Program outline

We will host a two-part pre-conference workshop that combines training on the All of Us Researcher Workbench with applied causal inference methods for observational health research. The workshop is specifically designed to address the program’s ongoing transition from the legacy Workbench environment to Researcher Workbench 2.0, powered by Verily, which introduces a new user interface, expanded cloud-based analysis capabilities, and new tools for cohort exploration and data analysis, including integration with external code repositories.

The first session will provide an overview of the evolving Workbench ecosystem, including how researchers access the platform, create workspaces, build cohorts, and navigate the new tools available in Researcher Workbench 2.0. The session will also discuss practical considerations during the migration period, as researchers transition existing projects and workflows from the legacy environment to the updated Verily based platform.

The second session participants will learn how to apply Propensity Score Matching (PSM) to generate balanced comparison, treated-control groups and estimating treatment effects in observational biomedical datasets. Hands-on demonstrations will use example cohorts derived from the All of Us Researcher Workbench environment and illustrate implementation in R using packages such as MatchIt and WeightIt.

Objectives

• Introduce participants to the structure, data resources, and analytic workflows within the All of Us Researcher Workbench.
• Explain key changes associated with the transition to Researcher Workbench 2.0, including the updated user interface, enhanced cloud computing environment, and new tools such as the Data Explorer and JupyterLab.
• Demonstrate approaches for building research cohorts and datasets within the updated Workbench environment.
• Explain the conceptual framework and assumptions underlying Propensity Score Matching in observational research.
• Provide practical guidance on implementing propensity score matching and weighting methods in R using MatchIt and WeightIt.
• Illustrate how large-scale biomedical datasets available through the Workbench can be combined with causal inference methods to address clinical, policy, and population health research questions.

This 120-minute interactive session, "The RCMI Innovation Pipeline: Transforming Health Disparities Research into Market-Ready Solutions," addresses the critical gap between academic discovery and real-world implementation. While academic publishing and grant awards are essential milestones, they are often insufficient to fully eliminate health disparities. True community impact requires "impact-scaling", the strategic translation of research into sustainable, market-ready products and services. Led by Morehouse School of Medicine (MSM), in coordination with RCMI PI/PDs and Technology Transfer Offices, the workshop opens with a high-level walkthrough of the MSM Technology Transfer model. This case study demonstrates how an RCMI institution can secure 180+ patents and launch startups, specifically highlighting the journey of Vanrafia/Atrasentan from an initial RCMI pilot award to successful commercial licensing. Participants will be introduced to the Heilmeier Framework as a rigorous method to vet disclosures and plan for commercial viability. The "How" of the RCMI Innovation Pipeline is demystified through a deep dive into non-dilutive funding and strategic partnerships. The session will discuss the SBIR/STTR (R41-R44) grants for non-dilutive funding. The final segment features a multi-regional panel examining diverse RCMI technology transfer models from Historically Black Medical Schools (HBMS), East, Southwest, and Pacific cohorts. This final session focuses on overcoming the "infrastructure gap" and how to build an innovation culture within institutions with limited resources. Attendees will leave with a practical playbook to transform health disparities research into scalable solutions that directly serve the needs of underserved populations. Attendees will have the option to join future post-conference sessions on I-CORPS hubs, and on demystifying regulatory FDA/CLIA pathways.

Overarching Goal: To equip participants with the knowledge, skills, and practical strategies needed to effectively integrate advanced technologies into health disparities research and to foster innovative, collaborative solutions.

Objectives

• Highlight Transformative Technologies: Provide insights into the potential of AI and Machine Learning (AI/ML), Organ-on-a-Chip platforms, and Omics technologies in advancing health disparities research, with an emphasis on improving health outcomes for minority and underrepresented populations and promoting equitable healthcare solutions.
• Address Ethical and Data Integrity Challenges: Examine ethical considerations associated with the use of AI in health disparities research, including algorithmic bias, transparency, and issues such as AI hallucination and its implications for data accuracy, reliability, and integrity.
• Foster Translational and Community-Engaged Research: Discuss strategies for translating technological innovations into real-world applications, including community-engaged approaches that ensure research is culturally responsive, inclusive, and aligned with the needs of affected populations.

Overarching Goal: Engage Consortium-wide RCMI U54 Center Administrators to share best practices on effective research administration and research compliance. Optimize data collection for reporting and evaluation of U54 Centers.

Objectives

• Optimize data collection for reporting and evaluation of U54 Centers.
• Provide fiscal and administrative sessions for program managers and research administrators of the RCMI Consortium.