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StoryPatient experienceOutcome
K Health · Case Study

Designing Continuous
Care for Hypertension

An AI-supported chronic care experience that shifted hypertension management from isolated visits to ongoing care.

Patient measuring blood pressure at home
Role
Sole Product DesignerLed discovery, UX strategy, UI and prototyping across the full MVP.
Duration
3 monthsAn MVP designed for K Health and Cedars-Sinai.
Team
Cross-functionalProduct · Clinical · Data Science · Engineering
Scope
End to endDiscovery · UX · UI · Prototyping
Core shift

From reactive care to continuous care

The real product problem was the space between appointments. Patients were diagnosed and treated in visits, but support often disappeared once they returned home.

K Health and Cedars-Sinai piloted hypertension monitoring inside the existing K Health app as the first step toward a broader continuous-care model, with the intention of expanding ongoing monitoring to other chronic conditions.

Why hypertension
A practical condition for testing continuous care

Blood pressure can be measured at home, and regular monitoring can support earlier detection and intervention instead of waiting for the next reactive visit.

For patients
Support and clarity between appointments

Patients could understand what a reading meant, know what to do next and access care when a result required attention.

For the care system
Earlier, data-informed intervention

The program extended care beyond isolated visits and created a reusable foundation for additional chronic-care programs inside the K Health ecosystem.

My role
I led the experience end to end, from framing the care model to shaping the product details that made it usable.

I was the sole designer on the project, working closely with a Product Manager and with clinical, data and engineering partners. Before defining the experience, I reviewed existing solutions, interviewed primary care physicians and mapped the clinical and operational context. The work required system thinking across patient engagement, clinical workflows and future scalability.

DiscoveryMapped the patient journey, program goals and care-system constraints.
StrategyFramed the MVP around the moments that mattered between visits.
Interaction designDesigned the patient flows, onboarding and core reading experiences.
Cross-functional workTranslated clinical and data requirements into a coherent product experience.
Decisions

Three decisions shaped the system

01
Create a scalable home for continuous care, not only for hypertension.
02
Use conversation as the interaction model so the product could guide, adapt and grow.
03
Fit the program into the existing clinician workflow instead of creating a parallel tool.
Platform thinking

Creating a real home for ongoing care

One of the most important product moves was introducing My Health as a dedicated area in the app. It started as the home for hypertension, but was intentionally structured to become the entry point for additional longitudinal care programs over time.

My Health overview screenMy Health navigation and modular content screen
Swipe to explore
System flow

How the continuous-care loop worked

The program worked as a loop: invite, onboard, measure, interpret, guide and return to the next reading.

StartPush notification/home screen Eligible users receive a pushnotification to enroll.(Or once enrolled, reminders to login readings as medically indicated). Onboarding First time reading Within thenormalrange? No Severe (too high/low) Initiate visit CTA Visit waiting room Doctor visit Next reading Elevated Follow-up questions(Generative AI) User replies Personalized messageguiding the user tochange cadence and/orschedule a visit within x days. Yes Feedback:Great, your reported values arewithin the normal range, see youat the next reading! End
StartPush notification/home screen
Eligible users receive a push notification to enroll. Once enrolled, reminders to log readings as medically indicated.
↓
Onboarding
↓
First time reading
↓
Within the normal range?
Yes · Normal
Feedback: Great, your reported values are within the normal range, see you at the next reading!
↓
End
No · Elevated
Follow-up questions (Generative AI)
↓ User replies
Personalized message guiding the user to change cadence and/or schedule a visit within x days.
↓
Next reading
No · Severe (too high/low)
Initiate visit CTA
↓
Visit waiting room
↓
Doctor visit
↓
Next reading
The elevated and severe paths return to the next reading, continuing the care loop.
Patient experience

From invitation to
ongoing guidance

The patient journey needed to feel supportive from the first invitation and remain clear when a reading required more attention.

Enrollment

Meeting patients where they already were

Enrollment had to feel native to the existing app. Eligible patients could be invited through a push notification, while the same prompt appeared naturally inside the homepage task area, making the program visible at the right moment without creating a new destination.

Push notification inviting an eligible patient to enroll Hypertension monitoring task shown in the existing K Health home screen
Onboarding

Building confidence before the first reading

The first-time experience had to do more than explain the program. It needed to reassure patients, confirm that they had the right device and make them feel ready to begin.

Program welcome screenBlood pressure monitor guidance screenHow to measure blood pressure correctly
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Core interaction

From a single reading to ongoing guidance

The reading flow used the conversational model patients already knew from K Health. It introduced the task, offered measurement guidance, collected the values and returned an immediate, understandable response, turning a one-time input into the beginning of the next care step.

Patient reading flow screen 1Patient reading flow screen 2Patient reading flow screen 3Patient reading flow screen 4Patient reading flow screen 5
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Adaptive support

Different readings required different levels of support

Clinical and Data Science defined the medical logic. My role was to translate it into clear patient states, understandable questions and appropriate next actions, so the experience remained calm when possible and unambiguous when urgency mattered.

Elevated readings

Ask more only when the context requires it

When readings were elevated, the AI-supported conversation gathered relevant symptoms and context before guiding the patient toward the next step.

Elevated readings flow exactly as shown in the Figma case study
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Severe readings

Remove uncertainty when urgency matters

For severe readings, the experience reduced conversation and moved directly toward care, with a clear route to an immediate visit.

Severe readings flow exactly as shown in the Figma case study
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Clinician workflow

Working inside the existing clinician product

The clinician experience was designed to fit directly into the product physicians already used. That allowed them to enroll patients and review post-enrollment insights without switching to a separate destination or creating a parallel workflow.

Pre-enrollment widget inside the clinician productPost-enrollment insights inside the clinician product
Implemented experience

End-to-end walkthrough

The final flow brings the individual decisions together: program entry, onboarding, measurements, conversational guidance and ongoing monitoring.

Outcome

What happened next

The MVP launched, but the Cedars-Sinai partnership changed direction before enough time had passed to measure long-term outcomes. The project nevertheless established a practical foundation for continuous care and proved how the existing K Health ecosystem could support ongoing monitoring.

Man measuring blood pressure at home
Reflection

What this project taught me

The difficult part was not collecting a blood-pressure reading. It was creating a care loop that stayed clear for patients, respected clinical judgment and supported a real business model. The project strengthened how I balance patient needs, clinical safety, business goals and future growth.

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