Reducing the Risk of Upcoding in DRG Grouping Through a Two-Stage DRG Grouper Based on Machine Learning.
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 empl...
| Publicado en: | Inquiry (00469580) Vol. 62; pp. 1 - 10 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
11/5/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189325295&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189325295 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00469580 INQ jtl: Inquiry (00469580) issn: 00469580 maglogo: Y pubinfo: dt: 11/5/2025 vid: 62 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 189325295 189325295 189325295 10.1177/00469580251389813 189325295 ppf: 1 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Reducing the Risk of Upcoding in DRG Grouping Through a Two-Stage DRG Grouper Based on Machine Learning. aug: au: Wang, Haitian Luo, Li Ma, Dongyuan Xie, Zhecheng Fang, Yuanchen affil: Business School, Sichuan University, Chengdu, China sug: subj: Diagnosis-Related Groups Economics Coding Standards Reimbursement Mechanisms Fraud Prevention and Control Machine Learning Methods Human Funding Source China Comparative Studies Descriptive Statistics Mann-Whitney U Test Kruskal-Wallis Test Prediction Models Health Care Costs Intracranial Hemorrhage Diagnosis Respiratory Tract Infections Diagnosis Inflammation Diagnosis Hospitals Economics Health Information Management Machine Learning Algorithms ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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