In the past 2 decades, emergency department (ED) overcrowding has become an issue large enough to receive coverage in the popular press, and to spawn research around its causes and effects.116 At the same time, nurses and physicians on the inpatient wards have been urged to shorten the length of stay for patients as health system leaders face an aging population but limited capital to build new beds or hire additional clinical staff. Capacity managementencompassing the flow of patients from ED triage to inpatient dischargehas become a shared concern of clinicians and hospital administrators alike.

How to achieve the goals of diagnosing and healing while ushering patients ever more quickly through the modern hospital is not yet entirely clear. Past research and work by business groups suggests that demand for inpatient beds starts early in the day, but discharges typically occur in the late afternoon.17 This creates a potential bottleneck in patient flow. Many hospitals have implemented measures to improve patient throughput.1821 However, formal research has focused on factors leading to an additional inpatient day.2226 We have found no peer‐reviewed publications that address the problem of same‐day delays by describing hour of day for each step in the discharge process and variables associated with late‐day discharges. To fill this gap, we conducted a prospective cohort study of 209 consecutive discharges from a general medical ward to: (1) describe the natural history of hospital discharge, (2) measure time of day and duration for each step, and (3) identify factors associated with discharges that occur later in the day. We hypothesized that time and duration of discharge would be associated with 5 factors: patient demographics and clinical characteristics, departmental occupancy, type of inpatient testing done immediately prior to discharge, and discharge characteristics such as discharge to a location other than home.

Patients and Methods

Results

Data were collected on 216 patients. Seven patients were excluded from the study, because they were discharged against medical advice. Since these patients left before their decisive test was completed, there was no way to assess duration of the discharge process. Of the remaining 209 patients, 6 patients lacked necessary data to complete analysis (5 without survey data; 1 without administrative data). Two additional patients were eliminated from the final analysis because they bypassed the normal discharge process and were extreme outliers in either discharge time (1 discharged at 1 _AM), or discharge duration (1 with discharge duration of 400+ hours). A total of 201 patients were included in the final analyses.

The hospitalist program primarily serves an indigent, local adult population with general medical problems, and this is reflected in the patient characteristics (Table 1). We compared the characteristics of patients discharged early and quickly (discharged prior to median hour of 3:00 _PM, and discharge process lasting less than 24 hours) to all other discharges, to identify factors associated with later and longer discharges.

Overall, 81% of patients were admitted from the ED, and 40% of all patients were insured by Medicaid or were self‐pay at time of discharge. Median expected charges were similar to the national average, as demonstrated by the median APRDRGwt of 1.0. Patients stayed an average of 6 days (median = 3 days). Patients with the longest adjusted lengths of stay (>20 days) were never early and short discharges. The most common discharge diagnoses were: congestive heart failure, chest pain or myocardial infarction, pneumonia, asthma/chronic obstructive pulmonary disease, and sickle‐cell disease. Thirty‐nine percent of all patients carried the diagnosis of alcohol or drug dependence or abuse, although for most this was not their discharge diagnosis. None of these demographic or clinical factors were associated with a late or long discharge.

The types of tests patients required on discharge were categorized into 4 groups: consults (18.4%), laboratory tests (22.9%), procedures (26.4%), and physical exam (32.3%). Distribution differed significantly between early and short discharges, and all other discharges (P < 0.001). Procedures and consults were less frequent among early and short discharges (procedures: 10.7% versus 35.7%; consults: 8.0% versus 24.6%).

For all patients, there was fragmentation within the consult and procedure categories. Within the consult group, there were 12 different types of consults ordered, with the dominant category (35.1%) being other. The next highest volume consult was physical/occupational therapy (27.0%). Within the procedure group, there were 11 different types of procedures, with the most common being stress echocardiograms (28.3%). Non‐MRI radiology procedures made up the next largest category (20.8%) and the third was other (18.9%).

Many patients had immediate postdischarge needs, as demonstrated by the 20% of patients not discharged home. The majority (66%) of those who were discharged to a facility required an ambulance. Early and short discharges were less likely to use an ambulance to leave the hospital (8.0% versus 21.4%; P = 0.01), and more likely to be discharged directly to home (99.3% versus 71.4%; P < 0.001).

Based on process mapping, we defined a 4‐step sequential discharge process for all patients (Figure 1). The first step was: decisive test needed by physician to discharge patient. Subgroup analysis demonstrated no significant difference in the timing of this step by individual physician (P = 0.44). The remaining 3 steps were as follows: physician aware of test results, discharge paperwork complete by physician, and patient leaves the unit. Each of the 4 steps showed wide variability in hour of occurrence.

Figure 1

Total discharge duration showed even more variability than the time of day when steps were likely to occur (Figure 2). Median duration of the discharge process was 7.6 hours (25th% to 75th%: 4 to 22 hours). Median duration from decisive test needed until resulted (duration 1) was 2 hours (25th% to 75th%: 0 to 8 hours); between test resulted until discharge paperwork complete (duration 2) was 1.4 hours (25th% to 75th%: 0.3 to 4.2 hours); and between discharge paperwork complete and patient leaving the unit (duration 3) was 2.0 hours (25th% to 75th%: 1.1 to 3.1 hours). All durations were skewed to the right, with durations 1 and 2 each taking at least 24 hours to occur in 10% of patients.

Figure 2

The final multivariate model for time of discharge contained 6 covariates: age, sex, race, test type, prescriptions filled prior to discharge, and need for an ambulance on discharge (Table 2). Special discharge needs continued to be associated with later discharges. Those patients who required an ambulance for transport had mean discharge times that were later by 1.5 hours (95% CI, 0.42.5). If staff obtained discharge medications for patients, these patients left 1.4 hours later than those patients who filled their prescriptions on their own (95% CI, 0.32.4). Patients requiring a consult or procedure also had significantly later discharges (1.2 hours for consults, 95% CI, 0.12.4; 1.1 hours for procedures, 95% CI, 0.12.1) than those needing a bedside exam. Age, sex, and race remained insignificant at the P 0.05 level in the final multivariate model. Length of stay was significantly associated with discharge time in crude analysis, but this variable dropped out of the final multivariate model.

We used duration of discharge as a secondary outcome measure. The final multivariate model for discharge duration included: age, sex, race, test type, and discharge to a facility (Table 2). Those who went to a facility on average left 28.9 hours (95% CI, 21.935.9) later than those who went home. Test type continued to show a significant association with discharge duration, although the estimates were slightly lower in the adjusted model. Need for a consult was associated with a discharge that was on average 14.8 hours (95% CI, 6.523.1) longer than discharges contingent on a physical exam. Similarly, those patients who had procedures had discharges that were on average 13.4 hours (95% CI, 6.020.7) longer than those whose last test was an exam. Several factors that were significantly associated with discharge duration in unadjusted analyses dropped out of the final multivariate model. These included: need for an ambulance, length of stay, insurance status, and medical complexity as estimated by APRDRGwt.

Conclusions

We found that discharge time and duration had wide variability and that certain factors were associated with only one outcome variabledischarge time or duration. Two factorsneed for an ambulance and filling of prescriptions prior to dischargewere associated with later hour of discharge. Discharge to a location other than home was associated with prolonged discharge duration. Test characteristicsin particular need for a procedure and consultwere significantly associated with both longer and later discharges.

In bivariate analysis, several factors were not associated with discharge time or duration. These were: African‐American race, sex, age, Department of Medicine occupancy on day of discharge, source of admission, psychiatric comorbidity, and substance abuse comorbidity. We had expected higher occupancy to delay discharge as demand exceeded capacity for tests, consults, etc. Our findings suggest that even though our study was conducted during the winter months when hospital occupancy is typically at its peak, supply of staff was still adequate enough to meet high demand. We had also expected that psychiatric and substance abuse comorbidities would prolong discharge as prior studies have found some of these diagnoses to be associated with longer lengths of stay.3134 However, our results do not support such an association, and may reflect our decision to group all psychiatric diagnoses together due to limited sample size.

The main strength of our study is the use of 2 outcome variablestime and durationto define delayed discharges. Our findings demonstrate that few factors are associated with both later and longer discharges. In an era when avoiding emergency room walkouts through early morning hospital discharges can be as important as managing overall length of stay, identifying factors associated with both duration and timing of discharges addresses actual challenges faced by hospitals with limited resources. Prior studies have rarely addressed both outcomes. An additional strength of our study is our use of an interdisciplinary survey. The discharge process is a key component of the inpatient stay, but it is also one for which no group is entirely responsible. Through the development and administration of an interdisciplinary survey, our study adds detail to existing descriptions of this fragmented process, and identifies potential areas for improvement.

Several limitations of our study deserve comment. First, we examined patients discharged from a hospitalist unit without house‐staff at an urban tertiary care hospital. Our findings may need additional interpretation prior to their application in dissimilar settings such as: (1) resident‐covered units in which workflow is shaped by teaching rounds, and (2) nonacademic hospitals in which incentives to provide rapid consults and procedures may be different. Second, we relied on self‐reporting for certain variables such as time decisive test needed. This may be subject to recall bias, as we did not have staff to independently verify recalled times. However, since the discharge process is generally a linear one, we were able to verify the general scope of recalled times with times date‐stamped by the computer during the discharge process (eg, checking that time decisive test needed did not occur after the discharge worksheet had been finalized in the electronic order‐entry system). Third, our sample size was not large enough to control for disease‐specific quality measures. Of note, prior studies have not identified a consistently positive or negative relationship between quality of care and efficiency.3538

Past work has used administrative and survey data to analyze the effect of discharge planning interventions on financial or quality outcomes. Outcomes have included readmissions, mortality, patient satisfaction, length of stay, and inappropriate bed days.22, 3845 However, as capacity management has become a more pressing issue for hospitals, greater focus is being placed on the mismatch between supply and demand of patients at each hour of the day. The relevant unit of measure for efficient discharges has become hour of day, in addition to total length of stay. Some hospital improvement projects have already addressed this shift in thinking.20, 21 Our study adds to this work by formally describing the precise timing and duration of steps in the discharge process, and identifying factors associated with both time and duration.

We believe the results of our study have several implications for hospital administrators and patients interested in more timely care. First, the methods used provide a tangible framework for addressing problems that cross disciplines (eg, nursing, physician, social work) and departments (eg, medicine and radiology), and have a multitude of potential causes and confounders. The survey results offer guidance on where to focus resources, provide a shared baseline metric for improvement, and suggest the cross‐functional team that should be involved in change efforts. Such an approach may be useful for addressing common system‐based challenges in inpatient quality and safety.

Second, with specific regard to discharge planning, our study supports the notion that modifiable factors are associated with discharge time and duration. However, we also describe a fragmented discharge process, with no single bottleneck or department responsible for the majority of late and long discharges. Although procedures and consults were both associated with longer and later discharges, only 26% of patients required a procedure prior to discharge, and 18% required a consult. Moreover, among procedures, different people and events are needed to carry out the 2 most popular procedures: stress echocardiograms, and non‐MRI radiology procedures. Hospital leadership at the highest levels will be required to improve efficiency based on local usage patterns, and to increase coordination among the multiple interdepartmental processes that make up the more general categories of procedure and consult.

In the past 2 decades, emergency department (ED) overcrowding has become an issue large enough to receive coverage in the popular press, and to spawn research around its causes and effects.116 At the same time, nurses and physicians on the inpatient wards have been urged to shorten the length of stay for patients as health system leaders face an aging population but limited capital to build new beds or hire additional clinical staff. Capacity managementencompassing the flow of patients from ED triage to inpatient dischargehas become a shared concern of clinicians and hospital administrators alike.

How to achieve the goals of diagnosing and healing while ushering patients ever more quickly through the modern hospital is not yet entirely clear. Past research and work by business groups suggests that demand for inpatient beds starts early in the day, but discharges typically occur in the late afternoon.17 This creates a potential bottleneck in patient flow. Many hospitals have implemented measures to improve patient throughput.1821 However, formal research has focused on factors leading to an additional inpatient day.2226 We have found no peer‐reviewed publications that address the problem of same‐day delays by describing hour of day for each step in the discharge process and variables associated with late‐day discharges. To fill this gap, we conducted a prospective cohort study of 209 consecutive discharges from a general medical ward to: (1) describe the natural history of hospital discharge, (2) measure time of day and duration for each step, and (3) identify factors associated with discharges that occur later in the day. We hypothesized that time and duration of discharge would be associated with 5 factors: patient demographics and clinical characteristics, departmental occupancy, type of inpatient testing done immediately prior to discharge, and discharge characteristics such as discharge to a location other than home.

