USING QUEUEING MODEL TO ANALYZE PATIENT FLOW IN EMERGENCY HEALTH CARE DEPARTMENT

Pushpandra Kumar; Kavita Chaudhary

doi:10.29121/shodhkosh.v5.i6.2024.2011

Authors

Pushpandra Kumar Department of Mathematics, Constituent Government College, Richha, Baheri, Bareilly(M.J.P., Rohilkhand University, Bareilly) ,U.P.,India
Kavita Chaudhary Department of Mathematics Constituent Government College, Hasanpur, Amroha (M.J.P.,Rohilkhand University,Bareilly),U.P.,India

DOI:

https://doi.org/10.29121/shodhkosh.v5.i6.2024.2011

Keywords:

Queue Discipline, Emergency Department, Waiting Time

Abstract [English]

In this paper, Overcrowding in emergency departments (EDs) is a prevalent issue that might compromise the standard and accessibility of medical care. Examining the emergency department presentations over the past three years, we have seen a steady rise in the quantity of presentations. It is a struggle for every ED to reduce patient wait times, deliver care on time, and raise patient satisfaction. According to patient satisfaction surveys, the most common concern is over lengthy wait times. We have analyzed 1890 questions for a period of three years (2019-2022). The most common complaints, with an overall satisfaction rating of 78, 66%, are about the lengthy wait times, the waiting staff room which is small area, and the inadequate staff. In order to properly handle these scenarios, we suggested using queuing models for our investigation, since they may yield pretty accurate assessments of the functionality of our system. The case study's data set comprehensive information from January 1 to December 31, 2022, a total of 48.218 patients who were registered during that time. The study's findings can aid in our comprehension of the scope of the issue at hand, the connection between waiting times and available resources, and how to monitor and assess performance in order to identify areas for improvement and resolve day to day crises.

References

Fomundam, S., & Herrmann, J. (2007). A survey of Queuing Theory Applications in healthcare. ISR Technical Report 24 . https://dl.acm.org/doi/10.5555/2693848.2694015

Vaericourt, f. D., & Jennings, O. (2011). Nurse staffing in medical units: a queuing perspective. Operation Researche 59 , 1320-1331. DOI: 10.1287/opre.1110.0968 DOI: https://doi.org/10.1287/opre.1110.0968

Vazquez-Serrano, J. I., Peimbert-Garcıa, R. E., & Cardenas-Barron, L.(2021). Discrete-event simulation modeling in healthcare: A comprehensive review. International Journal of Environmental Research and Public Health, 18(22), 12262. https://doi.org/10.3390/ijerph182212262 DOI: https://doi.org/10.3390/ijerph182212262

Shahverdi, B., Miller-Hooks, E., Tariverdi, M., Ghayoomi, H., Prentiss, D., & Kirsch, T. D. (2022). Models for assessing strategies for improving hospital capacity for handling patients during a pandemic. Disaster Medicine and Public Health Preparedness, 3(2), 1–10. https://doi.org/10.1017/dmp.2022.12

Bai, J., Fugener, A., Gonsch, J., Brunner, J. O., & Blobner, M. (2021). Managing admission and discharge processes in intensive care units. Health Care Management Science, 24(4), 666–685. https://doi.org/10. 1007/s10729-021-09560-6 DOI: https://doi.org/10.1007/s10729-021-09560-6

Baril, C., Gascon, V., & Miller, J. (2020). Design of experiments and discrete-event simulation to study oncology nurse workload. IISETransactions on Healthcare Systems Engineering, 10(1), 74–86. https://doi.org/10.1080/24725579.2019.1680581 DOI: https://doi.org/10.1080/24725579.2019.1680581

Anders, R.L. (2021). Patient safety time for federally mandated registered nurse to patient ratio. Nursing Forum, 56(4), 1038-1043. doi: 10.1111/nuf. DOI: https://doi.org/10.1111/nuf.12625

N.R.Hoot, & Aronsky, D. (2008). Systematic Review of Emergency Department Crowding: Causes, Effects, and Solutions. Annals of Emergency Medicine 52 , 126-136. doi: 10.1016/j.annemergmed.2008.03.014. DOI: https://doi.org/10.1016/j.annemergmed.2008.03.014

Cavallo, J. J., Donoho, D. A., & Forman, H. P. (2020). Hospital Capacity and Operations in the Coronavirus Disease 2019 (COVID-19) pandemic—Planning for the Nth patient. JAMA Health Forum, 1(3), e200345. https://doi.org/10.1001/jamahealthforum.2020.0345. DOI: https://doi.org/10.1001/jamahealthforum.2020.0345

