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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.
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Formulating and Validating Nursing Diagnosis I01:26

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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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Nursing Diagnosis01:22

Nursing Diagnosis

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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.
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Formulating and Validating Nursing Diagnosis II01:25

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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.
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Role of Communication in the Nursing Process I: Assessment and Diagnosis01:25

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The nursing process uses scientific reasoning, problem-solving, and critical thinking to guide nurses in providing patients with appropriate care. This process is a systematic approach to recognize, avoid, and treat current or potential health issues while promoting the patient's well-being.
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Nursing Process for Patient and Caregiver Teaching I: Assessment and Diagnosis01:24

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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.
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Developing a Classification Algorithm for Prediabetes Risk Detection From Home Care Nursing Notes: Using Natural

Eunjoo Jeon1, Aeri Kim, Jisoo Lee

  • 1Author Affiliations: Technology Research, SamsungSDS (Dr Jeon); College of Nursing, Seoul National University (Mss Kim, J. Lee, and H. Lee and Dr Woo); and Seoul National University Hospital (Ms Heo), Seoul, South Korea.

Computers, Informatics, Nursing : CIN
|May 11, 2023
PubMed
Summary
This summary is machine-generated.

A new algorithm uses natural language processing to identify prediabetes risk from nursing notes. This tool can help detect at-risk individuals early for timely intervention.

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Area of Science:

  • Medical Informatics
  • Public Health

Background:

  • Prediabetes poses a significant risk for developing type 2 diabetes.
  • Early detection of prediabetes is crucial for implementing timely interventions.
  • Home care nursing notes contain valuable clinical information for risk assessment.

Purpose of the Study:

  • To develop and validate a rule-based classification algorithm for prediabetes risk detection.
  • To utilize natural language processing (NLP) for analyzing home care nursing notes.
  • To identify prediabetes-related symptomatic terms in both English and Korean.

Main Methods:

  • Development of prediabetes-related symptomatic terms in English and Korean.
  • Application of NLP for preprocessing nursing notes.
  • Creation and validation of a rule-based classification algorithm using a large dataset (31,484 notes) and a gold standard testing set (400 notes).
  • Performance evaluation using accuracy, precision, recall, and F1 score.

Main Results:

  • The developed terms included 11 categories, with 1639 Korean and 1181 English words.
  • Approximately 42.2% of the analyzed nursing notes indicated one or more prediabetic symptoms.
  • The algorithm demonstrated high performance on the gold standard testing set.

Conclusions:

  • A novel rule-based NLP algorithm effectively classifies prediabetes risk from home care nursing notes.
  • The algorithm's ability to detect prediabetes symptomatic terms can aid in early risk group identification.
  • Integration into electronic health records systems offers potential for diabetes prevention through early intervention.