Case study•10 min
Automating 85% of prior-auth requests at ScaleHealth.
SO
AUTHOR
ScaleHealth Ops
READ TIME
10 min

How Healytix Conductor agents handles insurance follow-ups autonomously with 99.8% semantic accuracy.
Introduction
In the rapidly evolving landscape of clinical AI, the bottleneck is rarely the model itself. Instead, it's the friction between static patient records and the dynamic logic required for real-time decision support. This case study explores how Healytix bridges that gap.
Key Takeaways
- 1Standardizing on FHIR R4/R5 is a prerequisite for clinical agent scale.
- 2Grounding LLMs in patient context reduces hallucination rates by 94%.
- 3Humans must remain in the loop for every chart-modifying action.
Technical Architecture
Every case study we publish is backed by the Healytix Logic Spine. This ensures that the insights discussed here aren't just theoretical — they are executable across any standard hospital infrastructure.