American Research Journal of Nursing

Background:Antibiotics are overused. Resistance rates continue to increase, unnecessary money is spent, and often unwanted side effects follow. Reducing antibiotic resistance rates is a call to action for healthcare providers (HCP), pharmaceutical companies, patients, patient family members, and agricultural breeders.The purpose of this QI project wasto use an algorithm as a clinical support tool to assist providers practicing in an urgent care setting to reduce the number of antibiotics prescribed for common acute respiratory infections (ARIs) such as pharyngitis, sinusitis, and bronchitis. Methods: The project setting was one urgent care system between two locations within the Dallas Metroplex with an annual volume of 16,000 patients. As a QI project, six providers were educated on and asked to review an algorithm tool prior to prescribing antibiotics for ARIs related complaints based on designate diagnoses (acute pharyngitis, acute sinusitis, and acute bronchitis). Using data obtained from EHR, the project compared the number of antibiotics prescribed for ARI related complaints in the intervention period compared to baseline period from same timeframe of previous year’s prescribing rates. Conclusion:A clinical decision support tool such as an algorithm along with patient and provider education can reduce antibiotic overuse burden. Unnecessary antibiotic prescribing for the treatment of acute respiratory illnesses has continued risks to patients and the community. Results:Project findings confirmed the project aim for this quality improvement project that using a clinical decision support algorithm tool leads to significant decreased antibiotic prescribing rates for the acute respiratory illnesses of acute pharyngitisX2 (1, N = 95) = 23.69, p = 0.000, acute sinusitisX2 (1, N = 94) = 9.94, p = 0.003, and acute bronchitisX2 (1, N = 95) = 23.69, p = 0.000.