A Chatbot Utilizing Machine Learning and Natural Language Processing to Implement the Brief Negotiation Interview to Improve Engagement in Buprenorphine Treatment among Justice-Involved Individuals

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PROJECT SUMMARY/ABSTRACT The people at greatest risk of dying from an opioid overdose are the least likely to get life-saving medication. Justice-involved individuals coming out of prison have the highest risk of death by overdose (8x greater than the general population), yet only 1 in 20 of these individuals receive buprenorphine (bup), a safe, effective medication that has been shown to reduce a person’s risk of death by overdose by half. There is an urgent need to facilitate an increase in bup treatment engagement among these individuals. Two of the top barriers to receiving bup for these individuals are 1) system level barriers and, 2) low levels of individual motivation. Our prior research shows that delivering individual level treatment engagement interventions increase the rate at which individuals receive bup. Thus, our solution is to improve engagement in bup treatment among justice-involved individuals by 1) “disrupting” system level barriers by circumventing the pieces of the probation system that are stigmatizing and reduce the chances of a bup referral by using an artificial intelligence (AI)-based chatbot to make the referral, and 2) addressing low individual motivation by programming the chatbot to deliver the BNI itself, without the need for a trained professional. Aim 1: Design and develop a functional prototype chatbot to motivate bup engagement. Milestones: (a) human-centered design (including focus groups) with all stakeholders; and (b) creation of a functional chatbot using ML and NLP that is integrated with a mobile application, an application program interface server, and an administrator portal. Aim 2: Conduct a 4-week pilot study with 60 probationers randomly assigned to BNI Chatbot or Treatment-as-Usual (TAU). Hypothesis 1. The BNI Chatbot group will have a higher percentage of participants attending their first bup appointment than the TAU group at 4 weeks (Primary outcome). Hypothesis 2. The BNI Chatbot group will demonstrate higher ratings of readiness and intention to engage in bup treatment, and lower opioid use, as measured by urine toxicology tests, than the TAU group at 4 weeks (Secondary outcomes). Hypothesis 3. The BNI Chatbot group will demonstrate higher ratings of satisfaction than the TAU group.