Evidence map›Paper›PMID 40109424›Full record

ArticleFrontiers in public health2025

Impact of DRG policy on the performance of tertiary hospital inpatient services in Chongqing, China: an interrupted time series study, 2020-2023.

Yunyu Liu, Zusong Liao, Juntao Tan, Yongjie Yan, Yuting Wang

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 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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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

9 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

5 authors.

Yunyu LiuAffiliated Banan Hospital of Chongqing Medical University, Chongqing, China.
Zusong LiaoAffiliated Banan Hospital of Chongqing Medical University, Chongqing, China.
Juntao TanCollege of Medical Informatics, Chongqing Medical University, Chongqing, China.
Yongjie YanDepartment of Information, Daping Hospital, Army Medical University, Chongqing, China.
Yuting WangDepartment of Public Health, Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Implementing the diagnosis-related groups (DRG) payment policy in 2021 marked a significant step in increasing the capacity and efficiency of public hospital services in Chongqing, China. However, the adaptability and effectiveness of DRG policy in middle-income regions like Chongqing remain understudied. This study evaluates the impact of DRG on tertiary hospital inpatient services in Chongqing, focusing on challenges unique to resource-constrained settings. Methods: Using an interrupted time series design, we analyzed monthly data of 14 DRG performance measures obtained from the DRG comprehensive management system, covering two public hospitals in Chongqing from 2020 to 2023. To evaluate both immediate and long-term effects of the DRG policy, we employed an interrupted time series analysis model to analyze changes in indicator levels and trends pre- and post-intervention. Results: We found significant changes in the following indicators since the implementation of the DRG policy: case-mix index (CMI) level increased by 0.0661 ( Conclusion: The DRG policy in Chongqing led to unintended trade-offs: tertiary hospitals prioritized high-volume, low-complexity cases, eroding service capacity for severe conditions. Middle-income regions faced implementation barriers, including fragmented health IT systems and insufficient administrative capacity, which diminished policy effectiveness. Policymakers must tailor DRG implementation to local contexts, balancing efficiency with equity and quality.

Indexed as

Diagnosis-Related GroupsHealth PolicyInpatientsTertiary Care CentersChinaHumansInterrupted Time Series AnalysisLength of StayDRGperformance evaluationservice capacityservice cost interrupted time seriesservice efficiency

Identifiers

PMID40109424
PMCPMC11922081

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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.