A Robust ORMS Framework for Taiwanese Healthcare: Taguchi’s Dynamic Method in Action

TL;DR

This study designed a robust operating room management system for a hospital in Taiwan using Taguchi’s dynamic method and optimization techniques to improve patient care and hospital efficiency.

Contribution

A novel application of Taguchi’s dynamic method and genetic algorithms to optimize operating room management under resource constraints in a teaching hospital.

Findings

A robust ORMS was developed to improve operating room planning and scheduling.

Sensitivity analysis revealed the relative impact of staffing and resource parameters on performance.

Optimal parameter settings were identified, achieving a performance score of 0.91.

Abstract

The study focused on the design of an ORMS in a medical center in central Taiwan, which also functions as a teaching hospital. Background/Objectives: The research objectives were to design an ORMS simulation system based on the status quo of the operating room planning and scheduling in the medical center, obtain the optimal parameter setting in the ORMS, and find improvement strategies according to the sensitivity analysis based on the optimal parameter setting for total performance. Methods: Taguchi’s dynamic method was adopted to design the ORMS under human and material resource constraints. The scope of the study was internal medicine patients of the ORMS. A neural network was used to construct a relationship between parameters and performances. A genetic algorithm was used to obtain the optimal parameter setting for optimal performance. Results: This study successfully established a robust operating room management system (ORMS) to help hospital manager to plan and schedule operating rooms and take the ORMS into account to meet patient needs. Decision-makers can use the insights from the sensitivity analysis to refine their strategies effectively. The sensitivity analysis showed that the impact power (the percentage change in d) of the “number of circulating nurses (−0.15 to −1.25; −0.25 to −1.85)” factor was less than (<) that of the “number of holding nurses (−0.85 to −2.04; −0.91 to −2.07)” factor < that of the “number of preoperative beds (−2.57 to −4.53; −2.23 to −4.10)” factor < that of the “number of anesthetists (−3.13 to −7.50)” factor. Conclusions: In the optimal parameter setting obtained, the number of holding nurses was 18, the number of circulating nurses was 20, the number of anesthetists was 15, and the number of preoperative beds was 12. The optimal performance was 0.91.

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