Patient Flow Control in Emergency Departments Using Simulation Modeling and the Random Forest Algorithm


Authors : Pyelshak Yusuf; Fatima Umar Zambuk; Badamasi Imam Yau; Solomon Rifkatu Aaron; Atangs Ishaku; Aminu Agabus; Solomon Panshak Dawal; Ismail Zahraddeen Yakubu

Volume/Issue : Volume 9 - 2024, Issue 3 - March


Google Scholar : https://tinyurl.com/2vye9ks5

Scribd : https://tinyurl.com/5ykxtexn

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAR1035

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.


Abstract : The proposed thesis aims to optimize patient flow and reduce waiting times in emergency departments using simulation modeling and the Random Forest algorithm. Emergency departments face significant challenges in managing patient flow and reducing waiting times, which can lead to increased patient dissatisfaction and decreased quality of care. The proposed solution uses simulation modeling to create a virtual model of the emergency department and simulate patient flow under different scenarios. The Random Forest algorithm is then used to analyze the simulation results and identify the factors impacting patient flow and waiting times. By optimizing these factors, the proposed solution aims to reduce waiting times and improve the overall patient experience. The research involves the development and validation of the simulation model and the implementation of the Random Forest algorithm using real-world emergency department data. The outcomes of the implemented Random Forest Model in Chapter Four showcase its efficacy with an accuracy rate of 0.85, sensitivity rate of 0.99, and other favorable metrics. The proposed solution has the potential to improve patient outcomes and reduce costs associated with emergency department overcrowding and delays.

Keywords : Emergency Department, Patient Flow Control, Machine Learning Algorithm, Simulation Model.

References :

  1. Alenany, E., & Cadi, A. A. E. (2020). Modeling patient flow in the emergency department using machine learning and simulation. arXiv preprint arXiv:2012.01192.
  2. Elalouf, A., & Wachtel, G. (2021, December). Queueing problems in emergency departments: a review of practical approaches and research methodologies. In Operations Research Forum (Vol. 3, No. 1, p. 2). Cham: Springer International Publishing.
  3. Finkelstein, S. M., Wong, E., Lin, H., & Li, Y. (2021). Reducing Patient Waiting Time in Emergency Departments Using Reinforcement Learning and Queueing Theory. INFORMS Journal on Applied Analytics, 51(3), 162–179. https://doi.org/10.1287/inte.2020.1064
  4. Haripriya, G., Abinaya, K., Aarthi, N., & Kumar, P. P. (2021). Random Forest Algorithms in Health Care Sectors: A Review of Applications.
  5. Jarvis, P. R. E. (2016). Improving emergency department patient flow. Clinical and experimental emergency medicine, 3(2), 63.
  6. Khalilnejad Tabari, M., Aghajari, S., & Salahshour, S. (2022). Reinforcement Learning for Scheduling in Emergency Departments. In 2022 IEEE International Conference on Healthcare Informatics (ICHI) (pp. 10-16). IEEE.
  7. Lee, H., Lee, J., Lee, H., Lee, J., Lee, Y., & Kim, Y. (2021). Reducing Patient Waiting Time in Emergency Departments Using Reinforcement Learning and Multi-Agent Systems. Applied Sciences, 11(8), 3429. https://doi.org/10.3390/app11083429
  8. Li, J., Cao, W., Yang, Y., Hu, S., & Wei, S. (2021). Data-driven optimization of emergency department operations using machine learning algorithms. Health Care Management Science, 24(1), 32-46.
  9. Liu, J., Xie, J., Cheng, X., Zhang, Z., Li, X., & Li, Z. (2022). A Novel Reinforcement Learning Algorithm for Emergency Department Patient Flow Optimization. Journal of Healthcare Engineering, 2022, 1-11.
  10. Liu, Y., Liu, X., Li, Q., Zhu, Y., Ma, J., & Wang, Y. (2021). An intelligent triage system for an emergency department based on machine learning and queuing theory. Journal of Medical Systems, 45(9), 1-10.
  11. Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR). [Internet], 9, 381-386.
  12. Oh, S. H., Park, J., Lee, S. J., Kang, S., & Mo, J. (2022). Reinforcement learning-based expanded personalized diabetes treatment recommendation using South Korean electronic health records. Expert Systems with Applications, 206, 117932.
  13. Razavi, S., de la Hoz, E., & Akhavan-Tabatabaei, R. (2021). Optimizing Patient Flow in Emergency Departments Using Discrete Event Simulation and Deep Reinforcement Learning. International Journal of Environmental Research and Public Health, 18(9), 4722. https://doi.org/10.3390/ijerph18094722

 

The proposed thesis aims to optimize patient flow and reduce waiting times in emergency departments using simulation modeling and the Random Forest algorithm. Emergency departments face significant challenges in managing patient flow and reducing waiting times, which can lead to increased patient dissatisfaction and decreased quality of care. The proposed solution uses simulation modeling to create a virtual model of the emergency department and simulate patient flow under different scenarios. The Random Forest algorithm is then used to analyze the simulation results and identify the factors impacting patient flow and waiting times. By optimizing these factors, the proposed solution aims to reduce waiting times and improve the overall patient experience. The research involves the development and validation of the simulation model and the implementation of the Random Forest algorithm using real-world emergency department data. The outcomes of the implemented Random Forest Model in Chapter Four showcase its efficacy with an accuracy rate of 0.85, sensitivity rate of 0.99, and other favorable metrics. The proposed solution has the potential to improve patient outcomes and reduce costs associated with emergency department overcrowding and delays.

Keywords : Emergency Department, Patient Flow Control, Machine Learning Algorithm, Simulation Model.

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe