National payer
Prior-auth triage model
We cleared a prior-authorization backlog with clinical NLP, without taking clinicians out of the loop.
6d → 36h
median turnaround
flat
override rate
Context
A national payer processing high volumes of prior-authorization requests.
The challenge
Prior authorization was a bottleneck. Requests were reviewed by hand, median turnaround ran about six days, providers were frustrated, and clinical staff were buried in documentation. Leadership wanted speed without loosening clinical judgment or compliance.
What we delivered
- A clinical NLP model that reads incoming documentation and routes each request by complexity, so straightforward cases move quickly and complex ones reach the right reviewer.
- Clinicians stay in the loop on every decision. The model adds triage, not autonomy.
- More validation, not less: every output is tested, traceable, and logged, on our NIH-developed framework.
- Integrated into existing workflows and built to SOC 2 Type II and HITRUST-aligned controls, with HIPAA and a complete audit trail.
The outcome
- Median turnaround moved from six days to under 36 hours.
- Clinician override rates held flat, so speed did not cost accuracy.
- Reviewers spend their time on the cases that actually need judgment.
AI earns its place in healthcare when it raises the bar. Here it cleared a backlog while keeping clinicians in control and every decision on the record.
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