Everyone knew the risk. Nobody knew what to do.
Cardiocare was a Philips concept for Indian adults over 30. Heart risk feels far away, so my job was to turn it into something people act on every day.
Problem
The gap was the next action.
Prevention has no natural trigger, so the real question was where to step in. A 141-source literature review mapped the known risk drivers of coronary artery disease, and no single fact was missing.
What I found
All 18 knew their risks. None had a next action.
I ran 18 semi-structured interviews about daily routines, then coded the transcripts until the same behavior themes kept repeating.
- The themes: time, motivation, personalization, and awareness of the disease.
- The new brief: one next action inside a routine people already have.
What it cost
The finding holds for working adults; people already in cardiac care, and clinicians, are who it still has to be tested with.

What I decided
I chose a system.
SCAMPER, a checklist of ways to change an idea, opened directions from a single tracker to a full ecosystem. I shortlisted the ones the Philips design system could carry and scored them in a Pugh matrix, each against a baseline, on four design principles, feasibility, and viability.
What it cost
A matrix makes the comparison formal, but we wrote the criteria: it reduces bias, it doesn’t remove judgment.
Proof
The connected system won because it linked learning, routines, tracking, guidance, devices, and WhatsApp in one service.
See the principles and the matrix


What shipped
Learn explains the risk. Routine sets today’s action.
I tested the core loop, connect, learn, act, in moderated prototype sessions: could people understand their risk and find their next action without help? The team concept went on to win an iF Design Award in 2024.
- Connect: the wearable syncs steps, stress, and heart rate; WhatsApp pulls you back.
- Learn: a Mayo Clinic video and articles; Health Wiz answers in plain language.
- Act: Routine turns the risk and the learning into a weekly plan for that person.
My accessibility gap
The intended users included adults over 30 with heart risk, but the accessibility claim came from using the Philips design system rather than testing the experience with people at the edges.
If the target users were not represented in task-based testing, the work could claim conformance properties but not accessibility.


Looking back
Next, I set the target before testing.
Next time I’d agree the target and the minimum sample before testing, and watch task success beside the survey. I’d also close the consent gap: people linked their devices, the web app, the AI companion, and WhatsApp themselves. I’d connect them by default and ask for consent per data source.
Two things were outside the brief. Nobody had scoped interviews, so I wrote the objectives, recruited people, and ran them myself. Developers and usability specialists taught me the product, so the interface became one part of a service with hardware and clinical claims.

