Most people underestimate what data management really means in medtech and IVD
In pharma, data management is typically focused on getting clean, consistent, and traceable clinical data—which means that data anomalies are usually handled during data cleaning. But in medtech and IVD, anomalies can signal something else entirely.
Example: a scanned patient ID gets misinterpreted → the wrong patient is linked downstream.
It looks like a data error, but it could be a device or system issue, which changes the role of data management:
• Don’t just clean data—understand it
• Don’t just fix errors—trace their origin
• Don’t disregard anomalies—learn from them
From a data management perspective, the goal isn’t perfect data, but to understand real device performance—including its limitations. -Sebastian Carlsson, Data Manager at Aurevia
At Aurevia, we combine our data management knowledge with our deep expertise of devices, complex data flows, and regulatory expectations for medtech and IVD.
The result? Data you can trust—and evidence that stands up to scrutiny.
Learn more about how we can support your next clinical study.
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