How far is too far? Identifying suspicious travel patterns in healthcare claims using machine learning

• Published in: 2023 International Conference on Machine Learning and Applications (ICMLA) pp. 610 - 617
• Main Authors: Wang, Kai, Lasaga, Daniel, Helms, John, Bowen, Edward, Bhattacharya, Sanmitra
• Format: Conference Proceeding
• Language: English
• Published: IEEE 15.12.2023
• Subjects: Anomaly Detection; Codes; Detectors; Gastroenterology; Geographic Information Systems; GIS; Machine learning; Medical services; Mental health; Neural Networks; Pain
• DOI: 10.1109/ICMLA58977.2023.00090

Fraud in healthcare services and claims poses a significant threat to healthcare expenditure, accessibility to health services, and quality of care of members. One important type of member-provider collusion is where members travel unreason-able distances seeking healthcare services. Such activities could be indicators of "pill mills", doctor shopping, or referral kickback schemes. Previous research on the identification of suspicious travel distances have focused mostly on the billed amount and considered select diagnosis conditions and travel distances at zip code or county levels. Compared to these studies, our proposed framework focuses on claims across various diagnoses and takes into account population densities of members' zip codes, and provider densities for various specialties, among other features, which are critical to the prediction of travel distances. We exper-iment with two approaches - i) a regression model paired with a statistical anomalous distance detector, and ii) a neural network-based model paired with a likelihood estimator for anomalous distance detection. The evaluation of these models on a manually annotated dataset shows that the second approach outperforms the first one in identifying anomalous travel distances.