Real-world evaluation of user engagement with an artificial intelligence-powered clinical trial application in oncology Journal Article
| Authors: | Hung, T. K. W.; Mao, J. J.; Ho, A. L.; Sherman, E. J.; Robson, M.; Park, J.; Stein, E. M.; Kuperman, G. J.; Pfister, D. G. |
| Article Title: | Real-world evaluation of user engagement with an artificial intelligence-powered clinical trial application in oncology |
| Abstract: | Objectives This 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 Methods We 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.Results During 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.Conclusion This 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. |
| Keywords: | artificial intelligence; clinical trials; quality improvement; decision support; accrual; cancer care delivery; lookuptrials; user engagement |
| Journal Title: | Journal of the American Medical Informatics Association |
| ISSN: | 1067-5027 |
| Publisher: | Oxford University Press |
| Publication status: | Online ahead of print |
| Date Published: | 2025-01-01 |
| Online Publication Date: | 2025-01-01 |
| Language: | English |
| ACCESSION: | WOS:001537440400001 |
| DOI: | 10.1093/jamia/ocaf129 |
| PROVIDER: | wos |
| Notes: | Article; Early Access -- Source: Wos |
Altmetric
Citation Impact
BMJ Impact Analytics
Related MSK Work