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

You might also read

Related Articles

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

Sort by
Same author

Ultrasonography demonstrates vagus nerve atrophy in Parkinson's disease: a meta-analysis.

Journal of neurology·2026
Same author

Perceived Cognitive Load Among Emergency Department Code Blue Teams: Distribution, Correlates and Relationship with Team Performance.

Resuscitation·2026
Same author

Pain prevalence and intensity in advanced pancreatic cancer: a nationwide cohort study.

Pain·2026
Same author

The DOAC-FRAIL Study-Same Dose, Different Story: Prevalence of Deviant Direct Oral Anticoagulant Levels in Nursing Home Residents.

Journal of the American Geriatrics Society·2026
Same author

External vacuum expansion in autologous fat transfer as total breast reconstruction: Is it worth it? The interim analysis of the multicentre randomised controlled EVE trial.

Journal of plastic, reconstructive & aesthetic surgery : JPRAS·2026
Same author

Radiological-pathological correlation of tumour size and depth of invasion of oral squamous cell carcinoma using Dual-Energy CT in comparison to MRI and the impact on T-stage.

Dento maxillo facial radiology·2026

Related Experiment Video

Updated: Feb 27, 2026

Utilizing a 3D Printed Laparoscopic Nissen Fundoplication Model to Shorten a Resident's Learning Curve
08:21

Utilizing a 3D Printed Laparoscopic Nissen Fundoplication Model to Shorten a Resident's Learning Curve

Published on: August 15, 2025

774

Improving the Prediction of Total Surgical Procedure Time Using Linear Regression Modeling.

Eric R Edelman1, Sander M J van Kuijk2, Ankie E W Hamaekers3

  • 1Faculty of Health, Medicine and Life Sciences, Department of Health Services Research, CAPHRI School for Public Health and Primary Care, Maastricht University, Maastricht, Netherlands.

Frontiers in Medicine
|July 5, 2017
PubMed
Summary

Accurate operating room (OR) scheduling requires precise prediction of total procedure time (TPT). Linear regression models using estimated surgeon-controlled time (eSCT) and other factors significantly improve TPT prediction accuracy over fixed ratios.

Keywords:
anesthesia timeoperating room utilizationpredictionprocedure timeregressionsurgeon timesurgical time

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

8.9K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

777

Related Experiment Videos

Last Updated: Feb 27, 2026

Utilizing a 3D Printed Laparoscopic Nissen Fundoplication Model to Shorten a Resident's Learning Curve
08:21

Utilizing a 3D Printed Laparoscopic Nissen Fundoplication Model to Shorten a Resident's Learning Curve

Published on: August 15, 2025

774
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

8.9K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

777

Area of Science:

  • Health Services Research
  • Medical Informatics
  • Operations Research

Background:

  • Efficient operating room (OR) utilization is critical for healthcare efficiency.
  • Accurate prediction of total procedure time (TPT) is essential for effective OR scheduling and resource allocation.
  • Current methods for predicting TPT may lack the necessary precision for optimal OR management.

Purpose of the Study:

  • To enhance the accuracy of total procedure time (TPT) predictions for surgical cases.
  • To evaluate the effectiveness of linear regression models incorporating estimated surgeon-controlled time (eSCT) and other variables for TPT prediction.
  • To compare the performance of developed linear regression models against a fixed ratio model and separate prediction of anesthesia-controlled time (ACT).

Main Methods:

  • Utilized a Dutch benchmarking database of 79,983 surgeries from six academic hospitals (2012-2016).
  • Developed and tested various linear regression models to predict TPT using eSCT, patient age, operation type, ASA classification, and anesthesia type.
  • Compared model performance against a fixed ratio model (eSCT * 1.33) and models predicting ACT separately.

Main Results:

  • The most accurate TPT prediction was achieved using a linear regression model incorporating eSCT, operation type, ASA classification, and anesthesia type.
  • This optimized linear regression model demonstrated significantly superior performance compared to the fixed ratio model.
  • The developed model also outperformed methods that predicted anesthesia-controlled time (ACT) separately.

Conclusions:

  • Linear regression models integrating eSCT and key patient/procedural variables offer superior TPT prediction accuracy.
  • Improved TPT prediction accuracy can enhance OR scheduling and sequencing, leading to increased OR utilization.
  • Adoption of these advanced prediction models can yield substantial financial and productivity benefits in healthcare settings.