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Documentation of Nursing Diagnosis

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...

Guidelines for Nursing Documentation II

Guidelines for Nursing Documentation II

Effective documentation is an integral part of nursing practice. Here are some essential guidelines to follow when documenting patient care:
Timely documentation is crucial to ensure continuity of care for patients. Any delays in recording or reporting medical information can result in medical errors and even adverse patient outcomes. From medication administration to diagnostic test results, every detail must be accurately and promptly documented to provide the best possible care for patients.

Guidelines for Nursing Documentation I

Guidelines for Nursing Documentation I

Quality documentation and reporting share essential characteristics that ensure they are practical and valuable resources for those who use them. These characteristics are:
Factual:
The following points emphasize the significance of upholding accurate and unbiased documentation in healthcare.

Formulating and Validating Nursing Diagnosis II

Formulating and Validating Nursing Diagnosis II

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...

Nursing Diagnosis

Nursing Diagnosis

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...

Formulating and Validating Nursing Diagnosis I

Formulating and Validating Nursing Diagnosis I

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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The Immersive Cleveland Clinic Virtual Reality Shopping Platform for the Assessment of Instrumental Activities of Daily Living

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Generative AI-Based Nursing Diagnosis and Documentation Recommendation Using Virtual Patient Electronic Nursing

Hongshin Ju^1,2, Minsul Park^1,3, Hyeonsil Jeong¹

¹DKMediInfo, Hwaseong, Korea.

Healthcare Informatics Research

|May 19, 2025

Summary

Generative artificial intelligence (AI) significantly cut nursing documentation time by 40%, improving efficiency. While AI shows promise, further development is needed to enhance accuracy for seamless clinical integration.

Keywords:

Electronic Health Records Generative Artificial Intelligence Nursing Diagnosis Nursing Informatics Nursing Records

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Area of Science:

Nursing Informatics
Artificial Intelligence in Healthcare
Clinical Documentation

Background:

Nursing documentation accounts for approximately 30% of nurses' time, impacting workflow and patient safety.
Optimizing documentation efficiency is crucial for modern healthcare settings.
Traditional electronic nursing records (ENRs) present challenges in time management for nursing staff.

Purpose of the Study:

To compare the efficiency of traditional nursing documentation with a generative AI-based system.
To evaluate the impact of AI on reducing documentation time and improving accuracy.
To assess the overall efficiency and quality of AI-assisted nursing documentation.

Main Methods:

Forty nurses with at least 6 months of experience participated in the study.
A pre-assessment phase used traditional ENRs, followed by a post-assessment phase using a generative AI system (SmartENR, based on ChatGPT 4.0).
Documentation quality was assessed using a 5-point scale for accuracy, comprehensiveness, usability, ease of use, and fluency.

Main Results:

AI-assisted documentation reduced time by approximately 40% (467.18s traditional vs. 182.68s AI).
AI-generated documentation received high scores for ease of use (4.80/5) and fluency (4.50/5).
Scores for accuracy (3.62/5) and usability (3.50/5) indicate areas for AI model refinement.

Conclusions:

Generative AI substantially decreases nursing documentation workload and enhances efficiency.
AI systems have the potential to improve nursing documentation quality and efficiency in clinical practice.
Further AI model refinement is necessary to optimize accuracy and facilitate seamless integration into clinical workflows.