Designing an AI-powered Recommendation System that turns health reports into personalized action
Diagnostics • consumer app
A unified upload experience for lab reports, prescriptions, and scans
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Context
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After successfully helping users understand their lab reports through health insights, we noticed something interesting.
Users appreciated knowing what was wrong, but they still struggled with the most important question:
"What should I do now?"
Reading insights doesn't improve health.
Taking action does.
This realization led us to design the second phase of Health Insights Hub—a recommendation system capable of converting medical insights into personalized health plans.
Unlike traditional recommendation engines built using rigid medical rules, our goal was to build a scalable AI-assisted recommendation framework that could continuously evolve with new medical knowledge, user preferences, and healthcare services.
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The Problem
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Initially, recommendations were generated through a rule engine.While medically correct, the experience had several limitations. Recommendations looked generic.
Every user received almost identical advice. There was no explanation of why a recommendation appeared.
Adding new recommendation pathways required engineering work and medical validation every single time.
Most importantly, recommendations felt disconnected from the user's personal health story.
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Approach
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We interviewed 35 users who had recently viewed their AI-generated health insights.
The objective was to understand how people perceived recommendations after understanding their reports.
The findings completely changed our direction.
Supplements were viewed as medical interventions rather than informational suggestions.
This meant recommendations required a stronger trust layer.
Doctor validation became equally important as AI intelligence.
Users preferred:
- visual guidance
- structured plans
- simple checklistslear priorities
Research showed recommendations needed better information architecture, not more information.
This suggested that behavior change should become the first recommendation layer before supplements or medications.
Users repeatedly asked:
"Why am I seeing this recommendation?"
We realized personalization wasn't just about generating different advice.
It was about making users understand why that advice belonged to them.
03
Defining the Design Challenge
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We weren't designing a recommendation page. We were designing a recommendation platform — one that had to satisfy three competing constraints at once.
Every design decision lived at the intersection of medical accuracy, personal relevance, and product scalability. Lean too far toward medical correctness and recommendations felt generic. Over-index on personalization and you risk unsafe advice. Optimize for product reuse and you lose the human feel.
The framework above became our decision filter. If a design didn't serve all three, it didn't ship.
04
Existing Approach
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The conventional healthcare approach looked like this:
Medical team creates protocols
Engineering builds rule engine
Design creates screens
Recommendation appears
Every new recommendation category required rebuilding logic. This process took months. It also limited experimentation.
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Reframing the Problem
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Instead of asking
"How do we generate recommendations?"
we asked
"What are recommendations fundamentally made of?"
That question changed the project.
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Breaking Recommendations into Building Blocks
04
We analyzed hundreds of recommendation examples across medicine, nutrition, preventive care, and consumer health.
Instead of treating recommendations as paragraphs of text, we decomposed them into reusable UI patterns.
Each pattern became a reusable design component. We called these our recommendation "lego blocks."
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Teaching AI the Design System
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Rather than asking AI to invent interfaces, we taught it how each component worked.
Every block contained metadata describing
purpose
interaction
medical intent
constraints
supported content
hierarchy
when it should appear
when it should never appear
Instead of generating UI,
AI selected and assembled existing product components.
This dramatically improved consistency while keeping recommendations personalized.
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Expanding Medical Possibilities
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Research showed users had different mental models.
Some wanted
"Tell me the single most important thing."
Others wanted
"Give me a complete improvement plan."
Others preferred
"Only show me diet."
Instead of forcing one journey, we designed multiple recommendation pathways from the same recommendation engine.
Users could explore recommendations by
overall priority
supplements
diet
lifestyle
tests
doctor consultation
The recommendation engine adapted presentation without changing underlying medical logic.
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Designing for Trust
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The biggest product challenge wasn't personalization.
It was trust.
Research consistently showed users trusted doctors significantly more than AI for higher-risk decisions.
We redesigned recommendation hierarchy around trust.
Lower-risk interventions like diet and lifestyle appeared first.
Supplements were paired with doctor consultation when appropriate.
High-risk biomarkers surfaced stronger clinical recommendations.
Recommendations also explained why they appeared, connecting every suggestion back to specific biomarkers and report findings.
This shifted recommendations from "AI advice" toward "health guidance."
Trust hierarchy
Users are more comfortable acting on low-risk suggestions. The system surfaces those first and gates higher-risk actions behind doctor consultation.
Design implication
The AI doesn't just select what to recommend — it determines how to present it. Diet cards are actionable with one tap. Supplement cards always pair with a "Consult your doctor" prompt. Clinical suggestions link to the biomarker detail page, never to a purchase flow.
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Outcome
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The project fundamentally changed how recommendations were generated inside Health Insights Hub.
Instead of relying solely on hardcoded medical rules, we created an AI-assisted recommendation framework that combined structured medical knowledge with reusable product components.
The same recommendation engine could now power
Full recommendation pages
Biomarker pages
Insight cards
Future follow-up journeys
Historical health tracking
Doctor consultation experiences
Most importantly, the architecture became extensible.
Adding new recommendation categories no longer required redesigning the entire product.
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Key Design Contributions
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Led end-to-end product design for AI-powered recommendations.
Conducted synthesis of qualitative user research to identify trust and personalization gaps.
Defined a modular recommendation design system reusable across multiple health surfaces.
Partnered with AI engineers to establish a component-driven prompting strategy instead of free-form generation.
Designed contextual recommendation experiences embedded directly within biomarker detail pages.
Created scalable interaction patterns that balanced AI flexibility with medical safety.