Patients and Methods

Results

Data were collected on 216 patients. Seven patients were excluded from the study, because they were discharged against medical advice. Since these patients left before their decisive test was completed, there was no way to assess duration of the discharge process. Of the remaining 209 patients, 6 patients lacked necessary data to complete analysis (5 without survey data; 1 without administrative data). Two additional patients were eliminated from the final analysis because they bypassed the normal discharge process and were extreme outliers in either discharge time (1 discharged at 1 _AM), or discharge duration (1 with discharge duration of 400+ hours). A total of 201 patients were included in the final analyses.

The hospitalist program primarily serves an indigent, local adult population with general medical problems, and this is reflected in the patient characteristics (Table 1). We compared the characteristics of patients discharged early and quickly (discharged prior to median hour of 3:00 _PM, and discharge process lasting less than 24 hours) to all other discharges, to identify factors associated with later and longer discharges.

Overall, 81% of patients were admitted from the ED, and 40% of all patients were insured by Medicaid or were self‐pay at time of discharge. Median expected charges were similar to the national average, as demonstrated by the median APRDRGwt of 1.0. Patients stayed an average of 6 days (median = 3 days). Patients with the longest adjusted lengths of stay (>20 days) were never early and short discharges. The most common discharge diagnoses were: congestive heart failure, chest pain or myocardial infarction, pneumonia, asthma/chronic obstructive pulmonary disease, and sickle‐cell disease. Thirty‐nine percent of all patients carried the diagnosis of alcohol or drug dependence or abuse, although for most this was not their discharge diagnosis. None of these demographic or clinical factors were associated with a late or long discharge.

The types of tests patients required on discharge were categorized into 4 groups: consults (18.4%), laboratory tests (22.9%), procedures (26.4%), and physical exam (32.3%). Distribution differed significantly between early and short discharges, and all other discharges (P < 0.001). Procedures and consults were less frequent among early and short discharges (procedures: 10.7% versus 35.7%; consults: 8.0% versus 24.6%).

For all patients, there was fragmentation within the consult and procedure categories. Within the consult group, there were 12 different types of consults ordered, with the dominant category (35.1%) being other. The next highest volume consult was physical/occupational therapy (27.0%). Within the procedure group, there were 11 different types of procedures, with the most common being stress echocardiograms (28.3%). Non‐MRI radiology procedures made up the next largest category (20.8%) and the third was other (18.9%).

Many patients had immediate postdischarge needs, as demonstrated by the 20% of patients not discharged home. The majority (66%) of those who were discharged to a facility required an ambulance. Early and short discharges were less likely to use an ambulance to leave the hospital (8.0% versus 21.4%; P = 0.01), and more likely to be discharged directly to home (99.3% versus 71.4%; P < 0.001).

Based on process mapping, we defined a 4‐step sequential discharge process for all patients (Figure 1). The first step was: decisive test needed by physician to discharge patient. Subgroup analysis demonstrated no significant difference in the timing of this step by individual physician (P = 0.44). The remaining 3 steps were as follows: physician aware of test results, discharge paperwork complete by physician, and patient leaves the unit. Each of the 4 steps showed wide variability in hour of occurrence.

Figure 1

Total discharge duration showed even more variability than the time of day when steps were likely to occur (Figure 2). Median duration of the discharge process was 7.6 hours (25th% to 75th%: 4 to 22 hours). Median duration from decisive test needed until resulted (duration 1) was 2 hours (25th% to 75th%: 0 to 8 hours); between test resulted until discharge paperwork complete (duration 2) was 1.4 hours (25th% to 75th%: 0.3 to 4.2 hours); and between discharge paperwork complete and patient leaving the unit (duration 3) was 2.0 hours (25th% to 75th%: 1.1 to 3.1 hours). All durations were skewed to the right, with durations 1 and 2 each taking at least 24 hours to occur in 10% of patients.

Figure 2

The final multivariate model for time of discharge contained 6 covariates: age, sex, race, test type, prescriptions filled prior to discharge, and need for an ambulance on discharge (Table 2). Special discharge needs continued to be associated with later discharges. Those patients who required an ambulance for transport had mean discharge times that were later by 1.5 hours (95% CI, 0.42.5). If staff obtained discharge medications for patients, these patients left 1.4 hours later than those patients who filled their prescriptions on their own (95% CI, 0.32.4). Patients requiring a consult or procedure also had significantly later discharges (1.2 hours for consults, 95% CI, 0.12.4; 1.1 hours for procedures, 95% CI, 0.12.1) than those needing a bedside exam. Age, sex, and race remained insignificant at the P 0.05 level in the final multivariate model. Length of stay was significantly associated with discharge time in crude analysis, but this variable dropped out of the final multivariate model.

We used duration of discharge as a secondary outcome measure. The final multivariate model for discharge duration included: age, sex, race, test type, and discharge to a facility (Table 2). Those who went to a facility on average left 28.9 hours (95% CI, 21.935.9) later than those who went home. Test type continued to show a significant association with discharge duration, although the estimates were slightly lower in the adjusted model. Need for a consult was associated with a discharge that was on average 14.8 hours (95% CI, 6.523.1) longer than discharges contingent on a physical exam. Similarly, those patients who had procedures had discharges that were on average 13.4 hours (95% CI, 6.020.7) longer than those whose last test was an exam. Several factors that were significantly associated with discharge duration in unadjusted analyses dropped out of the final multivariate model. These included: need for an ambulance, length of stay, insurance status, and medical complexity as estimated by APRDRGwt.

Conclusions

We found that discharge time and duration had wide variability and that certain factors were associated with only one outcome variabledischarge time or duration. Two factorsneed for an ambulance and filling of prescriptions prior to dischargewere associated with later hour of discharge. Discharge to a location other than home was associated with prolonged discharge duration. Test characteristicsin particular need for a procedure and consultwere significantly associated with both longer and later discharges.

In bivariate analysis, several factors were not associated with discharge time or duration. These were: African‐American race, sex, age, Department of Medicine occupancy on day of discharge, source of admission, psychiatric comorbidity, and substance abuse comorbidity. We had expected higher occupancy to delay discharge as demand exceeded capacity for tests, consults, etc. Our findings suggest that even though our study was conducted during the winter months when hospital occupancy is typically at its peak, supply of staff was still adequate enough to meet high demand. We had also expected that psychiatric and substance abuse comorbidities would prolong discharge as prior studies have found some of these diagnoses to be associated with longer lengths of stay.3134 However, our results do not support such an association, and may reflect our decision to group all psychiatric diagnoses together due to limited sample size.

The main strength of our study is the use of 2 outcome variablestime and durationto define delayed discharges. Our findings demonstrate that few factors are associated with both later and longer discharges. In an era when avoiding emergency room walkouts through early morning hospital discharges can be as important as managing overall length of stay, identifying factors associated with both duration and timing of discharges addresses actual challenges faced by hospitals with limited resources. Prior studies have rarely addressed both outcomes. An additional strength of our study is our use of an interdisciplinary survey. The discharge process is a key component of the inpatient stay, but it is also one for which no group is entirely responsible. Through the development and administration of an interdisciplinary survey, our study adds detail to existing descriptions of this fragmented process, and identifies potential areas for improvement.

Several limitations of our study deserve comment. First, we examined patients discharged from a hospitalist unit without house‐staff at an urban tertiary care hospital. Our findings may need additional interpretation prior to their application in dissimilar settings such as: (1) resident‐covered units in which workflow is shaped by teaching rounds, and (2) nonacademic hospitals in which incentives to provide rapid consults and procedures may be different. Second, we relied on self‐reporting for certain variables such as time decisive test needed. This may be subject to recall bias, as we did not have staff to independently verify recalled times. However, since the discharge process is generally a linear one, we were able to verify the general scope of recalled times with times date‐stamped by the computer during the discharge process (eg, checking that time decisive test needed did not occur after the discharge worksheet had been finalized in the electronic order‐entry system). Third, our sample size was not large enough to control for disease‐specific quality measures. Of note, prior studies have not identified a consistently positive or negative relationship between quality of care and efficiency.3538

Past work has used administrative and survey data to analyze the effect of discharge planning interventions on financial or quality outcomes. Outcomes have included readmissions, mortality, patient satisfaction, length of stay, and inappropriate bed days.22, 3845 However, as capacity management has become a more pressing issue for hospitals, greater focus is being placed on the mismatch between supply and demand of patients at each hour of the day. The relevant unit of measure for efficient discharges has become hour of day, in addition to total length of stay. Some hospital improvement projects have already addressed this shift in thinking.20, 21 Our study adds to this work by formally describing the precise timing and duration of steps in the discharge process, and identifying factors associated with both time and duration.

We believe the results of our study have several implications for hospital administrators and patients interested in more timely care. First, the methods used provide a tangible framework for addressing problems that cross disciplines (eg, nursing, physician, social work) and departments (eg, medicine and radiology), and have a multitude of potential causes and confounders. The survey results offer guidance on where to focus resources, provide a shared baseline metric for improvement, and suggest the cross‐functional team that should be involved in change efforts. Such an approach may be useful for addressing common system‐based challenges in inpatient quality and safety.

Second, with specific regard to discharge planning, our study supports the notion that modifiable factors are associated with discharge time and duration. However, we also describe a fragmented discharge process, with no single bottleneck or department responsible for the majority of late and long discharges. Although procedures and consults were both associated with longer and later discharges, only 26% of patients required a procedure prior to discharge, and 18% required a consult. Moreover, among procedures, different people and events are needed to carry out the 2 most popular procedures: stress echocardiograms, and non‐MRI radiology procedures. Hospital leadership at the highest levels will be required to improve efficiency based on local usage patterns, and to increase coordination among the multiple interdepartmental processes that make up the more general categories of procedure and consult.

In the past 2 decades, emergency department (ED) overcrowding has become an issue large enough to receive coverage in the popular press, and to spawn research around its causes and effects.116 At the same time, nurses and physicians on the inpatient wards have been urged to shorten the length of stay for patients as health system leaders face an aging population but limited capital to build new beds or hire additional clinical staff. Capacity managementencompassing the flow of patients from ED triage to inpatient dischargehas become a shared concern of clinicians and hospital administrators alike.

How to achieve the goals of diagnosing and healing while ushering patients ever more quickly through the modern hospital is not yet entirely clear. Past research and work by business groups suggests that demand for inpatient beds starts early in the day, but discharges typically occur in the late afternoon.17 This creates a potential bottleneck in patient flow. Many hospitals have implemented measures to improve patient throughput.1821 However, formal research has focused on factors leading to an additional inpatient day.2226 We have found no peer‐reviewed publications that address the problem of same‐day delays by describing hour of day for each step in the discharge process and variables associated with late‐day discharges. To fill this gap, we conducted a prospective cohort study of 209 consecutive discharges from a general medical ward to: (1) describe the natural history of hospital discharge, (2) measure time of day and duration for each step, and (3) identify factors associated with discharges that occur later in the day. We hypothesized that time and duration of discharge would be associated with 5 factors: patient demographics and clinical characteristics, departmental occupancy, type of inpatient testing done immediately prior to discharge, and discharge characteristics such as discharge to a location other than home.

Patients and Methods

Results

Data were collected on 216 patients. Seven patients were excluded from the study, because they were discharged against medical advice. Since these patients left before their decisive test was completed, there was no way to assess duration of the discharge process. Of the remaining 209 patients, 6 patients lacked necessary data to complete analysis (5 without survey data; 1 without administrative data). Two additional patients were eliminated from the final analysis because they bypassed the normal discharge process and were extreme outliers in either discharge time (1 discharged at 1 _AM), or discharge duration (1 with discharge duration of 400+ hours). A total of 201 patients were included in the final analyses.

The hospitalist program primarily serves an indigent, local adult population with general medical problems, and this is reflected in the patient characteristics (Table 1). We compared the characteristics of patients discharged early and quickly (discharged prior to median hour of 3:00 _PM, and discharge process lasting less than 24 hours) to all other discharges, to identify factors associated with later and longer discharges.

Overall, 81% of patients were admitted from the ED, and 40% of all patients were insured by Medicaid or were self‐pay at time of discharge. Median expected charges were similar to the national average, as demonstrated by the median APRDRGwt of 1.0. Patients stayed an average of 6 days (median = 3 days). Patients with the longest adjusted lengths of stay (>20 days) were never early and short discharges. The most common discharge diagnoses were: congestive heart failure, chest pain or myocardial infarction, pneumonia, asthma/chronic obstructive pulmonary disease, and sickle‐cell disease. Thirty‐nine percent of all patients carried the diagnosis of alcohol or drug dependence or abuse, although for most this was not their discharge diagnosis. None of these demographic or clinical factors were associated with a late or long discharge.

