Journal of Primeasia
Integrative Disciplinary Research | Online ISSN 3064-9870 | Print ISSN 3069-4353
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Volume 6 Number 1 2025
RESEARCH ARTICLE (Open Access)
Bridging Queueing Theory and Data-Driven Analytics to Reduce Patient Wait Times: A Cross-Sectional Survey of Healthcare Users in the United States
Md Samiul Alam Mazumder1*, Mithra Rani Hur2
Journal of Primeasia 6 (1) 1-11 https://doi.org/10.25163/primeasia.6110837
Submitted: 27 January 2025 Revised: 08 April 2025 Accepted: 11 April 2025 Published: 14 April 2025
Abstract
Background: Long waits, registration bottlenecks, and diagnostic delays remain stubborn features of everyday hospital life, and they wear on patients and staff alike. Whether queueing theory and data-driven analytics are actually understood, together, as a workable remedy by the people who use these systems is less well documented.
Methods: We carried out a cross-sectional survey of 185 healthcare service users recruited through purposive and convenience sampling across outpatient and diagnostic settings in the United States. A structured, five-point Likert questionnaire captured perceived causes of flow inefficiency, waiting-time experience, and attitudes toward queueing-based and analytics-based interventions; descriptive statistics and Pearson correlation analysis were used to examine relationships among flow delay, waiting time, and adoption of these two approaches.
Results: Registration delay (76.2%) and diagnostic delay (75.2%) emerged as the most frequently cited drivers of inefficiency, followed closely by staff shortages (74.6%) and scheduling weaknesses (74.0%). Flow delay correlated positively with waiting time (r = 0.68), while both queueing theory and analytics adoption correlated negatively with delay and waiting time (r = -0.61 to -0.64, and r = -0.57 to -0.59, respectively); the two approaches were themselves strongly associated (r = 0.72).
Conclusion: Respondents' experience suggests that queueing theory and data-driven analytics are not competing tools but complementary ones, and that hospitals stand to gain more from combining them than from pursuing either in isolation.
Keywords: Patient Flow Optimization; Queueing Theory; Data-Driven Analytics; Healthcare Efficiency; Cross-Sectional Survey
References
Ahsan, K. B., Alam, M. R., Morel, D. G., & Karim, M. A. (2019). Emergency department resource optimisation for improved performance: A review. Journal of Industrial Engineering International, 15(S1), 253–266. https://doi.org/10.1007/s40092-019-00335-x
Akenroye, T. O., Oyedijo, A., Rajan, V. C., Zsidisin, G. A., Mkansi, M., & Baz, J. E. (2023). Connecting the dots: Uncovering the relationships between challenges confronting Africa's organ transplant supply chain systems. Supply Chain Management: An International Journal, 28(7), 43–61. https://doi.org/10.1108/scm-12-2022-0457
Back, C. O., Manataki, A., Papanastasiou, A., & Harrison, E. (2021). Stochastic workflow modeling in a surgical ward: Towards simulating and predicting patient flow. In Communications in computer and information science (pp. 565–591). Springer. https://doi.org/10.1007/978-3-030-72379-8_28
Bae, K., Jones, M., Evans, G., & Antimisiaris, D. (2017). Simulation modelling of patient flow and capacity planning for regional long-term care needs: A case study. Health Systems, 8(1), 1–16. https://doi.org/10.1080/20476965.2017.1405873
Bakker, M., & Tsui, K. (2017). Dynamic resource allocation for efficient patient scheduling: A data-driven approach. Journal of Systems Science and Systems Engineering, 26(4), 448–462. https://doi.org/10.1007/s11518-017-5347-3
Bard, J. F., Shu, Z., Morrice, D. J., Wang, D., Poursani, R., & Leykum, L. (2014). Improving patient flow at a family health clinic. Health Care Management Science, 19(2), 170–191. https://doi.org/10.1007/s10729-014-9294-y
Baron, O. (2021). Business analytics in service operations—Lessons from healthcare operations. Naval Research Logistics, 68(5), 517–533. https://doi.org/10.1002/nav.22011
