Intelligent decision support in beds management and hospital planning

Abstract

Worldwide, due to the constant overcrowding experienced in hospitals, hospital beds are one of the most needed resources, proving to be an extremely important feature in hospitalization planning and management, since the main purpose is to optimize their occupancy rate. This study aims to predict the future flow of patients after admission to a particular inpatient specialty to allow a more assertive planning based on demographic data. All data sources were made available by the Centro Hospitalar do Tâmega e Sousa (CHTS) and are relative to a 5-year period, 2017 to 2021. From the results achieved with the Machine Learning (ML) models developed was possible to conclude that these can prove to be an asset for the hospital, since being known the flow of patients allows a more informed and careful management of the management of beds.