented by hospitalist researchers. This agenda should emphasize three areas: detection, intervention, and reflection.

With respect to detection, attention needs to be paid to better tools for quantifying patient risk at the time a decision to admit to the ward is made. It is likely that such tools will need to combine the attributes of our severity score (LAPS) with those of the manually assigned scores.30, 34 In some cases, use of these tools could lead a physician to change the locus of admission from the ward to the TCU or ICU, which could improve outcomes by ensuring more timely provision of intensive care. Since problems with initial triage could be due to factors other than the failure to suspect or anticipate impending instability, future research should also include a cognitive component (eg, quantifying what proportion of subsequent patient deteriorations could be ascribed to missed diagnoses35). Additional work also needs to be done on developing mathematical models that can inform electronic monitoring of ward (not just ICU) patients.

Research on interventions that hospitalists can use to prevent the need for intensive care or to improve the rescue rate should take two routes. The first is a disease‐specific route, which builds on the fact that a relatively small set of conditions (pneumonia, sepsis, gastrointestinal bleeding) account for most transfers to a higher level of care. Condition‐specific protocols, checklists, and bundles36 tailored to a ward environment (as opposed to the ICU or to the entire hospital) might prevent deteriorations in these patients, as has been reported for sepsis.37 The second route is to improve the overall capabilities of rapid response and code blue teams. Such research would need to include a more careful assessment of what commonalities exist among patients who were and were not successfully rescued by these teams. This approach would probably yield more insights than the current literature, which focuses on whether rapid response teams are a good thing or not.

Finally, research also needs to be performed on how hospitalists reflect on adverse outcomes among ward patients. Greater emphasis needs to be placed on moving beyond trigger tool approaches that rely on manual chart review. In an era of expanding use of electronic medical record systems, more work needs to be done on how to harness these to provide hospitalists with better quantitative and risk‐adjusted information. This information should not be limited to simply reporting rates of transfers and deaths. Rather, finer distinctions must be provided with respect of the type of patients (ie, more diagnostic detail), the clinical status of patients (ie, more physiologic detail), as well as the effects of including or excluding patients in whom therapeutic options may be limited (ie, do not resuscitate and comfort care patients) on reported rates. Ideally, researchers should develop better process and outcomes measures that could be tested in collaborative networks that include multiple nonacademic general medical‐surgical wards.

Considerable research and public attention is being paid to the quantification, risk adjustment, and reporting of inpatient mortality.15 Inpatient mortality is reported as aggregate mortality (for all hospitalized patients or those with a specific diagnosis3, 6) or intensive care unit (ICU) mortality.7, 8 While reporting aggregate hospital or aggregate ICU mortality rates is useful, it is also important to develop reporting strategies that go beyond simply using data elements found in administrative databases (eg, diagnosis and procedure codes) to quantify practice variation. Ideally, such strategies would permit delineating processes of careparticularly those potentially under the control of hospitalists, not only intensiviststo identify improvement opportunities. One such process, which can be tracked using the bed history component of a patient's electronic medical record, is the transfer of patients between different units within the same hospital.

Several studies have documented that risk of ICU death is highest among patients transferred from general medical‐surgical wards, intermediate among direct admissions from the emergency department, and lowest among surgical admissions.911 Opportunities to reduce subsequent ICU mortality have been studied among ward patients who develop sepsis and are then transferred to the ICU,12 among patients who experience cardiac arrest,13, 14 as well as among patients with any physiological deterioration (eg, through the use of rapid response teams).1517 Most of these studies have been single‐center studies and/or studies reporting only an ICU denominator. While useful in some respects, such studies are less helpful to hospitalists, who would benefit from better understanding of the types of patients transferred and the total impact that transfers to a higher level of care make on general medical‐surgical wards. In addition, entities such as the Institute for Healthcare Improvement recommend the manual review of records of patients who were transferred from the ward to the ICU18 to identify performance improvement opportunities. While laudable, such approaches do not lend themselves to automated reporting strategies.

