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

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.