Brief Reports

Diabetes care in the inpatient setting requires coordination between multiple service providers. Breakdowns in this process occur at all levels leading to potential serious harm.1 Error rates focusing on multiple areas related to diabetes care, including the inpatient provision of insulin, have been described as high as 19.5% in 14,000 patients surveyed in the United Kingdom.2 Missteps are important, as insulin prescribing errors are more commonly associated with patient harm.3 In the United States, medication errors related to provision of care to critically ill patients has been documented, but, to our knowledge, no such reports regarding general medical or surgical wards exist.4

Insulin errors can result from a wide range of possible reasons including: incorrect medication reconciliation, prescribing errors, dispensing errors, administration errors, suboptimal meal timing, or errors in communication for discharge plans regarding diabetes care. Examining each of these areas as a whole could be a daunting task. As such, we sought to examine 1 portion of insulin provision as an initial focus for performance improvement at our institution. Our purpose was to describe the rates of errors associated with insulin administration at our single academic medical center on general medicine and surgical wards.

Methods

Study patients for this observational, prospective snapshot were identified by electronic medical records in 4 consecutive weeks in April 2009 at Barnes‐Jewish Hospital (St Louis, MO), a 1200 bed academic medical center. This study was approved by the Washington University in St Louis School of Medicine Human Studies Committee, and the requirement for informed consent was waived.

On day 1 of each snapshot period, all patients on the identified wards were examined to determine if insulin was currently active as part of the inpatient medication orders. If active, this patient was enrolled into the evaluation data set. No patients were excluded if insulin was currently ordered. Four inpatient areas were selected to provide a representation of the non‐critically ill patient population at our institution. The 4 areas selected were: a cardiac care ward (typical census = 24), a general medicine ward (typical census = 24), an abdominal transplant ward (typical census = 18), and a general surgery ward (typical census = 22). Taken together, these areas represent about 20% of the total non‐critically ill patient population at our hospital. The transplant area was chosen because it represents a high‐risk population with medication (corticosteroid)‐induced diabetes. Nursing and physician care are typically exclusive in these areas, and very little crossover among these healthcare providers would have occurred among the units surveyed during the study period.

Each patient included on day 1 of each audit period was followed for a total of up to 7 days. Patients were only enrolled on day 1 of each audit period. Four survey periods were conducted, providing an evaluation of 28 days of insulin therapy in the studied units. Four periods were selected to pick up more patients on day 1 of each audit period. Electronic records of medication administration and evaluation of paper chart orders provided the information for insulin administration error rates. Additionally, physician notes regarding patients' histories and home insulin use were reviewed for background information for our patient population. Prospective daily assessments of insulin orders, doses charted, nursing notes, and blood glucose values were conducted for potential errors in insulin administration.

All definitions of insulin administration errors were defined prior to data collection. The investigators reviewed available literature involving insulin errors, and found no standardized definitions or previously published assessments at the time of inception of our study. As such, we examined our own clinical practice for areas of potential concern related to insulin administration. The following error categories were identified: transcription errors (eg, insulin glargine 10 units qpm written, but order transcribed and carried out as 20 units qpm); greater than 1 hour between obtained point‐of‐care blood glucose value and provision of correctional (sliding‐scale) insulin; insulin held without a physician order present in the medical records; missing documentation of insulin doses (glucose value of 150 mg/dL present, but no documented correctional dose corresponding to this value present in medical record); premeal and correctional insulin given at separate times; and no documentation of physician notification for hypoglycemia. Other reasons for potential insulin administration errors were collected if deemed pertinent by the individual auditors.

At the time of our survey, a standardized subcutaneous insulin administration order set was utilized in all of the surveyed units. As computerized physician order entry was not yet available at our institution, all orders were transcribed electronically from paper orders. This insulin order set has been in place for 5 years. Once initiated, all portions of the order set are initiated, including communication to nurses regarding glucose measuring times, requirement for documentation of hypoglycemia, and proposed glucose goals. A survey of insulin orders during the audit time revealed that >97% of all insulin orders were initiated from this standardized order set. These order sets encouraged the provision of physiological insulin (basal‐bolus) using insulin glargine and insulin aspart in eligible individuals. Although no systematic, standardized goal for glucose attainment was promoted, a fasting blood glucose of 90‐130 mg/dL and post‐prandial value of 180 mg/dL was encouraged. The order sets had a stated requirement of physician contact for all blood glucose values 70 mg/dL. Although lack of documentation of hypoglycemia may not be directly considered an error associated with administration of insulin, the research group decided to include this provision in the definition of administration errors, given the ability of this parameter to provide a sense of overall completeness of insulin orders and as a marker of collaborative practice in the management of inpatient hyperglycemia.

Nurses documented glucose values and responses in electronic medical administration records as a matter of routine. Point‐of‐care glucose values were obtained by either patient care technicians or nurses on each individual ward. As an academic medical institution, physicians were frequently paged by other members of the healthcare team.

Each auditor (E.N.D., A.L., L.L.W., K.A.H.) reviewed 1 consistent unit during the audit period. All data for insulin administration errors were tabulated, and descriptive rates of errors were used on a per‐patient or per‐stay basis

Results

A total of 116 patient‐audit periods were identified during the 28‐day study period (Table 1). Sixty‐five patients were on surgical services, and 51 were on medicine services, representing 378 inpatient days. Median length of stay was 3.5 days. Home insulin use was evident in 49% of the surveyed population. Patients' mean A1C (data available within 3 months prior to admission) was 8.1% (n = 41). Inpatient insulin regimens on day 1 included correctional insulin only (51.7% of cases). Regimens containing neutral protamine Hagedorn (NPH) or glargine also included correctional insulin in 95% of cases, and premeal insulin in 35%. Regimens including both premeal insulin and correctional insulin occurred in 25% of the patients. Diet status indicated that 83% of the population was taking an oral diet on day 1, and 13% were nil per os (nothing by mouth [NPO]).

A total of 199 administration errors occurred at a rate of 1.72 errors/patient‐period and 0.53 errors/patient day (Table 2). Missing documentation of doses (15.5% of all patients) and insulin being held without an order (25% of patients) were the most frequently occurring events. Errors classified as other were found in 13.1% of the defined events. These other errors consisted of not carrying out correctional dose insulin orders appropriately (eg, blood sugar value of 149 mg/dL should have resulted in a correctional dose of 2 units, but 3 units were documented as given instead), timing errors related to provision of mealtime insulin apart from documented provision of a meal, or not following the required documentation for insulin pumps.

Forty‐two patients (36%) experienced no errors in insulin administration, 18 patients experienced 1 error, 21 patients had 2 errors, and 11 patients had 3 errors. The remainder of the patients (n = 23; 19.9%) had 4 or more errors during their observation period. Were similar across the units surveyed. Frequency of errors remained consistent regardless of reason for admission, history of diabetes or insulin use at home, or length of stay. Most errors occurred on days 1 and 2 of the hospital stay. Error rates and types were consistent across all units surveyed.

Discussion/Conclusion

We found that insulin administration errors were common in our inpatient snapshot of non‐critically ill patients. In our observational evaluation, 64% of patients had at least 1 error related to insulin administration. Errors related to missing documentation of scheduled doses, or doses held without a prescriber order, were the most common. Implications of missed or held doses could range from unclear approaches for dose adjustment due to missing information, incorrect titration due to incomplete information, or hypoglycemia and hyperglycemia.

This observed rate of error is much higher than the described error rate of 19.5% reported in the United Kingdom.2 This difference in error rates most likely reflects a difference in focus, as investigators in that national effort focused on prescriber error, aberrations in blood glucose values, and readmission rates. Our evaluation in assessing error rates regarding insulin administration supports the use of personnel keenly aware of the processes related to insulin administration, and provides insight into the importance of evaluating small portions of insulin provision (administration vs prescribing, etc) in assessing grounds for improvement in care. It is important to note that our findings may be exaggerated and are not entirely comparable to a study with a different scope and size.

Our snapshot tool and baseline evaluation is a simple method that could be undertaken at many institutions. As such, this methodology and error estimate serves as a gauge for future comparisons and areas for intervention. Limitations of our assessment include the small portion of patients audited during our evaluation versus using a snapshot of our entire hospital, utilizing nonstandardized criteria for determination of insulin errors, and the lack of correlation of clinical significance (aberrations in glucose values) with errors observed. Also, this single‐institution review may not be generalizable to all institutions. Additionally, we only examined errors related to administration of insulin. Other areas that would complete the picture, related to diabetic therapies and outcomes, would need to include prescribing errors or dispensing errors and relate these to glycemic outcomes. Assessment of these additional errors may have revealed more clinically important events that were not revealed in this small snapshot. Lastly, clinical endpoints such as intensive care unit (ICU) transfers, mortality, or readmissions were not assessed in this small study.

We are fortunate that many of these errors were apparently clinically silent, but in a subset of patients, the risk is real and life‐threatening. Risk occurs at both ends of the glucose spectrum, with the low end receiving the greatest attention. Severe hypoglycemia with harm and inpatient diabetic ketoacidosis have been qualified as newer events by Medicare. Hypoglycemia in the ICU population (40 mg/dL) is an independent marker of mortality.5 Hypoglycemia (50 mg/dL) has been associated with heart attacks, strokes, and death in the outpatient setting.6

The ability to safely control blood sugar in the hospital requires that medications are administered on time, and that communication occurs between the prescribing provider and the nursing staff providing care. Along with the case‐by‐case implications regarding the need for accurate administration of insulin for subsequent titration and determination of discharge prescriptions for patients with diabetes, there are many implications regarding the assessment of inpatient provision of insulin on determining institutional practices based on previous performance. If insulin administration is not accurately provided or documented, institutions will find it difficult to correctly make changes to insulin protocols for targeting future improvements. Our evaluation indicates an obvious need for quality improvement with 18.1% of the errors reflecting holding insulin without an order, and 12.6% of the errors showing no documentation for the physician being notified of hypoglycemia requiring treatment. The need to foster structured nurse‐physician communication will play a critical role in any process improvement. Communication is key for the optimal provision of insulin in the inpatient setting. Computerized order entry and bar‐code guided administration of doses of insulin may fix some types of the errors (transcription and missed documentation, respectively). That said, one of the largest impacts of this survey may reveal that these errors may not be fixed by technology, but may require more targeted and difficult interventions, such as continuing education and holding clinicians accountable. This study provides insight into the complicated issues regarding inpatient insulin administration and, due to its systematic approach, has given direction for process and system improvements.

Diabetes care in the inpatient setting requires coordination between multiple service providers. Breakdowns in this process occur at all levels leading to potential serious harm.1 Error rates focusing on multiple areas related to diabetes care, including the inpatient provision of insulin, have been described as high as 19.5% in 14,000 patients surveyed in the United Kingdom.2 Missteps are important, as insulin prescribing errors are more commonly associated with patient harm.3 In the United States, medication errors related to provision of care to critically ill patients has been documented, but, to our knowledge, no such reports regarding general medical or surgical wards exist.4

Insulin errors can result from a wide range of possible reasons including: incorrect medication reconciliation, prescribing errors, dispensing errors, administration errors, suboptimal meal timing, or errors in communication for discharge plans regarding diabetes care. Examining each of these areas as a whole could be a daunting task. As such, we sought to examine 1 portion of insulin provision as an initial focus for performance improvement at our institution. Our purpose was to describe the rates of errors associated with insulin administration at our single academic medical center on general medicine and surgical wards.

Methods

Study patients for this observational, prospective snapshot were identified by electronic medical records in 4 consecutive weeks in April 2009 at Barnes‐Jewish Hospital (St Louis, MO), a 1200 bed academic medical center. This study was approved by the Washington University in St Louis School of Medicine Human Studies Committee, and the requirement for informed consent was waived.

On day 1 of each snapshot period, all patients on the identified wards were examined to determine if insulin was currently active as part of the inpatient medication orders. If active, this patient was enrolled into the evaluation data set. No patients were excluded if insulin was currently ordered. Four inpatient areas were selected to provide a representation of the non‐critically ill patient population at our institution. The 4 areas selected were: a cardiac care ward (typical census = 24), a general medicine ward (typical census = 24), an abdominal transplant ward (typical census = 18), and a general surgery ward (typical census = 22). Taken together, these areas represent about 20% of the total non‐critically ill patient population at our hospital. The transplant area was chosen because it represents a high‐risk population with medication (corticosteroid)‐induced diabetes. Nursing and physician care are typically exclusive in these areas, and very little crossover among these healthcare providers would have occurred among the units surveyed during the study period.

Each patient included on day 1 of each audit period was followed for a total of up to 7 days. Patients were only enrolled on day 1 of each audit period. Four survey periods were conducted, providing an evaluation of 28 days of insulin therapy in the studied units. Four periods were selected to pick up more patients on day 1 of each audit period. Electronic records of medication administration and evaluation of paper chart orders provided the information for insulin administration error rates. Additionally, physician notes regarding patients' histories and home insulin use were reviewed for background information for our patient population. Prospective daily assessments of insulin orders, doses charted, nursing notes, and blood glucose values were conducted for potential errors in insulin administration.

All definitions of insulin administration errors were defined prior to data collection. The investigators reviewed available literature involving insulin errors, and found no standardized definitions or previously published assessments at the time of inception of our study. As such, we examined our own clinical practice for areas of potential concern related to insulin administration. The following error categories were identified: transcription errors (eg, insulin glargine 10 units qpm written, but order transcribed and carried out as 20 units qpm); greater than 1 hour between obtained point‐of‐care blood glucose value and provision of correctional (sliding‐scale) insulin; insulin held without a physician order present in the medical records; missing documentation of insulin doses (glucose value of 150 mg/dL present, but no documented correctional dose corresponding to this value present in medical record); premeal and correctional insulin given at separate times; and no documentation of physician notification for hypoglycemia. Other reasons for potential insulin administration errors were collected if deemed pertinent by the individual auditors.

At the time of our survey, a standardized subcutaneous insulin administration order set was utilized in all of the surveyed units. As computerized physician order entry was not yet available at our institution, all orders were transcribed electronically from paper orders. This insulin order set has been in place for 5 years. Once initiated, all portions of the order set are initiated, including communication to nurses regarding glucose measuring times, requirement for documentation of hypoglycemia, and proposed glucose goals. A survey of insulin orders during the audit time revealed that >97% of all insulin orders were initiated from this standardized order set. These order sets encouraged the provision of physiological insulin (basal‐bolus) using insulin glargine and insulin aspart in eligible individuals. Although no systematic, standardized goal for glucose attainment was promoted, a fasting blood glucose of 90‐130 mg/dL and post‐prandial value of 180 mg/dL was encouraged. The order sets had a stated requirement of physician contact for all blood glucose values 70 mg/dL. Although lack of documentation of hypoglycemia may not be directly considered an error associated with administration of insulin, the research group decided to include this provision in the definition of administration errors, given the ability of this parameter to provide a sense of overall completeness of insulin orders and as a marker of collaborative practice in the management of inpatient hyperglycemia.

Nurses documented glucose values and responses in electronic medical administration records as a matter of routine. Point‐of‐care glucose values were obtained by either patient care technicians or nurses on each individual ward. As an academic medical institution, physicians were frequently paged by other members of the healthcare team.

Each auditor (E.N.D., A.L., L.L.W., K.A.H.) reviewed 1 consistent unit during the audit period. All data for insulin administration errors were tabulated, and descriptive rates of errors were used on a per‐patient or per‐stay basis

Results

A total of 116 patient‐audit periods were identified during the 28‐day study period (Table 1). Sixty‐five patients were on surgical services, and 51 were on medicine services, representing 378 inpatient days. Median length of stay was 3.5 days. Home insulin use was evident in 49% of the surveyed population. Patients' mean A1C (data available within 3 months prior to admission) was 8.1% (n = 41). Inpatient insulin regimens on day 1 included correctional insulin only (51.7% of cases). Regimens containing neutral protamine Hagedorn (NPH) or glargine also included correctional insulin in 95% of cases, and premeal insulin in 35%. Regimens including both premeal insulin and correctional insulin occurred in 25% of the patients. Diet status indicated that 83% of the population was taking an oral diet on day 1, and 13% were nil per os (nothing by mouth [NPO]).

A total of 199 administration errors occurred at a rate of 1.72 errors/patient‐period and 0.53 errors/patient day (Table 2). Missing documentation of doses (15.5% of all patients) and insulin being held without an order (25% of patients) were the most frequently occurring events. Errors classified as other were found in 13.1% of the defined events. These other errors consisted of not carrying out correctional dose insulin orders appropriately (eg, blood sugar value of 149 mg/dL should have resulted in a correctional dose of 2 units, but 3 units were documented as given instead), timing errors related to provision of mealtime insulin apart from documented provision of a meal, or not following the required documentation for insulin pumps.

