Accelerating Adoption of Patient-Facing Technologies in Clinical Trials: A Pharmaceutical Industry Perspective on Opportunities and Challenges
Bibliographic record
Abstract
BACKGROUND: Patient-facing digital technologies (also called "Patient Technology" [PT]) have the potential to serve a variety of functions in clinical trials, such as capturing clinical endpoints, engaging patients, and facilitating remote study conduct. However, these technologies are not yet accepted as mainstream research tools, and the opportunities, challenges, and facilitators associated with their implementation in clinical trials have not been fully characterized. METHODS: In order to understand the factors affecting PT adoption, the TransCelerate Patient Technology Initiative conducted a series of surveys, interviews, and focus groups with approximately 600 subject matter experts, including pharmaceutical company representatives, clinical trial investigators at a number of trial sites worldwide, and clinical trial participants. All interview and survey responses were blinded and aggregated by a third-party consultant and themes were extracted. RESULTS: There was general consensus around the potential value of patient-facing technology as a clinical research tool, though a variety of challenges faced by each stakeholder were discussed. Detailed accounts of opportunities (improved patient experience, compliance, and engagement; clinical trial efficiencies; improved data quality and insights) and barriers (organizational and corporate cultural challenges, business-related challenges, user willingness and burden, and regulatory challenges) are reported. CONCLUSIONS: While the barriers to PT adoption explored here were numerous, they were also generally consistent. A number of proposals for establishing more holistic, collaborative, and strategic approaches to PT implementation in clinical trials are discussed. Such approaches could facilitate more effective, widespread adoption of PT, and thereby a more patient-centric clinical trial paradigm.
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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 | 0.302 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".