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.

Chronic hypertension affects 32% of adults in the United States.1 Each year, over 20 million emergency department visits involve hypertension.2 While many studies describe the epidemiology and outcomes of chronic hypertension, less is known about severe acute hypertension (AH). Often classified as either hypertensive urgency or emergency, it receives little attention in national treatment guidelines.3 There is a limited understanding of the epidemiology of, and the relationship between, this syndrome and patient outcomes among hospitalized patients. One registry study suggested that severe AH was associated with high rates of new organ damage, need for treatment in the intensive care unit, and a 90‐day readmission rate of 10%.4 This investigation, however, lacked generalizability, because it only enrolled subjects requiring therapy with an intravenous antihypertensive agent and did not provide information on the prevalence of severe AH. Qureshi et al.5 analyzed information in a more representative sample from the national hospital ambulatory care survey, however, the only outcome examined was the correlation between acute blood pressure and stroke. Studies focusing on a selected patient population may be of limited value to hospitalists, because they are commonly required to treat a range of patients presenting to the hospital.

In light of severe syndromes that may be associated with, or complicated by, severe AH presented early during acute care, hospitalists require a better understanding of the prevalence and the relationship between severe AH and attendant mortality and morbidity. In addition, an assessment of the association of severe AH on the need for intensive care unit (ICU) admission and mechanical ventilation (MV) may aid the initial treatment assessments and triage decisions required of hospitalists.

Our objective was to describe the prevalence and implications of severe AH present at the time of evaluation in the ED among patients eventually hospitalized, using clinical data collected on all consecutive admissions across a range of clinical conditions. We sought to determine the independent contribution, if any, of severe AH to hospital mortality, need for MV on admission, as well as hospital length of stay (LOS).

METHODS

RESULTS

Prevalence of Acute Hypertension

A total of 763,634 (59.2%) patients had at least 1 SBP measurement of 140 mmHg during the ED stay, including 178,197 (13.8%) with SBP >180 mmHg. Body systems associated with the highest prevalence of severe AH (SBP >180 mmHg) were nervous (29.0%), circulatory (16.0%), endocrine (14.7%), and kidney/urinary (13.5%) (Figure 1 presents the data in graphic form; Supporting Appendix B, in the online version of this article, presents corresponding data in table form).

Figure 1

Mortality

Univariable analysis revealed a graded relationship between SBP stratum and mortality risk (Figure 2a; and see Supporting Appendix C in the online version of this article). This relationship was most pronounced for nervous system diseases; mortality rates for each 10 mmHg increase in SBP from 180 to >220 mmHg were 6.5%, 8.1%, 10.0%, 12.0%, and 19.7%, respectively (trending P < 0.0001). The risk‐adjusted increase in mortality odds ratio ranged from 1.04 (95% CI: 0.89, 1.21) to 1.44 (95% CI: 1.25, 1.67) for patients in the severe AH strata compared to patients with SBP of 101 to 139 mmHg (Figure 3). Severe AH was not an independent mortality predictor in other disease categories.

Figure 2

Figure 3

Mechanical Ventilation on Admission

Univariable analysis revealed a graded relationship between severe AH and a need for MV on admission, especially for respiratory, circulatory, and infectious conditions (trending P < 0.0001) (Figure 2b; and see Supporting Appendix C in the online version of this article). In the multivariable analysis, there was a relationship between severe AH stratum and adjusted risk for MV on admission across nearly all disease categories (Figure 4).

Figure 4

Length of Stay

Univariable analysis revealed a graded relationship between severe AH strata and LOS for nearly all disease categories in survivors (trending P < 0.0001), except for digestive, kidney, and infectious diseases (Figure 2c; and see Supporting Appendix C in the online version of this article). For patients with nervous system diseases, the unadjusted LOS for each 10 mmHg increase in SBP from 180 to >220 was 5.8, 6.1, 6.4, 6.8, and 8.0 days, respectively (trending P < 0.0001). The relationship was similar for other disease categories which showed significant trending.

In the multivariable analysis, the relationship between severe AH strata and adjusted attributable LOS was graded across most disease categories, especially nervous, circulatory, and hepatobiliary diseases (Figure 5). The total adjusted number of hospital days attributable to severe AH was 0.43 days per case for all survivors with severe AH.

Figure 5

DISCUSSION

This large‐scale analysis demonstrated that severe AH was present in 13.8% of inpatients admitted through the ED. The prevalence of severe AH varied based on the primary reason for an acute care admission, ranging from 7% in infectious syndromes to nearly 30% in acute neurologic processes. Specific to patients with neurologic disease, initial severe blood pressure elevations independently correlated with mortality. Severe blood pressure elevations at ED were independently associated with an increased need for MV on admission and a prolonged LOS across a range of disease states.

Prior work on hypertension at admission has generally included single‐center analyses or only focused on patients with specific admitting diagnoses. For example, in a single ED analysis, Tilman et al.17 reported that 16% of 10,000 patients presented with elevated blood pressure (140/90 mmHg). In a multicenter review of 7000 persons, Karras et al.18, 19 described 423 patients with severe AH (180/110 mmHg) who comprised 6% of patients seen in the ED during a 1‐week period. Qureshi et al.5 noted severe AH in 13% of patients with acute stroke. While other ED‐based studies examined all ED patients, including those admitted and discharged from the ED, our study focused on those requiring hospitalization. This disparity in illness severity may, in part, explain the higher prevalence of severe AH we noted relative to others.

We further found that the prevalence of severe AH varied based on admitting diagnosis. This difference in prevalence rates by condition seems clinically plausible. Recognition of this pattern may prove valuable to hospitalists who, by the nature of their responsibilities, will encounter a broad range of patients. Because our data were derived from the largest analysis of blood pressure assessments for ED patients who were eventually hospitalized, and encompassed a multiplicity of hospitals, our findings are likely generalizable. Moreover, our large sample size enabled us to examine severe AH at each 10 mmHg increment across a variety of disease states, rather than restricting our analysis to 1 admitting diagnosis.

The independent relationship between severe blood pressure elevation and mortality was detected only in those with neurologic conditions. Incremental increases in SBP beyond 180 mmHg were associated with a stepwise escalation in the risk for death. Although recognition of the importance of blood pressure management in both ischemic and hemorrhagic stroke remains a cornerstone of therapy for these diseases, the stepwise relationship between escalating blood pressure and outcome suggests that further study is needed to determine the optimal management of severe AH among these patients. This relationship, along with the independent association between severe AH at presentation and the need for MV, underscores the importance of severe AH in critical care, representing a major challenge for intensivists and hospitalists, particularly those who practice in neurologic ICUs.

The independent association of severe AH and prolonged LOS represents a novel finding. Few reports have correlated the initial blood pressure with measures of resource use. Katz et al.4 found a median LOS of 6 days among 1000 patients who presented with severe AH and end organ dysfunction in 25 US hospitals. These investigators, however, did not explore the incremental independent contribution of initial blood pressure to LOS. Biologically, severe hypertension may exacerbate both acute and chronic conditions, thus complicating their management and resulting in longer hospitalizations.

Our analysis has limitations. While exposure misclassification is a potential concern, unlike other population‐based studies that use ICD‐9‐CM codes to identify AH cases, we relied on actual measures of blood pressure to identify subjects, thus minimizing this threat to validity. Similarly, since our end pointsmortality, MV on admission, and LOSwere also objective measures, the probability of their misclassification is minimal.

Another concerning contributor to exposure misclassification is the possibility that, in some instances, the initial elevation in BP meeting the inclusion criteria in our study cohort does not reflect the true BP. Indeed, a substantial body of research about BP measurement in the ED suggests that we may have included some persons who likely did not have AH. For example, Pitts and Adams described a regression to the mean phenomenon, with serial BP measurements in the ED, wherein the BP fell by approximately 11 mmHg over 4 hours.20 Baumann and colleagues reported a similar pattern.21 However, the findings of these 2 analyses do not necessarily apply to our study population; we focused on patients admitted to the hospital with an acute condition, while Pitts and Adams20 and Baumann et al.21 examined all ED patients. This distinction is crucial in that patients not admitted are likely less severely ill and systematically different from those who do merit hospitalization. Moreover, both Pitts and Adams20 and Cienki et al.22 observed that the regression to the mean and fall in serial BP measurements were less pronounced in those with the most extensive BP elevations. In our study, we found the strongest relationship between adverse outcomes and BP in patients with the most extreme BP elevations. Thus, misclassification is perhaps less likely to be an issue for these subjects.

In addition, misclassification may result when initially elevated BP simply represents the impact of untreated pain or anxiety in ED patients. However, Backer et al.23 and Tanabe and colleagues24 specifically explored the impact of pain and anxiety on BP in ED subjects, and neither group found a correlation between BP and either acute pain and/or anxiety scores. Our difficulties with case definitions and BP measurements for severe AH demonstrate the need for the creation and adoption of a formal, systematic approach to this syndrome, along with the need for prospective analyses to confirm our findings.

Selection bias represents a second potential threat to validity in our observational study, although this bias is mitigated by including all consecutive acute inpatient admissions to the participating hospitals. Furthermore, inclusion of the 9% of patients who had only 1 ED measurement of SBP collected did not alter the estimate of severe AH prevalence or associated outcomes.

Third, confounding may introduce the potential for false associations derived from observational data. The large sample size of our cohort allowed us to address this concern by adjusting for a large array of confounders. In addition, unlike other large‐scale population‐based studies which typically rely on administrative ICD‐9‐CM codes for risk adjustment, our analysis incorporated actual physiologic and laboratory results measured on admission, as well as a validated severity‐of‐illness scoring system for risk adjustment.12, 13

Although both SBP and diastolic blood pressure (DBP) thresholds are included in traditional definitions of hypertension, selecting SBP as the primary measure is reasonable because SBP >180 mmHg is a more important risk factor for cardiovascular disease than elevated DBP.25 Previous studies reported the relationship between the trend of SBP over time and clinical outcomes,26, 27 but we were not able to investigate the relationship of SBP trend and outcomes because the BP measurements in the our study were not collected in predefined intervals.

It would be ideal if serial blood pressure measures were to be collected at pre‐specified intervals and if more sophisticated schemas were to be used to refine the AH definition. This type of study may be possible in the future when vital signs can be collected automatically with advanced technology. Likewise, electronically captured treatment data could further help researchers to study the impact of process‐of‐care variables, including medications and other management strategies, in relation to outcomes. Finally, outpatient management of chronic hypertension is an integral part of clinical management. Unfortunately, these types of data are not available in our existing database. These limitations notwithstanding, an in‐depth understanding of the association between severe AH and potential adverse clinical and economic outcomes may direct further research in this field.

CONCLUSION

Severe AH appears common and its prevalence varies by underlying clinical condition in patients admitted from the ED. In those with acute neurologic syndromes, the degree of blood pressure elevation correlated with mortality, need for MV, and longer LOS. For many other conditions, elevation of blood pressure appeared to be linked to an increased need for MV and a prolongation in LOS. Future studies are needed to examine the potential impact of both 1) improved long‐term outpatient BP management, and 2) optimal management of severe AH upon admission on improving outcomes of patients hospitalized from the ED with severe AH.

