Toward Comprehensive Attribution of Healthcare Cost Changes

Health insurance companies wish to understand themain drivers behind changes in their costs to enable targeted and proactive management of their operations. This paper presents a comprehensive approach to cost change attribution that encompasses a range of factors represented in insurance transaction data, including medical procedures, healthcare provider characteristics, patient features, and geographic locations. To allow consideration of such a large number of features and their combinations, we combine feature selection, using regularization and significance testing, with a multiplicative model to account for the nonlinear nature of multi-morbidities. The proposed regression procedure also accommodates real-world aspects of the healthcare domain such as hierarchical relationships among factors and the insurer's differing abilities to address different factors. We describe deployment of the method for a large health insurance company in the United States. Compared to the company's expert analysis on the same dataset, the proposedmethod offers multiple advantages: 1) a unified view of themost significant cost factors across all categories, 2) discovery of smaller-scale anomalous factors missed by the experts, 3) early identification of emerging factors before all claims have been processed, and 4) an efficient automated process that can save months of manual effort.