Artificial intelligence and hospital demand prediction: systematic review and meta-analysis

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Published May 6, 2025
https://doi.org/10.5281/zenodo.16783120%20
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Statistic
Vol. 7 No. 1 (2025): Edición Mayo 2025

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Juan Carlos García Morales
Ministerio de Salud Pública, Loja, Loja, Ecuador
https://orcid.org/0000-0002-9247-6414
Lester Wong Vázquez
Universidad Técnica Particular de Loja, Loja, Ecuador
https://orcid.org/0000-0001-6054-1958

Abstract

The hospital capacity crises suffered by health systems are evidence of the inadequacy of traditional reactive models to anticipate fluctuations in the demand for care. This study seeks to systematize artificial intelligence models applied to hospital demand prediction, quantify their accuracy through a meta-analysis and determine biases and challenges of clinical implementation. We conducted a systematic review of studies published between 2020 and 2025 in English, Spanish and Portuguese, using the PRISMA protocol and extracting performance metrics such as MAE, RMSE and AUC. The results showed that models such as XGBoost, Bi-LSTM and N-BEATS outperformed ARIMA and classical regressions, achieving error reductions of up to 50% and improvements in robustness to abrupt variations in demand. It was identified that the inclusion of contextual variables and external validation improved applicability and that interpretability and data governance were critical for adoption. It was concluded that the incorporation of predictive AI optimized operational planning, reduced costs and strengthened hospital resilience, recommending validation protocols and interdisciplinary structures for its sustainable implementation

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite
García Morales , J. C., & Wong Vázquez , L. (2025). Artificial intelligence and hospital demand prediction: systematic review and meta-analysis. Revista Científica ARISTAS, 7(1), 38–55. https://doi.org/10.5281/zenodo.16783120

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