The clinician's guide to grounding AI in patient data.

The Problem: Model Hallucinations in Clinical Settings

Large Language Models (LLMs) are inherently probabilistic. In a hospital setting, probability isn't enough. When a clinician asks for the last HbA1c result, 'guessing' based on training data is a liability. Grounding is the process of tethering an LLM's response to an immutable source of truth: the Electronic Health Record (EHR).

Retrieval-Augmented Generation (RAG) Architecture

Healytix implements a multi-stage RAG pipeline. First, we vectorize the patient's longitudinal record. When a query is made, our system retrieves the most relevant FHIR resources (observations, conditions, medications) and injects them into the prompt context. This ensures the model only 'knows' what is in the patient's record.

Citations and Verifiability

Every claim made by a Healytix agent must be backed by a FHIR resource ID. We use semantic mapping to ensure that if an agent says 'The patient started Metformin in 2022', it also provides a deep link to the original MedicationRequest resource. This closes the trust loop for the clinician.

Key Takeaways

• 1
  Grounding LLMs in FHIR data minimizes clinical verification times.
• 2
  Semantic citations are mandatory for physician trust.
• 3
  RAG pipelines must be HIPAA-compliant and logically isolated.