AI-Based Fidelity Feedback to Enhance CBT

This study is being conducted together by researchers at the University of Pennsylvania and Lyssn.io, Inc., ("Lyssn"), a technology start-up developing digital tools to support evidence-based psychotherapies (EBPs) for mental health disorders and addiction. This study will implement a technology to assess and enhance the quality of EBPs like Cognitive Behavioral Therapy (CBT) that includes a user interface geared to clinical, supervision, and administrative workflows and needs, and then assess this technology for effectiveness in comparison to usual care. There is a tremendous global burden of mental illness: Over 50 million American adults have a diagnosable mental health disorder, and major depression on its own is the leading cause of disability worldwide. In the face of this burden, clinical research has documented a variety of effective EBPs (e.g. CBT), and these psychotherapies are utilized on a massive scale. Systems have invested over $2 billion in training providers in specific EBPs. Once trained, however, therapists' adherence to the EBP, also called fidelity, is both crucial for effectiveness and difficult to assess. There is no scalable method to assess the fidelity and quality of EBPs in community practice settings. This is a foundational problem for healthcare systems. Advances in speech processing and machine learning make technology a promising solution to this problem. The use of technology - instead of humans - to evaluate EBPs means that objective, performance-based feedback can be provided quickly, efficiently, cost-effectively, and without human error. If successful, the present research will be among the first examples of a method for building, monitoring, and assessing the quality of therapy that can scale up to large, real-world healthcare settings. In this study, the investigators will implement an existing, fully-functional prototype (LyssnCBT) that includes a user interface geared to community mental health (CMH) clinical, supervision, and administrative workflows and needs, and then assess for effectiveness of psychotherapy supported by LyssnCBT in comparison to usual care. This study will implement LyssnCBT in 5 community mental health agencies, beginning with a single-arm pilot field trial to identify and address any specific barriers to implementing the tool in a community mental health context. The study team will then conduct a larger study in community mental health agencies comparing LyssnCBT to services as usual.