Evaluating clinical trial inclusion/exclusion criteria from claims using generative artificial intelligence
Abstract
e13566 Background: Increasing enrollment in oncology clinical trials (CTs) requires identification of trial-eligible patients from routine health data. However, identification of CT-eligible patients is limited by inadequate access to EHR and its relevant clinical features to evaluate inclusion/exclusion (I/E) criteria in large populations. Artificial Intelligence (AI) applied to large-scale administrative claims may accelerate pre-screening of large patient cohorts against common I/E criteria related to stage and performance status (PS). Methods: This retrospective cohort study included 92,895 adult patients from ConcertAI’s real-world data: linked claims and deeply curated de-identified EHRs sourced from data-sharing agreements with 900+ community oncology clinics in the US. Patients had solid malignancies and an oncology encounter between January 6, 2014, and August 12, 2022. Patients with ≥1 claim record within one week of a PS entry and ≥25 claims within three years of a clinical encounter were included. We trained a Generative AI Transformer Model to predict good PS (ECOG 0-1 or Karnofsky 70-100) and stage IV disease at baseline, both of which are common inclusion criteria for therapeutic CTs in advanced malignancies. Model features included 7,745 timestamped claim codes and 8 time-independent demographic and socioeconomic variables. A state-of-the-art Generative AI Pre-trained Transformer Model was fine-tuned via compute-efficient transfer-learning to obtain…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 92,895 participants or records.
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