To facilitate and sustain community-engaged research (CEnR) conducted by academic-community partnerships (ACPs), a Clinical Translational Science Award (CTSA)-funded Community Engagement Core (CEC) and Community Partner Council (CPC) co-created two innovative microgrant programs. The Community Health Grant (CHG) and the Partnership Development Grant (PDG) programs are designed to specifically fund ACPs conducting pilot programs aimed at improving health outcomes. Collectively, these programs have engaged 94 community partner organizations while impacting over 55,000 individuals and leveraging $1.2 million to fund over $10 million through other grants and awards. A cross-sectional survey of 57 CHG awardees demonstrated high overall satisfaction with the programs and indicated that participation addressed barriers to CEnR, such as building trust in research and improving partnership and program sustainability. The goal of this paper is to (1) describe the rationale and development of the CHG and PDG programs; (2) their feasibility, impact, and sustainability; and (3) lessons learned and best practices. Institutions seeking to implement similar programs should focus on integrating community partners throughout the design and review processes and prioritizing projects that align with specific, measurable goals.

Adrenal vein sampling (AVS) is a complicated procedure requiring clinical expertise, collaboration, and patient involvement to ensure it occurs successfully. Implementation science offers unique insights into the barriers and enablers of service delivery of AVS. The primary aim of this review was to identify implementation components as described within clinical studies, that contribute to a successful AVS procedure. The secondary aim was to inform practice considerations to support the scale-up of AVS. A scoping review of clinical papers that discussed factors contributing to effective AVS implementation was included. A phased approach was employed to extract implementation science data from clinical studies. Implementation strategies were named and defined, allowing for implementation learnings to be synthesized, in the absence of dedicated research examining implementation process and findings only. Ten implementation components reported as contributing to a successful AVS procedure were identified. These components were categorized according to actions required pre-AVS, during AVS, and post-AVS. Using an implementation science approach, the findings of this review and analysis provide practical considerations to facilitate AVS service delivery design. Extracting implementation science information from clinical research has provided a mechanism that accelerates the translation of evidence into practice where implementation research is not yet available.

This paper explores the development of the Dissemination and Implementation Science Collaborative (DISC) at the Medical University of South Carolina, established through the Clinical and Translational Science Award program. DISC aims to accelerate clinical and translational science by providing training, mentorship, and collaboration opportunities in dissemination and implementation (D&I) science. Through DISC, investigators, trainees, and community partners are equipped with the knowledge and skills to conduct D&I research and translate findings into practice, particularly in South Carolina’s public health and healthcare landscape. We describe efforts to achieve the major overarching aims of DISC, which include conducting scientific workforce training, providing mentorship and consultation, and advancing methods and processes for D&I research. By sharing DISC experiences, successes, and challenges, this paper aims to support the growth of D&I research and capacity-building programs, fostering collaboration and shared resources in the field.

Organizations supporting translational research and translational science, including Clinical and Translational Science Award (CTSA) hubs, provide a diverse and often changing array of resources, support, and services to a myriad of researchers and research efforts. While a wide-ranging scope of programs is essential to the advancement of translational research and science, it also complicates a systematic and unified process for tracking activities, studying research processes, and examining impact. To overcome these challenges, the Duke University School of Medicine’s CTSA hub created a data platform, Translational Research Accomplishment Cataloguer (TRACER), that provides capacity to enhance strategic decision-making, impact assessment, and equitable resource distribution. This article reviews TRACER development processes, provides an overview of the TRACER platform, addresses challenges in the development process, and describes avenues for addressing or overcoming these challenges. TRACER development allowed our hub to conceptually identify key processes and goals within programs and linkages between programs, and it sets the stage for advancing evidence-based improvement across our hub. This platform development provides key insight into facilitators that can inform other initiatives seeking to collect and align organizational data for strategic decision-making and impact assessment. TRACER or similar platforms are additionally well positioned to advance the study of translational science.

The Stanford Population Health Sciences Data Ecosystem was created to facilitate the use of large datasets containing health records from hundreds of millions of individuals. This necessitated technical solutions optimized for an academic medical center to manage and share high-risk data at scale. Through collaboration with internal and external partners, we have built a Data Ecosystem to host, curate, and share data with hundreds of users in a secure and compliant manner. This platform has enabled us to host unique data assets and serve the needs of researchers across Stanford University, and the technology and approach were designed to be replicable and portable to other institutions. We have found, however, that though these technological advances are necessary, they are not sufficient. Challenges around making data Findable, Accessible, Interoperable, and Reusable remain. Our experience has demonstrated that there is a high demand for access to real-world data, and that if the appropriate tools and structures are in place, translational research can be advanced considerably. Together, technological solutions, management structures, and education to support researcher, data science, and community collaborations offer more impactful processes over the long-term for supporting translational research with real-world data.

