Can Machine Learning Target Health Care Fraud? Evidence From Medicare Hospitalizations
Journal of Policy Analysis and Management · 2026 · Shubhranshu Shekhar、Jetson Leder-Luis、Leman Akoglu
中文摘要
美国每年在医疗保健上的支出超过4万亿美元，其服务主要由私立医疗机构提供，并由保险公司进行偿付。该体系中的一个主要隐患是医院的过度收费与欺诈行为，医院面临虚报索赔以获取更高偿付的激励。在本研究中，我们开发了新型机器学习工具，用以识别向保险公司过度收费的医院，这些工具可用于指导对可疑医院的调查与审计，适用于公共和私人医疗保险体系。利用联邦医疗保险（Medicare，美国面向老年人和残疾人的联邦医疗保险计划）的大规模索赔数据，我们识别出住院服务中与欺诈相一致的模式。我们提出的欺诈检测方法完全无监督，不依赖任何有标签的训练数据，并且对终端用户具有可解释性，能够解释哪些诊断、操作和计费代码导致医院被标记为可疑。利用新收集的来自司法部关于面临反欺诈诉讼的医院的数据，以及对可疑医院的案例研究，我们验证了我们的方法和发现。我们的方法相对于随机锁定医院的效率提升接近5倍。我们还进行了事后分析，以了解哪些未用于检测的医院特征与可疑性相关。
Abstract
The US spends more than $4 trillion per year on health care, largely conducted by private providers and reimbursed by insurers. A major concern in this system is overbilling and fraud by hospitals, who face incentives to misreport their claims to receive higher payments. In this work, we develop novel machine learning tools to identify hospitals that overbill insurers, which can be used to guide investigations and auditing of suspicious hospitals for both public and private health insurance systems. Using large-scale claims data from Medicare, the US federal health insurance program for the elderly and disabled, we identify patterns consistent with fraud among inpatient hospitalizations. Our proposed approach for fraud detection is fully unsupervised, not relying on any labeled training data, and is explainable to end users, providing interpretations for which diagnosis, procedure, and billing codes lead to hospitals being labeled suspicious. Using newly collected data from the Department of Justice on hospitals facing anti-fraud lawsuits, and case studies of suspicious hospitals, we validate our approach and findings. Our method provides a nearly 5-fold lift over random targeting of hospitals. We also perform a post-analysis to understand which hospital characteristics, not used for detection, are associated with suspiciousness.