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

Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

833
The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic...
833
Variability: Analysis01:11

Variability: Analysis

415
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
415
Peripheral Artery Disease V: Postoperative Nursing Management01:23

Peripheral Artery Disease V: Postoperative Nursing Management

347
During the postoperative period, it is crucial to focus on maintaining circulation, identifying and managing potential complications, and planning for discharge.Nursing AssessmentVital signs monitoring: Regularly monitor vital signs, including blood pressure, heart rate, respiratory rate, and temperature, to detect early signs of complications such as bleeding and infection.Circulation assessment: Monitor pulses, perform Doppler assessments, and check capillary refill, color, temperature, and...
347
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

1.5K
Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
1.5K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

376
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,...
376
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

You might also read

Related Articles

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

Sort by
Same author

Technological Evolution in the Perioperative Environment.

AORN journal·2025
Same author

Using AORN's standardized data framework for documentation.

AORN journal·2009
Same author

Perioperative fluid management.

AORN journal·2009
Same journal

Guideline Quick View: Environmental Hygiene.

AORN journal·2026
Same journal

Air Quality as a Cornerstone of Sterile Technique.

AORN journal·2026
Same journal

Brief Limb-Focused Prewarming in Adults Undergoing General Anesthesia: A Randomized Trial.

AORN journal·2026
Same journal

Clinical Issues - July 2026.

AORN journal·2026
Same journal

The Power of Learning From Mishaps and Missteps.

AORN journal·2026
Same journal

Embracing the Future of Care.

AORN journal·2026
See all related articles

Related Experiment Video

Updated: Jan 7, 2026

Reduced Procedure Time and Variability with Active Esophageal Cooling During Radiofrequency Ablation for Atrial Fibrillation
04:58

Reduced Procedure Time and Variability with Active Esophageal Cooling During Radiofrequency Ablation for Atrial Fibrillation

Published on: August 25, 2022

2.5K

Using Analytics to Reduce Perioperative Clinical Variance.

Sharon Giarrizzo-Wilson, Nancy Stimson

    AORN Journal
    |December 26, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Artificial intelligence and advanced analytics can reduce healthcare waste by analyzing surgical data. Cohorting electronic health records improves efficiency, leading to cost savings and better patient care.

    Keywords:
    artificial intelligence (AI)clinical variancecohortinghealth care analyticssurgical data

    More Related Videos

    Non-fluoroscopic Catheter Tracking for Fluoroscopy Reduction in Interventional Electrophysiology
    10:46

    Non-fluoroscopic Catheter Tracking for Fluoroscopy Reduction in Interventional Electrophysiology

    Published on: May 26, 2015

    13.7K
    Collecting And Measuring Wound Exudate Biochemical Mediators In Surgical Wounds
    04:58

    Collecting And Measuring Wound Exudate Biochemical Mediators In Surgical Wounds

    Published on: October 20, 2012

    12.4K

    Related Experiment Videos

    Last Updated: Jan 7, 2026

    Reduced Procedure Time and Variability with Active Esophageal Cooling During Radiofrequency Ablation for Atrial Fibrillation
    04:58

    Reduced Procedure Time and Variability with Active Esophageal Cooling During Radiofrequency Ablation for Atrial Fibrillation

    Published on: August 25, 2022

    2.5K
    Non-fluoroscopic Catheter Tracking for Fluoroscopy Reduction in Interventional Electrophysiology
    10:46

    Non-fluoroscopic Catheter Tracking for Fluoroscopy Reduction in Interventional Electrophysiology

    Published on: May 26, 2015

    13.7K
    Collecting And Measuring Wound Exudate Biochemical Mediators In Surgical Wounds
    04:58

    Collecting And Measuring Wound Exudate Biochemical Mediators In Surgical Wounds

    Published on: October 20, 2012

    12.4K

    Area of Science:

    • Health Services Research
    • Medical Informatics
    • Artificial Intelligence in Healthcare

    Background:

    • US healthcare spending faces significant waste from hospital care variance and inefficient practices.
    • Surgical procedures represent a major source of cost variance, contributing 70% to 80% of the total.
    • Reducing unwarranted clinical variation is crucial for improving perioperative care quality and controlling costs.

    Purpose of the Study:

    • To explore the application of advanced healthcare analytics and artificial intelligence in identifying costs and variability in surgical services.
    • To demonstrate how electronic health record (EHR) data cohorting can enhance efficiency and effectiveness in surgical departments.
    • To present a clinical example of cost savings achieved through reduced product variability in perioperative care.

    Main Methods:

    • Utilizing a suite of artificial intelligence approaches to analyze complex patient care data.
    • Employing advanced healthcare analytics to process electronic health record (EHR) data.
    • Cohorting EHR data based on similar surgical traits to identify patterns and variations.

    Main Results:

    • Identification of hidden costs and variability within daily surgical services operations.
    • Improved efficiency and effectiveness in surgical care delivery through data-driven insights.
    • Demonstrated cost savings in a clinical example by reducing product variability in surgical procedures.

    Conclusions:

    • Advanced healthcare analytics, particularly AI-driven approaches, can effectively pinpoint sources of waste in surgical care.
    • Cohorting EHR data is a viable strategy for reducing unwarranted clinical variation and improving perioperative outcomes.
    • Implementing these analytical methods leads to significant cost reductions and enhanced quality of care in surgical services.