An Intelligent Trial Eligibility Screening Tool Using Natural Language Processing With a Block-Based Visual Programming Interface: Development and Usability Study.

Background: Clinical trial eligibility screening using electronic medical records (EMRs) is challenging due to the complexity of patient data and the varied clinical terminologies. Manual screening is time-consuming, requires specialized knowledge, and can lead to inconsistent participant selection, potentially compromising patient safety and research outcomes. This is critical in time-sensitive conditions like acute ischemic stroke. While computerized clinical decision support tools offer solutions, most require software engineering expertise to update, limiting their practical utility when eligibility criteria change. Objective: We developed and evaluated the intelligent trial eligibility screening tool (iTEST), which combines natural language processing with a block-based visual programming interface designed to enable clinicians to create and modify eligibility screening rules independently. In this study, we assessed iTEST's rule evaluation module using pre-configured rules and compared its effectiveness with that of standard EMR interfaces. Methods: We conducted an experiment at a tertiary teaching hospital in Taiwan with 12 clinicians using a 2-period crossover design. The clinicians assessed the eligibility of 4 patients with stroke for 2 clinical trials using both standard EMR and iTEST in a counterbalanced order, resulting in 48 evaluation scenarios. The iTEST comprised a rule authoring module using Google Blockly and a rule evaluation module utilizing MetaMap Lite for extracting medical concepts from unstructured EMR documents and structured laboratory data. 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 (range 0-100, with lower scores indicating a lower cognitive workload), and system usability through the system usability scale (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 min, P=.004) compared to the standard EMR interface. Users reported lower cognitive workload (National Aeronautics and Space Administration Task Load Index scale, 39.7 vs 62.8, P=.02) and higher system usability scale scores (71.3 vs 46.3, P=.01) with the iTEST. Particularly notable improvements in perceived cognitive workload were observed in temporal demand, effort, and frustration levels. Conclusions: The iTEST demonstrated superior performance in clinical trial eligibility screening, delivering improved accuracy, reduced completion time, lower cognitive workload, and better usability when evaluating preconfigured eligibility rules. The improved accuracy is critical for patient safety, as the misidentification of eligibility criteria could expose patients to inappropriate treatments or exclude them from beneficial trials. The adaptability and ability of the iTEST to process both structured and unstructured data make it valuable for time-sensitive scenarios and evolving research protocols. Future research should evaluate clinicians' ability to create and modify eligibility rules using the block-based authoring interface, as well as assess the iTEST across diverse types of clinical trials and health care settings.