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

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

170
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:
170
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

76
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
76
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

97
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
97

You might also read

Related Articles

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

Sort by
Same author

Screening for Missed Opportunities for Diagnosis in the ED Using eTriggers and Large Language Models.

JAMA network open·2026
Same author

Effectiveness of Interruptive Clinical Decision Support Alerts on Intravenous vs. Oral Acetaminophen Prescribing.

Applied clinical informatics·2026
Same author

Multidimensional resiliency factors and psychopathology after acute trauma: Results from a prospective cohort study.

Psychological trauma : theory, research, practice and policy·2026
Same author

Improving End-of-Life Screening in the Emergency Department With Collaborative Artificial Intelligence.

Annals of emergency medicine·2026
Same author

Medical Scribe and Ambient Artificial Intelligence Impact on Emergency Physician Documentation Burden and Clinical Productivity.

Annals of emergency medicine·2026
Same author

Smartphone Keystroke Biomarkers as Predictors of Adverse Neuropsychiatric Sequelae After Trauma in Trauma Survivors: Prospective Observational Cohort Study.

Journal of medical Internet research·2026

Related Experiment Video

Updated: Aug 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

7.2K

Machine Learning Methods for Predicting Patient-Level Emergency Department Workload.

Joshua W Joseph1, Evan L Leventhal2, Anne V Grossestreuer2

  • 1Department of Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts.

The Journal of Emergency Medicine
|November 30, 2022
PubMed
Summary
This summary is machine-generated.

Deep learning models accurately predict physician work relative value units (wRVUs) using triage data. These AI tools can improve emergency department physician workload balancing and resource allocation.

Keywords:
clinical decision supportmachine learningoperations managementquality assurance

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Related Experiment Videos

Last Updated: Aug 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

7.2K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Area of Science:

  • Emergency Medicine
  • Health Informatics
  • Artificial Intelligence

Background:

  • Work Relative Value Units (wRVUs) are key to physician compensation and reflect patient care effort.
  • Predicting wRVUs at triage can optimize physician workload balancing and operational efficiency in emergency departments.

Purpose of the Study:

  • To evaluate deep-learning models for predicting patient visit wRVUs using data available at triage.
  • To compare the predictive accuracy of various machine learning models against baseline methods.

Main Methods:

  • Utilized deidentified triage data (structured and unstructured chief complaints) from an urban academic emergency department (2016-2020).
  • Compared five models: chief complaint averages, linear regression, gradient-boosted trees, and neural networks (structured/unstructured data).
  • Evaluated models using mean absolute error (MAE).

Main Results:

  • Analyzed 204,064 patient visits; median wRVUs were 3.80.
  • Chief complaint averages had an MAE of 2.17 wRVUs.
  • Advanced models showed improved accuracy: linear regression (1.00), gradient-boosted tree (0.85), neural network (structured, 0.86), and neural network (unstructured, 0.78).

Conclusions:

  • Deep learning, particularly using unstructured chief complaint data, significantly improves wRVU prediction accuracy over simple methods.
  • These predictive algorithms offer potential for equitable physician compensation, workload management, and bias reduction in triage.
  • AI-driven insights can enhance emergency department operations and resource allocation.