Forty‐two patients (36%) experienced no errors in insulin administration, 18 patients experienced 1 error, 21 patients had 2 errors, and 11 patients had 3 errors. The remainder of the patients (n = 23; 19.9%) had 4 or more errors during their observation period. Were similar across the units surveyed. Frequency of errors remained consistent regardless of reason for admission, history of diabetes or insulin use at home, or length of stay. Most errors occurred on days 1 and 2 of the hospital stay. Error rates and types were consistent across all units surveyed.

Discussion/Conclusion

We found that insulin administration errors were common in our inpatient snapshot of non‐critically ill patients. In our observational evaluation, 64% of patients had at least 1 error related to insulin administration. Errors related to missing documentation of scheduled doses, or doses held without a prescriber order, were the most common. Implications of missed or held doses could range from unclear approaches for dose adjustment due to missing information, incorrect titration due to incomplete information, or hypoglycemia and hyperglycemia.

This observed rate of error is much higher than the described error rate of 19.5% reported in the United Kingdom.2 This difference in error rates most likely reflects a difference in focus, as investigators in that national effort focused on prescriber error, aberrations in blood glucose values, and readmission rates. Our evaluation in assessing error rates regarding insulin administration supports the use of personnel keenly aware of the processes related to insulin administration, and provides insight into the importance of evaluating small portions of insulin provision (administration vs prescribing, etc) in assessing grounds for improvement in care. It is important to note that our findings may be exaggerated and are not entirely comparable to a study with a different scope and size.

Our snapshot tool and baseline evaluation is a simple method that could be undertaken at many institutions. As such, this methodology and error estimate serves as a gauge for future comparisons and areas for intervention. Limitations of our assessment include the small portion of patients audited during our evaluation versus using a snapshot of our entire hospital, utilizing nonstandardized criteria for determination of insulin errors, and the lack of correlation of clinical significance (aberrations in glucose values) with errors observed. Also, this single‐institution review may not be generalizable to all institutions. Additionally, we only examined errors related to administration of insulin. Other areas that would complete the picture, related to diabetic therapies and outcomes, would need to include prescribing errors or dispensing errors and relate these to glycemic outcomes. Assessment of these additional errors may have revealed more clinically important events that were not revealed in this small snapshot. Lastly, clinical endpoints such as intensive care unit (ICU) transfers, mortality, or readmissions were not assessed in this small study.

We are fortunate that many of these errors were apparently clinically silent, but in a subset of patients, the risk is real and life‐threatening. Risk occurs at both ends of the glucose spectrum, with the low end receiving the greatest attention. Severe hypoglycemia with harm and inpatient diabetic ketoacidosis have been qualified as newer events by Medicare. Hypoglycemia in the ICU population (40 mg/dL) is an independent marker of mortality.5 Hypoglycemia (50 mg/dL) has been associated with heart attacks, strokes, and death in the outpatient setting.6

The ability to safely control blood sugar in the hospital requires that medications are administered on time, and that communication occurs between the prescribing provider and the nursing staff providing care. Along with the case‐by‐case implications regarding the need for accurate administration of insulin for subsequent titration and determination of discharge prescriptions for patients with diabetes, there are many implications regarding the assessment of inpatient provision of insulin on determining institutional practices based on previous performance. If insulin administration is not accurately provided or documented, institutions will find it difficult to correctly make changes to insulin protocols for targeting future improvements. Our evaluation indicates an obvious need for quality improvement with 18.1% of the errors reflecting holding insulin without an order, and 12.6% of the errors showing no documentation for the physician being notified of hypoglycemia requiring treatment. The need to foster structured nurse‐physician communication will play a critical role in any process improvement. Communication is key for the optimal provision of insulin in the inpatient setting. Computerized order entry and bar‐code guided administration of doses of insulin may fix some types of the errors (transcription and missed documentation, respectively). That said, one of the largest impacts of this survey may reveal that these errors may not be fixed by technology, but may require more targeted and difficult interventions, such as continuing education and holding clinicians accountable. This study provides insight into the complicated issues regarding inpatient insulin administration and, due to its systematic approach, has given direction for process and system improvements.

Diabetes care in the inpatient setting requires coordination between multiple service providers. Breakdowns in this process occur at all levels leading to potential serious harm.1 Error rates focusing on multiple areas related to diabetes care, including the inpatient provision of insulin, have been described as high as 19.5% in 14,000 patients surveyed in the United Kingdom.2 Missteps are important, as insulin prescribing errors are more commonly associated with patient harm.3 In the United States, medication errors related to provision of care to critically ill patients has been documented, but, to our knowledge, no such reports regarding general medical or surgical wards exist.4

Insulin errors can result from a wide range of possible reasons including: incorrect medication reconciliation, prescribing errors, dispensing errors, administration errors, suboptimal meal timing, or errors in communication for discharge plans regarding diabetes care. Examining each of these areas as a whole could be a daunting task. As such, we sought to examine 1 portion of insulin provision as an initial focus for performance improvement at our institution. Our purpose was to describe the rates of errors associated with insulin administration at our single academic medical center on general medicine and surgical wards.

Methods

Study patients for this observational, prospective snapshot were identified by electronic medical records in 4 consecutive weeks in April 2009 at Barnes‐Jewish Hospital (St Louis, MO), a 1200 bed academic medical center. This study was approved by the Washington University in St Louis School of Medicine Human Studies Committee, and the requirement for informed consent was waived.

On day 1 of each snapshot period, all patients on the identified wards were examined to determine if insulin was currently active as part of the inpatient medication orders. If active, this patient was enrolled into the evaluation data set. No patients were excluded if insulin was currently ordered. Four inpatient areas were selected to provide a representation of the non‐critically ill patient population at our institution. The 4 areas selected were: a cardiac care ward (typical census = 24), a general medicine ward (typical census = 24), an abdominal transplant ward (typical census = 18), and a general surgery ward (typical census = 22). Taken together, these areas represent about 20% of the total non‐critically ill patient population at our hospital. The transplant area was chosen because it represents a high‐risk population with medication (corticosteroid)‐induced diabetes. Nursing and physician care are typically exclusive in these areas, and very little crossover among these healthcare providers would have occurred among the units surveyed during the study period.

Each patient included on day 1 of each audit period was followed for a total of up to 7 days. Patients were only enrolled on day 1 of each audit period. Four survey periods were conducted, providing an evaluation of 28 days of insulin therapy in the studied units. Four periods were selected to pick up more patients on day 1 of each audit period. Electronic records of medication administration and evaluation of paper chart orders provided the information for insulin administration error rates. Additionally, physician notes regarding patients' histories and home insulin use were reviewed for background information for our patient population. Prospective daily assessments of insulin orders, doses charted, nursing notes, and blood glucose values were conducted for potential errors in insulin administration.

All definitions of insulin administration errors were defined prior to data collection. The investigators reviewed available literature involving insulin errors, and found no standardized definitions or previously published assessments at the time of inception of our study. As such, we examined our own clinical practice for areas of potential concern related to insulin administration. The following error categories were identified: transcription errors (eg, insulin glargine 10 units qpm written, but order transcribed and carried out as 20 units qpm); greater than 1 hour between obtained point‐of‐care blood glucose value and provision of correctional (sliding‐scale) insulin; insulin held without a physician order present in the medical records; missing documentation of insulin doses (glucose value of 150 mg/dL present, but no documented correctional dose corresponding to this value present in medical record); premeal and correctional insulin given at separate times; and no documentation of physician notification for hypoglycemia. Other reasons for potential insulin administration errors were collected if deemed pertinent by the individual auditors.

At the time of our survey, a standardized subcutaneous insulin administration order set was utilized in all of the surveyed units. As computerized physician order entry was not yet available at our institution, all orders were transcribed electronically from paper orders. This insulin order set has been in place for 5 years. Once initiated, all portions of the order set are initiated, including communication to nurses regarding glucose measuring times, requirement for documentation of hypoglycemia, and proposed glucose goals. A survey of insulin orders during the audit time revealed that >97% of all insulin orders were initiated from this standardized order set. These order sets encouraged the provision of physiological insulin (basal‐bolus) using insulin glargine and insulin aspart in eligible individuals. Although no systematic, standardized goal for glucose attainment was promoted, a fasting blood glucose of 90‐130 mg/dL and post‐prandial value of 180 mg/dL was encouraged. The order sets had a stated requirement of physician contact for all blood glucose values 70 mg/dL. Although lack of documentation of hypoglycemia may not be directly considered an error associated with administration of insulin, the research group decided to include this provision in the definition of administration errors, given the ability of this parameter to provide a sense of overall completeness of insulin orders and as a marker of collaborative practice in the management of inpatient hyperglycemia.

Nurses documented glucose values and responses in electronic medical administration records as a matter of routine. Point‐of‐care glucose values were obtained by either patient care technicians or nurses on each individual ward. As an academic medical institution, physicians were frequently paged by other members of the healthcare team.

Each auditor (E.N.D., A.L., L.L.W., K.A.H.) reviewed 1 consistent unit during the audit period. All data for insulin administration errors were tabulated, and descriptive rates of errors were used on a per‐patient or per‐stay basis

Results

A total of 116 patient‐audit periods were identified during the 28‐day study period (Table 1). Sixty‐five patients were on surgical services, and 51 were on medicine services, representing 378 inpatient days. Median length of stay was 3.5 days. Home insulin use was evident in 49% of the surveyed population. Patients' mean A1C (data available within 3 months prior to admission) was 8.1% (n = 41). Inpatient insulin regimens on day 1 included correctional insulin only (51.7% of cases). Regimens containing neutral protamine Hagedorn (NPH) or glargine also included correctional insulin in 95% of cases, and premeal insulin in 35%. Regimens including both premeal insulin and correctional insulin occurred in 25% of the patients. Diet status indicated that 83% of the population was taking an oral diet on day 1, and 13% were nil per os (nothing by mouth [NPO]).

A total of 199 administration errors occurred at a rate of 1.72 errors/patient‐period and 0.53 errors/patient day (Table 2). Missing documentation of doses (15.5% of all patients) and insulin being held without an order (25% of patients) were the most frequently occurring events. Errors classified as other were found in 13.1% of the defined events. These other errors consisted of not carrying out correctional dose insulin orders appropriately (eg, blood sugar value of 149 mg/dL should have resulted in a correctional dose of 2 units, but 3 units were documented as given instead), timing errors related to provision of mealtime insulin apart from documented provision of a meal, or not following the required documentation for insulin pumps.

Forty‐two patients (36%) experienced no errors in insulin administration, 18 patients experienced 1 error, 21 patients had 2 errors, and 11 patients had 3 errors. The remainder of the patients (n = 23; 19.9%) had 4 or more errors during their observation period. Were similar across the units surveyed. Frequency of errors remained consistent regardless of reason for admission, history of diabetes or insulin use at home, or length of stay. Most errors occurred on days 1 and 2 of the hospital stay. Error rates and types were consistent across all units surveyed.

Discussion/Conclusion

We found that insulin administration errors were common in our inpatient snapshot of non‐critically ill patients. In our observational evaluation, 64% of patients had at least 1 error related to insulin administration. Errors related to missing documentation of scheduled doses, or doses held without a prescriber order, were the most common. Implications of missed or held doses could range from unclear approaches for dose adjustment due to missing information, incorrect titration due to incomplete information, or hypoglycemia and hyperglycemia.

This observed rate of error is much higher than the described error rate of 19.5% reported in the United Kingdom.2 This difference in error rates most likely reflects a difference in focus, as investigators in that national effort focused on prescriber error, aberrations in blood glucose values, and readmission rates. Our evaluation in assessing error rates regarding insulin administration supports the use of personnel keenly aware of the processes related to insulin administration, and provides insight into the importance of evaluating small portions of insulin provision (administration vs prescribing, etc) in assessing grounds for improvement in care. It is important to note that our findings may be exaggerated and are not entirely comparable to a study with a different scope and size.

Our snapshot tool and baseline evaluation is a simple method that could be undertaken at many institutions. As such, this methodology and error estimate serves as a gauge for future comparisons and areas for intervention. Limitations of our assessment include the small portion of patients audited during our evaluation versus using a snapshot of our entire hospital, utilizing nonstandardized criteria for determination of insulin errors, and the lack of correlation of clinical significance (aberrations in glucose values) with errors observed. Also, this single‐institution review may not be generalizable to all institutions. Additionally, we only examined errors related to administration of insulin. Other areas that would complete the picture, related to diabetic therapies and outcomes, would need to include prescribing errors or dispensing errors and relate these to glycemic outcomes. Assessment of these additional errors may have revealed more clinically important events that were not revealed in this small snapshot. Lastly, clinical endpoints such as intensive care unit (ICU) transfers, mortality, or readmissions were not assessed in this small study.

We are fortunate that many of these errors were apparently clinically silent, but in a subset of patients, the risk is real and life‐threatening. Risk occurs at both ends of the glucose spectrum, with the low end receiving the greatest attention. Severe hypoglycemia with harm and inpatient diabetic ketoacidosis have been qualified as newer events by Medicare. Hypoglycemia in the ICU population (40 mg/dL) is an independent marker of mortality.5 Hypoglycemia (50 mg/dL) has been associated with heart attacks, strokes, and death in the outpatient setting.6

The ability to safely control blood sugar in the hospital requires that medications are administered on time, and that communication occurs between the prescribing provider and the nursing staff providing care. Along with the case‐by‐case implications regarding the need for accurate administration of insulin for subsequent titration and determination of discharge prescriptions for patients with diabetes, there are many implications regarding the assessment of inpatient provision of insulin on determining institutional practices based on previous performance. If insulin administration is not accurately provided or documented, institutions will find it difficult to correctly make changes to insulin protocols for targeting future improvements. Our evaluation indicates an obvious need for quality improvement with 18.1% of the errors reflecting holding insulin without an order, and 12.6% of the errors showing no documentation for the physician being notified of hypoglycemia requiring treatment. The need to foster structured nurse‐physician communication will play a critical role in any process improvement. Communication is key for the optimal provision of insulin in the inpatient setting. Computerized order entry and bar‐code guided administration of doses of insulin may fix some types of the errors (transcription and missed documentation, respectively). That said, one of the largest impacts of this survey may reveal that these errors may not be fixed by technology, but may require more targeted and difficult interventions, such as continuing education and holding clinicians accountable. This study provides insight into the complicated issues regarding inpatient insulin administration and, due to its systematic approach, has given direction for process and system improvements.

Serum sickness is an immunological condition characterized by fever, rash, arthralgia/arthritis, myalgia, edema, and localized lymphadenopathy. Historically, this syndrome was seen as an immunologic response to heterologous protein components administered for therapeutic purposes, such as in the treatment of diphtheria and scarlet fever. Following the decline in use of such heterologous proteins, this same condition is now seen with equine antitoxins, monoclonal antibodies, and some drugs.13 Specifically, the immunologic response to these drugs is referred to as serum sickness‐like reaction (SSLR). The classic serum sickness is described as a prototype Gell and Coombs type III or immune complex‐mediated hypersensitivity disease.4 When a foreign protein antitoxin is administered into human serum, immune system recognition and antibody production occurs. Antibodies become attached to antigens and, when there are sufficient antibody/antigen bonds, a lattice‐like aggregate called the immune complex forms. Normally these immune complexes are cleared from the blood by the reticulo‐endothelial system, but if the system is defective, or the complexes are in a sufficiently large quantity, then deposition into various tissues like the internal elastic lamina of arteries, perivascular regions, synovia, and glomeruli occurs. Following deposition, complement is activated, causing inflammation in these same tissues, resulting in fever, rash, arthralgia, and myalgia.5 A similar reaction has been seen with certain drug exposures as well. The mechanism for this reaction is less clear, but thought to be similar to haptens attaching to plasma proteins and inciting the immunological response.6

Case

A 57‐year‐old white female presented with rash and generalized body aches. She had no significant past medical history, except for sinusitis several years ago; she was prescribed clarithromycin but did not report any problem with this medication at that time. The patient was diagnosed with acute sinusitis 4 days before this presentation. She had visited a primary care physician for her sinusitis and had been prescribed clarithromycin 500 mg twice daily for 7 days. The patient did not use any prescribed or nonprescribed medications in the last 6 months, except the current use of clarithromycin. She used the medication for 3 days as directed, when she developed a generalized rash. The rash first developed on both arms and then migrated to involve the rest of the body within 1 day. The following day, she developed generalized weakness, muscle aches, and symmetric joint pain in the wrists, arms, fingers, and knees. She stopped taking the medication after her sixth dose because she thought her symptoms might be related to its use. Her rash began to fade away slightly. On the 4th day, her myalgias and arthralgias acutely worsened, limiting her normal activities. She developed shortness of breath, ultimately prompting her visit to the emergency department. On presentation, her temperature was 98F, pulse 76, blood pressure 115/73, and oxygen saturation 99% on room air. She was in no acute distress, had no signs of acute airway compromise, and was comfortable at rest. On examination, she had a pruritic morbilliform rash which was most prominent on her upper extremities. There was no muscular tenderness elicited on her body. The joint examination was entirely normal. Ear, nose, and throat examination was normal; there was no lip swelling, erythema, or swelling in the oral cavity or stridor. The chest was clear to auscultation, and the heart examination was normal. Pertinent labs (and normal ranges) included: C3, 83 mg/dL (79‐152 mg/dL); C4, 11 mg/dL (16‐38 mg/dL); total complement, 24 mg/dL (30‐75 mg/dL); erythrocyte sedimentation rate (ESR), 21 mm/hr (20 mm/hr); and C‐reactive protein (CRP), 0.8 mg/dL (normal, 0.8 mg/dL). Basic chemistries were unremarkable. Serum creatinine was 0.8 mg/dL, and blood urea nitrogen was 11 mg/dL. Creatine phosphokinase was 54 U/L. Liver function tests were normal. Complete blood count with differential showed: Hb, 12.5 g/dL; platelets, 228,000/mm3; polymorphonuclear cells, 76%; lymphocytes, 15%; and eosinophils, 5%. Given the history, the temporal association of symptoms with medication use, physical examination findings, low complement level, and elevated ESR, the diagnosis of serum sickness‐like reaction was made. The patient received intravenous dexamethasone 4 mg once and, following an observation period in the emergency department, was discharged on an oral prednisone taper, with diphenhydramine to use as needed. The patient responded well, and recovered uneventfully.

