Case Study — Healthcare Forms Platform

Built a product analytics system for healthcare. Zero PHI exposure. 68 safe data fields across 10 tables.

A HIPAA-compliant forms platform needed analytics that could predict churn, track adoption, and measure revenue — without touching patient data.

Safe data fields mapped

Analytics workflows designed

15+

Churn prediction signals

$12K–$21K

Annual labor savings per practice

Before.

Healthcare analytics is hard because the data that makes analytics powerful — names, dates, medical records — is exactly the data you can't touch under HIPAA. A healthcare forms platform had 2,700+ clinics on their platform but couldn't answer basic questions: Which clinics are at risk of churning? Which features drive retention? What does a healthy engagement pattern look like?

The platform had data spread across 10+ database tables, but no one had mapped which fields were safe to use for analytics and which contained protected health information. Their previous analytics tool captured potential PHI that created compliance risk. The product roadmap was based on assumptions, not evidence.

The Situation

No clinic-level engagement visibility
Churn detected only when clinics cancelled — no early warning
Feature adoption unknown — product roadmap based on assumptions
Analytics tools (Mixpanel) captured potential PHI that created compliance risk

What we did.

A systematic approach: audit every data field for HIPAA compliance, design analytics workflows that never touch PHI, and build the architecture to make it repeatable.

Step 1 — Data Audit

Cataloged every data field across 10+ database tables. Classified each as safe, restricted, or prohibited under HIPAA. Identified 68 fields that could power analytics without PHI exposure.

Step 2 — Churn Prediction Model Design

Designed a 15-signal early warning system: active doctors (30/60/90-day cohorts), appointment volume trends, form completion rates, login frequency, invoice aging, and payment collection metrics.

Step 3 — Analytics Workflow Design

Built 5 workflows: clinic churn prediction, field-level form abandonment analysis, doctor productivity & capacity analytics, revenue & payment analytics, and feature adoption tracking by role.

Step 4 — ETL Architecture

Designed a separate analytics database with HIPAA-compliant hashing. Patient identifiers never enter the analytics pipeline. Python implementation with automated extraction.

Step 5 — Compliance Documentation

Created audit trail specifications, security checklist, and data flow diagrams showing exactly which fields move where — ready for compliance review.

After.

Safe data fields across 10+ tables, zero PHI exposure

Analytics workflows — churn, abandonment, productivity, revenue, adoption

15+

Churn signals — early warning 30–60 days before cancellation

$12K–$21K/yr

Labor savings per practice from form automation insights

PHI fields in analytics pipeline — full HIPAA compliance

2,700+

Clinics can now be segmented by engagement, revenue, and risk

What you can do now.

Your product team can see which clinics are healthy and which are at risk — 30 to 60 days before they cancel. Churn prevention moves from reactive to predictive.

Your compliance team has documentation showing exactly which data fields are used, how they're hashed, and why patient identifiers never enter the analytics pipeline.

Your growth team can segment 2,700+ clinics by engagement pattern, revenue tier, and feature adoption — enabling targeted retention campaigns for the first time.

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Jake McMahon

ProductQuant

10 years building growth systems for B2B SaaS companies at $1M–$50M ARR. BSc Behavioural Psychology, MSc Data Science. This engagement required navigating HIPAA compliance, legacy data architecture, and a simultaneous product launch — the kind of work where the strategy matters as much as the execution.

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