Scheduling patient appointment in an infusion center: a mixed integer robust optimization approach.
Infusion centers are experiencing greater demand, resulting in long patient wait times. The duration of chemotherapy treatment sessions often varies, and this uncertainty also contributes to longer patient wait times and to staff overtime, if not managed properly. The impact of such long wait times...
| Publicado en: | Health Care Management Science Vol. 24; no. 1; pp. 117 - 140 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Mar2021
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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=150747727&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150747727 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13869620 BSE jtl: Health Care Management Science issn: 13869620 maglogo: N pubinfo: dt: Mar2021 vid: 24 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 150747727 146437309 150747727 NLM33044667 10.1007/s10729-020-09519-z NLM33044667 150747727 ppf: 117 ppct: 23 formats: tig: atl: Scheduling patient appointment in an infusion center: a mixed integer robust optimization approach. aug: au: Issabakhsh, Mona Lee, Seokgi Kang, Hyojung affil: Department of Industrial Engineering, University of Miami, 1251 Memorial Drive, 281, 33146, Coral Gables, FL, USA sug: subj: Ambulatory Care Facilities Administration Appointments and Schedules Drug Therapy Health Facility Departments Administration Models, Theoretical Virginia Academic Medical Centers Time Factors Organizational Efficiency Clinical Assessment Tools Impact of Events Scale ab: Infusion centers are experiencing greater demand, resulting in long patient wait times. The duration of chemotherapy treatment sessions often varies, and this uncertainty also contributes to longer patient wait times and to staff overtime, if not managed properly. The impact of such long wait times can be significant for cancer patients due to their physical and emotional vulnerability. In this paper, a mixed integer programming infusion appointment scheduling (IAS) mathematical model is developed based on patient appointment data, obtained from a cancer center of an academic hospital in Central Virginia. This model minimizes the weighted sum of the total wait times of patients, the makespan and the number of beds used through the planning horizon. A mixed integer programming robust slack allocation (RSA) mathematical model is designed to find the optimal patient appointment schedules, considering the fact that infusion time of patients may take longer than expected. Since the models can only handle a small number of patients, a robust scheduling heuristic (RSH) is developed based on the adaptive large neighborhood search (ALNS) to find patient appointments of real size infusion centers. Computational experiments based on real data show the effectiveness of the scheduling models compared to the original scheduling system of the infusion center. Also, both robust approaches (RSA and RSH) are able to find more reliable schedules than their deterministic counterparts when infusion time of patients takes longer than the scheduled infusion time. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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