ArticleJournal of the American Medical Informatics Association : JAMIA2025
Real-world evaluation of user engagement with an artificial intelligence-powered clinical trial application in oncology.
Article in Journal of the American Medical Informatics Association : JAMIA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Development and evaluation of a large language model-based, retrieval-augmented generation application for query response in early oncology clinical trials.ESMO real world data and digital oncology · 2026Article
- Development of a clinical trial knowledge management application for community oncology.JAMIA open · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
objectivesThis quality improvement study implemented and prospectively examined user engagement with an artificial intelligence (AI)-powered clinical trial knowledge management application at an NCI-designated comprehensive cancer center. MATERIALS AND
methodsWe prospectively auto-captured user engagement measures from July 1, 2022 to February 29, 2024. Measurement included: (1) event: an app interaction; (2) session: group of events within single setting; (3) engaged session: session longer than 10 s; (4) engagement time; (5) app downloads; (6) active user; and (7) stickiness: monthly active users per normalized total downloads. We analyzed the measures using time series and linear regression.
resultsDuring a 20-month evaluation, the application supported 138 clinical trials, recorded 136 632 user interactions, including 2754 engaged sessions with an average engagement time of 6 min 31 s. Of 243 downloads, 228 (94%) users remained active, with an estimated stickiness score of 3.12 (SD 0.91), indicating sustained provider engagement. DISCUSSION: This study provided insights into the feasibility and potential for integrating an AI-powered clinical trial knowledge management application into oncology workflows, with sustained engagement among providers over a 20-month period. High rates of active users and session stickiness suggest that such application offered meaningful utility in real-world clinical settings, underscoring the need for future studies to assess optimal integration strategies and impact on clinical trial accrual.
conclusionThis study addresses an important gap in the literature regarding the real-world integration of AI technologies in oncology care and offers valuable insights for future research and clinical practice.
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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 Socratic graph.