Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm.
The number of emergency cases or emergency room visits rapidly increases annually, thus leading to an imbalance in supply and demand and to the long-term overcrowding of hospital emergency departments (EDs). However, current solutions to increase medical resources and improve the handling of patient...
| Publicado en: | Health Care Management Science Vol. 20; no. 1; pp. 55 - 76 |
|---|---|
| Autores principales: | , , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Mar2017
|
| 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=121442066&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 121442066 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13869620 BSE jtl: Health Care Management Science issn: 13869620 maglogo: N pubinfo: dt: Mar2017 vid: 20 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 121442066 121442066 NLM26242590 121442066 10.1007/s10729-015-9335-1 NLM26242590 121442066 ppf: 55 ppct: 21 formats: tig: atl: Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm. aug: au: Feng, Yen-Yi Wu, I-Chin Chen, Tzu-Li affil: Department of Emergency Medicine , Mackay Memorial Hospital , Taipei 10449 Republic of China sug: subj: Emergency Service Administration Resource Allocation Administration Length of Stay Statistics and Numerical Data Emergency Service Statistics and Numerical Data Management Statistics Algorithms Resource Allocation Statistics and Numerical Data Human ab: The number of emergency cases or emergency room visits rapidly increases annually, thus leading to an imbalance in supply and demand and to the long-term overcrowding of hospital emergency departments (EDs). However, current solutions to increase medical resources and improve the handling of patient needs are either impractical or infeasible in the Taiwanese environment. Therefore, EDs must optimize resource allocation given limited medical resources to minimize the average length of stay of patients and medical resource waste costs. This study constructs a multi-objective mathematical model for medical resource allocation in EDs in accordance with emergency flow or procedure. The proposed mathematical model is complex and difficult to solve because its performance value is stochastic; furthermore, the model considers both objectives simultaneously. Thus, this study develops a multi-objective simulation optimization algorithm by integrating a non-dominated sorting genetic algorithm II (NSGA II) with multi-objective computing budget allocation (MOCBA) to address the challenges of multi-objective medical resource allocation. NSGA II is used to investigate plausible solutions for medical resource allocation, and MOCBA identifies effective sets of feasible Pareto (non-dominated) medical resource allocation solutions in addition to effectively allocating simulation or computation budgets. The discrete event simulation model of ED flow is inspired by a Taiwan hospital case and is constructed to estimate the expected performance values of each medical allocation solution as obtained through NSGA II. Finally, computational experiments are performed to verify the effectiveness and performance of the integrated NSGA II and MOCBA method, as well as to derive non-dominated medical resource allocation solutions from the algorithms. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|