We recently described a new risk adjustment methodology for inpatient mortality based entirely on automated data preceding hospital admission and not restricted to ICU patients. This methodology, which has been externally validated in Ottawa, Canada, after development in the Kaiser Permanente Medical Care Program (KPMCP), permits quantification of a patient's pre‐existing comorbidity burden, physiologic derangement at the time of admission, and overall inpatient mortality risk.19, 20 The primary purpose of this study was to combine this methodology with bed history analysis to quantify the in‐hospital mortality and length of stay (LOS) of patients who experienced intra‐hospital transfers in a large, multihospital system. As a secondary goal, we also wanted to assess the degree to which these transfers could be predicted based on information available prior to a patient's admission.

Materials and Methods

This project was approved by the Northern California KPMCP Institutional Review Board for the Protection of Human Subjects.

The Northern California KPMCP serves a total population of approximately 3.3 million members. Under a mutual exclusivity arrangement, physicians of The Permanente Medical Group, Inc., care for Kaiser Foundation Health Plan, Inc. members at facilities owned by Kaiser Foundation Hospitals, Inc. All Northern California KPMCP hospitals and clinics employ the same information systems with a common medical record number and can track care covered by the plan but delivered elsewhere. Databases maintained by the KPMCP capture admission and discharge times, admission and discharge diagnoses and procedures (assigned by professional coders), bed histories, inter‐hospital transfers, as well as the results of all inpatient and outpatient laboratory tests. The use of these databases for research has been described in multiple reports.2124

Our setting consisted of all 19 hospitals owned and operated by the KPMCP, whose characteristics are summarized in the Supporting Information Appendix available to interested readers. These include the 17 described in our previous report19 as well as 2 new hospitals (Antioch and Manteca) which are similar in size and type of population served. Our study population consisted of all patients admitted to these 19 hospitals who met these criteria: 1) hospitalization began from November 1st, 2006 through January 31st, 2008; 2) initial hospitalization occurred at a Northern California KPMCP hospital (ie, for inter‐hospital transfers, the first hospital stay occurred within the KPMCP); 3) age 15 years; and 4) hospitalization was not for childbirth.

We defined a linked hospitalization as the time period that began with a patient's admission to the hospital and ended with the patient's discharge (home, to a nursing home, or death). Linked hospitalizations can thus involve more than 1 hospital stay and could include a patient transfer from one hospital to another prior to definitive discharge. For linked hospitalizations, mortality was attributed to the admitting KPMCP hospital (ie, if a patient was admitted to hospital A, transferred to B, and died at hospital B, mortality was attributed to hospital A). We defined total LOS as the exact time in hours from when a patient was first admitted to the hospital until death or final discharge home or to a nursing home, while total ICU or transitional care unit (TCU, referred to as stepdown unit in some hospitals) LOS was calculated for all individual ICU or TCU stays during the hospital stay.

Results

During the study period, a total of 249,129 individual hospital stays involving 170,151 patients occurred at these 19 hospitals. After concatenation of inter‐hospital transfers, we were left with 237,208 linked hospitalizations. We excluded 26,738 linked hospitalizations that began at a non‐KPMCP hospital (ie, they were transported in), leaving a total of 210,470 linked hospitalizations involving 150,495 patients. The overall linked hospitalization mortality rate was 3.30%.

Table 1 summarizes cohort characteristics based on initial hospital location. On admission, ICU patients had the highest degree of physiologic derangement as well as the highest predicted mortality. Considerable inter‐hospital variation was present in both predictors and outcomes; details on these variations are provided in the Supporting Information Appendix.

Table 2 summarizes data from 3 groups of patients: patients initially admitted to the ward, or TCU, who did not experience a transfer to a higher level of care and patients admitted to these 2 units who did experience such a transfer. Patients who experienced a transfer constituted 5.3% (6,484/121,237) of ward patients and 6.7% (1,384/20,556) of TCU patients. Transferred patients tended to be older, have more acute physiologic derangement (higher LAPS), a greater pre‐existing illness burden (higher COPS), and a higher predicted mortality risk. Among ward patients, those with the following admission diagnoses were most likely to experience a transfer to a higher level of care: gastrointestinal bleeding (10.8% of all transfers), pneumonia (8.7%), and other infections (8.2%). The diagnoses most likely to be associated with death following transfer were cancer (death rate among transferred patients, 48%), renal disease (death rate, 36%), and liver disease (33%). Similar distributions were observed for TCU patients.