Discussion

Serum sickness‐like reaction has been described for many drugs, especially antibiotics.7 A clarithromycin‐associated reaction has not been reported previously. Diagnosis of SSLR in this case was suggested by several factors, including the temporal association between clarithromycin ingestion, as well as consistent physical examination and laboratory findings. The patient's past history of clarithromycin use caused the reaction to occur within 36 hours of drug ingestion. Important diagnoses that were considered included angioedema, systemic lupus erythematosus, StevensJohnson syndrome or other drug eruptions, viral exanthemata, reactive arthritis, and acute rheumatic fever. However, the typical morbilliform skin eruptions with mucosal sparing made both lupus and StevensJohnson syndrome unlikely. Without facial or lip edema, angioedema also seemed less probable. Typical features of viral exanthem were also not seen in this patient. The lack of a prior history of a similar reaction and prompt recovery with antiinflammatories also supported a diagnosis of SSLR. Clarithromycin is a very commonly prescribed antibiotic for the treatment of upper respiratory tract infections; this case emphasizes that clinicians should remain aware that its use may rarely be associated with SSLR.

Serum sickness is an immunological condition characterized by fever, rash, arthralgia/arthritis, myalgia, edema, and localized lymphadenopathy. Historically, this syndrome was seen as an immunologic response to heterologous protein components administered for therapeutic purposes, such as in the treatment of diphtheria and scarlet fever. Following the decline in use of such heterologous proteins, this same condition is now seen with equine antitoxins, monoclonal antibodies, and some drugs.13 Specifically, the immunologic response to these drugs is referred to as serum sickness‐like reaction (SSLR). The classic serum sickness is described as a prototype Gell and Coombs type III or immune complex‐mediated hypersensitivity disease.4 When a foreign protein antitoxin is administered into human serum, immune system recognition and antibody production occurs. Antibodies become attached to antigens and, when there are sufficient antibody/antigen bonds, a lattice‐like aggregate called the immune complex forms. Normally these immune complexes are cleared from the blood by the reticulo‐endothelial system, but if the system is defective, or the complexes are in a sufficiently large quantity, then deposition into various tissues like the internal elastic lamina of arteries, perivascular regions, synovia, and glomeruli occurs. Following deposition, complement is activated, causing inflammation in these same tissues, resulting in fever, rash, arthralgia, and myalgia.5 A similar reaction has been seen with certain drug exposures as well. The mechanism for this reaction is less clear, but thought to be similar to haptens attaching to plasma proteins and inciting the immunological response.6

Case

A 57‐year‐old white female presented with rash and generalized body aches. She had no significant past medical history, except for sinusitis several years ago; she was prescribed clarithromycin but did not report any problem with this medication at that time. The patient was diagnosed with acute sinusitis 4 days before this presentation. She had visited a primary care physician for her sinusitis and had been prescribed clarithromycin 500 mg twice daily for 7 days. The patient did not use any prescribed or nonprescribed medications in the last 6 months, except the current use of clarithromycin. She used the medication for 3 days as directed, when she developed a generalized rash. The rash first developed on both arms and then migrated to involve the rest of the body within 1 day. The following day, she developed generalized weakness, muscle aches, and symmetric joint pain in the wrists, arms, fingers, and knees. She stopped taking the medication after her sixth dose because she thought her symptoms might be related to its use. Her rash began to fade away slightly. On the 4th day, her myalgias and arthralgias acutely worsened, limiting her normal activities. She developed shortness of breath, ultimately prompting her visit to the emergency department. On presentation, her temperature was 98F, pulse 76, blood pressure 115/73, and oxygen saturation 99% on room air. She was in no acute distress, had no signs of acute airway compromise, and was comfortable at rest. On examination, she had a pruritic morbilliform rash which was most prominent on her upper extremities. There was no muscular tenderness elicited on her body. The joint examination was entirely normal. Ear, nose, and throat examination was normal; there was no lip swelling, erythema, or swelling in the oral cavity or stridor. The chest was clear to auscultation, and the heart examination was normal. Pertinent labs (and normal ranges) included: C3, 83 mg/dL (79‐152 mg/dL); C4, 11 mg/dL (16‐38 mg/dL); total complement, 24 mg/dL (30‐75 mg/dL); erythrocyte sedimentation rate (ESR), 21 mm/hr (20 mm/hr); and C‐reactive protein (CRP), 0.8 mg/dL (normal, 0.8 mg/dL). Basic chemistries were unremarkable. Serum creatinine was 0.8 mg/dL, and blood urea nitrogen was 11 mg/dL. Creatine phosphokinase was 54 U/L. Liver function tests were normal. Complete blood count with differential showed: Hb, 12.5 g/dL; platelets, 228,000/mm3; polymorphonuclear cells, 76%; lymphocytes, 15%; and eosinophils, 5%. Given the history, the temporal association of symptoms with medication use, physical examination findings, low complement level, and elevated ESR, the diagnosis of serum sickness‐like reaction was made. The patient received intravenous dexamethasone 4 mg once and, following an observation period in the emergency department, was discharged on an oral prednisone taper, with diphenhydramine to use as needed. The patient responded well, and recovered uneventfully.

Discussion

Serum sickness‐like reaction has been described for many drugs, especially antibiotics.7 A clarithromycin‐associated reaction has not been reported previously. Diagnosis of SSLR in this case was suggested by several factors, including the temporal association between clarithromycin ingestion, as well as consistent physical examination and laboratory findings. The patient's past history of clarithromycin use caused the reaction to occur within 36 hours of drug ingestion. Important diagnoses that were considered included angioedema, systemic lupus erythematosus, StevensJohnson syndrome or other drug eruptions, viral exanthemata, reactive arthritis, and acute rheumatic fever. However, the typical morbilliform skin eruptions with mucosal sparing made both lupus and StevensJohnson syndrome unlikely. Without facial or lip edema, angioedema also seemed less probable. Typical features of viral exanthem were also not seen in this patient. The lack of a prior history of a similar reaction and prompt recovery with antiinflammatories also supported a diagnosis of SSLR. Clarithromycin is a very commonly prescribed antibiotic for the treatment of upper respiratory tract infections; this case emphasizes that clinicians should remain aware that its use may rarely be associated with SSLR.

Serum sickness is an immunological condition characterized by fever, rash, arthralgia/arthritis, myalgia, edema, and localized lymphadenopathy. Historically, this syndrome was seen as an immunologic response to heterologous protein components administered for therapeutic purposes, such as in the treatment of diphtheria and scarlet fever. Following the decline in use of such heterologous proteins, this same condition is now seen with equine antitoxins, monoclonal antibodies, and some drugs.13 Specifically, the immunologic response to these drugs is referred to as serum sickness‐like reaction (SSLR). The classic serum sickness is described as a prototype Gell and Coombs type III or immune complex‐mediated hypersensitivity disease.4 When a foreign protein antitoxin is administered into human serum, immune system recognition and antibody production occurs. Antibodies become attached to antigens and, when there are sufficient antibody/antigen bonds, a lattice‐like aggregate called the immune complex forms. Normally these immune complexes are cleared from the blood by the reticulo‐endothelial system, but if the system is defective, or the complexes are in a sufficiently large quantity, then deposition into various tissues like the internal elastic lamina of arteries, perivascular regions, synovia, and glomeruli occurs. Following deposition, complement is activated, causing inflammation in these same tissues, resulting in fever, rash, arthralgia, and myalgia.5 A similar reaction has been seen with certain drug exposures as well. The mechanism for this reaction is less clear, but thought to be similar to haptens attaching to plasma proteins and inciting the immunological response.6

Case

A 57‐year‐old white female presented with rash and generalized body aches. She had no significant past medical history, except for sinusitis several years ago; she was prescribed clarithromycin but did not report any problem with this medication at that time. The patient was diagnosed with acute sinusitis 4 days before this presentation. She had visited a primary care physician for her sinusitis and had been prescribed clarithromycin 500 mg twice daily for 7 days. The patient did not use any prescribed or nonprescribed medications in the last 6 months, except the current use of clarithromycin. She used the medication for 3 days as directed, when she developed a generalized rash. The rash first developed on both arms and then migrated to involve the rest of the body within 1 day. The following day, she developed generalized weakness, muscle aches, and symmetric joint pain in the wrists, arms, fingers, and knees. She stopped taking the medication after her sixth dose because she thought her symptoms might be related to its use. Her rash began to fade away slightly. On the 4th day, her myalgias and arthralgias acutely worsened, limiting her normal activities. She developed shortness of breath, ultimately prompting her visit to the emergency department. On presentation, her temperature was 98F, pulse 76, blood pressure 115/73, and oxygen saturation 99% on room air. She was in no acute distress, had no signs of acute airway compromise, and was comfortable at rest. On examination, she had a pruritic morbilliform rash which was most prominent on her upper extremities. There was no muscular tenderness elicited on her body. The joint examination was entirely normal. Ear, nose, and throat examination was normal; there was no lip swelling, erythema, or swelling in the oral cavity or stridor. The chest was clear to auscultation, and the heart examination was normal. Pertinent labs (and normal ranges) included: C3, 83 mg/dL (79‐152 mg/dL); C4, 11 mg/dL (16‐38 mg/dL); total complement, 24 mg/dL (30‐75 mg/dL); erythrocyte sedimentation rate (ESR), 21 mm/hr (20 mm/hr); and C‐reactive protein (CRP), 0.8 mg/dL (normal, 0.8 mg/dL). Basic chemistries were unremarkable. Serum creatinine was 0.8 mg/dL, and blood urea nitrogen was 11 mg/dL. Creatine phosphokinase was 54 U/L. Liver function tests were normal. Complete blood count with differential showed: Hb, 12.5 g/dL; platelets, 228,000/mm3; polymorphonuclear cells, 76%; lymphocytes, 15%; and eosinophils, 5%. Given the history, the temporal association of symptoms with medication use, physical examination findings, low complement level, and elevated ESR, the diagnosis of serum sickness‐like reaction was made. The patient received intravenous dexamethasone 4 mg once and, following an observation period in the emergency department, was discharged on an oral prednisone taper, with diphenhydramine to use as needed. The patient responded well, and recovered uneventfully.

Discussion

Serum sickness‐like reaction has been described for many drugs, especially antibiotics.7 A clarithromycin‐associated reaction has not been reported previously. Diagnosis of SSLR in this case was suggested by several factors, including the temporal association between clarithromycin ingestion, as well as consistent physical examination and laboratory findings. The patient's past history of clarithromycin use caused the reaction to occur within 36 hours of drug ingestion. Important diagnoses that were considered included angioedema, systemic lupus erythematosus, StevensJohnson syndrome or other drug eruptions, viral exanthemata, reactive arthritis, and acute rheumatic fever. However, the typical morbilliform skin eruptions with mucosal sparing made both lupus and StevensJohnson syndrome unlikely. Without facial or lip edema, angioedema also seemed less probable. Typical features of viral exanthem were also not seen in this patient. The lack of a prior history of a similar reaction and prompt recovery with antiinflammatories also supported a diagnosis of SSLR. Clarithromycin is a very commonly prescribed antibiotic for the treatment of upper respiratory tract infections; this case emphasizes that clinicians should remain aware that its use may rarely be associated with SSLR.

Discharge summaries (DS) correlate with rates of rehospitalization1, 2 and adverse events after discharge.3 The Joint Commission on the Accreditation of Healthcare Organizations acknowledges their importance and mandates that certain elements be included.4 Thus far, however, DS are not standardized across institutions and there is no expectation that they be available at postdischarge visits. There have been numerous attempts to improve the quality of DS by using more structured formats or computer generated summaries with positive results in term of comprehensiveness, clarity, and practitioner satisfaction58 but with persistence of serious errors and omissions.9

Postgraduate training is often the first opportunity for physicians to learn information transfer management skills. Unfortunately, DS are created by house staff who have minimal training in this area¹¹ and feel like they have to learn by osmosis,12 resulting in poor quality DS and lack of availability at the point of care.1315

Previous research suggested that individualized feedback sessions for Internal Medicine residents improved the quality of certain aspects of their completed DS.10 We postulated that an audit and feedback educational intervention on DS for first year geriatric medicine fellows would also improve their quality. This technique involves chart or case review of clinical practice behaviors for a specific task followed by recommendation of new behaviors when applicable.16 Audit and feedback incorporates adult learning theory,1719 an essential part of continuous quality improvement that fits within the Accreditation Council for Graduate Medical Education (ACGME) competency of practice based learning and improvement,20 as an educational activity.

Methods

Intervention

Audit #1

All available DS for each fellow's first month of inpatient service were audited for completeness of the 21 item discharge summary template by 1 author (AD). The 21 items were focused on 4 distinct periods of the hospitalization: admission, hospital course, discharge planning, and postdischarge care (Figure 1).

Figure 1

Content under each of the 21 items was classified as complete, partially complete, or absent. An item was considered complete if most information was present and appropriate medical terms were used, partially complete if information was unclear, and absent if no information was present for that area of the DS. To ensure investigator reliability, a random sample of 25% of each fellow's DS was scored by 2 additional investigators (RK and HF) and all disagreements were reviewed and resolved by consensus.

Feedback

Between December 2006 and January 2007, one‐on‐one formative feedback sessions were scheduled. The sessions were approximately 30 minutes long, confidential, performed by 1 of the authors (AD) and followed a written format. During these sessions, each fellow received the results of their discharge summary audit, each partially complete or absent item was discussed, and the importance of DS was emphasized.

Audit #2

All available DS for each fellow's second month of inpatient service were audited for completeness, using the same 21 item assessment tool and the same scoring system.

Results

Five fellows participated, 4 of whom were women; 2 were in postgraduate year 4, 3 in year 5. A total of 158 DS were audited, 89 prefeedback and 79 postfeedback. Each fellow dictated an average of 17 DS during each inpatient month.

During Audit #1, the 21 item DS were complete among 71%, incomplete among 18%, absent among 11%. Admission items, hospital course items, and discharge planning items were complete among 70%, 78%, and 77% of DS respectively, but postdischarge items were complete among only 57%. Examining individual items, the lowest completion rates were found for test result follow‐up (42%), caregiver information (10%), and home services (64%), as well for assessment at admission and discharge of cognitive and mental status (56% and 53% respectively) and functional status (57% and 40%). Of note, all these items are of particular importance to geriatric care.

After receiving the audit and feedback intervention, fellows were more likely to complete all required discharge summary data when compared to prior‐to‐feedback (91% vs. 71%, P 0.001). Discharge summary completeness improved for all composite outcomes examining the four domains of care: admission (93% vs. 70%, P 0.001), hospital course (93% vs. 78%, P 0.001), discharge planning (93% vs. 77%, P 0.02), and postdischarge care (83% vs. 57%., P 0.001) (Table 1).

Discussion

Our study found that audit and feedback sessions significantly improved the completeness of DS dictated by geriatric medicine fellows at 1 academic medical center. Before feedback, completeness was high in most traditional areas of the DS including admission data, hospital course, and discharge planning, but was low in other areas critical for safe transitions of older adults such as postdischarge care, test follow‐up, caregiver information, and cognitive and functional status changes. These findings were surprising, as using a template should render a completion rate close to 100%. Notably, during feedback sessions, fellows suggested low completion rates were due to lack of awareness regarding the importance of completing all 21 items of the template and missing documentation in patient medical records.

