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
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 EHR workflow containing exclusively guideline concordant choices (SA, for
Suggested Alternatives); Accountable Justification (AJ) triggered by discordant prescriptions
that populate the note with provider's rationale for guideline exceptions ; and performance
feedback that benchmarks providers' own performance to that of their peers (PC, for Peer
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.