Rapid Prototyping with AI
We are entering a new AI-driven tool development era where domain expertise trumps coding ability. Large language models have democratized software development. If you can articulate the digital tool you want to create, AI can help you build it. Quickly, cheaply, and customized to your vision.
Medical students, with cutting-edge clinical knowledge and direct access to patients and workflows, are uniquely positioned to lead in this next era of healthcare innovation. But they need new forms of training and hands-on experience to develop the necessary knowledge, skills, and (perhaps most importantly) mindsets. The Medical Design Program’s “Health Design Sprint” focuses on all three.
Core Learning + Doing Loop
The course runs on a single repeating workflow that builds muscle memory: Claude Chat → Product Requirements Document (PRD) → App Builder (Lovable or Claude Code) → Fresh Build. Students generate a fresh build every day from their enriched PRD—not iterating on yesterday’s build. By comparing daily builds, they see directly how richer planning yields richer apps.
The valuable skill isn’t coding syntax — it’s strategic thinking about what problems matter and how to build solutions iteratively.
Framework: Knowledge, Skills, Mindsets
Every session develops all three dimensions:
Knowledge
How Large Language Models (LLMs) work, PRD-driven development, design thinking methodology, healthcare data and privacy considerations
Skills
Structured planning with AI, rapid prototyping in Lovable, user research, iterative PRD layering, user testing
Mindsets
Comfort with ambiguity, embrace messiness before structure, AI as partner not oracle, process over polish
Layered PRD Builds
Students build real apps from Day 1. Each day introduces a new design dimension that gets added to the PRD set—and each updated PRD produces a new, more sophisticated app version.
AI Tools Integration
The curriculum emphasizes why to choose specific tools, not just how to use them—building transferable judgment as the landscape evolves.
Course Structure
This elective builds on the established foundation of the UVA Medical Design Program, which has trained 200+ students in human-centered design methods since 2017. The Health Design Sprint extends that work into the AI era — integrating vibe coding tools while preserving the program’s core commitment to understanding users and problems before building solutions.
The methodology is designed to be transferable — institution-agnostic, documented, and evidence-backed. Any medical school with domain experts, design thinking facilitation, and access to AI tools can run a sprint.
What We Measure
Each sprint is designed as a learning experience for the institution, not just for students:
- How non-technical domain experts use AI tools in intensive learning contexts
- Time-to-functional-prototype for domain experts using vibe coding workflows
- Impact of layered PRD approach on prototype quality and student learning
- Failure modes, pedagogical pivots, and what scales beyond each cohort
The 10-Year Foundation
The Health Design Sprint builds on a decade of teaching design thinking to medical students through the UVA Medical Design Program.
- 2017–2022: Monthly workshops and real-world client projects — emergency department redesign, patient experience research, community health partnerships. Proved that medical students can do rigorous design thinking. Iterated the pedagogy across 5+ cohorts and 200+ students.
- 2023–2025: The AI inflection. Large language models went from curiosity to capability. The design thinking methodology acquired a powerful new output: students could go from insight to working prototype in the same sprint.
- 2026: The breakthrough. Two weeks, eight students, five production-quality healthcare applications. A fully documented, replicable methodology — the Signal-to-Prototype Loop.
Why This Matters
Small cohort, intensive format, documented outcomes, transferable framework. Success here provides a template that any institution can adapt. The data we gather informs decisions about AI tool access, curriculum design, and the role of domain expertise in the age of generative AI.