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Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data

机译：基于异常值的健康保险欺诈检测美国医疗补助数据

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Fraud, waste, and abuse in the U.S. healthcare system are estimated at $700 billion annually. Predictive analytics offers government and private payers the opportunity to identify and prevent or recover such billings. This paper proposes a data-driven method for fraud detection based on comparative research, fraud cases, and literature review. Unsupervised data mining techniques such as outlier detection are suggested as effective predictors for fraud. Based on a multi-dimensional data model developed for Medicaid claim data, specific metrics for dental providers were developed and evaluated in analytical experiments using outlier detection applied to claim, provider, and patient data in a state Medicaid program. The proposed methodology enabled successful identification of fraudulent activity, with 12 of the top 17 suspicious providers (71%) referred to officials for investigation with clearly anomalous and inappropriate activity. Future research is underway to extend the method to other specialties and enable its use by fraud analysts.
机译：美国医疗保健系统的欺诈，浪费和虐待估计每年估计为7000亿美元。预测分析提供政府和私人付款人有机会识别和预防或恢复这些账单。本文提出了一种基于比较研究，欺诈案件和文献综述的欺诈检测数据驱动方法。诸如异常值检测的无监督数据挖掘技术被建议为欺诈的有效预测因子。基于为Medicadad索赔数据开发的多维数据模型，使用在状态医疗补助计划中的索赔，提供商和患者数据应用于索赔，提供者和患者数据的分析实验，在分析实验中开发和评估了牙科提供商的特定度量。拟议的方法能够成功地识别欺诈活动，其中12位十大可疑提供者（71％）提到官员进行调查，以清晰的异常和不恰当的活动。今后的研究正在进行将该方法扩展到其他专业，并通过欺诈分析师实现其使用。

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