NRC Health helps US hospitals and health systems collect, analyze and act on the experience data of their patients and their employees, through four product suites, a business worth several hundred million dollars in revenue. When I joined, there was no design team. This page covers what came next: the team, the organization, the design system, and three use cases that show how we worked over the past two years.
SaaS · Healthcare · Patient Experience · Employee Experience · Design to Code · Management · AI · Analytics
1. Building the team
01
Assign one designer per suite
Four suites, a team of three. Each suite has its own named designer instead of a shared queue, and every product team knows who to go to.
02
Ship quick wins first
A newborn function has no credit. So the first deliveries were small, visible and fast, because that is what earns the right to take on bigger topics later.
03
Pool the cross-product layer
Design system, analytics and front-end are handled as one whole.
Done
Design system
Design system foundations: Figma library launched, adopted by all four suites and Huey
Product design
First product quick wins
Huey
Design principles defined and presented (Relevant, Inviting, Adaptive)
Analytics
Amplitude auto-capture in place
Now
Design system
Components built in Storybook by the Vietnam dev squad, then integrated phase by phase into the legacy product through the Patient Experience redesign
Product design
Patient Experience redesign
Product consistency, the basics: Unified Login, App Switcher, user management on tokens, cross-product Executive Dashboard
Huey
Cross-product rollout sequencing
Analytics
Custom tracking, product by product, and wider auto-capture
Strategy · Innovation
Product suite strategy: executive mandate for design system adoption, quarterly cross-product planning cadence, roadmap aligned with the product tenets
Next
Design system
Online design system: documentation and adoption guides, integration hackathons with developers, design review process
MCP and AI agent workflow, from a personal setup to a team capability
Product design
Quick wins on Market Insights, Consumer Insights, reporting and Rounding
Huey
General capabilities (data query, insights, actions), analytics-ready components by design
Analytics
Requirements for data bridges (Amplitude, CRM, support tools), trial of analytics add-ons
Team growth
Clear career paths (designer, senior, lead)
Monthly brown-bag lunch in rotation, annual onsite
Conference and training budget
Future
Design system
Tokens integrated in every product, a prerequisite for the rebrand
Product design
Product consistency, advanced: Explore Trends folded into Market Insights, component adoption across all products
Huey
Use cases per product: Ask Huey for Patient Experience, Market Insights report summaries, Rounding agent
Analytics
AI insights repository consolidating customer feedback
Strategy · Innovation
Brand identity and rebrand
3 to 5 day AI-athons with cross-functional teams
CX explorations
Design in service of product strategy, and integrated at its core.
The team stays shared rather than embedded: product teams call on design the way they call on any other function, but design's cross-cutting responsibilities, team cohesion and the design system chief among them, are what keep the whole product suite coherent. That structure is what makes design a driver of product and business strategy, not a downstream service.
2. The design system
Four products, several front-end stacks, one design system.
Build on Flowbite
Rather than rebuilding primitives, add a semantic layer on top, shaped around the company and its needs.
Make design tokens stack-agnostic
To cover heterogeneous environments: Angular, Vue and legacy code all consume the same tokens.
Share patterns across products
Navigation, filters and the overall page template are common to every product, which eases development on one side, and onboarding and everyday use on the other.
Make Figma and Storybook the shared source of truth
Design and engineering look at the same component at the same time, which ends the debate between what was designed and what shipped.
Experiment with AI-assisted code generation
A design-to-code workflow that lets non-technical profiles ship changes consistent with the design system, and generates code ready for developer review.
Flowbite, the open-source base of the system, extracted into tokens and 16 live components.
Three problems of a different nature: redesigning the company's most profitable product, lifting conversion on a major data-collection channel, and a strategic call on AI.
a
Redesigning the Patient Experience suite
The oldest product in the portfolio, layered with years of iterations without a sustained product strategy, and weighed down by technical debt. Hospitals rely on it every day, so the redesign could not disrupt any of their workflows.
Tackle the suite in phases rather than as one single project, each with its own scope and success criteria.
Open each phase with a round of user research, to confirm in finer detail the problems identified upstream.
Pair with a dedicated front-end developer squad based in Vietnam, which shortens the path from a design decision to production.
Keep the product usable at every step, so the work could pause or be reordered without leaving a half-redesigned suite behind.
Where it stands: phase 1 shipped, design tokens and primitive components (buttons, input fields). Phase 2 shipped, the navigation bar. Phase 3 shipped, the filter system. Tables and data visualization remain.
Visual to come
Before and after, one phase of the suite
b
The outreach email
These emails converted at 8%, which is decent, but the email looked like a scam, with no reading hierarchy and not a single basic UX rule respected.
List 101 quick wins across every product, then prioritize them. This one came out on top: the widest reach for the lowest cost of change.
Rebuild the email around a single action, instead of a wall of content.
Ship it straight through the design-to-code pipeline, with no spec in between.
Present the result to the executive committee, before and after side by side.
+24% click-through rate.
Visual to come
The email, before and after
c
Huey, one door for AI
AI features were multiplying across the portfolio, each with its own entry point and its own definition.
Bring everything under Huey: a single assistant federating the AI capabilities, extended into smart features and components shared by every suite.
Settle on three entry points and no more: an agent to query the data directly, a widget component that pushes information (summaries, insights, next logical actions...), and features embedded in the products where the work already happens (writing assistant, incident handling, ambient listening...).
Make the entry point a product decision, rather than a choice revisited from scratch with every feature.
Ambient listening, for example, raised nurse documentation from 55% to 90%.
The research behind it distilled into three principles every Huey interaction has to pass: inviting, relevant, adaptive. They became an evaluation test, then a marker and a drawer shared by every product.
A SaaS product can ship for years without ever knowing which features deserve to exist. Building product analytics with Product, Engineering and the executive committee shifted design decisions from argument to measurement.
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Back every design decision with research and A/B testing, so each change ships with a measurable hypothesis.
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Define KPIs per feature, never per release.
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Bring an Analytics Specialist into the design team, to put a data-driven practice at the heart of design.