The Variables Look Neutral. The Pattern Isn’t.
AequiPath — FHIR Audit & Scheduling Parity Platform
AequiPath watches four ordinary-looking fields in Epic’s prior-authorization and scheduling data, and flags when they quietly reproduce discrimination no one coded in.
Purpose, Mission & Values
Fairer Pathways. Healthier Communities.
Our Mission
To advance systemic fairness, structural transparency, and algorithmic governance across healthcare infrastructure.
Our Vision
A healthcare system where every person, in every community, has equitable access to timely, high-quality care, powered by trustworthy technology.
Integrity
Do what is right, always.
Service
People over profit.
Justice
Fairness in every decision.
Equity
Remove barriers. Expand access.
Excellence
Build better. Go further.
Evidence-Based
Use data. Drive lasting impact.
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The Problem
Neutral-Looking Data, Discriminatory Pattern
Prior authorization queues and scheduling systems routinely use claim density, zip code, Medicaid coverage, and missed-appointment history to decide who gets expedited and who waits. None of these variables mention race or income. All four are well-documented statistical proxies for them.
A prioritization rule built on these inputs can produce a discriminatory outcome without a single protected characteristic ever appearing in the code, which is exactly what makes it hard to catch, and easy to defend as “just operations.”
What AequiPath Does
Two Layers, One Always Watching, One In the Room
Retrospective Audit
Runs continuously against Epic’s prior-authorization and scheduling data, risk-adjusted against clinical acuity, so it isolates the part of any gap acuity can’t explain.
Real-Time Flag
Sits alongside a live prior-authorization decision and flags it for human review the moment it lands in a known disparity pattern, before the patient feels the delay.
See It In Action
Inside the AequiPath Dashboard
One-click audits, live disparity-ratio trend lines, and auto-generated disclosure packages, built for the compliance and quality teams who have to act on what it finds.
How It Integrates With Epic
Built Entirely on Epic’s Published APIs
AequiPath runs on Epic’s Da Vinci and native FHIR APIs, nothing proprietary, nothing that requires custom Epic configuration.
52 APIs in scope: 43 incoming APIs that read or subscribe to Epic data, and 9 outgoing services that return review flags and documentation. The audit itself is read-only: it never submits claims or books appointments.
No protected-class attribute is ever used as a model input, only claims, coverage, and scheduling data Epic already exposes.
Who’s Behind This
Built by a Former State Health-Policy Leader
January Montaño is the founder of Aequitas Technologies, Inc. She served as Deputy Director of Sourcing on Colorado’s COVID-19 Innovation Response Team, where she helped secure 27.8 million PPE items internationally, and later helped architect equity-driven policy for the 1.3 million members of Health First Colorado, the state’s Medicaid program. She has testified before the Colorado General Assembly on algorithmic bias in AI systems.
Why Now
Two Deadlines, One Date
The federal CMS Interoperability and Prior Authorization Final Rule requires health plans to run live FHIR prior-authorization APIs and supply specific denial reasons. Colorado’s HB 26-1139, governing AI use in health insurance coverage decisions, takes effect the same day.
How It Works
From Sandbox to Pilot in 4 Clear Steps
Discovery Call
A 30-minute consultation to understand your Epic build and current prior-auth and scheduling workflows.
Sandbox Proof-of-Concept
AequiPath runs against sandbox data to validate the API connections and the disparity model.
Pilot Validation
Runs against your live data with your compliance team, producing the first real disparity report.
Go Live & Scale
Full deployment with ongoing monitoring, reporting, and Aequitas Technologies as your engineering partner.
Ready to See What Your Data Shows?
AequiPath is built for a health plan, hospital system, or Medicaid MCO ready to see what its own prior-authorization and scheduling data actually shows.
Request a Demo → january@aequipath.com