A Software Product That Empowers Individuals Affected By Substance Use Disorders and Their Care Teams with Health and Social Resources

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Project Summary Access and participation in community resource programs such as transportation, housing and medication assistance - also known as social determinants of health (SDOH) is inextricably linked to a successful treatment and recovery in substance use disorders (SUD). Therefore, health and social service providers dedicate a significant amount of time to curate local community resource listings or “referral binders”. These “referral binders” are often highly duplicated and fragmented across organizations and by using expensive non-scalable solutions technology vendors in this domain have not fully addressed this problem. Furthermore, participation in such community programs is also hindered by the absence of highly efficient program eligibility screening tools. Undoubtedly, these shortcomings contribute to lack of direct access to recovery capital for individuals affected by SUD. We report two feasibility outcomes from our SBIR Phase I study. 1) A novel co-creation led business model that leverages local partnerships with subject matter expertise agencies on SUD and SDOH - can provide enhanced access to community resource programs at the point-of-care. 2) A highly adaptive digital assisted SDOH screening tool powered by novel conversational artificial intelligence (AI) and natural language processing (NLP) technologies can enhance patients’ participation in SDOH related community programs. This is achieved by using these technologies to perform program eligibility screenings through both web and text messaging channels coupled with patient triaging within a case work team. The purpose of this Phase II study is to: 1) Optimize and scale the community resource co-creation business model established in Phase I by onboarding eight co-creation partners in Texas within 2 years. 2) Examine the relationship between the conversational AI and NLP powered SDOH screening tool piloted in Phase I with SUD outcomes among youths and young adults. Specifically, we will partner with the Harris Center for Mental Health and IDD - the largest outpatient mental health provider in Texas to examine whether youths screened and enrolled into a treatment program using our technology report improved SUD outcomes. As AI and NLP are core pillars of our technology we will also investigate and subsequently correct for potential societal biases and stereotypes (e.g. race, gender) encoded in such technologies. This effort will avoid such biases to inadvertently determine outcomes in our downstream prediction tasks. The key Phase II milestones include; 1) Demonstrable evidence that the co-creation business model leads to enhanced access to SDOH and SUD community resources at the point-of-care. 2) An understanding on whether screening and eventual participation in a treatment program as supported by our conversational AI technology can lead to reduced encounters with law enforcement including CPS among youths with mental health and SUD. In summary, the proposed Phase II work will lead to enhanced access to SUD recovery capital and facilitate patients’ participation in local health and SDOH community programs.