Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Jul 4, 2025
Date Accepted: Nov 27, 2025
An Intelligent Trial Eligibility Screening Tool Using Natural Language Processing with a Block-Based Visual Programming Interface: Development and Usability Study
ABSTRACT
Background:
Clinical trial eligibility screening using electronic medical records (EMRs) presents significant challenges due to the complexity and volume of patient data. Researchers must navigate through extensive documentation, interpret varied clinical terminologies, and manually extract relevant information. This time-consuming process often requires specialized knowledge and can lead to inconsistent participant selection, potentially compromising research outcomes and patient safety.
Objective:
This study assesses the effectiveness of the intelligent trial eligibility screening tool (iTEST), a clinical decision support tool powered by natural language processing (NLP), compared to standard EMR interfaces for eligibility screening.
Methods:
Twelve clinicians assessed four patients' eligibility for two clinical trials using both the standard EMR interface and iTEST. Primary outcomes included accuracy in determining eligibility. Secondary outcomes measured task completion time, cognitive workload using the National Aeronautics and Space Administration Task Load Index scale (NASA-TLX scale; range 0–100, with lower scores indicating a lower cognitive workload), and system usability through the System Usability Scale (SUS; range: 0–100, with higher scores indicating higher system usability).
Results:
The iTEST significantly improved accuracy scores (from 0.91 to 1.00, P <.001) and reduced completion time (from 3.18 to 2.44 minutes, P = .004) when compared to the standard EMR interface. Users reported lower cognitive workload (NASA-TLX scale, 39.7 vs. 62.8, P = .016) and higher SUS scores (71.3 vs. 46.3, P = .011) with iTEST. Particularly notable improvements in perceived cognitive workload were observed in temporal demand, effort, and frustration levels.
Conclusions:
The iTEST performed superior to standard EMR interfaces in the eligibility screening process, delivering improved accuracy, reduced task completion time, and lower cognitive workload. These findings indicate that NLP-powered tools can enhance the efficiency and reliability of clinical trial participant selection while alleviating clinician burdens.
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Copyright
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