Use of Behavioral Economics to Improve Treatment of Acute Respiratory Infections (Pilot 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 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 Comparison). 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.

Each consented provider will be randomized to 1 of 8 cells in a factorial design with equal probability. If results of retrospective data analysis imply that design will be improved by stratification, randomization will be stratified by factors that could influence outcomes. Data will be collected from Northwestern University's Enterprise Data Warehouse which houses copies of data recorded in the Epic electronic health record. Data elements from qualifying office visits will be collected from coded portions of the electronic health record. An encounter is eligible for intervention if the patient's diagnosis is in the selected group of acute respiratory infections. The intervention EHR functions will be triggered when clinicians initiate an antibiotic prescription or enter a diagnosis for an acute respiratory infection that has a defined Order Set. If an antibiotic from a list of frequently misprescribed antibiotics is ordered and a diagnosis has not yet been entered, providers will be prompted to enter a diagnosis. If the diagnosis entered is acute nasopharyngitis; acute laryngeopharyngitis/acute upper respiratory infection; acute bronchitis; bronchitis not specified as acute or chronic; or flu; the interventions will be triggered. The diagnosis-appropriate order set will pop-up for providers in the Suggested Alternatives (SA) arm, while clinicians randomized to the Accountable Justification (AJ) arm will receive an alert and be required to enter a brief statement justifying their antibiotic prescription if antibiotics are not indicated for the diagnosis entered. This note will then be added to the patient's medical record. Clinicians randomized to the Peer Comparison (PC) condition will receive monthly updates about their antibiotic prescribing practices relative to other clinicians in their practice.