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The Application of GSCM in Eliminating Healthcare Waste: Hospital EDC as an Example

Author

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  • Huan-Cheng Chang

    (Division of Nephrology, Department of Medicine, Landseed International Hospital, No. 77, Guangtai Road, Pingzhen Dist., Taoyuan 324, Taiwan
    Department of Health Care Management, Chang Gung University, No. 259, Wenhua 1st Road, Guishan Dist., Taoyuan 33302, Taiwan)

  • Mei-Chin Wang

    (Noble Health Management Center, Landseed International Hospital, No. 77, Guangtai Road, Pingzhen Dist., Taoyuan 324, Taiwan)

  • Hung-Chang Liao

    (Department of Medical Management, Chung Shan Medical University Hospital, No. 110, Section 1, Jian-Koa N. Road, Taichung 402, Taiwan
    Department of Health Services Administration, Chung Shan Medical University, No. 110, Section 1, Jian-Koa N. Road, Taichung 402, Taiwan)

  • Ya-huei Wang

    (Department of Medical Management, Chung Shan Medical University Hospital, No. 110, Section 1, Jian-Koa N. Road, Taichung 402, Taiwan
    Department of Applied Foreign Languages, Chung Shan Medical University, No. 110, Section 1, Jian-Koa N. Road, Taichung 402, Taiwan)

Abstract

Eliminating unnecessary healthcare waste in hospitals and providing better healthcare quality are the core issues of green supply chain management (GSCM). Hence, this study used a hospital’s emergency department crowding (EDC) problem to illustrate how to establish an emergency medicine service (EMS) simulation system to obtain a robust parameters setting for solving hospitals’ EDC and waste problems, thereby increasing healthcare quality. Inappropriate resource allocation results in more serious EDC; more serious EDC results in increasing operating costs. Therefore, in the healthcare system, waste includes inappropriate costs and inappropriate resource allocation. The EMS of a medical center in central Taiwan was the object of the study. In this study, the dynamic Taguchi method was used to set the signal factor, noise factor, and control factors to simulate the EMS system to obtain the optimal parameters setting. The performance was set to Emergency Department Work Index ( EDWIN C ) and system time (waiting time and service time) per patient. The signal factor was set to the number of physicians; the noise factor was set to patient arrival rate; the control factors included persuading Triage 4 and Triage 5 outpatients, checkup process, bed occupation rate in the emergency department (ED), and medical checkup sequence for Triage 4 and Triage 5 patients. This study makes two significant contributions. First, the study introduces the GSCM concept to the healthcare setting to bring green innovation to hospitals. Hospital administrators may hence design better GSCM activities to facilitate healthcare processes to provide better healthcare outcomes. Second, the study applied the dynamic Taguchi method to the EMS and neural network (NN) to construct a computational model revealing the cause (factors) and effect (performances) relationship. In addition, the genetic algorithm (GA), a solution method, was used to obtain the optimal parameters setting of the EDC in Taiwan. Hence, after obtaining the solutions, the unnecessary waste in EDC—inappropriate costs and inappropriate resource allocation—is reduced.

Suggested Citation

  • Huan-Cheng Chang & Mei-Chin Wang & Hung-Chang Liao & Ya-huei Wang, 2019. "The Application of GSCM in Eliminating Healthcare Waste: Hospital EDC as an Example," IJERPH, MDPI, vol. 16(21), pages 1-13, October.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:21:p:4087-:d:279677
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    References listed on IDEAS

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