Inteligencia artificial y predicción de la demanda hospitalaria: revisión sistemática y metaanálisis

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Publicado may 6, 2025
https://doi.org/10.5281/zenodo.16783120%20
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Statistic
Vol. 7 Núm. 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

Resumen

Las crisis de capacidad hospitalaria que sufren los sistemas de salud evidencian la insuficiencia de los modelos reactivos tradicionales para prever fluctuaciones en la demanda asistencial. Este estudio busca sistematizar los modelos de inteligencia artificial aplicados a la predicción de la demanda hospitalaria, cuantificar su precisión a través de un metaanálisis y determinar sesgos y desafíos de implementación clínica. Se realizó una revisión sistemática de estudios publicados entre 2020 y 2025 en inglés, español y portugués, empleando el protocolo PRISMA y extrayendo métricas de desempeño como MAE, RMSE y AUC. Los resultados mostraron que modelos como XGBoost, Bi-LSTM y N-BEATS superaron a ARIMA y regresiones clásicas, logrando reducciones de error de hasta un 50 % y mejoras de robustez ante variaciones abruptas de la demanda. Se identificó que la inclusión de variables contextuales y la validación externa mejoraron la aplicabilidad y que la interpretabilidad y gobernanza de datos fueron críticas para la adopción. Se concluyó que la incorporación de IA predictiva optimizó la planificación operativa, redujo costos y reforzó la resiliencia hospitalaria, recomendándose protocolos de validación y estructuras interdisciplinarias para su implementación sostenible.

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Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.

Cómo citar
García Morales , J. C., & Wong Vázquez , L. (2025). Inteligencia artificial y predicción de la demanda hospitalaria: revisión sistemática y metaanálisis. Revista Científica ARISTAS, 7(1), 38–55. https://doi.org/10.5281/zenodo.16783120

