A prediction-based approach for online dynamic patient scheduling: a case study in radiotherapy treatment

TL;DR

This paper introduces a prediction-based method for online dynamic radiotherapy scheduling that adapts to patient arrivals, improving urgent patient treatment timeliness while maintaining overall efficiency.

Contribution

It presents a novel regression model that predicts optimal waiting times based on patient arrival patterns, enhancing scheduling adaptiveness and explainability.

Findings

Better prevention of overdue treatments for emergency patients

Maintains comparable waiting times for non-urgent patients

Supports explainability with SHAP values

Abstract

Patient scheduling is a difficult task involving stochastic factors such as the unknown arrival times of patients. Similarly, the scheduling of radiotherapy for cancer treatments needs to handle patients with different urgency levels when allocating resources. High priority patients may arrive at any time, and there must be resources available to accommodate them. A common solution is to reserve a flat percentage of treatment capacity for emergency patients. However, this solution can result in overdue treatments for urgent patients, a failure to fully exploit treatment capacity, and delayed treatments for low-priority patients. This problem is especially severe in large and crowded hospitals. In this paper, we propose a prediction-based approach for online dynamic radiotherapy scheduling that dynamically adapts the present scheduling decision based on each incoming patient and the current allocation of resources. Our approach is based on a regression model trained to recognize the links between patients' arrival patterns, and their ideal waiting time in optimal offline solutions where all future arrivals are known in advance. When our prediction-based approach is compared to flat-reservation policies, it does a better job of preventing overdue treatments for emergency patients, while also maintaining comparable waiting times for the other patients. We also demonstrate how our proposed approach supports explainability and interpretability in scheduling decisions using SHAP values.