title: Prescription Extraction from Clinical Notes: Towards Automating EMR Medication Reconciliation.
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.
authors: IBM T. J. Watson Research Center, Yorktown Heights, NY.
authors: Yajuan
authors: Y
authors: Wang
authors: Scripps Transitional Science Institute, La Jolla, CA.
authors: Steven R
authors: SR
authors: Steinhubl
authors: University of Maryland Medical Center, MD.
authors: Chrisopher
authors: C
authors: Defilippi
authors: IBM T. J. Watson Research Center, Yorktown Heights, NY.
authors: Kenney
authors: K
authors: Ng
authors: IBM T. J. Watson Research Center, Yorktown Heights, NY.
authors: Shahram
authors: S
authors: Ebadollahi
authors: Sutter Health, Walnut Creek, CA.
authors: Walter F
authors: WF
authors: Stewart
authors: IBM T. J. Watson Research Center, Yorktown Heights, NY.
authors: Roy J
authors: RJ
authors: Byrd