With the rise of interconnected medical technologies, materiovigilance has emerged as a critical priority. This discipline involves tracking, assessing, and documenting safety risks tied to marketed medical devices. Through ongoing surveillance, regulators and companies can pinpoint hazards—such as defects, usability flaws, or software failures—that threaten patient well-being.
Dr. Padmalakshmi Jnaneshwar, IQVIA’s Director of Safety Operations, views materiovigilance as a vital part of overseeing market safety. Whereas pharmacovigilance centers on drug safety, this field looks at how users interact with medical devices in everyday life.
The Challenge of Materiovigilance
Many firms are still working to develop the skills needed for this expanded role. While drug companies know a lot about pharmacovigilance, managing devices needs different methods, skills, and proof. A problem might look simple at first, but the root cause could be in the device’s design, software, user actions, or how the product was used.
If there is no clear way to operate, teams might experience delays in reporting, incomplete checks, and compliance risks. The life sciences sector has seen how hard it is to turn digital goals into real results. Deloitte reports that only 22% of life sciences leaders claim they have successfully scaled AI, and just 9% see significant returns.
The execution gap in safety operations affects groups using manual steps and scattered data. To fix this, materiovigilance teams need to build unique procedures separate from pharmacovigilance that focus on physical devices. These new processes must set rules for receiving complaints via different channels and classifying these events.
Governance and Data Strategy
Governance is a key element in developing materiovigilance. It enables teams to coordinate with other safety groups while ensuring clear responsibility across safety, regulatory affairs, quality, clinical, manufacturing, and tech functions. Without governance, teams might not know what they are in charge of, leading to missed deadlines and lost critical info.
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Creating a unified data strategy is also essential for materiovigilance teams. They do not have complete data sets, which hinders the development of trend analysis. A set of common definitions, consistent coding practices, and clear pathways for information to move between functions can help teams catch and analyze safety signals more effectively.
As materiovigilance teams create new processes independent of their pharmacovigilance counterparts, some level of collaboration is still expected. Combination products, such as insulin delivery systems, often create safety questions that bridge the two operating models. Patients may submit complaints about adverse events related to their medication or comment on the quality of the device.
In the middle of this evolution, it’s clear that materiovigilance will play a critical role in ensuring patient safety. As therapies and treatments become more digitally connected, the importance of materiovigilance will only continue to grow. Organizations that invest now in integrated data, device-specific expertise, and practical automation will be able to detect and respond to events faster.
Artificial intelligence is a major asset for pharma. To fully use AI’s power, materiovigilance teams must pinpoint specific operational problems and apply the tech there. AI can show its worth by finding duplicate records, routing complaints to the right teams, or spotting trends humans might miss.
Even as AI, automation, and other digital tools impact materiovigilance, human knowledge is still key. Human oversight provides the context that automated tools often lack. For instance, teams may need to evaluate device usability or software performance. Although AI and automation can process large data sets quickly, human review is the standard for validating conclusions and explaining safety decisions.
Organizations should prioritize technology that is compliance-ready, audit-defensible, and globally scalable. The most valuable systems are not necessarily the most advanced; they are the ones that support clear workflows, consistent classification, traceability, and reporting across regions. AI and automation can strengthen materiovigilance operations, but only when they are built on a controlled foundation that includes audit trails, role-based decision-making, version management, and explainable outputs.
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