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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
Summary
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.
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.
