PROJECT SUMMARY Institutional discrimination has limited access to important resources for Black, Indigenous, People of Color (BIPOC) and Lesbian, Gay, Bisexual, Transgender, and Queer (LGBTQ+) communities, and have impacted the quality of mental healthcare that these communities receive. Researchers have focused on promoting cultural competency (CC) among therapists to address mental healthcare disparities for BIPOC and LGBTQ+ communities. Therapist CC is broadly associated with improved clinical outcomes and therapeutic alliance. However, current methods of assessing therapist CC are infeasible to implement in clinical settings. Therapist-client power differentials limit clients from providing specific feedback, and behavioral coding is infeasible to implement in healthcare systems given difficulties with scaling up session evaluations. Natural language processing (NLP) provides a promising alternative to current methods of assessing and providing feedback on therapist CC, and recent applications of NLP based tools to psychotherapy settings indicate that they are viable methods of improving culturally responsive care. The purpose of the current study is to develop a prototype of a NLP-based therapist CC feedback tool (HEAL). We will first create an annotated dataset by coding 300 therapy sessions with a behavioral coding system assessing culturally responsive care (Aim 1). We will then create a prototype of HEAL (Aim 2) by (a) developing a feedback visualizer based on input from community advisory boards (CABs) comprised of supervisors and therapists, and BIPOC LGBTQ+ clients, (b) selecting a speech recognition software, and (c) developing and validating NLP models based on the annotated dataset from Aim 1. We will use rapid cycle prototyping and testing to iteratively revise HEAL, meeting monthly with CAB members. We will assess acceptability, appropriateness, feasibility, and usability through interviews with CAB members, and quantitative measures. Finally, we will pilot the prototype of HEAL with 15 therapists using standardized patients, and assess acceptability, feasibility, appropriateness, and usability through individual feedback interviews and quantitative measures (Aim 3). The results of this study will culminate in the development of a novel therapist CC support tool, and ideally position me to pursue R01 funding to evaluate its effectiveness in a randomized clinical trial. The proposed aims are in line with NIMH's strategic plan to develop innovative service delivery models to dramatically improve the outcomes of mental health services received in diverse communities and populations. The current study and training plan will allow me to develop expertise in community partnership in user centered design, and understand how cultural contexts and biases impact NLP models, and strongly position me to be a leader in multidisciplinary community-based research developing innovative methods of addressing mental healthcare inequities.