P226: AI as an Enabler: Reducing Human Error in eCOA Localization Without Replacing Linguistic Expertise
Poster Presenter
Jonathan Norman
Director, Localization & Scale Management
Yprime United Kingdom
Objectives
Localization is a bottleneck in eCOA deployment. This study evaluates if AI-powered migration can eliminate preventable human transcription errors during the transition from paper to electronic formats, reducing review cycles, and accelerating study startup without replacing human linguistic skill.
Method
YPrime reviewed 15 projects to identify errors from human-led migration. The process was repeated using AI-powered migration, keeping all other steps identical. Outputs were compared to linguist-approved versions to measure error rates and the number of review rounds required for each method.
Results
Analysis of the convenience sample (n=15) revealed that human-led migration consistently introduced mechanical errors such as character omissions, incorrect formatting of specialized scales, and data entry discrepancies when transferring content from source documents to the digital environment. These preventable errors were the primary drivers for additional screen report rounds and linguist re-proofreading cycles observed in the sample.
When the migration was repeated using the AI-powered methodology, only a negligible fraction of the original human errors reoccurred, typically stemming from ambiguities in the original source material rather than the migration process itself. Most significantly, the AI-powered process introduced zero new errors, maintaining consistency to the provided source text.
By eliminating the need for iterative review cycles caused by human transcription slips, the data suggests that most projects would have achieved final linguistic sign-off at least one review round earlier using AI-powered migration, with an average timeline saving of around 67%.
Conclusion
YPrime identified that mechanical error in human-led migration is a key driver of repeated review rounds and a persistent bottleneck in eCOA deployment. While the migration of translations from paper to electronic formats is often treated as a simple technical step, it is a critical quality control measure where small human errors can trigger delays and extended timelines.
The findings support the conclusion that AI-powered migration largely eliminates these preventable errors by removing the manual transcription element that is prone to human variability. By serving as an enabler rather than a replacement for linguistic expertise, AI preserves the quality of localized content while ensuring it is migrated into the eCOA system with near-perfect fidelity. This methodology prevents the multi-review cycles that currently act as a limiting factor for sponsors attempting to accelerate study startup.
Ultimately, by replacing human-led migration with AI-powered migration, sponsors can achieve higher quality outputs and significant timeline savings.
