A landscape assessment of CTSA evaluators and their work in the CTSA consortium, 2021 survey findings.

This article presents a landscape assessment of the findings from the 2021 Clinical and Translational Science Award (CTSA) Evaluators Survey. This survey was the most recent iteration of a well established, national, peer-led systematic snapshot of the CTSA evaluators, their skillsets, listed evaluation resources, preferred methods, and identified best practices. Three questions guided our study: who are the CTSA evaluators, what competencies do they share and how is their work used within hubs.

Distinguishing between translational science and translational research in CTSA pilot studies: A collaborative project across 12 CTSA hubs.

Introduction: The institutions (i.e., hubs) making up the National Institutes of Health (NIH)-funded network of Clinical and Translational Science Awards (CTSAs) share a mission to turn observations into interventions to improve public health.

Learning health system benefits: Development and initial validation of a framework.

Introduction: Implementation of research findings in clinical practice often is not realized or only partially achieved, and if so, with a significant delay. Learning health systems (LHSs) hold promise to overcome this problem by embedding clinical research and evidence-based best practices into care delivery, enabling innovation and continuous improvement. Implementing an LHS is a complex process that requires participation and resources of a wide range of stakeholders, including healthcare leaders, clinical providers, patients and families, payers, and researchers.

Engaging community in the translational process: Environmental scan of adaptive capacity and preparedness of Clinical and Translational Science Award Program hubs.

This paper is part of the Environmental Scan of Adaptive Capacity and Preparedness of Clinical and Translational Science Award (CTSA) hubs, illuminating challenges, practices, and lessons learned related to CTSA hubs' efforts of engaging community partners to reduce the spread of the virus, address barriers to COVID-19 testing, identify treatments to improve health outcomes, and advance community participation in research. CTSA researchers, staff, and community partners collaborated to develop evidence-based, inclusive, accessible, and culturally appropriate strategies and resources helping community members stay healthy, informed, and connected during the pandemic. CTSA institutions have used various mechanisms to advance co-learning and co-sharing of knowledge, resources, tools, and experiences between academic professionals, patients, community partners, and other stakeholders.

The Access Technology Program of the Indiana Clinical Translational Sciences Institute (CTSI): A model to facilitate access to cutting-edge technologies across a state.

Introduction: Access to cutting-edge technologies is essential for investigators to advance translational research. The Indiana Clinical and Translational Sciences Institute (CTSI) spans three major and preeminent universities, four large academic campuses across the state of Indiana, and is mandate to provide best practices to a whole state.

Methods: To address the need to facilitate the availability of innovative technologies to its investigators, the Indiana CTSI implemented the Access Technology Program (ATP).

Understanding and responding to COVID-19 in Wales: protocol for a privacy-protecting data platform for enhanced epidemiology and evaluation of interventions.

Introduction: The emergence of the novel respiratory SARS-CoV-2 and subsequent COVID-19 pandemic have required rapid assimilation of population-level data to understand and control the spread of infection in the general and vulnerable populations. Rapid analyses are needed to inform policy development and target interventions to at-risk groups to prevent serious health outcomes. We aim to provide an accessible research platform to determine demographic, socioeconomic and clinical risk factors for infection, morbidity and mortality of COVID-19, to measure the impact of COVID-19 on healthcare utilisation and long-term health, and to enable the evaluation of natural experiments of policy interventions.

Classifying publications from the clinical and translational science award program along the translational research spectrum: a machine learning approach.

Background: Translational research is a key area of focus of the National Institutes of Health (NIH), as demonstrated by the substantial investment in the Clinical and Translational Science Award (CTSA) program. The goal of the CTSA program is to accelerate the translation of discoveries from the bench to the bedside and into communities. Different classification systems have been used to capture the spectrum of basic to clinical to population health research, with substantial differences in the number of categories and their definitions.

Developing Common Metrics for the Clinical and Translational Science Awards (CTSAs): Lessons Learned.

The National Institutes of Health (NIH) Roadmap for Medical Research initiative, funded by the NIH Common Fund and offered through the Clinical and Translational Science Award (CTSA) program, developed more than 60 unique models for achieving the NIH goal of accelerating discoveries toward better public health. The variety of these models enabled participating academic centers to experiment with different approaches to fit their research environment. A central challenge related to the diversity of approaches is the ability to determine the success and contribution of each model.

Project development teams: a novel mechanism for accelerating translational research.

The trend in conducting successful biomedical research is shifting from individual academic labs to coordinated collaborative research teams. Teams of experienced investigators with a wide variety of expertise are now critical for developing and maintaining a successful, productive research program. However, assembling a team whose members have the right expertise requires a great deal of time and many resources.

Use of social network analysis tools to validate a resources infrastructure for interinstitutional translational research: a case study.

Question: How can knowledge management and innovative technology, cornerstones of library practice, be leveraged to validate the progress of Clinical and Translational Science Awards?

Setting: The Indiana Clinical and Translational Sciences Institute (Indiana CTSI) promotes interdisciplinary research across academic institutions.

Methods: Using social networking tools and knowledge management skills enabled the department of knowledge informatics and translation to create a visualization of utilization of resources across different Indiana CTSI programs and coauthorship and citation patterns.

Results: Contacts with different resources per investigator increased; every targeted program was shown to be linked to another.