A hybrid Bayesian network for medical device risk assessment and management

TL;DR

This paper introduces a hybrid Bayesian network approach for medical device risk assessment, addressing limitations of traditional methods like Fault Tree Analysis, and demonstrates its application on a defibrillator with validation against real data.

Contribution

It proposes a novel hybrid Bayesian network method for medical device risk management that overcomes data limitations of classical approaches.

Findings

Effective risk estimation with limited data

Applicable to various medical devices

Validated on real-world defibrillator data

Abstract

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.