The types of tests patients required on discharge were categorized into 4 groups: consults (18.4%), laboratory tests (22.9%), procedures (26.4%), and physical exam (32.3%). Distribution differed significantly between early and short discharges, and all other discharges (P < 0.001). Procedures and consults were less frequent among early and short discharges (procedures: 10.7% versus 35.7%; consults: 8.0% versus 24.6%).

For all patients, there was fragmentation within the consult and procedure categories. Within the consult group, there were 12 different types of consults ordered, with the dominant category (35.1%) being other. The next highest volume consult was physical/occupational therapy (27.0%). Within the procedure group, there were 11 different types of procedures, with the most common being stress echocardiograms (28.3%). Non‐MRI radiology procedures made up the next largest category (20.8%) and the third was other (18.9%).

Many patients had immediate postdischarge needs, as demonstrated by the 20% of patients not discharged home. The majority (66%) of those who were discharged to a facility required an ambulance. Early and short discharges were less likely to use an ambulance to leave the hospital (8.0% versus 21.4%; P = 0.01), and more likely to be discharged directly to home (99.3% versus 71.4%; P < 0.001).

Based on process mapping, we defined a 4‐step sequential discharge process for all patients (Figure 1). The first step was: decisive test needed by physician to discharge patient. Subgroup analysis demonstrated no significant difference in the timing of this step by individual physician (P = 0.44). The remaining 3 steps were as follows: physician aware of test results, discharge paperwork complete by physician, and patient leaves the unit. Each of the 4 steps showed wide variability in hour of occurrence.

Figure 1

Total discharge duration showed even more variability than the time of day when steps were likely to occur (Figure 2). Median duration of the discharge process was 7.6 hours (25th% to 75th%: 4 to 22 hours). Median duration from decisive test needed until resulted (duration 1) was 2 hours (25th% to 75th%: 0 to 8 hours); between test resulted until discharge paperwork complete (duration 2) was 1.4 hours (25th% to 75th%: 0.3 to 4.2 hours); and between discharge paperwork complete and patient leaving the unit (duration 3) was 2.0 hours (25th% to 75th%: 1.1 to 3.1 hours). All durations were skewed to the right, with durations 1 and 2 each taking at least 24 hours to occur in 10% of patients.

Figure 2

The final multivariate model for time of discharge contained 6 covariates: age, sex, race, test type, prescriptions filled prior to discharge, and need for an ambulance on discharge (Table 2). Special discharge needs continued to be associated with later discharges. Those patients who required an ambulance for transport had mean discharge times that were later by 1.5 hours (95% CI, 0.42.5). If staff obtained discharge medications for patients, these patients left 1.4 hours later than those patients who filled their prescriptions on their own (95% CI, 0.32.4). Patients requiring a consult or procedure also had significantly later discharges (1.2 hours for consults, 95% CI, 0.12.4; 1.1 hours for procedures, 95% CI, 0.12.1) than those needing a bedside exam. Age, sex, and race remained insignificant at the P 0.05 level in the final multivariate model. Length of stay was significantly associated with discharge time in crude analysis, but this variable dropped out of the final multivariate model.

We used duration of discharge as a secondary outcome measure. The final multivariate model for discharge duration included: age, sex, race, test type, and discharge to a facility (Table 2). Those who went to a facility on average left 28.9 hours (95% CI, 21.935.9) later than those who went home. Test type continued to show a significant association with discharge duration, although the estimates were slightly lower in the adjusted model. Need for a consult was associated with a discharge that was on average 14.8 hours (95% CI, 6.523.1) longer than discharges contingent on a physical exam. Similarly, those patients who had procedures had discharges that were on average 13.4 hours (95% CI, 6.020.7) longer than those whose last test was an exam. Several factors that were significantly associated with discharge duration in unadjusted analyses dropped out of the final multivariate model. These included: need for an ambulance, length of stay, insurance status, and medical complexity as estimated by APRDRGwt.

Conclusions

We found that discharge time and duration had wide variability and that certain factors were associated with only one outcome variabledischarge time or duration. Two factorsneed for an ambulance and filling of prescriptions prior to dischargewere associated with later hour of discharge. Discharge to a location other than home was associated with prolonged discharge duration. Test characteristicsin particular need for a procedure and consultwere significantly associated with both longer and later discharges.

In bivariate analysis, several factors were not associated with discharge time or duration. These were: African‐American race, sex, age, Department of Medicine occupancy on day of discharge, source of admission, psychiatric comorbidity, and substance abuse comorbidity. We had expected higher occupancy to delay discharge as demand exceeded capacity for tests, consults, etc. Our findings suggest that even though our study was conducted during the winter months when hospital occupancy is typically at its peak, supply of staff was still adequate enough to meet high demand. We had also expected that psychiatric and substance abuse comorbidities would prolong discharge as prior studies have found some of these diagnoses to be associated with longer lengths of stay.3134 However, our results do not support such an association, and may reflect our decision to group all psychiatric diagnoses together due to limited sample size.

The main strength of our study is the use of 2 outcome variablestime and durationto define delayed discharges. Our findings demonstrate that few factors are associated with both later and longer discharges. In an era when avoiding emergency room walkouts through early morning hospital discharges can be as important as managing overall length of stay, identifying factors associated with both duration and timing of discharges addresses actual challenges faced by hospitals with limited resources. Prior studies have rarely addressed both outcomes. An additional strength of our study is our use of an interdisciplinary survey. The discharge process is a key component of the inpatient stay, but it is also one for which no group is entirely responsible. Through the development and administration of an interdisciplinary survey, our study adds detail to existing descriptions of this fragmented process, and identifies potential areas for improvement.

Several limitations of our study deserve comment. First, we examined patients discharged from a hospitalist unit without house‐staff at an urban tertiary care hospital. Our findings may need additional interpretation prior to their application in dissimilar settings such as: (1) resident‐covered units in which workflow is shaped by teaching rounds, and (2) nonacademic hospitals in which incentives to provide rapid consults and procedures may be different. Second, we relied on self‐reporting for certain variables such as time decisive test needed. This may be subject to recall bias, as we did not have staff to independently verify recalled times. However, since the discharge process is generally a linear one, we were able to verify the general scope of recalled times with times date‐stamped by the computer during the discharge process (eg, checking that time decisive test needed did not occur after the discharge worksheet had been finalized in the electronic order‐entry system). Third, our sample size was not large enough to control for disease‐specific quality measures. Of note, prior studies have not identified a consistently positive or negative relationship between quality of care and efficiency.3538

Past work has used administrative and survey data to analyze the effect of discharge planning interventions on financial or quality outcomes. Outcomes have included readmissions, mortality, patient satisfaction, length of stay, and inappropriate bed days.22, 3845 However, as capacity management has become a more pressing issue for hospitals, greater focus is being placed on the mismatch between supply and demand of patients at each hour of the day. The relevant unit of measure for efficient discharges has become hour of day, in addition to total length of stay. Some hospital improvement projects have already addressed this shift in thinking.20, 21 Our study adds to this work by formally describing the precise timing and duration of steps in the discharge process, and identifying factors associated with both time and duration.

We believe the results of our study have several implications for hospital administrators and patients interested in more timely care. First, the methods used provide a tangible framework for addressing problems that cross disciplines (eg, nursing, physician, social work) and departments (eg, medicine and radiology), and have a multitude of potential causes and confounders. The survey results offer guidance on where to focus resources, provide a shared baseline metric for improvement, and suggest the cross‐functional team that should be involved in change efforts. Such an approach may be useful for addressing common system‐based challenges in inpatient quality and safety.

Second, with specific regard to discharge planning, our study supports the notion that modifiable factors are associated with discharge time and duration. However, we also describe a fragmented discharge process, with no single bottleneck or department responsible for the majority of late and long discharges. Although procedures and consults were both associated with longer and later discharges, only 26% of patients required a procedure prior to discharge, and 18% required a consult. Moreover, among procedures, different people and events are needed to carry out the 2 most popular procedures: stress echocardiograms, and non‐MRI radiology procedures. Hospital leadership at the highest levels will be required to improve efficiency based on local usage patterns, and to increase coordination among the multiple interdepartmental processes that make up the more general categories of procedure and consult.

A 52‐year‐old woman presented with a 3‐month history of progressive bilateral leg edema and dyspnea while climbing a flight of stairs or while walking up a steep slope. She also complained of a tingling sensation in both hands and fingers, which started about 2 months prior to the onset of edema. She did not describe sensory problems in the lower extremities and did not have any other neurological complaints. She denied fever, cough, chest pain, palpitations, orthopnea, paroxysmal nocturnal dyspnea, and dark stools. She had no history of hypertension, diabetes, dyslipidemia, or asthma and had never been hospitalized. She did not smoke or consume alcohol and used no medications, including over‐the‐counter drugs or dietary supplements. The patient was born in Japan and had not traveled outside the country since her birth. She was a homemaker and had worked occasionally as a manual laborer in sugar cane agriculture. A review of systems revealed no history of polydipsia, polyuria, or cold or heat intolerance but did identify new hair growth, especially on the extremities.

This middle‐aged woman shows progressive changes in her general health status that are characterized by edema and dyspnea on effort. The differential diagnosis of edema includes a broad spectrum of illnesses, such as cardiac, lung, renal, endocrine, and hepatic diseases. Because of the life‐threatening potential, my first concern is cardiac disease, although the patient is not experiencing typical symptoms of ischemic heart disease or congestive failure. Bilateral and distal distribution of neuropathic symptoms is likely due to diseases of peripheral nerves rather than those of the central nervous system. Her complaint of a bilateral tingling sensation in the hands may suggest carpal tunnel syndrome as a result of her long‐term agricultural work. Other possible causes include radiculopathy of the cervical spine or polyneuropathy. Clues in the physical examination may help narrow the differential diagnosis to a cardiac, hepatic, or endocrine disorder.

The patient appeared ill. Her weight had increased from 48 to 61 kg since she was last weighed 6 months previously. Her blood pressure was 140/78 mm Hg, her heart rate was 72 beats/minute with a regular rhythm, her respiratory rate was 18/minute, and her temperature was 37.5C. The jugular venous pressure was elevated at 10 cm above the sternal angle. A grade III/VI systolic ejection murmur was evident at the second interspace along the left sternal border. The second heart sound was fixed and split. There were decreased breath sounds and complete dullness to percussion over both lower lung fields. Shifting dullness was noted on abdominal examination. There was pitting edema from the feet to the thighs, with slow pit‐recovery time in both legs, and she exhibited generalized hirsutism on the face, body, and extremities. There was no lymphadenopathy. On neurological examination, her mental status was normal. The cranial nerves were normal, as was coordination. There was mild generalized distal‐dominant motor weakness with generalized hyporeflexia. Sensory testing demonstrated glove‐and‐stocking type loss of sensation to pinpricks as well as dysesthesia in all extremities. Phalen and Tinel tests were negative.

The elevated venous pressure and pitting edema with slow recovery suggest high venous pressure edema rather than hypoproteinemic edema. Complete bilateral dullness of the chest and shifting dullness of the abdomen indicate the presence of bilateral pleural effusion and ascites. Edema from high venous pressure is usually caused by right, left, or biventricular cardiac failure. A fixed splitting of the second heart sound suggests an atrial septal defect, which is a rare cause of progressive right heart failure in adults. I recommend checking the patient's thyroid function to investigate the possibility of hypothyroidism, which is a common illness among middle‐aged women and could contribute to her edema as well as hirsutism. The neurological findings suggest a generalized polyneuropathy. The unusual combination of high venous pressure edema and polyneuropathy may indicate a rare multisystem disorder such as amyloidosis. Alternatively, the patient might have developed multiple diseases during the same time period. For instance, diabetic polyneuropathy is the most common cause of polyneuropathy among the middle‐aged. Finally, the differential diagnosis of hirsutism includes ovarian, adrenal, or pituitary sources of hyperandrogenism in addition to hypothyroidism. I would first evaluate for diabetes, thyroid disease, and cardiac disease and would like to see the results of laboratory tests for thyrotropin and plasma glucose as well as chest radiography and electrocardiography.

