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<p>Few adult patients with cancer enroll in oncology clinical trials. A rate-limiting step to trial enrollment is prescreening, involving clinical research staff manually abstracting unstructured health records to identify patients who meet eligibility criteria. Prescreening is time-consuming, labor-intensive, and prone to human error, resulting in under-identification of eligible patients. Neurosymbolic AI language models may approximate or improve the accuracy of prescreening through automated abstraction of enrollment criteria from longitudinal unstructured patient charts. We conduct a randomized noninferiority trial using retrospectively collected clinical charts to compare the accuracy and efficiency of prescreening by trained research staff alone (Human-alone) vs. augmented with a pre-trained language model (Human+AI), among a cohort of 355 patients with non-small cell lung or colorectal cancer. Sample size is determined from analyses of a preliminary dataset as well as a prespecified, interim dataset of 74 charts. Chart-level accuracy, the primary endpoint of Human+AI prescreening is noninferior and superior to Human-alone (76.5% vs. 71.1%). However, efficiency is unchanged with similar average time per chart review, the secondary endpoint, (37.4 vs. 37.8 min). AI-assisted abstraction most improves accuracy for biomarker, staging, and response criteria. Performance is limited in some domains due to automation bias. Although improvements are modest, this large randomized trial evaluating a human-AI framework for oncology prescreening shows that AI language models can approximate and augment human-driven prescreening to enhance identification of trial-eligible patients, potentially increasing enrollment. The trial is registered on ClinicialTrials.gov (NCT06561217).</p>

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Human-AI teaming to improve accuracy and efficiency of eligibility criteria prescreening for oncology trials: a randomized evaluation trial using retrospective electronic health records

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月3日

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  • Ravi B. Parikh,
  • Likhitha Kolla,
  • Elizabeth A. Beothy,
  • William J. Ferrell,
  • Brenda Laventure,
  • Matthew Guido,
  • Anthony Girard,
  • Yang Li,
  • Khaled Essam Mahmoud Dosoky,
  • Karim Tarabishy,
  • Parth S. Patel,
  • Ayana Andalcio,
  • Kristin Maloney,
  • Jose Ulises Mena,
  • Wael Salloum,
  • Jinbo Chen,
  • Ezekiel J. Emanuel

摘要

Few adult patients with cancer enroll in oncology clinical trials. A rate-limiting step to trial enrollment is prescreening, involving clinical research staff manually abstracting unstructured health records to identify patients who meet eligibility criteria. Prescreening is time-consuming, labor-intensive, and prone to human error, resulting in under-identification of eligible patients. Neurosymbolic AI language models may approximate or improve the accuracy of prescreening through automated abstraction of enrollment criteria from longitudinal unstructured patient charts. We conduct a randomized noninferiority trial using retrospectively collected clinical charts to compare the accuracy and efficiency of prescreening by trained research staff alone (Human-alone) vs. augmented with a pre-trained language model (Human+AI), among a cohort of 355 patients with non-small cell lung or colorectal cancer. Sample size is determined from analyses of a preliminary dataset as well as a prespecified, interim dataset of 74 charts. Chart-level accuracy, the primary endpoint of Human+AI prescreening is noninferior and superior to Human-alone (76.5% vs. 71.1%). However, efficiency is unchanged with similar average time per chart review, the secondary endpoint, (37.4 vs. 37.8 min). AI-assisted abstraction most improves accuracy for biomarker, staging, and response criteria. Performance is limited in some domains due to automation bias. Although improvements are modest, this large randomized trial evaluating a human-AI framework for oncology prescreening shows that AI language models can approximate and augment human-driven prescreening to enhance identification of trial-eligible patients, potentially increasing enrollment. The trial is registered on ClinicialTrials.gov (NCT06561217).