Samiedaluie, S., & Verter, V. (2019). The impact of specialization of hospitals on patient access to care; A queuing analysis with an application to a neurological hospital. Health Care Management Science, 22(4), 709–726. https://doi.org/10.1007/s10729-018-9453-7 DOI: https://doi.org/10.1007/s10729-018-9453-7

Zayas-Caban, G., Xie, J., Green, L. V., & Lewis, M. E. (2019). Policies for physician allocation to triage and treatment in emergency departments. IISE Transactions on Healthcare Systems Engineering,9(4), 342–356. https://doi.org/10.1080/24725579.2019.1620384 DOI: https://doi.org/10.1080/24725579.2019.1620384

Pegoraro, F., Santos, E. A. P., De, E., & Loures, F. R. (2020). A support framework for decision making in emergency department management.Computers & Industrial Engineering, 146, 106477. https://doi.org/10.1016/j.cie.2020.106477 DOI: https://doi.org/10.1016/j.cie.2020.106477

TariVerdi, M., Miller-Hooks, E., Kirsch, T., & Levin, S. (2019). A resource-constrained, multi-unit hospital model for operational strategies evaluation under routine and surge demand scenarios. IISE Transactions on Healthcare Systems Engineering, 9(2), 103–119. https://doi.org/10.1080/24725579.2019.1584132 DOI: https://doi.org/10.1080/24725579.2019.1584132

Miller-Hooks, E., Tariverdi, M., Prentiss, D., & Thomas, D. K. (2021). A flatter curve affords hospitals greater time to prepare and improved response to a pandemic surge. International Journal of Medical Informatics, 2(3), 12. https://doi.org/10.2139/ssrn.3991 DOI: https://doi.org/10.2139/ssrn.3991691

Ordu, M., Demir, E., Tofallis, C., & Gunal, M. M. (2021). A novel healthcare resource allocation decision support tool: A forecasting-simulation- optimization approach. Journal of the Operational Research Society, 72(3), 485–500. https://doi.org/10.1080/01605682.2019.1700186 DOI: https://doi.org/10.1080/01605682.2019.1700186

Yong, E. (2021). How the pandemic now ends. The Atlantic. https:// www.theatlantic.com/health/archive/2021/08/delta-has-changed-pandemic endgame/619726/

D., & Kirsch, T. D. (2022). Models for assessing strategies for improving hospital capacity for handling patients during a pandemic. Disaster Medicine and Public Health Preparedness, 3(2), 1–10. https://doi.org/10.1017/dmp.2022.12 DOI: https://doi.org/10.1017/dmp.2022.12

Ghayoomi, H., Laskey, K., Miller-Hooks, E., Hooks, C., & Tariverdi, M. (2021). Assessing resilience of hospitals to cyberattack. Digital Health, 7, 20552076211059366–20552076211059325. https://doi.org/ 10.1177/20552076211059366 DOI: https://doi.org/10.1177/20552076211059366

Pandey, N., Kaushal, V., Puri, G. D., Taneja, S., Biswal, M., Mahajan, P., Guru, R. R., Malhotra, P., Sehgal, I. S., Dhooria, S., Muthu, V., & Agarwal, R. (2020). Transforming a general hospital to an infectious disease hospital for COVID-19 over 2 weeks. Frontiers in Public Health, 8, 382. https://doi.org/10.3389/fpubh.2020.00382 DOI: https://doi.org/10.3389/fpubh.2020.00382

Mockel, M., Bachmann, U., Behringer, W., Pfafflin, F., & Stegemann, M. S. (2020). How emergency departments prepare for virus disease outbreaks like COVID-19. European Journal of Emergency Medicine: Official Journal of the European Society for Emergency Medicine, 27(3), 161–162. https://doi.org/10.1097/MEJ.0000000000000703 DOI: https://doi.org/10.1097/MEJ.0000000000000703

Cao, Y., Li, Q., Chen, J., Guo, X., Miao, C., Yang, H., Chen, Z., Li, C., & Li, L. (2020). Hospital emergency management plan during the COVID-19 epidemic. Academic Emergency Medicine: Official Journal of the Society for Academic Emergency Medicine, 27(4), 309–311. https://doi.org/10.1111/acem.13951 DOI: https://doi.org/10.1111/acem.13951