Comparing Gold-standard Copayment and Coinsurance Values From Claims Processing Engines to Values Derived From Behavioral Health Claims Databases

Background: While researchers use patient expenditures in claims data to estimate insurance benefit features, little evidence exists to indicate whether the resulting measures are accurate. Objective: To develop and test an algorithm for deriving copayment and coinsurance values from behavioral health claims data. Subjects: Employer-sponsored insurance plans from 2011 to 2013 for a national managed behavioral health organization (MBHO). Measures: Twelve benefit features, distinguishing between carve-in and carve-out, in-network and out-of-network, inpatient and outpatient, and copayment and coinsurance, were created. Measures drew from claims (claims-derived measures), and benefit feature data from a claims processing engine database (true measures). Study Design: We calculate sensitivity and specificity of the claims-derived measures' ability to accurately determine if a benefit feature was required and for plan-years requiring the benefit feature, the accuracy of the claims-derived measures. Accuracy rates using the minimum, 25th, 50th, 75th, and maximum claims value for a plan-year were compared. Principal Findings: Sensitivity (82% or higher for all but 3 benefit features) and specificity (95% or higher for all but 2 benefit features) were relatively high. Accuracy rates were highest using the 75th or maximum claims value, depending on the benefit feature, and ranged from 69% to 99% for all benefit features except for out-of-network inpatient coinsurance. Conclusions: For most plan-years, claims-derived measures correctly identify required specialty mental health copayments and coinsurance, although the claims-derived measures' accuracy varies across benefit design features. This information should be considered when creating claims-derived benefit features to use for policy analysis.