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

836
Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
836
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

5.6K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
5.6K
Introduction To Health Care Delivery System01:18

Introduction To Health Care Delivery System

2.7K
The healthcare system is constantly changing and complex. Various services are available from different healthcare providers, but gaining access to these services has become challenging for people with limited healthcare insurance. Uninsured people present a challenge to healthcare because they frequently postpone or forego treatment.
The Institute of Medicine (IOM) advocates for a patient-centered, effective, safe, timely, equitable, and effective healthcare system. The National Priorities...
2.7K
Methods Of Healthcare Delivery System01:26

Methods Of Healthcare Delivery System

3.3K
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...
3.3K
Integrated Healthcare System01:20

Integrated Healthcare System

1.6K
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,...
1.6K
Nursing Clinical Information System01:27

Nursing Clinical Information System

771
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:
771

You might also read

Related Articles

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

Sort by
Same author

Unveiling Mild OSA: Oximetry Clusters Reveal Hidden Sleep Disruption Independent of AHI.

Journal of sleep research·2026
Same author

Large Language Model-Generated Patient Instructions for Prescriptions in Primary Health Care: Preclinical Algorithm Validation.

Journal of medical Internet research·2026
Same author

Comparison of Allergic Rhinitis Treatments on Utilities and Quality of Life: A MASK-air Study.

Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology·2026
Same author

Culturally Adapted Lifestyle and Mental Health Intervention for Low-Income Pregnant Women: A Feasibility Study.

Western journal of nursing research·2025
Same author

Assessment of the Effectiveness of Allergic Rhinitis Medications Using a Target Trial Emulation Approach Based on Mobile Health Data.

Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology·2025
Same author

Interventions Based on Biofeedback Systems to Improve Workers' Psychological Well-Being, Mental Health, and Safety: Systematic Literature Review.

Journal of medical Internet research·2025

Related Experiment Video

Updated: Jun 29, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.1K

Fast Healthcare Interoperability Resources-Based Support System for Predicting Delivery Type: Model Development and

João Coutinho-Almeida1,2,3, Alexandrina Cardoso4, Ricardo Cruz-Correia1,2,3

  • 1Faculty of Medicine, University of Porto, Porto, Portugal.

JMIR Formative Research
|April 8, 2024
PubMed
Summary

A machine learning system can identify potentially unnecessary cesarean deliveries, aiding clinical decisions and potentially reducing costs in Portugal. Further analysis is needed for real-world application.

Keywords:
algorithmalgorithmscesareancesarean deliveriescesarean deliveryclinical decision supportdecision supportinteroperabilityinteroperablemachine-learningmaternalobstetricobstetricspregnancypregnantsimulationsimulations

More Related Videos

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

791
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

7.6K

Related Experiment Videos

Last Updated: Jun 29, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.1K
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

791
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

7.6K

Area of Science:

  • Medical Informatics
  • Machine Learning in Healthcare
  • Health Economics

Background:

  • Cesarean delivery rates are rising globally, impacting maternal and newborn health.
  • Portugal's cesarean delivery rate reached 36.3% in 2020, with unclear contributing factors.
  • National initiatives aim to reduce cesarean delivery occurrences in Portugal.

Purpose of the Study:

  • Develop a machine learning (ML) based decision support system to identify potentially unnecessary cesarean deliveries.
  • Utilize interoperability standards for clinical decision support systems in obstetrics.
  • Identify predictive factors for delivery type and assess the economic impact of the ML tool.

Main Methods:

  • Retrospective analysis of maternal and fetal data from 9 Portuguese public hospitals (2019-2020).
  • Development and deployment of ML models, with LightGBM selected for efficiency.
  • Comparison of ML model outputs with clinician assessments and economic simulations.

Main Results:

  • The LightGBM model achieved an 88% area under the ROC curve.
  • 3.8% of deliveries in a trial phase triggered alarms for potentially unnecessary cesarean deliveries.
  • Economic simulations suggest potential benefits for 30% of Portuguese public hospitals.

Conclusions:

  • The ML system shows promise in identifying potentially incorrect cesarean delivery decisions, impacting medical practice and health economics.
  • Challenges include model biases and the need for further evaluation of clinical decision-making impacts.
  • Careful implementation and robust analysis are crucial for realizing the system's full potential.