SBIR Phase I: Predicting Healthcare Fraud, Waste and Abuse by Automatically Discovering Social Networks in Health Insurance Claims Data through Machine Learning

小型企业创新研究(SBIR)第一阶段项目的广泛影响/商业潜力将为新型社交网络分析检测异常铺平道路，这可能导致更准确、更快地识别欺诈、浪费和滥用(FWA)、关键意见领袖(即有影响力的人)和细分市场。联邦医疗保险和其他医疗保健提供者给FWA造成了数亿美元的损失。这项研究提出使用一种新的方法来发现实体(例如，医生)之间的关系，并使用机器学习将关于实体的信息(例如，处方历史)组合在一起。这样做的目的是减少索赔调查员的工作量，同时保持检测FWA的高准确性。总之，本研究的结果不仅将提高FWA的检测效率，而且能够检测出新类型的FWA。社会影响包括通过更好的FWA检测降低纳税人对政府支持的项目(如联邦医疗保险)的成本。更广泛地说，该系统可用于发现恐怖分子和犯罪网络，检测可能的阿片类药物或物质滥用流行队列、用药不足、用药过度，甚至不正确的药物。拟议的项目将应用一种新的机器学习方法来解决医疗保险中的欺诈、浪费和滥用(FWA)问题。技术问题是如何将医生等实体之间的关系与有关医生的信息(例如，医生的处方历史)结合起来。该项目通过开发一种新的方法来自动发现这些关系，然后通过机器学习将这些关系与医生的信息结合起来，从而极大地提高了预测精度。该方法仅用关系信息来填补实体信息的空白，反之亦然。相信这种方法将极大地提高对FWA的检测能力。其目标是在政府每月发布的欺诈定罪医生数据库中实现50%的真正阳性率。该项目的范围包括分析几种不同类型的医疗保险索赔格式(例如，联邦医疗保险)，并产生欺诈分数，然后其他人可以使用。预期的结果包括美国大多数医生(至少是那些与联邦医疗保险打交道的医生)的欺诈分数，这些分数的API，以及一个交互式视觉系统，声称调查人员可以用来减少他们的工作量，同时准确地识别FWA。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will pave the way for new types of social network analysis to detect anomalies, which could lead to more accurate and faster identification of Fraud, Waste, and Abuse (FWA), key opinion leaders (i.e., influentials), and market segments. Medicare and other healthcare providers lose hundreds of millions of dollars to FWA. This research proposes using a novel way to discover and combine relationships between entities (e.g., doctors) with information about the entities (e.g., prescription history) using machine learning. The goal is to reduce a claims investigator's workload while maintaining high accuracy in detecting FWA. In short, the results of this research will not only improve FWA detection efficiency, but enable detecting new types of FWA. Societal impact includes reduced costs to the taxpayer for government supported programs such as Medicare through better FWA detection. More broadly, the system could be used to find terrorist and crime networks, detect possible opioid or substance abuse epidemic cohorts, under-medication, over-medication, and even incorrect medications.The proposed project will apply a novel machine learning method to solve the Fraud, Waste, and Abuse (FWA) problem in health insurance. The technical problem is how to combine relations between entities such as doctors with information about doctors (e.g., a doctor's prescription history). This project advances the state of the art by developing a new way to automatically discover those relations and then combining those relations with the information about doctors through machine learning, thus vastly improving prediction accuracy. The method uses relation information to fill in the gaps of entity information alone and vice versa. It is believed that this method will hugely improve the ability to detect FWA. The goal is to achieve a 50% true positive rate in a database of fraud-convicted doctors published monthly by the government. The scope of the project involves analyzing several different types of health insurance claims formats (e.g., Medicare) and producing a fraud score, which then others can use. The anticipated results include a fraud score for most doctors in the U.S. (at least those who deal with Medicare), APIs to these scores, and an interactive visual system that claims investigators can use to reduce their workload while accurately identifying FWA.