Bio6 Clinic Platform
Build a compliant, multi-tenant clinic SaaS that scales without breaking clinical workflows.
Problem
Clinic operators were managing patients across spreadsheets, disconnected portals, and paper forms. Onboarding a new clinic required weeks of manual configuration, and there was no unified way to enforce compliance workflows across tenants. The fragmented tooling created risk, slowed growth, and eroded trust with clinical staff.
Details
Tools
Highlights
This project began as a platform redesign but evolved into a full systems architecture engagement. The freedom to build from the ground up allowed us to establish patterns that would scale across dozens of tenants.
CHAPTER 1
Discovery
POWERED BY AI (LOW CONFIDENCE)
Understanding the clinical context
The core challenge was building a single platform that could flex across specialties — from physiotherapy to dermatology — without fragmenting the experience. We conducted interviews with 8 clinic operators and mapped their existing workflows to identify shared patterns and specialty-specific divergences.
- Operators spent 40% of their day on manual data entry across disconnected systems
- Compliance workflows varied by specialty but shared a common approval structure
CHAPTER 2
Architecture
Designing for multi-tenancy from day one
I designed a token-based UI system where each tenant could apply brand overrides without touching core components. Feature flags were surfaced as first-class UI controls, letting clinic admins toggle modules independently of engineering deploys.
CHAPTER 3
Delivery
Shipping a modular component library
The final system shipped with 80+ components across scheduling, billing, and compliance surfaces. Every component was built with accessibility-first constraints and documented with usage guidelines for the engineering team.
Outcomes
12+
Clinic tenants onboarded
40%
Reduction in onboarding time
98%
HIPAA workflow compliance
Retrospective
Lessons
The biggest unlock was treating the design system as a product in itself — with versioning, changelogs, and adoption metrics — rather than a by-product of feature work.
Tradeoffs
We traded feature breadth for depth on core scheduling flows. Some specialty modules had to wait for v2, but the core experience was significantly more stable.