Global adoption of remote technologies to enable patient-centric oncology clinical trials: Analysis by the Bloomberg New Economy International Cancer Coalition.
Bibliographic record
Abstract
1584 Background: Fewer than 5% of patients with cancer enroll in a clinical trial, partly due to the significant financial and logistical burden on patients, especially among underserved populations. The COVID-19 pandemic marked a significant shift in the adoption of remote technologies and decentralized trial operations by major pharmaceutical companies. We sought to determine the current global state of adoption of these technologies, understand factors that are driving or preventing adoption of them, and highlight aspirations and direction for industry to enable more patient-centric trials. Methods: The multi-stakeholder Bloomberg New Economy International Cancer Coalition composed of patient advocacy, industry, government regulators, and academic medical centers developed a survey directed to global biopharmaceutical companies of the Coalition with a focus on registrational clinical trials. The survey was organized into three main sections: 1) Impact of different remote monitoring and data collection technologies on patient-centricity; 2) Adoption of these technologies in oncology and all therapeutic areas; 3) Barriers/facilitators to adoption. Results: Administered from October 1 to December 31, 2022, a total of 8 companies completed the survey (response rate: 100%), representing 33% of oncology market by revenues in 2021. Across nearly all remote monitoring and data collection technologies, adoption in oncology trials lags that of all trials. In the current state, eDiary/eCOA is the most utilized technology with 56% and 51% adoption for all trials and oncology trials, respectively, whereas visits in local physician networks is the least adopted at 12% and 7%, respectively. Looking forward, the difference between the current and aspired adoption rate in 5 years for oncology is large, with respondents expecting a 40% or greater absolute adoption increase in 8 out of the 11 technologies (Table). Furthermore, respondents identified digitally enabled recruitment, local or mobile imaging capabilities, and local physician networks as those technologies that would be most impactful for improving patient centricity in the long term. Conclusions: This survey is the first by a coalition of global stakeholders to determine the current state and future aspirations for the use of remote technologies in oncology clinical trials. These efforts may galvanize momentum towards greater adoption of enabling technologies supporting a new paradigm of trials that are more accessible, less burdensome, and more inclusive.[Table: see text]
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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 | 0.047 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".