Abstract
The combination of the phenomenon of overcrowding with inefficient management of resources is a major obstacle to the good performance of hospital units and consequently the degradation of the medical service provided. This paper provides an analysis to understand the correlation between poor bed allocation and hospital performance. The lack of an efficient resource planning among the various medical specialties can negatively impact the quality of service. Four different techniques were compared to realize which is better suited for optimizing the allocation of beds in Hospital units. Hill Climbing and the Genetic Algorithm stood out the others, the latter presenting greater consistency and a shorter computation time. When tested with real data from Centro Hospitalar do Tâmega e Sousa, attained a total of 0 wrongly allocated patients against 92 when compared with former methods. This translates into better patient service, reduced waiting time and staff workload, which means increased performance in all adjacent medical issues.
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This work has been supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.
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Lobo, A., Barbosa, A., Guimarães, T., Lopes, J., Peixoto, H., Santos, M.F. (2023). Better Medical Efficiency by Means of Hospital Bed Management Optimization—A Comparison of Artificial Intelligence Techniques. In: Moniz, N., Vale, Z., Cascalho, J., Silva, C., Sebastião, R. (eds) Progress in Artificial Intelligence. EPIA 2023. Lecture Notes in Computer Science(), vol 14116. Springer, Cham. https://doi.org/10.1007/978-3-031-49011-8_21
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