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Leveraging EA8191 to Assess AI-Augmented EHR Abstraction

NCT06561230 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 300

Last updated 2026-03-12

No results posted yet for this study

Summary

The goal of this prospective study is to assess the performance of AI (artificial intelligence) augmentation (compared against historical controls) to identify oncology patients who meet inclusion criteria for a clinical trial. The study staff will leverage a natural language processing (NLP)-based AI algorithm that rank-orders patients most likely to meet inclusion criteria for a trial. We hypothesize that this collaborative Human+AI workflow can improve the efficiency, accuracy, and diversity of trial prescreening.

Conditions

Interventions

OTHER

Chart review

Patients eligible for prescreening will be identified using structured criteria (e.g., anyone with an appointment in the upcoming 2 months with a relevant provider). All individual charts will be de-identified. De-identified EHRs will then be transferred to Mendel's secure data abstraction platform, where trial eligibility criteria will be abstracted. Patients will be rank-ordered by number of eligibility criteria met, with patients meeting the most eligibility criteria at the top of the queue. The study team will then review eligibility criteria and flag eligible patients.

Sponsors & Collaborators

Eligibility

Min Age
18 Years
Sex
MALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-04-04
Primary Completion
2026-05-31
Completion
2026-08-31

Countries

  • United States

Study Locations

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Entities

Diseases
Companies

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06561230 on ClinicalTrials.gov