Manual data entry and the query cycles it generates can consume a meaningful share of a data manager’s working day on a single study, and most of those queries are for errors that were preventable at the point of entry. That is the operational problem CRScube and its recently acquired US partner Mednet are building toward, with AI-assisted tools designed to catch transcription mistakes and coding inconsistencies before they ever reach a monitor’s queue.
The core mechanics are straightforward. An integration-free EHR-to-EDC connection lets the system identify and map source data directly into case report forms, cutting the manual transcription step at the site. On the coding side, a generative AI and NLP-based matching engine handles MedDRA and WHODrug assignments in real time, which reduces the downstream review cycles that compound when initial coding is wrong. Every AI suggestion goes through expert review before it is finalized, so the team retains sign-off authority while the system handles the repetitive matching work. Importantly, CRScube acquired Mednet in November 2025 specifically to extend this platform into the North American market, so the tools described here reflect a combined eClinical stack rather than a standalone product pitch.
The downstream effect matters more than the point-of-entry efficiency. When fewer errors reach monitors, their time shifts from routine data scrubbing to high-risk site management and decisions that actually require clinical judgment. Data managers move from issuing queries reactively to overseeing quality proactively. Neither of those shifts is dramatic on a single study, but across a growing trial portfolio the compounding effect on cost and cycle time is real. The platform also operates in a closed-loop environment, meaning proprietary trial data is not used to train public models, which addresses a specific concern that has slowed AI adoption at sites handling sensitive patient data.
For operations teams evaluating EDC vendors this year, the metric worth watching is query rate per data point across sites using AI-assisted entry versus those on manual workflows. If CRScube and Mednet publish that comparison from actual studies, it will be the clearest test of whether the point-of-entry approach holds up at scale.
Source link: https://www.mednetsolutions.com/blog/ai-assisted-data-entry-reducing-the-query-burden-in-clinical-trials/
Moe Alsumidaie, MBA, MSF, is founder and Chief Editor of Vanguard Publications, which publishes Clinical Trial Vanguard, Pharma Vanguard and BullScope, and Head of Research at CliniBiz. He has two decades in clinical trial operations and data science, with earlier roles at Genentech, Abbott Vascular and Stanford University Medical Center, and is a guest lecturer in clinical trial sciences at Rutgers University.
