• Grant D Innes, Robert Stenstrom, Eric Grafstein, and James M Christenson.
• St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada. ginnes2@providencehealth.bc.ca
• Can J Emerg Med. 2005 Sep 1; 7 (5): 299-308.

BackgroundA reliable emergency department (ED) workload measurement tool would provide a method of quantifying clinical productivity for performance evaluation and physician incentive programs; it would enable health administrators to measure ED outputs; and it could provide the basis for an equitable formula to estimate ED physician staffing requirements. Our objectives were to identify predictors that correlate with physician time needed to treat patients and to develop a multivariable model to predict physician workload.MethodsDuring 31 day, evening, night and weekend shifts, a research assistant (RA) shadowed 20 emergency physicians, documenting time spent performing clinical and non-clinical functions for 585 patient visits. The RA recorded key predictors including patient gender, age, vital signs and Glasgow Coma Scale (GCS) score, and the mode of arrival, triage level assigned, comorbidity and procedures performed. Multiple linear regression was used to describe the associations between predictor variables and total physician time per patient visit (TPPV), and to derive an equation for physician workload. Model derivation was based on 16 shifts and 314 patient visits; model validation was based on 15 shifts and 271 additional patient visits.ResultsThe strongest predictor variables were: procedure required, triage level, arrival by ambulance, GCS, age, any comorbidity, and number of prior visits. The derived regression equation is: TPPV = 29.7 + 8.6 (procedure required [Yes]) - 3.8 (triage level [1-5]) + 7.1 (ambulance arrival) - 1.1 (GCS [3-15]) + 0.1 (age in years) - 0.05 (n of previous visits) + 3.1 (any comorbidity). This model predicted 31.3% of the variance in physician TPPV (F [12, 29] = 13.2; p < 0.0001).ConclusionsThis study clarifies important determinants of emergency physician workload. If validated in other settings, the predictive formula derived and internally validated here is a potential alternative to current simplistic models based solely on patient volume and perceived acuity. An evidence-based workload estimation tool like that described here could facilitate ED productivity measurement, benchmarking, physician performance evaluation, and provide the substrate for an equitable formula to estimate ED physician staffing requirements.

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