Evaluates the impact of Biodesign Sprints on innovation in medical education, suggesting improvements in collaboration and outcomes.
PROBLEM: Many complex health care challenges demand integration of medicine and engineering for effective solutions. However, such collaboration is hindered by funding, communication barriers, and traditional research structures. Improved interdisciplinary models are needed to teach health care and engineering professionals new problem-solving approaches. APPROACH: The authors evaluated the integration of medicine and engineering in health care education through the student-led EnMed Health Innovations (EHI) Biodesign Sprint at Texas A&M's School of Engineering Medicine. The program teaches an innovation framework to empower health care professionals to drive change. Physicians submitted clinical problems through a competitive application process, after which multidisciplinary teams of students, physician mentors, and engineering experts collaborated over several months to conduct needs assessment, market analysis, concept generation, and implementation planning. The program culminated in a half-day Biodesign Sprint workshop focused on rapid ideation, project refinement, and pitch presentations. OUTCOMES: Using quantitative surveys and qualitative analysis, the authors assessed the Sprint's impact on Houston Methodist Neurology and Neurosurgery on project acceleration, participants' comfort with Biodesign tools, and overall satisfaction. Twenty one participants completed the pre-Sprint survey and 18 completed the post-Sprint survey. Participants reported increased familiarity with key aspects of the Biodesign process, including market analysis, prototyping, regulatory strategy, and implementation planning. Findings suggest the Biodesign Sprint fosters interdisciplinary collaboration, accelerates innovation, and produces tangible medical outcomes by combining engineering and health care expertise. NEXT STEPS: Project complexity and the Biodesign Sprint's short timeline highlight areas for refinement. Future iterations will add funding to support sustained progress.
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Dahlberg et al. (2026) studied this question.
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