The US federal government spends more than a trillion dollars per year on health care, largely provided by private third parties and reimbursed by the government. A major concern in this system is overbilling, waste and fraud by providers, who face incentives to misreport on their claims in order to receive higher payments. In this paper, we develop novel machine learning tools to identify providers that overbill Medicare, the US federal health insurance program for elderly adults and the disabled. Using large-scale Medicare claims data, we identify patterns consistent with fraud or overbilling among inpatient hospitalizations. Our proposed approach for Medicare fraud detection is fully unsupervised, not relying on any labeled training data, and is explainable to end users, providing reasoning and interpretable insights into the potentially suspicious behavior of the flagged providers. Data from the Department of Justice on providers facing anti-fraud lawsuits and several case studies validate our approach and findings both quantitatively and qualitatively.

翻译：美国联邦政府每年在医疗保健上的支出超过一万亿美元，其中大部分由私营第三方提供并由政府报销。该系统面临的主要问题是医疗服务提供者因面临虚报以获取更高报销的动机而导致的过度收费、浪费和欺诈行为。本文开发了新型机器学习工具，用于识别向美国联邦老年人及残障人士医疗保险计划（Medicare）过度收费的医疗服务提供者。通过利用大规模Medicare索赔数据，我们识别出住院治疗中与欺诈或过度收费一致的行为模式。所提出的Medicare欺诈检测方法完全采用无监督学习，不依赖任何标注训练数据，同时具备面向最终用户的可解释性，能够对标记医疗服务提供者的可疑行为提供推理依据和可解读的洞察。来自司法部的涉及反欺诈诉讼医疗服务提供者的数据以及多项案例研究，从定量和定性两方面验证了我们的方法及结论。