Clinical Intent Extraction: A FHIR-Aligned Representation and the CIRCA Benchmark

arXiv:2609.29479 · cs.CL · Submitted 2026-08-24 · Read on arXiv

cs.CL

Submitted: 2026-08-24

Updated: 2026-08-24

Terminology

• Accuracy — 33× in this paper · explained in 1 episode
• Benchmark — 16× in this paper · explained in 2 episodes
• Provenance — 11× in this paper · explained in 4 episodes
• Core — 9× in this paper · explained in 2 episodes
• Consistency — 6× in this paper · explained in 3 episodes
• Arbiter — 4× in this paper · explained in 1 episode
• Benchmarking — 4× in this paper · explained in 3 episodes
• Clinical Intent Representation — 4× in this paper
• Adaptation — 3× in this paper · explained in 1 episode
• Clinical Intent Extraction — 3× in this paper
• Generalization — 3× in this paper · explained in 13 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Annotator Agreement — 2× in this paper · explained in 1 episode
• Grounding — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Self-consistency — 2× in this paper · explained in 2 episodes
• Amazon Web Services — 1× in this paper
• Data Provenance — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• HIPAA Business Associate Agreement — 1× in this paper
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Three Generations of Healthcare IT: From the Digital Record to the Computable Care Process
• PaniniQA: Enhancing Patient Education Through Interactive Question Answering
• Overview of the MEDIQA-OE 2025 Shared Task on Medical Order Extraction from Doctor-Patient Consultations
• RadGraph: Extracting Clinical Entities and Relations from Radiology Reports
• Predicting Clinical Intent from Free Text Electronic Health Records
• Extraction and Analysis of Clinically Important Follow-up Recommendations in a Large Radiology Dataset
• AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators
• ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning
• LLMs Accelerate Annotation for Medical Information Extraction
• IRC-Bench: Recognizing Entities from Contextual Cues in First-Person Reminiscences
• Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
• Self-Consistency Improves Chain of Thought Reasoning in Language Models
• Self-Refine: Iterative Refinement with Self-Feedback
• CoEval: Ranking Language Models for Custom Tasks Without Labeled Data or Trustworthy Benchmarks
• Automated Annotation with Generative AI Requires Validation
• Towards Automatic Generation of Shareable Synthetic Clinical Notes Using Neural Language Models
• Does Synthetic Data Generation of LLMs Help Clinical Text Mining?
• Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes
• Reliable Extraction of Clinical Follow-Up Instructions: A Hybrid Neural-Symbolic Pipeline

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