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

Ravi B Parikh¹, Likhitha Kolla², Elizabeth A Beothy³, William J Ferrell⁴, Brenda Laventure³, Matthew Guido⁴, Anthony Girard⁵, Yang Li³, Khaled Essam Mahmoud Dosoky⁶, Karim Tarabishy⁶, Parth S Patel⁶, Ayana Andalcio⁶, Kristin Maloney⁶, Jose Ulises Mena⁶, Wael Salloum⁶, Jinbo Chen², Ezekiel J Emanuel⁴

¹Winship Cancer Institute, Emory University School of Medicine, Atlanta, GA, USA. ravi.bharat.parikh@emory.edu.

²Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

³Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

⁴Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

⁵Winship Cancer Institute, Emory University School of Medicine, Atlanta, GA, USA.

⁶Mendel.ai, San Jose, CA, USA.

PubMed PMC DOI PDF

Abstract

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. Neurosymb…

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