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Monitoring Perioperative Services Using 3D Multi-Objective Performance Frontiers.

Andrea J Elhajj1, Donna M Rizzo2, Gary C An3

  • 1College of Engineering and Mathematical Sciences, University of Vermont, Burlington, VT, USA.

Journal of Medical Systems
|February 6, 2021
PubMed
Summary
This summary is machine-generated.

The implementation of an Acute Care Surgery model improved operating room efficiency for General Surgery by optimizing under-utilized and spillover time. This analysis used Pareto optimality and Bayesian inference to assess the impact on surgical services.

Keywords:
Acute care surgeryMarkov Chain Monte CarloMulti-objective optimizationOver-utilized timeParetoSchedulingTactical decisionsUnder-utilized time

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Area of Science:

  • Healthcare Management
  • Surgical Operations Research
  • Health Services Research

Background:

  • The Acute Care Surgery (ACS) model is increasingly adopted in US hospitals, shifting management of Emergency General Surgery (EGS) patients from General Surgery (GS) to ACS services.
  • Evaluating the operational and financial impact of ACS implementation on existing GS departments is crucial for resource allocation and service optimization.

Purpose of the Study:

  • To assess the impact of an ACS service model on General Surgery operations at the University of Vermont Medical Center.
  • To analyze changes in under-utilized time, spillover time, and financial efficiency (work Relative Value Units/clinical Full Time Equivalents) before and after ACS implementation.

Main Methods:

  • Utilized three key metrics: under-utilized time, spillover time, and a financial ratio (work RVUs/clinical FTEs).
  • Applied multi-objective Pareto analysis and Bayesian breakpoint analysis to operating room metrics.
  • Employed Markov Chain Monte Carlo (MCMC) modeling for Bayesian Inference to substantiate findings.

Main Results:

  • Post-ACS implementation, Pareto fronts moved closer to the origin in 2D analyses, indicating more optimal solutions for productivity and reduced under-utilized/spillover time.
  • 3D Pareto analysis revealed a smaller volume for the post-ACS allocation surface compared to the pre-ACS surface.
  • A slight decrease in productivity (lower z-axis value) was observed post-ACS implementation.

Conclusions:

  • The ACS model demonstrates potential for improving operating room efficiency and resource management within General Surgery departments.
  • The applied methodology, combining Pareto optimality and Bayesian inference, offers a robust framework for benchmarking and monitoring perioperative services.
  • Visualizing operational implications post-tactical decisions in operating room management is vital for strategic healthcare planning.