Case study10 min

Automating 85% of prior-auth requests at ScaleHealth.

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

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

  • 1
    Standardizing on FHIR R4/R5 is a prerequisite for clinical agent scale.
  • 2
    Grounding LLMs in patient context reduces hallucination rates by 94%.
  • 3
    Humans 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.

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