Understand patterns in daily context
People need help understanding how glucose patterns relate to meals, routines, and follow up decisions.
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Diabetes management concept
GlucoGuide: Turning glucose data into safer next steps
GlucoGuide is a continuous glucose monitoring concept designed to help people understand patterns, recognize when attention may be needed, and prepare better questions for their care team without turning the product into a diagnostic authority.
AudienceB2C
DomainHealthtech
RoleProduct designer
PlatformMobile
Year2025
ScopeCase study and prototype
Reading time6 min
View the product
The full 25-screen Figma prototype spans CGM device pairing, glucose history, AI insight cards, medication logging, and the community flow.
30 Second Summary
OWNERSHIP
Product designer across the health app structure, critical screen flows, and plain language interaction patterns.
SAFETY DECISION
Use CGM data, severity tiers, and carefully limited guidance to reduce interpretation burden without presenting a diagnosis.
EVIDENCE
Four product states show device setup, daily context, dosage insight, and a predictive warning with explicit care boundaries.
Overview
A safer way to interpret daily glucose patterns.
GlucoGuide is a continuous glucose monitoring concept for people managing diabetes. I designed the product structure, critical screen flows, and plain language patterns that connect sensor setup, daily context, dosage insight, and carefully limited guidance.
Problem
More glucose data did not automatically create clarity.
People could see readings without knowing what mattered, when attention was needed, or what to discuss with a care team. The product had to reduce interpretation burden without presenting itself as a diagnostic authority.
Patient need and safety constraint
The product had to explain patterns without pretending to diagnose them.
Global diabetes prevalence provides important context for the scale of the problem, but it is not a product result. The design focus remained on understandable patterns, restrained guidance, and a clear boundary between product support and clinical care.
Avoid anxiety and false reassurance
Overconfident language, excessive alerts, and unexplained AI confidence can create anxiety or false reassurance.
Explain evidence and preserve care boundaries
Use plain language, visible evidence, restrained alert tiers, and clear escalation language while preserving the role of the care team.
Four critical product moments
From setup to a warning with a clear next step.
The sequence connects sensor setup, daily patterns, dosage context and a predicted low.
01 · Setup
Connect the device with clear setup steps.
The setup state explains why the CGM connection matters and keeps the primary action unambiguous.
02 · Daily context
Explain the day before asking users to read a chart.
Current glucose, time in range, variability, and daily context appear as a calm hierarchy rather than a wall of measurements.
03 · Dosage insight
Keep dosage history beside the trend.
The dosage view places recent insulin context beside the weekly pattern while avoiding a claim that the product determines treatment.
04 · Predictive warning
Explain the risk and the limits of the advice.
The warning names the expected timing, current context, and a clear next action without presenting the prediction as a diagnosis.
Safety boundary decision record
How should a predictive warning help without sounding like a clinician?
The monitor state is treated as a high consequence communication problem, not another notification style.
Question
How much certainty should the warning communicate?
Signal
A predicted low based on the current trend and the time window shown in the product.
My decision
Lead with plain language, make timing explicit, and place the recommended next action beside the warning.
Why
People need to understand urgency without translating a dense chart or interpreting model confidence alone.
Product consequence
The warning receives stronger hierarchy than routine insights, while the detailed chart remains available for context.
Boundary
The product can surface a pattern and encourage an appropriate follow up. It cannot diagnose, prescribe, or replace the care team.
Design Decisions
Design choices that made health data feel understandable
Use plain language by default and introduce clinical terminology only where accuracy, medication context, or care team communication requires it.
Clinical terminology or plain language throughout the interface?
The interface separates current status, trend context, and follow-up actions so a single reading never carries more certainty than the data supports.
How should alerts communicate different levels of attention?
Use three communication tiers: Informational pattern, Attention needed, and Urgent follow up.
Where should AI confidence appear?
Keep confidence visible on insight cards and connect it to the evidence behind the observation.
Trade offs and validation
Healthcare UX required restraint more than feature density.
Plain language reduces interpretation burden, while precise terminology remains available when accuracy and care communication require it.
Simplification versus medical nuance
I kept the interface calm and explicit about limits, even when a denser dashboard could have surfaced more measurements at once.
Guidance versus overconfidence
The product can surface observations and next questions, but it should not sound like a clinician or imply diagnosis or treatment authority.
Data visibility versus overwhelm
Current glucose, range, variability, and focused snapshot cards create a progressive path into deeper data rather than exposing every metric at once.
Reflection
Fewer signals support clearer decisions
Health design demands a different kind of restraint. The instinct is to surface everything available, but the patient’s goal is to make fewer, clearer decisions rather than process every available data point. The most important design decision in this project was choosing what not to show.
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