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基于疾病诊断相关分组的住院服务管理对中国上海市区域住院服务绩效的影响:一项 2013-2019 年的中断时间序列研究。

Impact of DRGs-based inpatient service management on the performance of regional inpatient services in Shanghai, China: an interrupted time series study, 2013-2019.

机构信息

Department of Health Policy Research, Shanghai Health Development Research Center (Shanghai Medical Information Center), No.1477 Beijing (W) Road, Jing'an District, Shanghai, 200040, China.

Jiading Health Affair Management Center, Shanghai, China.

出版信息

BMC Health Serv Res. 2020 Oct 12;20(1):942. doi: 10.1186/s12913-020-05790-6.

DOI:10.1186/s12913-020-05790-6
PMID:33046076
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7552463/
Abstract

BACKGROUND

The asymmetry of information brings difficulty for government to manage public hospitals. Therefore, Jiading District of Shanghai has been establishing DRGs-based inpatient service management system (ISMS) to effectively compare the output of different hospitals through DRGs, reward desired hospital performance and enhance inpatient service capacity. However, the impact of the implementation of DRGs-based inpatient service management (ISM) policy in Jiading district is still unknow. We therefore conducted this study to evaluate the impact of DRGs-based ISM policy on the performance of inpatient service since its implementation in Jiading District, Shanghai, China in 2017.

METHODS

Using an interrupted time series design, we analyzed quarterly data of seven DRGs-based performance measures from the ISMS which covered all five public hospitals in Jiading District from 2013 to 2019. We utilized the segmented linear regression model to assess the change of level and trend of performance indicators before and after ISM policy. Dickey-Fuller test was used to examine the stationary of the data. Durbin-Watson test was performed to check the series autocorrelation of indicators.

RESULTS

Significant changes in the following indicators were observed since the implementation of ISM policy. The case-mix index (CMI) level decreased by 0.103 (P < 0.05), the trend increased by 0.008 (P < 0.05). The total weight level decreased by 3719.05 (P < 0.05), and the trend increased by 250.13 (P < 0.05). The time efficiency index (TEI) level increased by 0.12 (P < 0.05), and the trend decreased by 0.01 (P < 0.05). The cost efficiency index (CEI) level increased by 0.31 (P < 0.05), and the trend decreased by 0.02 (P < 0.05). No significant difference was found in the change of DRGs number, inpatient mortality of low-risk group cases (IMLRG) and inpatient mortality of medium-to-low risk group cases (IMMLRG).

CONCLUSIONS

Findings highlight the role of ISM policy in improving the capacity and efficiency of regional inpatient service. Three prerequisites, including a good information system, high-quality EMR data, and a management team, are needed for other countries to implement their own ISM policy to help government manage public hospitals and improve the performance of regional inpatient service.

摘要

背景

信息不对称给政府管理公立医院带来了困难。因此,上海市嘉定区一直在建立基于 DRGs 的住院服务管理系统(ISMS),通过 DRGs 有效比较不同医院的产出,奖励期望的医院绩效,并增强住院服务能力。然而,嘉定区实施基于 DRGs 的住院服务管理(ISM)政策的影响仍不清楚。因此,我们开展了这项研究,以评估自 2017 年在中国上海嘉定区实施基于 DRGs 的 ISM 政策以来,该政策对住院服务绩效的影响。

方法

我们采用中断时间序列设计,分析了 2013 年至 2019 年嘉定区 5 家公立医院基于 ISMS 的 7 项基于 DRGs 的绩效指标的季度数据。我们利用分段线性回归模型评估政策实施前后绩效指标水平和趋势的变化。利用 Dickey-Fuller 检验检验数据的平稳性。利用 Durbin-Watson 检验检验指标的序列自相关性。

结果

自 ISM 政策实施以来,以下指标发生了显著变化。病例组合指数(CMI)水平下降了 0.103(P<0.05),趋势增加了 0.008(P<0.05)。总权重水平下降了 3719.05(P<0.05),趋势增加了 250.13(P<0.05)。时间效率指数(TEI)水平提高了 0.12(P<0.05),趋势下降了 0.01(P<0.05)。成本效率指数(CEI)水平提高了 0.31(P<0.05),趋势下降了 0.02(P<0.05)。DRGs 数量、低危组病例住院死亡率(IMLRG)和中低危组病例住院死亡率(IMMLRG)的变化无显著差异。

结论

研究结果强调了 ISM 政策在提高区域住院服务能力和效率方面的作用。其他国家在实施自己的 ISM 政策时,需要具备良好的信息系统、高质量的电子病历数据和管理团队这三个前提条件,以帮助政府管理公立医院并提高区域住院服务绩效。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5be1/7552463/2ff8a8b35107/12913_2020_5790_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5be1/7552463/97d9b55cac3e/12913_2020_5790_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5be1/7552463/2ff8a8b35107/12913_2020_5790_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5be1/7552463/97d9b55cac3e/12913_2020_5790_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5be1/7552463/2ff8a8b35107/12913_2020_5790_Fig2_HTML.jpg

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