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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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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.
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Patient-centered care involves delivering care beyond inpatient hospitalization. Reflective practice can enhance a patient-centered approach. Reflective practice is a process of reasoning that considers all aspects of the present situation, including practicalities, learning from personal practice, and consideration of patient needs. Patients appreciate care decisions made while considering their input. Involving the patient in their care provides the patient with a sense of contribution rather...
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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.
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Updated: Jan 13, 2026

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A Natural Language Processing Approach to Identify Negative Patient Descriptors in Electronic Health Records for

Azade Tabaie1,2, Angela D Thomas3,4, Emily K Mutondo3

  • 1Center for Biostatistics, Informatics, and Data Science, MedStar Health Research Institute, Columbia, Maryland, United States.

Applied Clinical Informatics
|October 28, 2025
PubMed
Summary

Negative patient descriptors in electronic health records (EHRs) are disproportionately documented for Black women, indicating potential implicit bias in maternal care. Addressing this biased language is crucial for improving health equity.

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Area of Science:

  • Health Services Research
  • Medical Informatics
  • Health Equity

Background:

  • Maternal harm is a critical healthcare issue, particularly affecting Black women.
  • Unstructured clinical notes within electronic health records (EHRs) may contain information on unsafe maternal care.
  • Natural language processing (NLP) studies indicate that note sentiment correlates with preventable safety events.

Purpose of the Study:

  • To investigate the association between negative patient descriptors in EHR clinical notes and adverse maternal outcomes.
  • To analyze demographic variations in the utilization of negative patient descriptors.

Main Methods:

  • Retrospective cohort study of women delivering between January 2016 and March 2020 in Washington, DC.
  • Utilized NLP and a predefined keyword list to identify sentences with negative descriptors in clinical notes.
  • Subject matter experts manually reviewed and labeled keywords; a logistic regression model classified the corpus.
  • Evaluated descriptor prevalence by race, age, insurance, and pregnancy outcomes, calculating adjusted odds ratios.

Main Results:

  • Of 9,302 patients, 444 had notes containing negative descriptors (719 notes total).
  • Negative descriptors were significantly more prevalent in notes of Black patients (70.5%) compared to White (10.1%) and Other (19.4%) racial groups.
  • These descriptors were more common in younger patients and those with public insurance (Medicare/Medicaid).
  • Patients with adverse outcomes (postpartum readmission, severe maternal morbidity) had higher adjusted odds of negative descriptors.
  • Black patients and those with public insurance showed higher odds of having negative descriptors documented.

Conclusions:

  • Negative patient descriptors are disproportionately documented for Black patients and those with public insurance, suggesting implicit bias in clinical documentation.
  • Addressing biased language in EHRs is essential for mitigating disparities and advancing health equity in maternal care.