PocketCare — Designing clarity in digital healthcare
A digital health records platform that transforms complex medical data into clear, actionable insights through AI-powered summaries and conversational guidance.
The Problem
Why health records feel overwhelming
For most non-medical users, health records are not just data—they are high-stakes, emotionally loaded information scattered across systems and filled with jargon. PocketCare focuses on reducing that cognitive and emotional load.
- Records are fragmented across multiple hospitals, clinics, and portals.
- Medical terminology creates anxiety and confusion at the moment of reading.
- Raw lab values are shown without clear context or plain-language meaning.
- Most tools stop at “showing” data instead of guiding next steps.
Goals & Design Principles
Reduce Cognitive Overload
Simplify complex information into digestible insights
Build Trust
Transparent, clear communication to establish user confidence
Explain Health Data
Translate medical jargon into plain, understandable language
Design Responsibly with AI
Ethical AI implementation with human oversight
What we learned
A concise view of how people read health data, where they get stuck, and the design moves that make them feel confident and guided.
How we researched
Quick qualitative loop: stakeholder talks, walkthroughs of current record tools, and domain deep-dives to surface emotional/cognitive friction—not just UI bugs.
What we observed
- People scan first—dates, status, “normal/abnormal” cues.
- Medical terms spike anxiety and mental load.
- Raw data is skipped; users want plain-language meaning.
- Visual clarity drives trust; messy layouts break it.
- Guidance beats data dumps; users want “what now?”.
What we designed for
- Make key signals scannable (dates, status, trends).
- Lead with plain language; show medical terms second.
- Provide guided, actionable summaries—not just access.
- Keep UI calm and consistent to reinforce trust.
- Surface only what’s relevant now; depth stays available.
User Personas
Understanding the diverse people who interact with health data—their needs, frustrations, and motivations.
Information Architecture
Wireframes
Hi-Fidelity Design
Design System
Outcome & Impact
Before
- ✗ Fragmented medical records
- ✗ Complex medical jargon
- ✗ No contextual guidance
- ✗ High user anxiety
After
- ✓ Unified health dashboard
- ✓ Plain-language summaries
- ✓ AI-powered guidance
- ✓ Increased user confidence
Key Outcomes
- Improved clarity in health data presentation
- Increased user confidence in understanding medical information
- Higher engagement with health records
- Reduced anxiety through clear communication
Learnings & Reflection
Healthcare UX requires extra empathy
Designing for health anxiety means every word, color, and interaction must be carefully considered to reduce stress and build trust.
AI is a tool, not a replacement
The most effective AI implementations provide guidance while maintaining human oversight and clear boundaries on medical advice.
Clarity over cleverness
In healthcare, straightforward communication and clear visual hierarchy are more valuable than creative design patterns.
