Can Machine Learning Target Health Care Fraud? Evidence From Medicare Hospitalizations
研究开发了无监督机器学习工具，利用美国联邦医保住院数据识别医院过度收费和欺诈模式，可指导公共和私人保险系统的审计，将调查效率提升近5倍。
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.