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

This study is enrolling participants by invitation only.
Sponsor:
Collaborator:
Information provided by (Responsible Party):
University of Southern California
ClinicalTrials.gov Identifier:
NCT01454960
First received: August 4, 2011
Last updated: October 18, 2011
Last verified: October 2011

August 4, 2011
October 18, 2011
July 2011
July 2012   (final data collection date for primary outcome measure)
Antibiotic Prescribing Rate for 5 Specific Acute Respiratory Infection Diagnoses [ Time Frame: 2 years ] [ Designated as safety issue: No ]

Changes in antibiotic prescribing rate for the following ICD-9 diagnoses:

460 Acute nasopharyngitis (common cold)

465 Acute laryngeopharyngitis/acute upper respiratory infection

466 Acute bronchitis

490 Bronchitis not specified as acute or chronic

487 Flu

Same as current
Complete list of historical versions of study NCT01454960 on ClinicalTrials.gov Archive Site
Antibiotic Prescribing Rates for Expanded List of Acute Respiratory Infection Diagnoses [ Time Frame: 2 years ] [ Designated as safety issue: No ]
We will monitor overall prescribing for the specified diagnoses and other Acute Respiratory Infection diagnoses, including cough/fever and pneumonia.
Same as current
Not Provided
Not Provided
 
Use of Behavioral Economics to Improve Treatment of Acute Respiratory Infections (Pilot Study)
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 (AP, for Alternative Prescriptions); Justification Alerts triggered by discordant prescriptions that populate the note with provider's rationale for guideline exceptions (JA); and performance feedback that benchmarks providers' own performance to that of their peers (SN, for social norms).

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 AP arm, while clinicians randomized to the justification 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 social norm condition will receive monthly updates about their antibiotic prescribing practices relative to other clinicians in their practice.

Interventional
Not Provided
Allocation: Randomized
Intervention Model: Factorial Assignment
Masking: Single Blind (Subject)
Primary Purpose: Treatment
Acute Respiratory Infections (ARIs)
  • Behavioral: Clinical Decision Support: Justification Alerts
    Justification Alerts triggered by discordant prescriptions that populate the EHR note with provider's rationale for guideline exceptions (JA).
    Other Names:
    • JA
    • Justification Alerts
  • Behavioral: Audit and Feedback: Social Norms
    Performance feedback that benchmarks providers' own performance to that of their peers (SN, for Social Norms).
    Other Names:
    • SN
    • Social Norms
  • Behavioral: CDS Order Sets: Alternative Prescriptions
    Order Sets that are triggered by EHR workflow containing exclusively guideline concordant choices (AP, for Alternative Prescriptions).
    Other Names:
    • AP
    • Alternative Prescription
  • Experimental: AP, JA, SN
    Participants are given all 3 interventions.
    Interventions:
    • Behavioral: Clinical Decision Support: Justification Alerts
    • Behavioral: Audit and Feedback: Social Norms
    • Behavioral: CDS Order Sets: Alternative Prescriptions
  • Experimental: AP, JA
    Participants receive the Alternative Prescribing and Justification Alerts interventions, but not the Social Norms intervention.
    Interventions:
    • Behavioral: Clinical Decision Support: Justification Alerts
    • Behavioral: CDS Order Sets: Alternative Prescriptions
  • Experimental: AP, SN
    Participants receive the Alternative Prescriptions and Social Norms interventions, but not the Justification Alerts intervention.
    Interventions:
    • Behavioral: Audit and Feedback: Social Norms
    • Behavioral: CDS Order Sets: Alternative Prescriptions
  • Experimental: JA, SN
    Participants receive the Justification Alerts and Social Norms interventions, but not the Alternative Prescriptions intervention.
    Interventions:
    • Behavioral: Clinical Decision Support: Justification Alerts
    • Behavioral: Audit and Feedback: Social Norms
  • Experimental: Social Norms
    Participants receive the Social Norms intervention, but do not receive the Alternative Prescribing or Justification Alerts interventions.
    Intervention: Behavioral: Audit and Feedback: Social Norms
  • Experimental: Alternative Prescriptions
    Participants receive the Alternative Prescription intervention, but not the Justification Alerts or Social Norms interventions.
    Intervention: Behavioral: CDS Order Sets: Alternative Prescriptions
  • Experimental: Justification Alerts
    Participants receive the Justification Alerts intervention, but do not receive the Alternative Prescriptions or Social Norms interventions.
    Intervention: Behavioral: Clinical Decision Support: Justification Alerts
  • No Intervention: Control
    Participants do not receive any of the 3 interventions.
Not Provided

*   Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
 
Enrolling by invitation
45
May 2013
July 2012   (final data collection date for primary outcome measure)

Inclusion Criteria:

A practicing attending physician or advanced practice nurse ("provider") at Northwestern University's NMFF GIM Clinic in 2011-2013 who sees acute respiratory infection patients.

Both
18 Years and older
No
Contact information is only displayed when the study is recruiting subjects
United States
 
NCT01454960
1RC4AG039115-01-2, 1RC4AG039115-01
Yes
University of Southern California
University of Southern California
National Institute on Aging (NIA)
Principal Investigator: Stephen Persell, MD Northwestern University
Study Director: Jason N Doctor, PhD University of Southern California
University of Southern California
October 2011

ICMJE     Data element required by the International Committee of Medical Journal Editors and the World Health Organization ICTRP