Bayesian Risk Identification Model (BRIM)

Abstract

Increasing pressure from both regulatory agencies and the consumer market has expanded the need for medical use error reduction. BRIM integrates user performance with risk management to quantifiably predict human factors issues and illuminate design mitigation strategies during development of medical devices. Upfront analytical modeling permits a significant reduction in required expertise and application of empirical methodologies. BRIM asserts that a common set of performance influencing conditions (PICs) determine how a user will interact with a medical device and that a unique set of resulting human response failures (HRFs) manifest differently depending on the specific product interface design. Probability of HRF occurrence can be derived via a Bayesian Belief Network representation of PICs. By understanding the root causes of why a combination of interface, environment, or contextual influences lead to human error, we can predict how a product will perform with respect to human interaction. And, by testing BRIM’s targeted set of design characteristics across human performance metrics, we can specify this use error likelihood per product interface.

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