Assessing the Performance of Artificial Intelligence (AI)-Augmented Electronic Health Record (EHR) Data Abstraction for Clinical Trial Patient Screening

Brief summary (registry, in English)

Identifying eligible patients is a key process in the clinical trial enterprise. Currently, this process relies on time-intensive manual chart review, creating a rate-limiting step for trial participation. The integration of AI technology into the trial screening process has potential to improve participation rates. This study aims to assess the performance (accuracy, efficiency) of AI-augmented patient identification and inform optimal integration into clinical research screening processes.

What you commit to

• Study type: Observational
• Participants enrolled: 355
• From 18 Aug 2023 to the primary completion date, 12 Jul 2024
• 2 sites in 1 country
• Visits and tests: ask the trial team, the registry does not list them

Built from the registry fields of ClinicalTrials.gov. It describes the study, not your situation.

Who cannot take part

Copied verbatim from ClinicalTrials.gov and grouped by topic. Only the trial team decides who can take part.

Who the study is looking for

Sex: All · Does not accept healthy volunteers

Full criteria on ClinicalTrials.gov

Sites (2)

Sites in registry order. Pick your city on the disease page to sort them by distance.

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