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, Meta Map, 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 Meta Map to enhance its accuracy. This integration effort will provide guidance in retargeting existing tools for better processing of clinical text.