A configurable, big data system for on-demand healthcare cost prediction

Predictive modeling is becoming increasingly common in healthcare. Existing healthcare cost prediction solutions are tailor-made to accomplish specific tasks for certain populations, hence requiring expensive modifications to adapt to a different task or population. In this paper, we present a modular and extensible solution for healthcare cost prediction, which can be easily configured for various prediction tasks and populations. Our solution incorporates efficient high-dimensional data handling, smart feature engineering, flexible predictive learning, individualized assessment of cost impacts of predictors, and a management system that allows for reuse of partial results. We configure two distinct applications using the proposed system and present results on prediction accuracy and cost impact assessment. The first application predicts healthcare costs for a commercial population, and the second predicts the cost of care for a Medicaid population using an entirely different set of data, predictors, and assumptions.

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