Table 3 compares outcomes among ward and TCU patients who did and did not experience a transfer to a higher level of care. The table shows that transferred patients were almost 3 times as likely to die, even after controlling for severity of illness, and that their hospital LOS was 9 days higher than expected. This increased risk was seen in all hospitals and among all transfer types (ward to ICU, ward to TCU, and TCU to ICU).

Table 3 also shows that, among decedent patients, those who never left the ward or TCU died much sooner than those who died following transfer. Among direct admits to the ICU, the median LOS at time of death was 3.9 days, with a mean of 9.4 standard deviation of 19.9 days, while the corresponding times for TCU direct admits were a median and mean LOS of 6.5 and 11.7 19.5 days.

Table 4 summarizes outcomes among different patient subgroups that did and did not experience a transfer to a higher level of care. Based on location, patients who experienced a transfer from the TCU to the ICU had the highest crude death rate, but patients transferred from the ward to the ICU had the highest OEMR. On the other hand, if one divides patients by the degree of physiologic derangement, patients with low LAPS who experienced a transfer had the highest OEMR. With respect to LOS, patients transferred from the TCU to the ICU had the highest OMELOS (13.4 extra days).

Transfers to a higher level of care at a different hospital, which in the KPMCP are usually planned, experienced lower mortality than transfers within the same hospital. For ward to TCU transfers, intra‐hospital transfers had a mortality of 12.1% while inter‐hospital transfers had a mortality of 5.7%. Corresponding rates for ward to ICU transfers were 21.7% and 11.2%, and for TCU to ICU transfers the rates were 25.9% and 12.5%, respectively.

Among patients initially admitted to the ward, a model to predict the occurrence of a transfer to a higher level of care (within 48 hours after admission) that included age, sex, admission type, primary condition, LAPS, COPS, and interaction terms had poor discrimination, with an area under the receiver operator characteristic (c statistic) of only 0.64. The c statistic for a model to predict transfer after 48 hours was 0.66. The corresponding models for TCU admits had c statistics of 0.67 and 0.68. All four models had poor calibration.

Discussion

Using automated bed history data permits characterizing a patient population with disproportionate mortality and LOS: intra‐hospital transfers to special care units (ICUs or TCUs). Indeed, the largest subset of these patients (those initially admitted to the ward or TCU) constituted only 3.7% of all admissions, but accounted for 24.2% of all ICU admissions, 21.7% of all hospital deaths, and 13.2% of all hospital days. These patients also had very elevated OEMRs and OMELOS. Models based on age, sex, preadmission laboratory test results, and comorbidities did not predict the occurrence of these transfers.

We performed multivariate analyses to explore the degree to which electronically assigned preadmission severity scores could predict these transfers. These analyses found that, compared to our ability to predict inpatient or 30‐day mortality at the time of admission, which is excellent, our ability to predict the occurrence of transfer after admission is much more limited. These results highlight the limitations of severity scores that rely on automated data, which may not have adequate discrimination when it comes to determining the risk of an adverse outcome within a narrow time frame. For example, among the 121,237 patients initially admitted to the ward who did not experience an intra‐hospital transfer, the mean LAPS was 18.9, while the mean LAPS among the 6,484 ward patients who did experience a transfer was 25.5. Differences between the mean and median LAPS, COPS, and predicted mortality risk among transferred and non‐transferred patients were significant (P < 0.0001 for all comparisons). However, examination of the distribution of LAPS, COPS, and predicted mortality risk between these two groups of patients showed considerable overlap.

