Research studies involving human subjects require collection of and reporting on demographic data related to race and ethnicity. However, existing practices lack standardized guidelines, leading to misrepresentation and biased inferences and conclusions for underrepresented populations in research studies. For instance, sometimes there is a misconception that self-reported racial or ethnic identity may be treated as a biological variable with underlying genetic implications, overlooking its role as a social construct reflecting lived experiences of specific populations. In this manuscript, we use the We All Count data equity framework, which organizes data projects across seven stages: Funding, Motivation, Project Design, Data Collection, Analysis, Reporting, and Communication. Focusing on data collection and analysis, we use examples – both real and hypothetical – to review common practice and provide critiques and alternative recommendations. Through these examples and recommendations, we hope to provide the reader with some ideas and a starting point as they consider embedding a lens of justice, equity, diversity, and inclusivity from research conception to dissemination of findings.

Efficient evidence generation to assess the clinical and economic impact of medical therapies is critical amid rising healthcare costs and aging populations. However, drug development and clinical trials remain far too expensive and inefficient for all stakeholders. On October 25–26, 2023, the Duke Clinical Research Institute brought together leaders from academia, industry, government agencies, patient advocacy, and nonprofit organizations to explore how different entities and influencers in drug development and healthcare can realign incentive structures to efficiently accelerate evidence generation that addresses the highest public health needs. Prominent themes surfaced, including competing research priorities and incentives, inadequate representation of patient population in clinical trials, opportunities to better leverage existing technology and infrastructure in trial design, and a need for heightened transparency and accountability in research practices. The group determined that together these elements contribute to an inefficient and costly clinical research enterprise, amplifying disparities in population health and sustaining gaps in evidence that impede advancements in equitable healthcare delivery and outcomes. The goal of addressing the identified challenges is to ultimately make clinical trials faster, more inclusive, and more efficient across diverse communities and settings.

The Master of Science in Clinical Research Management program at Rutgers Biomedical Health Sciences underwent significant restructuring aligned with the Clinical and Translational Science Award funding parameters. This evolution necessitated formal evaluation through accreditation by the Commission on Accreditation of Allied Health Education Programs. The years-long accreditation process posed challenges, particularly regarding the collection of course learning outcomes data aligned with accreditation competency standards. The objective of this special communication is to report the rationale behind pursuing accreditation for clinical research degrees, the data collection challenges during the accreditation process, and a potential solution. In order to address existing university metric data gaps, Research Electronic Data Capture (REDCap) software was used to develop a data collection tool that streamlined the accreditation process and reduced the administrative burden. REDCap was effective in allowing faculty to self-report 3 years of course outcomes data for accreditation. There was an elevated level of user satisfaction compared to alternative data collection methods. A SWOT analysis identified the strengths and weaknesses of using REDCap, emphasizing strengths in functionality that include customizability, data validation, and compliance with regulatory standards. Overall, the advantages of leveraging REDCap for accreditation data collection, including customization, data security, and user-friendliness outweigh the key disadvantage of REDCap, which is its limited reporting capabilities.

Evaluation of benefits beyond quantitative academic outputs is essential in determining translational research value. We used the Translational Science Benefits Model (TSBM) to examine the impact of the QUARTET USA trial using 30 benefits across 4 domains: Clinical, Community, Economic, and Policy. We found that the QUARTET USA trial demonstrated impact in six areas within the Clinical, and Community domains and had potential impact in two additional areas within the Community and Economic domains. Use of the TSBM supports the value of the QUARTET USA trial, which can be used as a template for future cardiovascular trials.