For many years, those involved in running clinical trials have recognised that the approach to collecting clinical trial data needed a shake up. There were clear inefficiencies that took up time and money for both the hospital study sites and the pharmaceutical companies behind the trials.
The inefficiencies of clinical trials
Up to 70% of clinical trial data is duplicated between Electronic Data Capture (EDC) systems used for clinical trials, and Electronic Health Record (EHR) systems used in hospitals. Huge amounts of time are spent manually re-keying this data from EHR systems into EDC systems. The associated costs of the time spent transferring data, quality checking, and overseeing the whole process, can run into many millions of dollars for a typical clinical trial.
So, for many years, there was a sense that there must be an easier way of transferring this data from one system to the other. Study sites have tried developing their own bespoke solutions for a digital transfer of data between systems (known as EHR2EDC), but these were not scalable solutions capable of large-scale impact.
At IgniteData, we’re proud to be in the leading battalion of a sea-change in clinical trial processes. Our solution, Archer, is the first scalable EHR2EDC solution capable of integrating with all the major EHR and EDC systems. But we’re also grateful for the significant body of work that has happened over the last decade, which has helped pave the way for this EHR2EDC revolution.
Read more about the Evolving Landscape of Clinical Trial Data
Significant studies into EHR2EDC
Over the last 11 years, there have been a number of pan-European projects and collaborations that have looked at the potential benefits of making better use of the data available in EHR systems. Those studies have contributed to a growing body of evidence supporting the case for automated EHR2EDC. In tandem, EHR systems have become increasingly widespread and sophisticated during this time, meaning the imperative to achieve efficiencies has become stronger and stronger.
Here are a few of the key European projects that have been crucial milestones in the journey to automated EHR2EDC:
2011-16: The Electronic Health Records Systems for Clinical Research (EHR4CR) project
The EHR4CR project made a significant contribution to our understanding of the possible benefits of being able to efficiently re-use the data that is already held in EHR systems.
Under the umbrella of Innovative Medicines Initiative (IMI), this 4-year European project involved numerous research institutions and companies, including AstraZeneca, GlaxoSmithKline, Merck and Sanofi.
The project aimed to tap into the emerging opportunity around digitisation of patient records for clinical research design and recruitment purposes. One of the outcomes of the project was a technical proof of concept and framework for using the data already available in EHRs to validate the design of study protocols and find sites that would be a good fit for studies. This would reduce workload and wasted effort on sites that would be unable to recruit, and speed up the delivery of innovations and treatment for patients.
Importantly, it found that the proof of concept was both ethically acceptable, and could potentially save vast amounts of time and money in a typical clinical trial.
The project also included a cost-benefit study of a typical phase two or three oncology trial.
This included a full evaluation of the detailed costs of performing three clinical research scenarios (S1 – protocol feasibility assessment, S2 – patient identification for recruitment, and S3 – clinical study execution), under current practices. Using input from a range of experts, the project gathered consensus about the minimum efficiency gains that could be expected by using an automated system.
This assessment of the potential benefits took into account that the estimated reduction in actual person-time and costs of performing trials would accelerate time to market (TTM) for new drugs.
Ultimately, the expected benefits for the global pharmaceutical oncology sector were estimated at between $54.2M and $2516.8M (depending on level of application across the different scenarios).
2017: The EIT Health EHR2EDC consortium studies
In 2017 attention began to move from clinical trial design and recruitment, to study conduct.
The EIT Health EHR2EDC consortium was a collaborative effort, driven by Sanofi and the European Institute for Innovation through Health Data (i~HD), industry partners, hospital partners, and technology providers. It aimed to promote and catalyse the most efficient and trustworthy uses of health data and interoperability, and it developed standards, mapping, and validation for the use of automatic data capture from hospital records into EDCs.
The consortium conducted further cost-benefit studies, looking at the costs of the current processes used in studies at a more granular level.
It looked at the costs of staff being employed to manually re-key data during a study, as well as to quality-check data (known as source data verification (SDV)) in a typical oncology study.
Taking a lower-than-average estimate of the number of data points per patient in a study, a conservative estimate of the number of minutes’ effort per data point, and the associated personnel costs per hour, they calculated the cost of manual transcription per patient. At a whopping $30,000, this cost represents almost half of the $62,000 total cost per patient in an oncology trial.
Again using a conservative estimate – of 50% of data being transferred via an automated system, rather than through manual data re-entry – this was shown to translate to a cost-saving of $15,000 per patient.
Across a single trial including 400 patients, this scales to potential savings of $6,000,000. And, remember, these calculations were using conservative estimates!
2018: The TransFAIR study
The TransFAIR study ran from 2018-19. Sanofi, AstraZeneca, Jansen and UCB worked with four hospitals in four different European countries (Spain, France, Germany and Italy) to define requirements and plug in technology to run mirror studies of real-world clinical trials from different sponsors.
Focusing on structured data such as laboratory data, medical history, adverse events, vital signs, demographics and medications, the study aimed to prove that at least 15% of data usually manually entered could be automatically transferred. The study significantly over-delivered on this goal, and it was found that 37% of the necessary data could be successfully transferred. (Since the TransFAIR study, other analysis published in 2021 has identified that up to 55% of clinical trial data is available in digital format in EHRs, therefore having potential to be transferred automatically.)
Perhaps more importantly, it also showed that the investigator was able to control the data being transferred. From a data privacy perspective, the fact that only the required data – as vetted and approved by ethics committees – was able to be extracted, was an important point to prove.
The above studies and work were crucial to proving the concept of EHR2EDC, as well as demonstrating the benefits that were there for the taking. But in 2021, we went beyond proof of concept, when we were able to use HL7 FHIR and Smart on FHIR technology to develop a scalable solution for EHR2EDC.
2021: UCLH, AstraZeneca and IgniteData pilot study
In 2021 IgniteData formed a collaboration with AstraZeneca to pilot the use of our EHR2EDC software, Archer, in clinical trials. Running Archer in parallel with traditional methods at University College London Hospitals NHS Foundation Trust (UCLH), the pilot project used a mirror image of the live health record, with synthetic non-sensitive patient data. It aimed to test and measure the efficiencies gained in time, cost and effort, before scaling to roll out the solution across other hospitals in the UK and Europe.
Findings from this pilot will be published soon and we will be updating our followers via our Linkedin company page when they become available.
Are you new to IgniteData?
IgniteData is the company shaping the future of clinical trials. Through our innovative digihealth platform, Archer, we are enhancing interoperability between Electronic Health Records (EHR) and key research applications such as Electronic Data Capture (EDC). Find out more about Archer here.
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