Comparing Gold-standard Copayment and Coinsurance Values From Claims Processing Engines to Values Derived From...

While researchers use patient expenditures in claims data to estimate insurance benefit features, little evidence exists to indicate whether the resulting measures are accurate.

To develop and test an algorithm for deriving copayment and coinsurance values from behavioral health claims data.

Employer-sponsored insurance plans from 2011 to 2013 for a national managed behavioral health organization (MBHO).

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).

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.

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.

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.