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

Emerging Adulthood01:27

Emerging Adulthood

686
Jeffrey Arnett's concept of emerging adulthood offers a framework to understand the unique developmental stage between adolescence and full-fledged adulthood, generally from ages 18 to 25. This period is marked by extensive exploration and shifts in identity, relationships, and career choices, a process known in psychology as role experimentation. Emerging adulthood reflects the evolving cultural expectations surrounding adulthood and the dynamic process of personal transformation during...
686
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

45.8K
VSEPR Theory for Determination of Electron Pair Geometries
45.8K
Introduction Cardiac Emergencies01:30

Introduction Cardiac Emergencies

365
Cardiac emergencies are critical situations involving the heart that require immediate medical intervention to prevent severe complications or death. These emergencies often arise from underlying heart conditions that impair the heart's ability to function correctly.Types of Cardiac EmergenciesThe most common types of cardiac emergencies include Acute Coronary Syndrome (ACS), myocardial infarction (MI), cardiac arrest, and heart failure.Acute Coronary Syndrome (ACS)Acute Coronary Syndrome (ACS)...
365
Prediction Intervals01:03

Prediction Intervals

3.4K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.4K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

1.2K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
1.2K
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

1.3K
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Use of BacterioScan 216Dx to reduce antibiotic use for suspected urinary tract infection in the emergency department.

Antimicrobial stewardship & healthcare epidemiology : ASHE·2025
Same author

Health insurance and transportation barriers impact access to epilepsy care in the United States.

Epilepsy research·2024
Same author

Model performance and variable selection method - A reply to the reader.

The American journal of emergency medicine·2017
Same author

Predicting 72-hour emergency department revisits.

The American journal of emergency medicine·2017
Same author

Revisiting hospital length of stay: what matters?

The American journal of managed care·2015
Same author

Racial, Income, and Marital Status Disparities in Hospital Readmissions Within a Veterans-Integrated Health Care Network.

Evaluation & the health professions·2013

Related Experiment Video

Updated: Feb 2, 2026

Author Spotlight: Unveiling Prognostic Indicators in Heart Failure - The Role of Phase Angle and Bioelectrical Impedance Analysis
04:05

Author Spotlight: Unveiling Prognostic Indicators in Heart Failure - The Role of Phase Angle and Bioelectrical Impedance Analysis

Published on: June 30, 2023

2.9K

Predicting 30-day emergency department revisits.

Kelly Gao, Gene Pellerin, Laurence Kaminsky1

  • 1Stratton VA Medical Center, US Department of Veterans Affairs, 113 Holland Ave, Albany, NY 12208.

The American Journal of Managed Care
|November 20, 2018
PubMed
Summary
This summary is machine-generated.

A new predictive model identifies patients at high risk of emergency department (ED) revisits within 30 days. This tool helps hospitals reduce unnecessary ED use through targeted interventions for frequent ED visitors.

More Related Videos

Accurate and Phenol Free DNA Sexing of Day 30 Porcine Embryos by PCR
10:16

Accurate and Phenol Free DNA Sexing of Day 30 Porcine Embryos by PCR

Published on: February 14, 2016

10.5K
Expired CO2 Measurement in Intubated or Spontaneously Breathing Patients from the Emergency Department
07:52

Expired CO2 Measurement in Intubated or Spontaneously Breathing Patients from the Emergency Department

Published on: January 29, 2011

17.0K

Related Experiment Videos

Last Updated: Feb 2, 2026

Author Spotlight: Unveiling Prognostic Indicators in Heart Failure - The Role of Phase Angle and Bioelectrical Impedance Analysis
04:05

Author Spotlight: Unveiling Prognostic Indicators in Heart Failure - The Role of Phase Angle and Bioelectrical Impedance Analysis

Published on: June 30, 2023

2.9K
Accurate and Phenol Free DNA Sexing of Day 30 Porcine Embryos by PCR
10:16

Accurate and Phenol Free DNA Sexing of Day 30 Porcine Embryos by PCR

Published on: February 14, 2016

10.5K
Expired CO2 Measurement in Intubated or Spontaneously Breathing Patients from the Emergency Department
07:52

Expired CO2 Measurement in Intubated or Spontaneously Breathing Patients from the Emergency Department

Published on: January 29, 2011

17.0K

Area of Science:

  • Health Services Research
  • Predictive Analytics
  • Healthcare Management

Background:

  • Emergency department (ED) revisits represent a significant burden on healthcare systems.
  • Identifying high-risk patients for ED revisits is crucial for optimizing resource allocation and improving patient outcomes.
  • Proactive interventions can mitigate unnecessary ED utilization.

Purpose of the Study:

  • To develop and validate a predictive model for identifying patients at high risk of 30-day ED revisits.
  • To provide hospitals with a tool for early detection of frequent ED users ('frequent flyers').
  • To facilitate the implementation of targeted interventions aimed at reducing recurrent ED visits.

Main Methods:

  • Retrospective analysis of administrative data from four Veterans Affairs hospitals.
  • Development of a predictive model using logistic regression, incorporating patient demographics, prior-year utilization, and comorbidities.
  • Model validation using a split-sample method and assessment of predictive power via C statistics.

Main Results:

  • The final predictive model, including demographics, prior utilization, and comorbidities, achieved a C statistic of 0.773 (development) and 0.763 (validation).
  • Models incorporating prior-year utilization and comorbidities demonstrated significantly higher predictive power compared to demographics alone.
  • The model's predictive performance was robust, as indicated by the C statistics in both development and validation datasets.

Conclusions:

  • The developed predictive model is effective in identifying patients likely to revisit the ED within 30 days.
  • The model offers superior predictive power compared to previously reported models.
  • Its straightforward implementation allows healthcare systems to proactively intervene with high-risk patients, thereby reducing ED revisits.