Large Language Models for Automating Clinical Trial Criteria Conversion to Observational Medical Outcomes Partnership Common Data Model Queries: Validation and Evaluation Study.

BACKGROUND

Real-world data-based feasibility assessments enhance clinical trial design, but automating eligibility criteria conversion to database queries is hindered by challenges related to ensuring high accuracy and generating clear, usable outputs.

OBJECTIVE

The aim of this study is to develop an automated system converting free-text eligibility criteria from ClinicalTrials.gov into Observational Medical Outcomes Partnership Common Data Model (OMOP CDM)-compatible Structured Query Language (SQL) queries and systematically evaluate hallucination patterns across multiple large language models (LLMs) to identify the optimal deployment strategies.

METHODS

Our system employs a three-stage preprocessing pipeline (segmentation, filtering, and simplification) achieving 58.2% token reduction while preserving clinical semantics. We compared GPT-4 concept mapping performance against USAGI using 357 clinical terms from 30 trials. For comprehensive evaluation, we analyzed 760 SQL generation attempts (19 trials×8 LLMs×5 prompting strategies) using the SynPUF (Synthetic Public Use Files) dataset and validated selected queries against National COVID Cohort Collaborative reference concept sets using Asan Medical Center's OMOP CDM database.

RESULTS

GPT-4 achieved a 48.5% concept mapping accuracy versus USAGI's 32.0% (P<.001), with domain-specific performance ranging from 72.7% (drug) to 38.3% (measurement). Surprisingly, the open-source llama3: 8b model achieved the highest effective SQL rate (75.8%) compared to GPT-4 (45.3%), attributed to lower hallucination rates (21.1% vs 33.7%). The overall hallucination rate was 32.7%, with wrong domain assignments (34.2%) and placeholder insertions (28.7%) being the most common. Clinical validation revealed mixed performance: high concordance for type 1 diabetes (Jaccard=0.81), complete failure for pregnancy (Jaccard=0.00), and minimal overlap for type 2 diabetes (Jaccard=0.03), despite perfect overlap coefficients in both diabetes cases. Moderate performance was observed for uncontrolled hypertension (Jaccard=0.18).

CONCLUSIONS

While LLMs can accelerate eligibility criteria transformation, hallucination rates of 21-50% necessitate careful model selection and validation strategies. Our findings challenge assumptions about model superiority, demonstrating that smaller, cost-effective models can outperform larger commercial alternatives. Future work should focus on hybrid approaches combining LLM capabilities with rule-based methods for handling complex clinical concepts.