Our methodology resembles Silber et al.'s27, 28 concept of failure to rescue in that it focuses on events occurring after hospitalization. Silber et al. argue that a hospital's quality can be measured by quantifying the degree to which patients who experience new problems are successfully rescued. Furthermore, quantification of those situations where rescue attempts are unsuccessful is felt to be superior to simply comparing raw or adjusted mortality rates because these are primarily determined by underlying case mix. The primary difference between Silber et al.'s approach and ours is at the level of detailthey specified a specific set of complications, whereas our measure is more generic and would include patients with many of the complications specified by Silber et al.27, 28

Most of the patients transferred to a higher level of care in our cohort survived (ie, were rescued), indicating that intensive care is beneficial. However, the fact that these patients had elevated OEMRs and OMELOS indicates that the real challenge facing hospitalists involves the timing of provision of a beneficial intervention. In theory, improved timing could result from earlier detection of problems, which is the underlying rationale for employing rapid response teams. However, the fact that our electronic tools (LAPS, COPS) cannot predict patient deteriorations within a narrow time frame suggests that early detection will remain a major challenge. Manually assigned vital signs scores designed for this purpose do not have good discrimination either.29, 30 This raises the possibility that, though patient groups may differ in terms of overall illness severity and mortality risk, differences at the individual patient level may be too subtle for clinicians to detect. Future research may thus need to focus on scores that combine laboratory data, vital signs, trends in data,31, 32 and newer proteomic markers (eg, procalcitonin).33 We also found that most transfers occurred early (within <72 hours), raising the possibility that at least some of these transfers may involve issues around triage rather than sudden deterioration.

Our study has important limitations. Due to resource constraints and limited data availability, we could not characterize the patients as well as might be desirable; in particular, we could not make full determinations of the actual reasons for patients' transfer for all patients. Broadly speaking, transfer to a higher level of care could be due to inappropriate triage, appropriate (preventive) transfer (which could include transfer to a more richly staffed unit for a specific procedure), relentless progression of disease despite maximal therapy, the occurrence of management errors, patient and family uncertainty about goals of care or inadequate understanding of treatment options and prognoses, or a combination of these factors. We could not make these distinctions with currently available electronic data. This is also true of postsurgical patients, in whom it is difficult to determine which transfers to intensive care might be planned (eg, in the case of surgical procedures where ICU care is anticipated) as opposed to the occurrence of a deterioration during or following surgery. Another major limitation of this study is our inability to identify code or no code status electronically. The elapsed LOS at time of death among patients who experienced a transfer to a higher level of care (as compared to patients who died in the ward without ever experiencing intra‐hospital transfer) suggests, but does not prove, that prolonged efforts were being made to keep them alive. We were also limited in terms of having access to other process data (eg, physician staffing levels, provision and timing of palliative care). Having ICU severity of illness scores would have permitted us to compare our cohort to those of other recent studies showing elevated mortality rates among transfer patients,911 but we have not yet developed that capability.

Consideration of our study findings suggests a possible research agenda that could be implemented by hospitalist researchers. This agenda should emphasize three areas: detection, intervention, and reflection.

With respect to detection, attention needs to be paid to better tools for quantifying patient risk at the time a decision to admit to the ward is made. It is likely that such tools will need to combine the attributes of our severity score (LAPS) with those of the manually assigned scores.30, 34 In some cases, use of these tools could lead a physician to change the locus of admission from the ward to the TCU or ICU, which could improve outcomes by ensuring more timely provision of intensive care. Since problems with initial triage could be due to factors other than the failure to suspect or anticipate impending instability, future research should also include a cognitive component (eg, quantifying what proportion of subsequent patient deteriorations could be ascribed to missed diagnoses35). Additional work also needs to be done on developing mathematical models that can inform electronic monitoring of ward (not just ICU) patients.

