Resident productivity as a function of emergency department volume, shift time of day, and cumulative time in the emergency department

Am J Emerg Med. 2009 Mar;27(3):313-9. doi: 10.1016/j.ajem.2008.03.002.

Abstract

Objectives: We sought to determine if resident productivity changed based on emergency department (ED) volume, shift time of day, or over time during a shift.

Methods: This is a retrospective review of patients evaluated in the ED by emergency medicine residents. Data were collected using the computerized tracker that provides time of physician assignment and daily volume. Regression analysis was used to determine relationship between productivity and volume as well as relationship between productivity and accumulated time in the ED. Analysis of variance was used to assess for productivity differences by shift time of day.

Results: One hundred sixty-one postgraduate year-1 (PGY-1), 264 PGY-2, and 193 PGY-3 shifts were included. PGY-1, PGY-2, and PGY-3 residents saw 0.85, 1.13, and 1.25 patients per hour, respectively. PGY-3 and PGY-2 productivity had a weak relationship to ED volume (R = 0.28, P = .03; and R = 0.36, P = .03), whereas PGY-1 productivity had a moderate relationship to ED volume (R = 0.44, P = .0001). There were no differences in productivity based on shift time of day. Accumulated time in the ED had a strongly negative relationship to productivity, with R values from -0.79 to -0.93 (P < .002 for all comparisons).

Conclusions: Resident productivity is not strongly linked to volume or time of day. If specific times have statistically higher volume, they should be staffed with larger numbers of residents. In addition, emergency medicine resident productivity declines reliably over shift time. Therefore, scheduling should be adjusted to create larger shift overlaps to aid in smoother patient flow.

MeSH terms

  • Analysis of Variance
  • Efficiency*
  • Emergency Medicine / education*
  • Emergency Service, Hospital / statistics & numerical data
  • Humans
  • Internship and Residency / organization & administration*
  • Regression Analysis
  • Retrospective Studies
  • Work Schedule Tolerance*
  • Workload*