Citas

  1. Angelina, Q., Begum, K., Kim, H.-C., Tripathy, S., Singhal, D., & Singh, S. (2025). A Structural Analysis of AI Implementation Challenges in Healthcare. Algorithms, 18(4), 189. https://doi.org/10.3390/a18040189
  2. Brossard, C., Goetz, C., Catoire, P., Jabbour, E., Foucrier, A., Auroy, M., & Lesur, D. (2025). Predicting emergency department admissions using a machine-learning algorithm: a proof of concept with retrospective study. BMC Emergency Medicine, 25, 3. https://doi.org/10.1186/s12873-024-01141-4
  3. Cao, L., & Zhang, L. (2025). Daily hospital outpatient visits prediction based on Seasonal Autoregressive Integrated Moving Average model [Preprint]. medRxiv. https://doi.org/10.1101/2025.04.15.25325872
  4. Chua, M., Lee, G. J. W., & Teo, Y. N. (2025). Deep learning modelling to forecast emergency department visits using calendar, meteorological, internet search data and stock market price. Computer Methods and Programs in Biomedicine, 267, 108808. https://doi.org/10.1016/j.cmpb.2025.108808
  5. Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. https://doi.org/10.1016/j.patrec.2005.10.010
  6. Hassan, M., Kushniruk, A., & Borycki, E. (2024). Barriers to and Facilitators of Artificial Intelligence Adoption in Health Care: Scoping Review. JMIR Human Factors, 11, e48633. https://doi.org/10.2196/48633
  7. Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001
  8. Jiang, S., Liu, Q., & Ding, B. (2023). A systematic review of the modelling of patient arrivals in emergency departments. Quantitative Imaging in Medicine and Surgery, 13(3), 1957–1971. https://doi.org/10.21037/qims-22-268 https://n9.cl/hs4vo
  9. Kim, J. N., & Oh, I. H. (2024). Optimizing Hospital Bed Capacity and Resource Allocation Using Inflow and Outflow Indices for Effective Healthcare Management. Inquiry, 61. https://doi.org/10.1177/00469580241304244
  10. Maleki Varnosfaderani, S., & Forouzanfar, M. (2024). The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering, 11(4), 337. https://doi.org/10.3390/bioengineering11040337
  11. Matheny, M. E., Goldsack, J. C., Saria, S., Shah, N. H., Gerhart, J., Cohen, I. G., Price, W. N., Patel, B., Payne, P. R. O., Embí, P. J., Anderson, B., & Horvitz, E. (2025). Artificial Intelligence in Health and Health Care: Priorities For Action. Health Affairs, 44(2), 163–170. https://doi.org/10.1377/hlthaff.2024.01003
  12. Methley, A. M., Campbell, S., Chew-Graham, C., McNally, R., & Cheraghi-Sohi, S. (2014). PICO, PICOS and SPIDER: a comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews. BMC Health Services Research, 14(1), 579. https://doi.org/10.1186/s12913-014-0579-0
  13. Nair, M., Svedberg, P., Larsson, I., & Nygren, J. M. (2024). A comprehensive overview of barriers and strategies for AI implementation in healthcare: mixed-method design. PLOS ONE, 19(8), e0305949. https://doi.org/10.1371/journal.pone.0305949
  14. National Library of Medicine. (2025). MeSH Browser. Internet Archive Wayback Machine. https://n9.cl/u53ps
  15. Organización Mundial de la Salud (2021). WHO issues first global report on AI in health and six guiding principles for its design and use. https://n9.cl/7bhcl
  16. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. PLOS Medicine, 18(3), e1003583. https://doi.org/10.1371/journal.pmed.1003583
  17. Peláez Rodríguez, C., Torres-López, R., Pérez-Aracil, J., López-Laguna, N., Sánchez-Rodríguez, S., & Salcedo-Sanz, S. (2024). An explainable machine learning approach for hospital emergency department visits forecasting using continuous training and multi-model regression. Computer Methods and Programs in Biomedicine, 245, 108033. https://doi.org/10.1016/j.cmpb.2024.108033
  18. Petravić, L., Gril Rogina, K., Albreht, T., et al. (2025). Prognostic models for predicting patient arrivals in emergency departments: An updated systematic review and research agenda. BMC Emergency Medicine, 25 (106). https://doi.org/10.1186/s12873-025-01250-8
  19. Porto, B. M., & Fogliatto, F. S. (2024). Enhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning. BMC Medical Informatics and Decision Making, 24, 377. https://doi.org/10.1186/s12911-024-02788-6
  20. Roquette, B. P., Nagano, H., Marujo, E. C., & Maiorano, A. C. (2020). Prediction of admission in pediatric emergency department with deep neural networks and triage textual data. Neural Networks, 126, 170–177. https://doi.org/10.1016/j.neunet.2020.03.012
  21. Sakib, M., Mustajab, S., & Alam, M. (2025). Ensemble deep learning techniques for time series analysis: a comprehensive review, applications, open issues, challenges, and future directions. Cluster Computing, 28(73). https://doi.org/10.1007/s10586-024-04684-0
  22. Seo, H., Ahn, I., Gwon, H., Kang, H., Kim, Y., Choi, H., Kim, M., Han, J., Kee, G., Park, S., Ko, S., Jung, H., Kim, B., Oh, J., Jun, T. J., & Kim, Y.-H. (2024). Forecasting hospital room and ward occupancy using static and dynamic information concurrently: Retrospective single-center cohort study. JMIR Medical Informatics, 12, e53400. https://doi.org/10.2196/53400
  23. Silva, E., Pereira, M. F., Vieira, J. T., Ferreira-Coimbra, J., Henriques, M., & Rodrigues, N. F. (2023). Predicting hospital emergency department visits accurately: A systematic review. International Journal of Health Planning and Management, 38(4), 904–917. https://doi.org/10.1002/hpm.3629
  24. Sivasundaralingam, D., Smith, N., & Lee, A. (2025). Predicting inpatient bed demand using machine learning. Unpublished manuscript. ResearchGate. https://n9.cl/8hq2ba
  25. Suresh, N. V., Selvakumar, A., Sridhar, G., & S., C. (2024). Operational Efficiency and Cost Reduction: The Role of AI in Healthcare Administration. In B. Singla, K. Shalender, & K. Stamer (Eds.), Revolutionizing the Healthcare Sector with AI (pp. 262-272). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-3731-8.ch013
  26. Suryawanshi, V., Kanyal, D., Sabale, S., & Bhoyar, V. (2024). The role of AI in enhancing hospital operational efficiency and patient care. Multidisciplinary Reviews, 8(5), 2025153. https://doi.org/10.31893/multirev.2025153
  27. Tuominen, J., Pulkkinen, E., Peltonen, J., Kanniainen, J., Oksala, N., Palomäki, A., & Roine, A. (2023). Forecasting emergency department crowding with advanced machine learning models and multivariable input. arXiv. https://doi.org/10.48550/arXiv.2308.16544

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