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Sensitivity, Specificity, and Predicted Value01:13

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A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
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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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Diagnostic excellence: turning to diagnostic performance improvement.

Andrew Auerbach1, Katie Raffel2, Irit R Rasooly3

  • 1San Francisco Department of Medicine, Division of Hospital Medicine, University of California, Division of Hospital Medicine, University of California, San Francisco, CA, USA.

Diagnosis (Berlin, Germany)
|September 18, 2025
PubMed
Summary

Diagnostic Excellence Programs are crucial for patient safety, reducing diagnostic errors through systematic surveillance and addressing system and individual factors. Patient engagement and AI integration are key to improving diagnostic accuracy and care.

Keywords:
diagnostic excellenceorganizational behaviorpatient safety

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Area of Science:

  • Healthcare Improvement
  • Patient Safety
  • Medical Diagnostics

Background:

  • Diagnosis is a patient safety priority, with diagnostic errors causing significant harm.
  • Diagnostic Excellence Programs are organizational initiatives to reduce errors and improve performance.
  • Systematic surveillance is essential for identifying and addressing diagnostic errors.

Purpose of the Study:

  • To outline the evolution and components of Diagnostic Excellence Programs.
  • To highlight strategies for improving diagnostic processes at both system and individual levels.
  • To emphasize the role of patient engagement and artificial intelligence in diagnostic excellence.

Main Methods:

  • Review of advancements in diagnostic excellence and error reduction.
  • Discussion of frameworks like the DEER Taxonomy and root/success cause analyses.
  • Exploration of system factors (EHR, workload, communication) and individual clinician performance enhancement.

Main Results:

  • Effective programs integrate system improvements, clinician support, and patient engagement.
  • Artificial intelligence offers potential for enhanced surveillance, workflow optimization, and communication.
  • Embedding diagnostic safety into standards of care is vital for sustained improvement.

Conclusions:

  • Diagnostic Excellence Programs require a multi-faceted approach addressing systems, individuals, and patients.
  • Strategic implementation of AI can significantly enhance diagnostic surveillance and improvement.
  • Fostering a culture of diagnostic safety is paramount for reducing errors and improving patient outcomes.