Technology-supported, measurement-based supervision for Motivational Interviewing

Millions of Americans receive evidence-based counseling for substance use problems each year. Manyevidence-based treatments for substance abuse are “talk based” therapies, such as motivationalinterviewing (MI), but the existing research-based methodology for evaluating counseling quality isto record sessions and use human rating teams to evaluate them. However, using humans as theassessment tool via behavioral coding is prohibitive in cost and time, can be error prone, and isvirtually never used in the real world. Technology is needed that can analyze the speech patterns and spoken language of counselingsessions, provide automatic and intuitive quality scores, and summarize these in actionablefeedback. Rapid, performance-based quality metrics could support training, ongoing supervision, andquality assurance for millions of evidence-based counseling sessions for substance abuse each year. Lyssn.io is a start-up targeting the development of implementation-focused technology to supportevidence-based counseling.Our goal is to develop innovative health technology solutions that areobjective, scalable, and cost efficient. Lyssn.io includes expertise in speech signal processing,machine learning, user-centered design, software engineering, and clinical expertise in evidence-based counseling.Previous NIH-funded research laid a computational foundation for generating MI quality metrics fromspeech and language features in MI sessions, and led to a prototype of a clinical software supporttool, the Counselor Observer Ratings Expert for MI (CORE-MI). The current Fast-Track SBIR proposal includes Phase I, which will focus on understanding clinicalworkflows, assessing usability, and initial validation of machine learning of MI fidelity measuresin the opioid treatment program at Evergreen Treatment Services (ETS) clinic in Seattle, WA. PhaseII will focus on robust validation of the speech and language technologies underlying the CORE-MItool, and development of scalable supervision protocols that integrate CORE-MI supported feedbackfor counselors. Finally, we will conduct a quasi-experimental evaluation of CORE-MI supportedsupervision and training at a second ETS clinic in the Puget Sound, focusing on acceptability,usability, and adoption, the impact on supervision, improved MI fidelity and preliminary evidenceof increased client retention.The successful execution of this project will break the reliance onhuman judgment for providing performance-based feedback to MI and will massively expand thecapacity to train, supervise, and provide quality assurance in MI for substance abuse.Most evidence-based treatments for substance abuse are in-person psychotherapy and counselinginterventions, such as motivational interviewing. There are currently no methods for evaluating thequality of such counseling interventions in the real world to support training, supervision, andquality assurance. Building on an existing prototype, Lyssn.io – a technology start-up focused onscalable and cost-efficient human-centered technologies – will enhance and evaluate a cloud-based, HIPAA-compliant clinical support software tool that uses automated speech recognition and machine learning in an communitybased opioid replacement clinic.