Quality data and the EHR-to-EDC dream – where and why we need to focus our energies

April, 2023
Quality data and the EHR-to-EDC dream – where and why we need to focus our energies

In 2011, when colleagues and I first began our multi-partner EHR4CR project, I had high hopes for the future use of electronic health record (EHR) data in clinical trials. Many of my other colleagues had strong doubts at the time about the potential for real world healthcare data to be used in trial execution. But over a decade later, I’m pleased to say attitudes have changed.

The time is truly ripe for us to realise the many benefits that can come from streamlining the transfer of EHR data into clinical trials – also known as EHR-to-EDC. The advent of SMART on FHIR apps that facilitate the connection of EHRs to other systems, the growing adoption and maturity of EHR systems themselves, technology innovations and the mass move towards digitisation of healthcare data – all come together to create the perfect environment to really leverage the power of real-world data for the benefit of all.

What are the benefits?

When I say, ‘for the benefit of all’, it’s worth remembering what’s at stake here. High quality healthcare data, and the seamless use of that data for clinical trials via EHR-to-EDC technology, means:

  • Huge efficiency improvements for hospitals running clinical trials.
  • Faster, better trials for pharma companies.
  • Enhanced primary care services, with higher quality data leading to higher quality, more responsive care.
  • Enabling faster access to innovative treatments.
  • With all these added efficiencies, the potential for capacity to do research that otherwise wouldn’t happen (including research hospitals having more freedom for their internal research programmes).

But, more fundamental than these benefits, I believe that this change in the way we use data for clinical trials will create a catalyst for much larger change and improvement in how we conduct trials. The technology for EHR-to-EDC data transfer sits right in the middle of a clinical trial, between the source (the EHR) and the sponsor (the EDC). It allows us to use ‘true’ source data. And, most importantly, the technology sits with hospitals and study sites. This opens the door for a shift in the dynamics of the relationship between key stakeholders in clinical trials.

Traditionally, pharma companies design clinical trials in splendid isolation. Communications between sponsor and trial site regarding the trial criteria tend to be one-way, with sponsors directing hospitals on requirements. There is very little consideration given to the data that is stored in the hospital’s EHR, and a lack of dialogue on how the trial design can better fit the hospital’s existing data.

The introduction of EHR-to-EDC technology as a bridge between the two worlds will bring a deeper understanding of what data is stored, with important learnings. It will allow a completely different level of collaboration between trial sponsors and hospitals, an interesting new dialogue about how we conduct trials and better feedback and transparency between the key partners. In the future, I see this being enhanced further with technologies such as artificial intelligence (AI) being used to ensure that important data elements that are captured in EHRs are included in clinical trial data.

Ultimately, I see this leading to better clinical trial design, with trials that are actually tailored to the real world, optimised protocols, and open dialogue around data and new data elements that are needed.


While I said earlier that attitudes have changed, I should couch that with the recognition that the shift towards the convergence of real-world data and clinical trials is still evolving. Much of the drive for change so far has come from regulatory agencies and non-profit bodies interested in improving the quality and use of healthcare data, as well as technology innovators looking to solve challenges. Within the pharma industry, the drive doesn’t yet exist – in the way it needs to – to see large-scale change and adoption of new technologies. What I am hearing from my former colleagues in the pharma industry, however, is a shift in attitude and a new eagerness to be convinced about what needs to be done.

What needs to happen?

So, what does need to be done?

eSource-readiness in hospitals

eSource-readiness (with the original source data required for clinical trials being captured and available digitally) is vital for study sites. To achieve eSource-readiness, hospitals obviously need their house in order:

  • The right systems to capture data.
  • High quality, accurate data being captured.
  • Data that is ordered and structured, using the right ontology and terminologies.

To get there, however, we must not forget the importance of everything that needs to be wrapped around these points:

  • Training programmes for staff – not only on the mechanics of using the systems and building coding capabilities, but also on the importance of data quality and its impact on patient care.
  • Processes in place, including standard operating procedures (SOPs) that clarify roles and responsibilities, and ensure quality.
  • A change management approach that recognises the importance of shifting the data culture of the organisation.
  • A stepwise implementation of AI support where it can be used to enhance usability. This would alidating concomitant medications and, for the future, being able to enhance safety signaling capabilities from EHRs.

Investment in data quality from pharma companies

For pharma companies, the recognition is increasingly growing that hospitals need support to do these things, and investment is needed for the pharma industry to truly act as a catalyst for change. More needs to be done on large-scale:

  • Industry-sponsored education programmes.
  • Industry-sponsored accreditation programmes for hospitals achieving eSource readiness and high-quality data.
  • Funded schemes that could supply expert teams to hospitals to set up systems, provide training, establish processes and procedures, and more.

As well as these, there is much that pharma companies can and are doing to improve their own internal capabilities. Change management and training programmes, process changes and a re-evaluation of relationships with partners are just as important for pharma companies as they are for study sites.

And, even more usefully, pharma companies could come together to agree on the high value datasets that they need most. As much as 50% of the data used in a clinical trial is data that is easily structured – data such as lab results, concomitant medications, and vital signs. These data domains should be deemed a priority for hospitals to focus on when improving their data quality. I often talk about taking a ‘stepwise’ approach to achieving the full potential for data to be re-used in clinical trials – this is what I mean by that. We cannot tackle the challenge of improving data quality in one go. But if we tackle one domain at a time, beginning with those that are most valuable, we will begin to see the benefits almost immediately.

Cross-industry and pre-competitive collaboration

To achieve this level of agreement on the route forward, however, the focus on cross-industry and pre-competitive collaboration needs to be much stronger. Pharma companies, in particular, need to come together with one consolidated voice, to agree those high value datasets that they all need, so that clarity and focus can be applied to the efforts to improve data quality. By working together in this way, I see a clear opportunity to drive the adoption of highly structured data in EHR systems.

At a tipping point

Having spoken about data quality, real world data, and the opportunities for technology to further support clinical trials for many years now, I feel we’re finally at a tipping point. The realisation of what we can achieve is gathering momentum, and, with it, the recognition that we will need to work together to make high quality, structured healthcare data the norm.

As a final thought on overcoming the challenges to get there, I think of the explosion, within the last five years of data points being brought into clinical trials. I believe this stems, at least in part, from pharma companies’ desire to realise the promise of new gadgets, devices, decentralised trials and so on. But pharma companies should think carefully before making a trial so complex that it becomes overly costly and painful for study sites to implement. Perhaps I’ll write more here about this trend another time!


Mats Sundgren

Mats Sundgren, PhD, MSc, is a renowned expert in Health Data Strategy for Industry and Academia, and formerly global integration lead for Electronic Health Records (EHR) services at AstraZeneca.

Mats Sundgren
Mats Sundgren

Academic and strategic industry advisor

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