For years, clinical research has relied on a frustratingly manual process: patient information is captured in the hospital’s electronic medical record (EMR) or electronic health record (EHR), then entered again into the electronic data capture (EDC) system. Industry surveys indicate that this duplicate workflow results in more than half of trial data being entered twice, with research coordinators often spending 12 to 15 hours per week per study on redundant data entry and tracking activities. Beyond the administrative burden, these manual handoffs delay study visibility and introduce unnecessary opportunities for transcription errors.
Many organizations have turned to third-party eSource or integration platforms to close this gap. While effective in some cases, those approaches often bring additional vendors, longer implementation timelines, trial-specific mapping, and ongoing system maintenance.
At Mednet, we believe simplifying clinical research should not require adding more technology to the stack.
Through our partnership with CRScube, sponsors and CROs can leverage cubeCDMS’s built-in AI-driven EHR data intake capability. Embedded directly within the EDC workflow, this new and seamless eSource feature enables sites to copy patient data from the EHR/EMR through screen capture technology, thereby completely removing the need for manual transcription and complex data integration. Mednet and CRScube provide an EHR-to-EDC solution, without the overhead and complexity implied by existing solutions in our industry.
Why reducing data silos matters
Research sites are under constant pressure to balance clinical documentation, protocol requirements, scheduling, regulatory responsibilities, and participant care. When information must be entered into multiple systems, coordinators lose valuable time that could be spent on higher-value work. Manual EHR-to-EDC transcription can also delay data entry by 14 to 30 days, slowing the flow of information to sponsors and CROs.
Much of a trial’s source data already exists in electronic health records (EHRs) and/or electronic medical records (EMRs), but it is still often re-entered manually into research systems. Each additional handoff introduces more opportunities for delay and error. The goal is not simply to collect data, but to move trusted clinical information into research workflows as efficiently as possible.
One platform. One workflow.
Rather than relying on a heavy-lift and costly EHR-to-EDC data integration, cubeCDMS takes a different approach. Its AI-driven EHR data intake capability allows authorized users to capture information from their active clinical workflow and populate the appropriate electronic case report form (eCRF).
Clinical Research Coordinators (CRCs) follow a very simple process to capture the data:
1. They open the patient’s electronic health records, as they usually do in routine practice.
2. CRScube’s AI assistant reads the content on screen from the electronic health records.
3. The AI assistant matches what is on screen with what is required in the eCRF and copies the relevant data.
Because the capability is embedded within cubeCDMS, organizations can avoid much of the complexity associated with traditional integration projects, including trial-specific mapping and separate deployments. Instead of stitching together multiple solutions, sponsors and CROs benefit from EHR-to-EDC data capture without the need for a complex and expensive third-party integration.
Benefits across the clinical trial ecosystem
For research sites
Cutting manual transcription gives coordinators back their time to support study participants and manage research activities.
For sponsors and CROs
Cleaner data improves study oversight, provides earlier visibility into data flow and issue trends, and reduces downstream query management.
For clinical data management teams
Less manual entry improves first-pass data quality and allows teams to focus on meaningful review instead of routine corrections.
Reducing technology fatigue for sites as a competitive advantage
As the number and complexity of clinical trials continue to grow, research sites are being asked to do more with the same finite time and resources. Administrative demands have risen alongside that growth, creating workflow bottlenecks that can delay study startup and enrollment. For sponsors and CROs, solutions that meaningfully reduce the burden related to clinical technology has become an important differentiator, and many sites now consider the technology they’ll use as part of the study selection process.
This is where practical AI delivers meaningful value. Native AI capabilities within cubeCDMS reduce repetitive administrative tasks without introducing another application for sites to manage. By reducing friction in day-to-day study execution, sponsors and CROs can offer research sites a more streamlined experience while gaining faster access to study data.
Breaking down more than data silos
Clinical research has long struggled with disconnected systems, fragmented workflows, and duplicate effort. Breaking those silos is not just about connecting technologies, it’s about creating a more unified experience for everyone involved in a study.
By combining AI-powered EHR data ingestion with a modern EDC platform, Mednet and CRScube are helping sponsors and CROs simplify study execution, improve data quality, and reduce the administrative burden on research sites. Sometimes the best innovation is not another system. It is making the systems you already rely on work smarter.