Evidence map›Paper›PMID 41179796›Full record

ArticleFrontiers in public health2025

Interrupted time series analysis of the impact of DIP reform on hospitalization costs in different types of hospitals.

Yan Chun-Hong, Lin Ke-Xin, Zheng Xin-Yue, Meng Xue-Hui

Abstract read
In one paragraph

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 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.

Yan Chun-Hong *School of Humanities and Management, Zhejiang Chinese Medical University, Hangzhou, China.
Lin Ke-Xin *School of Humanities and Management, Zhejiang Chinese Medical University, Hangzhou, China.
Zheng Xin-YueSchool of Humanities and Management, Zhejiang Chinese Medical University, Hangzhou, China.
Meng Xue-HuiSchool of Humanities and Management, Zhejiang Chinese Medical University, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: In the context of medical insurance payment reform, this study aims to evaluate the impact of the Diagnosis-Intervention Packet (DIP) payment policy on hospitalization costs across different types and levels of hospitals. In order to provide empirical evidence to support the high-quality collaboration between hospitals and medical insurance, while reducing the economic burden on patients. Method: Our study collected medical insurance reimbursement data from January 2019 to December 2022 in S city, covering 2,467,746 patients. Based on the intervention time point of the DIP reform implementation in 2021, an interrupted time series analysis was conducted on a monthly basis to compare the trend changes in hospitalization costs between traditional Chinese medicine hospitals (TCMHs) and general hospitals (GHs), as well as to examine the differences in impacts across hospitals of various levels. Results: Firstly, our study found that tertiary hospitals had the highest average hospitalization costs ( Conclusion: The government should adjust policies in a differentiated and refined manner based on the type and level of hospitals to achieve the goals of controlling medical costs and improving the incentive mechanisms. Meanwhile, optimizing the healthcare service structure can improve quality and efficiency, as well as better meet patient needs.

Indexed as

Health Care ReformHospital CostsHospitalizationHospitalsChinaFemaleHospitals, GeneralHumansInterrupted Time Series AnalysisMaleDiagnosis-Intervention Packet (DIP)general hospitalshospitalization costsinterrupted time series analysistraditional Chinese medicine hospitals

Identifiers

PMID41179796
PMCPMC12571719

What Socratic holds

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LicenceCC BY
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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 Socratic graph.