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

A framework for classifying decision support systems.

Ida Sim1, Amy Berlin

  • 1University of California, San Francisco, CA, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|January 20, 2004
PubMed
Summary
This summary is machine-generated.

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

The patient the trial cannot see: Clinical reasoning and mechanism in the age of precision medicine.

The American journal of medicine·2026
Same author

Biography is the dark matter of disease pathogenesis.

The American journal of medicine·2026
Same author

The person-population paradox in clinical medicine.

The American journal of medicine·2026
Same author

Integrating biology and biography in medicine: The mind and body are not separate.

PLOS mental health·2026
Same author

Reconceptualizing obesity and the modern definition of disease.

The American journal of medicine·2026
Same author

Automating Individualized Notification of Drug Recalls to Patients: Complex Challenges and Qualitative Evaluation.

JMIRx med·2026
Same journal

Sensitivity Analyses of a Scoring System for a Contraception Decision Aid.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Improving electronic health record processing of large language models via retrieval-augmented generation: A case study on dietary supplements.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Developing a User-Centered Mobile Application Prototype: Bridging Lower-Limb Fracture Care from Skilled Nursing Facility and Back to the Community.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Automating Adjudication of Cardiovascular Events Using Large Language Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Predictive Factors and State-Level Barriers to Postpartum Birth Control Usage in the United States: Insights from PRAMS Phase 8.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
See all related articles

A new taxonomy classifies computer-based clinical decision support systems (CDSSs) by their features. This comprehensive system aids in understanding CDSS variety and effectiveness in medical literature.

Area of Science:

  • Health Informatics
  • Medical Decision Making
  • Information Science

Background:

  • Computer-based clinical decision support systems (CDSSs) exhibit significant variation in design and functionality.
  • A standardized classification system is needed to understand the diversity of CDSSs in research.
  • Such a taxonomy can help identify factors influencing CDSS effectiveness and applicability.

Purpose of the Study:

  • To develop and validate a taxonomy for characterizing CDSSs.
  • The taxonomy focuses on contextual, technical, and workflow attributes of these systems.

Main Methods:

  • A systematic review of 150 English-language articles on CDSSs published between 1975 and 2002 was conducted.
  • Key aspects of CDSS structure and function were identified to refine the taxonomy iteratively.

Related Experiment Videos

  • The taxonomy was finalized after ensuring no new descriptors emerged from further analysis.

    • Main Results:

    • The developed taxonomy includes 95 descriptors across 24 descriptive axes.
    • These axes are categorized into Context, Knowledge and Data Source, Decision Support, Information Delivery, and Workflow.
    • The taxonomy demonstrated good reliability, with 75% of descriptors achieving an inter-rater agreement kappa > 0.6.

    Conclusions:

    • A comprehensive and multi-faceted taxonomy for classifying CDSSs has been successfully defined and tested.
    • The taxonomy shows promising reliability for categorizing CDSSs described in scientific literature.
    • This tool facilitates a more structured understanding and comparison of clinical decision support systems.