Developing a Clinical Decision Support Tool that Assesses Risk of Opioid Use Disorder Using Natural Language Processing, Machine Learning, and Social Determinants of Health from Clinical Notes

PROJECT SUMMARY/ABSTRACT
In 2017, 1.7 million Americans suffered from opioid use disorders (OUD), which led to 47,000 American deaths
from opioid overdose. Social determinates of health (SDoH) affect patients' OUD risk level and
physicians' opioid prescribing. Physicians lack the tools to quickly and accurately assess SDoH associated
with OUD, and lack knowledge of relevant resource for intervention. Clinical decision support (CDS) could
quickly assess a patients' SDoH factors associated with OUD risk and provide actionable recommendations,
which would reduce OUD risk assessment time and address knowledge gaps. In 2018, UCSF researchers
created the Compendium of Medical Terminology Codes for Social Risk Factors that maps SDoH risks to
medical vocabularies. However, most SDoH are documented in clinical notes. My long-term career goal is
research independence with expertise in: 1) OUD risk assessment, 2) SDoH research, and 3) intervention
development, implementation, and evaluation. Related to these goals, this study will use natural language
processing (NLP) to identify SDoH in clinical notes, examine associations between SDoH and OUD,
and develop a CDS tool to assess OUD risk. We will then assess usability, acceptability, and feasibility
of using the CDS tool in clinical settings. This research will help physicians quickly and accurately assess
OUD risk, intervene earlier, and improve care. Our research aims include: Aim 1. Use NLP to identify SDoH in
clinical notes and examine associations between SDoH and OUD. We will use the Compendium and NLP to
extract new SDoH in clinical notes. Two raters will manually validate the new SDoH, and use descriptive
statistics to characterize associations between SDoH and OUD. (training goals 1 and 2). Aim 2: Develop a
CDS tool to assess OUD risk. We will use SDoH and OUD associations from aim 1 to develop a supervised
machine learning algorithm for our CDS tool. We will validate the CDS tool by measuring its ability to correctly
assess OUD risk in patients' EHR data (training goals 1 and 2). Aim 3: Test the usability, acceptability, and
feasibility of physicians' use of the CDS tool. 40 physicians will be asked to assess sample patient cases, then
given CDS results on those same cases. Physicians will indicate whether they would follow the CDS's
recommendations. Additionally, participants will be asked to complete an interview and questionnaire to
evaluate usability and acceptability. We will assess feasibility by examining recruitment, implementation, and
metadata. (training goal 3). These aims are achievable because I have experience in NLP and machine
learning and my mentors are experts in OUD research, SDoH research, and intervention design; and have an
outstanding record in career development. This K01 will help me achieve researcher independence by
providing a) skills to develop an OUD risk assessment intervention; b) expertise in a novel growing SDoH field;
c) an innovative trial-ready scalable intervention; and d) preliminary data for an R01.

Grant Number: 5K01DA055081-04
NIH Institute/Center: NIH
Principal Investigator: William Brown III