Evidence map›Paper›PMID 41057433›Full record

ArticleScientific reports2025

Assessment of patient satisfaction with the hemodialysis experience using an integrated decision-making approach with rough numbers.

Jie Gao, Meihong Han, Bo Ban

Abstract readMulticenter StudyValidation Study
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Jie GaoDepartment of Endocrinology, Genetics and Metabolism, Affiliated Hospital of Jining Medical University, Jining Medical University, Jining, 250000, Shandong, China. 1424733059@qq.com.
Meihong HanDepartment of Infectious Diseases, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No.324, Jingwu Road, Jinan, 250000, Shandong, China.
Bo BanDepartment of Endocrinology, Genetics and Metabolism, Affiliated Hospital of Jining Medical University, Jining Medical University, Jining, 250000, Shandong, China. banbo2011@163.com.

Funding

National Natural Science Foundation of China No. 82400793
6 · The paper itself

Abstract

Patient satisfaction is a critical dimension of healthcare quality and hospital management, particularly in chronic treatments such as hemodialysis, where patients undergo frequent and time-intensive procedures. Assessing patient satisfaction is inherently a multi-attribute group decision-making (MAGDM) problem that involves both subjectivity and uncertainty, which many existing approaches fail to address adequately. To overcome these challenges, this study proposes a novel patient satisfaction assessment framework that integrates rough numbers with the best-worst method (BWM) and the gained and lost dominance score (GLDS) method. A comprehensive criteria system for evaluating hemodialysis experiences is developed through literature review and expert consultation. Rough numbers are employed to represent the linguistic and uncertain judgments of patients and experts, while the rough BWM determines robust criteria weights and the rough GLDS ranks hospital alternatives by considering both overall and criterion-level performance. The proposed method is validated through a case study of five hospitals in Jinan, China. The results confirm that the framework produces reliable and interpretable rankings, identifies Hospital A as the best-performing institution, and demonstrates robustness under sensitivity analysis. These findings highlight the value of the rough BWM-GLDS method as a practical and transparent tool for evaluating patient satisfaction in healthcare services.

Indexed as

Decision MakingKidney Failure, ChronicPatient SatisfactionRenal DialysisChinaHumansReview Literature as TopicGained and lost dominance scoreHemodialysisMulti-attribute group decision-makingPatient satisfactionRough number

Identifiers

PMID41057433
PMCPMC12504758

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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.