ABSTRACT In this SBIR Phase 1 proposal, we aim to develop and automate the workflow for a comprehensive transdiagnostic mental health assessment tool for increased precision in the screening of mental health. The majority of instruments presently in use have been designed based on DSM criteria, which has been critiqued for being theoretically defined and ignoring the heterogeneity of symptoms across patients. Furthermore, individual tools are incomplete and heterogeneous in their assessment of symptoms for the same disorder. Thus, individuals are placed into pre-defined categories that do not reflect their overall symptom experience and life factors, and diagnosis can vary based on the choice of tool. This makes it difficult and time consuming to understand individual patients to determine treatment trajectories, effectively recruit patients into research trials, and understand outcomes based on complete symptom phenotypes. In order to address these challenges, a novel assessment tool, the cMHQ, has been developed by our partner non-profit research organization, Sapien Labs, and provides a comprehensive patient symptom profile that maps across 10 DSM-based mental health disorders, as well as dimensional scores across six functional areas, thereby forming a bridge between the current diagnostic environment and the more research oriented RDoC framework. The proposed research focuses on transitioning the cMHQ and its outcomes from lab to marketplace to enable deeper clinical research insights and provide more informed treatment and referral regimens. In aim 1, we will build the cMHQ’s input assessment and diagnostic analysis by developing a responsive front-end assessment application and coding analysis scripts to generate data scoring metrics and diagnostic criteria. In aim 2, we will develop multiple data- output formats for the cMHQ that are tested for usability and acceptability, including an automatically generated cMHQ clinician report that provides a clear and comprehensive analysis of the patient’s mental health profile across multiple dimensions and disorders, as well as an API to enable tabular data outputs for integration into research systems. As part of this aim, we will also integrate all of the elements into a scalable architecture for testing and ensure HIPAA-compliance of the data-flow. If this project is successful, a further Phase 2 project will be warranted to pilot test its application in clinical research and practice domains to demonstrate the benefits to outcomes in both research and treatment.