Evaluation of Initial Progress to Implement Common Metrics Across the NIH Clinical and Translational Science Awards (CTSA) Consortium

Published in: Journal of Clinical and Translational Science (2020). doi: 10.1017/cts.2020.517

Posted on RAND.org on October 02, 2020

by Lisa C. Welch, Andrada Tomoaia-Cotisel, Farzad Noubary, Hong Chang, Peter Mendel, Anshu Parajulee, Marguerite Fenwood-Hughes, Jason Michel Etchegaray, Nabeel Shariq Qureshi, Redonna Chandler, Harry P. Selker

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Introduction

The Clinical and Translational Science Awards (CTSA) Consortium, about 60 National Institutes of Health (NIH)-supported CTSA hubs at academic health care institutions nationwide, is charged with improving the clinical and translational research enterprise. Together with the NIH National Center for Advancing Translational Sciences (NCATS), the Consortium implemented Common Metrics and a shared performance improvement framework.

Methods

Initial implementation across hubs was assessed using quantitative and qualitative methods over a 19-month period. The primary outcome was implementation of three Common Metrics and the performance improvement framework. Challenges and facilitators were elicited.

Results

Among 59 hubs with data, all began implementing Common Metrics, but about one-third had completed all activities for three metrics within the study period. The vast majority of hubs computed metric results and undertook activities to understand performance. Differences in completion appeared in developing and carrying out performance improvement plans. Seven key factors affected progress: hub size and resources, hub prior experience with performance management, alignment of local context with needs of the Common Metrics implementation, hub authority in the local institutional structure, hub engagement (including CTSA Principal Investigator involvement), stakeholder engagement, and attending training and coaching.

Conclusions

Implementing Common Metrics and performance improvement in a large network of research-focused organizations proved feasible but required substantial time and resources. Considerable heterogeneity across hubs in data systems, existing processes and personnel, organizational structures, and local priorities of home institutions created disparate experiences across hubs. Future metric-based performance management initiatives across heterogeneous local contexts should anticipate and account for these types of differences.

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