Trade-offs between elective surgery rescheduling and length-of-stay prediction accuracy

2025-07-21

AI Summary

This study addresses the trade-off between prediction error in elective surgery length-of-stay (LOS) forecasts and patient rescheduling strategies to mitigate bed overflow and improve resource utilization. Method: We propose a simulation-based framework that integrates machine learning–based LOS prediction models with dynamic rescheduling policies—including delayed admissions and inter-ward transfers—to quantify the substitution effect between prediction accuracy and operational flexibility under multiple scenarios. Contribution/Results: Our key insight is that high prediction accuracy is not strictly necessary: moderately accurate forecasts, when coupled with flexible rescheduling, significantly reduce bed overflow rates, enhance bed turnover efficiency, and diminish reliance on computationally expensive, high-precision predictive models. The findings establish a new paradigm for hospital operations—one that balances robustness and cost-effectiveness in surgical scheduling optimization.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

The availability of downstream resources plays a critical role in planning the admission of patients undergoing elective surgery, with inpatient beds being one of the most crucial resources. When planning patient admissions, predictions on their length-of-stay (LOS) made by machine learning (ML) models are used to ensure bed availability. However, the actual LOS for each patient may differ considerably from the predicted value, potentially making the schedule infeasible. To address such infeasibilities, rescheduling strategies that take advantage of operational flexibility can be implemented. For example, adjustments may include postponing admission dates, relocating patients to different wards, or even transferring patients who are already admitted. The common assumption is that more accurate LOS predictions reduce the impact of rescheduling. However, training ML models that can make such accurate predictions can be costly. Building on previous work that proposed simulated ac{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.

Problem

• Balancing elective surgery rescheduling with LOS prediction accuracy
• Optimizing bed resource use despite LOS prediction errors
• Evaluating rescheduling strategies to prevent hospital bed overflows

Innovation

• Uses machine learning for LOS prediction
• Implements flexible patient rescheduling strategies
• Optimizes resource use under prediction errors