Chronic hypertension affects 32% of adults in the United States.1 Each year, over 20 million emergency department visits involve hypertension.2 While many studies describe the epidemiology and outcomes of chronic hypertension, less is known about severe acute hypertension (AH). Often classified as either hypertensive urgency or emergency, it receives little attention in national treatment guidelines.3 There is a limited understanding of the epidemiology of, and the relationship between, this syndrome and patient outcomes among hospitalized patients. One registry study suggested that severe AH was associated with high rates of new organ damage, need for treatment in the intensive care unit, and a 90‐day readmission rate of 10%.4 This investigation, however, lacked generalizability, because it only enrolled subjects requiring therapy with an intravenous antihypertensive agent and did not provide information on the prevalence of severe AH. Qureshi et al.5 analyzed information in a more representative sample from the national hospital ambulatory care survey, however, the only outcome examined was the correlation between acute blood pressure and stroke. Studies focusing on a selected patient population may be of limited value to hospitalists, because they are commonly required to treat a range of patients presenting to the hospital.

In light of severe syndromes that may be associated with, or complicated by, severe AH presented early during acute care, hospitalists require a better understanding of the prevalence and the relationship between severe AH and attendant mortality and morbidity. In addition, an assessment of the association of severe AH on the need for intensive care unit (ICU) admission and mechanical ventilation (MV) may aid the initial treatment assessments and triage decisions required of hospitalists.

Our objective was to describe the prevalence and implications of severe AH present at the time of evaluation in the ED among patients eventually hospitalized, using clinical data collected on all consecutive admissions across a range of clinical conditions. We sought to determine the independent contribution, if any, of severe AH to hospital mortality, need for MV on admission, as well as hospital length of stay (LOS).

METHODS

RESULTS

Prevalence of Acute Hypertension

A total of 763,634 (59.2%) patients had at least 1 SBP measurement of 140 mmHg during the ED stay, including 178,197 (13.8%) with SBP >180 mmHg. Body systems associated with the highest prevalence of severe AH (SBP >180 mmHg) were nervous (29.0%), circulatory (16.0%), endocrine (14.7%), and kidney/urinary (13.5%) (Figure 1 presents the data in graphic form; Supporting Appendix B, in the online version of this article, presents corresponding data in table form).

Figure 1

Mortality

Univariable analysis revealed a graded relationship between SBP stratum and mortality risk (Figure 2a; and see Supporting Appendix C in the online version of this article). This relationship was most pronounced for nervous system diseases; mortality rates for each 10 mmHg increase in SBP from 180 to >220 mmHg were 6.5%, 8.1%, 10.0%, 12.0%, and 19.7%, respectively (trending P < 0.0001). The risk‐adjusted increase in mortality odds ratio ranged from 1.04 (95% CI: 0.89, 1.21) to 1.44 (95% CI: 1.25, 1.67) for patients in the severe AH strata compared to patients with SBP of 101 to 139 mmHg (Figure 3). Severe AH was not an independent mortality predictor in other disease categories.

Figure 2

Figure 3

Mechanical Ventilation on Admission

Univariable analysis revealed a graded relationship between severe AH and a need for MV on admission, especially for respiratory, circulatory, and infectious conditions (trending P < 0.0001) (Figure 2b; and see Supporting Appendix C in the online version of this article). In the multivariable analysis, there was a relationship between severe AH stratum and adjusted risk for MV on admission across nearly all disease categories (Figure 4).

Figure 4

Length of Stay

Univariable analysis revealed a graded relationship between severe AH strata and LOS for nearly all disease categories in survivors (trending P < 0.0001), except for digestive, kidney, and infectious diseases (Figure 2c; and see Supporting Appendix C in the online version of this article). For patients with nervous system diseases, the unadjusted LOS for each 10 mmHg increase in SBP from 180 to >220 was 5.8, 6.1, 6.4, 6.8, and 8.0 days, respectively (trending P < 0.0001). The relationship was similar for other disease categories which showed significant trending.

In the multivariable analysis, the relationship between severe AH strata and adjusted attributable LOS was graded across most disease categories, especially nervous, circulatory, and hepatobiliary diseases (Figure 5). The total adjusted number of hospital days attributable to severe AH was 0.43 days per case for all survivors with severe AH.

Figure 5

DISCUSSION

This large‐scale analysis demonstrated that severe AH was present in 13.8% of inpatients admitted through the ED. The prevalence of severe AH varied based on the primary reason for an acute care admission, ranging from 7% in infectious syndromes to nearly 30% in acute neurologic processes. Specific to patients with neurologic disease, initial severe blood pressure elevations independently correlated with mortality. Severe blood pressure elevations at ED were independently associated with an increased need for MV on admission and a prolonged LOS across a range of disease states.

Prior work on hypertension at admission has generally included single‐center analyses or only focused on patients with specific admitting diagnoses. For example, in a single ED analysis, Tilman et al.17 reported that 16% of 10,000 patients presented with elevated blood pressure (140/90 mmHg). In a multicenter review of 7000 persons, Karras et al.18, 19 described 423 patients with severe AH (180/110 mmHg) who comprised 6% of patients seen in the ED during a 1‐week period. Qureshi et al.5 noted severe AH in 13% of patients with acute stroke. While other ED‐based studies examined all ED patients, including those admitted and discharged from the ED, our study focused on those requiring hospitalization. This disparity in illness severity may, in part, explain the higher prevalence of severe AH we noted relative to others.

We further found that the prevalence of severe AH varied based on admitting diagnosis. This difference in prevalence rates by condition seems clinically plausible. Recognition of this pattern may prove valuable to hospitalists who, by the nature of their responsibilities, will encounter a broad range of patients. Because our data were derived from the largest analysis of blood pressure assessments for ED patients who were eventually hospitalized, and encompassed a multiplicity of hospitals, our findings are likely generalizable. Moreover, our large sample size enabled us to examine severe AH at each 10 mmHg increment across a variety of disease states, rather than restricting our analysis to 1 admitting diagnosis.

The independent relationship between severe blood pressure elevation and mortality was detected only in those with neurologic conditions. Incremental increases in SBP beyond 180 mmHg were associated with a stepwise escalation in the risk for death. Although recognition of the importance of blood pressure management in both ischemic and hemorrhagic stroke remains a cornerstone of therapy for these diseases, the stepwise relationship between escalating blood pressure and outcome suggests that further study is needed to determine the optimal management of severe AH among these patients. This relationship, along with the independent association between severe AH at presentation and the need for MV, underscores the importance of severe AH in critical care, representing a major challenge for intensivists and hospitalists, particularly those who practice in neurologic ICUs.

The independent association of severe AH and prolonged LOS represents a novel finding. Few reports have correlated the initial blood pressure with measures of resource use. Katz et al.4 found a median LOS of 6 days among 1000 patients who presented with severe AH and end organ dysfunction in 25 US hospitals. These investigators, however, did not explore the incremental independent contribution of initial blood pressure to LOS. Biologically, severe hypertension may exacerbate both acute and chronic conditions, thus complicating their management and resulting in longer hospitalizations.

Our analysis has limitations. While exposure misclassification is a potential concern, unlike other population‐based studies that use ICD‐9‐CM codes to identify AH cases, we relied on actual measures of blood pressure to identify subjects, thus minimizing this threat to validity. Similarly, since our end pointsmortality, MV on admission, and LOSwere also objective measures, the probability of their misclassification is minimal.

Another concerning contributor to exposure misclassification is the possibility that, in some instances, the initial elevation in BP meeting the inclusion criteria in our study cohort does not reflect the true BP. Indeed, a substantial body of research about BP measurement in the ED suggests that we may have included some persons who likely did not have AH. For example, Pitts and Adams described a regression to the mean phenomenon, with serial BP measurements in the ED, wherein the BP fell by approximately 11 mmHg over 4 hours.20 Baumann and colleagues reported a similar pattern.21 However, the findings of these 2 analyses do not necessarily apply to our study population; we focused on patients admitted to the hospital with an acute condition, while Pitts and Adams20 and Baumann et al.21 examined all ED patients. This distinction is crucial in that patients not admitted are likely less severely ill and systematically different from those who do merit hospitalization. Moreover, both Pitts and Adams20 and Cienki et al.22 observed that the regression to the mean and fall in serial BP measurements were less pronounced in those with the most extensive BP elevations. In our study, we found the strongest relationship between adverse outcomes and BP in patients with the most extreme BP elevations. Thus, misclassification is perhaps less likely to be an issue for these subjects.

In addition, misclassification may result when initially elevated BP simply represents the impact of untreated pain or anxiety in ED patients. However, Backer et al.23 and Tanabe and colleagues24 specifically explored the impact of pain and anxiety on BP in ED subjects, and neither group found a correlation between BP and either acute pain and/or anxiety scores. Our difficulties with case definitions and BP measurements for severe AH demonstrate the need for the creation and adoption of a formal, systematic approach to this syndrome, along with the need for prospective analyses to confirm our findings.

Selection bias represents a second potential threat to validity in our observational study, although this bias is mitigated by including all consecutive acute inpatient admissions to the participating hospitals. Furthermore, inclusion of the 9% of patients who had only 1 ED measurement of SBP collected did not alter the estimate of severe AH prevalence or associated outcomes.

Third, confounding may introduce the potential for false associations derived from observational data. The large sample size of our cohort allowed us to address this concern by adjusting for a large array of confounders. In addition, unlike other large‐scale population‐based studies which typically rely on administrative ICD‐9‐CM codes for risk adjustment, our analysis incorporated actual physiologic and laboratory results measured on admission, as well as a validated severity‐of‐illness scoring system for risk adjustment.12, 13

Although both SBP and diastolic blood pressure (DBP) thresholds are included in traditional definitions of hypertension, selecting SBP as the primary measure is reasonable because SBP >180 mmHg is a more important risk factor for cardiovascular disease than elevated DBP.25 Previous studies reported the relationship between the trend of SBP over time and clinical outcomes,26, 27 but we were not able to investigate the relationship of SBP trend and outcomes because the BP measurements in the our study were not collected in predefined intervals.

It would be ideal if serial blood pressure measures were to be collected at pre‐specified intervals and if more sophisticated schemas were to be used to refine the AH definition. This type of study may be possible in the future when vital signs can be collected automatically with advanced technology. Likewise, electronically captured treatment data could further help researchers to study the impact of process‐of‐care variables, including medications and other management strategies, in relation to outcomes. Finally, outpatient management of chronic hypertension is an integral part of clinical management. Unfortunately, these types of data are not available in our existing database. These limitations notwithstanding, an in‐depth understanding of the association between severe AH and potential adverse clinical and economic outcomes may direct further research in this field.

CONCLUSION

Severe AH appears common and its prevalence varies by underlying clinical condition in patients admitted from the ED. In those with acute neurologic syndromes, the degree of blood pressure elevation correlated with mortality, need for MV, and longer LOS. For many other conditions, elevation of blood pressure appeared to be linked to an increased need for MV and a prolongation in LOS. Future studies are needed to examine the potential impact of both 1) improved long‐term outpatient BP management, and 2) optimal management of severe AH upon admission on improving outcomes of patients hospitalized from the ED with severe AH.

Chronic hypertension affects 32% of adults in the United States.1 Each year, over 20 million emergency department visits involve hypertension.2 While many studies describe the epidemiology and outcomes of chronic hypertension, less is known about severe acute hypertension (AH). Often classified as either hypertensive urgency or emergency, it receives little attention in national treatment guidelines.3 There is a limited understanding of the epidemiology of, and the relationship between, this syndrome and patient outcomes among hospitalized patients. One registry study suggested that severe AH was associated with high rates of new organ damage, need for treatment in the intensive care unit, and a 90‐day readmission rate of 10%.4 This investigation, however, lacked generalizability, because it only enrolled subjects requiring therapy with an intravenous antihypertensive agent and did not provide information on the prevalence of severe AH. Qureshi et al.5 analyzed information in a more representative sample from the national hospital ambulatory care survey, however, the only outcome examined was the correlation between acute blood pressure and stroke. Studies focusing on a selected patient population may be of limited value to hospitalists, because they are commonly required to treat a range of patients presenting to the hospital.

In light of severe syndromes that may be associated with, or complicated by, severe AH presented early during acute care, hospitalists require a better understanding of the prevalence and the relationship between severe AH and attendant mortality and morbidity. In addition, an assessment of the association of severe AH on the need for intensive care unit (ICU) admission and mechanical ventilation (MV) may aid the initial treatment assessments and triage decisions required of hospitalists.

Our objective was to describe the prevalence and implications of severe AH present at the time of evaluation in the ED among patients eventually hospitalized, using clinical data collected on all consecutive admissions across a range of clinical conditions. We sought to determine the independent contribution, if any, of severe AH to hospital mortality, need for MV on admission, as well as hospital length of stay (LOS).

METHODS

RESULTS

Prevalence of Acute Hypertension

A total of 763,634 (59.2%) patients had at least 1 SBP measurement of 140 mmHg during the ED stay, including 178,197 (13.8%) with SBP >180 mmHg. Body systems associated with the highest prevalence of severe AH (SBP >180 mmHg) were nervous (29.0%), circulatory (16.0%), endocrine (14.7%), and kidney/urinary (13.5%) (Figure 1 presents the data in graphic form; Supporting Appendix B, in the online version of this article, presents corresponding data in table form).

Figure 1

Mortality

Univariable analysis revealed a graded relationship between SBP stratum and mortality risk (Figure 2a; and see Supporting Appendix C in the online version of this article). This relationship was most pronounced for nervous system diseases; mortality rates for each 10 mmHg increase in SBP from 180 to >220 mmHg were 6.5%, 8.1%, 10.0%, 12.0%, and 19.7%, respectively (trending P < 0.0001). The risk‐adjusted increase in mortality odds ratio ranged from 1.04 (95% CI: 0.89, 1.21) to 1.44 (95% CI: 1.25, 1.67) for patients in the severe AH strata compared to patients with SBP of 101 to 139 mmHg (Figure 3). Severe AH was not an independent mortality predictor in other disease categories.

Figure 2

Figure 3

Mechanical Ventilation on Admission

Univariable analysis revealed a graded relationship between severe AH and a need for MV on admission, especially for respiratory, circulatory, and infectious conditions (trending P < 0.0001) (Figure 2b; and see Supporting Appendix C in the online version of this article). In the multivariable analysis, there was a relationship between severe AH stratum and adjusted risk for MV on admission across nearly all disease categories (Figure 4).

Figure 4

Length of Stay

Univariable analysis revealed a graded relationship between severe AH strata and LOS for nearly all disease categories in survivors (trending P < 0.0001), except for digestive, kidney, and infectious diseases (Figure 2c; and see Supporting Appendix C in the online version of this article). For patients with nervous system diseases, the unadjusted LOS for each 10 mmHg increase in SBP from 180 to >220 was 5.8, 6.1, 6.4, 6.8, and 8.0 days, respectively (trending P < 0.0001). The relationship was similar for other disease categories which showed significant trending.

In the multivariable analysis, the relationship between severe AH strata and adjusted attributable LOS was graded across most disease categories, especially nervous, circulatory, and hepatobiliary diseases (Figure 5). The total adjusted number of hospital days attributable to severe AH was 0.43 days per case for all survivors with severe AH.

Figure 5

DISCUSSION

This large‐scale analysis demonstrated that severe AH was present in 13.8% of inpatients admitted through the ED. The prevalence of severe AH varied based on the primary reason for an acute care admission, ranging from 7% in infectious syndromes to nearly 30% in acute neurologic processes. Specific to patients with neurologic disease, initial severe blood pressure elevations independently correlated with mortality. Severe blood pressure elevations at ED were independently associated with an increased need for MV on admission and a prolonged LOS across a range of disease states.

Prior work on hypertension at admission has generally included single‐center analyses or only focused on patients with specific admitting diagnoses. For example, in a single ED analysis, Tilman et al.17 reported that 16% of 10,000 patients presented with elevated blood pressure (140/90 mmHg). In a multicenter review of 7000 persons, Karras et al.18, 19 described 423 patients with severe AH (180/110 mmHg) who comprised 6% of patients seen in the ED during a 1‐week period. Qureshi et al.5 noted severe AH in 13% of patients with acute stroke. While other ED‐based studies examined all ED patients, including those admitted and discharged from the ED, our study focused on those requiring hospitalization. This disparity in illness severity may, in part, explain the higher prevalence of severe AH we noted relative to others.

We further found that the prevalence of severe AH varied based on admitting diagnosis. This difference in prevalence rates by condition seems clinically plausible. Recognition of this pattern may prove valuable to hospitalists who, by the nature of their responsibilities, will encounter a broad range of patients. Because our data were derived from the largest analysis of blood pressure assessments for ED patients who were eventually hospitalized, and encompassed a multiplicity of hospitals, our findings are likely generalizable. Moreover, our large sample size enabled us to examine severe AH at each 10 mmHg increment across a variety of disease states, rather than restricting our analysis to 1 admitting diagnosis.

The independent relationship between severe blood pressure elevation and mortality was detected only in those with neurologic conditions. Incremental increases in SBP beyond 180 mmHg were associated with a stepwise escalation in the risk for death. Although recognition of the importance of blood pressure management in both ischemic and hemorrhagic stroke remains a cornerstone of therapy for these diseases, the stepwise relationship between escalating blood pressure and outcome suggests that further study is needed to determine the optimal management of severe AH among these patients. This relationship, along with the independent association between severe AH at presentation and the need for MV, underscores the importance of severe AH in critical care, representing a major challenge for intensivists and hospitalists, particularly those who practice in neurologic ICUs.

The independent association of severe AH and prolonged LOS represents a novel finding. Few reports have correlated the initial blood pressure with measures of resource use. Katz et al.4 found a median LOS of 6 days among 1000 patients who presented with severe AH and end organ dysfunction in 25 US hospitals. These investigators, however, did not explore the incremental independent contribution of initial blood pressure to LOS. Biologically, severe hypertension may exacerbate both acute and chronic conditions, thus complicating their management and resulting in longer hospitalizations.

Our analysis has limitations. While exposure misclassification is a potential concern, unlike other population‐based studies that use ICD‐9‐CM codes to identify AH cases, we relied on actual measures of blood pressure to identify subjects, thus minimizing this threat to validity. Similarly, since our end pointsmortality, MV on admission, and LOSwere also objective measures, the probability of their misclassification is minimal.

Another concerning contributor to exposure misclassification is the possibility that, in some instances, the initial elevation in BP meeting the inclusion criteria in our study cohort does not reflect the true BP. Indeed, a substantial body of research about BP measurement in the ED suggests that we may have included some persons who likely did not have AH. For example, Pitts and Adams described a regression to the mean phenomenon, with serial BP measurements in the ED, wherein the BP fell by approximately 11 mmHg over 4 hours.20 Baumann and colleagues reported a similar pattern.21 However, the findings of these 2 analyses do not necessarily apply to our study population; we focused on patients admitted to the hospital with an acute condition, while Pitts and Adams20 and Baumann et al.21 examined all ED patients. This distinction is crucial in that patients not admitted are likely less severely ill and systematically different from those who do merit hospitalization. Moreover, both Pitts and Adams20 and Cienki et al.22 observed that the regression to the mean and fall in serial BP measurements were less pronounced in those with the most extensive BP elevations. In our study, we found the strongest relationship between adverse outcomes and BP in patients with the most extreme BP elevations. Thus, misclassification is perhaps less likely to be an issue for these subjects.

In addition, misclassification may result when initially elevated BP simply represents the impact of untreated pain or anxiety in ED patients. However, Backer et al.23 and Tanabe and colleagues24 specifically explored the impact of pain and anxiety on BP in ED subjects, and neither group found a correlation between BP and either acute pain and/or anxiety scores. Our difficulties with case definitions and BP measurements for severe AH demonstrate the need for the creation and adoption of a formal, systematic approach to this syndrome, along with the need for prospective analyses to confirm our findings.

Selection bias represents a second potential threat to validity in our observational study, although this bias is mitigated by including all consecutive acute inpatient admissions to the participating hospitals. Furthermore, inclusion of the 9% of patients who had only 1 ED measurement of SBP collected did not alter the estimate of severe AH prevalence or associated outcomes.

Third, confounding may introduce the potential for false associations derived from observational data. The large sample size of our cohort allowed us to address this concern by adjusting for a large array of confounders. In addition, unlike other large‐scale population‐based studies which typically rely on administrative ICD‐9‐CM codes for risk adjustment, our analysis incorporated actual physiologic and laboratory results measured on admission, as well as a validated severity‐of‐illness scoring system for risk adjustment.12, 13

Although both SBP and diastolic blood pressure (DBP) thresholds are included in traditional definitions of hypertension, selecting SBP as the primary measure is reasonable because SBP >180 mmHg is a more important risk factor for cardiovascular disease than elevated DBP.25 Previous studies reported the relationship between the trend of SBP over time and clinical outcomes,26, 27 but we were not able to investigate the relationship of SBP trend and outcomes because the BP measurements in the our study were not collected in predefined intervals.

It would be ideal if serial blood pressure measures were to be collected at pre‐specified intervals and if more sophisticated schemas were to be used to refine the AH definition. This type of study may be possible in the future when vital signs can be collected automatically with advanced technology. Likewise, electronically captured treatment data could further help researchers to study the impact of process‐of‐care variables, including medications and other management strategies, in relation to outcomes. Finally, outpatient management of chronic hypertension is an integral part of clinical management. Unfortunately, these types of data are not available in our existing database. These limitations notwithstanding, an in‐depth understanding of the association between severe AH and potential adverse clinical and economic outcomes may direct further research in this field.

CONCLUSION

Severe AH appears common and its prevalence varies by underlying clinical condition in patients admitted from the ED. In those with acute neurologic syndromes, the degree of blood pressure elevation correlated with mortality, need for MV, and longer LOS. For many other conditions, elevation of blood pressure appeared to be linked to an increased need for MV and a prolongation in LOS. Future studies are needed to examine the potential impact of both 1) improved long‐term outpatient BP management, and 2) optimal management of severe AH upon admission on improving outcomes of patients hospitalized from the ED with severe AH.

Resident duty hour restrictions were initially implemented in New York in 1989 with New York State Code 405 in response to a patient death in a New York City Emergency Department.1 This case initiated an evaluation of potential risks to patient safety when residents were inadequately supervised and overfatigued. In 2003, the Accreditation Council for Graduate Medical Education (ACGME) implemented resident duty hours nationally due to concerns for patient safety and quality of care.2 These restrictions involved the implementation of the 80‐hour work week (averaged over 4 weeks), a maximum duty length of 30 hours, and prescriptive supervision guidelines. In December 2008, the Institute of Medicine (IOM) proposed additional changes to further restrict resident duty hours which also included overnight protected sleep periods and additional days off per month.3 The ACGME responded by mandating new resident duty hour restrictions in October 2010 which will be implemented in July 2011. The ACGME's new changes include a change in the maximum duty hour length for residents in their first year of training (PGY‐1) of 16 hours. Residents in their second year of training (PGY‐2) level and above may work a maximum of 24 hours with an additional 4 hours for transition of care and resident education. The ACGME strongly recommends strategic napping, but do not have a protected overnight sleep period in place4 (Table 1).

