Prediction accuracy versus rescheduling flexibility in elective surgery management

TL;DR

This paper investigates how the accuracy of machine learning-based length-of-stay predictions impacts the effectiveness of rescheduling strategies in elective surgery management, aiming to prevent bed overflows and optimize resource use.

Contribution

It analyzes the relationship between LOS prediction accuracy and rescheduling flexibility, evaluating different corrective policies to improve hospital bed management under prediction errors.

Findings

Better LOS predictions can reduce rescheduling needs.

Rescheduling strategies improve bed utilization despite prediction errors.

Certain policies are more effective in handling prediction inaccuracies.

Abstract

The availability of downstream resources plays is critical in planning the admission of elective surgery patients. The most crucial one is inpatient beds. To ensure bed availability, hospitals may use machine learning (ML) models to predict patients' length-of-stay (LOS) in the admission planning stage. However, the real value of the LOS for each patient may differ from the predicted one, potentially making the schedule infeasible. To address such infeasibilities, it is possible to implement rescheduling strategies that take advantage of operational flexibility. For example, planners may postpone admission dates, relocate patients to different wards, or even transfer patients who are already admitted among wards. A straightforward assumption is that better LOS predictions can help reduce the impact of rescheduling. However, the training process of ML models that can make such accurate predictions can be very costly. Building on previous work that proposed simulated ML for evaluating data-driven approaches, this paper explores the relationship between LOS prediction accuracy and rescheduling flexibility across various corrective policies. Specifically, we examine the most effective patient rescheduling strategies under LOS prediction errors to prevent bed overflows while optimizing resource utilization