Research on interventions that hospitalists can use to prevent the need for intensive care or to improve the rescue rate should take two routes. The first is a disease‐specific route, which builds on the fact that a relatively small set of conditions (pneumonia, sepsis, gastrointestinal bleeding) account for most transfers to a higher level of care. Condition‐specific protocols, checklists, and bundles36 tailored to a ward environment (as opposed to the ICU or to the entire hospital) might prevent deteriorations in these patients, as has been reported for sepsis.37 The second route is to improve the overall capabilities of rapid response and code blue teams. Such research would need to include a more careful assessment of what commonalities exist among patients who were and were not successfully rescued by these teams. This approach would probably yield more insights than the current literature, which focuses on whether rapid response teams are a good thing or not.

Finally, research also needs to be performed on how hospitalists reflect on adverse outcomes among ward patients. Greater emphasis needs to be placed on moving beyond trigger tool approaches that rely on manual chart review. In an era of expanding use of electronic medical record systems, more work needs to be done on how to harness these to provide hospitalists with better quantitative and risk‐adjusted information. This information should not be limited to simply reporting rates of transfers and deaths. Rather, finer distinctions must be provided with respect of the type of patients (ie, more diagnostic detail), the clinical status of patients (ie, more physiologic detail), as well as the effects of including or excluding patients in whom therapeutic options may be limited (ie, do not resuscitate and comfort care patients) on reported rates. Ideally, researchers should develop better process and outcomes measures that could be tested in collaborative networks that include multiple nonacademic general medical‐surgical wards.

Considerable research and public attention is being paid to the quantification, risk adjustment, and reporting of inpatient mortality.15 Inpatient mortality is reported as aggregate mortality (for all hospitalized patients or those with a specific diagnosis3, 6) or intensive care unit (ICU) mortality.7, 8 While reporting aggregate hospital or aggregate ICU mortality rates is useful, it is also important to develop reporting strategies that go beyond simply using data elements found in administrative databases (eg, diagnosis and procedure codes) to quantify practice variation. Ideally, such strategies would permit delineating processes of careparticularly those potentially under the control of hospitalists, not only intensiviststo identify improvement opportunities. One such process, which can be tracked using the bed history component of a patient's electronic medical record, is the transfer of patients between different units within the same hospital.

Several studies have documented that risk of ICU death is highest among patients transferred from general medical‐surgical wards, intermediate among direct admissions from the emergency department, and lowest among surgical admissions.911 Opportunities to reduce subsequent ICU mortality have been studied among ward patients who develop sepsis and are then transferred to the ICU,12 among patients who experience cardiac arrest,13, 14 as well as among patients with any physiological deterioration (eg, through the use of rapid response teams).1517 Most of these studies have been single‐center studies and/or studies reporting only an ICU denominator. While useful in some respects, such studies are less helpful to hospitalists, who would benefit from better understanding of the types of patients transferred and the total impact that transfers to a higher level of care make on general medical‐surgical wards. In addition, entities such as the Institute for Healthcare Improvement recommend the manual review of records of patients who were transferred from the ward to the ICU18 to identify performance improvement opportunities. While laudable, such approaches do not lend themselves to automated reporting strategies.

We recently described a new risk adjustment methodology for inpatient mortality based entirely on automated data preceding hospital admission and not restricted to ICU patients. This methodology, which has been externally validated in Ottawa, Canada, after development in the Kaiser Permanente Medical Care Program (KPMCP), permits quantification of a patient's pre‐existing comorbidity burden, physiologic derangement at the time of admission, and overall inpatient mortality risk.19, 20 The primary purpose of this study was to combine this methodology with bed history analysis to quantify the in‐hospital mortality and length of stay (LOS) of patients who experienced intra‐hospital transfers in a large, multihospital system. As a secondary goal, we also wanted to assess the degree to which these transfers could be predicted based on information available prior to a patient's admission.

Materials and Methods

This project was approved by the Northern California KPMCP Institutional Review Board for the Protection of Human Subjects.

