Ingredient-based method to create medication lists and support granular data segmentation.

Objectives: Show the generalizability of an ingredient-based method to automatically create an up-to-date, error-free, complete list of medication codes (e.g., opioid medications with at least one opioid ingredient) from an ingredient list (e.g., opioid ingredients). The method, previously evaluated with the RxNorm terminology, was reused and applied in the National Drug Code (NDC) context to create opioid and antidepressant medication lists. Methods: The resulting medication lists were validated through automatic comparisons with curated medication lists (the CDC opioid medication code set and the HEDIS antidepressant medication code set), automatic comparisons with active medication lists (Federal Drug Administration (FDA) databases and RxNorm), and manual physicians' review. Results: The proposed ingredient-based method was validated with two clinical terminologies (RxNorm and NDC) and two use cases (opioid and antidepressant medication code sets), demonstrating generalizability, reusability, and high accuracy. Conclusion: Methodologies for creating lists of sensitive codes are essential to supporting patients' need to restrict access to potentially stigmatizing information. In contrast with data-driven, less accurate, and unexplainable methods to create clinical lists, our study innovated by proposing algorithms to automatically discover correct, complete, up-to-date, and ingredient-based medication lists.