A hybrid Bayesian network for medical device risk assessment and management
This addresses risk management for medical device manufacturers, offering a solution where traditional methods like FTA fail due to limited data, though it is incremental as it builds on existing Bayesian network techniques.
The authors tackled the problem of medical device risk assessment under data scarcity by proposing a hybrid Bayesian network method, which they validated on a defibrillator device using real-world data.
ISO 14971 is the primary standard used for medical device risk management. While it specifies the requirements for medical device risk management, it does not specify a particular method for performing risk management. Hence, medical device manufacturers are free to develop or use any appropriate methods for managing the risk of medical devices. The most commonly used methods, such as Fault Tree Analysis (FTA), are unable to provide a reasonable basis for computing risk estimates when there are limited or no historical data available or where there is second-order uncertainty about the data. In this paper, we present a novel method for medical device risk management using hybrid Bayesian networks (BNs) that resolves the limitations of classical methods such as FTA and incorporates relevant factors affecting the risk of medical devices. The proposed BN method is generic but can be instantiated on a system-by-system basis, and we apply it to a Defibrillator device to demonstrate the process involved for medical device risk management during production and post-production. The example is validated against real-world data.