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This paper describes the development, application, and evaluation of a set of methods for transforming standard pharmacy claims data into a clinically relevant database that can facilitate healthcare research. Prescription claims data represent relatively inexpensive and largely unexploited exploratory ground for understanding the relationships between prescription treatments and their healthcare and cost outcomes. A web-based, graphical interface was developed to solicit clinical expert opinions about how claims should be combined into prescription treatments. A classification tree methodology was then applied to the database in an attempt to induce expert decisions based on a flexible set of predictor variables generated directly from the prescription claims. Two different classification tree approaches and four versions of the predictor variable sets (PVSs) were compared with each other and with a fixed heuristic for data transformation in a sample of 11,654 expert reviewed claim pairs. The model-based classification rules significantly outperformed the simple rule when claim pairs were comprised of different drugs and performed as well as the simple rule when the drugs were the same. The best combination of classification tree approach and PVS was used to generate a set of rules that was subsequently applied to a larger dataset and used to generate and describe prescription treatment episodes. A sample analysis was conducted using the output database to specify inclusion/exclusion criteria, group assignment, stratification, and outcomes such as treatment discontinuation. Both visual and formal techniques were used in a way that would be commonly used in an outcomes or pharmacoeconomic research endeavor.
