Electronic health records (EHR) are a treasure trove of data. Data from EHRs can be useful in helping study teams better understand population health, which can guide more precise recruitment efforts and provide insights that lead to more effective study designs. It can also be mined to build highly detailed and in-depth patient registries. The lack of technological interoperability between common EHR platforms and the many electronic data capture (EDC) solutions is largely to blame for keeping researchers from accessing and taking advantage of the volumes of raw, unstructured data found in EHRs.
How is EHR Data Different?
EHRs are patient medical records in digital format. EHR systems collect and store data about patients to aid in their clinical care, moving with them should they change doctors, see a specialist, or need to visit the emergency room. These systems were built to facilitate sharing of information among care providers; they were not built with clinical research in mind. This means that data from EHRs, from a research perspective, is unstructured. It is then difficult to access and utilize this data without the right tools.
Causes of Interoperability Challenges for Clinical Researchers
- Many EHR Systems to Choose From – There are many different EHR systems and very little agreement between healthcare providers. Even within communities, there can be multiple healthcare provider networks with different records platforms, not to mention various physician practices. The features and functionality found in the various EHR systems, while largely similar, possess enough differences to create variances between what the data looks like from one EHR system to another.
- The Human Factor – Initial entry of patient records into the EHR system is still performed by humans. While EHR software largely does its best to guide users through data entry in order to prevent errors and encourage consistency, there is still room for variations in how data is entered from one clinical site versus another.
- Lack of the Right Software Architecture – Some clinical trial EDCs still struggle to play nice with EHR systems. Again, this makes sense as the two types of software platforms were initially built to serve different purposes. Just as there are multiple EHR platforms that healthcare providers can choose from, there are many EDC options for research teams. Further variation occurs when multiple sponsors and/or contract research organizations (CROs) are involved. So even if one party brings an effective EDC, a partner with a less effective solution can muddy the waters and slow things down.
TrialKit began as an open system over 12 years ago, and for the past four years, we have offered TrialKit AI, a solution that utilizes the extensive API framework to enable our customers to move huge amounts of data from any EHR system to TrialKit EDC quickly, simply, and securely.
The Difference the Right Architecture Can Make
The lack of interoperability between disparate technologies is nothing new. At the same time, giving our customers autonomy over their study data and the flexibility to move that data wherever and whenever they want is core to what we do at Crucial Data Solutions (CDS). This is why we began exploring solutions to take the complexity out of data exchange for users, in essence creating a system that allows users to get what they need without ever feeling the complicated nature of how different systems interact with each other. This led us to our open API architecture.
TrialKit began as an open system over 12 years ago, and for the past four years, we have offered TrialKit AI, a solution that utilizes the extensive API framework to enable our customers to move huge amounts of data from any EHR system to TrialKit EDC quickly, simply, and securely. Throughout this process, proprietary AI technology parses the unstructured EHR data and transforms it into clean, structured data within the EDC. By using an open API, TrialKit can also help any EDC get what it needs from any EHR without the need for direct integrations and without time-consuming and labor-intensive changes to workflow.
Additional Benefits of Open API Architecture for EHR to EDC Data Exchange
- Ease of anonymizing patient data from EHR, aiding compliance
- Automated data entry saves time and reduces costs
- No need for manual transcriptions, reducing errors
- Eliminates need for labor-intense source data verification (SDV)
- Cleaner data, faster
- 21 CFR Part 11 and HIPAA compliance are ensured
For more information about TrialKit and its open API architecture, or to start a conversation about how TrialKit can benefit your studies, contact us today.
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