DRG grouping by machine learning: from expert-oriented to data-based method.
Background: Diagnosis-related groups (DRGs) are a payment system that could effectively solve the problem of excessive increases in healthcare costs which are applied as a principal measure in the healthcare reform in China. However, expert-oriented DRG grouping is a black box with the drawbacks of...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 21; no. 1; pp. 1 - 12 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
11/9/2021
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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=153472834&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153472834 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 11/9/2021 vid: 21 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 153472834 153472834 NLM34753472 153472834 10.1186/s12911-021-01676-7 NLM34753472 153472834 ppf: 1 ppct: 11 formats: tig: atl: DRG grouping by machine learning: from expert-oriented to data-based method. aug: au: Liu, Xiaoting Fang, Chenhao Wu, Chao Yu, Jianxing Zhao, Qi affil: School of Public Affairs, Zhejiang University, Zijingang Campus, 310058, Hangzhou, Zhejiang Province, China sug: subj: Diagnosis-Related Groups Algorithms Resource Databases China Human Comparative Studies Multicenter Studies Evaluation Research Validation Studies Short Portable Mental Status Questionnaire ab: Background: Diagnosis-related groups (DRGs) are a payment system that could effectively solve the problem of excessive increases in healthcare costs which are applied as a principal measure in the healthcare reform in China. However, expert-oriented DRG grouping is a black box with the drawbacks of upcoding and high cost.Methods: This study proposes a method of data-based grouping, designed and updated by machine learning algorithms, which could be trained by real cases, or even simulated cases. It inherits the decision-making rules from the expert-oriented grouping and improves performance by incorporating continuous updates at low cost. Five typical classification algorithms were assessed and some suggestions were made for algorithm choice. The kappa coefficients were reported to evaluate the performance of grouping.Results: Based on tenfold cross-validation, experiments showed that data-based grouping had a similar classification performance to the expert-oriented grouping when choosing suitable algorithms. The groupings trained by simulated cases had less accuracy when they were tested by the real cases rather than simulated cases, but the kappa coefficients of the best model were still higher than 0.6. When the grouping was tested in a new DRGs system, the average kappa coefficients were significantly improved from 0.1534 to 0.6435 by the update; and with enough computation resources, the update process could be completed in a very short time.Conclusions: As a new potential option, the data-based grouping meets the requirements of the DRGs system and has the advantages of high transparency and low cost in the design and update process. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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