Background
Overuse and misuse of antibiotics is an urgent healthcare problem and one of the key
factors in antibiotic resistance. Validated clinical prediction rules have shown effectiveness
in guiding providers to an appropriate diagnosis and identifying when antibiotics
are the recommended choice for treatment.
Objective
We aimed to study the relative ability of registered nurses using clinical prediction
rules to guide the management of acute respiratory infections in a simulated environment
compared to practicing primary care physicians.
Design
We evaluated a case-based simulation of the diagnosis and treatment for acute respiratory
infections using clinical prediction rules. As a secondary outcome, we examined nursing
self-efficacy by administering a survey before and after case evaluations.
Participants
Participants included 40 registered nurses from three academic medical centers and
five primary care physicians as comparators. Participants evaluated six simulated
case studies, three for patients presenting with cough symptoms and three for sore
throat.
Key Results
Compared to physicians, nurses determined risk and treatment for simulated sore throat
cases using clinical prediction rules with nurses having 100% accuracy in
low-risk sore throat cases versus 80% for physicians. We found great variability in
the accuracy of the risk level and appropriate treatment for cough cases. Nurses reported
slight increases in self-efficacy from baseline to post-case evaluation suggesting
further information is needed to understand correlation.
Conclusions
Clinical prediction rules used by nurses in sore throat management workflows can guide
accurate diagnosis and treatment in simulated cases, while cough management requires
further exploration. Our results support the future implementation of automated prediction
rules in a clinical decision support tool and a thorough examination of their effect
on clinical practice and patient outcomes.
