Bridging barriers, integrating insights: The Gotham approach to CTSA collaborative evaluation

TL;DR

This paper explores how voluntary collaboration among research hubs can improve data-sharing and evaluation in translational science.

Contribution

The study introduces a voluntary, sustainable model for multi-hub data-sharing and evaluation without national mandates.

Findings

Education engagement percentages were stable across NYC hubs despite institutional differences.

Collaboration fostered professional relationships and strengthened evaluation capacity.

The model demonstrates potential for overcoming data silos in translational science.

Abstract

Collaboration across the Clinical and Translational Science Award (CTSA) consortium is essential for advancing translational science, yet institutional silos often hinder data-sharing and benchmarking efforts. This study examines the viability of a voluntary, multi-hub analysis of the CTSA education common metric on trainee and scholar engagement across five New York City-based sites or “hubs.” Using a structured framework for collaboration and field-tested operational guidelines, a team of evaluators dubbed “The Gotham Group” pooled de-identified common education data to assess post-training research engagement and demographic representation. Their primary objective was to establish a sustainable model for independent data-sharing without national mandates or technical support. A secondary goal was to reassess the metric’s usefulness as an impact benchmark. Results showed that NYC education engagement percentages remained stable despite institutional differences, suggesting the metric’s viability for regional comparison. More importantly, the collaboration itself proved as valuable as its outcomes, fostering professional relationships, facilitating knowledge exchange, and strengthening evaluation capacity within and across the hubs. This study highlights the potential of voluntary data-sharing partnerships to overcome data silos and to create valuable networks driving continuous improvement in translational science.

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