The white‐cell count was 5400/mm³ with a normal differential. Hemoglobin was 10.7 g/dL with normal red‐cell indices, and the platelet count was 276,000/mm³. The erythrocyte sedimentation rate was 29 mm/hour. Other laboratory tests revealed the following values: total protein, 6.2 g/dL; albumin, 3.3 g/dL; blood urea nitrogen, 12 mg/dL; creatinine, 0.7 mg/dL; aspartate aminotransferase, 6 U/L; alanine aminotransferase, 2 U/L; lactate dehydrogenase, 96 U/L; alkaline phosphatase, 115 U/L; creatine phosphokinase, 60 U/L; total bilirubin, 0.9 mg/dL; glucose, 96 mg/dL; hemoglobin A1c, 4.6%; total cholesterol, 111 mg/dL; and thyrotropin, 6.32 mIU/mL (normal range, 0.50‐5.00 mIU/mL). Serum free thyroxine, triiodothyronine, and urine testosterone were normal. Serum dehydroepiandrosterone sulfate was mildly elevated for her age (864 ng/mL: normal range, 180‐750 ng/mL). Serological studies for human immunodeficiency virus, human T‐lymphotrophic virus type 1, and syphilis were negative. Urinalysis was weakly positive for protein but negative for casts and occult blood. The stool was negative for occult blood.

A chest radiograph showed bilateral pleural effusions. Computed tomography demonstrated bilateral pleural effusions, ascites, mild hepatomegaly, and small, multiple, mediastinal lymph nodes. Her electrocardiogram was normal. A transesophageal echocardiogram with agitated saline contrast demonstrated normal ventricular systolic and diastolic function and no atrial septal defect. The inferior vena cava did not collapse with inspiration, and there was no evidence of infiltrative cardiomyopathy.

These laboratory results rule out diabetes as the cause of the polyneuropathy. The subclinical hypothyroidism would not explain profound edema and hirsutism. A serum albumin level of 3.3 g/dL confirms high venous pressure edema rather than hypoproteinemic edema. Normochromic, normocytic anemia and a mildly elevated sedimentation rate point to a chronic illness or inflammatory state. The mediastinal lymphadenopathy may reflect congestion as a result of the high venous pressure or reflect a systemic disease involving lymph nodes. Normal ventricular function with high venous pressure is suggestive of heart failure from diastolic dysfunction, although the patient does not have risk factors for diastolic dysfunction, such as hypertension, and has no other echocardiographic features of diastolic impairment. The combination of hyperandrogenism and neuropathy points to a systemic process, such as a paraneoplastic syndrome. I would next investigate the source of the excess androgens.

Because serum dehydroepiandrosterone sulfate was mildly elevated, I‐131 aldosterol scintigraphy was performed, and it was negative. Electromyography showed a pattern of generalized sensorimotor polyneuropathy.

At this point, it appears that cardiac, endocrine, hepatic, and renal diseases have been largely ruled out as a cause of her symptoms. Reframing and unifying the important clinical problems for this patient may be useful in resolving this diagnostic puzzle. They include (1) systemic high venous pressure edema; (2) generalized sensorimotor polyneuropathy; (3) hirsutism; (4) normocytic, normochromic anemia; (5) an elevated erythrocyte sedimentation rate; (6) mediastinal lymphadenopathy; and (7) subclinical hypothyroidism. At this point, I cannot unify these pieces of information into a single diagnosis. I would search the medical literature, focusing on these terms.

A general internist consultant performed MEDLINE and Google Scholar searches using the key words edema, polyneuropathy, and hirsutism. This search suggested the diagnosis of Crow‐Fukase syndrome, also known as POEMS (polyneuropathy, organomegaly, endocrinopathy, M protein, and skin changes) syndrome. Subsequent evaluations were performed. First, serum protein electrophoresis revealed the presence of monoclonal proteins, although hypergammaglobulinemia was not present. Second, a bone marrow examination demonstrated increased abnormal plasma cell proliferation (7%), although a radiographic skeletal survey found no lesions suggestive of plasmacytoma. Third, cerebrospinal fluid analysis showed normal cell counts but increased protein concentration (202 mg/dL). Fourth, a blood sample referred to an outside laboratory demonstrated elevated levels of vascular endothelial growth factor (3902 pg/mL: normal range, 150‐500 pg/mL). On the basis of these findings, the diagnosis of POEMS syndrome was made. After oral prednisolone (40 mg/day) was initiated, the systemic edema improved gradually, and she did well during the 2‐year follow‐up period.

Commentary

POEMS syndrome, also known as Crow‐Fukase syndrome, is a rare multisystem disorder first described by Crow in 1956.1, 2 It is characterized by polyneuropathy, organomegaly, endocrinopathy, monoclonal gammopathy, and skin changes, as indicated by the acronym. The diagnosis of POEMS syndrome is difficult as this syndrome is rare and requires high clinical suspicion. According to a nationwide cross‐sectional survey in Japan, the prevalence of POEMS syndrome is very low (about 3 patients per 1,000,000 persons),3 and its prevalence in Western countries is considered even lower than that in Japan. The average age at onset is around 45 to 50 years old, and men are twice as likely to have this syndrome as women.46 Table 1 shows the diagnostic criteria of POEMS syndrome, based on research by Dispenzieri and others at the Mayo Clinic, and Table 2 presents the relative frequency of these clinical features.6, 7 The initial symptomatology generally includes polyneuropathy, skin changes, and generalized edema, which are nonspecific symptoms, as are other well‐recognized associated conditions such as clubbing, weight loss, thrombocytosis, polycythemia, and hyperhidrosis. Thus, it is important to consider this syndrome when one is facing an undiagnosed illness involving multiple organ systems and to distinguish it from other conditions such as multiple myeloma, amyloidosis, and monoclonal gammopathy of undetermined significance. Vascular endothelial growth factor is thought to be involved in the edema of POEMS syndrome, as massive release from aggregated platelets increases vascular permeability and venous pressure.710

Data regarding treatment and survival are largely observational. Overall mean survival from diagnosis in the 2003 Dispenzieri cohort was 13.7 years, with death often due to infection or cardiorespiratory failure.6 When a solitary plasmacytoma or osteosclerotic myeloma is present, radiation to the lesion can often lead to clinical remission. Other treatment options include alkylating agents and/or high‐dose chemotherapy with peripheral stem‐cell transplantation, corticosteroids, and supportive care.7

Clinicians frequently use the internet to aid in the clinical decision process. In a survey of the Royal New Zealand College of General Practitioners,11 half reported that they used the Internet to search for clinical information. Two well‐known resources are MEDLINE, which contains over 11 million references dating back to the 1960s, and internet search engines such as Google (and a more recent product, Google Scholar, which attempts to sort search results by including factors such as the author, the publication in which the article appears, and how often the article has been cited).

MEDLINE searches a well‐defined set of journals and uses the Medical Subject Headings (MeSH) vocabulary, which consists of sets of descriptive terms organized in a hierarchical structure to allow searching with various levels of specificity. For instance, entering the term heart attack will map to the MeSH term myocardial infarction and will also include more specific terms such as myocardial stunning and cardiogenic shock.

Google, in comparison, explores resources beyond journals without any clear boundary to its scope, and its advanced search functions can be occasionally unreliable. For instance, search results are occasionally marred by outdated citation information and may include materials that are not truly scholarly. However, search engines can search through the actual text of manuscripts and access the gray literature, which includes open‐source material that is usually original but not widely distributed or often easily available, such as technical reports and dissertations. A direct study comparing the results of searches in PubMed (one of the MEDLINE search engines) and Google Scholar is difficult, but the critical characteristics of each can be compared and contrasted (Table 3).

Internet searches may also suggest diagnoses from a compilation of clinical features, such as in this case. To be successful, such a search must complement the cognitive process; a search engine cannot completely replace clinical judgment. Clinicians must be able to identify salient clinical features and generate high‐yield search terms and then exercise skill in sifting through the citations to arrive at the appropriate diagnosis. A recent study found that Google searches revealed the correct diagnosis in 58% of the case records of the New England Journal of Medicine,12 although each search query resulted in many results, which then had to be manually reviewed for appropriateness within the case's context.

Like a traditional diagnostic test, a search can be described by sensitivity, specificity, and the number of articles needed to read.13 For example, in a study comparing the performance of search strategies to identify clinical practice guidelines in Google Scholar and SUMSearch (another freely accessible search engine), using the term guideline yielded the highest sensitivity, and using the term practice guideline generated the highest specificity and the lowest number of articles needed to read (Table 4).14

Although there are several other popular hosts of web‐based search engines, a more robust decision‐support program may help physicians more efficiently consider relevant diagnoses. One program, named Isabel, has been developed through the indexing of a database of more than 11,000 diseases according to word patterns in journal articles associated with each disease, and it is updated as new and relevant articles emerge. One recent study demonstrated that the correct diagnosis was made in 48 of 50 cases (96%) with specific, key findings as search terms but in only 37 of the same 50 cases (74%) if the entire case history was simply pasted in, again emphasizing the importance of specific search terms.15

POEMS syndrome is a rare entity occasionally seen in middle‐aged individuals and marked by a multitude of nonspecific findings, particularly polyneuropathy and plasma cell dyscrasia. In this case, the diagnostic test was an internet search based on the most prominent clinical symptoms. Such a strategy can provide a powerful addition to traditional literature and MEDLINE resources. However, the efficiency of this process is heavily dependent on the quality of the search strategy and, therefore, the cognitive faculties of the treating physician to avoid the predictable shortcoming of low specificity. Garbage in, garbage out still applies whether the computer in question is the human mind or the desktop PC.

A 52‐year‐old woman presented with a 3‐month history of progressive bilateral leg edema and dyspnea while climbing a flight of stairs or while walking up a steep slope. She also complained of a tingling sensation in both hands and fingers, which started about 2 months prior to the onset of edema. She did not describe sensory problems in the lower extremities and did not have any other neurological complaints. She denied fever, cough, chest pain, palpitations, orthopnea, paroxysmal nocturnal dyspnea, and dark stools. She had no history of hypertension, diabetes, dyslipidemia, or asthma and had never been hospitalized. She did not smoke or consume alcohol and used no medications, including over‐the‐counter drugs or dietary supplements. The patient was born in Japan and had not traveled outside the country since her birth. She was a homemaker and had worked occasionally as a manual laborer in sugar cane agriculture. A review of systems revealed no history of polydipsia, polyuria, or cold or heat intolerance but did identify new hair growth, especially on the extremities.

This middle‐aged woman shows progressive changes in her general health status that are characterized by edema and dyspnea on effort. The differential diagnosis of edema includes a broad spectrum of illnesses, such as cardiac, lung, renal, endocrine, and hepatic diseases. Because of the life‐threatening potential, my first concern is cardiac disease, although the patient is not experiencing typical symptoms of ischemic heart disease or congestive failure. Bilateral and distal distribution of neuropathic symptoms is likely due to diseases of peripheral nerves rather than those of the central nervous system. Her complaint of a bilateral tingling sensation in the hands may suggest carpal tunnel syndrome as a result of her long‐term agricultural work. Other possible causes include radiculopathy of the cervical spine or polyneuropathy. Clues in the physical examination may help narrow the differential diagnosis to a cardiac, hepatic, or endocrine disorder.

The patient appeared ill. Her weight had increased from 48 to 61 kg since she was last weighed 6 months previously. Her blood pressure was 140/78 mm Hg, her heart rate was 72 beats/minute with a regular rhythm, her respiratory rate was 18/minute, and her temperature was 37.5C. The jugular venous pressure was elevated at 10 cm above the sternal angle. A grade III/VI systolic ejection murmur was evident at the second interspace along the left sternal border. The second heart sound was fixed and split. There were decreased breath sounds and complete dullness to percussion over both lower lung fields. Shifting dullness was noted on abdominal examination. There was pitting edema from the feet to the thighs, with slow pit‐recovery time in both legs, and she exhibited generalized hirsutism on the face, body, and extremities. There was no lymphadenopathy. On neurological examination, her mental status was normal. The cranial nerves were normal, as was coordination. There was mild generalized distal‐dominant motor weakness with generalized hyporeflexia. Sensory testing demonstrated glove‐and‐stocking type loss of sensation to pinpricks as well as dysesthesia in all extremities. Phalen and Tinel tests were negative.