Feedback sessions dramatically improved overall completeness of subsequent DS and in most of areas of specific importance for geriatric care, although we remain uncertain why all areas did not show improvement (for example, caregiver information completion remained low). One possible explanation is the lack of accurate documentation for all necessary items in the hospital medical record. Moreover, we did not observe completion improvement for other items, ie, diet and activity. Overall, we believe that drawing attention to areas of particular importance to geriatric care transitions and providing learners with individual reports on their performance increased their awareness and motivated changes to their practice, improving discharge summary completion.

Our study has limitations. This study was a pilot intervention without a control group, because of time and budgetary constraints. Also, we were unable to assess for sustainability because the fellows studied for this project graduated after the second audit. Third, we studied discharge summary completion; further research should focus on accuracy of discharge summary content. Finally, while we did not use any advanced technologies or materials, faculty time required to conduct the audit and feedback in this study was estimated at 45 hours. In our opinion this estimate would classify our audit and feedback intervention as a low external cost and moderately‐high human cost intervention, which may represent a potential barrier to generalizability. On the other hand, we believe that even an audit of a small sample of DS done by a physician could provide valuable data for feedback and would involve less faculty time.

Our finding that audit and feedback sessions improved the completeness of DS among house‐staff is important for 2 reasons. First, we were able to demonstrate that focused feedback targeted to areas of particular importance to the transition of older adults changed subsequent behavior and resulted in improved documentation of these areas. Second, our study provides evidence of a programmatic approach to address the ACGME competency of practice‐based learning and improvement. We believe that our intervention can be reproduced by training programs across the country and are hopeful that such interventions will result in improved patient outcomes during critical care transitions such as hospital discharge.

Discharge summaries (DS) correlate with rates of rehospitalization1, 2 and adverse events after discharge.3 The Joint Commission on the Accreditation of Healthcare Organizations acknowledges their importance and mandates that certain elements be included.4 Thus far, however, DS are not standardized across institutions and there is no expectation that they be available at postdischarge visits. There have been numerous attempts to improve the quality of DS by using more structured formats or computer generated summaries with positive results in term of comprehensiveness, clarity, and practitioner satisfaction58 but with persistence of serious errors and omissions.9

Postgraduate training is often the first opportunity for physicians to learn information transfer management skills. Unfortunately, DS are created by house staff who have minimal training in this area¹¹ and feel like they have to learn by osmosis,12 resulting in poor quality DS and lack of availability at the point of care.1315

Previous research suggested that individualized feedback sessions for Internal Medicine residents improved the quality of certain aspects of their completed DS.10 We postulated that an audit and feedback educational intervention on DS for first year geriatric medicine fellows would also improve their quality. This technique involves chart or case review of clinical practice behaviors for a specific task followed by recommendation of new behaviors when applicable.16 Audit and feedback incorporates adult learning theory,1719 an essential part of continuous quality improvement that fits within the Accreditation Council for Graduate Medical Education (ACGME) competency of practice based learning and improvement,20 as an educational activity.

Methods

Intervention

Audit #1

All available DS for each fellow's first month of inpatient service were audited for completeness of the 21 item discharge summary template by 1 author (AD). The 21 items were focused on 4 distinct periods of the hospitalization: admission, hospital course, discharge planning, and postdischarge care (Figure 1).

Figure 1

Content under each of the 21 items was classified as complete, partially complete, or absent. An item was considered complete if most information was present and appropriate medical terms were used, partially complete if information was unclear, and absent if no information was present for that area of the DS. To ensure investigator reliability, a random sample of 25% of each fellow's DS was scored by 2 additional investigators (RK and HF) and all disagreements were reviewed and resolved by consensus.

Feedback

Between December 2006 and January 2007, one‐on‐one formative feedback sessions were scheduled. The sessions were approximately 30 minutes long, confidential, performed by 1 of the authors (AD) and followed a written format. During these sessions, each fellow received the results of their discharge summary audit, each partially complete or absent item was discussed, and the importance of DS was emphasized.

Audit #2

All available DS for each fellow's second month of inpatient service were audited for completeness, using the same 21 item assessment tool and the same scoring system.

Results

Five fellows participated, 4 of whom were women; 2 were in postgraduate year 4, 3 in year 5. A total of 158 DS were audited, 89 prefeedback and 79 postfeedback. Each fellow dictated an average of 17 DS during each inpatient month.

During Audit #1, the 21 item DS were complete among 71%, incomplete among 18%, absent among 11%. Admission items, hospital course items, and discharge planning items were complete among 70%, 78%, and 77% of DS respectively, but postdischarge items were complete among only 57%. Examining individual items, the lowest completion rates were found for test result follow‐up (42%), caregiver information (10%), and home services (64%), as well for assessment at admission and discharge of cognitive and mental status (56% and 53% respectively) and functional status (57% and 40%). Of note, all these items are of particular importance to geriatric care.

After receiving the audit and feedback intervention, fellows were more likely to complete all required discharge summary data when compared to prior‐to‐feedback (91% vs. 71%, P 0.001). Discharge summary completeness improved for all composite outcomes examining the four domains of care: admission (93% vs. 70%, P 0.001), hospital course (93% vs. 78%, P 0.001), discharge planning (93% vs. 77%, P 0.02), and postdischarge care (83% vs. 57%., P 0.001) (Table 1).

Discussion

Our study found that audit and feedback sessions significantly improved the completeness of DS dictated by geriatric medicine fellows at 1 academic medical center. Before feedback, completeness was high in most traditional areas of the DS including admission data, hospital course, and discharge planning, but was low in other areas critical for safe transitions of older adults such as postdischarge care, test follow‐up, caregiver information, and cognitive and functional status changes. These findings were surprising, as using a template should render a completion rate close to 100%. Notably, during feedback sessions, fellows suggested low completion rates were due to lack of awareness regarding the importance of completing all 21 items of the template and missing documentation in patient medical records.

Feedback sessions dramatically improved overall completeness of subsequent DS and in most of areas of specific importance for geriatric care, although we remain uncertain why all areas did not show improvement (for example, caregiver information completion remained low). One possible explanation is the lack of accurate documentation for all necessary items in the hospital medical record. Moreover, we did not observe completion improvement for other items, ie, diet and activity. Overall, we believe that drawing attention to areas of particular importance to geriatric care transitions and providing learners with individual reports on their performance increased their awareness and motivated changes to their practice, improving discharge summary completion.

Our study has limitations. This study was a pilot intervention without a control group, because of time and budgetary constraints. Also, we were unable to assess for sustainability because the fellows studied for this project graduated after the second audit. Third, we studied discharge summary completion; further research should focus on accuracy of discharge summary content. Finally, while we did not use any advanced technologies or materials, faculty time required to conduct the audit and feedback in this study was estimated at 45 hours. In our opinion this estimate would classify our audit and feedback intervention as a low external cost and moderately‐high human cost intervention, which may represent a potential barrier to generalizability. On the other hand, we believe that even an audit of a small sample of DS done by a physician could provide valuable data for feedback and would involve less faculty time.

Our finding that audit and feedback sessions improved the completeness of DS among house‐staff is important for 2 reasons. First, we were able to demonstrate that focused feedback targeted to areas of particular importance to the transition of older adults changed subsequent behavior and resulted in improved documentation of these areas. Second, our study provides evidence of a programmatic approach to address the ACGME competency of practice‐based learning and improvement. We believe that our intervention can be reproduced by training programs across the country and are hopeful that such interventions will result in improved patient outcomes during critical care transitions such as hospital discharge.

Discharge summaries (DS) correlate with rates of rehospitalization1, 2 and adverse events after discharge.3 The Joint Commission on the Accreditation of Healthcare Organizations acknowledges their importance and mandates that certain elements be included.4 Thus far, however, DS are not standardized across institutions and there is no expectation that they be available at postdischarge visits. There have been numerous attempts to improve the quality of DS by using more structured formats or computer generated summaries with positive results in term of comprehensiveness, clarity, and practitioner satisfaction58 but with persistence of serious errors and omissions.9

Postgraduate training is often the first opportunity for physicians to learn information transfer management skills. Unfortunately, DS are created by house staff who have minimal training in this area¹¹ and feel like they have to learn by osmosis,12 resulting in poor quality DS and lack of availability at the point of care.1315

Previous research suggested that individualized feedback sessions for Internal Medicine residents improved the quality of certain aspects of their completed DS.10 We postulated that an audit and feedback educational intervention on DS for first year geriatric medicine fellows would also improve their quality. This technique involves chart or case review of clinical practice behaviors for a specific task followed by recommendation of new behaviors when applicable.16 Audit and feedback incorporates adult learning theory,1719 an essential part of continuous quality improvement that fits within the Accreditation Council for Graduate Medical Education (ACGME) competency of practice based learning and improvement,20 as an educational activity.

Methods

Intervention

Audit #1

All available DS for each fellow's first month of inpatient service were audited for completeness of the 21 item discharge summary template by 1 author (AD). The 21 items were focused on 4 distinct periods of the hospitalization: admission, hospital course, discharge planning, and postdischarge care (Figure 1).

Figure 1

Content under each of the 21 items was classified as complete, partially complete, or absent. An item was considered complete if most information was present and appropriate medical terms were used, partially complete if information was unclear, and absent if no information was present for that area of the DS. To ensure investigator reliability, a random sample of 25% of each fellow's DS was scored by 2 additional investigators (RK and HF) and all disagreements were reviewed and resolved by consensus.

Feedback

Between December 2006 and January 2007, one‐on‐one formative feedback sessions were scheduled. The sessions were approximately 30 minutes long, confidential, performed by 1 of the authors (AD) and followed a written format. During these sessions, each fellow received the results of their discharge summary audit, each partially complete or absent item was discussed, and the importance of DS was emphasized.

Audit #2

All available DS for each fellow's second month of inpatient service were audited for completeness, using the same 21 item assessment tool and the same scoring system.

Results

Five fellows participated, 4 of whom were women; 2 were in postgraduate year 4, 3 in year 5. A total of 158 DS were audited, 89 prefeedback and 79 postfeedback. Each fellow dictated an average of 17 DS during each inpatient month.

During Audit #1, the 21 item DS were complete among 71%, incomplete among 18%, absent among 11%. Admission items, hospital course items, and discharge planning items were complete among 70%, 78%, and 77% of DS respectively, but postdischarge items were complete among only 57%. Examining individual items, the lowest completion rates were found for test result follow‐up (42%), caregiver information (10%), and home services (64%), as well for assessment at admission and discharge of cognitive and mental status (56% and 53% respectively) and functional status (57% and 40%). Of note, all these items are of particular importance to geriatric care.

After receiving the audit and feedback intervention, fellows were more likely to complete all required discharge summary data when compared to prior‐to‐feedback (91% vs. 71%, P 0.001). Discharge summary completeness improved for all composite outcomes examining the four domains of care: admission (93% vs. 70%, P 0.001), hospital course (93% vs. 78%, P 0.001), discharge planning (93% vs. 77%, P 0.02), and postdischarge care (83% vs. 57%., P 0.001) (Table 1).

Discussion

Our study found that audit and feedback sessions significantly improved the completeness of DS dictated by geriatric medicine fellows at 1 academic medical center. Before feedback, completeness was high in most traditional areas of the DS including admission data, hospital course, and discharge planning, but was low in other areas critical for safe transitions of older adults such as postdischarge care, test follow‐up, caregiver information, and cognitive and functional status changes. These findings were surprising, as using a template should render a completion rate close to 100%. Notably, during feedback sessions, fellows suggested low completion rates were due to lack of awareness regarding the importance of completing all 21 items of the template and missing documentation in patient medical records.

Feedback sessions dramatically improved overall completeness of subsequent DS and in most of areas of specific importance for geriatric care, although we remain uncertain why all areas did not show improvement (for example, caregiver information completion remained low). One possible explanation is the lack of accurate documentation for all necessary items in the hospital medical record. Moreover, we did not observe completion improvement for other items, ie, diet and activity. Overall, we believe that drawing attention to areas of particular importance to geriatric care transitions and providing learners with individual reports on their performance increased their awareness and motivated changes to their practice, improving discharge summary completion.

Our study has limitations. This study was a pilot intervention without a control group, because of time and budgetary constraints. Also, we were unable to assess for sustainability because the fellows studied for this project graduated after the second audit. Third, we studied discharge summary completion; further research should focus on accuracy of discharge summary content. Finally, while we did not use any advanced technologies or materials, faculty time required to conduct the audit and feedback in this study was estimated at 45 hours. In our opinion this estimate would classify our audit and feedback intervention as a low external cost and moderately‐high human cost intervention, which may represent a potential barrier to generalizability. On the other hand, we believe that even an audit of a small sample of DS done by a physician could provide valuable data for feedback and would involve less faculty time.

Our finding that audit and feedback sessions improved the completeness of DS among house‐staff is important for 2 reasons. First, we were able to demonstrate that focused feedback targeted to areas of particular importance to the transition of older adults changed subsequent behavior and resulted in improved documentation of these areas. Second, our study provides evidence of a programmatic approach to address the ACGME competency of practice‐based learning and improvement. We believe that our intervention can be reproduced by training programs across the country and are hopeful that such interventions will result in improved patient outcomes during critical care transitions such as hospital discharge.

Patients discharged from the hospital with coronary disease complications experience an increased risk of sudden cardiac arrest (SCA), which afflicts over 200,000 people in the United States each year with an 80% to 90% mortality rate.17 Prompt delivery of cardiopulmonary resuscitation (CPR) can triple the probability of survival from SCA, yet less than 25% of SCA victims receive bystander CPR.8 Given that 80% of SCA events occur in the home environment, hospitalization could serve as an important point of capture for family instruction in CPR. Prior investigations have suggested conducting conventional CPR training courses before discharge for family members. However, significant barriers exist to this approach, including the requirement for a certified instructor and a large time commitment for standard training.912

To address these resource and time barriers, the American Heart Association recently established a video self‐instruction (VSI) course in CPR, eliminating the need for an instructor and reducing the time requirement for training to 25 minutes. The course consists of a digital video disc (DVD) and low‐cost inflatable mannequin in a self‐contained kit.13 Several investigations have shown that CPR performance skills of students after VSI courses are similar to those of students after traditional CPR training programs.1417 This VSI program presents the unique opportunity for secondary training, given that the DVD and mannequin may be shared by primary trainees with family members or friends. This VSI approach has not been evaluated in the hospital setting or with family members of patients at risk for SCA. We sought to test the feasibility of an in‐hospital CPR training program using the VSI tool, with the hypothesis that VSI training would be well‐accepted by family members of hospitalized patients with known or suspected coronary disease. We further hypothesized that subjects would be able to perform skills adequately and would be motivated to subsequently share the VSI course with others after their family member's hospital discharge.

METHODS

This prospective, multicenter investigation was approved by the University of Pennsylvania Institutional Review Board (IRB) and represents the initial component of an ongoing longitudinal study testing different methods of CPR education in the hospital setting. Enrollment was conducted at 3 hospitals: The Hospital of the University of Pennsylvania (a 700‐bed tertiary‐care academic medical center), Penn Presbyterian Medical Center (a 300‐bed tertiary‐care and community hospital) and Pennsylvania Hospital (a 400‐bed community hospital).

RESULTS

Subject Characteristics and Demographics

Subjects were recruited at the 3 hospital sites between May 2009 and January 2010. A total of 756 eligible individuals were approached, and 280 accepted enrollment for CPR training, representing a 37% enrollment rate (Figure 1). Of the 280 enrolled, 136 underwent instruction using the VSI training program as described, and 144 were enrolled using an experimental method of VSI training in CPR; this second cohort will be described elsewhere. When comparing the eligible individuals who declined enrollment versus those who accepted (Table 1), no significant differences were observed with regard to age, gender or race (P = NS for each). Common reasons cited for nonparticipation included lack of interest or lack of time (data not shown).

Figure 1

Table 1.Subject Characteristics

	Enrolled (%) n = 136	Screened/Not Enrolled (%) n = 476
NOTE: Percentages are rounded to the nearest integer. CPR training history was not assessed among subjects declining enrollment. Abbreviation: CPR, cardiopulmonary resuscitation. * Immediate family denotes sibling, parent or child of patient.
Age, years	52 15	46 26
Female	94 (69)	326 (68)
Race
White	101 (74)	316 (66)
Black	30 (22)	90 (19)
Hispanic	5 (4)	8 (2)
Other/no response	0 (0)	59 (13)
Relationship to patient
Spouse	49 (36)	171 (36)
Immediate family*	58 (43)	168 (35)
Other	28 (21)	76 (16)
No response	1 (1)	61 (13)
Highest Education
Elementary	1 (1)	1 (1)
Middle schoo1	1 (1)	7 (1)
High school	46 (34)	157 (33)
Some college/vocation	36 (26)	86 (18)
College	30 (22)	92 (19)
Graduate school	22 (16)	39 (8)
No response	0 (0)	94 (20)
Previous CPR training
No	78 (57)
Yes: within past 2 years	0 (0)
Yes: within past 25 years	13 (10)
Yes: within past 510 years	5 (4)
Yes: more than 10 years ago	40 (29)

Demographics of the enrolled subject cohort are detailed in Table 1. The mean age of subjects was 52 15, and 94 of 136 (69%) were female. Enrolled subjects represented spouses or immediate family members of the hospitalized patient in 107 of 136 (79%) of cases, and the vast majority, 118 of 136 (87%), had either never received CPR training or had received it over 10 years prior to current enrollment.

Subject Perspectives

A posttraining survey revealed that most respondents, 101 of 136 (74%) felt comfortable or very comfortable learning CPR from the VSI kit, and 127 of 136 (93%) felt likely or very likely to share the VSI kit (Figure 2).

Figure 2

DISCUSSION

In the current work, we demonstrated the feasibility of using the hospital environment as a point of capture for training family members of at‐risk patients in CPR skills. Given that most SCA events occur in the home setting, family member training may hold greater potential for CPR delivery during actual events than training a similar number of younger laypersons at large. Other investigators have identified the focused identification and CPR training of populations at risk of SCA as an important and potentially efficient step to improve survival.10, 12, 18, 19 CPR education of family members before hospital discharge represents a logical extension of other cardiac risk factor‐focused health care education and services before patients are discharged home, including delivery of dietary counseling, diabetic teaching, and education regarding cardiac symptoms. To our knowledge, our work represents the first hospital‐based, adult, layperson, CPR training program using VSI as an instructional approach.

CPR training via a 25‐minute VSI program has been shown to yield CPR performance quality in trainees that is similar to that generated from formal CPR classes that require 3 hours to 4 hours.1416 While VSI training does not provide CPR certification, it is unlikely that the lack of testing and certification is a barrier to participation for the lay public. Indeed, the removal of the pressures of a formal class and testing may increase interest in CPR training through the VSI method.13, 20, 21

Several prior investigations have exploited VSI methodology as an outreach tool to teach CPR in various settings. A recent study in Norway used VSI CPR kits as refresher tools for hospital employees.22 Other work has focused on use of VSI implementation in schools.20, 21, 23 An example of this latter approach was a Danish initiative in which 35,000 VSI kits were distributed to seventh graders.20 Over 15,000 laypersons received secondary training at home by the initially trained students, highlighting a key advantage of the VSI kit approach. It has been argued that this secondary training phenomenon is among the reasons the VSI educational approach may offer a cost effective means for targeted family training.13, 20, 21

While participants in our program were able to adequately perform CPR skills and expressed self‐reported motivation and empowerment, it must be acknowledged that many trained laypersons still do not act when confronted with an actual arrest event.8 In addition, CPR quality at the time of actual performance may be variable, attenuating the survival benefit.2426 However, several population‐based observational studies have supported the notion that training more laypersons in CPR translates into improved overall survival rates from cardiac arrest.25, 27, 28 Further work will be required to follow newly trained, at‐risk family members over time to determine if SCA events occur, and if so, whether CPR was initiated.

CONCLUSIONS

In this prospective study of hospital‐based CPR training, we have shown that targeted training of families before hospital discharge is feasible, well received by trainees, and has the benefits of secondary training in the home environment, where most SCA events take place. This program could be easily implemented in other hospital or practice settings. Through targeted CPR training programs such as the one described in this investigation, at risk populations that are underrepresented in conventional CPR training classes can be equipped with important life‐saving skills. Further work on a larger scale will be required to measure the impact of such programs on patient outcomes.

Patients discharged from the hospital with coronary disease complications experience an increased risk of sudden cardiac arrest (SCA), which afflicts over 200,000 people in the United States each year with an 80% to 90% mortality rate.17 Prompt delivery of cardiopulmonary resuscitation (CPR) can triple the probability of survival from SCA, yet less than 25% of SCA victims receive bystander CPR.8 Given that 80% of SCA events occur in the home environment, hospitalization could serve as an important point of capture for family instruction in CPR. Prior investigations have suggested conducting conventional CPR training courses before discharge for family members. However, significant barriers exist to this approach, including the requirement for a certified instructor and a large time commitment for standard training.912

To address these resource and time barriers, the American Heart Association recently established a video self‐instruction (VSI) course in CPR, eliminating the need for an instructor and reducing the time requirement for training to 25 minutes. The course consists of a digital video disc (DVD) and low‐cost inflatable mannequin in a self‐contained kit.13 Several investigations have shown that CPR performance skills of students after VSI courses are similar to those of students after traditional CPR training programs.1417 This VSI program presents the unique opportunity for secondary training, given that the DVD and mannequin may be shared by primary trainees with family members or friends. This VSI approach has not been evaluated in the hospital setting or with family members of patients at risk for SCA. We sought to test the feasibility of an in‐hospital CPR training program using the VSI tool, with the hypothesis that VSI training would be well‐accepted by family members of hospitalized patients with known or suspected coronary disease. We further hypothesized that subjects would be able to perform skills adequately and would be motivated to subsequently share the VSI course with others after their family member's hospital discharge.

METHODS

This prospective, multicenter investigation was approved by the University of Pennsylvania Institutional Review Board (IRB) and represents the initial component of an ongoing longitudinal study testing different methods of CPR education in the hospital setting. Enrollment was conducted at 3 hospitals: The Hospital of the University of Pennsylvania (a 700‐bed tertiary‐care academic medical center), Penn Presbyterian Medical Center (a 300‐bed tertiary‐care and community hospital) and Pennsylvania Hospital (a 400‐bed community hospital).

RESULTS

Subject Characteristics and Demographics

Subjects were recruited at the 3 hospital sites between May 2009 and January 2010. A total of 756 eligible individuals were approached, and 280 accepted enrollment for CPR training, representing a 37% enrollment rate (Figure 1). Of the 280 enrolled, 136 underwent instruction using the VSI training program as described, and 144 were enrolled using an experimental method of VSI training in CPR; this second cohort will be described elsewhere. When comparing the eligible individuals who declined enrollment versus those who accepted (Table 1), no significant differences were observed with regard to age, gender or race (P = NS for each). Common reasons cited for nonparticipation included lack of interest or lack of time (data not shown).

Figure 1

Table 1.Subject Characteristics

	Enrolled (%) n = 136	Screened/Not Enrolled (%) n = 476
NOTE: Percentages are rounded to the nearest integer. CPR training history was not assessed among subjects declining enrollment. Abbreviation: CPR, cardiopulmonary resuscitation. * Immediate family denotes sibling, parent or child of patient.
Age, years	52 15	46 26
Female	94 (69)	326 (68)
Race
White	101 (74)	316 (66)
Black	30 (22)	90 (19)
Hispanic	5 (4)	8 (2)
Other/no response	0 (0)	59 (13)
Relationship to patient
Spouse	49 (36)	171 (36)
Immediate family*	58 (43)	168 (35)
Other	28 (21)	76 (16)
No response	1 (1)	61 (13)
Highest Education
Elementary	1 (1)	1 (1)
Middle schoo1	1 (1)	7 (1)
High school	46 (34)	157 (33)
Some college/vocation	36 (26)	86 (18)
College	30 (22)	92 (19)
Graduate school	22 (16)	39 (8)
No response	0 (0)	94 (20)
Previous CPR training
No	78 (57)
Yes: within past 2 years	0 (0)
Yes: within past 25 years	13 (10)
Yes: within past 510 years	5 (4)
Yes: more than 10 years ago	40 (29)

Demographics of the enrolled subject cohort are detailed in Table 1. The mean age of subjects was 52 15, and 94 of 136 (69%) were female. Enrolled subjects represented spouses or immediate family members of the hospitalized patient in 107 of 136 (79%) of cases, and the vast majority, 118 of 136 (87%), had either never received CPR training or had received it over 10 years prior to current enrollment.

Subject Perspectives

A posttraining survey revealed that most respondents, 101 of 136 (74%) felt comfortable or very comfortable learning CPR from the VSI kit, and 127 of 136 (93%) felt likely or very likely to share the VSI kit (Figure 2).

Figure 2

DISCUSSION

In the current work, we demonstrated the feasibility of using the hospital environment as a point of capture for training family members of at‐risk patients in CPR skills. Given that most SCA events occur in the home setting, family member training may hold greater potential for CPR delivery during actual events than training a similar number of younger laypersons at large. Other investigators have identified the focused identification and CPR training of populations at risk of SCA as an important and potentially efficient step to improve survival.10, 12, 18, 19 CPR education of family members before hospital discharge represents a logical extension of other cardiac risk factor‐focused health care education and services before patients are discharged home, including delivery of dietary counseling, diabetic teaching, and education regarding cardiac symptoms. To our knowledge, our work represents the first hospital‐based, adult, layperson, CPR training program using VSI as an instructional approach.

CPR training via a 25‐minute VSI program has been shown to yield CPR performance quality in trainees that is similar to that generated from formal CPR classes that require 3 hours to 4 hours.1416 While VSI training does not provide CPR certification, it is unlikely that the lack of testing and certification is a barrier to participation for the lay public. Indeed, the removal of the pressures of a formal class and testing may increase interest in CPR training through the VSI method.13, 20, 21

Several prior investigations have exploited VSI methodology as an outreach tool to teach CPR in various settings. A recent study in Norway used VSI CPR kits as refresher tools for hospital employees.22 Other work has focused on use of VSI implementation in schools.20, 21, 23 An example of this latter approach was a Danish initiative in which 35,000 VSI kits were distributed to seventh graders.20 Over 15,000 laypersons received secondary training at home by the initially trained students, highlighting a key advantage of the VSI kit approach. It has been argued that this secondary training phenomenon is among the reasons the VSI educational approach may offer a cost effective means for targeted family training.13, 20, 21

While participants in our program were able to adequately perform CPR skills and expressed self‐reported motivation and empowerment, it must be acknowledged that many trained laypersons still do not act when confronted with an actual arrest event.8 In addition, CPR quality at the time of actual performance may be variable, attenuating the survival benefit.2426 However, several population‐based observational studies have supported the notion that training more laypersons in CPR translates into improved overall survival rates from cardiac arrest.25, 27, 28 Further work will be required to follow newly trained, at‐risk family members over time to determine if SCA events occur, and if so, whether CPR was initiated.

CONCLUSIONS

In this prospective study of hospital‐based CPR training, we have shown that targeted training of families before hospital discharge is feasible, well received by trainees, and has the benefits of secondary training in the home environment, where most SCA events take place. This program could be easily implemented in other hospital or practice settings. Through targeted CPR training programs such as the one described in this investigation, at risk populations that are underrepresented in conventional CPR training classes can be equipped with important life‐saving skills. Further work on a larger scale will be required to measure the impact of such programs on patient outcomes.

Patients discharged from the hospital with coronary disease complications experience an increased risk of sudden cardiac arrest (SCA), which afflicts over 200,000 people in the United States each year with an 80% to 90% mortality rate.17 Prompt delivery of cardiopulmonary resuscitation (CPR) can triple the probability of survival from SCA, yet less than 25% of SCA victims receive bystander CPR.8 Given that 80% of SCA events occur in the home environment, hospitalization could serve as an important point of capture for family instruction in CPR. Prior investigations have suggested conducting conventional CPR training courses before discharge for family members. However, significant barriers exist to this approach, including the requirement for a certified instructor and a large time commitment for standard training.912

To address these resource and time barriers, the American Heart Association recently established a video self‐instruction (VSI) course in CPR, eliminating the need for an instructor and reducing the time requirement for training to 25 minutes. The course consists of a digital video disc (DVD) and low‐cost inflatable mannequin in a self‐contained kit.13 Several investigations have shown that CPR performance skills of students after VSI courses are similar to those of students after traditional CPR training programs.1417 This VSI program presents the unique opportunity for secondary training, given that the DVD and mannequin may be shared by primary trainees with family members or friends. This VSI approach has not been evaluated in the hospital setting or with family members of patients at risk for SCA. We sought to test the feasibility of an in‐hospital CPR training program using the VSI tool, with the hypothesis that VSI training would be well‐accepted by family members of hospitalized patients with known or suspected coronary disease. We further hypothesized that subjects would be able to perform skills adequately and would be motivated to subsequently share the VSI course with others after their family member's hospital discharge.

METHODS

This prospective, multicenter investigation was approved by the University of Pennsylvania Institutional Review Board (IRB) and represents the initial component of an ongoing longitudinal study testing different methods of CPR education in the hospital setting. Enrollment was conducted at 3 hospitals: The Hospital of the University of Pennsylvania (a 700‐bed tertiary‐care academic medical center), Penn Presbyterian Medical Center (a 300‐bed tertiary‐care and community hospital) and Pennsylvania Hospital (a 400‐bed community hospital).

RESULTS

Subject Characteristics and Demographics

Subjects were recruited at the 3 hospital sites between May 2009 and January 2010. A total of 756 eligible individuals were approached, and 280 accepted enrollment for CPR training, representing a 37% enrollment rate (Figure 1). Of the 280 enrolled, 136 underwent instruction using the VSI training program as described, and 144 were enrolled using an experimental method of VSI training in CPR; this second cohort will be described elsewhere. When comparing the eligible individuals who declined enrollment versus those who accepted (Table 1), no significant differences were observed with regard to age, gender or race (P = NS for each). Common reasons cited for nonparticipation included lack of interest or lack of time (data not shown).

Figure 1

Table 1.Subject Characteristics

	Enrolled (%) n = 136	Screened/Not Enrolled (%) n = 476
NOTE: Percentages are rounded to the nearest integer. CPR training history was not assessed among subjects declining enrollment. Abbreviation: CPR, cardiopulmonary resuscitation. * Immediate family denotes sibling, parent or child of patient.
Age, years	52 15	46 26
Female	94 (69)	326 (68)
Race
White	101 (74)	316 (66)
Black	30 (22)	90 (19)
Hispanic	5 (4)	8 (2)
Other/no response	0 (0)	59 (13)
Relationship to patient
Spouse	49 (36)	171 (36)
Immediate family*	58 (43)	168 (35)
Other	28 (21)	76 (16)
No response	1 (1)	61 (13)
Highest Education
Elementary	1 (1)	1 (1)
Middle schoo1	1 (1)	7 (1)
High school	46 (34)	157 (33)
Some college/vocation	36 (26)	86 (18)
College	30 (22)	92 (19)
Graduate school	22 (16)	39 (8)
No response	0 (0)	94 (20)
Previous CPR training
No	78 (57)
Yes: within past 2 years	0 (0)
Yes: within past 25 years	13 (10)
Yes: within past 510 years	5 (4)
Yes: more than 10 years ago	40 (29)

Demographics of the enrolled subject cohort are detailed in Table 1. The mean age of subjects was 52 15, and 94 of 136 (69%) were female. Enrolled subjects represented spouses or immediate family members of the hospitalized patient in 107 of 136 (79%) of cases, and the vast majority, 118 of 136 (87%), had either never received CPR training or had received it over 10 years prior to current enrollment.

Subject Perspectives

A posttraining survey revealed that most respondents, 101 of 136 (74%) felt comfortable or very comfortable learning CPR from the VSI kit, and 127 of 136 (93%) felt likely or very likely to share the VSI kit (Figure 2).

Figure 2

DISCUSSION

In the current work, we demonstrated the feasibility of using the hospital environment as a point of capture for training family members of at‐risk patients in CPR skills. Given that most SCA events occur in the home setting, family member training may hold greater potential for CPR delivery during actual events than training a similar number of younger laypersons at large. Other investigators have identified the focused identification and CPR training of populations at risk of SCA as an important and potentially efficient step to improve survival.10, 12, 18, 19 CPR education of family members before hospital discharge represents a logical extension of other cardiac risk factor‐focused health care education and services before patients are discharged home, including delivery of dietary counseling, diabetic teaching, and education regarding cardiac symptoms. To our knowledge, our work represents the first hospital‐based, adult, layperson, CPR training program using VSI as an instructional approach.

CPR training via a 25‐minute VSI program has been shown to yield CPR performance quality in trainees that is similar to that generated from formal CPR classes that require 3 hours to 4 hours.1416 While VSI training does not provide CPR certification, it is unlikely that the lack of testing and certification is a barrier to participation for the lay public. Indeed, the removal of the pressures of a formal class and testing may increase interest in CPR training through the VSI method.13, 20, 21

Several prior investigations have exploited VSI methodology as an outreach tool to teach CPR in various settings. A recent study in Norway used VSI CPR kits as refresher tools for hospital employees.22 Other work has focused on use of VSI implementation in schools.20, 21, 23 An example of this latter approach was a Danish initiative in which 35,000 VSI kits were distributed to seventh graders.20 Over 15,000 laypersons received secondary training at home by the initially trained students, highlighting a key advantage of the VSI kit approach. It has been argued that this secondary training phenomenon is among the reasons the VSI educational approach may offer a cost effective means for targeted family training.13, 20, 21

While participants in our program were able to adequately perform CPR skills and expressed self‐reported motivation and empowerment, it must be acknowledged that many trained laypersons still do not act when confronted with an actual arrest event.8 In addition, CPR quality at the time of actual performance may be variable, attenuating the survival benefit.2426 However, several population‐based observational studies have supported the notion that training more laypersons in CPR translates into improved overall survival rates from cardiac arrest.25, 27, 28 Further work will be required to follow newly trained, at‐risk family members over time to determine if SCA events occur, and if so, whether CPR was initiated.

