Provider-Consumer Anomaly Detection for Healthcare Systems

Anomaly detection is an important task that has been widely applied to different scenarios. In particular, its application in public healthcare is a crucial management task that can improve the quality of the health services and avoid loss of huge amounts of money. In this work we propose and evaluate, in a real scenario, a method for anomaly detection in healthcare based on a provider-consumer model. Our method is divided into two phases. In the first phase it assigns anomaly scores to the cities (consumers) as a function of their demand, then, in the second phase, it transfers the scores from cities to hospitals (providers). We applied the method to a real database from the Brazilian public healthcare that records medical procedures which cost more than $8.5 billion from 2008 to 2012, and demonstrated our method's ability to find potentially fraudulent hospitals. The method is being adopted by the Brazilian government for selecting anomalous hospitals to be investigated. Our main contributions are (i) a simple and effective method for anomaly detection in healthcare; (ii) our method does not require information about the providers nor medical rules; (iii) the analysis from the consumer perspective allows the detection of anomalies that could not be detected with traditional methods; and (iv) we applied the method to a real database and performed a detailed validation.