See how prior authorization gets faster
Banjo Health automates decisions by reading prescriber notes directly from the EHR, aligned to your specific payer criteria.
What does it take to speed up prior authorization without sacrificing accuracy?
Our platform reads the clinician's notes directly from the EHR and applies your criteria using AI. The result is a decision in minutes, not days, with less staff effort and better member outcomes.
The people behind the platform
We build, tune, and support the system that reads prescriber notes and drives automated decisions. Small team, big focus.
Founder & CEO
Alex Ngo
Alex founded Banjo Health to reduce the time-to-decision in prior authorization. Background in AI and health plan operations.
Lead Machine Learning Engineer
Priya Kaur
Priya trains the models that extract criteria from prescriber notes. Her work drives the accuracy behind every automated decision.
Real outcomes from the platform
Health plans and PBMs using Banjo Health report faster prior authorization decisions without adding staff hours.
Integration took less than two weeks. The platform started reading our provider's EHR notes and applying our criteria the same day.
James Delaney
VP of Pharmacy Services, Mid-Atlantic Health Plan
Our time-to-decision dropped by more than half. Members get answers faster, and our clinical team focuses on exceptions instead of routine reviews.
Monica Reyes
Director of Utilization Management, Regional PBM
We wanted automation that respected our existing criteria, not a black box. Banjo Health built to our rules and produced decisions we could audit.
David Kim
Chief Pharmacy Officer, National Health Network
1 prior authorization engine built for payers
Our platform replaces manual review with automated decisions drawn directly from prescriber notes.
1
EHR integration
One connection that pulls data directly from prescriber notes.
x
Claims processed
Thousands of prior authorization decisions handled automatically per month.
99%
Accuracy rate
Decisions aligned to payer-specific criteria with machine learning precision.
y
Time saved per case
Reduces manual review time from hours to minutes.