Naijun Shen, Jun Wang, Jiacun Wang, Xingyun Liu, Lijun Yang, Yu Wang
{"title":"Patient Flow Congestion Control Based on Stochastic Timed Petri Net Model","authors":"Naijun Shen, Jun Wang, Jiacun Wang, Xingyun Liu, Lijun Yang, Yu Wang","doi":"10.1109/ICCSI53130.2021.9736225","DOIUrl":null,"url":null,"abstract":"Resource allocation has always been an important part of healthcare systems. The goal is to allocate resources more reasonably and thus reduce the waiting time of patients during their visits to healthcare facilities. In this paper, we use stochastic timed Petri nets (STPN) to model and simulate medical service processes and evaluate their performance. In particular, we propose a mechanism with which more doctors can be added to a service unit when a patient flow congestion is detected, and the added doctors will be freed when congestion is relieved. A case study is performed to show the effectiveness of this new adaptive resource allocation mechanism.","PeriodicalId":175851,"journal":{"name":"2021 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"155 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Cyber-Physical Social Intelligence (ICCSI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSI53130.2021.9736225","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Resource allocation has always been an important part of healthcare systems. The goal is to allocate resources more reasonably and thus reduce the waiting time of patients during their visits to healthcare facilities. In this paper, we use stochastic timed Petri nets (STPN) to model and simulate medical service processes and evaluate their performance. In particular, we propose a mechanism with which more doctors can be added to a service unit when a patient flow congestion is detected, and the added doctors will be freed when congestion is relieved. A case study is performed to show the effectiveness of this new adaptive resource allocation mechanism.