Regenix - Healthcare System Modernization for Regenix
Rishi Bajpai
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Posted Jan 19, 2026
Modernized a healthcare system for Regenix without disrupting patient care.
Regenix
Modernizing a decades-old healthcare system without disrupting patient care
The problem I started with
Regenix has been treating natural hair loss in the US for more than three decades.
The business worked.
The system behind it did not.
A legacy FileMaker setup was holding together patient data, treatments, subscriptions, inventory, and reporting. Staff had used it for decades. Workflows were deeply manual. Reporting was slow. Scaling was nearly impossible.
At the same time:
Orders were coming from multiple channels
Subscriptions were growing
Compliance requirements were strict
Downtime was not an option because this involved real patient care
The challenge was not just rebuilding software.
It was replacing the nervous system of a live healthcare business without breaking it.
My role
I worked as a full stack developer and patient workflow architect, owning the system end to end.
My responsibility went beyond code:
Redesigning patient and treatment workflows
Translating decades of tribal knowledge into software
Ensuring compliance without killing usability
Migrating without disrupting day to day operations
This was as much systems thinking as it was engineering.
Design philosophy
I followed three non negotiable principles while building Regenix.
1. Continuity of care comes before clean architecture
This system could not fail quietly.
I designed the migration around:
Parallel systems running side by side
Phased cutovers instead of big switches
Rollback paths for every major change
The goal was simple.
Patients should never notice the transition.
2. Legacy workflows deserve respect
Staff had been trained on the old system for decades.
Instead of forcing abrupt change, I designed:
Familiar interaction patterns
Gradual workflow upgrades
Training driven adoption instead of shock transitions
Modern software should feel empowering, not alienating.
3. Healthcare data must be structured for decisions
Data already existed. It was just trapped.
I redesigned the system so:
Patient profiles carried 12 plus structured parameters
Treatments followed a clear three stage digital workflow
Reports updated in real time instead of manual exports
Clarity replaced guesswork.
Key modules I designed and built
Patient and client management
At the center of the system.
Each patient profile unified:
Medical data
Treatment history
Orders and subscriptions
Microscopic hair analysis records
Communication history
Everything lived in one traceable place.
Treatment management workflow
Previously paper based.
I digitized the full three stage treatment process so:
Progress was visible
Errors were reduced
Specialists could focus on care instead of paperwork
This alone improved productivity significantly.
Multi channel order and fulfillment system
Orders came from direct sales, Shopify, Walmart, and Amazon.
I built a unified order engine that:
Syncs orders automatically
Handles different fulfillment paths
Keeps inventory accurate across channels
Manual reconciliation was removed entirely.
Subscription and formula management
Subscriptions were a major growth driver but also a major failure point.
I designed:
An intelligent recurring subscription engine
Formula code management for custom treatments
Automated renewals with clear audit trails
This reduced churn and errors without adding complexity.
Reporting and business intelligence
Reporting was slow and manual before.
I built:
Real time dashboards
More than twenty report types
Clear KPIs for operations, care, and revenue
Decisions could finally be made on current data.
Integrations
To support scale and reliability, the system integrates with:
Shopify, Walmart, and Amazon seller APIs
Stripe for payments
SendGrid for email communication
Twilio for SMS
AWS S3 for secure storage
Redis for performance and caching
Every integration was designed to fail gracefully without breaking patient workflows.
Tools and stack
I built the platform using:
React for the frontend
Node.js for backend services
PostgreSQL for structured healthcare data
Redis for performance critical paths
AWS for infrastructure and storage
Stripe and SendGrid for commerce and communication
Cursor, Figma, and AWS Console for rapid iteration and control
The stack was chosen for stability, not novelty.
The impact
The transformation delivered measurable results.
60 percent reduction in processing time
40 percent increase in specialist productivity
30 percent reduction in subscription churn
25 percent improvement in customer satisfaction
Full return on investment achieved within 12 months
Most importantly, patient care continued uninterrupted throughout the transition.