The elevated venous pressure and pitting edema with slow recovery suggest high venous pressure edema rather than hypoproteinemic edema. Complete bilateral dullness of the chest and shifting dullness of the abdomen indicate the presence of bilateral pleural effusion and ascites. Edema from high venous pressure is usually caused by right, left, or biventricular cardiac failure. A fixed splitting of the second heart sound suggests an atrial septal defect, which is a rare cause of progressive right heart failure in adults. I recommend checking the patient's thyroid function to investigate the possibility of hypothyroidism, which is a common illness among middle‐aged women and could contribute to her edema as well as hirsutism. The neurological findings suggest a generalized polyneuropathy. The unusual combination of high venous pressure edema and polyneuropathy may indicate a rare multisystem disorder such as amyloidosis. Alternatively, the patient might have developed multiple diseases during the same time period. For instance, diabetic polyneuropathy is the most common cause of polyneuropathy among the middle‐aged. Finally, the differential diagnosis of hirsutism includes ovarian, adrenal, or pituitary sources of hyperandrogenism in addition to hypothyroidism. I would first evaluate for diabetes, thyroid disease, and cardiac disease and would like to see the results of laboratory tests for thyrotropin and plasma glucose as well as chest radiography and electrocardiography.

The white‐cell count was 5400/mm³ with a normal differential. Hemoglobin was 10.7 g/dL with normal red‐cell indices, and the platelet count was 276,000/mm³. The erythrocyte sedimentation rate was 29 mm/hour. Other laboratory tests revealed the following values: total protein, 6.2 g/dL; albumin, 3.3 g/dL; blood urea nitrogen, 12 mg/dL; creatinine, 0.7 mg/dL; aspartate aminotransferase, 6 U/L; alanine aminotransferase, 2 U/L; lactate dehydrogenase, 96 U/L; alkaline phosphatase, 115 U/L; creatine phosphokinase, 60 U/L; total bilirubin, 0.9 mg/dL; glucose, 96 mg/dL; hemoglobin A1c, 4.6%; total cholesterol, 111 mg/dL; and thyrotropin, 6.32 mIU/mL (normal range, 0.50‐5.00 mIU/mL). Serum free thyroxine, triiodothyronine, and urine testosterone were normal. Serum dehydroepiandrosterone sulfate was mildly elevated for her age (864 ng/mL: normal range, 180‐750 ng/mL). Serological studies for human immunodeficiency virus, human T‐lymphotrophic virus type 1, and syphilis were negative. Urinalysis was weakly positive for protein but negative for casts and occult blood. The stool was negative for occult blood.

A chest radiograph showed bilateral pleural effusions. Computed tomography demonstrated bilateral pleural effusions, ascites, mild hepatomegaly, and small, multiple, mediastinal lymph nodes. Her electrocardiogram was normal. A transesophageal echocardiogram with agitated saline contrast demonstrated normal ventricular systolic and diastolic function and no atrial septal defect. The inferior vena cava did not collapse with inspiration, and there was no evidence of infiltrative cardiomyopathy.

These laboratory results rule out diabetes as the cause of the polyneuropathy. The subclinical hypothyroidism would not explain profound edema and hirsutism. A serum albumin level of 3.3 g/dL confirms high venous pressure edema rather than hypoproteinemic edema. Normochromic, normocytic anemia and a mildly elevated sedimentation rate point to a chronic illness or inflammatory state. The mediastinal lymphadenopathy may reflect congestion as a result of the high venous pressure or reflect a systemic disease involving lymph nodes. Normal ventricular function with high venous pressure is suggestive of heart failure from diastolic dysfunction, although the patient does not have risk factors for diastolic dysfunction, such as hypertension, and has no other echocardiographic features of diastolic impairment. The combination of hyperandrogenism and neuropathy points to a systemic process, such as a paraneoplastic syndrome. I would next investigate the source of the excess androgens.

Because serum dehydroepiandrosterone sulfate was mildly elevated, I‐131 aldosterol scintigraphy was performed, and it was negative. Electromyography showed a pattern of generalized sensorimotor polyneuropathy.

At this point, it appears that cardiac, endocrine, hepatic, and renal diseases have been largely ruled out as a cause of her symptoms. Reframing and unifying the important clinical problems for this patient may be useful in resolving this diagnostic puzzle. They include (1) systemic high venous pressure edema; (2) generalized sensorimotor polyneuropathy; (3) hirsutism; (4) normocytic, normochromic anemia; (5) an elevated erythrocyte sedimentation rate; (6) mediastinal lymphadenopathy; and (7) subclinical hypothyroidism. At this point, I cannot unify these pieces of information into a single diagnosis. I would search the medical literature, focusing on these terms.

A general internist consultant performed MEDLINE and Google Scholar searches using the key words edema, polyneuropathy, and hirsutism. This search suggested the diagnosis of Crow‐Fukase syndrome, also known as POEMS (polyneuropathy, organomegaly, endocrinopathy, M protein, and skin changes) syndrome. Subsequent evaluations were performed. First, serum protein electrophoresis revealed the presence of monoclonal proteins, although hypergammaglobulinemia was not present. Second, a bone marrow examination demonstrated increased abnormal plasma cell proliferation (7%), although a radiographic skeletal survey found no lesions suggestive of plasmacytoma. Third, cerebrospinal fluid analysis showed normal cell counts but increased protein concentration (202 mg/dL). Fourth, a blood sample referred to an outside laboratory demonstrated elevated levels of vascular endothelial growth factor (3902 pg/mL: normal range, 150‐500 pg/mL). On the basis of these findings, the diagnosis of POEMS syndrome was made. After oral prednisolone (40 mg/day) was initiated, the systemic edema improved gradually, and she did well during the 2‐year follow‐up period.

Commentary

POEMS syndrome, also known as Crow‐Fukase syndrome, is a rare multisystem disorder first described by Crow in 1956.1, 2 It is characterized by polyneuropathy, organomegaly, endocrinopathy, monoclonal gammopathy, and skin changes, as indicated by the acronym. The diagnosis of POEMS syndrome is difficult as this syndrome is rare and requires high clinical suspicion. According to a nationwide cross‐sectional survey in Japan, the prevalence of POEMS syndrome is very low (about 3 patients per 1,000,000 persons),3 and its prevalence in Western countries is considered even lower than that in Japan. The average age at onset is around 45 to 50 years old, and men are twice as likely to have this syndrome as women.46 Table 1 shows the diagnostic criteria of POEMS syndrome, based on research by Dispenzieri and others at the Mayo Clinic, and Table 2 presents the relative frequency of these clinical features.6, 7 The initial symptomatology generally includes polyneuropathy, skin changes, and generalized edema, which are nonspecific symptoms, as are other well‐recognized associated conditions such as clubbing, weight loss, thrombocytosis, polycythemia, and hyperhidrosis. Thus, it is important to consider this syndrome when one is facing an undiagnosed illness involving multiple organ systems and to distinguish it from other conditions such as multiple myeloma, amyloidosis, and monoclonal gammopathy of undetermined significance. Vascular endothelial growth factor is thought to be involved in the edema of POEMS syndrome, as massive release from aggregated platelets increases vascular permeability and venous pressure.710

Data regarding treatment and survival are largely observational. Overall mean survival from diagnosis in the 2003 Dispenzieri cohort was 13.7 years, with death often due to infection or cardiorespiratory failure.6 When a solitary plasmacytoma or osteosclerotic myeloma is present, radiation to the lesion can often lead to clinical remission. Other treatment options include alkylating agents and/or high‐dose chemotherapy with peripheral stem‐cell transplantation, corticosteroids, and supportive care.7

Clinicians frequently use the internet to aid in the clinical decision process. In a survey of the Royal New Zealand College of General Practitioners,11 half reported that they used the Internet to search for clinical information. Two well‐known resources are MEDLINE, which contains over 11 million references dating back to the 1960s, and internet search engines such as Google (and a more recent product, Google Scholar, which attempts to sort search results by including factors such as the author, the publication in which the article appears, and how often the article has been cited).

MEDLINE searches a well‐defined set of journals and uses the Medical Subject Headings (MeSH) vocabulary, which consists of sets of descriptive terms organized in a hierarchical structure to allow searching with various levels of specificity. For instance, entering the term heart attack will map to the MeSH term myocardial infarction and will also include more specific terms such as myocardial stunning and cardiogenic shock.

Google, in comparison, explores resources beyond journals without any clear boundary to its scope, and its advanced search functions can be occasionally unreliable. For instance, search results are occasionally marred by outdated citation information and may include materials that are not truly scholarly. However, search engines can search through the actual text of manuscripts and access the gray literature, which includes open‐source material that is usually original but not widely distributed or often easily available, such as technical reports and dissertations. A direct study comparing the results of searches in PubMed (one of the MEDLINE search engines) and Google Scholar is difficult, but the critical characteristics of each can be compared and contrasted (Table 3).

Internet searches may also suggest diagnoses from a compilation of clinical features, such as in this case. To be successful, such a search must complement the cognitive process; a search engine cannot completely replace clinical judgment. Clinicians must be able to identify salient clinical features and generate high‐yield search terms and then exercise skill in sifting through the citations to arrive at the appropriate diagnosis. A recent study found that Google searches revealed the correct diagnosis in 58% of the case records of the New England Journal of Medicine,12 although each search query resulted in many results, which then had to be manually reviewed for appropriateness within the case's context.

Like a traditional diagnostic test, a search can be described by sensitivity, specificity, and the number of articles needed to read.13 For example, in a study comparing the performance of search strategies to identify clinical practice guidelines in Google Scholar and SUMSearch (another freely accessible search engine), using the term guideline yielded the highest sensitivity, and using the term practice guideline generated the highest specificity and the lowest number of articles needed to read (Table 4).14

Although there are several other popular hosts of web‐based search engines, a more robust decision‐support program may help physicians more efficiently consider relevant diagnoses. One program, named Isabel, has been developed through the indexing of a database of more than 11,000 diseases according to word patterns in journal articles associated with each disease, and it is updated as new and relevant articles emerge. One recent study demonstrated that the correct diagnosis was made in 48 of 50 cases (96%) with specific, key findings as search terms but in only 37 of the same 50 cases (74%) if the entire case history was simply pasted in, again emphasizing the importance of specific search terms.15

POEMS syndrome is a rare entity occasionally seen in middle‐aged individuals and marked by a multitude of nonspecific findings, particularly polyneuropathy and plasma cell dyscrasia. In this case, the diagnostic test was an internet search based on the most prominent clinical symptoms. Such a strategy can provide a powerful addition to traditional literature and MEDLINE resources. However, the efficiency of this process is heavily dependent on the quality of the search strategy and, therefore, the cognitive faculties of the treating physician to avoid the predictable shortcoming of low specificity. Garbage in, garbage out still applies whether the computer in question is the human mind or the desktop PC.

A 52‐year‐old woman presented with a 3‐month history of progressive bilateral leg edema and dyspnea while climbing a flight of stairs or while walking up a steep slope. She also complained of a tingling sensation in both hands and fingers, which started about 2 months prior to the onset of edema. She did not describe sensory problems in the lower extremities and did not have any other neurological complaints. She denied fever, cough, chest pain, palpitations, orthopnea, paroxysmal nocturnal dyspnea, and dark stools. She had no history of hypertension, diabetes, dyslipidemia, or asthma and had never been hospitalized. She did not smoke or consume alcohol and used no medications, including over‐the‐counter drugs or dietary supplements. The patient was born in Japan and had not traveled outside the country since her birth. She was a homemaker and had worked occasionally as a manual laborer in sugar cane agriculture. A review of systems revealed no history of polydipsia, polyuria, or cold or heat intolerance but did identify new hair growth, especially on the extremities.

This middle‐aged woman shows progressive changes in her general health status that are characterized by edema and dyspnea on effort. The differential diagnosis of edema includes a broad spectrum of illnesses, such as cardiac, lung, renal, endocrine, and hepatic diseases. Because of the life‐threatening potential, my first concern is cardiac disease, although the patient is not experiencing typical symptoms of ischemic heart disease or congestive failure. Bilateral and distal distribution of neuropathic symptoms is likely due to diseases of peripheral nerves rather than those of the central nervous system. Her complaint of a bilateral tingling sensation in the hands may suggest carpal tunnel syndrome as a result of her long‐term agricultural work. Other possible causes include radiculopathy of the cervical spine or polyneuropathy. Clues in the physical examination may help narrow the differential diagnosis to a cardiac, hepatic, or endocrine disorder.

