Queueing Theory-Based Analysis of Patient Flow in Government Hospitals of India
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Public healthcare facilities in India frequently face operational inefficiencies due to high patient inflow and limited medical resources, resulting in prolonged waiting times and congestion. This study presents a queueing theory–based analytical framework to examine patient flow in government hospitals in India, with specific emphasis on outpatient and emergency services. Patient arrivals are modeled as stochastic processes, while service mechanisms are characterized by the availability of medical personnel and service counters. Single-server and multi-server queueing models are employed to evaluate key system performance indicators such as average waiting time, expected queue length, and server utilization. The findings indicate that peak-hour congestion significantly affects service efficiency, whereas off-peak periods exhibit resource underutilization. Numerical results show that strategic reallocation of existing staff and minor modifications in service configuration can lead to substantial reductions in patient waiting time without increasing infrastructure costs. The study demonstrates the effectiveness of operations research techniques in addressing real-world healthcare challenges and offers quantitative decision-support insights for improving operational efficiency in Indian government hospitals.
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References
Green, L. V. (2006). Queueing analysis in healthcare. Patient Flow: Reducing Delay in Healthcare Delivery, 281–307. Springer.
https://doi.org/10.1007/978-0-387-33636-7_15
Jun, J. B., Jacobson, S. H., & Swisher, J. R. (1999). Application of discrete-event simulation in health care clinics: A survey. Journal of the Operational Research Society, 50(2), 109–123.
https://doi.org/10.1057/palgrave.jors.2600669
Mak, H. Y., & Bates, D. W. (2009). Optimizing emergency department operations using queueing theory. Operations Research for Health Care, 1(1), 18–28.
Kim, S. H., Horowitz, I., Young, K. K., & Buckley, T. A. (2018). Analysis of capacity management of surgical services using queueing theory. Health Care Management Science, 21(1), 21–33.
https://doi.org/10.1007/s10729-016-9368-5
Lundgren, M., & Jansson, M. (2017). Queueing models for radiology departments: Performance analysis and capacity planning. European Journal of Operational Research, 258(2), 525–535.
https://doi.org/10.1016/j.ejor.2016.08.036
Sharma, A., & Gupta, D. (2014). A queueing model for patient flow management in outpatient departments of government hospitals. International Journal of Operational Research, 20(3), 345–360.
Patel, R., Mehta, P., & Shah, K. (2017). Performance evaluation of hospital services using M/M/s queueing models. Journal of Health Management, 19(2), 225–238.
https://doi.org/10.1177/0972063417692605
Reddy, K. S., & Rao, P. V. (2019). Priority queueing approach for emergency department performance improvement in Indian hospitals. International Journal of Healthcare Management, 12(4), 320–328.
https://doi.org/10.1080/20479700.2018.1453963
Verma, S., & Singh, R. (2023). Queueing theory and simulation-based analysis of patient waiting time in district hospitals of India. Journal of Public Health Engineering, 5(1), 45–56.
Gross, D., Shortle, J. F., Thompson, J. M., & Harris, C. M. (2018). Fundamentals of queueing theory (5th ed.). Wiley.
Hillier, F. S., & Lieberman, G. J. (2021). Introduction to operations research (11th ed.). McGraw-Hill Education.
Worthington, D. J. (1987). Queueing models for hospital waiting lists. Journal of the Operational Research Society, 38(5), 413–422.
https://doi.org/10.1057/jors.1987.70
Ministry of Health and Family Welfare. (2022). National health profile of India. Government of India.
World Health Organization. (2020). Health systems efficiency and patient flow management. WHO Press.
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