Transformation is one of the most vital steps within the clinical trial submission processes. Intended to protect patient privacy while providing the highest level of utility.
Data anonymization can be time-intensive & difficult to handle in-house. Historically transformation has been a manual process and there are a variety of data anonymization methods that each come with their own unique level of re-identification risk. Choosing the correct method for your team’s clinical study reports requires a thorough understanding of methodologies, as well as the resources and tools required to handle all data anonymization properly.
Clinical anonymization is the automated transformation process to mask patient identifiers in clinical study reports (CSRs) and clinical data sets that are shared both internally and externally.
Once data is collected, organizations are responsible for removing any Personal Identifiable Information (PII) from any CSR reports that are created. There are two options to do this:
Both options come with their own levels of risk, particularly if the study was specific or small. Many organizations rely on a combination of methods that fall into both categories in order to ensure true patient protection and regulatory compliance. It is important to be able to quantify and adjust as needed this risk of re-identification to legally protect your company.
If sponsors share documents that are useless for medical innovation, even though they are compliant, the whole purpose of the activity is meaningless. Transparency is the gateway to medical research as sharing high utility data sets and documents drives targeted treatments and vaccines. Compared to redaction and qualitative assessment, anonymization leverages software to automate manual processes and provides a numeric score that ensures regulatory compliance.
Intended to promote transparency and data sharing in the clinical trial space, EMA Policy 0070, Health Canada Public Release of Clinical Information (PRCI) requires pharmaceutical companies to de-identify clinical trial findings and encourage anonymization. With retroactive requests for disclosure and proactive publication of clinical information for all new drug submissions (both NDS-NAS and those not categorized as new active substance), failure to properly protect patient data can result in serious consequences.
There are a number of ways to make data anonymous. Each method has its benefits and can be used in combination with others.
Traditionally, many pharmaceutical companies have used redaction tactics to handle transformation. However, regulatory bodies are now pushing back on fully redacted components of CSRs and are promoting the use of sophisticated intelligent software to meet tight compliance timelines. Such software can be used to mitigate anonymization risks as well as leverage labor, cost, and time resources.
d-wise, the leader in transparency technology, enables sponsors to share data and documents on any scale with their outsourced Data & Document Anonymization Service offering. By combining a team of experts that leverage Blur, the #1 product in anonymization and quantifiable risk measurement, outsourcing transformation has never been simpler and safer.
Learn more about Data & Document Anonymization today.