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用于预测和预防医疗保险索赔处理错误的数据挖掘

Data Mining to Predict and Prevent Errors in Health Insurance Claims Processing
课程网址: http://videolectures.net/kdd2010_kumar_dmppeh/  
主讲教师: Mohit Kumar
开课单位: 卡内基梅隆大学
开课时间: 2010-10-01
课程语种: 英语
中文简介:
近年来,全世界的医疗保险费用惊人地增加。这种增加的一个主要原因是保险公司在处理索赔时所犯的付款错误。这些错误通常会导致额外的管理工作,以重新处理(或返工)索赔,该索赔占典型医疗保险公司管理人员的30%。我们描述了一个系统,通过预测需要重新设计的声明来帮助减少这些错误,生成解释以帮助审核员纠正这些声明,并尝试功能选择,概念漂移和主动学习以收集来自审计师随着时间的推移而改进。我们描述了来自美国大型医疗保险公司的索赔数据的框架,问题制定,评估指标和实验结果。我们表明,与现有方法相比,我们的系统产生了更高精度(命中率)的数量级,这足以准确地为典型的保险公司节省超过15,250万美元。我们还描述了该领域中有趣的研究问题以及为使系统易于跨健康保险公司部署而进行的设计选择。
课程简介: Health insurance costs across the world have increased alarmingly in recent years. A major cause of this increase are payment errors made by the insurance companies while processing claims. These errors often result in extra administrative effort to re-process (or rework) the claim which accounts for up to 30% of the administrative staff in a typical health insurer. We describe a system that helps reduce these errors using machine learning techniques by predicting claims that will need to be reworked, generating explanations to help the auditors correct these claims, and experiment with feature selection, concept drift, and active learning to collect feedback from the auditors to improve over time. We describe our framework, problem formulation, evaluation metrics, and experimental results on claims data from a large US health insurer. We show that our system results in an order of magnitude better precision (hit rate) over existing approaches which is accurate enough to potentially result in over $15-25 million in savings for a typical insurer. We also describe interesting research problems in this domain as well as design choices made to make the system easily deployable across health insurance companies.
关 键 词: 医疗保险; 概念漂移; 保险公司
课程来源: 视频讲座网
最后编审: 2019-05-11:lxf
阅读次数: 79