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Related Concept Videos

Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

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 assessment...
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

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...
Data Validation01:03

Data Validation

Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

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 illness...

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Related Experiment Video

Updated: May 24, 2026

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

Challenges in Using Clinical Data for AI-Enabled Diagnostic Support.

Mirela Prgomet1, Getiye Dejenu Kibret1, Judith Thomas1

  • 1Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

Secondary use of clinical data for research presents challenges. Key issues include data quality, context, and coding, impacting AI tool development in healthcare.

Keywords:
Artificial intelligenceclinical decision supportdata qualitypathology

Related Experiment Videos

Last Updated: May 24, 2026

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

Area of Science:

  • Clinical Informatics
  • Health Data Science
  • Artificial Intelligence in Medicine

Background:

  • Secondary use of clinical data is crucial for research and AI development.
  • Routinely collected clinical data often presents unique challenges for research applications.

Purpose of the Study:

  • To examine laboratory values and diagnoses from a multi-hospital dataset.
  • To identify interpretive considerations and challenges in using routinely collected clinical data.
  • To inform the development of AI-enabled clinical tools.

Main Methods:

  • Retrospective analysis of a multi-hospital dataset.
  • Examination of laboratory values and associated diagnoses for common tests.
  • Investigated unanticipated findings to understand data limitations.

Main Results:

  • Identified significant challenges in secondary data use.
  • Highlighted issues with data quality, completeness, temporality, clinical context, coding, and governance.
  • Unanticipated findings underscore the need for careful data interpretation.

Conclusions:

  • Routinely collected clinical data requires careful consideration for secondary use.
  • Data quality, context, and coding are critical barriers for AI development.
  • Addressing these challenges is essential for reliable AI tools in healthcare.