Evidence map›Paper›PMID 40529706›Full record

ArticleFrontiers in public health2025

The implementation effect of DIP payment method across different population in Southwest China based on multi-group interrupt time series.

Lixiang Wu, Ni Wu, Yuhan Cao, Xiaoyuan Zhou

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 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Lixiang WuWest China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, China.
Ni WuWest China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, China.
Yuhan CaoSchool of Public Health, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Xiaoyuan ZhouWest China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Since 2020, China has implemented a payment method known as "Diagnosis-Intervention Package" (DIP) in 71 cities nationwide to address the specific needs of the country. The objective of this study is to evaluate the impact of DIP on medical quality and the burden experienced by inpatients covered under the Urban Employee Basic Medical Insurance (UEBMI) and Urban and Rural Residents Basic Medical Insurance (URRBMI). Furthermore, it aims to investigate potential differences in these effects between inpatients enrolled in the two distinct types of insurance, thereby enhancing our understanding of how this reform in payment methods influences healthcare delivery, and refine the social security system. Methods: We conducted a multiple-group interrupted time series analyses (MGITSA) on outcome variables reflecting medical services quality, and the burden of UEBMI and URRBMI inpatients, based on a dataset containing 180,071 inpatient reimbursement records in City C spanning from January, 2019 to December, 2021. This dataset included 42,581 records for URRBMI inpatients and 137,490 records for UEBMI inpatients. Results: After DIP implementation, both UEBMI and URRBMI showed increased inpatient numbers (21.59% and 22.26%, respectively), reduced LOS (7.10% for UEBMI, 0.29% for URRBMI), and higher ACR (3.07% for UEBMI, 15.36% for URRBMI). Hospitalization costs increased slightly for both groups (2.97% for UEBMI, 10.44% for URRBMI). Subgroup analysis revealed age-specific differences: significant LOS and cost changes in <18-year-olds and >45-year-olds, but minimal effects in 18-45-year-olds. MGITSA showed URRBMI experienced significant LOS reduction (β3=-0.004, P=0.014), while UEBMI had more pronounced LOS and ACR trends, with no significant inter-group differences in cost slopes. Conclusion: DIP improved hospital efficiency (reduced LOS, increased admissions) and financial protection (higher ACR) for both insurance groups in the short term, though hospitalization costs rose, requiring attention to potential service intensity inflation or cost-shifting. Age disparities in DIP impacts highlight the need for targeted policies. Continuous monitoring and policy adjustments are essential to balance cost control, service quality, and equity, ensuring DIP's long-term effectiveness in China's healthcare reform.

Indexed as

Quality of Health CareReimbursement MechanismsAdolescentAdultAgedChinaFemaleHumansInsurance, HealthInterrupted Time Series AnalysisMaleMiddle AgedRural PopulationDiagnosis-Intervention Packetmandatory medical insurancemedical efficiencymedical services qualitythe burden of patients

Identifiers

PMID40529706
PMCPMC12171191

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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.