Timeline:
4 weeks
Role:
Systems Design / UX Designer
Industry:
Figma, Claude Code,
Cystic Fibrosis (CF) is a genetic disease that causes thick, sticky mucus in the lungs, digestive system, and other organs.
Emma is a 27-year-old nurse living with Cystic Fibrosis. She takes her medication, sees her care team every three months, and by most measures, she's doing fine.
But CF has a quiet danger: lung function can decline significantly while a person feels completely normal. Emma experienced this firsthand — her lung health numbers kept dropping across appointments while she felt healthy enough to work a full nursing shift. By the time she felt the difference, significant damage had already been done.
Through interviewing Emma, I learned of three key issues that interfere with her ability to receive timely care.
Subjective Vs Objective
Her lung health can decline significantly while she feels completely fine.
Monitoring is Episodic
Pulmonary function tests are effort-dependent, quarterly, and clinic-bound. Decline happens between appointments.
Continuity of Care
Providers change but context doesn't transfer. Emma bears the burden of needing to re-explain—generic baselines don't capture her normal.
The Question
How might we turn Emma's daily health data into an early warning system —
so she can live freely and act early?
Emma needs her data—both subjective and objective—needs to be captured and measured against her personal baseline, continuously, to surface health concerns and address them before she starts feeling sick.
The Answer
A system that sees what Emma can't feel.
Emma's smart watch and at-home breathing monitor upload health data via bluetooth to the digital cloud. From there, her data is used to create a digital twin—a predictive model of and for Emma's health. Emma can view insights and predictions of her twin through her iPhone and communicate health concerns predicted by her twin directly with her entire care team.
Life with Emma's Twin
Emma's morning routine is simple: two minutes, two steps.
Step 1
Emma breathes into her monitor for 60 seconds each morning.
Step 2
A daily check-in to record her mood and stress level follows.
Subjective and objective data reveal Emma's health trends.
Data is uploaded to reveal Emma's daily health stats, trends over time, and keep a record of her check-in history.
This data becomes Emma’s digital twin — a predictive model of her health, in real time. The more it learns, the better it predicts.
The app is the window into Emma's digital twin — surfacing the gap between how she’s feeling and what her body's actually doing.
Three mornings of feeling good, but her numbers tell a different story.
Emma's twin can predict health concerns before she starts feeling sick.
Emma sees the evidence and decides to act.
When Emma's health stats drift from her baseline, an alert button appears. She can then push the notification to her entire care team.
Her care team gets the full picture — before Emma gets sick.
Emma's care team steps in early.
Following review of drift alert, Emma’s care team can:
Emma's Twin keeps Emma in the loop on next steps.
What does this mean for Emma?
Reflection
Designing for a close friend made this meaningful beyond being a school project. It pushed me to think in full ecosystems rather than single screens, and to understand where existing systems create friction rather than replace them entirely. The problems Emma described — silent health decline, care that resets every time a provider changes — aren't problems a single app can solve. But a connected ecosystem, designed to work with what already exists and fill in the gaps, is a starting point. The goal was never to add complexity to Emma's life, but to make the invisible visible, quietly, in the background.
Next Steps
