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Management by constraints: considering patient volume when adding medical staff to the emergency department

Zeev Rotstein1, Rachel Wilf-Miron, Bruno Lavi

  • 1Department of Medical Management, Sheba Medical Center, Tel Hashomer, Israel. zeevr@post.tau.ac.il

Insights

Adding a physician to the emergency department (ED) staff significantly reduces patient length of stay (LOS) only when patient volume is between 80-119 admissions. This highlights the importance of patient volume in optimizing ED staffing and resource allocation.

Area of Science:

  • Health Services Research
  • Operations Research
  • Emergency Medicine

Background:

  • Emergency departments (EDs) are critical hospital bottlenecks.
  • Management by constraint theory suggests focusing on critical issues like physician availability.
  • Staffing decisions in EDs require careful consideration of system bottlenecks.

Purpose of the Study:

  • To develop a dynamic statistical model for forecasting medical staff needs in EDs.
  • To optimize emergency department efficiency by considering patient volume.
  • To evaluate the impact of additional physician staffing on patient flow.

Main Methods:

  • Analysis of daily non-trauma admissions to a general ED from 1992-1995.
  • Utilized hospital computerized database for patient data.
  • Employed a Gross Linear Model with SAS software to assess physician impact on patient length of stay (LOS) across varying patient volumes.

Main Results:

  • Adding a physician during evening shifts (noon to midnight) significantly reduced patient LOS by an average of 6.61 minutes for 80-119 daily admissions (P < 0.001).
  • No significant effect on LOS was observed when patient admissions were below 80 or above 120.
  • Physician availability as a bottleneck is volume-dependent.

Conclusions:

  • Patient volume is a key determinant of the effectiveness of additional staffing in EDs.
  • Applying the theory of constraints can aid in planning and coordinating emergency services.
  • ED managers can use patient volume data for logical staffing and resource allocation decisions.
Abstract

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