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Dalbir Rana
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Healthcare · AI knowledge base

Healthcare knowledge base

A healthcare software support team was answering the same tickets over and over, while past solutions sat locked in closed tickets. I designed an AI-powered knowledge base that turns resolved tickets into trusted, searchable articles — with a human always in the loop.

ClientHealthcare SaaS
RoleProduct Designer
URL
Healthcare knowledge base

Overview

The answer already exists — in a closed ticket nobody can find.

This is an AI-powered knowledge base for a healthcare software support team handling thousands of tickets across a dozen medical products. Every resolved ticket held a real answer, but it stayed locked in a closed case. I designed a platform that turns those tickets into structured, searchable articles — with AI drafting the first version and support experts reviewing before anything publishes.

Challenge

The hard part

A knowledge base lives or dies on trust. In healthcare support, a stale or wrong answer isn’t just annoying — it can misguide a clinic. AI could draft articles fast, but nothing could publish unchecked. The challenge was a system fast enough to keep up with ticket volume, yet safe enough that a human expert signs off on every article, with duplicates caught before they multiply.

Healthcare knowledge base — secure sign-in
Knowledge base dashboard — article health and review pipeline

Research & Insights

What the research told me

Support-team interviewsTicket analysisWorkflow mappingContent auditUsability testing
RepeatSame ticketsAgents kept solving problems that had already been solved — the answers were just trapped in closed cases.

TrustBefore speedIn healthcare, no one would use a knowledge base unless an expert had verified the answer first.

DupesThe real riskSimilar tickets could spawn conflicting articles, so duplicate detection had to happen before publishing.

Process

How I worked

01

Learn the support workflow

I interviewed agents and mapped how tickets get resolved to see where knowledge was being lost.

02

Design the AI-to-human flow

I shaped how AI drafts an article from a ticket and hands it to an expert for review.

03

Build in trust signals

Confidence scores, similarity checks, and validation states let reviewers approve with confidence.

04

Make it findable

A clean knowledge base with search, categories, and an AI assistant so answers surface fast.

Intelligent ticket processing — upload and auto-generate articles
Article editor — AI draft with validation and suggestions
Article review — confidence, source tickets and similar articles

Wireframes & Iterations

From rough to right

I iterated on the review and editor layouts — how to show AI confidence, similar-article matches, and validation checks without burying the article itself. Small, well-placed trust signals let a reviewer approve or reject in seconds. The design system kept every screen consistent across the platform.

Knowledge base design system — colour, typography and core components

Final UI

The shipped design

The platform ships as an AI-powered knowledge base with a human in the loop. AI drafts articles from resolved tickets, experts review with full context, and a clean, searchable library — plus an AI assistant — makes answers easy to find across every medical product.

  • A searchable, filterable knowledge base across all products
  • An AI assistant for natural-language answers
  • Analytics on usage, search behaviour and reuse impact
  • Admin controls for AI thresholds, categories, users and access
Knowledge base — searchable published articles
Published article view with source tickets and feedback
AI assistant — natural-language answers from the knowledge base
Analytics and insights — views, searches and reuse trends
Admin panel — AI processing controls and category management
User management — roles and access across the platform

Results

The impact

1,102
Articles published
Resolved tickets turned into trusted, searchable knowledge.
71%
Self-serve resolution
More answers found without opening a new ticket.
182h
Saved for authors
AI drafting plus reuse cut repetitive writing across the team.
The AI did the heavy lifting, but the human review is what earned trust. Once experts controlled what published, the team leaned on the knowledge base instead of re-solving the same tickets.