The survey investigates COVID-19 information source trust levels and Vietnamese Americans’ willingness to participate in clinical trials. An analysis of 212 completed surveys revealed that trust in coronavirus disease 2019 (COVID-19) clinical trial information from university hospitals and drug companies was associated with willingness to participate in clinical trials. Trust in COVID-19 information from federal governments and state governments was also associated with willingness to participate in clinical trials. However, trust in local health facilities was linked to trial participation reluctance. The results suggest that Vietnamese Americans’ participation in clinical trials can be increased by identifying and using trusted sources of information.

Clinical research professionals (CRPs) are essential contributors to clinical and translational research endeavors, encompassing roles such as research nurses, research coordinators, data managers, and regulatory affairs specialists. This paper reports on the implementation of a novel training program for the CRPs, the Co-mentoring Circles Program, developed by the University of Florida Health Clinical Research Professionals Consortium, and proposes an initial logic model of CRP workforce development informed by the observations, participant feedback, and the established Translational Workforce Logic Model. The co-mentoring program was delivered through an online didactic curriculum and bi-monthly meetings over nine months, from January to September 2022. The formative evaluation identified the factors that support CRP workforce development through knowledge acquisition and professional relationship building. Finally, this paper proposes a logic model of CRP workforce development, including financial and human inputs, didactic and co-mentoring activities, workforce outputs, outputs related to workforce and clinical research study progress, and resulting impacts of increased national capacity for translational research and increased rate of research translation.

Advancing the new field of translational science and developing innovative solutions to overcome translational roadblocks are key priorities of the Clinical and Translational Science Awards (CTSA) Program of the National Center for Advancing Translational Science (NCATS). However, interpreting this emerging concept of “translational science” (TS) as a field of inquiry distinct from “translational research” (TR) and developing real-world investigations in TS can be challenging. The goal of this paper is to share the obstacles the Einstein-Montefiore CTSA hub has faced in generating institutional interest and research in TS and to present potential strategies for addressing them. The aim is to stimulate dialog within the wider CTSA community and beyond about the need to systematically examine how TS should be efficiently and effectively pursued, that is, the science of translational science. The collective sharing of experiences and innovative approaches to overcoming TS challenges that arise at CTSA hubs is critical if the field is to grow and gain wider recognition and acceptance by the scientific and broader communities.

Translational research (TR) is the movement of fundamental scientific discoveries into healthcare settings and population health policy, and parallels the goals of DOHaD research. Unfortunately, there is little guidance on how to become a translational researcher. To understand the opinions of DOHaD trainees towards TR, we conducted a workshop at the DOHaD World Congress 2022. We found that trainees were enthusiastic for their work to have translational impact, and that they feel that holistic, multidisciplinary solutions may lead to more generalisable research. However, there lacks support for TR career pathways, which may stall the execution of the long-term vision of the DOHaD agenda. We put forward recommendations for trainees to clarify their purpose in pursuing TR and for seeking relevant people and patronages to support their training paths. For mentors, training institutions, and scientific societies, we recommend developing TR-specific programmes, and implementing training opportunities, networking events, and funding to support these endeavours.

Knowledge graphs have become a common approach for knowledge representation. Yet, the application of graph methodology is elusive due to the sheer number and complexity of knowledge sources. In addition, semantic incompatibilities hinder efforts to harmonize and integrate across these diverse sources. As part of The Biomedical Translator Consortium, we have developed a knowledge graph–based question-answering system designed to augment human reasoning and accelerate translational scientific discovery: the Translator system. We have applied the Translator system to answer biomedical questions in the context of a broad array of diseases and syndromes, including Fanconi anemia, primary ciliary dyskinesia, multiple sclerosis, and others. A variety of collaborative approaches have been used to research and develop the Translator system. One recent approach involved the establishment of a monthly “Question-of-the-Month (QotM) Challenge” series. Herein, we describe the structure of the QotM Challenge; the six challenges that have been conducted to date on drug-induced liver injury, cannabidiol toxicity, coronavirus infection, diabetes, psoriatic arthritis, and ATP1A3-related phenotypes; the scientific insights that have been gleaned during the challenges; and the technical issues that were identified over the course of the challenges and that can now be addressed to foster further development of the prototype Translator system. We close with a discussion on Large Language Models such as ChatGPT and highlight differences between those models and the Translator system.