Devapriya, P., Strömblad, C. T. B., Bailey, M. D., Frazier, S., Bulger, J., Kemberling, S. T., & Wood, K. E. (2015). StRATBAM: A discrete-event simulation model to support strategic hospital bed capacity decisions. Journal of Medical Systems, 39(10), Article 130. https://doi.org/10.1007/s10916-015-0325-0
Fan, X., Tang, J., Yan, C., Guo, H., & Cao, Z. (2019). Outpatient appointment scheduling problem considering patient selection behavior: Data modeling and simulation optimization. Journal of Combinatorial Optimization, 42(4), 677–699. https://doi.org/10.1007/s10878-019-00487-x
Fathi, M., & Khakifirooz, M. (2019). Kidney-related operations research: A review. IISE Transactions on Healthcare Systems Engineering, 9(3), 226–242. https://doi.org/10.1080/24725579.2019.1640318
Feng, D., Mo, Y., Tang, Z., Chen, Q., Zhang, H., Akerkar, R., & Song, X. (2021). Data-driven hospital personnel scheduling optimization through patient's prediction. CCF Transactions on Pervasive Computing and Interaction, 3(1), 40–56. https://doi.org/10.1007/s42486-020-00052-0
Feng, Y., & Jie, X. (2023). A data-driven pharmacists scheduling problem in a pharmacy with fairness concerns. In Lecture notes in operations research (pp. 363–378). Springer. https://doi.org/10.1007/978-981-99-2625-1_29
Ghanbari, E., Ghasbe, S. S., Aghsami, A., & Jolai, F. (2022). A novel mathematical optimization model for a preemptive multi-priority M/M/C queueing system of emergency department's patients, a real case study in Iran. IISE Transactions on Healthcare Systems Engineering, 12(4), 305–321. https://doi.org/10.1080/24725579.2022.2083730
Gombolay, M., Golen, T., Shah, N., & Shah, J. (2017). Queueing theoretic analysis of labor and delivery. Health Care Management Science, 22(1), 16–33. https://doi.org/10.1007/s10729-017-9418-2
He, L., Madathil, S. C., Oberoi, A., Servis, G., & Khasawneh, M. T. (2018). A systematic review of research design and modeling techniques in inpatient bed management. Computers & Industrial Engineering, 127, 451–466. https://doi.org/10.1016/j.cie.2018.10.033
Hu, X., Barnes, S., & Golden, B. (2017). Applying queueing theory to the study of emergency department operations: A survey and a discussion of comparable simulation studies. International Transactions in Operational Research, 25(1), 7–49. https://doi.org/10.1111/itor.12400
Hu, Y., Dong, J., Perry, O., Cyrus, R. M., Gravenor, S., & Schmidt, M. J. (2021). Use of a novel patient-flow model to optimize hospital bed capacity for medical patients. The Joint Commission Journal on Quality and Patient Safety, 47(6), 354–363. https://doi.org/10.1016/j.jcjq.2021.02.008
Humphreys, P., Spratt, B., Tariverdi, M., Burdett, R. L., Cook, D., Yarlagadda, P. K. D. V., & Corry, P. (2022). An overview of hospital capacity planning and optimisation. Healthcare, 10(5), Article 826. https://doi.org/10.3390/healthcare10050826
Hurwitz, J. E., Lee, J. A., Lopiano, K. K., McKinley, S. A., Keesling, J., & Tyndall, J. A. (2014). A flexible simulation platform to quantify and manage emergency department crowding. BMC Medical Informatics and Decision Making, 14(1), Article 50. https://doi.org/10.1186/1472-6947-14-50
Konrad, R., DeSotto, K., Grocela, A., McAuley, P., Wang, J., Lyons, J., & Bruin, M. (2013). Modeling the impact of changing patient flow processes in an emergency department: Insights from a computer simulation study. Operations Research for Health Care, 2(4), 66–74. https://doi.org/10.1016/j.orhc.2013.04.001
Li, N., Li, X., & Forero, P. (2021). Physician scheduling for outpatient department with nonhomogeneous patient arrival and priority queue. Flexible Services and Manufacturing Journal, 34(4), 879–915. https://doi.org/10.1007/s10696-021-09414-x
Luo, L., Li, J., Xu, X., Shen, W., & Xiao, L. (2019). A data-driven hybrid three-stage framework for hospital bed allocation: A case study in a large tertiary hospital in China. Computational and Mathematical Methods in Medicine, 2019, Article 7370231. https://doi.org/10.1155/2019/7370231
