Integrating electronic health record (EHR) data into electronic data capture (EDC) systems has the potential to enhance data management in clinical research. As promising as it sounds, though, the EHR-to-EDC solution also comes with its challenges. An EHR represents patients' digitalized medical records. An EDC system, on the other hand, stores the data of patients participating in medical trials.
This article discusses the novelty of EHR-to-EDC (EHR2EDC) integration in research as well as the benefits and issues associated with it.
The integration of EHR data into EDC is a vital process in clinical research. It makes use of software to transfer medical records into EDC systems.
EDC systems are data management services that collect, correct, and process health data. They offer research coordinators information on population health, disease prevalence, and drug side effects. Additionally, the data is made available to clinical research associates (CRAs), clinical research organizations (CROs), sponsors, and pharmaceutical companies.
Research trends are moving toward standardizing and automating EHR-to-EDC integration.
Creating an automated model is necessary to streamline the flow of EHR data into EDC:
The potential of such a model is exciting. A study in Germany found that automated EHR-to-EDC transfer is a feasible process that accelerates the development of new drugs. [1] This approach can simplify the conduct of studies and data management for both study sites and sponsors. An automated model implements smooth workflows, machine learning (ML), and natural language processing (NLP) to achieve its goals.
There are three main groups of patient data that can be transferred into EDC systems:
Medical data is generally transferred manually from a site’s EHR to the EDC systems. An automated, scalable EHC-to-EDC solution has the potential to replace manual labor and boost transfer efficiency.
Integrating EHRs into EDC systems entails numerous benefits for clinical research. It allows researchers to get results faster and produce efficient outcome analyses. Standardising and automating the EHR-to-EDC process could only add to the benefits! Below, we elaborate on seven remarkable potential advantages.
Fast and High-Quality EHR Data Entry
A recent study estimates that 70% of clinical trial data is duplicated between the EDC and EHR systems.[2] A standardised, automated transfer of medical records into EDC systems can reduce double data entry. Automatization can also allow for more accurate EHR analysis and fewer transcription errors than manual eCRF use. In the end, you get high-quality data while saving 87,500 hours of precious research time per trial.[3]
Reduced Queries
Integrating EHR data into EDC systems lowers the number of inconsistencies found in the data. This results in a reduced number of queries, as CRAs and sponsors need to perform fewer validation checks.
Reduced Time Spent on SDV
The Source Data Verification (SDV) process checks if the research data matches the source data at the trial site. It compares the source data against the CRF data. By reducing errors, a standardized and digitized EHR-to-EDC process also shortens the SDV process, saving time.
Quicker Accomplishment of a Clinical Study Milestone
Let’s face it: manually entering and sorting large databases is quite the hassle. It takes up a lot of time and energy. Digitalizing data management services allows researchers to focus on the trial and meet clinical study milestones.
Early Detection of Safety Signals
A safety signal represents data about a potential adverse drug effect. The analysis and transfer of electronic medical records into electronic data capture systems provide faster access to data. Hence, safety signals could be detected early on and investigated further.[4]
Seamless Site Workflows
The automation of EHR-to-EDC integration can make life easier for all involved parties, including the monitoring bodies and management team. It lowers the error rate and enhances productivity, creating a smooth, seamless site workflow.
Lower Cost
Transferring electronic health records to EDC systems can invariably reduce clinical trial costs. It addresses sponsor concerns about costs related to monitoring and repeated validity checks. According to the EIT Health EHR2EDC Consortium, this practice can save $15,000 per patient in a given oncological trial. [3]
Automating EHR-to-EDC integration in clinical trials does come with a few challenges.
First, the wide adoption of a standard is slow. Currently, different points of care employ different transfer systems and methods. Some have yet to incorporate patient records into electronic health records.
Second, data mapping is complicated. An automated model cannot be expected to recognize all the variability in the data. So, human verification remains necessary.
Third, establishing site connections takes time and effort. The installation and configuration of EHR-to-EDC solutions can last for months. Besides, dedicated IT resources must be instated at every site, adding to the challenge.
Approaching and overcoming these challenges at scale can begin with addressing the concerns of sites, partners, and sponsors. The first steps in this direction have already been taken.
In 2019, the ONC (Office of the National Coordinator for Health Information Technology) presented the HL7 Fast Healthcare Interoperability Resources (FHIR) model [5] HL7 FHIR has been deemed “the next generation” framework standard for EHR-to-EDC integration. In 2022, the European Commission also approved the recommendation to simplify the exchange of EHR data between European Union countries for clinical research. [6] This follows a similar action by the FDA in the US in 2018.
Automating the integration of EHR into EDC systems can enhance the conduct of clinical studies and data management for both study sites and sponsors. It allows for a high-quality, quick, and secure transfer of structured and unstructured data necessary for clinical research. This EHR-to-EDC solution also reduces time, queries, effort, and costs.
Creating a standard for data management services is challenging. Overcoming any challenges at scale involves addressing the concerns of sites, partners, and sponsors and forming a standard framework.
Transferring EHR data into EDC systems for clinical research is a fast-growing concept. It requires careful planning and expert supervision. At Therapyte, we understand the exciting potential and implications associated with EHR-to-EDC integration. Contact us to find out more.
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[1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10439471/
[2] https://www.appliedclinicaltrialsonline.com/view/innovations-in-data-capture-transforming-trial-delivery
[3] https://www.researchgate.net/publication/368654364_EHR-to-EDC_'Faster_and_Better'_automating_data_between_Electronic_Health_Records_and_Electronic_Data_Capture_systems_at_hospitals_-_oral_presentation_at_SCOPE_US_2023
[4]https://www.researchgate.net/publication/230756054_Using_Electronic_Health_Care_Records_for_Drug_Safety_Signal_Detection
[5] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6508843/
[6] https://digital-strategy.ec.europa.eu/en/policies/electronic-health-records