The Northern California KPMCP serves a total population of approximately 3.3 million members. Under a mutual exclusivity arrangement, physicians of The Permanente Medical Group, Inc., care for Kaiser Foundation Health Plan, Inc. members at facilities owned by Kaiser Foundation Hospitals, Inc. All Northern California KPMCP hospitals and clinics employ the same information systems with a common medical record number and can track care covered by the plan but delivered elsewhere. Databases maintained by the KPMCP capture admission and discharge times, admission and discharge diagnoses and procedures (assigned by professional coders), bed histories, inter‐hospital transfers, as well as the results of all inpatient and outpatient laboratory tests. The use of these databases for research has been described in multiple reports.2124

Our setting consisted of all 19 hospitals owned and operated by the KPMCP, whose characteristics are summarized in the Supporting Information Appendix available to interested readers. These include the 17 described in our previous report19 as well as 2 new hospitals (Antioch and Manteca) which are similar in size and type of population served. Our study population consisted of all patients admitted to these 19 hospitals who met these criteria: 1) hospitalization began from November 1st, 2006 through January 31st, 2008; 2) initial hospitalization occurred at a Northern California KPMCP hospital (ie, for inter‐hospital transfers, the first hospital stay occurred within the KPMCP); 3) age 15 years; and 4) hospitalization was not for childbirth.

We defined a linked hospitalization as the time period that began with a patient's admission to the hospital and ended with the patient's discharge (home, to a nursing home, or death). Linked hospitalizations can thus involve more than 1 hospital stay and could include a patient transfer from one hospital to another prior to definitive discharge. For linked hospitalizations, mortality was attributed to the admitting KPMCP hospital (ie, if a patient was admitted to hospital A, transferred to B, and died at hospital B, mortality was attributed to hospital A). We defined total LOS as the exact time in hours from when a patient was first admitted to the hospital until death or final discharge home or to a nursing home, while total ICU or transitional care unit (TCU, referred to as stepdown unit in some hospitals) LOS was calculated for all individual ICU or TCU stays during the hospital stay.

Results

During the study period, a total of 249,129 individual hospital stays involving 170,151 patients occurred at these 19 hospitals. After concatenation of inter‐hospital transfers, we were left with 237,208 linked hospitalizations. We excluded 26,738 linked hospitalizations that began at a non‐KPMCP hospital (ie, they were transported in), leaving a total of 210,470 linked hospitalizations involving 150,495 patients. The overall linked hospitalization mortality rate was 3.30%.

Table 1 summarizes cohort characteristics based on initial hospital location. On admission, ICU patients had the highest degree of physiologic derangement as well as the highest predicted mortality. Considerable inter‐hospital variation was present in both predictors and outcomes; details on these variations are provided in the Supporting Information Appendix.

Table 2 summarizes data from 3 groups of patients: patients initially admitted to the ward, or TCU, who did not experience a transfer to a higher level of care and patients admitted to these 2 units who did experience such a transfer. Patients who experienced a transfer constituted 5.3% (6,484/121,237) of ward patients and 6.7% (1,384/20,556) of TCU patients. Transferred patients tended to be older, have more acute physiologic derangement (higher LAPS), a greater pre‐existing illness burden (higher COPS), and a higher predicted mortality risk. Among ward patients, those with the following admission diagnoses were most likely to experience a transfer to a higher level of care: gastrointestinal bleeding (10.8% of all transfers), pneumonia (8.7%), and other infections (8.2%). The diagnoses most likely to be associated with death following transfer were cancer (death rate among transferred patients, 48%), renal disease (death rate, 36%), and liver disease (33%). Similar distributions were observed for TCU patients.

Table 3 compares outcomes among ward and TCU patients who did and did not experience a transfer to a higher level of care. The table shows that transferred patients were almost 3 times as likely to die, even after controlling for severity of illness, and that their hospital LOS was 9 days higher than expected. This increased risk was seen in all hospitals and among all transfer types (ward to ICU, ward to TCU, and TCU to ICU).

Table 3 also shows that, among decedent patients, those who never left the ward or TCU died much sooner than those who died following transfer. Among direct admits to the ICU, the median LOS at time of death was 3.9 days, with a mean of 9.4 standard deviation of 19.9 days, while the corresponding times for TCU direct admits were a median and mean LOS of 6.5 and 11.7 19.5 days.

