Optimization of Service Process in Emergency Department Using Discrete Event Simulation and Machine Learning Algorithm.
Introduction: Emergency departments are operating with limited resources and high levels of unexpected requests. This study aimed tominimize patients' waiting time and the percentage of units' engagement to improve the emergency department (ED) efficiency. Methods: A comprehensive combination method...
| Publicado en: | Archives of Academic Emergency Medicine Vol. 10; no. 1; pp. 1 - 9 |
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| Autores principales: | , , |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
Shahid Beheshti University of Medical Sciences
2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=162690895&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162690895 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 26454904 MFMK jtl: Archives of Academic Emergency Medicine issn: 26454904 maglogo: N pubinfo: dt: 2022 vid: 10 iid: 1 pid: 87963 pub: Shahid Beheshti University of Medical Sciences artinfo: ui: 162690895 162690895 162690895 10.22037/aaem.v10i1.1545 162690895 ppf: 1 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Optimization of Service Process in Emergency Department Using Discrete Event Simulation and Machine Learning Algorithm. aug: au: Hosseini_Shokouh, Sayyed_Morteza Mohammadi, Kasra Yaghoubi, Maryam affil: Health Management Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran sug: subj: Emergency Service Machine Learning Computer Simulation Health Care Delivery Waiting Lists Algorithms Neural Networks (Computer) Genetics Genetic Screening Instrument Validation Workflow Data Analysis Software Descriptive Statistics Human ab: Introduction: Emergency departments are operating with limited resources and high levels of unexpected requests. This study aimed tominimize patients' waiting time and the percentage of units' engagement to improve the emergency department (ED) efficiency. Methods: A comprehensive combination method involvingDiscrete Event Simulation (DES), Artificial Neural Network (ANN) algorithm, and finally solving the model by use of Genetic Algorithm (GA) was used in this study. After simulating the case and making sure about the validity of the model, experiments were designed to study the effects of change in individuals and equipment on the average time that patients wait, as well as units' engagement in ED. Objective functions determined using Artificial Neural Network algorithm and MATLAB software were used to train it. Finally, after estimating objective functions and adding related constraints to the problem, a fractional Genetic Algorithm was used to solve the model. Results: According to the model optimization result, it was determined that the hospitalization unit, as well as the hospitalization units' doctors, were in an optimized condition, but the triage unit, as well as the fast track units' doctors, should be optimized. After experiments in which the average waiting time in the triage section reached near zero, the average waiting time in the screening section was reduced to 158.97 minutes and also the coefficient of units' engagement in both sections were 69% and 84%, respectively. Conclusion: Using the service optimization method creates a significant improvement in patient's waiting time and stream at emergency departments, which is made possible through appropriate allocation of the human and material resources. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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