CONCLUSIONS

In this prospective study of hospital‐based CPR training, we have shown that targeted training of families before hospital discharge is feasible, well received by trainees, and has the benefits of secondary training in the home environment, where most SCA events take place. This program could be easily implemented in other hospital or practice settings. Through targeted CPR training programs such as the one described in this investigation, at risk populations that are underrepresented in conventional CPR training classes can be equipped with important life‐saving skills. Further work on a larger scale will be required to measure the impact of such programs on patient outcomes.

Shock has been defined as failure to deliver and/or utilize adequate amounts of oxygen1 and is a common cause of critical illness. Few studies have examined the predictive utility of bedside clinical examination in predicting the category of shock. Scholars have suggested a bedside approach that uses simple examination techniques and applied physiology to rapidly identify a patients' circulation as high vs. low cardiac output. Those with a high‐output examination are designated as high‐output, most often septic shock. Low‐output patients are further categorized as heart full or heart empty to distinguish cardiogenic from hypovolemic categories of shock, respectively.2 The predictive characteristics of this simple algorithm have not been studied. In this study, we examine the operating characteristics of selected elements of this algorithm when administered at the bedside by trainees in Internal Medicine.

Methods

This study was performed after approval of the Institutional Review Board; informed consent was waived. Patients with nonsurgical problems who present to the hospital or who develop sustained hypotension are managed by medical house officers on the intensive care and/or rapid response team with the supervision of patients' attending physicians. All house officers were asked to document explicitly in their assessment notes the following examination findings: finger capillary refill (same/quicker vs. slower than examiner's), hand skin temperature (same/warmer vs. cooler than examiner's) and pulse pressure (ie, same/wider vs. thinner than examiner's), presence or absence of crackles >1/3 from base on bilateral lung examination and jugular venous pressure (JVP) vs. 8 cmH₂O. The documented examinations of either the rapid response team (PGY2; n = 14) or intensive care unit (ICU) resident (PGY3; n = 14) for patients evaluated between September 2008 and February 2009 were used for this study. Resuscitation was administered entirely by house officers, occasionally guided in person, but always supervised by attending physicians.

In May 2009, clinical data, including electrocardiograms/echocardiograms and laboratory (eg, cardiac enzymes, culture) results were abstracted from medical records of subjects. These were presented to a blinded senior clinician (DK) to review and apply evidence‐based or consensus criteria,36 whenever possible, to categorize the type of shock (septic vs. cardiogenic vs. hypovolemic) based on data acquired after the onset of shock. For example, patients with microbiologic and/or radiologic evidence of infection were classified as septic shock,1, 3, 4 those with acute left or right ventricular dysfunction on echocardiogram were classified as cardiogenic shock,1, 6 and those with clinical evidence of acute hemorrhage with hypovolemic shock.1, 5 While some of the patients were examined by DK as part of clinical care, he was blinded to the identity of patients and their algorithm‐related physical examination findings when he reviewed the abstracted data (>2 months after study closure) to adjudicate the final diagnosis of shock. These diagnoses were considered the reference standard for this study. The operating characteristics (sensitivity = true positive/true positive + false negative; specificity = true negative/true negative + false positive; negative predictive value (NPV) = true negative/all negatives; positive predictive value (PPV) = true positive/all positives; accuracy = true results/all results) were calculated for combinations of physical examination findings and correct final diagnosis (Figure 1).

Figure 1

Results

A total of 68 patients, averaging 71 16 years, were studied; 57% were male, and 66% were White, and 20% were Black. Table 1 lists characteristics of patients. A total of 37 patients were diagnosed as having septic shock, 11 had cardiogenic shock and 10 hypovolemic shock. Operating characteristics of the bedside examination techniques for predicting mechanism of shock are listed in Table 2. Capillary refill and skin temperature were 100% concordant yielding sensitivity of 89% (95% confidence interval [CI], 75‐97%), specificity of 68% (95% CI, 46‐83%), PPV of 77% (95% CI, 61‐88%), NPV of 84% (95% CI, 64‐96%) and overall accuracy of 79% (95% CI, 68‐88%) for diagnosis of high output (ie, septic shock). JVP 8 cmH₂O was more accurate than crackles for predicting cardiogenic shock in low‐output patients with sensitivity of 82% (95% CI, 48‐98%), specificity of 79% (95% CI, 41‐95%), PPV of 75% (95% CI, 43‐95%), NPV of 85% (95% CI, 55‐98%), and overall accuracy of 80% (95%CI, 59‐93%). Using just skin temperature and JVP, the bedside approach misdiagnosed 19 of 75 cases (overall accuracy, 75%; 95% CI, 16‐37%).

Discussion

This is the first study to examine the predictive characteristics of simple bedside physical examination techniques in correctly predicting the category/mechanism of shock. Overall, the algorithm performed well, and accurately predicted the category of shock in three‐quarters of patients. It also has the benefit of being very rapid, taking only seconds to complete, using bedside techniques that even inexperienced clinicians can apply.

Very few studies have examined the accuracy of examination techniques specifically for diagnosis of shock. In humans injected with endotoxin, body temperature and cardiac output increased, but skin temperature and capillary refill times are not well described.79 Schriger and Baraff10 reported that capillary refill >2 seconds was only 59% sensitive for diagnosing hypovolemia in patients with hypovolemic shock or orthostatic changes in blood pressure. Sensitivity was 77% in 13 patients with hypovolemic shock.10 However, some studies have demonstrated that age, sex, external temperature11 and fever12 can affect capillary refill times. Otieno et al.13 demonstrated a kappa statistic value of 0.49 for capillary refill 4 seconds, suggesting that reproducibility of this technique could be a major drawback. McGee et al.14 reviewed examination techniques for diagnosing hypovolemic states and concluded that postural changes in heart rate and blood pressure were the most accurate; capillary refill was not recommended. Stevenson and Perloff15 demonstrated that crackles and elevated JVP were absent in 18 of 43 patients with pulmonary capillary wedge pressures >22 mmHg. Butman et al.16 showed that elevated JVP was 82% accurate for predicting a wedge pressure >18 mmHg. Connors et al.17 demonstrated that clinicians' predictions of heart filling pressures and cardiac output were accurate (relative to pulmonary artery catheter measurements) in less than 50% of cases, though the examination techniques used were not qualified or quantified. No previous study has combined simple, semiobjective physical examination techniques for the purpose of distinguishing categories of shock.

Since identification of the pathogenesis of shock has important treatment/prognostic implications (eg, fluid and vasopressor therapies, early search for drainable focus of infection in sepsis, reestablishing vessel patency in myocardial infarction and pulmonary embolus), we believe that this simple, rapidly administered algorithm will prove useful in clinical medicine. In some clinical situations, the approach can lead to timely identification of the causative mechanism, allowing prompt definitive treatment. For example, a patient presenting with high‐output hypotension is so often sepsis/septic shock that treatment with antibiotics is justified (since success is time‐sensitive) even when the exact site/microbe has not yet been identified. Acute right heart overfilled low‐output hypotension should be considered pulmonary embolism (which also requires time‐sensitive therapies) until proven otherwise. Yet, a sizeable number of cases do not fit neatly into a single category. For example, 11% of patients with septic shock presented with cool extremities in the early phases of illness. In clinical decision‐making, 2 diagnostic‐therapeutic paradigms are common. In the first, the diagnosis is relatively certain and narrowly‐directed, mechanism‐specific treatment is appropriate. The second paradigm is 1 of significant uncertainty, when clinicians must treat empirically the most likely causes until more data become available to permit safe narrowing of therapies. For example, a patient presenting with hypotension, cool extremities, leukocytosis and apparent pneumonia should be treated empirically for septic shock while exploring explanations for the incongruous low‐output state (eg, profound hypovolemia, adrenal insufficiency, concurrent or precedent myocardial dysfunction). Patients often have several mechanisms contributing to hypotension. Since patients are not ideal forms, there can be no perfect decision‐tool; clinicians would be fool‐hardy to prematurely close decision‐making prior to definitive diagnosis. In the case of shock, such diagnostic arrogance would delay time‐sensitive therapies and thus contribute to morbidity and mortality. Nonetheless, this physical examination algorithmunderstanding its operating characteristics and limitationsmay add to the bedside clinician's diagnostic armamentarium.

Our study has several notable limitations. First, bedside examinations were performed by multiple observers who had limited (1 electronic mail) instruction on how to perform and document the data gathered for this study. So these results should be generalized cautiously until reproduced at other centers with greater numbers of observers (than the 28 of this study). The central supposition, that skin cooler, capillary refill longer, and pulse pressure more narrow than theirs, presupposes reasonable homogeneity of the normal state which is not necessarily true.11 Interobserver variability of physical examination further compromises the fidelity of findings recorded for this study.13 Since we conducted a retrospective review, and because of the emergency nature of the clinical problem, it would be difficult to conduct a study in which multiple examiners performed the same physical examinations to quantify interobserver variability. Irrespective, we would expect interobserver variability to systematically reduce accuracy; it is all‐the‐more impressive that trainees' examination results correctly diagnosed mechanism of shock in three‐quarters of cases. Also, examiners were not blinded to clinical history, so results of their examination could have been biased by their pre‐examination hypotheses of pathogenesis. Of course, they were not aware of the expert's final categorization of mechanism performed much later in time. Since there is no absolute reference standard for classification of the pathogenesis of shock, we depended upon careful review of selected data (same parameters for each patient) by a single senior investigatoralbeit armed with evidence‐based or consensus‐based standards of diagnosing shock. Finally, it can be argued that all forms of shock are mixed (with hypovolemia) early in the course; sepsis requires refilling of a leaky and dilated vasculature and the noncompliant ischemic ventricle often requires a higher filling pressure than normal to empty. To complicate even more, patients may have preexistent conditions (eg, chronic congestive heart failure, cirrhosis) that limit cardiovascular responses to acute shock. Our diagnostic approach was to identify the principal cause of the acute decompensation, assuming that many patients will have more than 1 single mechanism accounting for hypotension.

In conclusion, this is the first study to examine the utility of this simple physical examination algorithm to diagnose the mechanism of shock. Some have discounted or underemphasized examination techniques in favor of more time‐intensive and labor‐intensive diagnostic modalities, such as bedside echocardiography, which may waste precious time and resources. The simple physical examination algorithm assessed in this study has favorable operating characteristics and can be performed readily by even novice clinicians. If replicated at other centers and by greater numbers of observers, this approach could assist clinicians and teachers who train clinicians to rapidly diagnose and manage patients with shock.

Shock has been defined as failure to deliver and/or utilize adequate amounts of oxygen1 and is a common cause of critical illness. Few studies have examined the predictive utility of bedside clinical examination in predicting the category of shock. Scholars have suggested a bedside approach that uses simple examination techniques and applied physiology to rapidly identify a patients' circulation as high vs. low cardiac output. Those with a high‐output examination are designated as high‐output, most often septic shock. Low‐output patients are further categorized as heart full or heart empty to distinguish cardiogenic from hypovolemic categories of shock, respectively.2 The predictive characteristics of this simple algorithm have not been studied. In this study, we examine the operating characteristics of selected elements of this algorithm when administered at the bedside by trainees in Internal Medicine.

Methods

This study was performed after approval of the Institutional Review Board; informed consent was waived. Patients with nonsurgical problems who present to the hospital or who develop sustained hypotension are managed by medical house officers on the intensive care and/or rapid response team with the supervision of patients' attending physicians. All house officers were asked to document explicitly in their assessment notes the following examination findings: finger capillary refill (same/quicker vs. slower than examiner's), hand skin temperature (same/warmer vs. cooler than examiner's) and pulse pressure (ie, same/wider vs. thinner than examiner's), presence or absence of crackles >1/3 from base on bilateral lung examination and jugular venous pressure (JVP) vs. 8 cmH₂O. The documented examinations of either the rapid response team (PGY2; n = 14) or intensive care unit (ICU) resident (PGY3; n = 14) for patients evaluated between September 2008 and February 2009 were used for this study. Resuscitation was administered entirely by house officers, occasionally guided in person, but always supervised by attending physicians.

In May 2009, clinical data, including electrocardiograms/echocardiograms and laboratory (eg, cardiac enzymes, culture) results were abstracted from medical records of subjects. These were presented to a blinded senior clinician (DK) to review and apply evidence‐based or consensus criteria,36 whenever possible, to categorize the type of shock (septic vs. cardiogenic vs. hypovolemic) based on data acquired after the onset of shock. For example, patients with microbiologic and/or radiologic evidence of infection were classified as septic shock,1, 3, 4 those with acute left or right ventricular dysfunction on echocardiogram were classified as cardiogenic shock,1, 6 and those with clinical evidence of acute hemorrhage with hypovolemic shock.1, 5 While some of the patients were examined by DK as part of clinical care, he was blinded to the identity of patients and their algorithm‐related physical examination findings when he reviewed the abstracted data (>2 months after study closure) to adjudicate the final diagnosis of shock. These diagnoses were considered the reference standard for this study. The operating characteristics (sensitivity = true positive/true positive + false negative; specificity = true negative/true negative + false positive; negative predictive value (NPV) = true negative/all negatives; positive predictive value (PPV) = true positive/all positives; accuracy = true results/all results) were calculated for combinations of physical examination findings and correct final diagnosis (Figure 1).

Figure 1

Results

A total of 68 patients, averaging 71 16 years, were studied; 57% were male, and 66% were White, and 20% were Black. Table 1 lists characteristics of patients. A total of 37 patients were diagnosed as having septic shock, 11 had cardiogenic shock and 10 hypovolemic shock. Operating characteristics of the bedside examination techniques for predicting mechanism of shock are listed in Table 2. Capillary refill and skin temperature were 100% concordant yielding sensitivity of 89% (95% confidence interval [CI], 75‐97%), specificity of 68% (95% CI, 46‐83%), PPV of 77% (95% CI, 61‐88%), NPV of 84% (95% CI, 64‐96%) and overall accuracy of 79% (95% CI, 68‐88%) for diagnosis of high output (ie, septic shock). JVP 8 cmH₂O was more accurate than crackles for predicting cardiogenic shock in low‐output patients with sensitivity of 82% (95% CI, 48‐98%), specificity of 79% (95% CI, 41‐95%), PPV of 75% (95% CI, 43‐95%), NPV of 85% (95% CI, 55‐98%), and overall accuracy of 80% (95%CI, 59‐93%). Using just skin temperature and JVP, the bedside approach misdiagnosed 19 of 75 cases (overall accuracy, 75%; 95% CI, 16‐37%).

Discussion

This is the first study to examine the predictive characteristics of simple bedside physical examination techniques in correctly predicting the category/mechanism of shock. Overall, the algorithm performed well, and accurately predicted the category of shock in three‐quarters of patients. It also has the benefit of being very rapid, taking only seconds to complete, using bedside techniques that even inexperienced clinicians can apply.

Very few studies have examined the accuracy of examination techniques specifically for diagnosis of shock. In humans injected with endotoxin, body temperature and cardiac output increased, but skin temperature and capillary refill times are not well described.79 Schriger and Baraff10 reported that capillary refill >2 seconds was only 59% sensitive for diagnosing hypovolemia in patients with hypovolemic shock or orthostatic changes in blood pressure. Sensitivity was 77% in 13 patients with hypovolemic shock.10 However, some studies have demonstrated that age, sex, external temperature11 and fever12 can affect capillary refill times. Otieno et al.13 demonstrated a kappa statistic value of 0.49 for capillary refill 4 seconds, suggesting that reproducibility of this technique could be a major drawback. McGee et al.14 reviewed examination techniques for diagnosing hypovolemic states and concluded that postural changes in heart rate and blood pressure were the most accurate; capillary refill was not recommended. Stevenson and Perloff15 demonstrated that crackles and elevated JVP were absent in 18 of 43 patients with pulmonary capillary wedge pressures >22 mmHg. Butman et al.16 showed that elevated JVP was 82% accurate for predicting a wedge pressure >18 mmHg. Connors et al.17 demonstrated that clinicians' predictions of heart filling pressures and cardiac output were accurate (relative to pulmonary artery catheter measurements) in less than 50% of cases, though the examination techniques used were not qualified or quantified. No previous study has combined simple, semiobjective physical examination techniques for the purpose of distinguishing categories of shock.

