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Related Concept Videos

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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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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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.
The nursing process considers the patient's emotional and physical well-being. The process can be repeated or stopped at any point if judged essential. Assessment is the first step in the nursing...
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Nursing Diagnosis01:22

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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.
The nursing diagnosis focuses on evidence-based...
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Identifying Symptom Information in Clinical Notes Using Natural Language Processing.

Theresa A Koleck, Nicholas P Tatonetti, Suzanne Bakken

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    Researchers developed a novel method to extract symptom information from electronic health records (EHRs). This approach significantly expands symptom vocabularies and accurately identifies symptoms in clinical notes, advancing symptom science research.

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    Area of Science:

    • Nursing Science
    • Biomedical Informatics
    • Clinical Research

    Background:

    • Symptom research is crucial in nursing.
    • Electronic health records (EHRs) offer vast data for symptom research.
    • Clinical notes contain complex symptom language, necessitating specialized extraction methods.

    Purpose of the Study:

    • To describe a method for generating comprehensive symptom vocabularies.
    • To identify symptom information within EHR notes.
    • To pilot the method with five key symptom concepts: constipation, depressed mood, disturbed sleep, fatigue, and palpitations.

    Main Methods:

    • Expanded symptom vocabularies using the Unified Medical Language System and clinical text corpora.
    • Employed NimbleMiner, an open-source natural language processing tool, for vocabulary expansion and symptom identification.
    • Evaluated performance against manually annotated EHR notes authored by nurses and physicians.

    Main Results:

    • Identified up to 11 times more synonyms per symptom concept compared to baseline lists.
    • Discovered abbreviations, misspellings, and unique multiword expressions.
    • Achieved excellent performance in symptom identification using the natural language processing system.

    Conclusions:

    • The developed method and comprehensive vocabularies enable accurate and scalable extraction of symptom information from EHR notes.
    • This capability significantly enhances the potential for advancing symptom science research.
    • The approach demonstrates excellent performance metrics for symptom labeling in clinical documentation.