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

922
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...
922
Documentation in Long-Term and Home Healthcare Setting01:29

Documentation in Long-Term and Home Healthcare Setting

1.5K
Documentation in long-term care facilities and home healthcare settings is crucial for ensuring continuous, coordinated, and comprehensive care for patients. Each setting has its specific documentation processes and tools:
Long-Term Care Facilities
1.5K
Variability: Analysis01:11

Variability: Analysis

944
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...
944
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

712
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
712
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

422
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
422
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

2.2K
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...
2.2K

You might also read

Related Articles

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

Sort by
Same author

Relationships between multimodal ocular imaging and white matter hyperintensity volume.

Quantitative imaging in medicine and surgery·2025
Same author

Atorvastatin pretreatment, ST-segment resolution and long-term prognosis for ST-segment elevation myocardial infarction with primary percutaneous coronary intervention.

International journal of cardiology. Heart & vasculature·2025
Same author

Age and gender-related changes in choroidal thickness: Insights from deep learning analysis of swept-source OCT images.

Photodiagnosis and photodynamic therapy·2025
Same author

Deep learning-based anterior segment identification and parameter assessment of primary angle closure disease in ultrasound biomicroscopy images.

BMJ open ophthalmology·2025
Same author

Evaluation of an acne lesion detection and severity grading model for Chinese population in online and offline healthcare scenarios.

Scientific reports·2025
Same author

Automated measurement and correlation analysis of fundus tessellation and optic disc characteristics in myopia.

Scientific reports·2024
Same journal

A GenAI Pipeline for Violinist Kinematic Data Management.

Studies in health technology and informatics·2026
Same journal

AMAL-For-Qatar: A Comprehensive AI Ecosystem for Fetal Ultrasound Analysis - Project Overview and Achievements.

Studies in health technology and informatics·2026
Same journal

Longitudinal Treatment-Aware Multimodal AI for Dermatology: A Scoping Review.

Studies in health technology and informatics·2026
Same journal

Predicting Postpartum Depression Using Imbalance-Aware Machine Learning.

Studies in health technology and informatics·2026
Same journal

Validation of Deep-Learning Models for Autosegmentation of Brain Metastases.

Studies in health technology and informatics·2026
Same journal

Delay-Dependent Gating in Modular RNNs.

Studies in health technology and informatics·2026
See all related articles

Related Experiment Video

Updated: Apr 25, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.6K

Automatic variance analysis of multistage care pathways.

Xiang Li1, Haifeng Liu1, Shilei Zhang1

  • 1IBM Research, Beijing, China.

Studies in Health Technology and Informatics
|August 28, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a method to automatically detect deviations in patient care pathways using electronic medical records. Analyzing these variances helps improve care quality and reduce medical errors.

More Related Videos

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.1K
Assessment of Dependence in Activities of Daily Living Among Older Patients in an Acute Care Unit
06:52

Assessment of Dependence in Activities of Daily Living Among Older Patients in an Acute Care Unit

Published on: September 30, 2020

9.8K

Related Experiment Videos

Last Updated: Apr 25, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.6K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.1K
Assessment of Dependence in Activities of Daily Living Among Older Patients in an Acute Care Unit
06:52

Assessment of Dependence in Activities of Daily Living Among Older Patients in an Acute Care Unit

Published on: September 30, 2020

9.8K

Area of Science:

  • Health Informatics
  • Clinical Process Analysis
  • Medical Data Mining

Background:

  • Care pathways (CP) standardize healthcare delivery but actual patient care often deviates.
  • Analyzing these deviations is crucial for refining care pathways and minimizing medical errors.

Purpose of the Study:

  • To propose an automated method for identifying deviations between actual patient traces in electronic medical records (EMR) and predefined multistage care pathways.
  • To enable clinicians to refine care pathways and enhance patient care quality.

Main Methods:

  • Developed a care pathway variance analysis method using hidden Markov models for trace alignment.
  • Employed temporal logic and binomial tests to identify three types of deviations: additional activities, absent activities, and violated constraints.

Main Results:

  • Successfully applied the method to a congestive heart failure care pathway using real-world EMR data.
  • Identified specific deviations within care stages, providing actionable insights for quality improvement.

Conclusions:

  • The proposed method effectively identifies deviations in patient care pathways from EMR data.
  • This analysis offers meaningful evidence for improving the quality of care and reducing medical errors.