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Development of a cost‐optimization model to reduce bottlenecks: A health service case study
Author(s) -
Lau Henry,
Dadich Ann,
Nakandala Dilupa,
Evans Huntley,
Zhao Li
Publication year - 2018
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12294
Subject(s) - computer science , workflow , genetic algorithm , risk analysis (engineering) , service (business) , operations research , machine learning , business , marketing , database , engineering
With the increasing use of emergency departments, many public hospitals experience bottlenecks that hinder patient flow within the health system. Mitigating bottlenecks can enhance workflow efficiency and reduce patient wait‐time. Yet given the complexity of health services, current techniques have a limited capacity to address this issue. This article introduces an innovative generic cost‐optimization model based on genetic algorithm to alleviate bottlenecks without the need for complex mathematical analysis. A case study is presented to validate its feasibility, demonstrating an evidence‐based, pragmatic way to alleviate bottlenecks that practitioners can readily implement.