There is growing concern regarding the impact of these new resident duty hour restrictions on the coverage of inpatient services, particularly during the overnight period. To our knowledge, there is no published national data on how pediatric inpatient teaching services are staffed at night. The objective of this study was to survey the current landscape of pediatric resident coverage of noncritical care inpatient teaching services. In addition, we sought to explore how changes in work hour restrictions might affect the role of pediatric hospitalists in training programs.

METHODS

We developed an institutional review board (IRB)‐approved Web‐based electronic survey. The survey consisted of 17 questions. The survey obtained information regarding the demographics of the program including: number of residents, daily patient census per ward intern, information regarding staff‐only pediatric ward services, overnight coverage, and current attending in‐house overnight coverage (see Appendix). We also examined the prevalence of pediatric hospitalists in training programs, their current role in staffing patients, and how that role may change with the implementation of additional resident duty hour restrictions. Initially, the survey was reviewed and tested by several pediatric hospitalists and program directors. It was then reviewed and approved by the Association of Pediatric Program Director (APPD) research task force. The survey was sent out to 196 US pediatric residency programs via the APPD listserve in January 2010. Program directors were given the option of completing it themselves or specifically designating someone else to complete it. Two reminders were sent. We then sent an additional request for program participation on the pediatric hospitalist listserve. All data was collected by February 2010.

RESULTS

One hundred twenty unique responses were received (61% of total pediatric residency programs). As of 2009, this represented 5201 pediatric residents (58% of total pediatric residents). The average program size was 43 residents (range: 12‐156 residents, median 43). The average daily patient census per ward intern during daytime hours was 6.65 patients (range: 3‐17, median 6). Twenty percent of training programs had staff‐only (no residents) pediatric ward services during daytime hours. In the programs with both staff‐only and resident pediatric ward services, only 19% of patients were covered by the staff‐only teams and 81% of patients were covered by resident teams.

During the overnight period, 86% of resident teams did not have caps on the number of new patient admissions. An average of 3.6 providers per training program were in‐house overnight to accept patient admissions to pediatric wards. Ninety‐four percent of these providers in‐house were residents (399 residents in‐house/425 total providers in‐house each night).

Twenty‐five percent of the training programs that responded to the survey had pediatric hospitalist attendings in‐house at night. This included both overnight and partial nights (ie, until midnight). Other attendings in‐house at night include: neonatal intensive care unit (NICU) attendings (53% of programs), pediatric intensive care unit (PICU) attendings (46% of programs), Pediatric Emergency Medicine attendings (65% of programs), and Pediatric Surgery attendings (6.4% of programs). Twenty‐two percent of programs had no in‐house attendings at night (Figure 1).

Figure 1

Pediatric hospitalists were involved with 84% (n = 97) of training programs. Sixty percent (n = 58) of the pediatric hospitalist teams were staffed with both teaching attendings and residents. Fourteen percent (n = 14) of the pediatric hospitalist teams did not involve residents (staff‐only) and 25% (n = 25) had both types of teams. Specifically, of the programs that had pediatric hospitalists, 20% (n = 19) of them had hospitalist attendings in‐house 24 hours per day and 13% (n = 12) of teams had hospitalist attendings in‐house into the evening hours for a varying amount of time. Of the programs with hospitalist attendings in‐house 24 hours per day, 52% (n = 11) had started this coverage within the past 3 years.

Looking towards the future, and prior to the enactment of the October 2010 ACGME standards, 31% (n = 35) of the training programs that lacked 24/7 hospitalist in‐house coverage in January 2010 anticipated adding this level of coverage within the next 5 years. Notably, 70% (n = 81) of training programs felt that further resident work hour restrictions, which have since been enacted, would likely require the addition of more hospitalist attendings at night. Our survey allowed program directors to make open‐ended comments on how further work hour restrictions may change inpatient staffing in noncritical care inpatient teaching services.

DISCUSSION

To our knowledge, this was the first national study of pediatric resident coverage in noncritical care inpatient teaching services. While there was significant variation in how inpatient teaching services were covered across these programs, in January 2010, residents were involved in the majority of patient care with only 20% of programs having attending‐only hospitalist teams during the daytime. During the overnight period, the proportion of patient care provided by residents became even more significant with residents representing 94% of the total in‐house providers accepting new admissions. While pediatric hospitalists were prevalent at these training programs, their role in direct patient care overnight was limited. Only 6% of total in‐house providers accepting admissions at night were pediatric hospitalists.

The comments made by program directors are representative of the overall concerns regarding changes to resident work hours (see Table 2). In a position statement by the Association of Pediatric Program Directors in regards to the IOM recommendations, concerns were raised stating that the recommendations of the IOM Committee are intended to enhance patient safety without appropriate consideration for the educational and professional development of trainees.5 While the newly mandated ACGME standards are different than the previous IOM recommendations, it is clear that there will be very significant changes to accommodate these new standards. Our study was done prior to the new ACGME's standards. At the time of the survey, less than a third of programs were anticipating the addition of 24/7 pediatric hospitalist coverage; however, if resident work hours were further restricted, 70% of programs felt that additional hospitalists would be needed. This is a significant increase in the previously anticipated need for overnight attending hospitalist coverage, especially in light of the further restrictions mandated by the ACGME. We know that the response of New York State programs to the 405 regulations varied by program size, but all made significant changes to accommodate the new standards.6 It is clear that many program directors nationally are anticipating significant changes to their residencies when these new restrictions are enacted. The respondents in our survey felt that pediatric hospitalists are going to have to play an even bigger role at night when additional resident work hour restrictions are put into place.

Pediatric hospital medicine remains a rapidly growing field.7 Eighty‐four percent of pediatric training programs utilize pediatric hospitalists. Over 60% of these pediatric hospitalist teams are involved in teaching teams with residents. While we did not directly study the supply and demand of pediatric hospitalists, there is some concern that even despite its rapid growth, the supply of pediatric hospitalists will not keep up with the demand when further resident work hours restrictions are implemented. At time of submission, a cost‐analysis has not yet been publicly published on the ACGME's new changes. There is data available based on the IOM's 2008 recommendations. A study by Nuckols and Escarce8 suggests that if the IOM's recommendations were implemented, the entire healthcare system nationally would have to develop and fill new full‐time positions equal to 5001 attending physicians, 5984 midlevel providers (nurse practitioners or physician assistants), 320 licensed vocational nurses, 229 nursing aides, and 45 laboratory technicians. This would be equivalent to adding an additional 8247 residency positions across all specialties.810 While the ACGME's new mandated changes are different than the IOM's recommendations, they will also restrict resident duty hours that we believe could lead to gaps in patient care requiring significant personnel changes in the healthcare system.

There are several limitations to our study. We did not study the role of pediatric subspecialty fellows and their involvement in pediatric inpatient services in these training programs. We also did not study the prevalence and use of resident night float systems. While night floats may be used in some programs, it may become more prevalent with the possible restriction in intern work hours down to 16 hours. As with any survey, there remains both volunteer and nonresponse bias with the programs that decide to complete or disregard the survey. Finally, there remains some concern over the data collection after the survey was sent out to the hospitalist listserve. Pediatric hospitalists may have incorrectly filled out the data for their program after their program director had already completed the survey. We attempted to minimize this problem by specifically instructing hospitalists to encourage their program director to fill out the survey if they had not already done so. We also compared computer Internet Protocol (IP) addresses and actual program responses, before and after the hospitalist e‐mail was sent, in an attempt to minimize the chance of including duplicated responses from the same program. Lastly, the January 2010 survey predated the October 2010 ACGME response to the IOM recommendations, and the responses may be different now that the specific restrictions have been mandated with an actual implementation date.

CONCLUSIONS

This study shows that pediatric teaching services varied significantly in how they provided overnight coverage in 2010 prior to new ACGME recommendations. Overall, residents were providing the overwhelming majority of the patient care overnight in pediatric training programs. While hospitalists were prevalent in pediatric training programs, in 2010 they had limited roles in direct patient care at night. The ACGME has now mandated additional residency work hour restrictions to be implemented July 2011. With these restrictions, hospitalists will likely need to expand their services, and additional hospitalists will be needed to provide overnight coverage. It is unclear where those hospitalists will come from and what their role will be. It is also unclear what the impact of increased demand and changed job description will be on the continued evolution of the field of Pediatric Hospital Medicine.

Future work needs to be done to establish benchmarks for inpatient coverage. The benchmarks could include guidelines on balancing patient safety with resident education. This may also involve the implementation of resident night float models. There needs to be monitoring on how changes in resident work hours and staffing affect coverage and, ultimately, how changes affect patient and resident outcomes.

Resident duty hour restrictions were initially implemented in New York in 1989 with New York State Code 405 in response to a patient death in a New York City Emergency Department.1 This case initiated an evaluation of potential risks to patient safety when residents were inadequately supervised and overfatigued. In 2003, the Accreditation Council for Graduate Medical Education (ACGME) implemented resident duty hours nationally due to concerns for patient safety and quality of care.2 These restrictions involved the implementation of the 80‐hour work week (averaged over 4 weeks), a maximum duty length of 30 hours, and prescriptive supervision guidelines. In December 2008, the Institute of Medicine (IOM) proposed additional changes to further restrict resident duty hours which also included overnight protected sleep periods and additional days off per month.3 The ACGME responded by mandating new resident duty hour restrictions in October 2010 which will be implemented in July 2011. The ACGME's new changes include a change in the maximum duty hour length for residents in their first year of training (PGY‐1) of 16 hours. Residents in their second year of training (PGY‐2) level and above may work a maximum of 24 hours with an additional 4 hours for transition of care and resident education. The ACGME strongly recommends strategic napping, but do not have a protected overnight sleep period in place4 (Table 1).

There is growing concern regarding the impact of these new resident duty hour restrictions on the coverage of inpatient services, particularly during the overnight period. To our knowledge, there is no published national data on how pediatric inpatient teaching services are staffed at night. The objective of this study was to survey the current landscape of pediatric resident coverage of noncritical care inpatient teaching services. In addition, we sought to explore how changes in work hour restrictions might affect the role of pediatric hospitalists in training programs.

METHODS

We developed an institutional review board (IRB)‐approved Web‐based electronic survey. The survey consisted of 17 questions. The survey obtained information regarding the demographics of the program including: number of residents, daily patient census per ward intern, information regarding staff‐only pediatric ward services, overnight coverage, and current attending in‐house overnight coverage (see Appendix). We also examined the prevalence of pediatric hospitalists in training programs, their current role in staffing patients, and how that role may change with the implementation of additional resident duty hour restrictions. Initially, the survey was reviewed and tested by several pediatric hospitalists and program directors. It was then reviewed and approved by the Association of Pediatric Program Director (APPD) research task force. The survey was sent out to 196 US pediatric residency programs via the APPD listserve in January 2010. Program directors were given the option of completing it themselves or specifically designating someone else to complete it. Two reminders were sent. We then sent an additional request for program participation on the pediatric hospitalist listserve. All data was collected by February 2010.

RESULTS

One hundred twenty unique responses were received (61% of total pediatric residency programs). As of 2009, this represented 5201 pediatric residents (58% of total pediatric residents). The average program size was 43 residents (range: 12‐156 residents, median 43). The average daily patient census per ward intern during daytime hours was 6.65 patients (range: 3‐17, median 6). Twenty percent of training programs had staff‐only (no residents) pediatric ward services during daytime hours. In the programs with both staff‐only and resident pediatric ward services, only 19% of patients were covered by the staff‐only teams and 81% of patients were covered by resident teams.