The patient appeared ill. Her weight had increased from 48 to 61 kg since she was last weighed 6 months previously. Her blood pressure was 140/78 mm Hg, her heart rate was 72 beats/minute with a regular rhythm, her respiratory rate was 18/minute, and her temperature was 37.5C. The jugular venous pressure was elevated at 10 cm above the sternal angle. A grade III/VI systolic ejection murmur was evident at the second interspace along the left sternal border. The second heart sound was fixed and split. There were decreased breath sounds and complete dullness to percussion over both lower lung fields. Shifting dullness was noted on abdominal examination. There was pitting edema from the feet to the thighs, with slow pit‐recovery time in both legs, and she exhibited generalized hirsutism on the face, body, and extremities. There was no lymphadenopathy. On neurological examination, her mental status was normal. The cranial nerves were normal, as was coordination. There was mild generalized distal‐dominant motor weakness with generalized hyporeflexia. Sensory testing demonstrated glove‐and‐stocking type loss of sensation to pinpricks as well as dysesthesia in all extremities. Phalen and Tinel tests were negative.

The elevated venous pressure and pitting edema with slow recovery suggest high venous pressure edema rather than hypoproteinemic edema. Complete bilateral dullness of the chest and shifting dullness of the abdomen indicate the presence of bilateral pleural effusion and ascites. Edema from high venous pressure is usually caused by right, left, or biventricular cardiac failure. A fixed splitting of the second heart sound suggests an atrial septal defect, which is a rare cause of progressive right heart failure in adults. I recommend checking the patient's thyroid function to investigate the possibility of hypothyroidism, which is a common illness among middle‐aged women and could contribute to her edema as well as hirsutism. The neurological findings suggest a generalized polyneuropathy. The unusual combination of high venous pressure edema and polyneuropathy may indicate a rare multisystem disorder such as amyloidosis. Alternatively, the patient might have developed multiple diseases during the same time period. For instance, diabetic polyneuropathy is the most common cause of polyneuropathy among the middle‐aged. Finally, the differential diagnosis of hirsutism includes ovarian, adrenal, or pituitary sources of hyperandrogenism in addition to hypothyroidism. I would first evaluate for diabetes, thyroid disease, and cardiac disease and would like to see the results of laboratory tests for thyrotropin and plasma glucose as well as chest radiography and electrocardiography.

The white‐cell count was 5400/mm³ with a normal differential. Hemoglobin was 10.7 g/dL with normal red‐cell indices, and the platelet count was 276,000/mm³. The erythrocyte sedimentation rate was 29 mm/hour. Other laboratory tests revealed the following values: total protein, 6.2 g/dL; albumin, 3.3 g/dL; blood urea nitrogen, 12 mg/dL; creatinine, 0.7 mg/dL; aspartate aminotransferase, 6 U/L; alanine aminotransferase, 2 U/L; lactate dehydrogenase, 96 U/L; alkaline phosphatase, 115 U/L; creatine phosphokinase, 60 U/L; total bilirubin, 0.9 mg/dL; glucose, 96 mg/dL; hemoglobin A1c, 4.6%; total cholesterol, 111 mg/dL; and thyrotropin, 6.32 mIU/mL (normal range, 0.50‐5.00 mIU/mL). Serum free thyroxine, triiodothyronine, and urine testosterone were normal. Serum dehydroepiandrosterone sulfate was mildly elevated for her age (864 ng/mL: normal range, 180‐750 ng/mL). Serological studies for human immunodeficiency virus, human T‐lymphotrophic virus type 1, and syphilis were negative. Urinalysis was weakly positive for protein but negative for casts and occult blood. The stool was negative for occult blood.

A chest radiograph showed bilateral pleural effusions. Computed tomography demonstrated bilateral pleural effusions, ascites, mild hepatomegaly, and small, multiple, mediastinal lymph nodes. Her electrocardiogram was normal. A transesophageal echocardiogram with agitated saline contrast demonstrated normal ventricular systolic and diastolic function and no atrial septal defect. The inferior vena cava did not collapse with inspiration, and there was no evidence of infiltrative cardiomyopathy.

These laboratory results rule out diabetes as the cause of the polyneuropathy. The subclinical hypothyroidism would not explain profound edema and hirsutism. A serum albumin level of 3.3 g/dL confirms high venous pressure edema rather than hypoproteinemic edema. Normochromic, normocytic anemia and a mildly elevated sedimentation rate point to a chronic illness or inflammatory state. The mediastinal lymphadenopathy may reflect congestion as a result of the high venous pressure or reflect a systemic disease involving lymph nodes. Normal ventricular function with high venous pressure is suggestive of heart failure from diastolic dysfunction, although the patient does not have risk factors for diastolic dysfunction, such as hypertension, and has no other echocardiographic features of diastolic impairment. The combination of hyperandrogenism and neuropathy points to a systemic process, such as a paraneoplastic syndrome. I would next investigate the source of the excess androgens.

Because serum dehydroepiandrosterone sulfate was mildly elevated, I‐131 aldosterol scintigraphy was performed, and it was negative. Electromyography showed a pattern of generalized sensorimotor polyneuropathy.

At this point, it appears that cardiac, endocrine, hepatic, and renal diseases have been largely ruled out as a cause of her symptoms. Reframing and unifying the important clinical problems for this patient may be useful in resolving this diagnostic puzzle. They include (1) systemic high venous pressure edema; (2) generalized sensorimotor polyneuropathy; (3) hirsutism; (4) normocytic, normochromic anemia; (5) an elevated erythrocyte sedimentation rate; (6) mediastinal lymphadenopathy; and (7) subclinical hypothyroidism. At this point, I cannot unify these pieces of information into a single diagnosis. I would search the medical literature, focusing on these terms.

A general internist consultant performed MEDLINE and Google Scholar searches using the key words edema, polyneuropathy, and hirsutism. This search suggested the diagnosis of Crow‐Fukase syndrome, also known as POEMS (polyneuropathy, organomegaly, endocrinopathy, M protein, and skin changes) syndrome. Subsequent evaluations were performed. First, serum protein electrophoresis revealed the presence of monoclonal proteins, although hypergammaglobulinemia was not present. Second, a bone marrow examination demonstrated increased abnormal plasma cell proliferation (7%), although a radiographic skeletal survey found no lesions suggestive of plasmacytoma. Third, cerebrospinal fluid analysis showed normal cell counts but increased protein concentration (202 mg/dL). Fourth, a blood sample referred to an outside laboratory demonstrated elevated levels of vascular endothelial growth factor (3902 pg/mL: normal range, 150‐500 pg/mL). On the basis of these findings, the diagnosis of POEMS syndrome was made. After oral prednisolone (40 mg/day) was initiated, the systemic edema improved gradually, and she did well during the 2‐year follow‐up period.

Commentary

POEMS syndrome, also known as Crow‐Fukase syndrome, is a rare multisystem disorder first described by Crow in 1956.1, 2 It is characterized by polyneuropathy, organomegaly, endocrinopathy, monoclonal gammopathy, and skin changes, as indicated by the acronym. The diagnosis of POEMS syndrome is difficult as this syndrome is rare and requires high clinical suspicion. According to a nationwide cross‐sectional survey in Japan, the prevalence of POEMS syndrome is very low (about 3 patients per 1,000,000 persons),3 and its prevalence in Western countries is considered even lower than that in Japan. The average age at onset is around 45 to 50 years old, and men are twice as likely to have this syndrome as women.46 Table 1 shows the diagnostic criteria of POEMS syndrome, based on research by Dispenzieri and others at the Mayo Clinic, and Table 2 presents the relative frequency of these clinical features.6, 7 The initial symptomatology generally includes polyneuropathy, skin changes, and generalized edema, which are nonspecific symptoms, as are other well‐recognized associated conditions such as clubbing, weight loss, thrombocytosis, polycythemia, and hyperhidrosis. Thus, it is important to consider this syndrome when one is facing an undiagnosed illness involving multiple organ systems and to distinguish it from other conditions such as multiple myeloma, amyloidosis, and monoclonal gammopathy of undetermined significance. Vascular endothelial growth factor is thought to be involved in the edema of POEMS syndrome, as massive release from aggregated platelets increases vascular permeability and venous pressure.710

Data regarding treatment and survival are largely observational. Overall mean survival from diagnosis in the 2003 Dispenzieri cohort was 13.7 years, with death often due to infection or cardiorespiratory failure.6 When a solitary plasmacytoma or osteosclerotic myeloma is present, radiation to the lesion can often lead to clinical remission. Other treatment options include alkylating agents and/or high‐dose chemotherapy with peripheral stem‐cell transplantation, corticosteroids, and supportive care.7

Clinicians frequently use the internet to aid in the clinical decision process. In a survey of the Royal New Zealand College of General Practitioners,11 half reported that they used the Internet to search for clinical information. Two well‐known resources are MEDLINE, which contains over 11 million references dating back to the 1960s, and internet search engines such as Google (and a more recent product, Google Scholar, which attempts to sort search results by including factors such as the author, the publication in which the article appears, and how often the article has been cited).

MEDLINE searches a well‐defined set of journals and uses the Medical Subject Headings (MeSH) vocabulary, which consists of sets of descriptive terms organized in a hierarchical structure to allow searching with various levels of specificity. For instance, entering the term heart attack will map to the MeSH term myocardial infarction and will also include more specific terms such as myocardial stunning and cardiogenic shock.

Google, in comparison, explores resources beyond journals without any clear boundary to its scope, and its advanced search functions can be occasionally unreliable. For instance, search results are occasionally marred by outdated citation information and may include materials that are not truly scholarly. However, search engines can search through the actual text of manuscripts and access the gray literature, which includes open‐source material that is usually original but not widely distributed or often easily available, such as technical reports and dissertations. A direct study comparing the results of searches in PubMed (one of the MEDLINE search engines) and Google Scholar is difficult, but the critical characteristics of each can be compared and contrasted (Table 3).

Internet searches may also suggest diagnoses from a compilation of clinical features, such as in this case. To be successful, such a search must complement the cognitive process; a search engine cannot completely replace clinical judgment. Clinicians must be able to identify salient clinical features and generate high‐yield search terms and then exercise skill in sifting through the citations to arrive at the appropriate diagnosis. A recent study found that Google searches revealed the correct diagnosis in 58% of the case records of the New England Journal of Medicine,12 although each search query resulted in many results, which then had to be manually reviewed for appropriateness within the case's context.

Like a traditional diagnostic test, a search can be described by sensitivity, specificity, and the number of articles needed to read.13 For example, in a study comparing the performance of search strategies to identify clinical practice guidelines in Google Scholar and SUMSearch (another freely accessible search engine), using the term guideline yielded the highest sensitivity, and using the term practice guideline generated the highest specificity and the lowest number of articles needed to read (Table 4).14

Although there are several other popular hosts of web‐based search engines, a more robust decision‐support program may help physicians more efficiently consider relevant diagnoses. One program, named Isabel, has been developed through the indexing of a database of more than 11,000 diseases according to word patterns in journal articles associated with each disease, and it is updated as new and relevant articles emerge. One recent study demonstrated that the correct diagnosis was made in 48 of 50 cases (96%) with specific, key findings as search terms but in only 37 of the same 50 cases (74%) if the entire case history was simply pasted in, again emphasizing the importance of specific search terms.15

POEMS syndrome is a rare entity occasionally seen in middle‐aged individuals and marked by a multitude of nonspecific findings, particularly polyneuropathy and plasma cell dyscrasia. In this case, the diagnostic test was an internet search based on the most prominent clinical symptoms. Such a strategy can provide a powerful addition to traditional literature and MEDLINE resources. However, the efficiency of this process is heavily dependent on the quality of the search strategy and, therefore, the cognitive faculties of the treating physician to avoid the predictable shortcoming of low specificity. Garbage in, garbage out still applies whether the computer in question is the human mind or the desktop PC.

He looked upon the earth so filled with misery and pox

On Cro‐Magnon Neurosurgeons taking tumors out with rocks

With the blood banks run by leeches and their pterodactyl nursing

And observed This can't be healthcare these mere creatures are rehearsing

What shall we do when their lifespan will exceed eleven years?

When they no longer drink from toilet pits or make hearts from used pig ears?

