Defining methods to improve eSource site start-up practices
Bibliographic record
Abstract
eSource software that transfers patient electronic health record data into a clinical trial electronic case report form holds promise for increasing data quality while reducing data collection, monitoring and source document verification costs. Integrating eSource into multicenter clinical trial start-up procedures could facilitate the use of eSource technologies in clinical trials. We conducted a qualitative integrative analysis to identify eSource site start-up key steps, challenges that might occur in executing those steps, and potential solutions to those challenges. We then conducted a value analysis to determine the challenges and solutions with the greatest impacts for eSource implementation teams. There were 16 workshop participants: 10 pharmaceutical sponsor, 3 academic site, and 1 eSource vendor representative. Participants identified 36 Site Start-Up Key Steps, 11 Site Start-Up Challenges, and 14 Site Start-Up Solutions for eSource-enabled studies. Participants also identified 77 potential impacts of the Challenges upon the Site Start-Up Key Steps and 70 ways in which the Solutions might impact Site Start-Up Challenges. The most important Challenges were: [1] not being able to identify a site eSource champion and [2] not agreeing on an eSource approach. The most important Solutions were: [1] eSource vendors accepting electronic data in the Health Level 7 Fast Healthcare Interoperability Resources (HL7® FHIR®) standard, [2] creating standard content for eSource-related legal documents, and [3] creating a common eSource site readiness checklist. Site start-up for eSource-enabled multi-center clinical trials is a complex socio-technical problem. This study's Start-Up Solutions provide initial steps for scalable eSource implementation. • ESource software that transfers patient electronic health record data into a clinical trial electronic case report form hold promise for improved data collection effectiveness. However, integrating eSource into multicenter clinical trial start-up procedures can be problematic. • We conducted a study to qualitative integrative analysis to identify eSource site start-up key steps, challenges that might occur in executing those steps, and potential solutions to those challenges. We then conducted a value analysis to determine the challenges and solutions with the greatest impacts for eSource implementation teams. • Participants identified 36 Site Start-Up Key Steps, 11 Site Start-Up Challenges, and 14 Site Start-Up Solutions for eSource-enabled studies. Participants also identified 77 potential impacts of the Challenges upon the Site Start-Up Key Steps and 70 ways in which the Solutions might impact Site Start-Up Challenges. • The most important Challenges were [1]: not being able to identify a site eSource champion and [2] not agreeing on an eSource approach. The most important Solutions were [1]: eSource vendors accepting electronic data in the Health Level 7 Fast Healthcare Interoperability Resources (HL7® FHIR®) standard [2], creating standard content for eSource-related legal documents, and [3] creating a common eSource site readiness checklist.
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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 | 0.341 | 0.379 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.024 | 0.030 |
| Open science | 0.014 | 0.034 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".