Chemotherapy appointment scheduling under uncertainty using mean-risk stochastic integer programming.
Oncology clinics are often burdened with scheduling large volumes of cancer patients for chemotherapy treatments under limited resources such as the number of nurses and chairs. These cancer patients require a series of appointments over several weeks or months and the timing of these appointments i...
| Publicado en: | Health Care Management Science Vol. 21; no. 1; pp. 87 - 105 |
|---|---|
| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Mar2018
|
| 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=127551178&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 127551178 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13869620 BSE jtl: Health Care Management Science issn: 13869620 maglogo: N pubinfo: dt: Mar2018 vid: 21 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 127551178 127551178 143982805 NLM27637491 10.1007/s10729-016-9380-4 NLM27637491 127551178 ppf: 87 ppct: 18 formats: tig: atl: Chemotherapy appointment scheduling under uncertainty using mean-risk stochastic integer programming. aug: au: Alvarado, Michelle Ntaimo, Lewis affil: Texas A&M University College Station College Station, Texas, USA sug: subj: Cancer Care Facilities Administration Personnel Staffing and Scheduling Administration Appointments and Schedules Drug Therapy Algorithms Statistics Cancer Care Facilities Labor Supply Time Factors Organizational Efficiency Oncology Nursing Labor Supply Ambulatory Care Facilities Administration Sickness Impact Profile ab: Oncology clinics are often burdened with scheduling large volumes of cancer patients for chemotherapy treatments under limited resources such as the number of nurses and chairs. These cancer patients require a series of appointments over several weeks or months and the timing of these appointments is critical to the treatment's effectiveness. Additionally, the appointment duration, the acuity levels of each appointment, and the availability of clinic nurses are uncertain. The timing constraints, stochastic parameters, rising treatment costs, and increased demand of outpatient oncology clinic services motivate the need for efficient appointment schedules and clinic operations. In this paper, we develop three mean-risk stochastic integer programming (SIP) models, referred to as SIP-CHEMO, for the problem of scheduling individual chemotherapy patient appointments and resources. These mean-risk models are presented and an algorithm is devised to improve computational speed. Computational results were conducted using a simulation model and results indicate that the risk-averse SIP-CHEMO model with the expected excess mean-risk measure can decrease patient waiting times and nurse overtime when compared to deterministic scheduling algorithms by 42 % and 27 %, respectively. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|