Evidence map›Paper›PMID 42368908›Full record

ArticleFrontiers in public health2026

Drivers and effects of DRG coding abuse based on hot cheese model: a qualitative study.

Shuo Liu, Chang-Chang Chen, Li Zhao, Zhi-Ping Yang, Dai-Ming Fan, Xue-Jun Hu

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Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Shuo Liu *First Affiliated Hospital of Air Force Medical University, Xi'an, China.
Chang-Chang Chen *Department of Nursing, Air Force Medical University, Xi'an, China.
Li ZhaoFirst Affiliated Hospital of Air Force Medical University, Xi'an, China.
Zhi-Ping YangFirst Affiliated Hospital of Air Force Medical University, Xi'an, China.
Dai-Ming FanFirst Affiliated Hospital of Air Force Medical University, Xi'an, China.
Xue-Jun HuAir Force Medical Training Base, Air Force Medical University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The national-level China Healthcare Security Diagnosis-Related Groups (CHS-DRG) reform in China has been underway for 6 years. However, the marginal benefit of controlling medical insurance fund expenditures has been decreasing, mainly due to the emergence of game behaviors among healthcare providers. The study aimed to explore the internal driving factors, effects and their interrelationships of DRG downcoding, upcoding and ambiguous cases to propose the intervention measures based on the hot cheese model. Methods: This study was conducted through semi-structured interviews with 22 participants from various positions in hospitals in Shaanxi Province, China. The hot cheese model was introduced for qualitative analysis to systematically analyze the manifestations, subjective and objective driving factors, and internal and external effects of DRG coding abuse. Results: The subjective, objective drivers and effects of DRG coding abuse were revealed, and a hot cheese model was established, consisting of three defensive layers: layers of hospital organization environment, doctor-filled first page of medical records, and medical record/insurance department review. Conclusion: DRG upcoding should be distinguished between subjective and objective driving factors for targeted intervention. The potential effect of DRG downcoding and ambiguous cases should not be underestimated. Future research should focus on intelligent early warning by natural language processing on pre-coding of medical record texts.

Indexed as

Clinical CodingDiagnosis-Related GroupsFraudHealth Care ReformModels, TheoreticalChinaHumansInterviews as TopicQualitative ResearchDRG coding abusedriverseffectshot cheese modelqualitative study

Identifiers

PMID42368908
PMCPMC13303593

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