Evidence map›Paper›PMID 40520293›Full record

ArticleFrontiers in public health2025

Facilitators and barriers to the implementation of DIP payment methodology reform in a public hospital in Guangzhou: a qualitative study based on the implementation of the meta-framework for research (CFIR) framework.

Jinghui Chang, Siyi Chen, Anqi Li, Xixi Yang, Hualian Luo, Maidina Yilamu, Bingqian Fu, Nuoyan Xu, Jing Liu, Huishu Tian

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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 6 papers.

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0cells of the map it votes in
6citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Jinghui Chang *School of Health Management, Southern Medical University, Guangzhou, China.
Siyi Chen *School of Health Management, Southern Medical University, Guangzhou, China.
Anqi LiSchool of Health Management, Southern Medical University, Guangzhou, China.
Xixi YangSchool of Health Management, Southern Medical University, Guangzhou, China.
Hualian LuoSchool of Health Management, Southern Medical University, Guangzhou, China.
Maidina YilamuSchool of Health Management, Southern Medical University, Guangzhou, China.
Bingqian FuSchool of Health Management, Southern Medical University, Guangzhou, China.
Nuoyan XuSchool of Health Management, Southern Medical University, Guangzhou, China.
Jing LiuGuangzhou First People's Hospital, Guangzhou, China.
Huishu TianGuangzhou First People's Hospital, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The Diagnosis-Intervention Packet (DIP), a medical insurance payment management system utilizing big data, has been piloted in 12 cities by the National Healthcare Security Administration in China starting in 2021. Guangzhou is one of the pilot cities, and it has demonstrated significant success in the DIP payment reform, with its practical experience being affirmed and promoted by the National Health Department. In this study, researchers conducted field visits to a public hospital in Guangzhou to understand the internal responses to the DIP reform and the cognitive attitudes of relevant personnel. The analysis of the positive and negative factors affecting the implementation of the reform and the proposed measures to optimize internal hospital management are expected to provide practical evidence for the implementation of DIP payment reform in other public hospitals. Methods: This study develops an interview guide based on the Consolidated Framework for Implementation Research (CFIR) and conducts one-on-one semi-structured interviews offline with personnel from a Grade A tertiary public hospital in Guangzhou. Employing rapid qualitative analysis techniques and utilizing NVivo 14.0 for coding CFIR-structured texts related to implementation, the study integrates five dimensions: innovation, inner and outer context, individuals, and the implementation process. It identifies factors that facilitate and hinder the implementation of the Diagnosis-Intervention Packet (DIP) payment reform, thereby proposing optimized internal management strategies for public hospitals to cope with DIP payment reforms. Discussion: This study will provide significant insights for optimizing the internal management of public hospitals in the context of DIP payment reform. It offers a reference for optimizing internal management in tertiary public hospitals in China, aiming to achieve standardized, healthy, collaborative, and high-quality development.

Indexed as

Diagnosis-Related GroupsHealth Care ReformHospitals, PublicChinaHumansInterviews as TopicQualitative ResearchDIP Medicare payment methodologyinternal managementmeta-framework for implementation research (CFIR) frameworkpublic health policypublic hospitals

Identifiers

PMID40520293
PMCPMC12162575

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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.