The Research Centers in Minority Institutions, (RCMI) Program was established by Congress to address the health research and training needs of minority populations, by preparing future generations of scientists at these institutions, with a track record of producing minority scholars in medicine, science, and technology. The RCMI Consortium consists of the RCMI Specialized Centers and a Coordinating Center (CC). The RCMI-CC leverages the scientific expertise, technologies, and innovations of RCMI Centers to accelerate the delivery of solutions to address health disparities in communities that are most impacted. There is increasing recognition that the gap in representation of racial/ethnic groups and women is perpetuated by institutional cultures lacking inclusion and equity. The objective of this work is to provide a framework for inclusive excellence by developing a systematic evaluation process with common data elements that can track the inter-linked goals of workforce diversity and health equity. At its core, the RCMI Program embodies the trinity of diversity, equity, and inclusion. We propose a realist evaluation framework and a logic model that integrates the institutional context to develop common data metrics for inclusive excellence. The RCMI-CC will collaborate with NIH-funded institutions and research consortia to disseminate and scale this model.

The United States witnessed a nearly 4‐fold increase in personal health care expenditures between 1980 and 2010. Despite innovations and obvious benefits to health, participants enrolled in clinical trials still do not accurately represent the racial and ethnic composition of patients nationally or globally. This lack of diversity in cohorts limits the generalizability and significance of results among all populations and has deep repercussions for patient equity. To advance diversity in clinical trials, robust evidence for the most effective strategies for recruitment of diverse participants is needed. A major limitation of previous literature on clinical trial diversity is the lack of control or comparator groups for different strategies. To date, interventions have focused primarily on (1) community‐based interventions, (2) institutional practices, and (3) digital health systems. This review article outlines prior intervention strategies across these 3 categories and considers health policy and ethical incentives for substantiation before US Food and Drug Administration approval. There are no current studies that comprehensively compare these interventions against one another. The American Heart Association Strategically Focused Research Network on the Science of Diversity in Clinical Trials represents a multicenter, collaborative network between Stanford School of Medicine and Morehouse School of Medicine created to understand the barriers to diversity in clinical trials by contemporaneous head‐to‐head interventional strategies accessing digital, institutional, and community‐based recruitment strategies to produce informed recruitment strategies targeted to improve underrepresented patient representation in clinical trials.

Community-Based Organizations as Effective Partners in the Battle Against Misinformation

Introduction: Adding developmental networks (DN) to grant-writing coaching can significantly enhance ESIs’ research careers. Herein, we present study design, ESIs’ characteristics and encountered challenges/lessons learned and their resolutions when deploying/implementing (a) NCR algorithm(s), (b) recruitment/retention and (c) implementing DN intervention. Methods: Nested Cluster Randomization (NCR) design governs this study implementation. The sample size is 220 ESIs intending to submit an NIH K, R, U, and/or Minority Supplement application(s). Primary outcome: intensity/sustainability of grant submission(s)/funding(s), measured by time to/between application(s). Outcome(s) analyses modes: summaries, Kaplan Meir and Cox proportional hazard models as a function of randomization groups and other predictors of outcomes. Results: In the present study, we recruited two cohorts of ESIs (N = 85): 39% African Americans, 18% Latinx, 18% Whites, 20% Asians and 6% Hawaiian/Pacific Islander/other ethnicities; 65% are women; 73% are assistant professors, 4% are Associate Professors and 23% are instructors/scientists/post-doctoral. Participants’ disciplines: 32% basic/biomedical, 36% clinical/translational and 32% social/behavioral. Proposal(s) mechanisms: 61% research grants (R series), 31% career development (K series), 7% support of competitive research (SCORE) and 1% National Science Foundation applications. NCR did produce balance in the distribution of ESIs’ demographics, sex at birth, ethnicity, professional appointments, background disciplines, and mechanism of sought funding. Lessons learned/challenges: NCR implementation was methodologically challenged during implementation by added constraints (e.g., assigning coaches to the same randomization arm of their participants as well as blinding them to ESIs’ randomization group). Recruitment and retention were hampered by the COVID-19 pandemic and more progressive and innovative strategies were needed to heighten the visibility and outreach of this program. DN delivery was also affected by the pandemic and monitoring of ESIs’ engagement and facilitation of communications interventions were needed. Resolution of these challenges effectively reconfigured NCR algorithms, recruitment/retention plans, and DN intervention delivery. We intend to recruit an additional 135 ESIs focusing on underrepresented scholars from RCMIs, CTSAs, and other programs. COVID-19 rendered this program 100% virtual, with recruitment/retention challenges and substantial disruption of ESIs’ research. We may extend the grant writing period, coaching, and Mock Study Section support.

Despite being disproportionately impacted by health disparities, Black, Hispanic, Indigenous, and other underrepresented populations account for a significant minority of graduates in biomedical data science-related disciplines. Given their commitment to educating underrepresented students and trainees, minority serving institutions (MSIs) can play a significant role in enhancing diversity in the biomedical data science workforce. Little has been published about the reach, curricular breadth, and best practices for delivering these data science training programs. The purpose of this paper is to summarize six Research Centers in Minority Institutions (RCMIs) awarded funding from the National Institute of Minority Health Disparities (NIMHD) to develop new data science training programs. A cross-sectional survey was conducted to better understand the demographics of learners served, curricular topics covered, methods of instruction and assessment, challenges, and recommendations by program directors. Programs demonstrated overall success in reach and curricular diversity, serving a broad range of students and faculty, while also covering a broad range of topics. The main challenges highlighted were a lack of resources and infrastructure and teaching learners with varying levels of experience and knowledge. Further investments in MSIs are needed to sustain training efforts and develop pathways for diversifying the biomedical data science workforce.

The COVID-19 pandemic has overwhelmed health care systems worldwide, particularly in underresourced communities of color with a high prevalence of pre-existing health conditions. Many state governments and health care entities responded by increasing their capacity for telemedicine and disease tracking and creating mobile apps for dissemination of medical information. Our experiences with state-sponsored apps suggest that because many of these eHealth tools did not include community participation, they inadvertently contributed to widening digital health disparities. We propose that, as eHealth tools continue to expand as a form of health care, more attention needs to be given to their equitable distribution, accessibility, and usage. In this viewpoint collaboratively written by a minority-serving community-based organization and an eHealth academic research team, we present our experience participating in a community advisory board working on the dissemination of the COVID Alert NY mobile app to illustrate the importance of public participation in app development. We also provide practical recommendations on how to involve community representatives in the app development process. We propose that transparency and community involvement in the process of app development ultimately increases buy-in, trust, and usage of digital technology in communities where they are needed most.