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 Video

Updated: Nov 6, 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

7.3K

Predicting Ambulance Patient Wait Times: A Multicenter Derivation and Validation Study.

Katie J Walker1, Jirayus Jiarpakdee2, Anne Loupis3

  • 1Cabrini Emergency Department, Malvern, Melbourne, Victoria, Australia; Cabrini Institute, Malvern, Melbourne, Victoria, Australia; Casey Emergency Department, Berwick, Melbourne, Victoria, Australia; School of Clinical Sciences at Monash Health, Monash University, Clayton, Melbourne, Victoria, Australia.

Annals of Emergency Medicine
|May 11, 2021
PubMed

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

Emergency Department Triage Nurses' Scope of Practice: An Observational Study.

Journal of clinical nursing·2026
Same author

Lifetime Physical Loading and Magnetic Resonance-Derived Intervertebral Disc Health in Adults With Chronic Low Back Pain: A Cross-Sectional Study.

JOR spine·2026
Same author

Reducing Post-Fall Emergency Department Transfer From Residential Aged Care Homes: The Falls Outreach and Residential Mobile Assessment Team (FORMAT) Before-and-After Study.

Emergency medicine Australasia : EMA·2026
Same author

Evaluation of an AI Scribe Tool in the Emergency Department: A Single-Arm Observational Study.

Emergency medicine Australasia : EMA·2026
Same author

Gender Differences in Risk Factors, Management, and Outcomes of Elderly Patients With Acute Coronary Syndrome: The Interplay of Frailty.

Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions·2026
Same author

Prevalence and screening of orthostatic hypotension in older adults presenting to the emergency department: A systematic review.

International emergency nursing·2026
Summary

Machine learning models can predict emergency ambulance patient wait times, offering valuable insights for emergency departments. These models utilize readily available data to improve operational efficiency and patient care.

Area of Science:

  • Emergency Medicine
  • Health Informatics
  • Machine Learning Applications

Background:

  • Emergency departments (EDs) face challenges in managing patient flow and wait times.
  • Predicting ambulance patient off-stretcher times is crucial for resource allocation and patient care.

Purpose of the Study:

  • To develop and validate machine learning (ML) models for predicting emergency ambulance patient door-to-off-stretcher wait times.
  • To ensure model applicability across diverse emergency departments.

Main Methods:

  • Utilized retrospective administrative data from nine Australian EDs (2017-2019).
  • Developed and validated statistical and ML models, including linear regression and elastic net.
  • Analyzed 421,894 patient episodes.

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.3K
Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
14:28

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver

Published on: June 27, 2025

598

Related Experiment Videos

Last Updated: Nov 6, 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

7.3K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.3K
Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
14:28

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver

Published on: June 27, 2025

598

Main Results:

  • Median off-load times varied significantly across sites (13-29 minutes).
  • Global ML models achieved median absolute errors of 11.7-12.8 minutes.
  • Individual site models showed higher accuracy (6.3-16.1 minutes), with key predictors including recent patient wait times, triage category, and age.

Conclusions:

  • Electronic demographic and flow data can accurately estimate ambulance patient off-stretcher times.
  • ML models can be built with reasonable accuracy for multiple hospitals using limited point-of-care variables.