图形关联分析用于识别关节置换手术供应商索赔的变化
James Kemp1, Christopher Barker2, Norm Good3
1University of New South Wales, Australia.
Studies in health technology and informatics
|January 25, 2024
概括
无监督机器学习识别了关节置换手术的不寻常的医疗提供者计费模式. 这种方法可以有效地检测潜在的欺诈性索赔,减少医疗保健浪费,提高计费完整性.
科学领域:
- 卫生经济学 卫生经济学
- 医疗数据分析 医疗数据分析
- 机器学习应用程序 机器学习应用程序
背景情况:
- 检测欺诈性或浪费性医疗保险索赔是具有挑战性的,因为数据量和人工努力.
- 供应商计费行为的变化可能表明潜在的问题.
研究的目的:
- 应用无监督机器学习来识别和排名不寻常的医疗服务提供者声称行为.
- 开发可解释的模型来分析与单边关节置换手术相关的索赔.
主要方法:
- 在澳大利亚医疗保险福利计划数据上利用了无监督的机器学习.
- 建立了手术手术的参考模型,并比较了个别提供者的要求.
- 根据典型索赔以外的额外费用对提供者进行排名.
主要成果:
- 识别了具有显著不寻常索赔模式的提供者.
- 偏远提供商的索赔可能会使程序成本增加高达192%.
- 开发的方法是高效的和可通用的.
结论:
- 无监督机器学习提供了一种有效和可解释的方法来检测不寻常的医疗计费.
- 这种方法可以整合到现有的工作流程中,以打击医疗保健浪费.
- 该模型的通用性使其可以应用于各种医疗程序.
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