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Ther Innov Regul Sci. 2019 Jan 1:2168479018815454. doi: 10.1177/2168479018815454. [Epub ahead of print]

Improving Clinical Trial Participant Prescreening With Artificial Intelligence (AI): A Comparison of the Results of AI-Assisted vs Standard Methods in 3 Oncology Trials.

Author information

1
1 Calaprice-Whitty Consulting, Inc.
2
2 Mendel AI, San Jose, CA, USA.

Abstract

BACKGROUND::

Delays in clinical trial enrollment and difficulties enrolling representative samples continue to vex sponsors, sites, and patient populations. Here we investigated use of an artificial intelligence-powered technology, Mendel.ai, as a means of overcoming bottlenecks and potential biases associated with standard patient prescreening processes in an oncology setting.

METHODS::

Mendel.ai was applied retroactively to 2 completed oncology studies (1 breast, 1 lung), and 1 study that failed to enroll (lung), at the Comprehensive Blood and Cancer Center, allowing direct comparison between results achieved using standard prescreening practices and results achieved with Mendel.ai. Outcome variables included the number of patients identified as potentially eligible and the elapsed time between eligibility and identification.

RESULTS::

For each trial that enrolled, use of Mendel.ai resulted in a 24% to 50% increase over standard practices in the number of patients correctly identified as potentially eligible. No patients correctly identified by standard practices were missed by Mendel.ai. For the nonenrolling trial, both approaches failed to identify suitable patients. An average of 19 days for breast and 263 days for lung cancer patients elapsed between actual patient eligibility (based on clinical chart information) and identification when the standard prescreening practice was used. In contrast, ascertainment of potential eligibility using Mendel.ai took minutes.

CONCLUSIONS::

This study suggests that augmentation of human resources with artificial intelligence could yield sizable improvements over standard practices in several aspects of the patient prescreening process, as well as in approaches to feasibility, site selection, and trial selection.

KEYWORDS:

artificial intelligence; clinical trial enrollment; clinical trial screening; clinical trial startup; feasibility; machine learning; real world data

PMID:
30599764
DOI:
10.1177/2168479018815454

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