# Translating Evidence into Practice: Adapting TrialGPT for Real-World Clinical Trial Eligibility Screening

## TL;DR

This paper evaluates a locally deployed version of TrialGPT, an AI system that helps identify patients eligible for clinical trials using electronic health records.

## Contribution

The novel contribution is adapting and validating TrialGPT for real-world clinical trial eligibility screening in a secure, local deployment.

## Key findings

- The system achieved 81.8% sensitivity and 97.8% specificity when benchmarked against an expert-adjudicated dataset.
- Compared to manual screening, TrialGPT identified over twice as many eligible patients while maintaining equivalent specificity.

## Abstract

To evaluate the performance of a locally deployed adaptation of TrialGPT, a large language model (LLM) system for identifying trial-eligible patients from unstructured electronic health record (EHR) data.

TrialGPT was re-engineered for secure, deployment at UT Health San Antonio using a locally hosted LLM. It was optimized for real-world data needs through a longitudinal patient–encounter–note hierarchy mirroring EHR documentation. Performance was evaluated in two stages: (1) benchmarking against an expert-adjudicated gold corpus (n = 149) and (2) comparative validation against manual screening (n = 55).

Against the expert-adjudicated corpus, the system achieved 81.8% sensitivity, 97.8% specificity, and a positive predictive value of 75.0%. Compared with manual screening, it identified more than twice as many truly eligible patients (81.8% vs 36.4%) while preserving equivalent specificity.

The adapted TrialGPT framework operationalizes trial matching, translating EHR data into actionable screening intelligence for efficient, scalable clinical trial recruitment.