Cargando…
The Application of GSCM in Eliminating Healthcare Waste: Hospital EDC as an Example
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...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6862180/ https://www.ncbi.nlm.nih.gov/pubmed/31652898 http://dx.doi.org/10.3390/ijerph16214087 |
_version_ | 1783471493950210048 |
---|---|
author | Chang, Huan-Cheng Wang, Mei-Chin Liao, Hung-Chang Wang, Ya-huei |
author_facet | Chang, Huan-Cheng Wang, Mei-Chin Liao, Hung-Chang Wang, Ya-huei |
author_sort | Chang, Huan-Cheng |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6862180 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-68621802019-12-05 The Application of GSCM in Eliminating Healthcare Waste: Hospital EDC as an Example Chang, Huan-Cheng Wang, Mei-Chin Liao, Hung-Chang Wang, Ya-huei Int J Environ Res Public Health Article 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. MDPI 2019-10-24 2019-11 /pmc/articles/PMC6862180/ /pubmed/31652898 http://dx.doi.org/10.3390/ijerph16214087 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chang, Huan-Cheng Wang, Mei-Chin Liao, Hung-Chang Wang, Ya-huei The Application of GSCM in Eliminating Healthcare Waste: Hospital EDC as an Example |
title | The Application of GSCM in Eliminating Healthcare Waste: Hospital EDC as an Example |
title_full | The Application of GSCM in Eliminating Healthcare Waste: Hospital EDC as an Example |
title_fullStr | The Application of GSCM in Eliminating Healthcare Waste: Hospital EDC as an Example |
title_full_unstemmed | The Application of GSCM in Eliminating Healthcare Waste: Hospital EDC as an Example |
title_short | The Application of GSCM in Eliminating Healthcare Waste: Hospital EDC as an Example |
title_sort | application of gscm in eliminating healthcare waste: hospital edc as an example |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6862180/ https://www.ncbi.nlm.nih.gov/pubmed/31652898 http://dx.doi.org/10.3390/ijerph16214087 |
work_keys_str_mv | AT changhuancheng theapplicationofgscmineliminatinghealthcarewastehospitaledcasanexample AT wangmeichin theapplicationofgscmineliminatinghealthcarewastehospitaledcasanexample AT liaohungchang theapplicationofgscmineliminatinghealthcarewastehospitaledcasanexample AT wangyahuei theapplicationofgscmineliminatinghealthcarewastehospitaledcasanexample AT changhuancheng applicationofgscmineliminatinghealthcarewastehospitaledcasanexample AT wangmeichin applicationofgscmineliminatinghealthcarewastehospitaledcasanexample AT liaohungchang applicationofgscmineliminatinghealthcarewastehospitaledcasanexample AT wangyahuei applicationofgscmineliminatinghealthcarewastehospitaledcasanexample |