Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

General Anesthesia: Overview

General Anesthesia: Overview

Anesthesia is a medical procedure that uses drugs for CNS suppression to enable painless surgeries and procedures. The selection of anesthetics is influenced by their pharmacokinetic properties, side effects, and patient characteristics. Various types of anesthesia include general, local, regional, spinal, and inhalational.
General anesthesia induces unconsciousness in the whole body, while the others target specific areas or sensations. It is administered to minimize adverse effects, maintain...

Stages of General Anesthesia

Stages of General Anesthesia

Various sedation levels offer significant advantages in facilitating procedural interventions for patients undergoing medical or invasive surgical procedures. These levels span from anxiolysis to general anesthesia, providing a spectrum of sedative effects to cater to specific patient needs. Anxiolysis reduces anxiety and is achieved through minimal sedation, enabling patients to remain awake and responsive while feeling more at ease during the procedure. This level can benefit minor...

Neural Control of Respiration

Neural Control of Respiration

The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
Two primary areas comprise the respiratory center: the medullary respiratory center in the medulla oblongata and the pontine respiratory group in the pons. The...

You might also read

Related Articles

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

Sort by

Same author

Retrospective analysis of adaptation frequencies and factors in offline in offline adaptive proton therapy.

Journal of applied clinical medical physics·2026

Same author

First multi-institutional systematic comparison of the neutron ambient dose equivalent produced by proton therapy systems.

Physics in medicine and biology·2025

Same author

Three-dimensional proton FLASH dose rate measurement at high spatiotemporal resolution using a novel multi-layer strip ionization chamber (MLSIC) device.

Medical physics·2025

Same author

Feasibility study on using low dose CT for adaptive proton therapy.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists·2025

Same author

High spatiotemporal resolution scintillation imaging of pulsed pencil beam scanning proton beams produced by a gantry-mounted synchrocyclotron.

Medical physics·2024

Same author

Risk factors for neurocognitive impairment, emotional distress, and poor quality of life in survivors of pediatric rhabdomyosarcoma: A report from the Childhood Cancer Survivor Study.

Cancer·2024

Search research articles

Related Experiment Video

Updated: Jan 15, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Anesthesia Practice Shift Scheduling With a Generative Deep Learning Model.

Wesley Emeneker¹, Stephen Heape^1,2, Gavin Hartman³

¹Anesthesia, PhySS LLC, Fincastle, USA.

|October 10, 2025

Summary

This summary is machine-generated.

Anesthesiology scheduling challenges are addressed by a new deep learning model. This machine learning approach automates shift scheduling, improving efficiency and provider satisfaction in anesthesia practices.

Keywords:

anesthesia scheduling deep learning lstm schedule generation staff scheduling

More Related Videos

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Related Experiment Videos

Last Updated: Jan 15, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Operations Research

Background:

Current anesthesiology scheduling methods are insufficient for modern practice demands, leading to provider dissatisfaction and burnout.
Inflexible schedules, increased workload, and complex practice environments contribute to job dissatisfaction among anesthesiologists.
Existing technological solutions have failed to adequately reduce workload or improve scheduling efficiency in anesthesia.

Purpose of the Study:

To introduce an alternative anesthesiology shift scheduling method utilizing machine learning (ML).
To develop and evaluate a deep learning (DL) model for automating the creation of compliant anesthesia shift schedules.
To demonstrate that DL can learn scheduling rules directly from historical data without explicit human codification.

Main Methods:

A deep learning model architecture was designed for anesthesia shift scheduling.
The DL model was trained using historical shift schedule data from the Reno-Tahoe Anesthesia (RTA) group.
Model performance was evaluated against specific practice requirements and scheduling rules.

Main Results:

The trained DL model achieved a Matthews Correlation Coefficient (MCC) of 0.9776 and a balanced accuracy of 0.9531.
The model demonstrated a reliable ability to learn and generate new shift schedules that comply with practice rules.
The DL model successfully inferred scheduling rules directly from past schedule examples.

Conclusions:

Advanced machine learning, specifically deep learning, offers a viable solution to the persistent challenges in anesthesiology shift scheduling.
The developed DL model significantly reduces the manual effort required for creating equitable and compliant anesthesia schedules.
This approach enhances operational efficiency and has the potential to improve anesthesiologist job satisfaction by automating complex scheduling tasks.