AI-based Metaheuristic Optimization Models for Surgery Scheduling Problems in Healthcare (Preprint)

Author:

Lopes Sr JoãoORCID,Guimarães Tiago,Duarte Júlio,Santos Manuel

Abstract

BACKGROUND

Healthcare is facing enormous challenges. The most recent pandemic has caused a global reflection on how clinical and organisational processes should be organised, optimising decision-making by managers and healthcare professionals to provide increasingly patient-oriented healthcare. One of the most debated topics is efficiency in surgical scheduling, a crucial sector for the good functioning of hospitals, related to the management of waiting lists, and highly vulnerable to bad decisions due to the high number of variables and restrictions involved.

OBJECTIVE

In this research, in collaboration with one of the leading hospitals in Portugal, Centro Hospitalar e Universitário de Santo António (CHUdSA), we propose a study on heuristic approaches to optimise the management of the surgical centre and reduce inherent costs.

METHODS

A study was carried out into the scheduling process for a given period conducted by CHUdSA. Using heuristic approaches, optimization algorithms were implemented to determine the possible scheduling dates for a waiting list, with the aim of minimizing the scheduling penalty. The penalty represents the monetary cost that the hospital must bear for surgeries that are not scheduled by the deadline.

RESULTS

The results obtained allow us to conclude that the implementation of these algorithms in a real context could represent a substantial advance in the scheduling process. This advance is evident in the ability of artificial intelligence algorithms not only to optimise the efficiency of the process, but also to make it possible to schedule surgeries for significantly earlier dates compared to the manual method used by hospital professionals.

CONCLUSIONS

This implementation clearly shows the benefit of using this proposal to increase the efficiency of this process and minimise the overall costs, highlighting the remarkable ability of algorithms to respond promptly and accurately to each context

Publisher

JMIR Publications Inc.

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