During the overnight period, 86% of resident teams did not have caps on the number of new patient admissions. An average of 3.6 providers per training program were in‐house overnight to accept patient admissions to pediatric wards. Ninety‐four percent of these providers in‐house were residents (399 residents in‐house/425 total providers in‐house each night).

Twenty‐five percent of the training programs that responded to the survey had pediatric hospitalist attendings in‐house at night. This included both overnight and partial nights (ie, until midnight). Other attendings in‐house at night include: neonatal intensive care unit (NICU) attendings (53% of programs), pediatric intensive care unit (PICU) attendings (46% of programs), Pediatric Emergency Medicine attendings (65% of programs), and Pediatric Surgery attendings (6.4% of programs). Twenty‐two percent of programs had no in‐house attendings at night (Figure 1).

Figure 1

Pediatric hospitalists were involved with 84% (n = 97) of training programs. Sixty percent (n = 58) of the pediatric hospitalist teams were staffed with both teaching attendings and residents. Fourteen percent (n = 14) of the pediatric hospitalist teams did not involve residents (staff‐only) and 25% (n = 25) had both types of teams. Specifically, of the programs that had pediatric hospitalists, 20% (n = 19) of them had hospitalist attendings in‐house 24 hours per day and 13% (n = 12) of teams had hospitalist attendings in‐house into the evening hours for a varying amount of time. Of the programs with hospitalist attendings in‐house 24 hours per day, 52% (n = 11) had started this coverage within the past 3 years.

Looking towards the future, and prior to the enactment of the October 2010 ACGME standards, 31% (n = 35) of the training programs that lacked 24/7 hospitalist in‐house coverage in January 2010 anticipated adding this level of coverage within the next 5 years. Notably, 70% (n = 81) of training programs felt that further resident work hour restrictions, which have since been enacted, would likely require the addition of more hospitalist attendings at night. Our survey allowed program directors to make open‐ended comments on how further work hour restrictions may change inpatient staffing in noncritical care inpatient teaching services.

DISCUSSION

To our knowledge, this was the first national study of pediatric resident coverage in noncritical care inpatient teaching services. While there was significant variation in how inpatient teaching services were covered across these programs, in January 2010, residents were involved in the majority of patient care with only 20% of programs having attending‐only hospitalist teams during the daytime. During the overnight period, the proportion of patient care provided by residents became even more significant with residents representing 94% of the total in‐house providers accepting new admissions. While pediatric hospitalists were prevalent at these training programs, their role in direct patient care overnight was limited. Only 6% of total in‐house providers accepting admissions at night were pediatric hospitalists.

The comments made by program directors are representative of the overall concerns regarding changes to resident work hours (see Table 2). In a position statement by the Association of Pediatric Program Directors in regards to the IOM recommendations, concerns were raised stating that the recommendations of the IOM Committee are intended to enhance patient safety without appropriate consideration for the educational and professional development of trainees.5 While the newly mandated ACGME standards are different than the previous IOM recommendations, it is clear that there will be very significant changes to accommodate these new standards. Our study was done prior to the new ACGME's standards. At the time of the survey, less than a third of programs were anticipating the addition of 24/7 pediatric hospitalist coverage; however, if resident work hours were further restricted, 70% of programs felt that additional hospitalists would be needed. This is a significant increase in the previously anticipated need for overnight attending hospitalist coverage, especially in light of the further restrictions mandated by the ACGME. We know that the response of New York State programs to the 405 regulations varied by program size, but all made significant changes to accommodate the new standards.6 It is clear that many program directors nationally are anticipating significant changes to their residencies when these new restrictions are enacted. The respondents in our survey felt that pediatric hospitalists are going to have to play an even bigger role at night when additional resident work hour restrictions are put into place.

Pediatric hospital medicine remains a rapidly growing field.7 Eighty‐four percent of pediatric training programs utilize pediatric hospitalists. Over 60% of these pediatric hospitalist teams are involved in teaching teams with residents. While we did not directly study the supply and demand of pediatric hospitalists, there is some concern that even despite its rapid growth, the supply of pediatric hospitalists will not keep up with the demand when further resident work hours restrictions are implemented. At time of submission, a cost‐analysis has not yet been publicly published on the ACGME's new changes. There is data available based on the IOM's 2008 recommendations. A study by Nuckols and Escarce8 suggests that if the IOM's recommendations were implemented, the entire healthcare system nationally would have to develop and fill new full‐time positions equal to 5001 attending physicians, 5984 midlevel providers (nurse practitioners or physician assistants), 320 licensed vocational nurses, 229 nursing aides, and 45 laboratory technicians. This would be equivalent to adding an additional 8247 residency positions across all specialties.810 While the ACGME's new mandated changes are different than the IOM's recommendations, they will also restrict resident duty hours that we believe could lead to gaps in patient care requiring significant personnel changes in the healthcare system.

There are several limitations to our study. We did not study the role of pediatric subspecialty fellows and their involvement in pediatric inpatient services in these training programs. We also did not study the prevalence and use of resident night float systems. While night floats may be used in some programs, it may become more prevalent with the possible restriction in intern work hours down to 16 hours. As with any survey, there remains both volunteer and nonresponse bias with the programs that decide to complete or disregard the survey. Finally, there remains some concern over the data collection after the survey was sent out to the hospitalist listserve. Pediatric hospitalists may have incorrectly filled out the data for their program after their program director had already completed the survey. We attempted to minimize this problem by specifically instructing hospitalists to encourage their program director to fill out the survey if they had not already done so. We also compared computer Internet Protocol (IP) addresses and actual program responses, before and after the hospitalist e‐mail was sent, in an attempt to minimize the chance of including duplicated responses from the same program. Lastly, the January 2010 survey predated the October 2010 ACGME response to the IOM recommendations, and the responses may be different now that the specific restrictions have been mandated with an actual implementation date.

CONCLUSIONS

This study shows that pediatric teaching services varied significantly in how they provided overnight coverage in 2010 prior to new ACGME recommendations. Overall, residents were providing the overwhelming majority of the patient care overnight in pediatric training programs. While hospitalists were prevalent in pediatric training programs, in 2010 they had limited roles in direct patient care at night. The ACGME has now mandated additional residency work hour restrictions to be implemented July 2011. With these restrictions, hospitalists will likely need to expand their services, and additional hospitalists will be needed to provide overnight coverage. It is unclear where those hospitalists will come from and what their role will be. It is also unclear what the impact of increased demand and changed job description will be on the continued evolution of the field of Pediatric Hospital Medicine.

Future work needs to be done to establish benchmarks for inpatient coverage. The benchmarks could include guidelines on balancing patient safety with resident education. This may also involve the implementation of resident night float models. There needs to be monitoring on how changes in resident work hours and staffing affect coverage and, ultimately, how changes affect patient and resident outcomes.

Resident duty hour restrictions were initially implemented in New York in 1989 with New York State Code 405 in response to a patient death in a New York City Emergency Department.1 This case initiated an evaluation of potential risks to patient safety when residents were inadequately supervised and overfatigued. In 2003, the Accreditation Council for Graduate Medical Education (ACGME) implemented resident duty hours nationally due to concerns for patient safety and quality of care.2 These restrictions involved the implementation of the 80‐hour work week (averaged over 4 weeks), a maximum duty length of 30 hours, and prescriptive supervision guidelines. In December 2008, the Institute of Medicine (IOM) proposed additional changes to further restrict resident duty hours which also included overnight protected sleep periods and additional days off per month.3 The ACGME responded by mandating new resident duty hour restrictions in October 2010 which will be implemented in July 2011. The ACGME's new changes include a change in the maximum duty hour length for residents in their first year of training (PGY‐1) of 16 hours. Residents in their second year of training (PGY‐2) level and above may work a maximum of 24 hours with an additional 4 hours for transition of care and resident education. The ACGME strongly recommends strategic napping, but do not have a protected overnight sleep period in place4 (Table 1).

There is growing concern regarding the impact of these new resident duty hour restrictions on the coverage of inpatient services, particularly during the overnight period. To our knowledge, there is no published national data on how pediatric inpatient teaching services are staffed at night. The objective of this study was to survey the current landscape of pediatric resident coverage of noncritical care inpatient teaching services. In addition, we sought to explore how changes in work hour restrictions might affect the role of pediatric hospitalists in training programs.

METHODS

We developed an institutional review board (IRB)‐approved Web‐based electronic survey. The survey consisted of 17 questions. The survey obtained information regarding the demographics of the program including: number of residents, daily patient census per ward intern, information regarding staff‐only pediatric ward services, overnight coverage, and current attending in‐house overnight coverage (see Appendix). We also examined the prevalence of pediatric hospitalists in training programs, their current role in staffing patients, and how that role may change with the implementation of additional resident duty hour restrictions. Initially, the survey was reviewed and tested by several pediatric hospitalists and program directors. It was then reviewed and approved by the Association of Pediatric Program Director (APPD) research task force. The survey was sent out to 196 US pediatric residency programs via the APPD listserve in January 2010. Program directors were given the option of completing it themselves or specifically designating someone else to complete it. Two reminders were sent. We then sent an additional request for program participation on the pediatric hospitalist listserve. All data was collected by February 2010.

RESULTS

One hundred twenty unique responses were received (61% of total pediatric residency programs). As of 2009, this represented 5201 pediatric residents (58% of total pediatric residents). The average program size was 43 residents (range: 12‐156 residents, median 43). The average daily patient census per ward intern during daytime hours was 6.65 patients (range: 3‐17, median 6). Twenty percent of training programs had staff‐only (no residents) pediatric ward services during daytime hours. In the programs with both staff‐only and resident pediatric ward services, only 19% of patients were covered by the staff‐only teams and 81% of patients were covered by resident teams.

During the overnight period, 86% of resident teams did not have caps on the number of new patient admissions. An average of 3.6 providers per training program were in‐house overnight to accept patient admissions to pediatric wards. Ninety‐four percent of these providers in‐house were residents (399 residents in‐house/425 total providers in‐house each night).

Twenty‐five percent of the training programs that responded to the survey had pediatric hospitalist attendings in‐house at night. This included both overnight and partial nights (ie, until midnight). Other attendings in‐house at night include: neonatal intensive care unit (NICU) attendings (53% of programs), pediatric intensive care unit (PICU) attendings (46% of programs), Pediatric Emergency Medicine attendings (65% of programs), and Pediatric Surgery attendings (6.4% of programs). Twenty‐two percent of programs had no in‐house attendings at night (Figure 1).

Figure 1

Pediatric hospitalists were involved with 84% (n = 97) of training programs. Sixty percent (n = 58) of the pediatric hospitalist teams were staffed with both teaching attendings and residents. Fourteen percent (n = 14) of the pediatric hospitalist teams did not involve residents (staff‐only) and 25% (n = 25) had both types of teams. Specifically, of the programs that had pediatric hospitalists, 20% (n = 19) of them had hospitalist attendings in‐house 24 hours per day and 13% (n = 12) of teams had hospitalist attendings in‐house into the evening hours for a varying amount of time. Of the programs with hospitalist attendings in‐house 24 hours per day, 52% (n = 11) had started this coverage within the past 3 years.

Looking towards the future, and prior to the enactment of the October 2010 ACGME standards, 31% (n = 35) of the training programs that lacked 24/7 hospitalist in‐house coverage in January 2010 anticipated adding this level of coverage within the next 5 years. Notably, 70% (n = 81) of training programs felt that further resident work hour restrictions, which have since been enacted, would likely require the addition of more hospitalist attendings at night. Our survey allowed program directors to make open‐ended comments on how further work hour restrictions may change inpatient staffing in noncritical care inpatient teaching services.

