ArticleJournal of clinical and translational science2024
A national unmet needs assessment for CTSA-affiliated electronic health record data networks: A customer discovery approach.
Article in Journal of clinical and translational science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Development of a Virtual Transition Program for Adolescents and Young Adults With Type 1 Diabetes: Three-Stage User-Centered Design Study.JMIR pediatrics and parenting · 2026Article
- Bridging the gap: Community insights on effectively sharing research findings with the public.Journal of clinical and translational science · 2026Article
- Building implementation science capacity: Adaptation of I-Corps™@NCATS training for rapid fit-to-context discovery and designing for scale-up and sustainability.Journal of clinical and translational science · 2026Article
- Building a collaborative ecosystem across the IDeA-CTR networks in response to a public health emergency.Journal of clinical and translational science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Introduction: The expansion of electronic health record (EHR) data networks over the last two decades has significantly improved the accessibility and processes around data sharing. However, there lies a gap in meeting the needs of Clinical and Translational Science Award (CTSA) hubs, particularly related to real-world data (RWD) and real-world evidence (RWE). Methods: We adopted a mixed-methods approach to construct a comprehensive needs assessment that included: (1) A Landscape Context analysis to understand the competitive environment; and (2) Customer Discovery to identify stakeholders and the value proposition related to EHR data networks. Methods included surveys, interviews, and a focus group. Results: Thirty-two CTSA institutions contributed data for analysis. Fifty-four interviews and one focus group were conducted. The synthesis of our findings pivots around five emergent themes: (1) CTSA segmentation needs vary according to resources; (2) Team science is key for success; (3) Quality of data generates trust in the network; (4) Capacity building is defined differently by researcher career stage and CTSA existing resources; and (5) Researchers' unmet needs. Conclusions: Based on the results, EHR data networks like ENACT that would like to meet the expectations of academic research centers within the CTSA consortium need to consider filling the gaps identified by our study: foster team science, improve workforce capacity, achieve data governance trust and efficiency of operation, and aid Learning Health Systems with validating, applying, and scaling the evidence to support quality improvement and high-value care. These findings align with the NIH NCATS Strategic Plan for Data Science.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.