There will need to be a better way to care for newer ills

A time when broadband wireless will be cheaper than their pills

He came up with a brilliant plan to revolutionize the health

To advance all medical outcomes and thereby spread the wealth

But for some strange combination of wisdom, luck, and quirk

He devised sufficient stakeholders to ensure this could not work

So a King might hire a knight to wipe out enemies with his lance

Then buy a plan to pay the cost of repairing his chain mail pants

Then along will come men with crosses of Blue who can manage that so much smarter

By inventing rules that convert poor fools from heroic docs to martyrs

He made tiny things that hide in meat and cause nasty cramps and rashes

That leave only the fittest alive to run in the royal 50 yard dashes

He made plants with spikes and purple leaves that can make one very sick

Then companies who turn green goop to gold that can flow thru a needle stick

He made medical schools to teach more tools, taking 10 years from students' lives

Then ruined careers with malpractice fears if they forget to wash their knives.

He made men whose pockets are filled with stuff from frivolous medical suits

When the experts forget the proper dosing of Peruvian medicinal fruits

He made routine birth a hazardous game between midwife, mom, and fetus

He made people who dress in masks and gloves to bravely retrieve and greet us

Then if anything goes wrong because one more time he throws snake eyes on the dice

He made lawyers to ensure that at least someone benefits while everyone else paid the price

Then along came the buildings with gadgets and learning, to find things we can't hope to fix

And those who get paid to know how NOT to pay the providers of care to the sick

He made organized giants that make tablets and gizmos from the minds of the cream of the crop

And made multiple races with all different faces whose subjective complaints will not stop

But alas came the gadgets, the photons and diodes, the software, the web and the data

Then the standards, the knowledge bases, multiuser interfaces, all in perpetual BETA

To automate the arcane, declare real what is feigned, and make INPUT like losing a toe

Then the last fatal strawhe made privacy laws to ensure they can't share what they know

Oh what have I done, this is really no fun, they now live to one hundred and thirty

But there's no more MDs and the few with degrees refuse to get their hands dirty

Next time when I try to take research to practice I'll start with a real I.O.M.

Evidence galore, so when we screw up once more I can put all the blame right on them

He looked upon the earth so filled with misery and pox

On Cro‐Magnon Neurosurgeons taking tumors out with rocks

With the blood banks run by leeches and their pterodactyl nursing

And observed This can't be healthcare these mere creatures are rehearsing

What shall we do when their lifespan will exceed eleven years?

When they no longer drink from toilet pits or make hearts from used pig ears?

There will need to be a better way to care for newer ills

A time when broadband wireless will be cheaper than their pills

He came up with a brilliant plan to revolutionize the health

To advance all medical outcomes and thereby spread the wealth

But for some strange combination of wisdom, luck, and quirk

He devised sufficient stakeholders to ensure this could not work

So a King might hire a knight to wipe out enemies with his lance

Then buy a plan to pay the cost of repairing his chain mail pants

Then along will come men with crosses of Blue who can manage that so much smarter

By inventing rules that convert poor fools from heroic docs to martyrs

He made tiny things that hide in meat and cause nasty cramps and rashes

That leave only the fittest alive to run in the royal 50 yard dashes

He made plants with spikes and purple leaves that can make one very sick

Then companies who turn green goop to gold that can flow thru a needle stick

He made medical schools to teach more tools, taking 10 years from students' lives

Then ruined careers with malpractice fears if they forget to wash their knives.

He made men whose pockets are filled with stuff from frivolous medical suits

When the experts forget the proper dosing of Peruvian medicinal fruits

He made routine birth a hazardous game between midwife, mom, and fetus

He made people who dress in masks and gloves to bravely retrieve and greet us

Then if anything goes wrong because one more time he throws snake eyes on the dice

He made lawyers to ensure that at least someone benefits while everyone else paid the price

Then along came the buildings with gadgets and learning, to find things we can't hope to fix

And those who get paid to know how NOT to pay the providers of care to the sick

He made organized giants that make tablets and gizmos from the minds of the cream of the crop

And made multiple races with all different faces whose subjective complaints will not stop

But alas came the gadgets, the photons and diodes, the software, the web and the data

Then the standards, the knowledge bases, multiuser interfaces, all in perpetual BETA

To automate the arcane, declare real what is feigned, and make INPUT like losing a toe

Then the last fatal strawhe made privacy laws to ensure they can't share what they know

Oh what have I done, this is really no fun, they now live to one hundred and thirty

But there's no more MDs and the few with degrees refuse to get their hands dirty

Next time when I try to take research to practice I'll start with a real I.O.M.

Evidence galore, so when we screw up once more I can put all the blame right on them

He looked upon the earth so filled with misery and pox

On Cro‐Magnon Neurosurgeons taking tumors out with rocks

With the blood banks run by leeches and their pterodactyl nursing

And observed This can't be healthcare these mere creatures are rehearsing

What shall we do when their lifespan will exceed eleven years?

When they no longer drink from toilet pits or make hearts from used pig ears?

There will need to be a better way to care for newer ills

A time when broadband wireless will be cheaper than their pills

He came up with a brilliant plan to revolutionize the health

To advance all medical outcomes and thereby spread the wealth

But for some strange combination of wisdom, luck, and quirk

He devised sufficient stakeholders to ensure this could not work

So a King might hire a knight to wipe out enemies with his lance

Then buy a plan to pay the cost of repairing his chain mail pants

Then along will come men with crosses of Blue who can manage that so much smarter

By inventing rules that convert poor fools from heroic docs to martyrs

He made tiny things that hide in meat and cause nasty cramps and rashes

That leave only the fittest alive to run in the royal 50 yard dashes

He made plants with spikes and purple leaves that can make one very sick

Then companies who turn green goop to gold that can flow thru a needle stick

He made medical schools to teach more tools, taking 10 years from students' lives

Then ruined careers with malpractice fears if they forget to wash their knives.

He made men whose pockets are filled with stuff from frivolous medical suits

When the experts forget the proper dosing of Peruvian medicinal fruits

He made routine birth a hazardous game between midwife, mom, and fetus

He made people who dress in masks and gloves to bravely retrieve and greet us

Then if anything goes wrong because one more time he throws snake eyes on the dice

He made lawyers to ensure that at least someone benefits while everyone else paid the price

Then along came the buildings with gadgets and learning, to find things we can't hope to fix

And those who get paid to know how NOT to pay the providers of care to the sick

He made organized giants that make tablets and gizmos from the minds of the cream of the crop

And made multiple races with all different faces whose subjective complaints will not stop

But alas came the gadgets, the photons and diodes, the software, the web and the data

Then the standards, the knowledge bases, multiuser interfaces, all in perpetual BETA

To automate the arcane, declare real what is feigned, and make INPUT like losing a toe

Then the last fatal strawhe made privacy laws to ensure they can't share what they know

Oh what have I done, this is really no fun, they now live to one hundred and thirty

But there's no more MDs and the few with degrees refuse to get their hands dirty

Next time when I try to take research to practice I'll start with a real I.O.M.

Evidence galore, so when we screw up once more I can put all the blame right on them

Many in‐hospital cardiac arrests and other adverse events are heralded by warning signs that are evident in the preceding 6 to 8 hours.1 By promptly intervening before further deterioration occurs, rapid response teams (RRTs) are designed to decrease unexpected intensive care unit (ICU) transfers, cardiac arrests, and inpatient mortality. While implementing RRTs is 1 of the 6 initiatives recommended by the Institute for Healthcare Improvement,2 data supporting their effectiveness is equivocal.3, 4

In October 2006, at Denver Health Medical Center, an academic, safety net hospital, we initiated a rapid response systemclinical triggers program (RRS‐CTP).5 In our RRS‐CTP, an abrupt change in patient status (Figure 1) triggers a mandatory call by the patient's nurse to the primary team, which is then required to perform an immediate bedside evaluation. By incorporating the primary team into the RRT‐CTP, we sought to preserve as much continuity of care as possible. Also, since the same house staff compose our cardiopulmonary arrest or cor team, and staff the ICUs and non‐ICU hospital wards, we did not feel that creating a separate RRT was an efficient use of resources. Our nurses have undergone extensive education about the necessity of a prompt bedside evaluation and have been instructed and empowered to escalate concerns to senior physicians if needed. We present a case that illustrates challenges to both implementing an RRS and measuring its potential benefits.

Figure 1

Case

A 59‐year‐old woman with a history of bipolar mood disorder was admitted for altered mental status. At presentation, she had signs of acute mania with normal vital signs. After initial laboratory workup, her altered mental status was felt to be multifactorial due to urinary tract infection, hypernatremia (attributed to lithium‐induced nephrogenic diabetes insipidus), and acute mania (attributed to medication discontinuation). Because she was slow to recover from the acute mania, her hospital stay was prolonged. From admission, the patient was treated with heparin 5000 units subcutaneously twice daily for venous thromboembolism prophylaxis.

On hospital day 7, at 21:32, the patient was noted to have asymptomatic tachycardia at 149 beats per minute and a new oxygen requirement of 3 L/minute. The cross‐cover team was called; however, although criteria were met, the RRS‐CTP was not activated and a bedside evaluation was not performed. A chest X‐ray was found to be normal and, with the exception of the oxygen requirement, her vital signs normalized by 23:45. No further diagnostic testing was performed at the time.

The next morning, at 11:58, the patient was found to have a blood pressure of 60/40 mmHg and heart rate of 42 beats per minute. The RRS‐CTP was activated. The primary team arrived at the bedside at 12:00 and found the patient to be alert, oriented, and without complaints. Her respiratory rate was 30/minute, and her oxygen saturation was 86% on 3 L/minute. An arterial blood gas analysis demonstrated acute respiratory alkalosis with hypoxemia and an electrocardiogram showed sinus tachycardia with a new S₁Q₃T₃ pattern. A computed tomography angiogram revealed a large, nearly occlusive pulmonary embolus (PE) filling an enlarged right pulmonary artery, as well as thrombus within the left main pulmonary artery. She was transferred to the medical ICU and alteplase was administered. The patient survived and was discharged in good clinical condition.

Discussion

Despite the strong theoretical benefit of the RRT concept, a recent review by Ranji et al.4 concluded that RRTs had not yet been shown to improve patient outcomes. In contrast to dedicated RRTs, this case illustrates a different type of RRS that was designed to address abrupt changes in patient status, while maintaining continuity of care and efficiently utilizing resources.

If one considers an RRS to have both afferent (criteria recognition) and efferent (RRT or primary team response) limbs, the afferent limb must be consistently activated in order to obtain the efferent limb's response.6 The greatest opportunities to improve RRSs are thought to lie in the afferent limb.3 Our RRS‐CTP was not triggered in 1 of 2 instances in which criteria for mandatory initiation of the system were met. This is consistent with the findings of the Medical Early Response Intervention and Therapy (MERIT) trial, in which RRTs were called in only 41% of the patients meeting criteria and subsequently having adverse events,7 and with the ongoing monitoring of the use of the system at our hospital. Had the cross‐covering team seen the patient at the bedside initially, the PE might have been diagnosed while the patient was hemodynamically stable, giving the patient nearly a 3‐fold lower relative mortality.8 When the RRS‐CTP was activated, a prompt bedside evaluation occurred, allowing for lytic therapy to be administered before cardiopulmonary arrest (attendant mortality of 90%).9

While rapid response criteria were originally based upon published sensitivity analyses, more recent studies suggest that these criteria lack diagnostic accuracy. As demonstrated by Cretikos et al,10 to reach a sensitivity of 70%, the corresponding specificity would be only 86%. Given that the prevalence of adverse events in the MERIT trial was only 0.6%, the resulting positive predictive value (PPV) of rapid response call criteria is 3%. Accordingly, 33 calls would be needed to prevent 1 unplanned ICU transfer, cardiac arrest, or death. Nurses' attempts to minimize false‐positive calls may help explain the low call rates for patients meeting RRT criteria. The 2 avenues to increase the PPV of criteria are:

• 1

  Increase the prevalence of disease in the population screened by risk factor stratification.

• 2

  Increase the specificity of the call criteria, which has been limited by the associated decrease in sensitivity.10

Regarding the efferent response limb of RRS, our case demonstrates that the primary team (rather than a separate group of caregivers), when alerted appropriately, can effectively respond to critical changes in patient status. Accordingly, our data show that since the inception of the program, cardiopulmonary arrests have decreased from a mean of 4.1 per month to 2.3 per month (P = 0.03).

Many clinical trials of RRTs would not capture the success demonstrated in this case. For example, due to the low prevalence of events, the MERIT trial used a composite endpoint that included unplanned ICU transfers, cardiac arrests, and mortality. Because our patient still required an unplanned ICU transfer after being evaluated by the responding team, she would have been counted as a system failure.

