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

Methods of Documentation VII: EMR01:30

Methods of Documentation VII: EMR

950
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...
950
Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

1.3K
The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Comparing characteristics and outcomes between hospitalized adults on a pea protein or dairy/soy protein formulas: initial findings.

Journal of comparative effectiveness research·2026
Same author

A dynamic risk prediction framework for Alzheimer's disease and related dementias with interpretability.

NPJ digital medicine·2026
Same author

Clinical document metadata extraction: A scoping review.

Journal of biomedical informatics·2026
Same author

Context matching is not reasoning when performing generalized clinical evaluation of generative language models.

NPJ digital medicine·2025
Same author

Risk of subsequent abdominopelvic surgery in women undergoing hysterectomy with ovarian conservation: A population-based cohort study.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics·2025
Same author

A Self-Explainable Dynamic Risk Monitoring Framework for Predicting Alzheimer's Disease and Related Dementias.

medRxiv : the preprint server for health sciences·2025

Related Experiment Video

Updated: Oct 1, 2025

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

7.2K

Ascertainment of Delirium Status Using Natural Language Processing From Electronic Health Records.

Sunyang Fu1,2, Guilherme S Lopes1, Sandeep R Pagali3

  • 1Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota.

The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences
|March 3, 2022
PubMed
Summary

Natural language processing (NLP) algorithms effectively identify delirium from electronic health records, improving upon manual chart review. These NLP tools offer a scalable and cost-effective method for delirium detection in clinical practice.

Keywords:
Confusion assessment methodDeliriumElectronic health recordsNatural language processing

More Related Videos

Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

9.3K
A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.0K

Related Experiment Videos

Last Updated: Oct 1, 2025

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

7.2K
Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

9.3K
A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.0K

Area of Science:

  • Medical Informatics
  • Clinical Natural Language Processing
  • Healthcare Data Science

Background:

  • Delirium is frequently underdiagnosed and undercoded in clinical practice, hindering accurate billing and research.
  • Manual chart review for delirium identification is labor-intensive and impractical for large-scale studies.
  • Natural Language Processing (NLP) offers a solution for automated text analysis in Electronic Health Records (EHRs).

Purpose of the Study:

  • To develop and validate NLP algorithms for the automatic identification of delirium occurrences within EHRs.
  • To compare the performance of NLP algorithms against manual chart review as a gold standard.
  • To examine the prevalence of delirium using different identification methods.

Main Methods:

  • A cohort of 300 adults aged 65+ from the Mayo Clinic Biobank was randomly selected.
  • Two NLP algorithms, NLP-CAM and NLP-mCAM, were developed based on the Confusion Assessment Method (CAM) criteria.
  • Algorithm performance was evaluated using sensitivity, specificity, and accuracy, with manual chart review as the gold standard.

Main Results:

  • NLP-CAM achieved a sensitivity of 0.919, specificity of 1.000, and accuracy of 0.967.
  • NLP-mCAM demonstrated a sensitivity of 0.827, specificity of 0.913, and accuracy of 0.827.
  • Delirium prevalence varied: NLP-CAM identified 9.4% of cases, NLP-mCAM identified 15.3% definite and 8.0% possible cases.

Conclusions:

  • NLP algorithms based on the CAM framework show high performance for delirium status delineation.
  • These NLP tools provide an expeditious and cost-effective approach to identifying delirium.
  • Automated NLP methods enhance the feasibility of large-scale delirium studies and improve clinical coding.