Development of simulation optimization methods for solving patient referral problems in the hospital-collaboration environment.
This research studied a patient referral problem among multiple cooperative hospitals for sharing imaging services' referrals. The proposed problem consisted of many types of patients and the uncertainty associated with the number of patients of each type, patients' arrival time, and patients' medic...
| Publicado en: | Journal of Biomedical Informatics Vol. 73; pp. 148 - 159 |
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| Autores principales: | , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Sep2017
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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=124939177&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 124939177 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Sep2017 vid: 73 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 124939177 124939177 NLM28802837 124939177 10.1016/j.jbi.2017.08.004 NLM28802837 124939177 ppf: 148 ppct: 11 formats: tig: atl: Development of simulation optimization methods for solving patient referral problems in the hospital-collaboration environment. aug: au: Chen, Ping-Shun Lin, Ming-Han affil: Department of Industrial and Systems Engineering, Chung Yuan Christian University, Chung Li District, Taoyuan City 320, Taiwan, ROC sug: subj: Algorithms Problem Solving Hospitals Referral and Consultation Computer Simulation Human ab: This research studied a patient referral problem among multiple cooperative hospitals for sharing imaging services' referrals. The proposed problem consisted of many types of patients and the uncertainty associated with the number of patients of each type, patients' arrival time, and patients' medical operation time, leading to a difficulty in finding solutions due to the uncertain environment. This research used system simulation to construct a model and develop a simulation optimization method, combining the heuristic algorithm (patient referral mechanism) with the particle swarm optimization (PSO) method, to determine a better way to refer patients from one hospital (referring hospital) to another (recipient hospital) to receive certain imaging services. After the simulated model was verified and validated, three patient referral mechanisms to dispatch referring patients to the appropriate recipient hospitals were proposed. Based on the numerical results, the findings showed that Mechanism 2, transferring patients to the hospital with the shortest waiting time, had good performance in both scenarios: allowing patient referrals among all hospitals and limiting the patients' waiting time. Finally, this study presents the conclusions and some directions for future research. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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