Conclusion

While local needs should inform the type of RRS implemented, this case illustrates one of the major obstacles ubiquitous to RRS implementation: failure of system activation. With appropriate activation, an RRS‐CTP can meet RRS goals while maintaining continuity of care and maximizing existing resources. This case also illustrates the difficulty of achieving a statistically relevant outcome, while demonstrating the potential benefits of evolving RRSs.

Many in‐hospital cardiac arrests and other adverse events are heralded by warning signs that are evident in the preceding 6 to 8 hours.1 By promptly intervening before further deterioration occurs, rapid response teams (RRTs) are designed to decrease unexpected intensive care unit (ICU) transfers, cardiac arrests, and inpatient mortality. While implementing RRTs is 1 of the 6 initiatives recommended by the Institute for Healthcare Improvement,2 data supporting their effectiveness is equivocal.3, 4

In October 2006, at Denver Health Medical Center, an academic, safety net hospital, we initiated a rapid response systemclinical triggers program (RRS‐CTP).5 In our RRS‐CTP, an abrupt change in patient status (Figure 1) triggers a mandatory call by the patient's nurse to the primary team, which is then required to perform an immediate bedside evaluation. By incorporating the primary team into the RRT‐CTP, we sought to preserve as much continuity of care as possible. Also, since the same house staff compose our cardiopulmonary arrest or cor team, and staff the ICUs and non‐ICU hospital wards, we did not feel that creating a separate RRT was an efficient use of resources. Our nurses have undergone extensive education about the necessity of a prompt bedside evaluation and have been instructed and empowered to escalate concerns to senior physicians if needed. We present a case that illustrates challenges to both implementing an RRS and measuring its potential benefits.

Figure 1

Case

A 59‐year‐old woman with a history of bipolar mood disorder was admitted for altered mental status. At presentation, she had signs of acute mania with normal vital signs. After initial laboratory workup, her altered mental status was felt to be multifactorial due to urinary tract infection, hypernatremia (attributed to lithium‐induced nephrogenic diabetes insipidus), and acute mania (attributed to medication discontinuation). Because she was slow to recover from the acute mania, her hospital stay was prolonged. From admission, the patient was treated with heparin 5000 units subcutaneously twice daily for venous thromboembolism prophylaxis.

On hospital day 7, at 21:32, the patient was noted to have asymptomatic tachycardia at 149 beats per minute and a new oxygen requirement of 3 L/minute. The cross‐cover team was called; however, although criteria were met, the RRS‐CTP was not activated and a bedside evaluation was not performed. A chest X‐ray was found to be normal and, with the exception of the oxygen requirement, her vital signs normalized by 23:45. No further diagnostic testing was performed at the time.

The next morning, at 11:58, the patient was found to have a blood pressure of 60/40 mmHg and heart rate of 42 beats per minute. The RRS‐CTP was activated. The primary team arrived at the bedside at 12:00 and found the patient to be alert, oriented, and without complaints. Her respiratory rate was 30/minute, and her oxygen saturation was 86% on 3 L/minute. An arterial blood gas analysis demonstrated acute respiratory alkalosis with hypoxemia and an electrocardiogram showed sinus tachycardia with a new S₁Q₃T₃ pattern. A computed tomography angiogram revealed a large, nearly occlusive pulmonary embolus (PE) filling an enlarged right pulmonary artery, as well as thrombus within the left main pulmonary artery. She was transferred to the medical ICU and alteplase was administered. The patient survived and was discharged in good clinical condition.

Discussion

Despite the strong theoretical benefit of the RRT concept, a recent review by Ranji et al.4 concluded that RRTs had not yet been shown to improve patient outcomes. In contrast to dedicated RRTs, this case illustrates a different type of RRS that was designed to address abrupt changes in patient status, while maintaining continuity of care and efficiently utilizing resources.

If one considers an RRS to have both afferent (criteria recognition) and efferent (RRT or primary team response) limbs, the afferent limb must be consistently activated in order to obtain the efferent limb's response.6 The greatest opportunities to improve RRSs are thought to lie in the afferent limb.3 Our RRS‐CTP was not triggered in 1 of 2 instances in which criteria for mandatory initiation of the system were met. This is consistent with the findings of the Medical Early Response Intervention and Therapy (MERIT) trial, in which RRTs were called in only 41% of the patients meeting criteria and subsequently having adverse events,7 and with the ongoing monitoring of the use of the system at our hospital. Had the cross‐covering team seen the patient at the bedside initially, the PE might have been diagnosed while the patient was hemodynamically stable, giving the patient nearly a 3‐fold lower relative mortality.8 When the RRS‐CTP was activated, a prompt bedside evaluation occurred, allowing for lytic therapy to be administered before cardiopulmonary arrest (attendant mortality of 90%).9

While rapid response criteria were originally based upon published sensitivity analyses, more recent studies suggest that these criteria lack diagnostic accuracy. As demonstrated by Cretikos et al,10 to reach a sensitivity of 70%, the corresponding specificity would be only 86%. Given that the prevalence of adverse events in the MERIT trial was only 0.6%, the resulting positive predictive value (PPV) of rapid response call criteria is 3%. Accordingly, 33 calls would be needed to prevent 1 unplanned ICU transfer, cardiac arrest, or death. Nurses' attempts to minimize false‐positive calls may help explain the low call rates for patients meeting RRT criteria. The 2 avenues to increase the PPV of criteria are:

• 1

  Increase the prevalence of disease in the population screened by risk factor stratification.

• 2

  Increase the specificity of the call criteria, which has been limited by the associated decrease in sensitivity.10

Regarding the efferent response limb of RRS, our case demonstrates that the primary team (rather than a separate group of caregivers), when alerted appropriately, can effectively respond to critical changes in patient status. Accordingly, our data show that since the inception of the program, cardiopulmonary arrests have decreased from a mean of 4.1 per month to 2.3 per month (P = 0.03).

Many clinical trials of RRTs would not capture the success demonstrated in this case. For example, due to the low prevalence of events, the MERIT trial used a composite endpoint that included unplanned ICU transfers, cardiac arrests, and mortality. Because our patient still required an unplanned ICU transfer after being evaluated by the responding team, she would have been counted as a system failure.

Conclusion

While local needs should inform the type of RRS implemented, this case illustrates one of the major obstacles ubiquitous to RRS implementation: failure of system activation. With appropriate activation, an RRS‐CTP can meet RRS goals while maintaining continuity of care and maximizing existing resources. This case also illustrates the difficulty of achieving a statistically relevant outcome, while demonstrating the potential benefits of evolving RRSs.

Many in‐hospital cardiac arrests and other adverse events are heralded by warning signs that are evident in the preceding 6 to 8 hours.1 By promptly intervening before further deterioration occurs, rapid response teams (RRTs) are designed to decrease unexpected intensive care unit (ICU) transfers, cardiac arrests, and inpatient mortality. While implementing RRTs is 1 of the 6 initiatives recommended by the Institute for Healthcare Improvement,2 data supporting their effectiveness is equivocal.3, 4

In October 2006, at Denver Health Medical Center, an academic, safety net hospital, we initiated a rapid response systemclinical triggers program (RRS‐CTP).5 In our RRS‐CTP, an abrupt change in patient status (Figure 1) triggers a mandatory call by the patient's nurse to the primary team, which is then required to perform an immediate bedside evaluation. By incorporating the primary team into the RRT‐CTP, we sought to preserve as much continuity of care as possible. Also, since the same house staff compose our cardiopulmonary arrest or cor team, and staff the ICUs and non‐ICU hospital wards, we did not feel that creating a separate RRT was an efficient use of resources. Our nurses have undergone extensive education about the necessity of a prompt bedside evaluation and have been instructed and empowered to escalate concerns to senior physicians if needed. We present a case that illustrates challenges to both implementing an RRS and measuring its potential benefits.

Figure 1

Case

A 59‐year‐old woman with a history of bipolar mood disorder was admitted for altered mental status. At presentation, she had signs of acute mania with normal vital signs. After initial laboratory workup, her altered mental status was felt to be multifactorial due to urinary tract infection, hypernatremia (attributed to lithium‐induced nephrogenic diabetes insipidus), and acute mania (attributed to medication discontinuation). Because she was slow to recover from the acute mania, her hospital stay was prolonged. From admission, the patient was treated with heparin 5000 units subcutaneously twice daily for venous thromboembolism prophylaxis.

On hospital day 7, at 21:32, the patient was noted to have asymptomatic tachycardia at 149 beats per minute and a new oxygen requirement of 3 L/minute. The cross‐cover team was called; however, although criteria were met, the RRS‐CTP was not activated and a bedside evaluation was not performed. A chest X‐ray was found to be normal and, with the exception of the oxygen requirement, her vital signs normalized by 23:45. No further diagnostic testing was performed at the time.

The next morning, at 11:58, the patient was found to have a blood pressure of 60/40 mmHg and heart rate of 42 beats per minute. The RRS‐CTP was activated. The primary team arrived at the bedside at 12:00 and found the patient to be alert, oriented, and without complaints. Her respiratory rate was 30/minute, and her oxygen saturation was 86% on 3 L/minute. An arterial blood gas analysis demonstrated acute respiratory alkalosis with hypoxemia and an electrocardiogram showed sinus tachycardia with a new S₁Q₃T₃ pattern. A computed tomography angiogram revealed a large, nearly occlusive pulmonary embolus (PE) filling an enlarged right pulmonary artery, as well as thrombus within the left main pulmonary artery. She was transferred to the medical ICU and alteplase was administered. The patient survived and was discharged in good clinical condition.

Discussion

Despite the strong theoretical benefit of the RRT concept, a recent review by Ranji et al.4 concluded that RRTs had not yet been shown to improve patient outcomes. In contrast to dedicated RRTs, this case illustrates a different type of RRS that was designed to address abrupt changes in patient status, while maintaining continuity of care and efficiently utilizing resources.

If one considers an RRS to have both afferent (criteria recognition) and efferent (RRT or primary team response) limbs, the afferent limb must be consistently activated in order to obtain the efferent limb's response.6 The greatest opportunities to improve RRSs are thought to lie in the afferent limb.3 Our RRS‐CTP was not triggered in 1 of 2 instances in which criteria for mandatory initiation of the system were met. This is consistent with the findings of the Medical Early Response Intervention and Therapy (MERIT) trial, in which RRTs were called in only 41% of the patients meeting criteria and subsequently having adverse events,7 and with the ongoing monitoring of the use of the system at our hospital. Had the cross‐covering team seen the patient at the bedside initially, the PE might have been diagnosed while the patient was hemodynamically stable, giving the patient nearly a 3‐fold lower relative mortality.8 When the RRS‐CTP was activated, a prompt bedside evaluation occurred, allowing for lytic therapy to be administered before cardiopulmonary arrest (attendant mortality of 90%).9

While rapid response criteria were originally based upon published sensitivity analyses, more recent studies suggest that these criteria lack diagnostic accuracy. As demonstrated by Cretikos et al,10 to reach a sensitivity of 70%, the corresponding specificity would be only 86%. Given that the prevalence of adverse events in the MERIT trial was only 0.6%, the resulting positive predictive value (PPV) of rapid response call criteria is 3%. Accordingly, 33 calls would be needed to prevent 1 unplanned ICU transfer, cardiac arrest, or death. Nurses' attempts to minimize false‐positive calls may help explain the low call rates for patients meeting RRT criteria. The 2 avenues to increase the PPV of criteria are:

• 1

  Increase the prevalence of disease in the population screened by risk factor stratification.

• 2

  Increase the specificity of the call criteria, which has been limited by the associated decrease in sensitivity.10

Regarding the efferent response limb of RRS, our case demonstrates that the primary team (rather than a separate group of caregivers), when alerted appropriately, can effectively respond to critical changes in patient status. Accordingly, our data show that since the inception of the program, cardiopulmonary arrests have decreased from a mean of 4.1 per month to 2.3 per month (P = 0.03).

Many clinical trials of RRTs would not capture the success demonstrated in this case. For example, due to the low prevalence of events, the MERIT trial used a composite endpoint that included unplanned ICU transfers, cardiac arrests, and mortality. Because our patient still required an unplanned ICU transfer after being evaluated by the responding team, she would have been counted as a system failure.

Conclusion

While local needs should inform the type of RRS implemented, this case illustrates one of the major obstacles ubiquitous to RRS implementation: failure of system activation. With appropriate activation, an RRS‐CTP can meet RRS goals while maintaining continuity of care and maximizing existing resources. This case also illustrates the difficulty of achieving a statistically relevant outcome, while demonstrating the potential benefits of evolving RRSs.