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Reducing the Risk of Upcoding in DRG Grouping Through a Two-Stage DRG Grouper Based on Machine Learning.

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Title: Reducing the Risk of Upcoding in DRG Grouping Through a Two-Stage DRG Grouper Based on Machine Learning.
Authors: Wang, Haitian1 (AUTHOR), Luo, Li1 (AUTHOR), Ma, Dongyuan1 (AUTHOR), Xie, Zhecheng2 (AUTHOR), Fang, Yuanchen1,3 (AUTHOR) fang_yuanchen@163.com
Source: Inquiry (00469580). 11/5/2025, Vol. 62, p1-9. 9p.
Subject Terms: *Machine learning, *Comparative studies, *Algorithms, Fraud prevention, Diagnosis related groups, Intracranial hemorrhage, Health insurance reimbursement, Research funding, Prediction models, Respiratory infections, Kruskal-Wallis Test, Descriptive statistics, Mann Whitney U Test, Hospitals, Medical coding, Inflammation, Management of medical records, Medical care costs
Geographic Terms: China
Abstract: In the implementation of diagnosis-related groups (DRGs), hospitals respond to price changes by incorporating more patients into the more profitable DRGs, thereby providing evidence for upcoding. This study proposes a two-stage DRGs grouper (ML-DRG) to alleviate the risk of upcoding. The ML-DRG employs machine learning methods to build a predictive model of patients' clinical resource consumption and assigns the model output as the resource consumption index, which comprehensively considers various patients characteristics and is challenging to modify. We utilize the data from the Chengdu Healthcare Security Administration of China, covering the period from 2011 to 2018, to compare the performance of the proposed method with the 3 mainstream approaches. Our findings indicate that the intracranial hemorrhagic disease (BR1) group and respiratory infection/inflammation disease (ES2) group of ADRG were divided into 4 DRGs, with the coefficient of variation of each group being less than.8. Among the 4 grouping methods, ML-DRG demonstrated the best performance. These findings suggest that the application of ML-DRG may reduce the risk of upcoding by helping hospitals avoid selecting incorrect DRG codes for higher reimbursement rates. [ABSTRACT FROM AUTHOR]
Copyright of Inquiry (00469580) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Reducing the Risk of Upcoding in DRG Grouping Through a Two-Stage DRG Grouper Based on Machine Learning.
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  Data: <searchLink fieldCode="JN" term="%22Inquiry+%2800469580%29%22">Inquiry (00469580)</searchLink>. 11/5/2025, Vol. 62, p1-9. 9p.
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  Data: In the implementation of diagnosis-related groups (DRGs), hospitals respond to price changes by incorporating more patients into the more profitable DRGs, thereby providing evidence for upcoding. This study proposes a two-stage DRGs grouper (ML-DRG) to alleviate the risk of upcoding. The ML-DRG employs machine learning methods to build a predictive model of patients' clinical resource consumption and assigns the model output as the resource consumption index, which comprehensively considers various patients characteristics and is challenging to modify. We utilize the data from the Chengdu Healthcare Security Administration of China, covering the period from 2011 to 2018, to compare the performance of the proposed method with the 3 mainstream approaches. Our findings indicate that the intracranial hemorrhagic disease (BR1) group and respiratory infection/inflammation disease (ES2) group of ADRG were divided into 4 DRGs, with the coefficient of variation of each group being less than.8. Among the 4 grouping methods, ML-DRG demonstrated the best performance. These findings suggest that the application of ML-DRG may reduce the risk of upcoding by helping hospitals avoid selecting incorrect DRG codes for higher reimbursement rates. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Inquiry (00469580) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=189325295
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        Value: 10.1177/00469580251389813
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      – Code: eng
        Text: English
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        PageCount: 9
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      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Comparative studies
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Fraud prevention
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      – SubjectFull: Diagnosis related groups
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      – SubjectFull: Intracranial hemorrhage
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      – SubjectFull: Health insurance reimbursement
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      – SubjectFull: Hospitals
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      – SubjectFull: China
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      – TitleFull: Reducing the Risk of Upcoding in DRG Grouping Through a Two-Stage DRG Grouper Based on Machine Learning.
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              Text: 11/5/2025
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