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Predicting Medical Provider Specialties to Detect Anomalous Insurance Claims

机译：预测医疗服务提供者专科以发现异常保险索赔

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摘要

The healthcare industry is a complex system with many moving parts. One issue in this field is the misuse of medical insurance systems, such as Medicare. In this paper, we build a machine learning model to detect when physicians exhibit anomalous behavior in their medical insurance claims. This new research has the potential to give some insight in determining if, and when, physicians are acting outside the norm of their respective specialty, which could indicate misuse, fraud, or lack of knowledge around billing procedures. We use a publicly available procedure billing dataset, released by the U. S. Medicare system. Due to the large size of the dataset, we sampled the dataset to include all physicians practicing within one state only. The model uses the multinomial Naïve Bayes algorithm and is evaluated by calculating precision, recall, and Fscore with 5-fold cross-validation. The model is able to successfully predict several classes of physicians with an F-score over 0.9. These results show that it is possible to effectively use machine learning in a novel way to classify physicians into their respective fields solely using the procedures they bill for. This research provides a model that can identify physicians who are potentially misusing insurance systems for further investigation.
机译：医疗保健行业是一个包含许多活动部件的复杂系统。该领域的一个问题是滥用医疗保险系统，例如Medicare。在本文中，我们建立了一个机器学习模型来检测医生何时在医疗保险索赔中表现出异常行为。这项新的研究有可能为确定医生是否以及何时行动超出其各自专业的规范而提供一些见识，这可能表明滥用，欺诈或对计费程序缺乏了解。我们使用由美国Medicare系统发布的可公开获得的程序计费数据集。由于数据集的规模很大，我们对数据集进行了采样，以包括仅在一个州内执业的所有医生。该模型使用多项朴素贝叶斯算法，并通过计算精度，召回率和具有5倍交叉验证的Fscore进行评估。该模型能够成功预测F分数超过0.9的几类医生。这些结果表明，仅通过医生开具的程序，就可以有效地以新颖的方式有效地使用机器学习将医生划分为各自领域。这项研究提供了一个模型，可以识别可能滥用保险系统进行进一步调查的医生。

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