The Dream Is EHR-To-EDC — eSource Is The Wake-Up Call

By Dan Schell, Chief Editor, Clinical Leader

I recently watched a webinar titled, “Bridging the Gap: EHR to EDC Integration in Clinical Trials – Reality Check.” That title alone made me pause. For years, I’ve heard industry people talk about the dream of pulling clinical trial data directly from EHRs into EDC systems. But the phrase “reality check” hit home. After hearing some of the questions during that session — and reviewing what others have written about the limits of EHRs versus the promise of eSource — I wanted to take a closer look at the pros and cons of this integration.

I get the appeal of direct EHR-to-EDC transfer. Many sites spend countless hours copying information from one system into another (There’s nothing like duplicate data entry to increase the frustration level of site staff!). In oncology trials alone, staff spend three to five minutes per data point, which can balloon into thousands of hours and millions in costs across a study. More than half of trial data is duplicated between EHRs and research systems, with about 20% of study budgets eaten up by this duplication.

Eliminate that double entry, and you eliminate time, errors, and cost. Early adopters of eSource-enabled integration — Mayo Clinic, City of Hope, MD Anderson — report reduced transcription errors, faster database locks, and lighter site workloads. Not surprisingly, webinar participants repeatedly asked about how automation could reduce their administrative burden.

On paper, the case for integration is a slam dunk.

Why It’s Not That Simple

But here’s the catch: EHRs weren’t designed for research. They were built to help clinicians document patient encounters and bill insurance companies. That’s a very different mission than running a clinical trial.

Research protocols demand precise visit schedules, study-specific data points (like pill counts or investigational product handling), and rigid workflows. EHR templates simply don’t fit those needs. As one report put it bluntly: No health system is going to reconfigure its EHR for a handful of trial patients when it’s already serving hundreds of thousands of care encounters.

This reality explains why many webinar questions circled around the same challenges:

• Interoperability headaches. Even with standards like FHIR, data mapping varies across sites and vendors.
• Unstructured data. Physician notes, imaging, and pathology reports don’t easily flow into neat fields. AI helps, but it’s still early days.
• Legal and compliance reviews. Institutions worry about HIPAA, GDPR, and 21 CFR Part 11 every time data moves across systems.
• Change management. Staff trained for years on manual entry don’t just flip a switch and trust automation overnight.

Where eSource Changes the Equation

This is where eSource steps in. Unlike EHRs, eSource systems are built for research. They allow sites to configure visit schedules, enforce protocol workflows, and capture trial-specific data directly at the point of entry.

The difference is night and day. Instead of juggling binders, annotated printouts, and copy-paste data entry, sites use structured templates with built-in quality checks. Monitors can review data remotely, and sponsors can push standardized eSource templates across studies.

The numbers back it up: CRIO (which sponsored the webinar I watched) reports that sites using their eSource were able to reduce protocol deviations by up to 50% and improve data quality by 70%. Meanwhile, early adopting hospitals show 50% reductions in site burden and transcription time cut from 15 minutes to under two per subject.

No wonder more than 2,000 sites worldwide are moving from paper or partial EHR reliance to eSource platforms.

Case Study: Scaling EHR-to-EDC At Memorial Sloan Kettering

Memorial Sloan Kettering (MSK) offers one of the clearest examples of what happens when a major research center moves beyond pilot projects and scales EHR-to-EDC integration. Using an HL7-FHIR based approach, MSK went from running two enabled trials to 14 within a single quarter.

The setup process that initially took 17 weeks dropped to just five weeks — a 71% reduction. Across those studies, more than 30,000 data points were transferred electronically, and data managers consistently rated ease-of-use and efficiency very high (4.2–5.0 out of 5).

The MSK experience proves two points: first, that large-scale integration is possible when the right infrastructure is in place; second, that the payoff isn’t just theoretical. Faster study start-up, higher efficiency, and reduced manual workload are tangible benefits that can ripple across an entire research portfolio.

The Sticking Points

So if eSource is so great, why aren’t we all there yet? The questions during the webinar hinted at the sticking points:

• Data standardization. Even champions like University Hospital of Essen admit it takes continuous updates to map hundreds of source systems into standardized repositories.
• Costs and resources. Building the mapping dictionaries and handling protocol amendments eats up time and money — especially when every trial has different data needs.
• Trust and adoption. Coordinators worry: What if the system misses something? Investigators worry: What if the FDA doesn’t accept it? Sponsors worry: What if one site refuses to change?
• Regulatory clarity. Everyone agrees agencies want better data integrity, but clear, consistent guidance on validating AI-driven eSource is still evolving.

The Questions Behind the Questions

When I looked at the list of webinar questions, I noticed something. Many were less about whether integration is possible and more about whether it’s safe, scalable, and compliant. That tells me the industry is past the “is this real?” stage. The focus now is “how do we do it without breaking something critical?”

That’s progress. But it’s also a reminder that technology alone isn’t enough. This shift requires collaboration among sponsors, sites, vendors, and regulators. Otherwise, “integration” risks becoming yet another patchwork solution that increases site burden instead of reducing it.

If there’s one takeaway from both the webinar and the literature, it’s this: EHR-to-EDC integration is coming — but not in the way many imagine.

We’re unlikely to see raw EHRs simply pouring data into trial databases anytime soon. The gulf between healthcare documentation and research precision is too wide. Instead, eSource-enabled automation, built to sit between EHR and EDC, will continue to gain ground.

Sites that adopt eSource report big wins in efficiency and accuracy. Sponsors that push for standardization across sites help tip the scales toward broader adoption. And regulators, slowly but surely, are shaping the frameworks that will make it all stick.

The “reality check” is this: The bridge between EHR and EDC is less a single span than a series of stepping stones. eSource is one of the biggest and most stable stones we have right now. Step on it carefully, and you’ll move forward. Ignore it, and you’ll keep slogging through the swamp of duplicate entry, ballooning costs, and endless queries.

Editor’s Note: Many of the stats for this article come from an April 2025 Applied Clinical Trials article — Scaling eSource-Enabled Clinical Trial Projects — co-written by Mats Sundgren, Ph.D., who was one of the panelists on the webinar I watched.