Table 4 summarizes outcomes among different patient subgroups that did and did not experience a transfer to a higher level of care. Based on location, patients who experienced a transfer from the TCU to the ICU had the highest crude death rate, but patients transferred from the ward to the ICU had the highest OEMR. On the other hand, if one divides patients by the degree of physiologic derangement, patients with low LAPS who experienced a transfer had the highest OEMR. With respect to LOS, patients transferred from the TCU to the ICU had the highest OMELOS (13.4 extra days).

Transfers to a higher level of care at a different hospital, which in the KPMCP are usually planned, experienced lower mortality than transfers within the same hospital. For ward to TCU transfers, intra‐hospital transfers had a mortality of 12.1% while inter‐hospital transfers had a mortality of 5.7%. Corresponding rates for ward to ICU transfers were 21.7% and 11.2%, and for TCU to ICU transfers the rates were 25.9% and 12.5%, respectively.

Among patients initially admitted to the ward, a model to predict the occurrence of a transfer to a higher level of care (within 48 hours after admission) that included age, sex, admission type, primary condition, LAPS, COPS, and interaction terms had poor discrimination, with an area under the receiver operator characteristic (c statistic) of only 0.64. The c statistic for a model to predict transfer after 48 hours was 0.66. The corresponding models for TCU admits had c statistics of 0.67 and 0.68. All four models had poor calibration.

Discussion

Using automated bed history data permits characterizing a patient population with disproportionate mortality and LOS: intra‐hospital transfers to special care units (ICUs or TCUs). Indeed, the largest subset of these patients (those initially admitted to the ward or TCU) constituted only 3.7% of all admissions, but accounted for 24.2% of all ICU admissions, 21.7% of all hospital deaths, and 13.2% of all hospital days. These patients also had very elevated OEMRs and OMELOS. Models based on age, sex, preadmission laboratory test results, and comorbidities did not predict the occurrence of these transfers.

We performed multivariate analyses to explore the degree to which electronically assigned preadmission severity scores could predict these transfers. These analyses found that, compared to our ability to predict inpatient or 30‐day mortality at the time of admission, which is excellent, our ability to predict the occurrence of transfer after admission is much more limited. These results highlight the limitations of severity scores that rely on automated data, which may not have adequate discrimination when it comes to determining the risk of an adverse outcome within a narrow time frame. For example, among the 121,237 patients initially admitted to the ward who did not experience an intra‐hospital transfer, the mean LAPS was 18.9, while the mean LAPS among the 6,484 ward patients who did experience a transfer was 25.5. Differences between the mean and median LAPS, COPS, and predicted mortality risk among transferred and non‐transferred patients were significant (P < 0.0001 for all comparisons). However, examination of the distribution of LAPS, COPS, and predicted mortality risk between these two groups of patients showed considerable overlap.

Our methodology resembles Silber et al.'s27, 28 concept of failure to rescue in that it focuses on events occurring after hospitalization. Silber et al. argue that a hospital's quality can be measured by quantifying the degree to which patients who experience new problems are successfully rescued. Furthermore, quantification of those situations where rescue attempts are unsuccessful is felt to be superior to simply comparing raw or adjusted mortality rates because these are primarily determined by underlying case mix. The primary difference between Silber et al.'s approach and ours is at the level of detailthey specified a specific set of complications, whereas our measure is more generic and would include patients with many of the complications specified by Silber et al.27, 28

Most of the patients transferred to a higher level of care in our cohort survived (ie, were rescued), indicating that intensive care is beneficial. However, the fact that these patients had elevated OEMRs and OMELOS indicates that the real challenge facing hospitalists involves the timing of provision of a beneficial intervention. In theory, improved timing could result from earlier detection of problems, which is the underlying rationale for employing rapid response teams. However, the fact that our electronic tools (LAPS, COPS) cannot predict patient deteriorations within a narrow time frame suggests that early detection will remain a major challenge. Manually assigned vital signs scores designed for this purpose do not have good discrimination either.29, 30 This raises the possibility that, though patient groups may differ in terms of overall illness severity and mortality risk, differences at the individual patient level may be too subtle for clinicians to detect. Future research may thus need to focus on scores that combine laboratory data, vital signs, trends in data,31, 32 and newer proteomic markers (eg, procalcitonin).33 We also found that most transfers occurred early (within <72 hours), raising the possibility that at least some of these transfers may involve issues around triage rather than sudden deterioration.

