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 Experiment Videos

Predicting Laboratory Test Ordering in Emergency Departments Using Integrated Structured and Unstructured Electronic

Xingyu Zhang1, Haipeng Ling2, Xin Zhang3

  • 1Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, United States.

JMIR Medical Informatics
|June 15, 2026
PubMed
Summary

Related Concept Videos

Methods of Documentation VII: EMR01:30

Methods of Documentation VII: EMR

Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare settings,...

You might also read

Related Articles

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

Sort by
Same author

Qualitative insights into a digital CBT program for AYAs with cancer: Participant perceptions of factors impacting feasibility and engagement.

Journal of psychosocial oncology·2026
Same author

Stewardship and Vision for Psycho-Oncology.

Psycho-oncology·2026
Same author

The role of sleep deficiency in the relationship between adverse childhood experiences and early adolescent pain outcomes.

JCPP advances·2025
Same author

Diet quality, eating behaviour, and mental health outcomes of prostate cancer survivors.

Journal of cancer survivorship : research and practice·2025
Same author

The Current State of Palliative Care Research for Adolescents and Young Adults With Cancer: A Systematic Review and Meta-Thematic Analysis of Empirical Literature.

Psycho-oncology·2025
Same author

AI-driven healthcare: Fairness in AI healthcare: A survey.

PLOS digital health·2025

Machine learning models integrating electronic health record data can predict laboratory test use in emergency departments (EDs). This approach aims to reduce unnecessary testing and improve healthcare efficiency.

Area of Science:

  • Health Informatics
  • Artificial Intelligence in Medicine
  • Clinical Decision Support

Background:

  • Laboratory testing is crucial for emergency department (ED) diagnosis but often overused, increasing healthcare costs.
  • Electronic health record (EHR) data offers potential for predictive models to optimize test utilization.

Purpose of the Study:

  • To develop and evaluate machine learning (ML) models for predicting laboratory test use in ED visits.
  • To integrate structured and unstructured EHR data for enhanced predictive accuracy.

Main Methods:

  • Analysis of 13,115 adult ED visits from the 2021 National Hospital Ambulatory Medical Care Survey-Emergency Department dataset.
  • Utilized structured data (demographics, vital signs, history) and unstructured data (chief complaints, injury descriptions) encoded with Bidirectional Encoder Representations from Transformers (BERT).
Keywords:
clinical decision supportelectronic health recordsemergency departmentlaboratory test usemachine learning

Related Experiment Videos

  • Compared four ML model configurations: structured-only, unstructured-only, combined data, and ensemble, evaluating performance using the area under the receiver operating characteristic curve (AUC).

    • Main Results:

    • The combined model, integrating both structured and unstructured data, achieved the highest predictive performance with an AUC of 0.83.
    • The combined model outperformed structured-only (AUC=0.78) and unstructured-only (AUC=0.74) models.
    • Key predictors included older age, ambulance arrival, abnormal vital signs, and chronic comorbidities; injury-related visits showed lower testing likelihood.

    Conclusions:

    • Integrating structured and unstructured EHR data significantly improves prediction of laboratory test use in EDs.
    • Findings support developing data-driven clinical decision support tools to enhance diagnostic efficiency.
    • This approach can help reduce unnecessary laboratory testing in emergency settings.