Manktelow, M., Iftikhar, A., Bucholc, M., McCann, M., & O'Kane, M. (2022). Clinical and operational insights from data-driven care pathway mapping: A systematic review. BMC Medical Informatics and Decision Making, 22(1), Article 42. https://doi.org/10.1186/s12911-022-01756-2
Nasir, M., Summerfield, N., Dag, A., & Oztekin, A. (2020). A service analytic approach to studying patient no-shows. Service Business, 14(2), 287–313. https://doi.org/10.1007/s11628-020-00415-8
Ortíz-Barrios, M. A., & Alfaro-Saíz, J. (2020). Methodological approaches to support process improvement in emergency departments: A systematic review. International Journal of Environmental Research and Public Health, 17(8), Article 2664. https://doi.org/10.3390/ijerph17082664
Rad, R. H., Baniasadi, S., Yousefi, P., Heravi, H. M., Al-Ani, M. S., & Ilani, M. A. (2022). Presented a framework of computational modeling to identify the patient admission scheduling problem in the healthcare system. Journal of Healthcare Engineering, 2022, 1–15. https://doi.org/10.1155/2022/1938719
Su, Q., Yao, X., Su, P., Shi, J., Zhu, Y., & Xue, L. (2010). Hospital registration process reengineering using simulation method. Journal of Healthcare Engineering, 1(1), 67–82. https://doi.org/10.1260/2040-2295.1.1.67
Tao, L., & Liu, J. (2019). An intelligent healthcare decision support system. In Health information science (pp. 131–154). Springer. https://doi.org/10.1007/978-3-030-15385-4_8
Tavakoli, M., Tavakkoli-Moghaddam, R., Mesbahi, R., Ghanavati-Nejad, M., & Tajally, A. (2022). Simulation of the COVID-19 patient flow and investigation of the future patient arrival using a time-series prediction model: A real-case study. Medical & Biological Engineering & Computing, 60(4), 969–990. https://doi.org/10.1007/s11517-022-02525-z
Uslu, B. Ç., Okay, E., & Dursun, E. (2020). Analysis of factors affecting IoT-based smart hospital design. Journal of Cloud Computing: Advances, Systems and Applications, 9(1), Article 67. https://doi.org/10.1186/s13677-020-00215-5
Van Hulzen, G., Martin, N., & Depaire, B. (2021). The need for interactive data-driven process simulation in healthcare: A case study. In Lecture notes in business information processing (pp. 317–329). Springer. https://doi.org/10.1007/978-3-030-72693-5_24
Wang, L., & Alexander, C. A. (2020). Big data analytics in medical engineering and healthcare: Methods, advances and challenges. Journal of Medical Engineering & Technology, 44(6), 267–283. https://doi.org/10.1080/03091902.2020.1769758
Wang, Z., & Liu, R. (2021). Managing appointments of outpatients considering the presence of emergency patients: The combination of the analytical and data-driven approach. International Journal of Production Research, 60(13), 4214–4228. https://doi.org/10.1080/00207543.2021.2007425
Wartelle, A., Mourad-Chehade, F., Yalaoui, F., Laplanche, D., & Sanchez, S. (2022). Forecasting saturation in the emergency department: A comparison of queuing data-driven approaches. IFAC-PapersOnLine, 55(10), 1556–1561. https://doi.org/10.1016/j.ifacol.2022.09.612
Xu, Y., Shang, X., Zhao, H., Zhang, R., & Wang, J. (2018). The characteristics of service efficiency and patient flow in heavy load outpatient service system. In Communications in computer and information science (pp. 17–30). Springer. https://doi.org/10.1007/978-981-13-3149-7_2
Zhai, Y., Li, R., & Yan, Z. (2022). Research on application of meticulous nursing scheduling management based on data-driven intelligent optimization technology. Computational Intelligence and Neuroscience, 2022, Article 3293806. https://doi.org/10.1155/2022/3293806
Zhang, Y., Luo, L., Yang, J., Liu, D., Kong, R., & Feng, Y. (2018). A hybrid ARIMA-SVR approach for forecasting emergency patient flow. Journal of Ambient Intelligence and Humanized Computing, 10(8), 3315–3323. https://doi.org/10.1007/s12652-018-1059-x
Zhong, X., Lee, H. K., & Li, J. (2017). From production systems to health care delivery systems: A retrospective look on similarities, difficulties and opportunities. International Journal of Production Research, 55(14), 4212–4227. https://doi.org/10.1080/00207543.2016.1277276
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