论文标题
机器学习的应用在识别异常主张的识别
Applications of Machine Learning to the Identification of Anomalous ER Claims
论文作者
论文摘要
由于欺诈和上编码而导致的健康保险付款不当，导致美国每年在美国每年超额医疗保健费用，激励机器学习研究人员为健康保险索赔建立异常检测模型。本文介绍了针对ER主张的两种此类策略。第一个是一个基于严重性代码分布的上编码模型，该模型由层次诊断代码簇分层。在独立的ERS和急性护理医院之间观察到平均升级异常得分的统计学显着差异，独立的ERS更为异常。第二个模型是一个随机的森林，可以通过在审查队列中最佳地对ER主张进行最佳分类来最大程度地减少付款。根据所审查索赔的百分比，随机森林将12％至40％的股票优先于基准方法，该方法优先考虑了索赔金额。
Improper health insurance payments resulting from fraud and upcoding result in tens of billions of dollars in excess health care costs annually in the United States, motivating machine learning researchers to build anomaly detection models for health insurance claims. This article describes two such strategies specifically for ER claims. The first is an upcoding model based on severity code distributions, stratified by hierarchical diagnosis code clusters. A statistically significant difference in mean upcoding anomaly scores is observed between free-standing ERs and acute care hospitals, with free-standing ERs being more anomalous. The second model is a random forest that minimizes improper payments by optimally sorting ER claims within review queues. Depending on the percentage of claims reviewed, the random forest saved 12% to 40% above a baseline approach that prioritized claims by billed amount.
