Extracting Rx information from clinical narrative.

OBJECTIVE: The authors used the i2b2 Medication Extraction Challenge to evaluate their entity extraction methods, contribute to the generation of a publicly available collection of annotated clinical notes, and start developing methods for ontology-based reasoning using structured information generated from the unstructured clinical narrative.
DESIGN: Extraction of salient features of medication orders from the text of de-identified hospital discharge summaries was addressed with a knowledge-based approach using simple rules and lookup lists. The entity recognition tool, MetaMap, was combined with dose, frequency, and duration modules specifically developed for the Challenge as well as a prototype module for reason identification. MEASUREMENTS: Evaluation metrics and corresponding results were provided by the Challenge organizers.
RESULTS: The results indicate that robust rule-based tools achieve satisfactory results in extraction of simple elements of medication orders, but more sophisticated methods are needed for identification of reasons for the orders and durations. LIMITATIONS: Owing to the time constraints and nature of the Challenge, some obvious follow-on analysis has not been completed yet.
CONCLUSIONS: The authors plan to integrate the new modules with MetaMap to enhance its accuracy. This integration effort will provide guidance in retargeting existing tools for better processing of clinical text.