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Related Concept Videos

Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

1.6K
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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Nursing Clinical Information System01:27

Nursing Clinical Information System

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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:
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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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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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Nursing Diagnosis01:22

Nursing Diagnosis

3.6K
Following assessment, a nursing diagnosis is the next step in the nursing process. It begins after the nurse has collected and recorded the patient data. The purpose of diagnosing is to identify how the client responds to actual or potential health processes, identify factors that bestow or that cause health problems, the etiologies, and identify resources or strengths the individual, group, or community can draw on to prevent or resolve problems.
The nursing diagnosis focuses on evidence-based...
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Nursing Process for Patient and Caregiver Teaching I: Assessment and Diagnosis01:24

Nursing Process for Patient and Caregiver Teaching I: Assessment and Diagnosis

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The nursing process provides a clinical decision-making framework for patients and families to establish and implement a personalized care plan. Since part of the nurse's duties is to teach patients, the steps of the nursing process are the most effective way to approach instruction. The nursing process and the teaching-learning process are inextricably linked.
It is critical to determine the patient's learning needs during the assessment. Determination of learning needs compounds data...
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Related Experiment Video

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A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis
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Developing an artificial intelligence-based clinical decision support system for nursing diagnoses.

Gülengün Türk1, Nihal Taşkıran1, Orhan Er2

  • 1Department of Fundamentals of Nursing, Aydin Adnan Menderes University Faculty of Nursing, Aydın, Turkey.

Nursing Outlook
|November 7, 2025
PubMed
Summary

Artificial intelligence (AI) enhances nursing diagnoses by improving accuracy and efficiency. An AI decision support system aids nurses in faster, more consistent clinical reasoning and care planning.

Keywords:
Artificial intelligenceClinical decision support systemMachine learningNursing diagnosis

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Area of Science:

  • Nursing Informatics
  • Clinical Decision Support Systems
  • Artificial Intelligence in Healthcare

Background:

  • Nursing diagnoses are vital for patient care planning and communication.
  • Diagnostic accuracy is often hindered by high workloads and complex patient data.
  • Artificial intelligence (AI) integration can improve clinical reasoning and standardize nursing language.

Purpose of the Study:

  • To develop an AI-based clinical decision support system (CDSS).
  • To enhance diagnostic accuracy in nursing using fewer defining characteristics.
  • To leverage AI for improved clinical decision-making in nursing.

Main Methods:

  • Utilized Gordon's Functional Health Patterns Model for data collection.
  • Collected data from 122 patients, including defining characteristics, risk factors, and nursing diagnoses.
  • Tested various machine learning algorithms for diagnostic accuracy.

Main Results:

  • The Naive Bayes algorithm achieved 96.33% accuracy with all indicators.
  • Gradient Boosting reached 82.94% accuracy using the 50 most important variables.
  • Identified key nursing diagnoses such as "Obesity" and "Acute Pain" effectively.

Conclusions:

  • The AI-based CDSS demonstrated high accuracy in nursing diagnoses.
  • The system shows potential to assist nurses in making faster diagnostic decisions.
  • AI can support more consistent and accurate nursing care planning.