Evidence map›Paper›PMID 31652898›Full record

ReviewInternational journal of environmental research and public health2019

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

Huan-Cheng Chang, Mei-Chin Wang, Hung-Chang Liao, Ya-Huei Wang

Abstract readReview
In one paragraph

Review in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Huan-Cheng ChangDivision of Nephrology, Department of Medicine, Landseed International Hospital, No. 77, Guangtai Road, Pingzhen Dist., Taoyuan 324, Taiwan. changhc@landseed.com.tw.
Mei-Chin WangNoble Health Management Center, Landseed International Hospital, No. 77, Guangtai Road, Pingzhen Dist., Taoyuan 324, Taiwan. wangmeria@landseed.com.tw.
Hung-Chang LiaoDepartment of Medical Management, Chung Shan Medical University Hospital, No. 110, Section 1, Jian-Koa N. Road, Taichung 402, Taiwan. hcliao@csmu.edu.tw.ORCID 0000-0002-2503-4405
Ya-Huei WangDepartment of Medical Management, Chung Shan Medical University Hospital, No. 110, Section 1, Jian-Koa N. Road, Taichung 402, Taiwan. yhuei@csmu.edu.tw.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 (

Indexed as

Conservation of Natural ResourcesCrowdingDelivery of Health CareEfficiency, OrganizationalEmergency Medical ServicesHumansTotal Quality Managementdynamic Taguchi methodemergency department crowding (EDC)emergency medicine service (EMS)genetic algorithm (GA)green supply chain management (GSCM)neural network (NN)

Identifiers

PMID31652898
PMCPMC6862180

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.