Our study has important limitations. Due to resource constraints and limited data availability, we could not characterize the patients as well as might be desirable; in particular, we could not make full determinations of the actual reasons for patients' transfer for all patients. Broadly speaking, transfer to a higher level of care could be due to inappropriate triage, appropriate (preventive) transfer (which could include transfer to a more richly staffed unit for a specific procedure), relentless progression of disease despite maximal therapy, the occurrence of management errors, patient and family uncertainty about goals of care or inadequate understanding of treatment options and prognoses, or a combination of these factors. We could not make these distinctions with currently available electronic data. This is also true of postsurgical patients, in whom it is difficult to determine which transfers to intensive care might be planned (eg, in the case of surgical procedures where ICU care is anticipated) as opposed to the occurrence of a deterioration during or following surgery. Another major limitation of this study is our inability to identify code or no code status electronically. The elapsed LOS at time of death among patients who experienced a transfer to a higher level of care (as compared to patients who died in the ward without ever experiencing intra‐hospital transfer) suggests, but does not prove, that prolonged efforts were being made to keep them alive. We were also limited in terms of having access to other process data (eg, physician staffing levels, provision and timing of palliative care). Having ICU severity of illness scores would have permitted us to compare our cohort to those of other recent studies showing elevated mortality rates among transfer patients,911 but we have not yet developed that capability.

Consideration of our study findings suggests a possible research agenda that could be implemented by hospitalist researchers. This agenda should emphasize three areas: detection, intervention, and reflection.

With respect to detection, attention needs to be paid to better tools for quantifying patient risk at the time a decision to admit to the ward is made. It is likely that such tools will need to combine the attributes of our severity score (LAPS) with those of the manually assigned scores.30, 34 In some cases, use of these tools could lead a physician to change the locus of admission from the ward to the TCU or ICU, which could improve outcomes by ensuring more timely provision of intensive care. Since problems with initial triage could be due to factors other than the failure to suspect or anticipate impending instability, future research should also include a cognitive component (eg, quantifying what proportion of subsequent patient deteriorations could be ascribed to missed diagnoses35). Additional work also needs to be done on developing mathematical models that can inform electronic monitoring of ward (not just ICU) patients.

Research on interventions that hospitalists can use to prevent the need for intensive care or to improve the rescue rate should take two routes. The first is a disease‐specific route, which builds on the fact that a relatively small set of conditions (pneumonia, sepsis, gastrointestinal bleeding) account for most transfers to a higher level of care. Condition‐specific protocols, checklists, and bundles36 tailored to a ward environment (as opposed to the ICU or to the entire hospital) might prevent deteriorations in these patients, as has been reported for sepsis.37 The second route is to improve the overall capabilities of rapid response and code blue teams. Such research would need to include a more careful assessment of what commonalities exist among patients who were and were not successfully rescued by these teams. This approach would probably yield more insights than the current literature, which focuses on whether rapid response teams are a good thing or not.

Finally, research also needs to be performed on how hospitalists reflect on adverse outcomes among ward patients. Greater emphasis needs to be placed on moving beyond trigger tool approaches that rely on manual chart review. In an era of expanding use of electronic medical record systems, more work needs to be done on how to harness these to provide hospitalists with better quantitative and risk‐adjusted information. This information should not be limited to simply reporting rates of transfers and deaths. Rather, finer distinctions must be provided with respect of the type of patients (ie, more diagnostic detail), the clinical status of patients (ie, more physiologic detail), as well as the effects of including or excluding patients in whom therapeutic options may be limited (ie, do not resuscitate and comfort care patients) on reported rates. Ideally, researchers should develop better process and outcomes measures that could be tested in collaborative networks that include multiple nonacademic general medical‐surgical wards.