SBIR Phase I: Harnessing Natural Language Processing for Scalable Text-based Behavioral Health Care

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to address a systematic behavioral health and substance use provider shortage, cost-effectively improve patient retention in treatment, and proactively treat chronic behavioral health patients. Left untreated, these conditions cost over $1 trillion annually and result in countless early deaths. Peer support is an effective tool to engage patients unwilling or unable to access clinical care, particularly in marginalized populations. However, scaling peer support is challenging, with current online support forums rife with trolling and abuse. Our Natural Language Processing (NLP) tools can extrapolate the emotional sentiment of text messages, automatically flagging clinically relevant or critical content. This allows clinicians to easily moderate groups by focusing their time on the patients most in need, while peers generate the touchpoints necessary for day-to-day engagement. This Small Business Innovation Research (SBIR) Phase I project will greatly enhance the ability of clinicians to track the mental health of patients within a support group. Currently, a challenge in managing peer groups is identifying the health of a group as a whole - some groups can be far more constructive than others. Given the volume of messages generated in an online support group, together with expected caseloads for care manager and peer support specialists, who may be managing dozens of groups, this is an impossible task without the aid of technology. To achieve this goal we focus on three main areas: 1) improving the performance of our existing NLP algorithms by developing novel techniques to identify and track multiple conversations that might be co-occurring in the group, 2) developing a method of tracking the overall health and stability of a group by analyzing interactions among peers and 3) design new interfaces that effectively display all of the insights generated by the algorithms. These NLP tools will power a platform to give patients more access to support. Providers will have access to a novel high-fidelity data source to better triage outreach and personalize care. 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.