Since identification of the pathogenesis of shock has important treatment/prognostic implications (eg, fluid and vasopressor therapies, early search for drainable focus of infection in sepsis, reestablishing vessel patency in myocardial infarction and pulmonary embolus), we believe that this simple, rapidly administered algorithm will prove useful in clinical medicine. In some clinical situations, the approach can lead to timely identification of the causative mechanism, allowing prompt definitive treatment. For example, a patient presenting with high‐output hypotension is so often sepsis/septic shock that treatment with antibiotics is justified (since success is time‐sensitive) even when the exact site/microbe has not yet been identified. Acute right heart overfilled low‐output hypotension should be considered pulmonary embolism (which also requires time‐sensitive therapies) until proven otherwise. Yet, a sizeable number of cases do not fit neatly into a single category. For example, 11% of patients with septic shock presented with cool extremities in the early phases of illness. In clinical decision‐making, 2 diagnostic‐therapeutic paradigms are common. In the first, the diagnosis is relatively certain and narrowly‐directed, mechanism‐specific treatment is appropriate. The second paradigm is 1 of significant uncertainty, when clinicians must treat empirically the most likely causes until more data become available to permit safe narrowing of therapies. For example, a patient presenting with hypotension, cool extremities, leukocytosis and apparent pneumonia should be treated empirically for septic shock while exploring explanations for the incongruous low‐output state (eg, profound hypovolemia, adrenal insufficiency, concurrent or precedent myocardial dysfunction). Patients often have several mechanisms contributing to hypotension. Since patients are not ideal forms, there can be no perfect decision‐tool; clinicians would be fool‐hardy to prematurely close decision‐making prior to definitive diagnosis. In the case of shock, such diagnostic arrogance would delay time‐sensitive therapies and thus contribute to morbidity and mortality. Nonetheless, this physical examination algorithmunderstanding its operating characteristics and limitationsmay add to the bedside clinician's diagnostic armamentarium.

Our study has several notable limitations. First, bedside examinations were performed by multiple observers who had limited (1 electronic mail) instruction on how to perform and document the data gathered for this study. So these results should be generalized cautiously until reproduced at other centers with greater numbers of observers (than the 28 of this study). The central supposition, that skin cooler, capillary refill longer, and pulse pressure more narrow than theirs, presupposes reasonable homogeneity of the normal state which is not necessarily true.11 Interobserver variability of physical examination further compromises the fidelity of findings recorded for this study.13 Since we conducted a retrospective review, and because of the emergency nature of the clinical problem, it would be difficult to conduct a study in which multiple examiners performed the same physical examinations to quantify interobserver variability. Irrespective, we would expect interobserver variability to systematically reduce accuracy; it is all‐the‐more impressive that trainees' examination results correctly diagnosed mechanism of shock in three‐quarters of cases. Also, examiners were not blinded to clinical history, so results of their examination could have been biased by their pre‐examination hypotheses of pathogenesis. Of course, they were not aware of the expert's final categorization of mechanism performed much later in time. Since there is no absolute reference standard for classification of the pathogenesis of shock, we depended upon careful review of selected data (same parameters for each patient) by a single senior investigatoralbeit armed with evidence‐based or consensus‐based standards of diagnosing shock. Finally, it can be argued that all forms of shock are mixed (with hypovolemia) early in the course; sepsis requires refilling of a leaky and dilated vasculature and the noncompliant ischemic ventricle often requires a higher filling pressure than normal to empty. To complicate even more, patients may have preexistent conditions (eg, chronic congestive heart failure, cirrhosis) that limit cardiovascular responses to acute shock. Our diagnostic approach was to identify the principal cause of the acute decompensation, assuming that many patients will have more than 1 single mechanism accounting for hypotension.

In conclusion, this is the first study to examine the utility of this simple physical examination algorithm to diagnose the mechanism of shock. Some have discounted or underemphasized examination techniques in favor of more time‐intensive and labor‐intensive diagnostic modalities, such as bedside echocardiography, which may waste precious time and resources. The simple physical examination algorithm assessed in this study has favorable operating characteristics and can be performed readily by even novice clinicians. If replicated at other centers and by greater numbers of observers, this approach could assist clinicians and teachers who train clinicians to rapidly diagnose and manage patients with shock.

Shock has been defined as failure to deliver and/or utilize adequate amounts of oxygen1 and is a common cause of critical illness. Few studies have examined the predictive utility of bedside clinical examination in predicting the category of shock. Scholars have suggested a bedside approach that uses simple examination techniques and applied physiology to rapidly identify a patients' circulation as high vs. low cardiac output. Those with a high‐output examination are designated as high‐output, most often septic shock. Low‐output patients are further categorized as heart full or heart empty to distinguish cardiogenic from hypovolemic categories of shock, respectively.2 The predictive characteristics of this simple algorithm have not been studied. In this study, we examine the operating characteristics of selected elements of this algorithm when administered at the bedside by trainees in Internal Medicine.

Methods

This study was performed after approval of the Institutional Review Board; informed consent was waived. Patients with nonsurgical problems who present to the hospital or who develop sustained hypotension are managed by medical house officers on the intensive care and/or rapid response team with the supervision of patients' attending physicians. All house officers were asked to document explicitly in their assessment notes the following examination findings: finger capillary refill (same/quicker vs. slower than examiner's), hand skin temperature (same/warmer vs. cooler than examiner's) and pulse pressure (ie, same/wider vs. thinner than examiner's), presence or absence of crackles >1/3 from base on bilateral lung examination and jugular venous pressure (JVP) vs. 8 cmH₂O. The documented examinations of either the rapid response team (PGY2; n = 14) or intensive care unit (ICU) resident (PGY3; n = 14) for patients evaluated between September 2008 and February 2009 were used for this study. Resuscitation was administered entirely by house officers, occasionally guided in person, but always supervised by attending physicians.

In May 2009, clinical data, including electrocardiograms/echocardiograms and laboratory (eg, cardiac enzymes, culture) results were abstracted from medical records of subjects. These were presented to a blinded senior clinician (DK) to review and apply evidence‐based or consensus criteria,36 whenever possible, to categorize the type of shock (septic vs. cardiogenic vs. hypovolemic) based on data acquired after the onset of shock. For example, patients with microbiologic and/or radiologic evidence of infection were classified as septic shock,1, 3, 4 those with acute left or right ventricular dysfunction on echocardiogram were classified as cardiogenic shock,1, 6 and those with clinical evidence of acute hemorrhage with hypovolemic shock.1, 5 While some of the patients were examined by DK as part of clinical care, he was blinded to the identity of patients and their algorithm‐related physical examination findings when he reviewed the abstracted data (>2 months after study closure) to adjudicate the final diagnosis of shock. These diagnoses were considered the reference standard for this study. The operating characteristics (sensitivity = true positive/true positive + false negative; specificity = true negative/true negative + false positive; negative predictive value (NPV) = true negative/all negatives; positive predictive value (PPV) = true positive/all positives; accuracy = true results/all results) were calculated for combinations of physical examination findings and correct final diagnosis (Figure 1).

Figure 1

Results

A total of 68 patients, averaging 71 16 years, were studied; 57% were male, and 66% were White, and 20% were Black. Table 1 lists characteristics of patients. A total of 37 patients were diagnosed as having septic shock, 11 had cardiogenic shock and 10 hypovolemic shock. Operating characteristics of the bedside examination techniques for predicting mechanism of shock are listed in Table 2. Capillary refill and skin temperature were 100% concordant yielding sensitivity of 89% (95% confidence interval [CI], 75‐97%), specificity of 68% (95% CI, 46‐83%), PPV of 77% (95% CI, 61‐88%), NPV of 84% (95% CI, 64‐96%) and overall accuracy of 79% (95% CI, 68‐88%) for diagnosis of high output (ie, septic shock). JVP 8 cmH₂O was more accurate than crackles for predicting cardiogenic shock in low‐output patients with sensitivity of 82% (95% CI, 48‐98%), specificity of 79% (95% CI, 41‐95%), PPV of 75% (95% CI, 43‐95%), NPV of 85% (95% CI, 55‐98%), and overall accuracy of 80% (95%CI, 59‐93%). Using just skin temperature and JVP, the bedside approach misdiagnosed 19 of 75 cases (overall accuracy, 75%; 95% CI, 16‐37%).

Discussion

This is the first study to examine the predictive characteristics of simple bedside physical examination techniques in correctly predicting the category/mechanism of shock. Overall, the algorithm performed well, and accurately predicted the category of shock in three‐quarters of patients. It also has the benefit of being very rapid, taking only seconds to complete, using bedside techniques that even inexperienced clinicians can apply.

Very few studies have examined the accuracy of examination techniques specifically for diagnosis of shock. In humans injected with endotoxin, body temperature and cardiac output increased, but skin temperature and capillary refill times are not well described.79 Schriger and Baraff10 reported that capillary refill >2 seconds was only 59% sensitive for diagnosing hypovolemia in patients with hypovolemic shock or orthostatic changes in blood pressure. Sensitivity was 77% in 13 patients with hypovolemic shock.10 However, some studies have demonstrated that age, sex, external temperature11 and fever12 can affect capillary refill times. Otieno et al.13 demonstrated a kappa statistic value of 0.49 for capillary refill 4 seconds, suggesting that reproducibility of this technique could be a major drawback. McGee et al.14 reviewed examination techniques for diagnosing hypovolemic states and concluded that postural changes in heart rate and blood pressure were the most accurate; capillary refill was not recommended. Stevenson and Perloff15 demonstrated that crackles and elevated JVP were absent in 18 of 43 patients with pulmonary capillary wedge pressures >22 mmHg. Butman et al.16 showed that elevated JVP was 82% accurate for predicting a wedge pressure >18 mmHg. Connors et al.17 demonstrated that clinicians' predictions of heart filling pressures and cardiac output were accurate (relative to pulmonary artery catheter measurements) in less than 50% of cases, though the examination techniques used were not qualified or quantified. No previous study has combined simple, semiobjective physical examination techniques for the purpose of distinguishing categories of shock.

Since identification of the pathogenesis of shock has important treatment/prognostic implications (eg, fluid and vasopressor therapies, early search for drainable focus of infection in sepsis, reestablishing vessel patency in myocardial infarction and pulmonary embolus), we believe that this simple, rapidly administered algorithm will prove useful in clinical medicine. In some clinical situations, the approach can lead to timely identification of the causative mechanism, allowing prompt definitive treatment. For example, a patient presenting with high‐output hypotension is so often sepsis/septic shock that treatment with antibiotics is justified (since success is time‐sensitive) even when the exact site/microbe has not yet been identified. Acute right heart overfilled low‐output hypotension should be considered pulmonary embolism (which also requires time‐sensitive therapies) until proven otherwise. Yet, a sizeable number of cases do not fit neatly into a single category. For example, 11% of patients with septic shock presented with cool extremities in the early phases of illness. In clinical decision‐making, 2 diagnostic‐therapeutic paradigms are common. In the first, the diagnosis is relatively certain and narrowly‐directed, mechanism‐specific treatment is appropriate. The second paradigm is 1 of significant uncertainty, when clinicians must treat empirically the most likely causes until more data become available to permit safe narrowing of therapies. For example, a patient presenting with hypotension, cool extremities, leukocytosis and apparent pneumonia should be treated empirically for septic shock while exploring explanations for the incongruous low‐output state (eg, profound hypovolemia, adrenal insufficiency, concurrent or precedent myocardial dysfunction). Patients often have several mechanisms contributing to hypotension. Since patients are not ideal forms, there can be no perfect decision‐tool; clinicians would be fool‐hardy to prematurely close decision‐making prior to definitive diagnosis. In the case of shock, such diagnostic arrogance would delay time‐sensitive therapies and thus contribute to morbidity and mortality. Nonetheless, this physical examination algorithmunderstanding its operating characteristics and limitationsmay add to the bedside clinician's diagnostic armamentarium.

Our study has several notable limitations. First, bedside examinations were performed by multiple observers who had limited (1 electronic mail) instruction on how to perform and document the data gathered for this study. So these results should be generalized cautiously until reproduced at other centers with greater numbers of observers (than the 28 of this study). The central supposition, that skin cooler, capillary refill longer, and pulse pressure more narrow than theirs, presupposes reasonable homogeneity of the normal state which is not necessarily true.11 Interobserver variability of physical examination further compromises the fidelity of findings recorded for this study.13 Since we conducted a retrospective review, and because of the emergency nature of the clinical problem, it would be difficult to conduct a study in which multiple examiners performed the same physical examinations to quantify interobserver variability. Irrespective, we would expect interobserver variability to systematically reduce accuracy; it is all‐the‐more impressive that trainees' examination results correctly diagnosed mechanism of shock in three‐quarters of cases. Also, examiners were not blinded to clinical history, so results of their examination could have been biased by their pre‐examination hypotheses of pathogenesis. Of course, they were not aware of the expert's final categorization of mechanism performed much later in time. Since there is no absolute reference standard for classification of the pathogenesis of shock, we depended upon careful review of selected data (same parameters for each patient) by a single senior investigatoralbeit armed with evidence‐based or consensus‐based standards of diagnosing shock. Finally, it can be argued that all forms of shock are mixed (with hypovolemia) early in the course; sepsis requires refilling of a leaky and dilated vasculature and the noncompliant ischemic ventricle often requires a higher filling pressure than normal to empty. To complicate even more, patients may have preexistent conditions (eg, chronic congestive heart failure, cirrhosis) that limit cardiovascular responses to acute shock. Our diagnostic approach was to identify the principal cause of the acute decompensation, assuming that many patients will have more than 1 single mechanism accounting for hypotension.

In conclusion, this is the first study to examine the utility of this simple physical examination algorithm to diagnose the mechanism of shock. Some have discounted or underemphasized examination techniques in favor of more time‐intensive and labor‐intensive diagnostic modalities, such as bedside echocardiography, which may waste precious time and resources. The simple physical examination algorithm assessed in this study has favorable operating characteristics and can be performed readily by even novice clinicians. If replicated at other centers and by greater numbers of observers, this approach could assist clinicians and teachers who train clinicians to rapidly diagnose and manage patients with shock.

In 2006, after introducing formal hospitalist programs at both an academic hospital and an affiliated community teaching hospital, we conducted a time study to gain insight into the effect of adopting a community model in an academic environment. This evaluation was conducted to identify similarities and differences between the 2 programs and to highlight opportunities for process and quality improvement. The hospitalist case mix index (CMI) was higher at the academic center (1.3) than at the community center (1.1). At both institutions documentation and most order entry were completed on paper, while lab and test results were electronically available. Both hospitalist programs were nonteaching services with day shifts staffed from 7:00 _AM to 7:00 _PM. At the academic center, a single hospitalist staffed the service for 7 days in a row with an average daily census of 10 patients. At the community hospital, 2 hospitalists carried the service, alternating days as the primary admitter. These hospitalists each carried an average census of 13 patients for 6 days in a row with staggered start/stop dates to ensure service continuity. The years of experience as a practicing hospitalist were similar between the 2 programs (median 4 years and range 1‐10 years for both programs); all hospitalists completed an internal medicine residency.

Methods

A paper‐based tool was used to collect data at 1‐minute intervals into 5 major categories validated through trial observation, content focus groups, and expert opinion. The 5 categories were Direct Patient Care, Indirect Patient Care, Travel, Personal, and Other (Table 1). Communication, a subcategory of Indirect Patient Care, was further classified by the job‐profession category and communication modality of the individual(s) interacting with the hospitalist. The tool allowed for more than 1 task category to be tracked at a time in order to capture multitasking. Three trained industrial engineers shadowed 9 different hospitalists during the day shifts, between 2 and 5 shifts per hospitalist, gathering approximately 355 hours of observational data over the 8 weeks of the study; 4 weeks at each hospital. Weekend and night shift data were not collected due to observer availability. Results for each setting were reported as the mean and standard deviation percentage of physician time observed for each task category. The results were also reported as the mean and standard deviation volume adjusted time per patient for each task category. The adjustment was made by dividing physician time by the number of patient encounters for that observation. Comparative analyses were calculated using a t‐test with a significance level of 0.05 and confidence intervals were reported at a 95% interval. Since this project was a quality improvement initiative analyzing the introduction of a new clinical service, Institutional Review Board (IRB) approval from our institution was not required.

Results

Hospitalist time allocations at the 2 programs were comparatively similar (Table 2). At the academic center, hospitalists spent the majority of their time providing indirect patient care (69.8%, CI: 66.3‐73.3%), followed by direct patient care (13.1%, CI: 11.2‐14.9%), with the remaining time distributed among travel, personal, and other administrative duties. Likewise, the community hospitalists spent the majority of their time providing indirect patient care (68.7%, CI: 63.0‐74.5%), followed by direct patient care (16.7%, CI: 14.1‐19.4%), with travel, personal, and administrative duties completing the day. Additionally, the percent of time spent multitasking, defined as more than 1 task category observed at the same time, was strikingly similar between the 2 groups (Academic: 47.6% 16.5%, Community: 47.9% 9.8%).

