Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Variability in epilepsy polygenic risk prediction across Taiwanese population and clinical cohorts.

Epilepsia·2026
Same author

Developing a clinical decision support tool for stratifying stroke risk in patients presenting with dizziness to the emergency department: A retrospective cohort study.

Digital health·2026
Same author

Enhanced Prediction of Atrial Fibrillation in Patients With Ischemic Stroke Through Electronic Medical Records and Text Mining: Algorithm Development and Validation.

JMIR medical informatics·2026
Same author

Taiwan's National Health Insurance Research Database (NHIRD): in the Era of Artificial Intelligence, Causal Inference, and Data Security.

Clinical epidemiology·2025
Same author

Multimodal Multitask Learning for Predicting Depression Severity and Suicide Risk Using Pretrained Audio and Text Embeddings: Methodology Development and Application.

JMIR medical informatics·2025
Same author

Transient anticholinergic burden and out-of-hospital cardiac arrest: a case-crossover study.

European heart journal·2025
Same journal

Selecting, Scaling, and Measuring the Value of Ambient AI in a Nonacademic Health System: Multiphase Pilot Study.

JMIR medical informatics·2026
Same journal

Prediction of Early Hospital Admission (≤24 Hours) After Stroke Using Machine Learning and Deep Learning: Multicenter Study From China.

JMIR medical informatics·2026
Same journal

Assessing the Feasibility and Acceptability of Implementing a Preclinic Vital Signs Assessment in Primary Care: Cross-Sectional Pilot Study.

JMIR medical informatics·2026
Same journal

Candidate Passive Sensor Suite Technologies for Tactical Combat Casualty Care Environments: Comparative Assessment Study.

JMIR medical informatics·2026
Same journal

Relevance of the uMap Collaborative Platform as Support for Choropleth Mapping: A Traffic‒Light Statistical Signal Atlas of All-Cause Mortality-First French Lockdown.

JMIR medical informatics·2026
Same journal

Ambient AI Scribe Implementation in an Ambulatory Setting in a Single Medical Group: Prospective Study.

JMIR medical informatics·2026
See all related articles

Related Experiment Video

Updated: Jan 8, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.9K

An Intelligent Trial Eligibility Screening Tool Using Natural Language Processing With a Block-Based Visual

Ya-Han Hu1,2, Yi-Ying Cheng1, Chung-Ching Lan1

  • 1Department of Information Management, National Central University, Taoyuan, Taiwan.

JMIR Medical Informatics
|December 11, 2025
PubMed
Summary
This summary is machine-generated.

The intelligent trial eligibility screening tool (iTEST) significantly improved clinical trial screening accuracy and efficiency. This tool enhances patient safety by ensuring correct participant selection for trials.

Keywords:
block-based visual programmingclinical decision supportclinical trialselectronic medical recordseligibility screeningnatural language processingpatient safety

More Related Videos

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools
11:29

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Published on: June 20, 2020

9.6K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.5K

Related Experiment Videos

Last Updated: Jan 8, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.9K
Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools
11:29

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Published on: June 20, 2020

9.6K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.5K

Area of Science:

  • Medical Informatics
  • Clinical Trial Management
  • Health Data Science

Background:

  • Electronic Medical Records (EMRs) present challenges in clinical trial eligibility screening due to data complexity and varied terminologies.
  • Manual screening is inefficient, requires expertise, and can lead to inconsistent participant selection, impacting patient safety and research outcomes, especially in critical situations like acute ischemic stroke.
  • Existing computerized tools often require software engineering expertise for updates, limiting their practical use when eligibility criteria change.

Purpose of the Study:

  • To develop and evaluate the intelligent trial eligibility screening tool (iTEST), which integrates natural language processing with a visual programming interface.
  • To enable clinicians to independently create and modify eligibility screening rules.
  • To assess the performance of iTEST's rule evaluation module against standard EMR interfaces.

Main Methods:

  • An experiment was conducted with 12 clinicians at a tertiary teaching hospital using a 2-period crossover design.
  • Clinicians evaluated the eligibility of stroke patients for two trials using both standard EMR and iTEST.
  • iTEST utilized Google Blockly for rule authoring and MetaMap Lite for concept extraction from EMR data; outcomes included accuracy, task completion time, cognitive workload (NASA-TLX), and system usability (SUS).

Main Results:

  • iTEST significantly improved accuracy (0.91 to 1.00, P<.001) and reduced completion time (3.18 to 2.44 min, P=.004) compared to standard EMR.
  • Users reported lower cognitive workload (NASA-TLX: 39.7 vs 62.8, P=.02) and higher system usability (SUS: 71.3 vs 46.3, P=.01) with iTEST.
  • Notable improvements in cognitive workload were seen in temporal demand, effort, and frustration.

Conclusions:

  • iTEST demonstrated superior performance in clinical trial eligibility screening, enhancing accuracy, efficiency, and usability.
  • Improved accuracy is crucial for patient safety, preventing inappropriate treatments or exclusion from beneficial trials.
  • iTEST's adaptability to structured/unstructured data and ease of modification make it valuable for time-sensitive research and evolving protocols.