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AMIA Jt Summits Transl Sci Proc. 2015 Mar 25;2015:188-93. eCollection 2015.

Prescription Extraction from Clinical Notes: Towards Automating EMR Medication Reconciliation.

Author information

1
IBM T. J. Watson Research Center, Yorktown Heights, NY.
2
Scripps Transitional Science Institute, La Jolla, CA.
3
University of Maryland Medical Center, MD.
4
Sutter Health, Walnut Creek, CA.

Abstract

Medication in for ma lion is one of [he most important clinical data types in electronic medical records (EMR) This study developed an NLP application (PredMED) to extract full prescriptions and their relevant components from a large corpus of unstructured ambulatory office visit clinical notes and the corresponding structured medication reconciliation (MED REC) data in the EMR. PredMED achieved an 84.4% F-score on office visit encounter notes and 95.0% on MED"REC data, outperforming two available medication extraction systems. To assess the potential for using automatically extracted prescriptions in the medication reconciliation task, we manually analyzed discrepancies between prescriptions found in clinical encounter notes and in matching MED_REC data for sample patient encounters.

PMID:
26306266
PMCID:
PMC4525262

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