Hierarchical Reinforcement Learning for Fair Operating Room Scheduling in Plastic Surgery.

ABSTRACT:

Background

Equitable operating room (OR) scheduling is critical for surgical training and resource usage. Traditional, hand-crafted schedules frequently perpetuate hidden biases. We present a hierarchical reinforcement learning (HRL) framework, coupled with statistical fairness analysis, to optimize OR assignment while maintaining institutional constraints.

Methods

A retrospective dataset of 1.5 years (July 2023 to December 2024) of weekly OR assignments from a plastic surgery service (n = 24 surgeons) was analyzed. A random forest model predicted future OR demand; feature importance was quantified with Shapley Additive Explanations. Fairness metrics (Gini, Theil, coefficient of variation, disparate-impact ratios) were computed at baseline. An HRL architecture-Proximal Polic

SUBMITTER: Wallner C 

PROVIDER: S-EPMC12448174 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature