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Authors: João Lopes 1 ; Gonçalo Vieira 1 ; Rita Veloso 2 ; Susana Ferreira 2 ; Maria Salazar 2 and Manuel Santos 1

Affiliations: 1 Department of Information Systems, University of Minho, Guimarães, Portugal ; 2 Centro Hospitalar Universitário de Santo António (CHUdSA), Portugal

Keyword(s): Prescriptive Analytics, Surgery Scheduling Problems.

Abstract: Surgery scheduling plays a crucial role in modern healthcare systems, ensuring efficient use of resources, minimising patient waiting times and improving organisations’ operational performance. Additionally, healthcare faces enormous challenges, with a general modernisation of all clinical and administrative processes expected, requiring organisations to keep up with the latest advances in Information Technology. The scheduling of surgeries is a crucial sector for the good functioning of hospitals, and the management of waiting lists is directly related to this process, which has seen the COVID-19 pandemic cause a significant increase in waiting times in some specialities. Surgery scheduling is considered a highly complex problem, influenced by numerous factors such as resource availability, operating shifts, patient priorities and scheduling restrictions, putting significant challenges to healthcare providers. In this research, in collaboration with one of the leading hospitals in P ortugal, the Centro Hospitalar Universitário de Santo António (CHUdSA), we propose an approach based on Prescriptive Analytics, using optimisation algorithms to evaluate their performance in the management of the operating room. The results allow identifying the feasibility of this approach, taking into account the number of surgeries to be scheduled and surgical spaces in a time perspective, prevailing the priority of each surgery in the waiting list. (More)

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Paper citation in several formats:
Lopes, J.; Vieira, G.; Veloso, R.; Ferreira, S.; Salazar, M. and Santos, M. (2023). Optimization of Surgery Scheduling Problems Based on Prescriptive Analytics. In Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-664-4; ISSN 2184-285X, SciTePress, pages 474-479. DOI: 10.5220/0012131700003541

@conference{data23,
author={João Lopes. and Gon\c{C}alo Vieira. and Rita Veloso. and Susana Ferreira. and Maria Salazar. and Manuel Santos.},
title={Optimization of Surgery Scheduling Problems Based on Prescriptive Analytics},
booktitle={Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA},
year={2023},
pages={474-479},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012131700003541},
isbn={978-989-758-664-4},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA
TI - Optimization of Surgery Scheduling Problems Based on Prescriptive Analytics
SN - 978-989-758-664-4
IS - 2184-285X
AU - Lopes, J.
AU - Vieira, G.
AU - Veloso, R.
AU - Ferreira, S.
AU - Salazar, M.
AU - Santos, M.
PY - 2023
SP - 474
EP - 479
DO - 10.5220/0012131700003541
PB - SciTePress