PATIENT ROUTING SYSTEM AS A TOOL FOR MANAGING HOSPITAL RESILIENCE DURING THE COVID-19 PANDEMIC
Authors
Khusanov Anvar Mirzakbarovich, Alimova Hilola Pulatovna

Share
Annotation
During the COVID-19 pandemic, patient routing became a key mechanism for maintaining hospital resilience. This study evaluated a multi-level dynamic routing model implemented at the Republican Specialized Hospital “Zangiota No. 1” for managing hospitalization flows. A retrospective–prospective analysis of admission and discharge data (2020–2025) assessed disease severity distribution, ICU transfer rates, and bed management efficiency. Formalized admission criteria, multi-stage triage, and regular clinical reassessment reduced inappropriate hospitalizations, optimized resource use, and improved timely access to intensive care. Predictive flow management helped prevent critical overload during peak epidemiological periods. The model demonstrated high adaptability, controllability, and scalability, supporting its use as an effective crisis-management tool and a foundation for sustainable multidisciplinary hospital operations.
Keywords
Authors
Khusanov Anvar Mirzakbarovich, Alimova Hilola Pulatovna

Share
References:
Zhong L, Pei S, et al. (2024). Patient flow networks absorb healthcare stress during pandemic crises. arXiv. https://doi.org/10.48550/arXiv.2410.060314
Litvak E, Keshavjee S, et al. (2021). How hospitals can save lives and themselves: Lessons on patient flow from the COVID-19 pandemic. Annals of Surgery, 274(1), 37–39. https://doi.org/10.1097/SLA.0000000000004871
Adelaja I, Sayma M, et al. (2020). A comprehensive hospital agile preparedness (CHAPs) tool for pandemic preparedness, based on the COVID-19 experience. Future Healthcare Journal, 7(2), 165–168. https://doi.org/10.7861/FHJ.2020-0030
Carrié A, Penmasta V, et al. (2024). Fuzzy approach to patient emergency routing: Rescuing patients from the abyss of uncertainty. https://doi.org/10.1109/ICEC59683.2024.10837158
Knight E. (2021). Smoothing variability in patient flow to improve the value of care delivery during the COVID-19 pandemic. https://doi.org/10.69554/folo2256
Parker F, Ganjkhanloo F, et al. (2024). Optimal hospital capacity management during demand surges. arXiv. https://doi.org/10.48550/arXiv.2403.15738
Karakra A, Lamine E, et al. (2020). HospiT’Win: A digital twin framework for patients’ pathways real-time monitoring and hospital organizational resilience capacity enhancement. In Proceedings of the International Workshop on Innovative Simulation for Health Care (IWISH) (pp. 62–71). https://doi.org/10.46354/I3M.2020.IWISH.012
Shi P. (2022). Operations (management) warp speed: Rapid deployment of hospital-focused predictive/prescriptive analytics for the COVID-19 pandemic. Production and Operations Management, 32(5), 1433–1452. https://doi.org/10.1111/poms.13648
Parker F, Mart’inez DA, et al. (2024). An interactive decision-support dashboard for optimal hospital capacity management. arXiv. https://doi.org/10.48550/arXiv.2403.15634
Lu Y, Guan Y, et al. (2021). Hospital beds planning and admission control policies for COVID-19 pandemic: A hybrid computer simulation approach. In Proceedings of the IEEE Conference on Automation Science and Engineering (pp. 956–961). https://doi.org/10.1109/CASE49439.2021.9551589
