The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to develop a remote monitoring system that can alert caregivers to relapses in opioid use. Over 23 million Americans are addicted to drugs and alcohol, and these addictions billions per year. Most tools to help people stay in recovery have low or mixed success rates. Reducing relapse saves lives and families and it reduces rearrests, reincarcerations, and rehospitalizations. In this proposal machine learning and pattern recognition, both forms of artificial intelligence (AI) will be aid identification of and response to potential relapse. Benefits include conserving emergency response resources, but more importantly, improving long-term intervention success.This Small Business Innovation Research (SBIR) Phase I project will establish the feasibility of identifying and predicting a future state of craving / obsession or relapse using physiological and smartphone data, a use-case where physiologically underpinned alerts alter current care coordination workflows, and a use-case where relapse after discharge from inpatient facilities for rehabilitation can be significantly averted. Technical objectives include: 1) devise a novel data-driven framework for accurately and objectively estimating probability for relapsing into opioid use using individualized classification models; 2) Deploy and assess efficacy of model risk stratification system and monitoring dashboard at addiction treatment centers through feedback from managed care providers; 3) Assess and compare efficacy of craving vs. prediction models for just-in-time interventions vs. standard practices.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
