Evidence map›Paper›PMID 40356831›Full record

ArticleFrontiers in public health2025

The impact of bundled payment on the economic burden and satisfaction of patients in Close-Knit County Medical Community in China.

Yanhong Guo, Jialin Qian, Xin Li, Jian Wang, Liangying Zhu, Erdan Huang, Yanchun Zhang, Sheng Nong

Abstract read
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Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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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

5 citing papers in PubMed.

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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

8 authors.

Yanhong GuoSchool of Public Health, Youjiang Medical University for Nationalities, Guangxi, China.
Jialin QianSchool of Public Health, Youjiang Medical University for Nationalities, Guangxi, China.
Xin LiSchool of Public Health, Youjiang Medical University for Nationalities, Guangxi, China.
Jian WangSchool of Public Health, Youjiang Medical University for Nationalities, Guangxi, China.
Liangying ZhuSchool of Public Health, Youjiang Medical University for Nationalities, Guangxi, China.
Erdan HuangHealth Development Research Center of the National Health Commission, Beijing, China.
Yanchun ZhangHealth Development Research Center of the National Health Commission, Beijing, China.
Sheng NongSchool of Public Health, Youjiang Medical University for Nationalities, Guangxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: China is setting up a Close-Knit County Medical Community (CCMC) to connect county hospitals, township health centers, and village clinics. The medical insurance agency will count the number of insured people in the CCMC area and distribute funds to the community as a whole. Then, the county hospital will work with local health facilities to decide how to use these funds. This reform aims to improve primary healthcare services, lower medical costs, support residents' health, and boost their satisfaction with healthcare. Methods: This study looked at data from counties in China monitored by County Medical Communities from 2018 to 2022. We used difference-in-differences (DID) to analyze how bundled payments affected patients' financial burdens, the income of healthcare providers and the satisfaction of patients and healthcare providers. Results: The bundled payment policy had no significant effect on the average cost per discharged patient ( Conclusion: Bundled medical insurance payments in the CCMC do not add financial stress for patients and help low-income families. They also boost the income of healthcare providers. However, there is still a need for improvements to enhance overall satisfaction with the healthcare system.

Indexed as

Health ExpendituresPatient SatisfactionPrimary Health CareReimbursement MechanismsChinaFemaleHospitals, CountyHumansMaleClose-Knit County Medical Communityeconomic burdeneconomic incentivemedical insurance bundled paymentsatisfaction

Identifiers

PMID40356831
PMCPMC12066574

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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.