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Towards a Standard Process enabling AI-support for Safety and Conformity of Medical Devices

Peter Pfeiffer; Heike Sander; Peter Fettke; Wolfgang Reisig
In: Proceedings of HEDA-2022. The International Health Data Workshop (HEDA-2022), located at International Conference on Application and Theory of Petri Nets and Concurrency, June 19-24, Bergen, Norway, CEUR, 11/2022.


Within the lifecycle of a medical device, Post-Market Surveillance (PMS) refers to the proactive, systematic and periodic monitoring of adverse events and other findings related to medical devices on the market, aiming to ensure the safety of patients, healthcare professionals and other users. PMS and associated activities, like Vigilance, are required by law and part of the obligatory risk management for medical products where regulatory requirements to be met are, for instance, defined by the Medical Device Regulation. Information to be monitored includes adverse event reports, clinical studies, scientific publications and other reports from any relevant internal or external data source. This requires the collection of heterogeneous data from various data sources in order to perform quantitative and qualitative analysis. In this work, we propose an initial version of a standard process with emphasis on data collection and analysis of data relevant for PMS and associated activities within the medical device lifecycle which is based on experience from regulatory specialists in this field. In addition, the challenges and potential for AI-support within these processes are discussed.


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