Federal Project Grant Award Summary

The University of Florida has been awarded $439,826 under the Drug Use and Addiction Research Programs (CFDA 93.279) by the National Institute on Drug Abuse, effective March 1, 2026, through February 28, 2028. The project focuses on developing and evaluating BUP-CARE, a machine-learning-based clinical decision support tool designed to predict buprenorphine care discontinuity risk among opioid use disorder patients. The tool will integrate multi-faceted risk factors to identify patients at high risk of discontinuing buprenorphine treatment within six months of initiation, addressing a critical gap in primary care management where over 50% of patients currently discontinue therapy despite its proven efficacy in reducing overdose and mortality risks.

The deliverables will include a validated machine-learning algorithm and risk stratification framework to enable primary care providers—who prescribe over 60% of buprenorphine prescriptions—to systematically identify high-risk patients and deploy targeted interventions such as long-acting injectable buprenorphine and peer recovery support services. By applying advanced machine-learning techniques to healthcare claims data, the project will overcome limitations of traditional regression-based predictive methods and provide clinicians with evidence-based, data-driven tools to improve treatment retention and outcomes in the context of the nation's ongoing opioid epidemic.