Title:	IDENTIFYING AND UNDERSTANDING OPIOID USE DISORDER IN CLINICAL NOTES
Author(s):	T. Elizabeth Workman, Joel Kupersmith, Joseph L. Goulet, Christopher Spevak, Cynthia Brandt, Friedhelm Sandbrink, Marc R. Blackman, Nawar M. Shara and Qing Zeng-Treitler
ISBN:	978-989-8704-18-4
Editors:	Mário Macedo
Year:	2020
Edition:	Single
Keywords:	Opioid Abuse Disorder, Machine Learning, U.S. Veterans
Type:	Full
First Page:	143
Last Page:	150
Language:	English
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Full Contents:	click to dowload
Paper Abstract:	Opioid use, abuse and misuse afflicts many populations, including Veterans. The objective of this ten-year retrospective study was to identify documentation of potential opioid abuse, both treated and untreated, in clinical notes, by developing and applying a natural language processing tool to a corpus of clinical notes documenting the healthcare of U.S. Veterans. To better understand the issue of opioid abuse among Veterans, we also extracted descriptive data on prescription counts, patient demographics, and diagnoses. The natural language processing tool we developed achieved F1 scores of 88% and 91% in identifying opioid abuse with treatment, and without treatment, respectively, among U.S. Veterans receiving healthcare in the Baltimore, Maryland and Washington DC VA service regions. This resulted in identifying 809 additional patients experiencing opioid abuse. The descriptive data give insight by elucidating trends that enhance understanding of opioid abuse among Veterans receiving healthcare in these service regions, and suggest future research.
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