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

Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

2.1K
Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
2.1K
Nursing Interventions II: Selecting and Classifying the Nursing Interventions01:29

Nursing Interventions II: Selecting and Classifying the Nursing Interventions

2.9K
Creating and executing a nursing diagnosis helps nurses plan care and guide patient, family, and community interventions. They are developed based on a patient's physical evaluation and support measuring the outcomes. It is not recommended to select random interventions throughout the planning process. Instead, consider the following six essential factors when choosing interventions:
2.9K
Integrated Healthcare System01:20

Integrated Healthcare System

2.1K
An integrated healthcare system (IHS) is a set of organizations that provides for or arranges to provide coordinated and continuous service to a defined population. The IHS takes responsibility for that particular population's health status and outcome, both clinically and fiscally. An integrated healthcare system is a well-organized, well-coordinated, and collaborative network. The integrated delivery system is a network that connects different healthcare providers to deliver organized,...
2.1K
Methods Of Healthcare Delivery System01:26

Methods Of Healthcare Delivery System

3.8K
At the different levels of the healthcare system, we see varying methods of healthcare used. These methods include managed care systems, case management, and primary healthcare.
Managed Care System:
The managed care system is designed to control the cost while maintaining the quality of care. The patient's care from admission to discharge is planned by the primary care provider or the case manager, also known as the gatekeeper. In a managed care system, the number of care providers is...
3.8K
Nursing Clinical Information System01:27

Nursing Clinical Information System

1.1K
Nursing Clinical Information System (NCIS)
A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
Critical attributes of NCIS include:
1.1K
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.3K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.3K

You might also read

Related Articles

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

Sort by
Same author

LLM-Based Extraction of Clinical Practice Guidelines into Structured Arguments.

Studies in health technology and informatics·2026
Same author

Double-Decoder U-Net for Improved Segmentation of the Overlapping Trapezium Bone in X-ray Images.

Studies in health technology and informatics·2026
Same author

Prospectively evaluating the environmental impacts of digital health applications: a case study and recommendations.

Journal of the American Medical Informatics Association : JAMIA·2026
Same author

Adoption and Use of Social Media in Health Care Among Medical Residents: Cross-Sectional Study.

JMIR medical education·2026
Same author

Generation of Training Data to Distinguish Adverse Events from Medical Conditions.

Studies in health technology and informatics·2026
Same author

Challenges and Opportunities in Postgraduate Digital Health Education.

Studies in health technology and informatics·2026

Related Experiment Video

Updated: Dec 16, 2025

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management
05:35

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

Published on: January 19, 2024

1.2K

Decision Support System for Selection of e-Health Interventions.

Dahbia Agher1,2, Marc Fouque1, Matteo Brandi1

  • 1INSERM, University Sorbonne Paris Nord, Sorbonne University, Laboratory of Medical Informatics and Knowledge Engineering in e-Health, LIMICS, Paris, France.

Studies in Health Technology and Informatics
|July 2, 2020
PubMed
Summary

This study developed a decision support system to personalize cardiovascular risk prevention. The system identifies patient behaviors and recommends tailored mobile health interventions for better goal achievement.

Keywords:
Non-drug intervention (NDI)cardiovascular risk factore-health interventionprevention

More Related Videos

E-Patient Counseling Trial E-PACO: Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy
06:28

E-Patient Counseling Trial E-PACO: Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy

Published on: August 1, 2019

8.7K
A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis
04:19

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

Published on: May 10, 2022

4.2K

Related Experiment Videos

Last Updated: Dec 16, 2025

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management
05:35

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

Published on: January 19, 2024

1.2K
E-Patient Counseling Trial E-PACO: Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy
06:28

E-Patient Counseling Trial E-PACO: Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy

Published on: August 1, 2019

8.7K
A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis
04:19

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

Published on: May 10, 2022

4.2K

Area of Science:

  • Cardiovascular disease prevention
  • Health informatics
  • Behavioral science

Background:

  • Cardiovascular disease remains a leading cause of mortality globally.
  • Effective prevention strategies require personalized approaches considering individual behaviors and risk factors.
  • Current e-health interventions lack tailored messaging and decision support.

Purpose of the Study:

  • To design a decision support system for personalized cardiovascular risk prevention.
  • To identify behavioral groups linked to clinical risk factors.
  • To recommend tailored mobile health interventions and prevention messages.

Main Methods:

  • Developed a decision support system integrating a data prediction model.
  • Incorporated clinical risk factors, risky behaviors, and social status into the model.
  • Embedded the system within a novel e-health application for patient engagement.

Main Results:

  • The decision support system is now operational.
  • The system effectively categorizes patients into behavioral groups.
  • Personalized intervention recommendations are generated based on identified needs.

Conclusions:

  • The developed system offers a novel approach to personalized cardiovascular risk prevention.
  • The system's operational status paves the way for clinical validation.
  • Future research will focus on large-scale studies to evaluate patient adherence and outcomes.