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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.
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A healthcare provider can diagnose a urinary tract infection (UTI) through several methods:Medical History and Symptoms: The provider will take a detailed medical history and ask about symptoms such as frequent urination, burning sensation during urination, and lower abdominal pain.Urinalysis: A clean-catch urine sample is collected in a sterile container and tested for the presence of bacteria, white blood cells (leukocytes), nitrites, blood, and protein. The presence of leukocytes and...
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Introduction to AEDAn Automated External Defibrillator (AED) is a portable medical device that analyzes the heart's rhythm and, if necessary, delivers an electrical shock to help the heart re-establish an effective rhythm during sudden cardiac arrest (SCA). SCA occurs when the heart suddenly and unexpectedly stops beating, leading to a loss of blood flow to the brain and other vital organs. In such emergencies, time is of the essence, and using an AED, combined with Cardiopulmonary...
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Identifying diagnostic errors in the emergency department using trigger-based strategies.

Mahsa Khalili1,2, Moein Enayati3, Shrinath Patel3

  • 1Department of Emergency Medicine, The University of British Columbia, Vancouver, British Columbia, Canada mahsa.khalili@ubc.ca.

BMJ Open Quality
|August 7, 2025
PubMed
Summary
This summary is machine-generated.

Electronic health record (EHR) triggers can identify some diagnostic errors but are insufficient alone. Machine learning strategies are recommended to improve diagnostic error detection accuracy in the emergency department (ED).

Keywords:
Diagnostic errorsElectronic Health RecordsEmergency department

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Area of Science:

  • Patient Safety
  • Clinical Informatics
  • Health Services Research

Background:

  • Diagnostic errors are a significant patient safety concern impacting outcomes.
  • Trigger-based strategies are used to identify diagnostic errors and improve care.
  • Existing electronic health record (EHR)-based triggers require evaluation for effectiveness.

Purpose of the Study:

  • To evaluate the performance of three pre-established EHR-based triggers in detecting diagnostic errors within an emergency department (ED).
  • To assess the effectiveness of these triggers in identifying potential diagnostic errors for further review.

Main Methods:

  • Retrospective observational study of consecutive cohorts in a US academic ED.
  • Utilized EHR data to identify trigger-positive and trigger-negative cases based on predefined criteria (unscheduled return visits, care escalation, in-ED/early post-ED deaths).
  • Reviewed a random sample of trigger-positive and trigger-negative cases using the SaferDx tool to confirm diagnostic errors.

Main Results:

  • Identified 5791 trigger-positive and 118,262 trigger-negative cases.
  • Positive predictive values (PPVs) for diagnostic errors were low: 5.4% (T1), 8.9% (T2), and 6.9% (T3).
  • Negative predictive value (NPV) was 100%, indicating no errors missed in trigger-negative cases. Sepsis was the most common error diagnosis.

Conclusions:

  • Pre-established EHR-based triggers can identify some diagnostic errors but are insufficient for comprehensive detection.
  • Further development and exploration of data-driven strategies, such as machine learning, are needed to enhance diagnostic error detection accuracy.
  • Improving diagnostic error detection is crucial for enhancing patient safety in the ED setting.