Human Factors Engineering in Medical Device Safety

Abstract

Use errors -- mistakes made by users interacting with medical devices -- are implicated in 60-70% of medical device adverse events reported to the FDA. Human factors engineering (HFE) applies systematic knowledge of human capabilities, limitations, and behaviour to device design, aiming to minimise use errors through intuitive interfaces, error-resistant designs, and effective labelling. The FDA's 2016 guidance on applying human factors to medical devices mandates HFE evaluation for all new device submissions, requiring use-related risk analysis, formative usability testing, and summative (validation) human factors studies. Yet despite these requirements, use errors persist: auto-injector misuse rates remain 15-30%, infusion pump programming errors cause 56% of medication error-related deaths, and patient-operated devices show higher error rates in elderly, cognitively impaired, and low-literacy populations. We present the Human Factors Device Safety Framework (HFDSF), evaluating five HFE methodologies -- task analysis with cognitive walkthrough, simulation-based usability testing, eye-tracking and physiological monitoring, AI-powered error prediction, and inclusive design for diverse populations -- across four device use scenarios (self-injection devices, infusion pump programming, wearable device management, and surgical instrument interfaces). Our Human Factors Safety Score (HFSS) measures use error reduction, task completion efficiency, user satisfaction, population inclusivity, and regulatory compliance. AI-powered error prediction achieves the highest HFSS (0.924) through real-time detection of error-prone interaction patterns that trigger preventive interventions, while inclusive design achieves the highest population inclusivity (0.960) by systematically addressing the needs of elderly, disabled, and low-literacy users.