Use of Behavioral Economics to Improve Treatment of Acute Respiratory Infections (Main Study)

Bacteria resistant to antibiotic therapy are a major public health problem. The evolution of multi-drug resistant pathogens may be encouraged by provider prescribing behavior. Inappropriate use of antibiotics for nonbacterial infections and overuse of broad spectrum antibiotics can lead to the development of resistant strains. Though providers are adequately trained to know when antibiotics are and are not comparatively effective, this has not been sufficient to affect critical provider practices.

The intent of this study is to apply behavioral economic theory to reduce the rate of antibiotic prescriptions for acute respiratory diagnoses for which guidelines do not call for antibiotics. Specifically targeted are infections that are likely to be viral.

The objective of this study is to improve provider decisions around treatment of acute respiratory infections.

The participants are practicing attending physicians or advanced practice nurses (i.e. providers) at participating clinics who see acute respiratory infection patients. A maximum of 550 participants will be recruited for this study.

Providers consenting to participate will fill out a baseline questionnaire online. Subsequent to baseline data collection and enrollment, participating clinic sites will be randomized to the study arms, as described below.

There will be a control arm, with clinic sites randomized in a multifactorial design to up to three interventions that leverage the electronic medical record: Order Sets that are triggered by electronic health record (EHR) workflow containing exclusively guideline concordant choices (SA, for Suggested Alternatives); Accountable Justifications triggered by discordant prescriptions that populate the note with provider's rationale for guideline exceptions (AJ); and performance feedback that benchmarks providers' own performance to that of their peers (PC, for Peer Comparisons).

The outcomes of interest are antibiotic prescribing patterns, including prescribing rates and changes in prescribing rates over time.

The intervention period will be over one year, with a one-year follow up period to measure persistence of the effect after EHR features are returned to the original state and providers no longer receive email alerts.