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基于多组中断时间序列的中国西南地区不同人群DIP支付方式实施效果

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

作者信息

Wu Lixiang, Wu Ni, Cao Yuhan, Zhou Xiaoyuan

机构信息

West China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, China.

School of Public Health, Chengdu University of Traditional Chinese Medicine, Chengdu, China.

出版信息

Front Public Health. 2025 Jun 2;13:1572475. doi: 10.3389/fpubh.2025.1572475. eCollection 2025.

DOI:10.3389/fpubh.2025.1572475
PMID:40529706
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12171191/
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.

摘要

背景

自2020年以来,中国在全国71个城市实施了一种名为“诊断-干预包”(DIP)的支付方式,以满足国家的特定需求。本研究的目的是评估DIP对城市职工基本医疗保险(UEBMI)和城乡居民基本医疗保险(URRBMI)覆盖的住院患者的医疗质量和负担的影响。此外,旨在调查这两种不同类型保险的住院患者在这些影响方面的潜在差异,从而加深我们对这种支付方式改革如何影响医疗服务提供的理解,并完善社会保障体系。

方法

我们基于包含C市2019年1月至2021年12月期间180,071条住院报销记录的数据集,对反映医疗服务质量以及UEBMI和URRBMI住院患者负担的结果变量进行了多组中断时间序列分析(MGITSA)。该数据集包括42,581条URRBMI住院患者记录和137,490条UEBMI住院患者记录。

结果

实施DIP后,UEBMI和URRBMI的住院患者数量均有所增加(分别为21.59%和22.26%),住院时间缩短(UEBMI为7.10%,URRBMI为0.2°/₀),病例组合指数(ACR)提高(UEBMI为3.07%,URRBMI为15.36%)。两组的住院费用均略有增加(UEBMI为2.97%,URRBMI为10.44%)。亚组分析显示了年龄特异性差异:18岁以下和45岁以上患者的住院时间和费用有显著变化,而18至45岁患者的影响最小。MGITSA显示URRBMI的住院时间显著缩短(β3=-0.004,P=0.014),而UEBMI的住院时间和ACR趋势更明显,成本斜率在组间无显著差异。

结论

DIP在短期内提高了两个保险组的医院效率(缩短住院时间、增加入院人数)和财务保障(提高ACR),尽管住院费用有所上升,需要关注潜在的服务强度膨胀或成本转移。DIP影响的年龄差异凸显了制定针对性政策的必要性。持续监测和政策调整对于平衡成本控制、服务质量和公平性至关重要,以确保DIP在中国医疗改革中的长期有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6602/12171191/4579a80086a5/fpubh-13-1572475-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6602/12171191/cdce371e67c8/fpubh-13-1572475-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6602/12171191/beb03974b147/fpubh-13-1572475-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6602/12171191/8edb1dba0b6f/fpubh-13-1572475-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6602/12171191/6efc3a888798/fpubh-13-1572475-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6602/12171191/52a3dfc97f4e/fpubh-13-1572475-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6602/12171191/4579a80086a5/fpubh-13-1572475-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6602/12171191/cdce371e67c8/fpubh-13-1572475-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6602/12171191/beb03974b147/fpubh-13-1572475-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6602/12171191/8edb1dba0b6f/fpubh-13-1572475-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6602/12171191/6efc3a888798/fpubh-13-1572475-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6602/12171191/52a3dfc97f4e/fpubh-13-1572475-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6602/12171191/4579a80086a5/fpubh-13-1572475-g006.jpg

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