Research•18 pages
Evaluating LLM calibration in clinical decision support.
HR
AUTHOR
Healytix Research
READ TIME
18 pages

Whitepaper on how Healytix measures model 'confidence' before routing a suggestion to a physician's dashboard.
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 research 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 research 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.