Predicting high-cost privately insured patients based on self-reported health and utilization data.

OBJECTIVES: To examine how well self-reported data on health, health behaviors, and healthcare utilization by a sample of privately insured patients predict whether they will incur high healthcare costs the following year.

STUDY DESIGN: A 2012 mail survey of autoworkers from Chrysler, Ford, and General Motors, with 3983 survey respondents linked to their health insurance claims data for 2012 and 2013.

METHODS: High healthcare costs are defined as being in the 75th percentile or higher of healthcare expenditures. Models that include combinations of claims-based measures of expenditures and morbidity and self-reported measures of health, health behaviors, and healthcare utilization are compared.

RESULTS: Claims-based measures of healthcare costs and comorbidity for 2012 were strong predictors of whether a patient would incur high healthcare costs in 2013 (C statistic = 0.78). Self-reported measures of chronic conditions, health status, health behaviors, and hospital use are also good predictors of high healthcare costs. However, even the most comprehensive model that included self-reported measures was not as accurate in predicting high healthcare costs (C statistic = 0.73).

CONCLUSIONS: Efficient targeting of high-cost patients is crucial to the success of innovative care delivery models that attempt to lower costs and improve quality of care through more intensive care management of patients. The results of this study show that in the absence of claims data on prior use and expenditures, patient-reported measures of health status and prior healthcare use are reasonable predictors of future healthcare costs for a privately insured population.