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

Detecting adverse events using information technology.

David W Bates1, R Scott Evans, Harvey Murff

  • 1Division of General Medicine, Department of Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA. dbates@partners.org

Journal of the American Medical Informatics Association : JAMIA
|February 22, 2003
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

Analysis of clinical decision support system malfunctions: a case series and survey.

Journal of the American Medical Informatics Association : JAMIA·2016
Same author

Impact of Hyponatremia Correction on the Risk for 30-Day Readmission and Death in Patients with Congestive Heart Failure.

The American journal of medicine·2016
Same author

Corrigendum to "Medication safety practices in hospitals: A national survey in Saudi Arabia" [Saudi Pharm. J. 21(2) (2013) 159-164].

Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society·2016
Same author

The frequency of inappropriate nonformulary medication alert overrides in the inpatient setting.

Journal of the American Medical Informatics Association : JAMIA·2016
Same author

A cross-sectional observational study of high override rates of drug allergy alerts in inpatient and outpatient settings, and opportunities for improvement.

BMJ quality & safety·2016
Same author

Appropriateness: A Key to Enabling the Use of Genomics in Clinical Practice?

The American journal of medicine·2016
Same journal

Digital divide in clinical and operational artificial intelligence adoption and implementation stages: US hospital diffusion patterns and AI deserts.

Journal of the American Medical Informatics Association : JAMIA·2026
Same journal

Extending the fundamental theorem of biomedical informatics: a proposal and illustrative examples.

Journal of the American Medical Informatics Association : JAMIA·2026
Same journal

Human factors methods for designing safe health information technology: what do the experts think?

Journal of the American Medical Informatics Association : JAMIA·2026
Same journal

Equity-by-design for socially assistive robots as digital health tools.

Journal of the American Medical Informatics Association : JAMIA·2026
Same journal

Orchestrator multi-agent clinical decision support system for secondary headache diagnosis in primary care.

Journal of the American Medical Informatics Association : JAMIA·2026
Same journal

CUI-Curate: a GraphRAG-based framework for automated clinical concept curation for NLP applications.

Journal of the American Medical Informatics Association : JAMIA·2026
See all related articles

Information technology can effectively detect patient safety issues like adverse drug events and nosocomial infections. These computerized methods offer a cost-effective solution for widespread adoption in healthcare organizations.

Area of Science:

  • Health Informatics
  • Patient Safety Research
  • Clinical Data Analysis

Background:

  • Traditional adverse event detection relies on spontaneous reporting, missing most safety issues.
  • Chart review is effective but too costly for routine use.
  • Information technology offers timely and cost-effective adverse event detection, enabling harm prevention.

Purpose of the Study:

  • To review information technology (IT) methodologies for detecting adverse events.
  • To analyze studies utilizing IT for adverse event detection.
  • To report study findings on specific adverse event types detected by IT.

Main Methods:

  • A structured review of English-language studies was conducted.
  • Studies using IT to detect adverse events were identified.

Related Experiment Videos

  • Only studies with original data were included in the review.

    • Main Results:

    • Event monitoring and natural language processing tools can detect adverse events in clinical databases cost-effectively.
    • IT methods show success in detecting adverse drug events and nosocomial infections.
    • These techniques are adaptable for a wider range of adverse events as medical information becomes more computerized.

    Conclusions:

    • Computerized detection of adverse events is becoming increasingly practical.
    • Widespread implementation of IT for adverse event detection is anticipated.
    • IT offers a scalable solution to improve patient safety.