Overuse of antibiotics for acute respiratory infections (ARIs) continues to drive increasing antibiotic resistance. Provider-driven clinical decision support (CDS) has had a minimal impact on decreasing antibiotic prescribing rates. The overall objectives of this application are to 1) implement registered nurse (RN)-driven CDS containing integrated clinical prediction rules (iCPRs) [decision aids embedded into the electronic health record which determine risk of bacterial infection based on elements of the patient history and physical examination] and 2) determine the impact of RN-driven CDS on inappropriate antibiotic prescribing and resource use. The central hypothesis is that RNs using iCPR will accurately determine patients? risk for bacterial ARI, recommend appropriate testing and treatment based on that risk and will have lower rates of inappropriate antibiotic prescribing compared to providers. The rationale for this project is that a CDS tool can allow RNs to work at the full scope of their licenses and decrease inappropriate antibiotic prescribing. This central hypothesis will be tested using three specific aims: 1) adapt and implement an RN driven iCPR tool for evaluation and treatment of patients with ARIs in primary care and urgent care practices; 2) determine the impact of the RN driven iCPR on antibiotic prescribing, diagnostic test-ordering, resource use patterns, and cost-effectiveness; and 3) use a theory-driven implementation framework to identify barriers and facilitators to, and optimize factors influencing, effective implementation of an RN-driven iCPR. For the first aim, the previously developed and studied provider iCPR will be adapted for RN workflow and a RN training program will be implemented to train RNs in its use for patient evaluation and treatment. For the second aim, a stepped wedge cluster randomized trial will be conducted at primary care and urgent care clinics at the four participating institutions. For the third aim, the Technology Acceptance Model (TAM3) and Proctor?s Framework will be used as guides to assess implementation outcomes and use findings to drive post-implementation tool optimization and create an implementation toolkit. The proposed project is innovative because it: 1) uses CDS tools with risk stratification to enable RNs to lead initial ARI assessment and treatment; 2) uses an evidence-based implementation framework to evaluate outcomes and identify barriers and facilitators; and 3) evaluates the cost of the intervention which is critical in increasing dissemination potential. The proposed research is significant because it is expected to provide a scalable implementation model leveraging CDS and RN training tools to decrease antibiotic overuse, lower the cost of care, and ultimately reduce antibiotic resistance.

Public Health Relevance

The proposed project is relevant to public health because it seeks to decrease inappropriate antibiotic use in acute respiratory infections which leads to antibiotic resistance. We will adapt our innovative clinical decision support, that uses evidence-based rules to determine risk of bacterial infection, for use in nurse led evaluation and treatment of patients with acute respiratory infections. We will determine if the nurse driven clinical decision support decreases antibiotic prescriptions, diagnostic testing and cost of care.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Research Project (R01)
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Dissemination and Implementation Research in Health Study Section (DIRH)
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Brown, Liliana L
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New York University
Internal Medicine/Medicine
Schools of Medicine
New York
United States
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Richardson, Safiya; Mishuris, Rebecca; O'Connell, Alexander et al. (2017) ""Think aloud"" and ""Near live"" usability testing of two complex clinical decision support tools. Int J Med Inform 106:1-8
Feldstein, David A; Hess, Rachel; McGinn, Thomas et al. (2017) Design and implementation of electronic health record integrated clinical prediction rules (iCPR): a randomized trial in diverse primary care settings. Implement Sci 12:37