Optimizing Care Delivery: A Contemporary Approach to Hospital Load Distribution and Queue Management

This paper presented a real-time, location-aware hospital load-distribution and queue-management system that closes the gap between hospital-side occupancy data and patient-side facility choice. By combining a capacity-normalized load-classification model with a distance-and-load composite ranking algorithm, and by delivering the recommendation through an integrated mobile experience that also supports navigation and appointment booking, the system addresses a gap left open by prior ED-crowding-prediction, bed-tracking, static hospital-recommendation, and queueing-theoretic work, none of which combine real-time cross-hospital visibility with an actionable, patient-facing recommendation. A simulation-based evaluation suggests the approach can meaningfully reduce average waiting time and improve load balance across a hospital network relative to naive nearest-hospital routing. Future work will proceed along several directions. Clinical triage priority will be incorporated so that severity, not only load and distance, influences routing for urgent cases. Machine-learning-based short-term wait-time prediction, in the spirit of [1] and [2], will be layered on top of the current real-time snapshot to anticipate load a few hours ahead rather than only reporting the present state. Ambulance and emergency-transport routing will be integrated so that first responders, not only walk-in patients, benefit from load-aware hospital selection. Bed-level (rather than aggregate) availability tracking, informed by IoT approaches such as [6], will improve the granularity of the capacity figure. Finally, integration with Electronic Health Records will be explored, subject to the applicable data-protection and consent requirements, to support continuity of care once a patient reaches the recommended facility, along with a dedicated emergency priority queue for time-critical cases.

[1]  T. Shou, Z. Ye, Y. Hong, Z. Wang, H. Zhu, Z. Jiang, D. Yang, B. Zhou, C. Wang, and L. Chen, “CrowdQ: Predicting the queue state of hospital emergency department using crowdsensing mobility data-driven models,” Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 7, no. 3, pp. 1–28, 2023. [2]  O. Vural, B. Ozaydin, K. Y. Aram, J. Booth, B. Freeman Lindsey, and A. Ahmed, “An artificial intelligence–based framework for predicting emergency department overcrowding: Development and evaluation study,” JMIR Med. Inform., vol. 13, art. e73960, 2025. [3]  L. Zhou, P. Zhao, D. Wu, C. Cheng, and H. Huang, “Time series model for forecasting the number of new admission inpatients,” BMC Med. Inform. Decis. Mak., vol. 18, art. 39, 2018. [4]  L.-H. Yao, K.-C. Leung, J.-H. Hong, C.-L. Tsai, and L.-C. Fu, “A system for predicting hospital admission at emergency department based on electronic health record using convolution neural network,” in Proc. 2020 IEEE Int. Conf. Systems, Man, and Cybernetics (SMC), Toronto, ON, Canada, 2020, pp. 546–551. [5]  A. Rehalia et al., “Real-time hospital bed information system during pandemic situation,” in Proc. 2023 Int. Conf. Computational Intelligence for Information, Security and Communication Applications (CIISCA), 2023, pp. 1–6. [6]  R. S. A. Corpuz, J. Usa, J. C. Obaña, J. N. Borbe, D. A. Salazar, and T. M. Valeza, “A hospital bed availability monitoring system using the Internet of Things,” Int. J. Inf. Technol. Secur., vol. 16, no. 3, pp. 13–26, 2024. [7]   C.-H. Wang, R. Tian, K. Hu, Y.-T. Chen, and T.-H. Ku, “A Markov decision optimization of medical service resources for two-class patient queues in emergency departments via particle swarm optimization algorithm,” Sci. Rep., vol. 15, art. 2942, 2025. [8]  M. Armony, S. Israelit, A. Mandelbaum, Y. N. Marmor, Y. Tseytlin, and G. B. Yom-Tov, “On patient flow in hospitals: A data-based queueing-science perspective,” Stochastic Systems, vol. 5, no. 1, pp. 146–194, 2015. [9]    Google, “Cloud Firestore Documentation,” Firebase, 2026. [Online]. Available: https://firebase.google.com/docs/firestore [10]   Google, “Flutter Documentation,” 2026. [Online]. Available: https://docs.flutter.dev