CJEM Articles: staffing
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July
2009
11
4
Chris K. Anderson, Gregory S. Zaric, Jonathan F. Dreyer, Michael W. Carter, Shelley L. McLeod
Introduction: The Canadian Emergency Department Triage and Acuity Scale (CTAS) is a 5-level triage tool used to determine the priority by which patients should be treated in Canadian emergency departments (EDs). To determine emergency physician (EP) workload and staffing needs, many hospitals in Ontario use a case-mix formula based solely on patient volume at each triage level. The purpose of our study was to describe the distribution of EP time by activity during a shift in order to estimate the amount of time required by an EP to assess and treat patients in each triage category and to determine the variability in the distribution of CTAS scoring between hospital sites.
Methods: Research assistants directly observed EPs for 592 shifts and electronically recorded their activities on a moment-by-moment basis. The duration of all activities associated with a given patient were summed to derive a directly observed estimate of the amount of EP time required to treat the patient.
Results: We observed treatment times for 11 716 patients in 11 hospital-based EDs. The mean time for physicians to treat patients was 73.6 minutes (95% confidence interval [CI] 63.6-83.7) for CTAS level 1, 38.9 minutes (95% CI 36.0-41.8) for CTAS-2, 26.3 minutes (95% CI 25.4-27.2) for CTAS-3, 15.0 minutes (95% CI 14.6-15.4) for CTAS-4 and 10.9 minutes (95% CI 10.1-11.6) for CTAS-5. Physician time related to patient care activities accounted for 84.2% of physicians’ ED shifts.
Conclusion: In our study, EPs had very limited downtime. There was significant variability in the distribution of CTAS scores between sites and also marked variation in EP time related to each triage category. This brings into question the appropriateness of using CTAS alone to determine physician staffing levels in EDs.
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September
2005
7
5
Eric Grafstein, Grant D. Innes, James M. Christenson, Robert Stenstrom
Background: A 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.
Methods: During 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.
Results: The 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).
Conclusions: This 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.
