AI Tools Reduce Manual Data Entry Hours in Clinical Trials

Research coordinators typically spend between 12 and 15 hours per week on a single study performing a tedious task: copying patient data from hospital records into separate trial databases. This manual duplication creates significant delays and introduces a high risk of human error. Mednet, in partnership with CRScube, has introduced an AI-driven tool designed to automate this specific workflow, aiming to remove the bottleneck that slows down clinical trial data processing.

The tool works by reading data directly from the screen of the electronic health record (EHR) and transferring it to the electronic data capture (EDC) system. This approach eliminates the need for complex middleware or separate integration projects. By automating the transcription process, the system addresses a core inefficiency in the industry where more than half of trial data is currently entered by hand twice.

Reducing delays and manual errors

Manual data entry does not just consume time; it creates a lag of 14 to 30 days before sponsors and contract research organizations can view the information. This delay means that oversight decisions are often made based on outdated data. Furthermore, a 2023 analysis published in PubMed found that manual abstraction of medical records carries an error rate of approximately 6.57 percent. By automating the transfer, the tool aims to catch errors at the source rather than relying on downstream corrections, which frees up data management teams to focus on complex cases that require human judgment.

The economic implications are significant. While the EDC market is projected to grow from $3.2 billion to $7.1 billion by 2030, market growth alone does not fix the underlying workflow inefficiencies. Sites are increasingly factoring in the administrative burden of technology when deciding which trials to host. By reducing this friction, sponsors gain a competitive advantage in recruiting sites, as the tool lowers the operational load on clinical staff.

Regulatory considerations remain open

Despite the operational benefits, the regulatory landscape for AI-driven data capture is not fully defined. The FDA has not issued specific guidance for this type of screen-based intake, leaving the characterization of such tools to broader conversations about AI in clinical investigations. Sponsors must still ensure that their specific protocols meet source data verification requirements. This means that while the tool automates the copy-paste process, human oversight remains a critical component of the data integrity workflow.

The practical success of this technology will be measured by whether it actually reduces the hours spent on transcription and whether it leads to cleaner data with fewer query cycles before database lock. According to GN technics/ai (en-US), the focus is on shifting the burden from routine entry mistakes to more meaningful data review. However, organizations must weigh the efficiency gains against the need for rigorous validation to ensure that automated data entry meets the strict standards required for clinical trials.