Healthcare AI
A Multi-Stage Validation Framework for Trustworthy Large-scale Clinical Information Extraction using Large Language Models
This paper presents a multi-stage validation framework for LLM-based clinical information extraction, enabling rigorous assessment under weak supervision. It integrates prompt calibration, rule-based filtering, semantic grounding, judge LLM evaluation, and expert review, applied to substance use disorder (SUD) diagnoses from 919,783 clinical notes. The framework removes unsupported extractions, shows strong agreement between judge LLM and experts (Gwet's AC1=0.80), and demonstrates that LLM-extracted diagnoses predict subsequent SUD specialty care more accurately than structured data (AUC=0.80). This work enables scalable, trustworthy deployment of LLM-based clinical information extraction without extensive manual annotation.
Executive Impact: Driving Trust and Scalability in Healthcare AI
The multi-stage validation framework offers a robust solution for deploying large language models in clinical settings, overcoming challenges of scalability and trustworthiness. By integrating automated checks with targeted expert review, it ensures high-fidelity information extraction from unstructured health records, improving diagnostic accuracy and patient care pathways.
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LLM Extractions Filtered
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Judge LLM Agreement (Gwet's AC1)
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F1 Score (Judge-Evaluated)
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Predictive Validity AUC
Deep Analysis & Enterprise Applications
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Validation Framework
Pre-extraction Reliability
Internal Filtering Impact
Predictive Validity
The proposed multi-stage framework ensures trustworthiness in LLM-based information extraction by sequentially filtering, validating, and assessing clinical relevance, minimizing reliance on manual annotation.
Enterprise Process Flow
Prompt Calibration (SME Annotated)
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Rule-based Filtering
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Semantic Grounding Assessment
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Judge LLM Confirmatory Validation
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Targeted SME Review
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Predictive Validity Assessment
Zero-shot chain-of-thought prompting significantly improved extraction performance, achieving a relaxed F1 score of 0.9004 on annotated data, reflecting the model's ability to reason over clinical evidence.
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Relaxed F1 Score (Chain-of-Thought)
Rule-based filtering and semantic grounding collectively removed 14.59% of initial LLM+ extractions, effectively identifying unsupported or implausible outputs across various substance categories, enhancing precision prior to deeper validation stages.
| Substance Category | Pre-filter LLM+ Count | Flagged by Rules/Grounding | Post-filter LLM+ Count |
|---|---|---|---|
| Alcohol | 261037 | 29692 + 1708 | 229637 |
| Opioid | 63123 | 14123 + 1057 | 47943 |
| Cannabis | 71777 | 5917 + 1315 | 64545 |
| Cocaine | 65796 | 4066 + 1025 | 60705 |
LLM-extracted SUD diagnoses demonstrated superior predictive validity for subsequent engagement in SUD specialty care compared to ICD-10 diagnoses alone. In the full cohort, LLM-extracted data achieved an AUC of 0.80, surpassing ICD-10's 0.76. When combined, performance further increased to 0.84. In the narrative-only cohort (where no ICD-10 codes were present), LLM-extracted diagnoses still achieved an AUC of 0.67, compared to ICD-10's 0.50, highlighting the value of narrative data for high-uncertainty cases.
LLM vs. ICD-10 for SUD Specialty Care Prediction
LLM-extracted SUD diagnoses demonstrated superior predictive validity for subsequent engagement in SUD specialty care compared to ICD-10 diagnoses alone. In the full cohort, LLM-extracted data achieved an AUC of 0.80, surpassing ICD-10's 0.76. When combined, performance further increased to 0.84. In the narrative-only cohort (where no ICD-10 codes were present), LLM-extracted diagnoses still achieved an AUC of 0.67, compared to ICD-10's 0.50, highlighting the value of narrative data for high-uncertainty cases.
- LLM-extracted diagnoses predicted SUD specialty care with an AUC of 0.80 in the full cohort.
- Combined LLM and ICD-10 data achieved the highest predictive performance (AUC 0.84).
- In narrative-only cases, LLM-extracted data had an AUC of 0.67, significantly better than ICD-10's 0.50.
Unlock the ROI of Trustworthy AI in Healthcare
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Your AI Implementation Roadmap
Our proven phased approach ensures a smooth, effective, and ROI-driven AI integration.
Phase 1: Prompt Calibration & Initial Filtering Setup
Systematic optimization of LLM prompts using small, SME-annotated datasets and configuring initial rule-based and semantic grounding filters to reduce obvious errors.
Phase 2: Targeted Validation & Judge LLM Deployment
Implementing Judge LLM for confirmatory validation on high-uncertainty cases and targeted expert review to calibrate automated assessments.
Phase 3: Predictive Validity & System Integration
Assessing external validity by linking LLM extractions to downstream clinical outcomes and integrating the validated system into existing clinical workflows.
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