Dynamic Queuing Algorithms for Optimized Healthcare Appointment and Patient Flow Management in OPD Systems
About this article
Keywords:
Dynamic queuing; Healthcare appointment management; Hospital queuing system; Outpatient department (OPD); Patient flow; Real-time scheduling; Resource optimizationAbstract
Efficient management of outpatient departments (OPDs) in hospitals is critical to ensure timely care and minimizing patient wait times. This paper introduces a dynamic queuing algorithm designed to optimize appointment scheduling and patient flow management in healthcare systems. The proposed solution dynamically adjusts patient queues based on real-time factors such as patient priority, appointment type, and resource availability. By implementing this system, healthcare providers can better manage fluctuations in patient load and improve overall operational efficiency. A key innovation of this approach is its ability to reallocate resources and redistribute appointments dynamically, enhancing patient satisfaction and reducing delays. The algorithm has been tested using a simulated hospital environment, and results demonstrate significant improvements in reducing waiting times and improving appointment adherence. This work contributes to the development of smarter healthcare systems that prioritize both patient outcomes and hospital workflow.
References
Dai, L., Gong, J., and Xu, S. (2018). Dynamic patient scheduling for multi-appointment health care programs. Production and Operations Manage-ment, 27(9), 1675–1689. https://doi.org/10.1111/poms.12783
Zhao, S., and Luo, L. (2022). Robust appointment scheduling in healthcare: A comprehensive review. Mathematics, 10(22), 4317. https://doi.org/10.3390/math10224317
Ye, Y., Zhu, X., and Wu, C. (2024). Asymptotically optimal appointment scheduling in the presence of patient unpunctuality. arXiv preprint, arXiv:2412.18215. https://arxiv.org/abs/2412.18215
Gupta, H., and Denton, M. (2014). Appointment scheduling algorithm considering routine and urgent patients. Expert Systems with Applications, 41(10), 4525–4534. https://doi.org/10.1016/j.eswa.2014.01.016
Chen, X., et al. (2019). Fuzzy logic for real-time queue management in hospitals. IEEE Transactions on Fuzzy Systems, 27(4), 851–862. https://doi.org/10.1109/TFUZZ.2018.2842019
View more references (15)
Chakraborty, T., Deshmukh, A. S., and Rajaram, V. (2018). Optimizing outpatient appointment system using machine learning algorithms and scheduling rules: A prescriptive analytics framework. Expert Systems with Applications, 105, 245–261. https://doi.org/10.1016/j.eswa.2018.03.006
He, Z., et al. (2022). Reinforcement learning for dynamic queue management in healthcare. Journal of Healthcare Informatics Research, 6(1), 48–62. https://doi.org/10.1007/s41666-021-00066-2
Turkcan, E., and Aktas, E. S. (2018). Performance of the smallest-variance-first rule in appointment sequencing. arXiv preprint, arXiv:1812.01467. https://arxiv.org/abs/1812.01467
Green, L. V. (2018). Patient flow modeling in healthcare systems. Production and Operations Management, 27(10), 1937–1950. https://doi.org/10.1111/poms.12846
Chakraborty, S., and Muthulakshmi, M. (2021). Predictive analytics for dynamic appointment scheduling in healthcare. Journal of Biomedical In-formatics, 115, 103674. https://doi.org/10.1016/j.jbi.2021.103674
Bilodeau, B., and Stanford, D. A. (2020). High-priority expected waiting times in the delayed accumulating priority queue with applications to health care KPIs. arXiv preprint, arXiv:2001.06054. https://arxiv.org/abs/2001.06054
Bauerhenne, C., Kolisch, R., and Schulz, A. S. (2024). Robust appointment scheduling with waiting time guarantees. arXiv preprint, arXiv:2402.12561. https://arxiv.org/abs/2402.12561
Liu, W., Lu, M., and Shi, P. (2024). Patient assignment and prioritization for multi-stage care with reentrance. arXiv preprint, arXiv:2406.12135. https://arxiv.org/abs/2406.12135
Farid Mehr, S., Venkatachalam, S., and Chinnam, R. B. (2019). Managing access to primary care clinics using robust scheduling templates. arXiv preprint, arXiv:1911.05129. https://arxiv.org/abs/1911.05129
Yousefi, N., Hasankhani, F., Kiani, M., and Yousefi, N. (2019). Appointment scheduling model in healthcare using clustering algorithms. arXiv preprint, arXiv:1905.03083. https://arxiv.org/abs/1905.03083
Oz, B., Shneer, S., and Ziedins, I. (2020). Static vs accumulating priorities in healthcare queues under heavy loads. arXiv preprint, arXiv:2003.14087. https://arxiv.org/abs/2003.14087
Safdar, K. A., Emrouznejad, A., and Dey, P. K. (2020). An optimized queue management system to improve patient flow in the absence of ap-pointment system. International Journal of Health Care Quality Assurance, 33(1), 1–15. https://doi.org/10.1108/IJHCQA-07-2019-0120
Guo, Y., and Yao, Y. (2019). On performance of prioritized appointment scheduling for healthcare. Journal of Service Science and Management, 12(5), 589–604. https://doi.org/10.4236/jssm.2019.125040
Tang, J., et al. (2020). A hybrid machine learning and optimization approach for healthcare appointment management. Journal of Healthcare Man-agement, 65(3), 157–168. https://doi.org/10.1177/1094670520903082
He, Z., et al. (2022). Reinforcement learning for dynamic queue management in healthcare. Journal of Healthcare Informatics Research, 6(1), 48–62. https://doi.org/10.1007/s41666-021-00066-2