Modernizing clinical trial accessibility: Integrating the AI-powered LookUpTrials app into the CTN program.
Bibliographic record
Abstract
e13558 Background: Clinical trials are essential for advancing cancer therapies, yet accrual rates remain under 10% due to various barriers. The Clinical Trials Navigator (CTN) program was developed to bridge this gap by guiding patients and healthcare professionals toward appropriate clinical trials. To further streamline this process, the CTN has integrated LookUpTrials, an AI-powered clinical trial management mobile application, into its workflow. We evaluated the preliminary effectiveness of pairing LookUpTrials within the CTN program in enhancing workflow efficiency and accessibility. Methods: We conducted a hybrid type-1 effectiveness-implementation study pre- and post-implementation of LookUpTrials into CTN. We assessed preliminary effectiveness of LookUpTrials by qualitative assessment based on Consolidated Framework for Implementation Research (CFIR) and surveyed user experience and workflow efficiency. Pre-implementation of LookUpTrials, navigators from CTN used to track ongoing clinical trials using Microsoft Word document. In July 2024, we piloted LookUpTrials and have since uploaded over 107 trials into the application. Currently, CTN navigators use LookupTrials to identify and share trial information with patients and physicians, with an ongoing pilot of LookUpTrials with CTN navigators into Multidisciplinary Case Conferences (MCCs). Results: Preliminary qualitative assessment found that LookupTrials supported CTN navigators in improving the speed and accuracy of identifying suitable clinical trials, with greater efficiency in filtering and sharing trial information, while enhancing physician collaboration. Furthermore, feedback indicated LookUpTrials increased trial visibility and potential improvements in patient referrals through CTN. The application was also recognized for enhancing accessibility, enabling healthcare professionals to discover trials they may not have been aware of due to the difficulty of manually searching for trials. Further data collection is ongoing to quantify these improvements, including time spent identifying trials and trial referral rates. Conclusions: Integrating LookupTrials into the CTN workflow represents a significant step toward modernizing clinical trial navigation and has shown promise to streamline navigator efficiency and improve trial discovery. The digitally enhanced CTN program aims to address suboptimal clinical trial accrual rate by simplifying trial identification and sharing, offering a novel approach to improving patient access to cutting-edge therapies. Further integration of CTNs into MCCs is in progress, with the potential to standardize clinical trial discussions across Ontario, ultimately enhancing patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".