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

Regression Toward the Mean01:52

Regression Toward the Mean

6.8K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.8K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

453
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
453
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

356
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
356
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

362
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
362
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

514
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
514
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

518
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
518

You might also read

Related Articles

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

Sort by
Same author

Non-Opioid Pharmaceutical Alternatives for Acute Pain Management in the Emergency Department: A Scoping Review.

The western journal of emergency medicine·2026
Same author

Naloxone and Clinical Outcomes in Suspected Opioid-Associated Out-of-Hospital Cardiac Arrests.

JAMA network open·2026
Same author

Naloxone administration associated with improved survival in PEA out-of-hospital cardiac arrests.

Resuscitation·2026
Same author

From Chaos to Coordination: Fundamentals of Teamwork and How Residents Can Practice Teaming to Improve Acute Care.

Annals of emergency medicine·2026
Same author

Trends in first-time psychedelic and other hallucinogen use in the United States: Results from the National Survey on Drug Use and Health.

Drug and alcohol dependence·2026
Same author

Obesity and Long COVID: intersecting epidemics?

BMC public health·2026

Related Experiment Video

Updated: Jan 3, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.9K

Predicting Emergency Department "Bouncebacks": A Retrospective Cohort Analysis.

Juan Carlos C Montoy1, Joshua Tamayo-Sarver2, Gregg A Miller2

  • 1University of California, San Francisco, Department of Emergency Medicine, San Francisco, California.

The Western Journal of Emergency Medicine
|November 19, 2019
PubMed
Summary
This summary is machine-generated.

Previous emergency department (ED) use strongly predicts return visits. Identifying frequent ED visitors can help target interventions, as their past usage is the highest risk factor for revisits.

More Related Videos

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.0K
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

7.5K

Related Experiment Videos

Last Updated: Jan 3, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.9K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.0K
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

7.5K

Area of Science:

  • Emergency Medicine
  • Health Services Research
  • Public Health

Background:

  • Short-term return visits to the emergency department (ED) are a key quality metric.
  • Identifying patients at high risk for ED revisits is crucial for developing targeted interventions.
  • Current strategies to reduce ED revisits have shown limited success.

Purpose of the Study:

  • To investigate whether a history of frequent ED visits increases the risk of short-term return visits.
  • To identify key predictors of 14-day ED revisits.

Main Methods:

  • Population-based, retrospective cohort study utilizing administrative data from 80 hospitals across seven states.
  • Inclusion of patients discharged from EDs between July 2014 and June 2016.
  • Multivariable logistic regression analysis to assess predictors of 14-day return visits.

Main Results:

  • The overall 14-day ED revisit rate was 12.6% among 6,699,717 index visits.
  • Frequent ED visitors constituted 18.7% of all visits but 40.2% of all 14-day revisits.
  • Frequent visitor status (OR 3.06) was the strongest predictor, followed by cellulitis, alcohol-related disorders, congestive heart failure, and public insurance.

Conclusions:

  • Past emergency department utilization, including a single previous visit, is a more significant predictor of return visits than other patient or community factors.
  • Clinicians and health system stakeholders should consider previous ED use when assessing patient risk and making treatment decisions.