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 :
- Alenany, E., & Cadi, A. A. E. (2020). Modeling patient flow in the emergency department using machine learning and simulation. arXiv preprint arXiv:2012.01192.
- 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.
- 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
- Haripriya, G., Abinaya, K., Aarthi, N., & Kumar, P. P. (2021). Random Forest Algorithms in Health Care Sectors: A Review of Applications.
- Jarvis, P. R. E. (2016). Improving emergency department patient flow. Clinical and experimental emergency medicine, 3(2), 63.
- 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.
- 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
- 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.
- 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.
- 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.
- Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR). [Internet], 9, 381-386.
- 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.
- 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.