| Increasing pressure from both regulatory agencies and the consumer market has led to the need for medical use error reduction. BRIM (Bayesian Risk Identification Model) integrates human performance testing 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 human 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. Probability of HRF occurrence can be derived via a Bayesian Belief Network representation of PICs. By understanding the root causes of why an interface, environment, or contextual combination 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. Swift and early identification of use-error can provide for increased design flexibility, ultimately leading toward development of a safer product, at a lower cost, that increases productivity and decreases patient mortality. |
