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

1.2K
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...
1.2K
The Availability Heuristic01:08

The Availability Heuristic

6.7K
A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
6.7K
Methods of Documentation II: POMR01:26

Methods of Documentation II: POMR

1.2K
The Problem-Oriented Medical Record (POMR) revolutionized medical record-keeping by introducing a systematic approach focusing on the patient's problems rather than merely listing symptoms. Dr. Lawrence Weed's introduction of this method in the 1960s marked a significant advancement in medical documentation. The POMR framework consists of four key components: the database, problem list, plan of care, and progress notes.
1.2K

You might also read

Related Articles

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

Sort by
Same author

Telemedicine Use and Access to Care for New Patients at an Academic Cardiovascular Center: An Analysis of Geographic Reach and Wait Times.

Journal of the American Heart Association·2026
Same author

Adoption of a Pediatric Acute Care Cardiology Inpatient Model.

JAMA network open·2026
Same author

Variation in Commercial Insurer Prior Authorization Rules.

Annals of internal medicine·2026
Same author

The state of the unit: variable care models in paediatric acute care cardiology units documented by the fourth iteration of the Paediatric Acute Care Cardiology Collaborative (PAC<sup>3</sup>) Hospital Survey.

Cardiology in the young·2026
Same author

Cost-Effectiveness of Continuous Glucose Monitoring With Remote Patient Monitoring in Pediatric Patients With Newly Diagnosed Type 1 Diabetes in the U.S.

Diabetes care·2026
Same author

A Quantitative Framework for Evaluating the Performance of Algorithm-Directed Whole-Population Remote Patient Monitoring: Tutorial for Type 1 Diabetes Care.

JMIR diabetes·2026

Related Experiment Video

Updated: Nov 19, 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

7.4K

Quantifying Electronic Health Record Data: A Potential Risk for Cognitive Overload.

Dana B Gal1,2, Brian Han1,2, Chistopher Longhurst3

  • 1Division of Pediatric Cardiology, Department of Pediatrics, School of Medicine and.

Hospital Pediatrics
|January 27, 2021
PubMed
Summary
This summary is machine-generated.

Pediatric cardiovascular ICU patients generate over 1400 health data points daily. Frontline providers face significant data exposure, risking cognitive overload and impacting patient safety.

More Related Videos

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
13:57

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

12.9K
Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach
10:13

Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach

Published on: February 14, 2014

14.0K

Related Experiment Videos

Last Updated: Nov 19, 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

7.4K
Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
13:57

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

12.9K
Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach
10:13

Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach

Published on: February 14, 2014

14.0K

Area of Science:

  • Pediatric critical care medicine
  • Health informatics
  • Patient-generated health data (PGHD)

Background:

  • Electronic health records (EHRs) generate substantial patient data.
  • High data volume may contribute to healthcare provider cognitive burden and overload.
  • Quantifying this data in pediatric intensive care is crucial for understanding its impact.

Purpose of the Study:

  • To quantify and describe patient-generated health data in a pediatric cardiovascular intensive care unit (ICU).
  • To assess the daily data volume generated by patients and its exposure to frontline providers.
  • To identify the types of data generated and their structure.

Main Methods:

  • Retrospective, single-center study of pediatric cardiovascular ICU patients from February 1-15, 2020.
  • Collected daily data points per patient from EHR.
  • Analyzed data by type and extrapolated provider exposure based on patient-to-provider ratios.

Main Results:

  • Thirty patients (19 surgical, 11 medical) were included.
  • Patients generated an average of 1460 data points daily.
  • Frontline providers were exposed to 4380 data points during day shifts and 16,060 overnight.
  • Over 80% of generated data was structured.

Conclusions:

  • Healthcare providers face significant daily data generation from EHRs, potentially causing cognitive overload.
  • This study quantifies data volume in a pediatric setting, highlighting the need for management strategies.
  • Structured data offers potential for optimization systems to mitigate cognitive overload and improve patient safety and provider well-being.