ArticleHealth care management science2021
An integrated quality and resilience engineering framework in healthcare with Z-number data envelopment analysis.
Article in Health care management science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Who cites it
6 citing papers in PubMed.
- A decision-making framework for evaluating medical equipment suppliers under uncertainty.Scientific reports · 2025Article
- Performance Evaluation of Hospitals under Data Uncertainty: An Uncertain Common-Weights Data Envelopment Analysis.Healthcare (Basel, Switzerland) · 2024Article
- Z-number network data envelopment analysis approach: A case study on the Iranian insurance industry.PloS one · 2024Article
- The Core of Healthcare Efficiency: A Comprehensive Bibliometric Review on Frontier Analysis of Hospitals.Healthcare (Basel, Switzerland) · 2022Review
- Dynamic Performance Assessment of Hospitals by Applying Credibility-Based Fuzzy Window Data Envelopment Analysis.Healthcare (Basel, Switzerland) · 2022Article
- Stability analysis with general fuzzy measure: An application to social security organizations.PloS one · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Supplier selection for medical equipment is a major challenge for hospitals in healthcare supply chains. The primary reason for measuring medical equipment supplier efficiency is to achieve the highest level of overall performance and productivity in healthcare supply chains. This study presents an integrated quality and resilience engineering (QRE) framework for evaluating medical equipment suppliers' performance using structural equation modeling and Z-number data envelopment analysis (Z-DEA). Noise analysis is used to select the best α-cut for the Z-DEA model, and fuzzy data are used to handle uncertainties. We show that flexibility, conformance to standards, redundancy, cost, quality certifications, and delivery time significantly affect the medical equipment suppliers' performance. In addition, we demonstrate that the proposed integrated QRE framework is more efficient and informative than stand-alone quality engineering or resiliency engineering. We present a case study in a cardiovascular hospital to illustrate the applicability of the proposed framework for medical equipment supplier evaluation and selection. To the best of our knowledge, this is the first study to integrate QRE and Z-DEA for supplier performance evaluation in healthcare.
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Identifiers
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Registered trials
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.