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通过基于机器学习的两阶段DRG分组器来减少DRG分组中的上编码风险

Haitian Wang¹, Li Luo¹, Dongyuan Ma¹

¹Business School, Sichuan University, Chengdu, China.

Inquiry : a journal of medical care organization, provision and financing

|November 6, 2025

概括

本研究介绍了一种基于机器学习的两阶段诊断相关组 (DRG) 群组 (ML-DRG),以打击医疗保健的升级编码. 在准确分组患者方面,ML-DRG表现出卓越的性能,降低了错误计费的风险,以获得更高的退款.

科学领域:

卫生经济学卫生经济学
医疗信息学医疗信息学
机器学习机器学习

背景情况:

诊断相关组 (DRG) 的实施激励医院升级代码以提高利能力.
现有的DRG分组方法容易被操纵,导致不准确的患者分类和经济收益.

研究的目的:

提出和评估一种新型的两级DRG群群 (ML-DRG),旨在减轻升级编码的风险.
开发一种机器学习模型,预测临床资源消耗,以便更强大的患者分类.

主要方法:

利用两阶段机器学习方法 (ML-DRG) 来预测患者的资源消耗.
开发了基于综合患者特征的资源消耗指数,使其难以改变.
使用中国医疗保健数据 (2011-2018) 对比ML-DRG与三种主流DRG分组方法的性能.

主要成果:

与现有方法相比,ML-DRG表现出优越的性能.
具体的疾病组 (内出血性疾病 - BR1,呼吸道感染/炎症疾病 - ES2) 被有效地划分为4个DRG,变化系数低 (<0.8).

结论:

通过提供更准确,更容易操纵的患者分类系统,ML-DRG群显示出在减少医疗保健升级编码方面的巨大潜力.
实施ML-DRG可以帮助医院避免选择错误的DRG代码以提高报销率,从而促进更公平的医疗保健计费.

关键词:

诊断相关的群体诊断相关的群体分组方法分组方法.医院的医疗费用补偿机器学习是机器学习.上升编码的上升编码.

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