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Nursing Performance Using Clinical Prediction Rules for Acute Respiratory Infection Management: A Case-Based

Victoria L Tiase1, Patrice Hicks2, Haddy Bah3

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Summary
This summary is machine-generated.

Registered nurses accurately managed simulated sore throat cases using clinical prediction rules. Further exploration is needed for cough cases, but this supports automated decision support tools for antibiotic stewardship.

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Area of Science:

  • Infectious Diseases
  • Health Policy
  • Nursing Practice

Background:

  • Antibiotic resistance is a major healthcare concern driven by overuse and misuse.
  • Clinical prediction rules aid in appropriate diagnosis and antibiotic prescribing.
  • Nurses' role in managing acute respiratory infections (ARIs) using these rules needs investigation.

Purpose of the Study:

  • To compare registered nurses' (RNs) ability to manage ARIs using clinical prediction rules against primary care physicians (PCPs) in a simulated setting.
  • To assess nursing self-efficacy in managing ARIs with clinical prediction rules.

Main Methods:

  • A case-based simulation evaluated diagnosis and treatment of ARIs using clinical prediction rules.
  • Participants included 40 RNs and 5 PCPs evaluating 6 simulated cases (3 cough, 3 sore throat).
  • Nursing self-efficacy was measured via pre- and post-simulation surveys.

Main Results:

  • RNs demonstrated 100% accuracy in managing low-risk simulated sore throat cases, outperforming PCPs (80%).
  • Accuracy in managing simulated cough cases showed significant variability among participants.
  • Nurses reported a slight increase in self-efficacy post-simulation.

Conclusions:

  • Clinical prediction rules can effectively guide RNs in diagnosing and treating simulated sore throat cases.
  • Cough case management using these rules requires further research and refinement.
  • Findings support the development of automated clinical decision support tools to enhance antibiotic stewardship.