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

Variability: Analysis01:11

Variability: Analysis

448
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...
448
Interpreting Run Charts01:25

Interpreting Run Charts

3.0K
Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
3.0K
Introduction to Statistical Process Control01:15

Introduction to Statistical Process Control

605
Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...
605
Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

860
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...
860
The X̄ Chart00:58

The X̄ Chart

446
The  x̄ chart is a statistical tool for monitoring the means in a process.
The x̄ chart, often known as the individual control chart, is a crucial tool in statistical process control. It is designed to monitor process behavior and performance over time and is widely used in various industries to ensure that processes are operating at their optimum capacity and within specified limits.
A x̄ chart is constructed by plotting individual measurements of a quality...
446
Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

3.3K
Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
3.3K

You might also read

Related Articles

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

Sort by
Same author

Real-world performance of point-of-care metre, blood gas and laboratory glucose methods in Australian and American hospital inpatient cohorts.

Diabetic medicine : a journal of the British Diabetic Association·2026
Same author

Isobolographic Analysis of the Interaction Between Remimazolam and Etomidate in General Anesthesia Induction: A Prospective, Sequential Allocation Study Based on the Dixon Up-and-Down Method.

Drug design, development and therapy·2026
Same author

Development of a Digital Phenotype for Immune-Related Colitis.

JCO clinical cancer informatics·2026
Same author

Methanol detection under variable humidity conditions using an improved wavelength modulation spectroscopy method based on first harmonic signal analysis.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026
Same author

Multisite Coadsorption of the *OOH Intermediate on NiFeOOH Hierarchical Nanosheet Arrays Boost Water Electro-Oxidation at Ultrahigh Current Densities.

ACS applied materials & interfaces·2026
Same author

GSC-YOLO: A Pedestrian Detection Method for Low-Light Security Surveillance Scenarios.

Sensors (Basel, Switzerland)·2026
Same journal

Evaluation of temporal preservation in synthetic longitudinal patient data.

Journal of biomedical informatics·2026
Same journal

ARKE: An ontology-driven framework for automated mapping of local radiology procedure terms to the LOINC-RadLex playbook using large language model.

Journal of biomedical informatics·2026
Same journal

A validation-driven training controller for cross-lingual biomedical NER via reinforcement learning-based adaptive loss weighting.

Journal of biomedical informatics·2026
Same journal

ASP-HR: An Adaptive Spatial Perception and Hierarchical Reasoning mechanism for document-level biomedical relation extraction.

Journal of biomedical informatics·2026
Same journal

Beyond Accuracy: Safety-Centered guidelines for the evaluation of LLM-based therapy recommendation systems for chronic multimorbidity patients.

Journal of biomedical informatics·2026
Same journal

DeepEN: A deep reinforcement learning framework for personalized enteral nutrition in critical care.

Journal of biomedical informatics·2026
See all related articles

Related Experiment Video

Updated: Jan 16, 2026

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
14:32

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Published on: February 16, 2011

24.3K

Measuring and visualizing healthcare process variability.

Pengfei Yin1, Abel Armas Cervantes1, Daniel Capurro2

  • 1School of Computing and Information Systems, University of Melbourne, Australia.

Journal of Biomedical Informatics
|September 25, 2025
PubMed
Summary
This summary is machine-generated.

Higher clinical variability in patient care significantly increases length of stay (LOS), especially for those undergoing coronary bypass surgery. Understanding this variability is key to improving patient outcomes and healthcare efficiency.

Keywords:
Clinical pathwaysClinical processProcess miningUnwarranted clinical variationValue-based careVariability analysisVariability visualization

More Related Videos

Author Spotlight: Workflow for Integrating POCUS Data into EHR for Managing Heart Failure Patients
03:47

Author Spotlight: Workflow for Integrating POCUS Data into EHR for Managing Heart Failure Patients

Published on: July 12, 2024

1.1K
Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
07:51

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

8.0K

Related Experiment Videos

Last Updated: Jan 16, 2026

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
14:32

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Published on: February 16, 2011

24.3K
Author Spotlight: Workflow for Integrating POCUS Data into EHR for Managing Heart Failure Patients
03:47

Author Spotlight: Workflow for Integrating POCUS Data into EHR for Managing Heart Failure Patients

Published on: July 12, 2024

1.1K
Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
07:51

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

8.0K

Area of Science:

  • Healthcare Management
  • Clinical Informatics
  • Patient Outcomes Research

Background:

  • Unwarranted clinical variability can negatively impact patient care, leading to adverse events and extended hospital stays.
  • Identifying excessive clinical variation is crucial for optimizing healthcare efficiency and patient outcomes.
  • The relationship between clinical variation and patient outcomes, such as length of stay (LOS), requires further investigation.

Purpose of the Study:

  • To explore the association between clinical variation and clinical outcomes, specifically length of stay (LOS).
  • To identify the critical point in time when clinical variation significantly impacts LOS.
  • To establish a standardized method for measuring clinical process variability and its effect on patient outcomes.

Main Methods:

  • A cohort study utilizing the MIMIC-IV database of electronic health records from Beth Israel Deaconess Medical Center.
  • Analysis focused on 847 adult patients undergoing elective coronary bypass surgery.
  • Regression analysis was employed to determine the temporal window where clinical variability independently and significantly influences LOS, controlling for demographic factors and comorbidities.

Main Results:

  • Patients in the highest quartile of variability experienced an 81% increase in LOS.
  • Higher Charlson Comorbidity Index (CCI) was associated with a 3.3% increase in LOS.
  • Insurance types (Medicare, Other) were linked to decreased LOS, while age and race showed no significant impact.

Conclusions:

  • Greater clinical variability in patient journeys is directly associated with longer LOS in a dose-response manner.
  • This study introduces a standardized approach to quantify and visualize clinical process variability.
  • The findings underscore the importance of managing clinical variability to improve patient outcomes and healthcare efficiency.