Mednet AI Reads EHR Screens to Eliminate Manual EDC Data Entry

Mednet AI Reads EHR Screens to Eliminate Manual EDC Data Entry

Research coordinators at clinical trial sites spend 12 to 15 hours each week copying data from electronic health records into separate electronic data capture systems—a purely manual task that duplicates information already stored digitally. Mednet, working with partner CRScube, has embedded an AI assistant directly into its cubeCDMS platform that reads what coordinators see on their EHR screens and populates the corresponding trial case report forms automatically.

The approach skips the integration middleware that typically connects hospital systems to EDC platforms. When a coordinator opens a patient's EHR, the AI assistant identifies relevant data elements, matches them to the appropriate eCRF fields, and transfers the information without requiring trial-specific mapping projects or third-party connectors.

Why it matters

Manual transcription introduces a 6.57 percent pooled error rate according to a 2023 PubMed meta-analysis, and the 14- to 30-day lag before sponsors see the data means oversight decisions rely on information that is already weeks old. Sites increasingly choose which trials to support based on administrative burden, giving sponsors that reduce coordinator workload a tangible recruitment advantage. Eliminating transcription at the source shifts data management teams from correcting routine entry errors to reviewing cases that genuinely require clinical judgment.

Cutting weeks from data visibility

The current workflow holds trial data in limbo for two weeks to a month while coordinators manually enter information that already exists in hospital systems. That delay cascades through query management cycles and database lock timelines. By capturing data at the point a coordinator views it, the AI intake feature compresses that window and delivers cleaner first-pass datasets that require fewer correction rounds.

The EDC market is projected to grow from $3.2 billion to $7.1 billion by 2030, but market expansion alone does not solve the workflow problem. More than half of trial data currently gets entered twice—once by clinical staff into the EHR during patient care, then again by research coordinators into the EDC system for sponsor use.

Regulatory and practical considerations

The FDA has not issued standalone guidance for AI-driven EDC capture, so this type of screen-based intake falls under broader regulatory frameworks for AI in clinical investigations. Sponsors evaluating cubeCDMS will need to verify that the system meets source data verification requirements specific to their protocols.

The practical test will be whether sites running studies on the platform see the promised reduction in transcription hours and whether cleaner initial data actually translates to fewer query cycles before database lock. Sites factor technology friction into trial selection decisions, making operational efficiency a competitive factor in study recruitment.

Details were first reported by Clinical Trial Vanguard, citing information from Mednet Solutions.