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

Nursing Clinical Information System01:27

Nursing Clinical Information System

912
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:
912
Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

989
Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
989
Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

653
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...
653
Critical Thinking II01:25

Critical Thinking II

3.5K
Critical thinking is a cognitive process with several attributes. The attributes of critical thinking include the following:
3.5K
Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

1.4K
The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters...
1.4K
Methods of Documentation III: PIE01:21

Methods of Documentation III: PIE

1.7K
Problem-intervention-evaluation (PIE) is a systematic approach to documentation used in healthcare settings for clinical decision-making and patient care planning. It is a structured approach to organizing patient data based on problems, interventions, and evaluations. Here's a breakdown of its key features and considerations:
1.7K

You might also read

Related Articles

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

Sort by
Same author

Evaluation of the Medical Utilization of the Telemedicine Pilot Project for Patients With Diabetes Based on Korean National Health Insurance Claims Data.

Journal of Korean medical science·2026
Same author

Investigation of the Impact of Body Mass Index in the 20s on Chronic Metabolic Diseases and Their Progression Rate After the Age of 65 Years.

Journal of Korean medical science·2025
Same author

Evolving Regulations in Telemedicine Pilot Project: Insights Into Law, Practice, and Patient Care through International Case Studies.

Journal of Korean medical science·2025
Same author

Dark Data in Real-World Evidence: Challenges, Implications, and the Imperative of Data Literacy in Medical Research.

Journal of Korean medical science·2024
Same author

Comorbidity Patterns and Management in Inpatients with Endocrine Diseases by Age Groups in South Korea: Nationwide Data.

Journal of personalized medicine·2024
Same author

Defining Medical AI Competencies for Medical School Graduates: Outcomes of a Delphi Survey and Medical Student/Educator Questionnaire of South Korean Medical Schools.

Academic medicine : journal of the Association of American Medical Colleges·2024
Same journal

The Role of YouTube as an Information Platform for Ovarian Cancer: the Educational Quality and Reliability.

Yonsei medical journal·2026
Same journal

Bone Bridge Effect for the Treatment of Acute Osteoporotic Vertebral Compression Fractures: A Multistrategic Approach Using an Anabolic Agent.

Yonsei medical journal·2026
Same journal

Outcomes and Prognostic Factors in Hepatopancreatoduodenectomy.

Yonsei medical journal·2026
Same journal

The Risk of Incident Diabetes Mellitus in Relation to Egg Consumption among Working-Aged Korean Adults.

Yonsei medical journal·2026
Same journal

SpyGlass Direct Visualization System: A Cost-Effective Approach Enhancing Nutritional and Immune Recovery in Difficult Bile Duct Stone Management.

Yonsei medical journal·2026
Same journal

Optimizing Antibiotic Use for Urinary Tract Infections: A Qualitative Assessment of Regular Prescribing Practices.

Yonsei medical journal·2026
See all related articles

Related Experiment Video

Updated: Oct 9, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

685

Physician Knowledge Base: Clinical Decision Support Systems.

Sira Kim1, Eung-Hee Kim2, Hun-Sung Kim3,4

  • 1Center of Smart Healthcare, Pyeonghwa IS, Seoul, Korea.

Yonsei Medical Journal
|December 16, 2021
PubMed
Summary
This summary is machine-generated.

Electronic medical records (EMRs) enable vast data accumulation for clinical decision support systems (CDSSs). Future CDSS development requires continuous algorithm updates and evaluation of their effectiveness with medical staff input.

Keywords:
Artificial intelligenceclinicaldecision support systemsdeep learning

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

4.8K
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

7.7K

Related Experiment Videos

Last Updated: Oct 9, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

685
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

4.8K
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

7.7K

Area of Science:

  • Medical Informatics
  • Health Information Technology
  • Clinical Decision Support

Background:

  • Electronic medical records (EMRs) facilitate the collection of substantial qualitative medical data, driving the integration of clinical decision support systems (CDSSs).
  • Current CDSSs aim to minimize physician-related medical errors but face challenges in technical maturity and data completeness for widespread medical adoption.
  • The continuous accumulation of medical data necessitates ongoing updates to CDSS algorithms to maintain their core functionalities, demanding significant time and resources.

Purpose of the Study:

  • To evaluate the potential effectiveness of future clinical decision support systems (CDSSs).
  • To analyze the developmental possibilities for advanced CDSS technology.
  • To incorporate the understanding of CDSS core functions by medical staff into the evaluation framework.

Main Methods:

  • Review and analysis of existing clinical decision support system (CDSS) technology.
  • Assessment of data accumulation trends from electronic medical records (EMRs).
  • Consideration of medical staff comprehension of CDSS functionalities in evaluating future potential.

Main Results:

  • The study highlights the critical need for continuous algorithm refinement in CDSSs as data volumes grow.
  • Current CDSS technology requires further development to meet rigorous medical standards.
  • No prior research has comprehensively assessed future CDSS effectiveness or development pathways.

Conclusions:

  • Future CDSS development must prioritize adaptability and continuous improvement of algorithms.
  • Integrating medical staff insights is crucial for developing effective and user-centric CDSS solutions.
  • Further research is warranted to explore novel approaches for CDSS advancement and validation.