Over the last several decades, the prevalence of Opioid Use Disorder (OUD) has escalated in the U.S., leading to an opioid epidemic that ravages the wellbeing of the nation. Despite all alleviating efforts, opioid-related morbidity and mortality continue to rise across most states, including Tennessee. Currently, medication-assisted treatment (MAT) is the most highly regarded method of treatment for OUD; however, MAT programs differ significantly on the pharmaceutical agent of use, adjuncts to treatment, and philosophy of care. Unfortunately, this inconsistency often leads to poor treatment retention in MAT programs. This project aimed to improve MAT retention for patients with OUD at two MAT clinics within a federally qualified health center (FQHC). The intervention was web-based OUD education grounded in the community reinforcement approach (CRA). The treatment group experienced increased 12-week MAT retention by 3.8% (p=0.026) compared to the control group who received standard treatment pre-implementation. This project implemented a web-based psychosocial intervention, in addition to MAT, to increase treatment retention with excellent patient satisfaction, improving treatment efficacy and outcomes for patients facing OUD.
Bentz, Jordan Taylor and Snell, April, "Computer-Based Learning for Opioid Use Disorder" (2022). Graduate Publications and Other Selected Works - Doctor of Nursing Practice (DNP).