Solving the EHR-to-EDC Challenge: A Scalable-first Approach

Electronic data capture (EDC) systems have long been used to collect, clean, transfer, and process clinical data. But despite the widespread adoption of electronic health record (EHRs) in the healthcare system, their implementation in clinical research has been slow and challenging—even though they’re recognized as a highly rich source of information that provide many benefits for clinical research, particularly with EHR to EDC integration, including:

• reduced trial costs
• faster trial completion
• increased generalizability of results, enhanced recruitment
• expanded scope of research
• earlier identification of safety events

Clinical researchers have long sought to repurpose EHR data at scale to support clinical research. As explained in our new white paper Solving the EHR-to-EDC Challenge: A Scalable-first Approach, multiple hurdles have hindered progress toward a truly scalable solution, including poor interoperability between EHRs and other systems, data quality issues, and the sheer volume of data in modern clinical trials. Although limited solutions have been developed for EHR-to-EDC data extraction, they lack scalability due to limited implementation options and the need for extensive IT infrastructure and data transfer agreements.

Why Solve the EHR-to-EDC Challenge? 

Approximately 70% of the data entered into EDC systems are duplicated from EHRs and other source systems, which has become a major pain point for site research coordinators. It’s not surprising that we often hear them say: 

"Why am I manually entering data that’s already available somewhere else?"

Today’s industry standards require research coordinators to identify and review specific patient and visit records in the EHR (and other systems) and then determine what data needs to be transferred to the EDC from specific reports. This is carried out by toggling between two systems and then manually entering the relevant data. This manual re-entry is an enormous challenge for sites and also negatively affects sponsors and partners. 

Industry Changes Have Finally Enabled a Scalable Solution

Key industry changes have paved the way for a scalable multidisciplinary approach to solving the EHR-to-EDC challenge, focusing on presenting data to users rather than solely mapping it. 

The 21st Century Cures Act (2016) aimed to enhance interoperability and reduce regulatory burdens associated with EHR systems. The Office of the National Coordinator for Health Information Technology (ONC) adopted API-enabled “read” services and recommended the HL7® FHIR® standard, which has let the health applications market leverage data from any EHR in a standardized format. Although the process of extracting data from EHRs and feeding them into EDC systems remains complex, these changes have provided a tailwind and a standardized approach for data exchange. Furthermore, regulatory agencies have recognized the value of EHR data in clinical research and encouraged its use in guidances and recommendations. 

Medidata’s Multi-pronged Approach to Overcome the EHR-to-EDC Challenge

Medidata has developed a uniquely scalable, easy-to-use solution for EHR-to-EDC data capture. Rave Companion is a data entry assistant for clinical trial sites using Rave EDC. When enabled with Medidata Health Record Connect, Companion presents matching EHR data for the EDC form, enabling completion of forms up to 90% faster than manual data entry. Health Record Connect is a healthcare data interoperability engine for securely and compliantly acquiring, transforming, and exchanging electronic health record (EHR) data. Health Record Connect has out-of-the-box connectivity to over 90% of the top research sites in the US and thousands more. Unlike other EHR-to-EDC solutions that can take months to implement on each study, Health Record Connect doesn’t require site-by-site EHR system integrations and data transfer agreements or complex study- and system-specific EHR-to-protocol mappings, so it’s up and running immediately. Also, sites don’t need to log in and use yet another system; Rave Companion automatically pops up when they open a Rave EDC form.

Conclusion

Integrating EHRs into clinical research holds immense potential for advancing medical knowledge and improving patient outcomes. By addressing interoperability challenges, ensuring data quality, and streamlining data transfer processes, researchers can leverage the rich information within EHRs to enhance various aspects of clinical research. Medidata’s Rave Companion aims to simplify the capture of EHR and other source data into EDC systems, providing a user-friendly and scalable solution for clinical research.

Download a copy of our white paper to learn how to revolutionize your EHR-to-EDC processes.