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What Data Governance and Data Accessibility Mean for Clinical Research?

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While Electronic Health Records (EHRs) open the door to the advantages of big data in clinical research, significant challenges related to patient consent, confidentiality, governance, traceability, and security must be addressed, especially within the healthcare context.


Secure and easy-to-implement data governance and accessibility practices are essential to address the challenges mentioned above. Data governance involves managing the availability, usability, integrity, and security of data coming from EHRs. It ensures that data is trustworthy, consistent across platforms and departments, and does not get misused.

In a clinical context, this means that EHRs, which can include structured data, images, lab results, triage and research notes, as well as information on temporal events, are coded using a consistent ontology and de-identified to protect patient privacy, as mandated by PIPEDA regulations. It also means that EHR data is protected against changes, deletions, misplacement, etc. 

Data accessibility, on the other hand, refers to the process of controlling a user’s ability to access, retrieve, move, or manipulate EHR data. The process of limiting access to sensitive information is done by establishing predetermined access brackets that dictate the type of data a user can and cannot access, and in how much detail. As such, a student scientist and a study physician, who fit into different access categories based on their roles, are granted different levels of accessibility to the EHR database.

In clinical research, setting up effective data governance and accessibility structure is not a small feat, but it is essential if researchers want to leverage the power of big data and new bioinformatics tools. 

Logibec NOAH is proposing a solution that codifies patient EHRs –whether the data is structured or unstructured–, creates concrete access categories, and protects the integrity of the database, all while preserving patient privacy.