Retrieval-Augmented Guardrails for AI-Drafted Patient-Portal Messages: Error Taxonomy Construction and Large-Scale Evaluation

Kari Jaaskelainen

29 Sep 2025 — 1 min read

Clinically grounded guardrails that make AI draft responses safer and more complete

As patient-portal messaging scales, clinicians need AI assistance that is accurate, empathetic, and workflow-aware. This work introduces a practical blueprint for building such guardrails. First, the authors craft a clinically grounded error ontology—spanning 5 domains and 59 fine-grained error codes—that captures omissions, inaccuracies, tone mismatches, and process missteps in AI-drafted replies. Second, they develop a Retrieval-Augmented Evaluation pipeline (RAEC) that leverages semantically similar historical message–response pairs to contextualize judgments. Third, a two-stage DSPy prompting architecture delivers scalable, hierarchical, and interpretable error detection.

Why this matters: evaluating an AI draft in isolation can miss critical context—prior patient communications, typical clinician phrasing, or institutional norms. By pulling in similar, real-world exemplars, RAEC improves the specificity and confidence of error identification, particularly in clinical completeness and workflow appropriateness.

Clinically grounded error taxonomy: 5 domains, 59 codes for precise labeling.
Retrieval augmentation: compares drafts to similar historical cases to refine judgments.
Two-stage DSPy pipeline: scalable, interpretable, and hierarchical detection.
Validated at scale: on 1,500+ messages, retrieval context boosts performance; on 100 messages, human validation shows higher concordance (50% vs. 33%) and F1 (0.500 vs. 0.256) versus baseline.

The takeaway: institution-aware, retrieval-augmented guardrails can better flag clinically meaningful issues and guide safer AI assistance—without demanding manual review of every draft. This is a clear path toward trustworthy co-writing tools that reduce clinician burden while keeping care standards front and center.

Paper: http://arxiv.org/abs/2509.22565v1
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#HealthcareAI #LLM #RAG #ClinicalNLP #Safety #Evaluation #DSPy #Guardrails

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