Authors:
Don Roosan
1
;
Yanting Wu
2
;
Jay Chok
3
;
Christopher Sanine
4
;
Tiffany Khou
1
;
Yawen Li
5
and
Hasiba Khan
6
Affiliations:
1
Western University of Health Sciences, College of Pharmacy, 309 E 2nd Street, Pomona, CA, U.S.A.
;
2
Indiana University School of Medicine, Division of Clinical Pharmacology, 340 W 10th Street, Indianapolis, IN, U.S.A.
;
3
Westcliff University, 17877 Von Karman Ave 4th floor, Irvine, CA 92614, U.S.A.
;
4
Emanate Health Inter-Community Hospital, 210 W San Bernardino Road, Covina, CA, U.S.A.
;
5
School of Social Work, California State University, San Bernardino, CA, U.S.A.
;
6
Tekurai Inc, 2000 NW Military Highway #10, San Antonio, TX, U.S.A.
Keyword(s):
Artificial Intelligence, Cognitive Task Analysis, Electronic Health Record, Data Visualization, Opioid Use Disorder.
Abstract:
The rise of big data in healthcare, particularly within electronic health records (EHRs), presents both challenges and opportunities for addressing complex public health issues such as opioid use disorder (OUD) and social determinants of health (SDoH). Traditional data analysis methods are often limited by their reliance on structured data, overlooking the wealth of valuable insights embedded within unstructured clinical narratives. Leveraging advancements in artificial intelligence (AI), Large Language Models (LLM) and natural language processing (NLP), this study proposes a novel approach to detect OUD by analyzing unstructured data within EHRs. Specifically, a Bidirectional Encoder Representations from Transformers (BERT)-based NLP method is developed and applied to clinical progress notes extracted from the EHR system of Emanate Health System. The study created a data analytics platform utilizing user-centered design for improving clinical decisions. This study contributes to the
ongoing effort to combat the opioid crisis by bridging the gap between technology-driven analytics and clinical practice, ultimately striving for improved patient wellbeing and equitable healthcare delivery.
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