Designing A Predictive Analytics Framework for Enhancing Patient Flow and Reducing Emergency Care Wait Times

References

[1] Araz, O. M., Olson, D., & Ramirez-Nafarrate, A. (2020). Predictive analytics for hospital admissions from the emergency department using triage data. International Journal of Production Economics, 228, 107741.

[2] Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big data in health care: Using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123–1131.

[3] Bernstein, S. L., Aronsky, D., Duseja, R., Epstein, S., Handel, D., Hwang, U., McCarthy, M., John McConnell, K., Pines, J. M., Rathlev, N., Schafermeyer, R., Zwemer, F., Schull, M., Asplin, B. R., & Society for Academic Emergency Medicine, Emergency Department Crowding Task Force. (2009). The effect of emergency department crowding on clinically oriented outcomes. Academic Emergency Medicine, 16(1), 1–10.

[4] Boyle, A., Coleman, J., Sultan, Y., Dhakshinamoorthy, V., Nambiar, B., & Hegarty, D. (2020). The future of emergency medicine: A transformative vision for the UK. Emergency Medicine Journal, 37(9), 579–584.

[5] Carter, E. J., Pouch, S. M., & Larson, E. L. (2014). The relationship between emergency department crowding and patient outcomes: A systematic review. Journal of Nursing Scholarship, 46(2), 106–115.

[6] Dickson, E. W., Anguelov, Z., Vetterick, D., Eller, A., & Singh, S. (2009). Use of lean in the emergency department: A case series of 4 hospitals. Annals of Emergency Medicine, 54(4), 504–510.

[7] Dubovitskaya, A., Novotny, P., Xu, Z., & Wang, F. (2020). Applications of artificial intelligence and machine learning in dynamic patient flow management in emergency departments. JAMIA Open, 3(3), 338–347.

[8] Green, L. V., Soares, J., Giglio, J. F., & Green, R. A. (2006). Using queueing theory to increase the effectiveness of emergency department provider staffing. Academic Emergency Medicine, 13(1), 61–68.

[9] Gul, M., & Guneri, A. F. (2022). A comprehensive review of emergency department simulation and a predictive analytics-based framework for its future development. Simulation Modelling Practice and Theory, 118, 102528.

[10] Hoot, N. R., & Aronsky, D. (2008). Systematic review of emergency department crowding: Causes, effects, and solutions. Annals of Emergency Medicine, 52(2), 126–136.

[11] Huang, Y., Benyoucef, M., & Zhang, J. (2020). Identifying and managing bottlenecks in the emergency department: A systematic review. Production and Operations Management, 29(7), 1679–1699.

[12] Katz, D., Blumberg, S., & Sun, Y. (2021). Operational research in emergency medicine: A comprehensive review. Operations Research for Health Care, 28, 100285.

[13] McManus, M. L., Long, M. C., Cooper, A., & Litvak, E. (2003). Queuing theory accurately models the need for critical care resources. Anesthesiology, 100(5), 1271–1276.

[14] Morley, C., Unwin, M., Peterson, G. M., Stankovich, J., & Kinsman, L. (2018). Emergency department crowding: A systematic review of causes, consequences and solutions. PLOS ONE, 13(8), e0203316.

[15] Sun, Y., Teow, K. L., Heng, B. H., Ooi, C. K., & Tay, S. Y. (2019). Real-time prediction of waiting time in the emergency department, using quantile regression. Annals of Emergency Medicine, 60(3), 299–308.

[16] Wessler, B. S., Kent, D. M., & Konstam, M. A. (2021). Predictive analytics in healthcare: A guide for clinicians. JAMA Cardiology, 6(3), 349–350.

[17] Wiler, J. L., Handel, D. A., Ginde, A. A., Aronsky, D., Genes, N. G., Hackman, J. L., ... & Pines, J. M. (2011). Predictors of patient length of stay in 9 emergency departments. The American Journal of Emergency Medicine, 29(8), 893–900.

[18] Wong, A., Montoy, J. C. C., Thaler, A., Etchemendy, E., Hernandez, A., Gharibo, C., ... & Liu, S. W. (2019). The use of a dashboard to improve emergency department throughput: A quality improvement initiative. BMJ Open Quality, 8(3), e000579.

@article{1711325,
      author = {Eunice Feyisayo Ogundipe},
      title = {Designing A Predictive Analytics Framework for Enhancing Patient Flow and Reducing Emergency Care Wait Times},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {4},
      pages = {1122-1127},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1711325.pdf},
      abstract = {The world is still experiencing serious issues in the management of patient flow in emergency departments (EDs), as overcrowding and long wait times pose a danger to the quality of care and patient safety. Old models of resource management and process control have been reactive in nature, whereby bottlenecks are only managed once they have been created. This research proposes a conceptual framework that integrates predictive analytics into emergency care, aiming to enhance patient flow and reduce waiting times. It is based on the theories of queuing, operations management, and health informatics, providing a multidisciplinary background for the study and treatment of emergency care dynamics. It recognizes four central elements, including sources of data, analytic processes, decision-support deliverables, and operational behavior, which interact to create a continuous prediction, decision-making, and intervention cycle. The connection between these constructs highlights the importance of converting raw data into actionable information that can be used in making clinical and managerial decisions to achieve a more efficient workflow and timely patient care. The model also defines assumptions and propositions, stating that success requires reliable access to data, technological support, and employee adherence. It has practical implications for clinicians, hospital managers, and policymakers, highlighting the limitations associated with data privacy and the potential bias in its algorithms. This framework represents a new approach to emergency care delivery, transitioning from reactive to proactive management. It provides the theoretical framework and a practical roadmap for using predictive analytics to enhance system efficiency, patient outcomes, and overall healthcare performance.},
      keywords = {Predictive Analytics; Patient Flow; Emergency Care; Waiting Times; Health Informatics; Operations Management},
      month = {October},
      doi = {https://doi.org/10.64388/IREV9I4-1711325-6501}
  }