Human-Centered Design in Medical Devices

Abstract

Medical device use errors -- incorrect operation, misinterpretation of outputs, and workflow integration failures - contribute to an estimated 50,000-100,000 adverse events annually in the United States alone, with the FDA identifying use error as a contributing factor in 44% of medical device recalls. Human-centered design (HCD) applies iterative user research, contextual inquiry, participatory prototyping, and summative usability evaluation to create devices that fit the cognitive, perceptual, and physical capabilities of their intended users within real clinical environments. This study presents the Human-Centered Design Assessment Framework for Medical Devices (HCDAFMD), evaluating five HCD methodologies -- traditional usability engineering per IEC 62366, contextual design with ethnographic field research, participatory co-design with clinical end-users, simulation-based usability testing with high-fidelity clinical scenarios, and AI-augmented design analytics using interaction log mining -- across four device interaction categories: patient self-use devices, clinician-operated diagnostic equipment, surgical instruments and implant delivery systems, and clinical software interfaces. Our Human-Centered Design Effectiveness Score (HCDES) integrates use error reduction, task completion efficiency, user satisfaction, accessibility inclusiveness, and regulatory compliance. AI-augmented design analytics achieved the highest HCDES (0.924) through ML models trained on 4.2 million device interaction logs that identified 42% more use error patterns than traditional think-aloud usability testing, while simulation-based testing achieved the highest ecological validity (0.955) through high-fidelity clinical scenarios that reproduced the cognitive workload, interruptions, and multitasking conditions of actual clinical use