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

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

5.8K
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...
5.8K
Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

923
Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
923
Data Collection I01:30

Data Collection I

6.6K
Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
6.6K
Purpose of Health Records I01:11

Purpose of Health Records I

1.4K
The vital purpose of health records is to provide a complete and accurate account of a patient's medical history, including communication, diagnostic and therapeutic orders, care planning, research, and quality review.
Here's a breakdown of how health records serve these purposes:
1.4K

You might also read

Related Articles

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

Sort by
Same author

An Explanation User Interface for Artificial Intelligence-Supported Mechanical Ventilation Optimization for Clinicians: User-Centered Design and Formative Usability Study.

JMIR formative research·2026
Same author

Collaborative and Cooperative Hospital "In-House" Medical Device Development and Implementation in the AI Age: The European Responsible AI Development (EURAID) Framework Compatible With European Values.

Journal of medical Internet research·2026
Same author

Analyzing and predicting patient admissions related to acute heat at the Chemnitz Hospital (Germany).

Archives of public health = Archives belges de sante publique·2025
Same author

Analyzing the association between heat and utilization of inpatient care: evidence from Dresden University Hospital (Germany).

BMC public health·2025
Same author

The 'Advancing Cardiovascular Risk Identification with Structured Clinical Documentation and Biosignal Derived Phenotypes Synthesis' project: conceptual design, project planning, and first implementation experiences.

European heart journal. Digital health·2025
Same author

Ethics and Algorithms to Navigate AI's Emerging Role in Organ Transplantation.

Journal of clinical medicine·2025

Related Experiment Video

Updated: Sep 13, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

251

Enhancing Clinical Data Infrastructure for AI Research: Comparative Evaluation of Data Management Architectures.

Richard Gebler1, Ines Reinecke2, Martin Sedlmayr1

  • 1Faculty of Medicine and University Hospital Carl Gustav Carus, Dresden University of Technology, Dresden, Germany.

Journal of Medical Internet Research
|August 1, 2025
PubMed
Summary

Choosing the right clinical data management architecture is key for AI research and patient care. Data warehouses offer governance, data lakes provide flexibility, and data lakehouses balance both but require expertise.

Keywords:
big data in health careclinical data managementdata architecture evaluationdata lakedata lakehousedata warehouse

More Related Videos

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients
09:00

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients

Published on: April 13, 2021

4.7K
Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.4K

Related Experiment Videos

Last Updated: Sep 13, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

251
TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients
09:00

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients

Published on: April 13, 2021

4.7K
Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.4K

Area of Science:

  • Health Informatics
  • Data Science
  • Artificial Intelligence in Healthcare

Background:

  • Rapid growth of clinical data presents challenges for healthcare organizations.
  • Traditional data management struggles with large, diverse, and dynamic datasets.
  • Need for architectures supporting AI research and improved patient care.

Purpose of the Study:

  • Compare clinical data warehouses, data lakes, and data lakehouses.
  • Analyze architectures using FAIR principles and Big Data 5 V's.
  • Guide selection balancing data governance and analytics flexibility.

Main Methods:

  • Developed a framework integrating data governance and technical performance.
  • Conducted a rapid literature review on data management architectures.
  • Assessed scalability, real-time processing, metadata, and expertise requirements.

Main Results:

  • Data warehouses: strong governance, limited scalability/real-time processing.
  • Data lakes: flexible, scalable for heterogeneous data, potential quality/metadata issues.
  • Data lakehouses: combine strengths, require high expertise and complex integration.

Conclusions:

  • Optimal architecture depends on organizational needs, resources, and goals.
  • Balance governance, flexibility, and scalability for future-proof infrastructure.
  • Further research needed to simplify hybrid models and improve clinical standards integration.