Clinician Perspectives on a Predictive Model for Recommending Opioid Use Disorder Treatment

Presentation Time: 08:30 AM - 08:45 AM

Abstract Keywords: Clinical Decision Support, Qualitative Methods, Surveys and Needs Analysis
Primary Track: Applications
Programmatic Theme: Clinical Informatics

Background: Predictive models that have been made available as clinical decision support systems have not always been used.

Objectives: This qualitative study aimed to identify factors that might impact the uptake of a predictive model recommending either methadone or buprenorphine as medication for opioid use disorder (MOUD) in the inpatient setting.

Methods: We conducted semi-structured interviews with clinicians who prescribe MOUD and performed a combined deductive and inductive content analysis using a socio-technical model.

Results: Thirteen clinicians were interviewed. Non-specialists trusted their specialist peers to lead MOUD decisions and claimed they would trust a tool endorsed by experts and the institution. Clinicians expected the model to follow clinical reasoning, which involves considering factors that are not well-captured by the electronic health record (e.g., housing status, access to care, facility preferences).

Conclusion: Predictive models for MOUD should be designed to foster appropriate trust given the tool’s purpose, process, limitation, and performance.

Speaker(s):
Leigh Anne Tang
Vanderbilt University Department of Biomedical Informatics

Author(s):
Leigh Anne Tang - Vanderbilt University Department of Biomedical Informatics; Michelle Gomez - Vanderbilt University; Uday Suresh, MS - Vanderbilt University Department of Biomedical Informatics; Kristopher Kast, MD - Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center; Robert Becker, MS - Department of Biomedical Informatics, Vanderbilt University; Thomas Reese, PharmD, PhD - Vanderbilt; Colin Walsh - Department of Biomedical Informatics, Vanderbilt University; Jessica Ancker, MPH, PhD, FACMI - Vanderbilt University Medical Center;