While the difference in total percent of time spent on direct patient care was statistically significant (P = 0.03), the values converged after adjusting for the differences in average daily census (Table 3). On average, both the academic and community hospitalists spent approximately 10 minutes per patient per day interacting face to face with the patient and/or family (10.0 2.9 minutes and 10.1 3.6 minutes respectively, P = 0.89). However, after volume adjusting, other workflow differences became statistically significant, primarily in indirect patient care (Academic: 54.7 11.1 minutes/patient, Community: 41.9 9.8 minutes/patient, P 0.001). The academic hospitalists spent more time writing orders (4.6 1.3 minutes/patient vs. 2.8 1.1 minutes/patient, P 0.001), looking up and reviewing medical reference materials (1.1 0.6 minutes/patient vs. 0.3 0.4 minutes/patient, P 0.001), and communicating with other providers (20.5 7.7 min/patient vs. 11.1 3.1 min/patient, P 0.001) than their community hospitalist counterparts. Nearly half the time that the academic hospitalists spent communicating was dedicated to speaking with other physicians (9.2 3.5 minutes/patient); more than double that of the community hospitalists (4.0 1.6 minutes). Additionally, the academic hospitalists spent more time speaking with pharmacists (0.7 0.6 minutes vs. 0.1 0.2 minutes, P = 0.001).

Discussion

In 2006, O'Leary et al.1 demonstrated that academic hospitalists spend approximately 20% of their time engaged in direct patient care. Our results are consistent with these data and further expand these findings to a community setting. Although we found subtle workflow differences between the academic and community programs, their similarities were more striking than their differences. We suspect that these differences can be largely attributed to the higher CMI at the academic program as well as the greater complexity and additional communication hand‐offs inherent to this tertiary academic medical center. For example, at the academic medical center, medicine admissions were screened by a medicine triage resident and subsequently handed off to a hospitalist. In most cases, this system did not preclude the need to speak directly with the emergency department (ED) attending, adding a layer of complexity that did not exist in the community hospital. Finally, in contrast to the community hospital, there was little comanagement at the academic medical center, necessitating frequent transfers to and from medical and subspecialty services.

It appears that hospitalists, irrespective of their work environment, spend far more time documenting, communicating, and coordinating care than at the bedside. It is unclear whether this represents a desirable outcome of hospitalists' role as managers of complex hospital stays or inefficient and ineffective effort that should be mitigated through care delivery redesign. Further research to optimize hospital information management, streamline care processes and eliminate low value‐added effort is clearly needed.

Another notable finding of our study is that hospitalists spend roughly half of their time performing more than 1 work category at the same time deemed as multitasking.2 The prevalence and effects of multitasking are well‐characterized in emergency medicine and likely apply to hospitalists.3, 4 Fractured attention due to multitasking may hamper communication, jeopardize care handoffs, and increase risk for medical errors and litigation.46 While it is likely that multitasking is inherent to the practice of hospital medicine, it is unclear how this could be mitigated or better facilitated. Perhaps this could be done through structured communication and information management. This too merits further investigation.

Lastly, this study found that it takes approximately an hour of a hospitalist's time each day to manage 1 patient's care. This in and of itself, is very important from the standpoint of both billing and workload. In today's professional services fee model, there are a number of components that contribute to the level of service that a hospitalist can bill. One of those components is time, specifically the time spent counseling and/or coordinating care, which as this study suggests, dominates a hospitalist's workday. It is therefore critical that hospitalists accurately and consistently document the amount of time they spend with each patient and specifically describe the counseling and/or activities to coordinate care. Additionally, recognizing how much time is required for a hospitalist to care for a patient has important workload implications. If we assume that it takes approximately an hour per patient and a typical workday is around 11 hours after subtracting personal time, then it would be reasonable to expect that a single hospitalist should have, on average, 11 patient encounters per day. This number is, of course, completely dependent on organizational factors such as a specific hospital's support systems and the mix of admissions, follow‐ups, and discharges on that service.

Our study has several limitations. The time study occurred at 2 hospitals, in 1 mid‐sized Midwestern city, and the results may not be generalizable to other settings. However, the congruence of our findings with those of O'Leary et al.1 suggests that our results maintain external validity. Second, at the time of the study the 2 programs were relatively new and workflows were still evolving. Additionally, the academic and community hospitalist programs were under unified management and 2 of the surveyed hospitalists worked at both programs. This may have artificially homogenized the work patterns observed at both programs. Finally, observing hospitalist activities exclusively during the weekday daytime shifts has the potential to bias the results. However, the night and weekend duties and responsibilities of the 2 programs differed significantly, which would have made it very difficult to derive meaningful comparisons for those observations.

Conclusion

We found that hospitalists in both academic and community settings spend the majority of their time multitasking and engaged in indirect patient care. Further studies are necessary to determine the extent to which this is a necessary feature of the hospitalist care model and whether hospitalists should restructure their workflow to improve outcomes.

In 2006, after introducing formal hospitalist programs at both an academic hospital and an affiliated community teaching hospital, we conducted a time study to gain insight into the effect of adopting a community model in an academic environment. This evaluation was conducted to identify similarities and differences between the 2 programs and to highlight opportunities for process and quality improvement. The hospitalist case mix index (CMI) was higher at the academic center (1.3) than at the community center (1.1). At both institutions documentation and most order entry were completed on paper, while lab and test results were electronically available. Both hospitalist programs were nonteaching services with day shifts staffed from 7:00 _AM to 7:00 _PM. At the academic center, a single hospitalist staffed the service for 7 days in a row with an average daily census of 10 patients. At the community hospital, 2 hospitalists carried the service, alternating days as the primary admitter. These hospitalists each carried an average census of 13 patients for 6 days in a row with staggered start/stop dates to ensure service continuity. The years of experience as a practicing hospitalist were similar between the 2 programs (median 4 years and range 1‐10 years for both programs); all hospitalists completed an internal medicine residency.

Methods

A paper‐based tool was used to collect data at 1‐minute intervals into 5 major categories validated through trial observation, content focus groups, and expert opinion. The 5 categories were Direct Patient Care, Indirect Patient Care, Travel, Personal, and Other (Table 1). Communication, a subcategory of Indirect Patient Care, was further classified by the job‐profession category and communication modality of the individual(s) interacting with the hospitalist. The tool allowed for more than 1 task category to be tracked at a time in order to capture multitasking. Three trained industrial engineers shadowed 9 different hospitalists during the day shifts, between 2 and 5 shifts per hospitalist, gathering approximately 355 hours of observational data over the 8 weeks of the study; 4 weeks at each hospital. Weekend and night shift data were not collected due to observer availability. Results for each setting were reported as the mean and standard deviation percentage of physician time observed for each task category. The results were also reported as the mean and standard deviation volume adjusted time per patient for each task category. The adjustment was made by dividing physician time by the number of patient encounters for that observation. Comparative analyses were calculated using a t‐test with a significance level of 0.05 and confidence intervals were reported at a 95% interval. Since this project was a quality improvement initiative analyzing the introduction of a new clinical service, Institutional Review Board (IRB) approval from our institution was not required.

Results

Hospitalist time allocations at the 2 programs were comparatively similar (Table 2). At the academic center, hospitalists spent the majority of their time providing indirect patient care (69.8%, CI: 66.3‐73.3%), followed by direct patient care (13.1%, CI: 11.2‐14.9%), with the remaining time distributed among travel, personal, and other administrative duties. Likewise, the community hospitalists spent the majority of their time providing indirect patient care (68.7%, CI: 63.0‐74.5%), followed by direct patient care (16.7%, CI: 14.1‐19.4%), with travel, personal, and administrative duties completing the day. Additionally, the percent of time spent multitasking, defined as more than 1 task category observed at the same time, was strikingly similar between the 2 groups (Academic: 47.6% 16.5%, Community: 47.9% 9.8%).

While the difference in total percent of time spent on direct patient care was statistically significant (P = 0.03), the values converged after adjusting for the differences in average daily census (Table 3). On average, both the academic and community hospitalists spent approximately 10 minutes per patient per day interacting face to face with the patient and/or family (10.0 2.9 minutes and 10.1 3.6 minutes respectively, P = 0.89). However, after volume adjusting, other workflow differences became statistically significant, primarily in indirect patient care (Academic: 54.7 11.1 minutes/patient, Community: 41.9 9.8 minutes/patient, P 0.001). The academic hospitalists spent more time writing orders (4.6 1.3 minutes/patient vs. 2.8 1.1 minutes/patient, P 0.001), looking up and reviewing medical reference materials (1.1 0.6 minutes/patient vs. 0.3 0.4 minutes/patient, P 0.001), and communicating with other providers (20.5 7.7 min/patient vs. 11.1 3.1 min/patient, P 0.001) than their community hospitalist counterparts. Nearly half the time that the academic hospitalists spent communicating was dedicated to speaking with other physicians (9.2 3.5 minutes/patient); more than double that of the community hospitalists (4.0 1.6 minutes). Additionally, the academic hospitalists spent more time speaking with pharmacists (0.7 0.6 minutes vs. 0.1 0.2 minutes, P = 0.001).

Discussion

In 2006, O'Leary et al.1 demonstrated that academic hospitalists spend approximately 20% of their time engaged in direct patient care. Our results are consistent with these data and further expand these findings to a community setting. Although we found subtle workflow differences between the academic and community programs, their similarities were more striking than their differences. We suspect that these differences can be largely attributed to the higher CMI at the academic program as well as the greater complexity and additional communication hand‐offs inherent to this tertiary academic medical center. For example, at the academic medical center, medicine admissions were screened by a medicine triage resident and subsequently handed off to a hospitalist. In most cases, this system did not preclude the need to speak directly with the emergency department (ED) attending, adding a layer of complexity that did not exist in the community hospital. Finally, in contrast to the community hospital, there was little comanagement at the academic medical center, necessitating frequent transfers to and from medical and subspecialty services.

It appears that hospitalists, irrespective of their work environment, spend far more time documenting, communicating, and coordinating care than at the bedside. It is unclear whether this represents a desirable outcome of hospitalists' role as managers of complex hospital stays or inefficient and ineffective effort that should be mitigated through care delivery redesign. Further research to optimize hospital information management, streamline care processes and eliminate low value‐added effort is clearly needed.

Another notable finding of our study is that hospitalists spend roughly half of their time performing more than 1 work category at the same time deemed as multitasking.2 The prevalence and effects of multitasking are well‐characterized in emergency medicine and likely apply to hospitalists.3, 4 Fractured attention due to multitasking may hamper communication, jeopardize care handoffs, and increase risk for medical errors and litigation.46 While it is likely that multitasking is inherent to the practice of hospital medicine, it is unclear how this could be mitigated or better facilitated. Perhaps this could be done through structured communication and information management. This too merits further investigation.

Lastly, this study found that it takes approximately an hour of a hospitalist's time each day to manage 1 patient's care. This in and of itself, is very important from the standpoint of both billing and workload. In today's professional services fee model, there are a number of components that contribute to the level of service that a hospitalist can bill. One of those components is time, specifically the time spent counseling and/or coordinating care, which as this study suggests, dominates a hospitalist's workday. It is therefore critical that hospitalists accurately and consistently document the amount of time they spend with each patient and specifically describe the counseling and/or activities to coordinate care. Additionally, recognizing how much time is required for a hospitalist to care for a patient has important workload implications. If we assume that it takes approximately an hour per patient and a typical workday is around 11 hours after subtracting personal time, then it would be reasonable to expect that a single hospitalist should have, on average, 11 patient encounters per day. This number is, of course, completely dependent on organizational factors such as a specific hospital's support systems and the mix of admissions, follow‐ups, and discharges on that service.

Our study has several limitations. The time study occurred at 2 hospitals, in 1 mid‐sized Midwestern city, and the results may not be generalizable to other settings. However, the congruence of our findings with those of O'Leary et al.1 suggests that our results maintain external validity. Second, at the time of the study the 2 programs were relatively new and workflows were still evolving. Additionally, the academic and community hospitalist programs were under unified management and 2 of the surveyed hospitalists worked at both programs. This may have artificially homogenized the work patterns observed at both programs. Finally, observing hospitalist activities exclusively during the weekday daytime shifts has the potential to bias the results. However, the night and weekend duties and responsibilities of the 2 programs differed significantly, which would have made it very difficult to derive meaningful comparisons for those observations.

Conclusion

We found that hospitalists in both academic and community settings spend the majority of their time multitasking and engaged in indirect patient care. Further studies are necessary to determine the extent to which this is a necessary feature of the hospitalist care model and whether hospitalists should restructure their workflow to improve outcomes.

In 2006, after introducing formal hospitalist programs at both an academic hospital and an affiliated community teaching hospital, we conducted a time study to gain insight into the effect of adopting a community model in an academic environment. This evaluation was conducted to identify similarities and differences between the 2 programs and to highlight opportunities for process and quality improvement. The hospitalist case mix index (CMI) was higher at the academic center (1.3) than at the community center (1.1). At both institutions documentation and most order entry were completed on paper, while lab and test results were electronically available. Both hospitalist programs were nonteaching services with day shifts staffed from 7:00 _AM to 7:00 _PM. At the academic center, a single hospitalist staffed the service for 7 days in a row with an average daily census of 10 patients. At the community hospital, 2 hospitalists carried the service, alternating days as the primary admitter. These hospitalists each carried an average census of 13 patients for 6 days in a row with staggered start/stop dates to ensure service continuity. The years of experience as a practicing hospitalist were similar between the 2 programs (median 4 years and range 1‐10 years for both programs); all hospitalists completed an internal medicine residency.

Methods

A paper‐based tool was used to collect data at 1‐minute intervals into 5 major categories validated through trial observation, content focus groups, and expert opinion. The 5 categories were Direct Patient Care, Indirect Patient Care, Travel, Personal, and Other (Table 1). Communication, a subcategory of Indirect Patient Care, was further classified by the job‐profession category and communication modality of the individual(s) interacting with the hospitalist. The tool allowed for more than 1 task category to be tracked at a time in order to capture multitasking. Three trained industrial engineers shadowed 9 different hospitalists during the day shifts, between 2 and 5 shifts per hospitalist, gathering approximately 355 hours of observational data over the 8 weeks of the study; 4 weeks at each hospital. Weekend and night shift data were not collected due to observer availability. Results for each setting were reported as the mean and standard deviation percentage of physician time observed for each task category. The results were also reported as the mean and standard deviation volume adjusted time per patient for each task category. The adjustment was made by dividing physician time by the number of patient encounters for that observation. Comparative analyses were calculated using a t‐test with a significance level of 0.05 and confidence intervals were reported at a 95% interval. Since this project was a quality improvement initiative analyzing the introduction of a new clinical service, Institutional Review Board (IRB) approval from our institution was not required.

Results

Hospitalist time allocations at the 2 programs were comparatively similar (Table 2). At the academic center, hospitalists spent the majority of their time providing indirect patient care (69.8%, CI: 66.3‐73.3%), followed by direct patient care (13.1%, CI: 11.2‐14.9%), with the remaining time distributed among travel, personal, and other administrative duties. Likewise, the community hospitalists spent the majority of their time providing indirect patient care (68.7%, CI: 63.0‐74.5%), followed by direct patient care (16.7%, CI: 14.1‐19.4%), with travel, personal, and administrative duties completing the day. Additionally, the percent of time spent multitasking, defined as more than 1 task category observed at the same time, was strikingly similar between the 2 groups (Academic: 47.6% 16.5%, Community: 47.9% 9.8%).

While the difference in total percent of time spent on direct patient care was statistically significant (P = 0.03), the values converged after adjusting for the differences in average daily census (Table 3). On average, both the academic and community hospitalists spent approximately 10 minutes per patient per day interacting face to face with the patient and/or family (10.0 2.9 minutes and 10.1 3.6 minutes respectively, P = 0.89). However, after volume adjusting, other workflow differences became statistically significant, primarily in indirect patient care (Academic: 54.7 11.1 minutes/patient, Community: 41.9 9.8 minutes/patient, P 0.001). The academic hospitalists spent more time writing orders (4.6 1.3 minutes/patient vs. 2.8 1.1 minutes/patient, P 0.001), looking up and reviewing medical reference materials (1.1 0.6 minutes/patient vs. 0.3 0.4 minutes/patient, P 0.001), and communicating with other providers (20.5 7.7 min/patient vs. 11.1 3.1 min/patient, P 0.001) than their community hospitalist counterparts. Nearly half the time that the academic hospitalists spent communicating was dedicated to speaking with other physicians (9.2 3.5 minutes/patient); more than double that of the community hospitalists (4.0 1.6 minutes). Additionally, the academic hospitalists spent more time speaking with pharmacists (0.7 0.6 minutes vs. 0.1 0.2 minutes, P = 0.001).