TY  - GEN
AB  - Accuracy of medication data in EHR is crucial for patient care and research. Previous work has shown frequent errors in medication lists include incomplete records, duplicated prescriptions, and failed discontinuation of medications. Since medication lists are inaccurate, physicians often record medication information in progress notes, which is difficult to automatically extract since notes are written as free-text narratives. In this study, we developed and validated a named entity recognition model to identify current medication and adherence from progress notes. Also, a prototype tool for medication reconciliation using the developed model was demonstrated.
AD  - Oregon Health and Science University
AD  - Oregon Health and Science University
AD  - Oregon Health and Science University
AD  - Oregon Health and Science University
AD  - Oregon Health and Science University
AD  - Oregon Health and Science University
AU  - Lin, Wei-Chun
AU  - Chen, Jimmy S.
AU  - Kaluzny, Joel
AU  - Chen, Aiyin
AU  - Chiang, Michael F.
AU  - Hribar, Michelle R.
DA  - 2021
DO  - 10.6083/q524jp38n
DO  - DOI
ID  - 9225
KW  - Medication Reconciliation
KW  - Data Collection
KW  - Electronic Health Records
KW  - Data Accuracy
KW  - Glaucoma
KW  - Natural Language Processing
KW  - Eye Diseases
KW  - Ocular Hypertension
KW  - Medical Records
KW  - Information Storage and Retrieval
KW  - EHR
KW  - medication lists
KW  - clinical notes
L1  - https://digitalcollections.ohsu.edu/record/9225/files/Lin-Wei-Chun-OHSU-ResearchWeek-2021.pdf
L2  - https://digitalcollections.ohsu.edu/record/9225/files/Lin-Wei-Chun-OHSU-ResearchWeek-2021.pdf
L4  - https://digitalcollections.ohsu.edu/record/9225/files/Lin-Wei-Chun-OHSU-ResearchWeek-2021.pdf
LA  - eng
LK  - https://digitalcollections.ohsu.edu/record/9225/files/Lin-Wei-Chun-OHSU-ResearchWeek-2021.pdf
N2  - Accuracy of medication data in EHR is crucial for patient care and research. Previous work has shown frequent errors in medication lists include incomplete records, duplicated prescriptions, and failed discontinuation of medications. Since medication lists are inaccurate, physicians often record medication information in progress notes, which is difficult to automatically extract since notes are written as free-text narratives. In this study, we developed and validated a named entity recognition model to identify current medication and adherence from progress notes. Also, a prototype tool for medication reconciliation using the developed model was demonstrated.
PB  - Oregon Health and Science University
PY  - 2021
T1  - Extraction of active medications and adherence using natural language processing for glaucoma patients
TI  - Extraction of active medications and adherence using natural language processing for glaucoma patients
UR  - https://digitalcollections.ohsu.edu/record/9225/files/Lin-Wei-Chun-OHSU-ResearchWeek-2021.pdf
Y1  - 2021
ER  -