DISCUSSION

To our knowledge, this was the first national study of pediatric resident coverage in noncritical care inpatient teaching services. While there was significant variation in how inpatient teaching services were covered across these programs, in January 2010, residents were involved in the majority of patient care with only 20% of programs having attending‐only hospitalist teams during the daytime. During the overnight period, the proportion of patient care provided by residents became even more significant with residents representing 94% of the total in‐house providers accepting new admissions. While pediatric hospitalists were prevalent at these training programs, their role in direct patient care overnight was limited. Only 6% of total in‐house providers accepting admissions at night were pediatric hospitalists.

The comments made by program directors are representative of the overall concerns regarding changes to resident work hours (see Table 2). In a position statement by the Association of Pediatric Program Directors in regards to the IOM recommendations, concerns were raised stating that the recommendations of the IOM Committee are intended to enhance patient safety without appropriate consideration for the educational and professional development of trainees.5 While the newly mandated ACGME standards are different than the previous IOM recommendations, it is clear that there will be very significant changes to accommodate these new standards. Our study was done prior to the new ACGME's standards. At the time of the survey, less than a third of programs were anticipating the addition of 24/7 pediatric hospitalist coverage; however, if resident work hours were further restricted, 70% of programs felt that additional hospitalists would be needed. This is a significant increase in the previously anticipated need for overnight attending hospitalist coverage, especially in light of the further restrictions mandated by the ACGME. We know that the response of New York State programs to the 405 regulations varied by program size, but all made significant changes to accommodate the new standards.6 It is clear that many program directors nationally are anticipating significant changes to their residencies when these new restrictions are enacted. The respondents in our survey felt that pediatric hospitalists are going to have to play an even bigger role at night when additional resident work hour restrictions are put into place.

Pediatric hospital medicine remains a rapidly growing field.7 Eighty‐four percent of pediatric training programs utilize pediatric hospitalists. Over 60% of these pediatric hospitalist teams are involved in teaching teams with residents. While we did not directly study the supply and demand of pediatric hospitalists, there is some concern that even despite its rapid growth, the supply of pediatric hospitalists will not keep up with the demand when further resident work hours restrictions are implemented. At time of submission, a cost‐analysis has not yet been publicly published on the ACGME's new changes. There is data available based on the IOM's 2008 recommendations. A study by Nuckols and Escarce8 suggests that if the IOM's recommendations were implemented, the entire healthcare system nationally would have to develop and fill new full‐time positions equal to 5001 attending physicians, 5984 midlevel providers (nurse practitioners or physician assistants), 320 licensed vocational nurses, 229 nursing aides, and 45 laboratory technicians. This would be equivalent to adding an additional 8247 residency positions across all specialties.810 While the ACGME's new mandated changes are different than the IOM's recommendations, they will also restrict resident duty hours that we believe could lead to gaps in patient care requiring significant personnel changes in the healthcare system.

There are several limitations to our study. We did not study the role of pediatric subspecialty fellows and their involvement in pediatric inpatient services in these training programs. We also did not study the prevalence and use of resident night float systems. While night floats may be used in some programs, it may become more prevalent with the possible restriction in intern work hours down to 16 hours. As with any survey, there remains both volunteer and nonresponse bias with the programs that decide to complete or disregard the survey. Finally, there remains some concern over the data collection after the survey was sent out to the hospitalist listserve. Pediatric hospitalists may have incorrectly filled out the data for their program after their program director had already completed the survey. We attempted to minimize this problem by specifically instructing hospitalists to encourage their program director to fill out the survey if they had not already done so. We also compared computer Internet Protocol (IP) addresses and actual program responses, before and after the hospitalist e‐mail was sent, in an attempt to minimize the chance of including duplicated responses from the same program. Lastly, the January 2010 survey predated the October 2010 ACGME response to the IOM recommendations, and the responses may be different now that the specific restrictions have been mandated with an actual implementation date.

CONCLUSIONS

This study shows that pediatric teaching services varied significantly in how they provided overnight coverage in 2010 prior to new ACGME recommendations. Overall, residents were providing the overwhelming majority of the patient care overnight in pediatric training programs. While hospitalists were prevalent in pediatric training programs, in 2010 they had limited roles in direct patient care at night. The ACGME has now mandated additional residency work hour restrictions to be implemented July 2011. With these restrictions, hospitalists will likely need to expand their services, and additional hospitalists will be needed to provide overnight coverage. It is unclear where those hospitalists will come from and what their role will be. It is also unclear what the impact of increased demand and changed job description will be on the continued evolution of the field of Pediatric Hospital Medicine.

Future work needs to be done to establish benchmarks for inpatient coverage. The benchmarks could include guidelines on balancing patient safety with resident education. This may also involve the implementation of resident night float models. There needs to be monitoring on how changes in resident work hours and staffing affect coverage and, ultimately, how changes affect patient and resident outcomes.

Feedback has long been recognized as pivotal to the attainment of clinical acumen and skills in medical training.1 Formative feedback can give trainees insight into their strengths and weaknesses, and provide them with clear goals and methods to attain those goals.1, 2 In fact, feedback given regularly over time by a respected figure has shown to improve physician performance.3 However, most faculty are not trained to provide effective feedback. As a result, supervisors often believe they are giving more feedback than trainees believe they are receiving, and residents receive little feedback that they perceive as useful.4 Most residents receive little to no feedback on their communications skills4 or professionalism,5 and rarely receive corrective feedback.6, 7

Faculty may fail to give feedback to residents for a number of reasons. Those barriers most commonly cited in the literature are discomfort with criticizing residents,6, 7 lack of time,4 and lack of direct observation of residents in clinical settings.810 Several studies have looked at tools to guide feedback and address the barrier of discomfort with criticism.6, 7, 11 Some showed improvements in overall feedback, though often supervisors gave only positive feedback and avoided giving feedback about weaknesses.6, 7, 11 Despite the recognition of lack of time as a barrier to feedback,4 most studies on feedback interventions thus far have not included setting aside time for the feedback to occur.6, 7, 11, 12 Finally, a number of studies utilized objective structured clinical examinations (OSCEs) coupled with immediate feedback to improve direct observation of residents, with success in improving feedback related to the encounter.9, 10, 13 To address the gaps in the current literature, the goals of our study were to address 2 specific barriers to feedback for residents: lack of time and discomfort with giving feedback.

The aim of this study was to improve Internal Medicine (IM) residents' and attendings' experiences with feedback on the wards using a pocket card and a dedicated feedback session. We developed and evaluated the pocket feedback card and session for faculty to improve the quality and frequency of their feedback to residents in the inpatient setting. We performed a randomized trial to evaluate our intervention. We hypothesized that the intervention would: 1) improve the quality and quantity of attendings' feedback given to IM ward residents; and 2) improve attendings' comfort with feedback delivery on the wards.

PARTICIPANTS AND METHODS

Intervention Design

We designed a pocket feedback card to guide a feedback session and assist attendings in giving useful feedback to IM residents on the wards (Figure 1).14 The individual items and categories were adapted from the Accreditation Council for Graduate Medical Education (ACGME) Common Program Requirements Core Competencies section and were revised via the expert consensus of the authors.14 We included 20 items related to resident skills, knowledge, attitudes, and behaviors important to the care of hospitalized patients, grouped under the 6 ACGME core competency domains.14 Many of these items correspond to competencies in the Society of Hospital Medicine (SHM) Core Competencies; in particular, the categories of Systems‐Based Practice and Practice‐Based Learning mirror competencies in the SHM Core Competencies Healthcare Systems chapter.15 Each item utilized a 5‐point Likert scale (1 = very poor, 3 = at expected level, 5 = superior) to evaluate resident performance (Figure 1). We created this card to serve as a directive and specific guide for attendings to provide feedback about specific domains and to give more constructive feedback. The card was to be used during a specific dedicated feedback session in order to overcome the commonly cited barrier of lack of time.

Figure 1

RESULTS

During the 6‐month study period, 34 teaching attendings (over 36 attending inpatient blocks) and 93 IM residents (over 111 resident inpatient blocks) participated in the study. Thirty‐four of 36 attending surveys and 96 of 111 resident surveys were completed. The overall survey response rates for residents and attendings were 85% and 94%, respectively. Two attendings participated during 2 separate blocks, first in the control group and then in the intervention group, and 18 residents participated during 2 separate blocks. No attendings or residents participated more than twice.

Resident survey response rate was 81.2% in the intervention group and 87.3% in the control group (Table 1). Residents in the intervention group reported receiving more feedback regarding skills they did well (89.7% vs 63.6%, P = 0.004) and skills needing improvement (51.3% vs 25.5%, P = 0.02) than those in the control group. In addition, more intervention residents reported receiving useful information regarding how to improve their skills (53.8% vs 27.3%, P = 0.01), and reported actually improving both their clinical skills (61.5% vs 27.8%, P = 0.001) and their professionalism/communication skills (51.3% vs 29.1%, P = 0.03) based on feedback received from attendings.

The attending survey response rates for the intervention and control groups were 100% and 90%, respectively. In general, both groups of attendings reported that they were comfortable giving feedback and that they did, in fact, give feedback in each area during their ward block (Table 2). More intervention attendings felt that at least 1 of their residents improved their professionalism/communication skills based on the feedback given (76.9% vs 31.1%, P = 0.02). There were no other significant differences between the groups of attendings.

Intervention attendings also shared their attitudes toward the feedback card and session. A majority felt that using 1 attending rounds as a feedback session helped create a dedicated time for giving feedback (68.8%), and that the feedback card helped them to give specific, constructive feedback (62.5%). Most attendings reported they would use the feedback card and session again during future inpatient blocks (81%), and would recommend them to other attendings (75%).

Qualitative data from intervention attending interviews revealed further thoughts about the feedback card and feedback session. Most attendings interviewed (7/8) felt that the card was useful for the structure and topic guidance it provided. Half felt that setting aside time for feedback was the more useful component. The other half reported that, because they usually set aside time for feedback regardless, the card was more useful. None of the attendings felt that the feedback card or session was detrimental for patient care or education, and many said that the intervention had positive effects on these areas. For example, 1 attending said that the session added to patient care because I used particular [patient] cases as examples for giving feedback.

DISCUSSION

In this randomized study, we found that a simple pocket feedback card and dedicated feedback session was acceptable to ward attendings and improved resident satisfaction with feedback. Unlike most prior studies of feedback, we demonstrated more feedback around skills needing improvement, and intervention residents felt the feedback they received helped them improve their skills. Our educational intervention was unique in that it combined a pocket card to structure feedback content and dedicated time to structure the feedback process, to address 2 of the major barriers to giving feedback: lack of time and lack of comfort.

The pocket card itself as a tool for improving feedback is innovative and valuable. As a short but directive guide, the card supports attendings' delivery of relevant and specific feedback about residents' performance, and because it is based on the ACGME competencies, it may help attendings focus feedback on areas in which they will later evaluate residents. The inclusion of a prespecified time for giving feedback was important as well, in that it allowed for face‐to‐face feedback to occur, as opposed to a passing comment after a presentation or brief notes in a written final evaluation. Both the card and the feedback session seemed equally important for the success of this intervention, with attitudes varying based on individual attending preferences. Those who usually set aside time for feedback on their own found the card more useful, whereas those who had more trouble finding time for feedback found the specific session more useful. Most attendings found the intervention as a whole helpful, and without any detrimental effects on patient care or education. The card and session may be particularly valuable for hospital attendings, given their growing presence as teachers and supervisors for residents, and their busy days on the wards.

