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Related Concept Videos

Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

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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...
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Formulating and Validating Nursing Diagnosis II01:25

Formulating and Validating Nursing Diagnosis II

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Nursing diagnoses represent a problem validated by major defining characteristics. There are four categories of nursing diagnoses: problem-focused, risk, health promotion or wellness, and syndrome. The anatomy of a nursing diagnosis includes three components: problem statement or diagnostic label, defining characteristics, and related factors.
Risk nursing diagnoses represent clinical judgments of an individual, family, or community more vulnerable to developing the health problem than others...
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Formulating and Validating Nursing Diagnosis I01:26

Formulating and Validating Nursing Diagnosis I

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A nursing diagnosis is written when the nurse recognizes a cluster of essential patient data indicating health problems treated with independent nursing interventions. The standardized terminologies of a nursing diagnosis help nurses identify and treat patients' problems. Every electronic health record that uses nursing diagnosis must employ standard diagnostic terminology. Developing an efficient, individualized care plan begins with accurate nursing diagnoses.
There are thirteen domains...
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Strategies for Assessing and Addressing Confounding01:25

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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
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Diagnostic and Statistical Manual of Mental Disorders (DSM)01:27

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The Diagnostic and Statistical Manual of Mental Disorders (DSM) serves as the primary classification system for mental health disorders, providing standardized diagnostic criteria for clinicians and researchers. First published by the American Psychiatric Association (APA) in 1952, the DSM has undergone several revisions to reflect evolving psychiatric understanding. The fifth edition, DSM-5, released in 2013, introduced key updates that expanded diagnostic categories and modified diagnostic...
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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
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Related Experiment Video

Updated: Sep 25, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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A novel methodological framework was described for detecting and quantifying overdiagnosis.

Katy Bell1, Jenny Doust2, Sharon Sanders3

  • 1Sydney School of Public Health, Sydney Medical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales 2006, Australia.

Journal of Clinical Epidemiology
|April 28, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework to quantify overdiagnosis in noncancer conditions, addressing a gap in current methods. The framework, demonstrated with gestational diabetes mellitus, aids in evaluating diagnostic strategies and their impact on patient outcomes.

Keywords:
Chronic diseaseClinical epidemiologyDiagnostic testsEvidence based medicineMedical overuseOverdiagnosis

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Area of Science:

  • Medical Diagnostics
  • Health Services Research
  • Epidemiology

Background:

  • Quantifying overdiagnosis is established for screen-detected cancers but lacks methods for noncancerous conditions.
  • Existing definitions of overdiagnosis are prognosis-based or utility-based.
  • Accurate quantification of overdiagnosis is crucial for evaluating diagnostic strategies.

Purpose of the Study:

  • To develop a methodological framework for quantifying overdiagnosis in both symptomatic and asymptomatic noncancer conditions.
  • To apply this framework using gestational diabetes mellitus as a case study.
  • To provide a tool for guiding future research on overdiagnosis.

Main Methods:

  • Development of a framework based on prognostic information and clinical utility of diagnoses.
  • Utilizing Causal Directed Acyclic Graphs (DAGs) to model relationships between diagnostic strategies and overdiagnosis.
  • Application of the "Fair Umpire" framework to estimate overdiagnosis using gestational diabetes mellitus data.

Main Results:

  • The study proposes a novel framework for quantifying overdiagnosis applicable to noncancer conditions.
  • The "Fair Umpire" framework, when applied to gestational diabetes mellitus, demonstrates a method for estimating overdiagnosis.
  • The framework illuminates the complex relationships between diagnostic strategies and the frequency of overdiagnosis.

Conclusions:

  • The developed framework can quantify overdiagnosis in noncancer conditions, complementing existing methods for cancer.
  • This approach provides a robust method for evaluating the clinical utility and prognostic information of diagnostic strategies.
  • The framework is intended to guide further research and improve diagnostic practices, potentially reducing unnecessary medical interventions.