OBJECTIVES: To develop a quantitative measure of emergency department (ED) crowding and busyness. METHODS: A five-week study in spring 2002 in an urban teaching ED compared a new index (the Emergency Department Work Index [EDWIN]) with attending physician and nurse ratings of crowding. EDWIN is defined as summation operator n(i)t(i)/N(a)(B(T)-B(A)), where n(i) = number of patients in the ED in triage category i, t(i) = triage category, N(a) = number of attending physicians on duty, B(T) = number of treatment bays, and B(A) = number of admitted patients in the ED. The triage system used is the Emergency Severity Index (ESI), which was modified by reversing the ranking of triage categories; that is, an ESI score of 1 represented the least acute patient and 5 the sickest. EDWIN was calculated every two hours in a convenience sample of 60 eight-hour shifts. With each measurement, the charge attending physician and nurse estimated how busy/crowded the ED was, using a Likert scale. Nurse and physician assessments were averaged and compared with EDWIN scores. Data were analyzed with SPSS 10.0 (SPSS Inc., Chicago, IL). RESULTS: A total of 2,647 patients aged 18 years and older were assessed at 225 time points over 35 consecutive days. Nurses and physicians showed good interrater agreement of crowding assessment (weighted kappa 0.61, 95% confidence interval = 0.53 to 0.69). Median EDWIN scores and interquartile ranges (IQRs) when the ED was rated as not busy, average, and very busy were 1.07 (IQR = 0.80 to 1.55), 1.55 (IQR = 1.16 to 1.93), and 1.83 (IQR = 1.42 to 2.45) (p < 0.001). The ED was on diversion for 17 time blocks (6.5% of all blocks), with a median EDWIN of 2.77 (IQR = 1.83 to 3.63), compared with an EDWIN of 1.45 (IQR = 1.05 to 2.00) when not on diversion (p < 0.001). EDWIN scores correlated weakly with various process-of-care measures chosen as secondary end points. CONCLUSIONS: EDWIN correlated well with staff assessment of ED crowding and diversion. The index can be programmed into tracking software for use as a "dashboard" to alert staff when the ED is approaching crisis. If validated across other sites, EDWIN may provide a tool to compare crowding levels among different EDs.
OBJECTIVES: To develop a quantitative measure of emergency department (ED) crowding and busyness. METHODS: A five-week study in spring 2002 in an urban teaching ED compared a new index (the Emergency Department Work Index [EDWIN]) with attending physician and nurse ratings of crowding. EDWIN is defined as summation operator n(i)t(i)/N(a)(B(T)-B(A)), where n(i) = number of patients in the ED in triage category i, t(i) = triage category, N(a) = number of attending physicians on duty, B(T) = number of treatment bays, and B(A) = number of admitted patients in the ED. The triage system used is the Emergency Severity Index (ESI), which was modified by reversing the ranking of triage categories; that is, an ESI score of 1 represented the least acute patient and 5 the sickest. EDWIN was calculated every two hours in a convenience sample of 60 eight-hour shifts. With each measurement, the charge attending physician and nurse estimated how busy/crowded the ED was, using a Likert scale. Nurse and physician assessments were averaged and compared with EDWIN scores. Data were analyzed with SPSS 10.0 (SPSS Inc., Chicago, IL). RESULTS: A total of 2,647 patients aged 18 years and older were assessed at 225 time points over 35 consecutive days. Nurses and physicians showed good interrater agreement of crowding assessment (weighted kappa 0.61, 95% confidence interval = 0.53 to 0.69). Median EDWIN scores and interquartile ranges (IQRs) when the ED was rated as not busy, average, and very busy were 1.07 (IQR = 0.80 to 1.55), 1.55 (IQR = 1.16 to 1.93), and 1.83 (IQR = 1.42 to 2.45) (p < 0.001). The ED was on diversion for 17 time blocks (6.5% of all blocks), with a median EDWIN of 2.77 (IQR = 1.83 to 3.63), compared with an EDWIN of 1.45 (IQR = 1.05 to 2.00) when not on diversion (p < 0.001). EDWIN scores correlated weakly with various process-of-care measures chosen as secondary end points. CONCLUSIONS: EDWIN correlated well with staff assessment of ED crowding and diversion. The index can be programmed into tracking software for use as a "dashboard" to alert staff when the ED is approaching crisis. If validated across other sites, EDWIN may provide a tool to compare crowding levels among different EDs.
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