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AI Health Companion
Behind the intelligence layer
Ruta Health
The public case study shows what we built. This page shows how we thought about the system underneath, the decisions behind the AI experience, not the AI itself.
PROTECTED
NDA
This case study focuses on how the experience was designed around the intelligence, not the underlying models or logic
The system is designed with HIPAA-aligned principles, including secure storage, access control, and auditability. Full HIPAA certification requires third-party auditing beyond the scope of this capstone, but the architecture is built to support it.
Health data is personal. We treated it that way.
THE DESIGN QUESTION
How do you design trust into something that thinks for you?
The AI can find patterns.
The real question is whether a woman, after being dismissed by doctors, would trust it.
The system was designed around building that trust.
The data is messy
Users log more on good days. Gaps are not random, they are signal.
The system had to work with inconsistency, not against it.
The user is exhausted
On the days users most need insight, they have the least energy to process it.
Every output had to work in a 5 second glance.
The stakes are personal
A wrong suggestion doesn't just waste time, it breaks trust with someone
who's already been let down by the medical system.
HOW THE EXPERIENCE FLOWS
From what a user shares to what they see back.
Literature, competitors, and real women navigating chronic conditions every day.
1
User shares something
Through chat, a weekly assessment, or daily logs. Structured and unstructured. Some days detailed, some days a single tap. The system accepts all of it.
2
The system looks for connections
Across sessions, across weeks, across symptom types. It connects what was logged today with what happened last Tuesday. The specific approach is proprietary but the goal is simple: find the patterns users cannot see on their own.
3
Before anything reaches the user, it gets checked
Every insight is attached to a source. Every pattern shows a confidence level 'emerging' or 'consistent,' never a percentage out of context. If it cannot be explained in one sentence with a citation, it does not appear.
4
The user sees a narrative, not a data dump
'Joint pain tends to peak 1–2 days after stressful periods.' Not a chart. Not a table.
A sentence they can take to their doctor. With a source link underneath and a suggested next step beside it.
If we cannot explain it in one human sentence with a source, it doesn't ship.
HOW WE HANDLE DATA BEFORE IT REACHES THE AI
The AI never sees who the user is.
Before any user input reaches the AI layer, it goes through a de-identification process names, locations, and personally identifiable information are stripped or anonymised.
The AI only sees what was reported, never who reported it.
HIPAA-ALIGNED DESIGN PRINCIPLES
Encrypted data
In transit and at rest, no exceptions
Role-based access
Every data interaction logged
Audit trails
Users understood next steps (improvement area)
User-controlled deletion
Remove everything with one action
THREE BOUNDARIES WE SET EARLY AND NEVER MOVED
No free memory storage
The AI does not retain conversation history freely. Every retention has a safeguard and a purpose.
Outputs as insights, not diagnosis
Every response is framed as a pattern to explore, never a condition to accept.
No data reuse without consent
Health data is never reused for model training without explicit, informed user consent.
WHAT WE DESIGNED - THE TRANSLATION LAYER
The space between what the system knows and what the user sees.
This is the layer that connects system output to human trust. The system can calculate patterns. The challenge is making sure a tired, skeptical, medically dismissed woman would actually trust it.
WHAT THE SYSTEM PRODUCES
{ "pattern": "stress→pain", "confidence": 0.72, "window": 14, "drivers": ["stress", "sleep"]}
Accurate. Unreadable. Frightening.
WHAT THE SYSTEM SEES
PATTERN IDENTIFIED
Joint pain tends to peak 1–2 days after stressful periods. This connection has shown up consistently over the past two weeks.
Source: Stanford Medicine, 2023
Same data. Made human. Always sourced.
9:41
Guide
AI companion
New symptoms
I’m sorry you’re dealing with new symptoms. Let’s figure this out.
What new symptoms are you experiencing?”
I’ve been having joint pain and hair loss.
When did these symptoms begin?
About 3 weeks ago.
Are the symptoms constant or do they come and go?
Mostly constant
Any recent changes medication, stress, diet, sleep, illness?
Stress has been high
Your joint pain and hair fall may be linked to stress-related inflammation.
The American Psychological Association notes chronic stress can impact immune regulation. [link]
Studies in the Journal of Autoimmunity show inflammatory markers may increase during prolonged stress exposure. [link]
Telogen effluvium (stress-related hair shedding) can occur several weeks after high-stress periods. [link]
After tracking my stress and adding light stretching......
(Source)
After tracking my stress and adding light stretching......
(Source)
Would you like to log this issue and review it later?
Yes, log it
No, not now
Type or speak to the mic
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Every insight shows its work. Sources linked, not hidden.
Three tensions we held
Accurate but not overconfident
The system could be right and still feel unsafe. Showing 72% confidence as a raw number made users anxious. Showing it as 'emerging' made them curious. Same data, different trust.
Helpful but not prescriptive
Users wanted guidance, not instructions. Every output was designed as an observation
'this connection has shown up' - never a conclusion. Every next step was a suggestion, never a directive.
Present but not intrusive
On days with the least energy, the system needed to communicate the most. Every insight had to work in a 5-second glance. Detail lived one tap away, never on the surface.
What we tested the Intelligence Layer for
Did users understand it?
Could they explain what a pattern meant in their own words without rereading?
92% said yes
Did users trust it?
Would they act on guidance, or verify it somewhere else first?
Sources reduced cross-checking
Did it feel actionable?
Were next steps doable on a hard day, not just a good one?
Partial - area for improvement
It helped me understand what might be happening, without telling me what is wrong.
- Usability session, participant 3
“
WHAT THIS PROJECT TAUGHT ME
The hardest design problem isn't the AI.
It's the moment between the AI and the person.
A single word in a chat bubble can reassure or push someone away. A confidence bar can help explain something or feel like too much. A suggestion can feel supportive or like judgment.
Designing that space between what a system calculates and what a person can take in is the kind of work I want to keep doing.
Thanks for scrolling.
Let's design something
worth coming back to.
Open to full-time product design roles.
Also happy to talk ideas, design systems, or anything in between.
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