Our study results have important implications for resident training in the hospital. Improving resident receipt of feedback about strengths and weaknesses is an ACGME training requirement, and specific guidance about how to improve skills is critical for focusing improvement efforts. Previous studies have demonstrated that directive feedback in medical training can lead to a variety of performance improvements, including better evaluations by other professionals,9, 16 and objective improvements in resident communication skills,17 chart documentation,18 and clinical management of patients.11, 15, 19 By improving the quality of feedback across several domains and facilitating the feedback process, our intervention may lead to similar improvements. Future studies should examine the global impact of guided feedback as in our study. Perhaps most importantly, attendings found the intervention acceptable and would recommend its use, implying longer term sustainability of its integration into the hospital routine.

One strength of our study was its prospective randomized design. Despite the importance of rigor in medical education research, there remains a paucity of randomized studies to evaluate educational interventions for residents in inpatient settings. Few studies of feedback interventions in particular have performed randomized trials,5, 6, 11 and only one has examined a feedback intervention in a randomized fashion in the inpatient setting.12 This evaluation of a 20‐minute intervention, and a reminder card for supervising attendings to improve written and verbal feedback to residents, modestly improved the amount of verbal feedback given to residents, but did not impact the number of residents receiving mid‐rotation feedback or feedback overall as our study did by report.12

There were several important limitations to our study. First, because this was a single institution study, we only achieved modest sample sizes, particularly in the attending groups, and were unable to assess all of the differences in attending attitudes related to feedback. Second, control and intervention participants were on service simultaneously, which may have led to contamination of the control group and an underestimation of the true impact of our intervention. Since residents were not exclusive to 1 study group on 1 occasion (18 of the 93 residents participated during 2 separate blocks), our results may be biased. In particular, those residents who had the intervention first, and were subsequently in the control group, may have rated the control experience worse than they would have otherwise, creating a bias in favor of a positive result for our intervention. Nonetheless, we believe this situation was uncommon and the potential associated bias minimal. Further, this study assessed attitudes related to feedback and self‐reported knowledge and skills, but did not directly assess resident knowledge, skills, or patient outcomes. We recognize the importance of these outcomes and hope that future interventions can determine these important downstream effects of feedback. We were also unable to assess the card and session's impact on attendings' comfort with feedback, because most attendings in both groups reported feeling comfortable giving feedback. This result may indicate that attendings actually are comfortable giving feedback, or may suggest some element of social desirability bias. Finally, in this study, we designed an intervention which combined the pocket card and dedicated feedback time. We did not quantitatively examine the effect of either component alone, and it is unclear if offering the feedback card without protected time or offering protected time without a guide would have impacted feedback on the wards. However, qualitative data from our study support the use of both components, and implementing the 2 components together is feasible in any inpatient teaching setting.

Despite these limitations, protected time for feedback guided by a pocket feedback card is a simple intervention that appears to improve feedback quantity and quality for ward residents, and guides them to improve their performance. Our low‐intensity intervention helped attendings give residents the tools to improve their clinical and communication skills. An opportunity to make a positive impact on resident education with such a small intervention is rare. The use of a feedback card with protected feedback time could be easily implemented in any training program, and is a valuable tool for busy hospitalists who are more commonly supervising residents on their inpatient rotations.

Feedback has long been recognized as pivotal to the attainment of clinical acumen and skills in medical training.1 Formative feedback can give trainees insight into their strengths and weaknesses, and provide them with clear goals and methods to attain those goals.1, 2 In fact, feedback given regularly over time by a respected figure has shown to improve physician performance.3 However, most faculty are not trained to provide effective feedback. As a result, supervisors often believe they are giving more feedback than trainees believe they are receiving, and residents receive little feedback that they perceive as useful.4 Most residents receive little to no feedback on their communications skills4 or professionalism,5 and rarely receive corrective feedback.6, 7

Faculty may fail to give feedback to residents for a number of reasons. Those barriers most commonly cited in the literature are discomfort with criticizing residents,6, 7 lack of time,4 and lack of direct observation of residents in clinical settings.810 Several studies have looked at tools to guide feedback and address the barrier of discomfort with criticism.6, 7, 11 Some showed improvements in overall feedback, though often supervisors gave only positive feedback and avoided giving feedback about weaknesses.6, 7, 11 Despite the recognition of lack of time as a barrier to feedback,4 most studies on feedback interventions thus far have not included setting aside time for the feedback to occur.6, 7, 11, 12 Finally, a number of studies utilized objective structured clinical examinations (OSCEs) coupled with immediate feedback to improve direct observation of residents, with success in improving feedback related to the encounter.9, 10, 13 To address the gaps in the current literature, the goals of our study were to address 2 specific barriers to feedback for residents: lack of time and discomfort with giving feedback.

The aim of this study was to improve Internal Medicine (IM) residents' and attendings' experiences with feedback on the wards using a pocket card and a dedicated feedback session. We developed and evaluated the pocket feedback card and session for faculty to improve the quality and frequency of their feedback to residents in the inpatient setting. We performed a randomized trial to evaluate our intervention. We hypothesized that the intervention would: 1) improve the quality and quantity of attendings' feedback given to IM ward residents; and 2) improve attendings' comfort with feedback delivery on the wards.

PARTICIPANTS AND METHODS

Intervention Design

We designed a pocket feedback card to guide a feedback session and assist attendings in giving useful feedback to IM residents on the wards (Figure 1).14 The individual items and categories were adapted from the Accreditation Council for Graduate Medical Education (ACGME) Common Program Requirements Core Competencies section and were revised via the expert consensus of the authors.14 We included 20 items related to resident skills, knowledge, attitudes, and behaviors important to the care of hospitalized patients, grouped under the 6 ACGME core competency domains.14 Many of these items correspond to competencies in the Society of Hospital Medicine (SHM) Core Competencies; in particular, the categories of Systems‐Based Practice and Practice‐Based Learning mirror competencies in the SHM Core Competencies Healthcare Systems chapter.15 Each item utilized a 5‐point Likert scale (1 = very poor, 3 = at expected level, 5 = superior) to evaluate resident performance (Figure 1). We created this card to serve as a directive and specific guide for attendings to provide feedback about specific domains and to give more constructive feedback. The card was to be used during a specific dedicated feedback session in order to overcome the commonly cited barrier of lack of time.

Figure 1

RESULTS

During the 6‐month study period, 34 teaching attendings (over 36 attending inpatient blocks) and 93 IM residents (over 111 resident inpatient blocks) participated in the study. Thirty‐four of 36 attending surveys and 96 of 111 resident surveys were completed. The overall survey response rates for residents and attendings were 85% and 94%, respectively. Two attendings participated during 2 separate blocks, first in the control group and then in the intervention group, and 18 residents participated during 2 separate blocks. No attendings or residents participated more than twice.

Resident survey response rate was 81.2% in the intervention group and 87.3% in the control group (Table 1). Residents in the intervention group reported receiving more feedback regarding skills they did well (89.7% vs 63.6%, P = 0.004) and skills needing improvement (51.3% vs 25.5%, P = 0.02) than those in the control group. In addition, more intervention residents reported receiving useful information regarding how to improve their skills (53.8% vs 27.3%, P = 0.01), and reported actually improving both their clinical skills (61.5% vs 27.8%, P = 0.001) and their professionalism/communication skills (51.3% vs 29.1%, P = 0.03) based on feedback received from attendings.

The attending survey response rates for the intervention and control groups were 100% and 90%, respectively. In general, both groups of attendings reported that they were comfortable giving feedback and that they did, in fact, give feedback in each area during their ward block (Table 2). More intervention attendings felt that at least 1 of their residents improved their professionalism/communication skills based on the feedback given (76.9% vs 31.1%, P = 0.02). There were no other significant differences between the groups of attendings.

Intervention attendings also shared their attitudes toward the feedback card and session. A majority felt that using 1 attending rounds as a feedback session helped create a dedicated time for giving feedback (68.8%), and that the feedback card helped them to give specific, constructive feedback (62.5%). Most attendings reported they would use the feedback card and session again during future inpatient blocks (81%), and would recommend them to other attendings (75%).

Qualitative data from intervention attending interviews revealed further thoughts about the feedback card and feedback session. Most attendings interviewed (7/8) felt that the card was useful for the structure and topic guidance it provided. Half felt that setting aside time for feedback was the more useful component. The other half reported that, because they usually set aside time for feedback regardless, the card was more useful. None of the attendings felt that the feedback card or session was detrimental for patient care or education, and many said that the intervention had positive effects on these areas. For example, 1 attending said that the session added to patient care because I used particular [patient] cases as examples for giving feedback.

DISCUSSION

In this randomized study, we found that a simple pocket feedback card and dedicated feedback session was acceptable to ward attendings and improved resident satisfaction with feedback. Unlike most prior studies of feedback, we demonstrated more feedback around skills needing improvement, and intervention residents felt the feedback they received helped them improve their skills. Our educational intervention was unique in that it combined a pocket card to structure feedback content and dedicated time to structure the feedback process, to address 2 of the major barriers to giving feedback: lack of time and lack of comfort.

The pocket card itself as a tool for improving feedback is innovative and valuable. As a short but directive guide, the card supports attendings' delivery of relevant and specific feedback about residents' performance, and because it is based on the ACGME competencies, it may help attendings focus feedback on areas in which they will later evaluate residents. The inclusion of a prespecified time for giving feedback was important as well, in that it allowed for face‐to‐face feedback to occur, as opposed to a passing comment after a presentation or brief notes in a written final evaluation. Both the card and the feedback session seemed equally important for the success of this intervention, with attitudes varying based on individual attending preferences. Those who usually set aside time for feedback on their own found the card more useful, whereas those who had more trouble finding time for feedback found the specific session more useful. Most attendings found the intervention as a whole helpful, and without any detrimental effects on patient care or education. The card and session may be particularly valuable for hospital attendings, given their growing presence as teachers and supervisors for residents, and their busy days on the wards.

Our study results have important implications for resident training in the hospital. Improving resident receipt of feedback about strengths and weaknesses is an ACGME training requirement, and specific guidance about how to improve skills is critical for focusing improvement efforts. Previous studies have demonstrated that directive feedback in medical training can lead to a variety of performance improvements, including better evaluations by other professionals,9, 16 and objective improvements in resident communication skills,17 chart documentation,18 and clinical management of patients.11, 15, 19 By improving the quality of feedback across several domains and facilitating the feedback process, our intervention may lead to similar improvements. Future studies should examine the global impact of guided feedback as in our study. Perhaps most importantly, attendings found the intervention acceptable and would recommend its use, implying longer term sustainability of its integration into the hospital routine.

One strength of our study was its prospective randomized design. Despite the importance of rigor in medical education research, there remains a paucity of randomized studies to evaluate educational interventions for residents in inpatient settings. Few studies of feedback interventions in particular have performed randomized trials,5, 6, 11 and only one has examined a feedback intervention in a randomized fashion in the inpatient setting.12 This evaluation of a 20‐minute intervention, and a reminder card for supervising attendings to improve written and verbal feedback to residents, modestly improved the amount of verbal feedback given to residents, but did not impact the number of residents receiving mid‐rotation feedback or feedback overall as our study did by report.12

There were several important limitations to our study. First, because this was a single institution study, we only achieved modest sample sizes, particularly in the attending groups, and were unable to assess all of the differences in attending attitudes related to feedback. Second, control and intervention participants were on service simultaneously, which may have led to contamination of the control group and an underestimation of the true impact of our intervention. Since residents were not exclusive to 1 study group on 1 occasion (18 of the 93 residents participated during 2 separate blocks), our results may be biased. In particular, those residents who had the intervention first, and were subsequently in the control group, may have rated the control experience worse than they would have otherwise, creating a bias in favor of a positive result for our intervention. Nonetheless, we believe this situation was uncommon and the potential associated bias minimal. Further, this study assessed attitudes related to feedback and self‐reported knowledge and skills, but did not directly assess resident knowledge, skills, or patient outcomes. We recognize the importance of these outcomes and hope that future interventions can determine these important downstream effects of feedback. We were also unable to assess the card and session's impact on attendings' comfort with feedback, because most attendings in both groups reported feeling comfortable giving feedback. This result may indicate that attendings actually are comfortable giving feedback, or may suggest some element of social desirability bias. Finally, in this study, we designed an intervention which combined the pocket card and dedicated feedback time. We did not quantitatively examine the effect of either component alone, and it is unclear if offering the feedback card without protected time or offering protected time without a guide would have impacted feedback on the wards. However, qualitative data from our study support the use of both components, and implementing the 2 components together is feasible in any inpatient teaching setting.

Despite these limitations, protected time for feedback guided by a pocket feedback card is a simple intervention that appears to improve feedback quantity and quality for ward residents, and guides them to improve their performance. Our low‐intensity intervention helped attendings give residents the tools to improve their clinical and communication skills. An opportunity to make a positive impact on resident education with such a small intervention is rare. The use of a feedback card with protected feedback time could be easily implemented in any training program, and is a valuable tool for busy hospitalists who are more commonly supervising residents on their inpatient rotations.

Feedback has long been recognized as pivotal to the attainment of clinical acumen and skills in medical training.1 Formative feedback can give trainees insight into their strengths and weaknesses, and provide them with clear goals and methods to attain those goals.1, 2 In fact, feedback given regularly over time by a respected figure has shown to improve physician performance.3 However, most faculty are not trained to provide effective feedback. As a result, supervisors often believe they are giving more feedback than trainees believe they are receiving, and residents receive little feedback that they perceive as useful.4 Most residents receive little to no feedback on their communications skills4 or professionalism,5 and rarely receive corrective feedback.6, 7

Faculty may fail to give feedback to residents for a number of reasons. Those barriers most commonly cited in the literature are discomfort with criticizing residents,6, 7 lack of time,4 and lack of direct observation of residents in clinical settings.810 Several studies have looked at tools to guide feedback and address the barrier of discomfort with criticism.6, 7, 11 Some showed improvements in overall feedback, though often supervisors gave only positive feedback and avoided giving feedback about weaknesses.6, 7, 11 Despite the recognition of lack of time as a barrier to feedback,4 most studies on feedback interventions thus far have not included setting aside time for the feedback to occur.6, 7, 11, 12 Finally, a number of studies utilized objective structured clinical examinations (OSCEs) coupled with immediate feedback to improve direct observation of residents, with success in improving feedback related to the encounter.9, 10, 13 To address the gaps in the current literature, the goals of our study were to address 2 specific barriers to feedback for residents: lack of time and discomfort with giving feedback.

The aim of this study was to improve Internal Medicine (IM) residents' and attendings' experiences with feedback on the wards using a pocket card and a dedicated feedback session. We developed and evaluated the pocket feedback card and session for faculty to improve the quality and frequency of their feedback to residents in the inpatient setting. We performed a randomized trial to evaluate our intervention. We hypothesized that the intervention would: 1) improve the quality and quantity of attendings' feedback given to IM ward residents; and 2) improve attendings' comfort with feedback delivery on the wards.

PARTICIPANTS AND METHODS

Intervention Design

We designed a pocket feedback card to guide a feedback session and assist attendings in giving useful feedback to IM residents on the wards (Figure 1).14 The individual items and categories were adapted from the Accreditation Council for Graduate Medical Education (ACGME) Common Program Requirements Core Competencies section and were revised via the expert consensus of the authors.14 We included 20 items related to resident skills, knowledge, attitudes, and behaviors important to the care of hospitalized patients, grouped under the 6 ACGME core competency domains.14 Many of these items correspond to competencies in the Society of Hospital Medicine (SHM) Core Competencies; in particular, the categories of Systems‐Based Practice and Practice‐Based Learning mirror competencies in the SHM Core Competencies Healthcare Systems chapter.15 Each item utilized a 5‐point Likert scale (1 = very poor, 3 = at expected level, 5 = superior) to evaluate resident performance (Figure 1). We created this card to serve as a directive and specific guide for attendings to provide feedback about specific domains and to give more constructive feedback. The card was to be used during a specific dedicated feedback session in order to overcome the commonly cited barrier of lack of time.

Figure 1

RESULTS

During the 6‐month study period, 34 teaching attendings (over 36 attending inpatient blocks) and 93 IM residents (over 111 resident inpatient blocks) participated in the study. Thirty‐four of 36 attending surveys and 96 of 111 resident surveys were completed. The overall survey response rates for residents and attendings were 85% and 94%, respectively. Two attendings participated during 2 separate blocks, first in the control group and then in the intervention group, and 18 residents participated during 2 separate blocks. No attendings or residents participated more than twice.

Resident survey response rate was 81.2% in the intervention group and 87.3% in the control group (Table 1). Residents in the intervention group reported receiving more feedback regarding skills they did well (89.7% vs 63.6%, P = 0.004) and skills needing improvement (51.3% vs 25.5%, P = 0.02) than those in the control group. In addition, more intervention residents reported receiving useful information regarding how to improve their skills (53.8% vs 27.3%, P = 0.01), and reported actually improving both their clinical skills (61.5% vs 27.8%, P = 0.001) and their professionalism/communication skills (51.3% vs 29.1%, P = 0.03) based on feedback received from attendings.

The attending survey response rates for the intervention and control groups were 100% and 90%, respectively. In general, both groups of attendings reported that they were comfortable giving feedback and that they did, in fact, give feedback in each area during their ward block (Table 2). More intervention attendings felt that at least 1 of their residents improved their professionalism/communication skills based on the feedback given (76.9% vs 31.1%, P = 0.02). There were no other significant differences between the groups of attendings.

Intervention attendings also shared their attitudes toward the feedback card and session. A majority felt that using 1 attending rounds as a feedback session helped create a dedicated time for giving feedback (68.8%), and that the feedback card helped them to give specific, constructive feedback (62.5%). Most attendings reported they would use the feedback card and session again during future inpatient blocks (81%), and would recommend them to other attendings (75%).

Qualitative data from intervention attending interviews revealed further thoughts about the feedback card and feedback session. Most attendings interviewed (7/8) felt that the card was useful for the structure and topic guidance it provided. Half felt that setting aside time for feedback was the more useful component. The other half reported that, because they usually set aside time for feedback regardless, the card was more useful. None of the attendings felt that the feedback card or session was detrimental for patient care or education, and many said that the intervention had positive effects on these areas. For example, 1 attending said that the session added to patient care because I used particular [patient] cases as examples for giving feedback.

DISCUSSION

In this randomized study, we found that a simple pocket feedback card and dedicated feedback session was acceptable to ward attendings and improved resident satisfaction with feedback. Unlike most prior studies of feedback, we demonstrated more feedback around skills needing improvement, and intervention residents felt the feedback they received helped them improve their skills. Our educational intervention was unique in that it combined a pocket card to structure feedback content and dedicated time to structure the feedback process, to address 2 of the major barriers to giving feedback: lack of time and lack of comfort.

The pocket card itself as a tool for improving feedback is innovative and valuable. As a short but directive guide, the card supports attendings' delivery of relevant and specific feedback about residents' performance, and because it is based on the ACGME competencies, it may help attendings focus feedback on areas in which they will later evaluate residents. The inclusion of a prespecified time for giving feedback was important as well, in that it allowed for face‐to‐face feedback to occur, as opposed to a passing comment after a presentation or brief notes in a written final evaluation. Both the card and the feedback session seemed equally important for the success of this intervention, with attitudes varying based on individual attending preferences. Those who usually set aside time for feedback on their own found the card more useful, whereas those who had more trouble finding time for feedback found the specific session more useful. Most attendings found the intervention as a whole helpful, and without any detrimental effects on patient care or education. The card and session may be particularly valuable for hospital attendings, given their growing presence as teachers and supervisors for residents, and their busy days on the wards.

Our study results have important implications for resident training in the hospital. Improving resident receipt of feedback about strengths and weaknesses is an ACGME training requirement, and specific guidance about how to improve skills is critical for focusing improvement efforts. Previous studies have demonstrated that directive feedback in medical training can lead to a variety of performance improvements, including better evaluations by other professionals,9, 16 and objective improvements in resident communication skills,17 chart documentation,18 and clinical management of patients.11, 15, 19 By improving the quality of feedback across several domains and facilitating the feedback process, our intervention may lead to similar improvements. Future studies should examine the global impact of guided feedback as in our study. Perhaps most importantly, attendings found the intervention acceptable and would recommend its use, implying longer term sustainability of its integration into the hospital routine.

One strength of our study was its prospective randomized design. Despite the importance of rigor in medical education research, there remains a paucity of randomized studies to evaluate educational interventions for residents in inpatient settings. Few studies of feedback interventions in particular have performed randomized trials,5, 6, 11 and only one has examined a feedback intervention in a randomized fashion in the inpatient setting.12 This evaluation of a 20‐minute intervention, and a reminder card for supervising attendings to improve written and verbal feedback to residents, modestly improved the amount of verbal feedback given to residents, but did not impact the number of residents receiving mid‐rotation feedback or feedback overall as our study did by report.12

There were several important limitations to our study. First, because this was a single institution study, we only achieved modest sample sizes, particularly in the attending groups, and were unable to assess all of the differences in attending attitudes related to feedback. Second, control and intervention participants were on service simultaneously, which may have led to contamination of the control group and an underestimation of the true impact of our intervention. Since residents were not exclusive to 1 study group on 1 occasion (18 of the 93 residents participated during 2 separate blocks), our results may be biased. In particular, those residents who had the intervention first, and were subsequently in the control group, may have rated the control experience worse than they would have otherwise, creating a bias in favor of a positive result for our intervention. Nonetheless, we believe this situation was uncommon and the potential associated bias minimal. Further, this study assessed attitudes related to feedback and self‐reported knowledge and skills, but did not directly assess resident knowledge, skills, or patient outcomes. We recognize the importance of these outcomes and hope that future interventions can determine these important downstream effects of feedback. We were also unable to assess the card and session's impact on attendings' comfort with feedback, because most attendings in both groups reported feeling comfortable giving feedback. This result may indicate that attendings actually are comfortable giving feedback, or may suggest some element of social desirability bias. Finally, in this study, we designed an intervention which combined the pocket card and dedicated feedback time. We did not quantitatively examine the effect of either component alone, and it is unclear if offering the feedback card without protected time or offering protected time without a guide would have impacted feedback on the wards. However, qualitative data from our study support the use of both components, and implementing the 2 components together is feasible in any inpatient teaching setting.

Despite these limitations, protected time for feedback guided by a pocket feedback card is a simple intervention that appears to improve feedback quantity and quality for ward residents, and guides them to improve their performance. Our low‐intensity intervention helped attendings give residents the tools to improve their clinical and communication skills. An opportunity to make a positive impact on resident education with such a small intervention is rare. The use of a feedback card with protected feedback time could be easily implemented in any training program, and is a valuable tool for busy hospitalists who are more commonly supervising residents on their inpatient rotations.