title: Efficacy of an evidence-based clinical decision support in primary care practices: a randomized clinical trial.
abstract: OBJECTIVE
  There is consensus that incorporating clinical decision support into electronic health records will improve quality of care, contain costs, and reduce overtreatment, but this potential has yet to be demonstrated in clinical trials.
  
  
  OBJECTIVE
  To assess the influence of a customized evidence-based clinical decision support tool on the management of respiratory tract infections and on the effectiveness of integrating evidence at the point of care.
  
  
  METHODS
  In a randomized clinical trial, we implemented 2 well-validated integrated clinical prediction rules, namely, the Walsh rule for streptococcal pharyngitis and the Heckerling rule for pneumonia. INTERVENTIONS AND MAIN OUTCOMES AND MEASURES: The intervention group had access to the integrated clinical prediction rule tool and chose whether to complete risk score calculators, order medications, and generate progress notes to assist with complex decision making at the point of care.
  
  
  RESULTS
  The intervention group completed the integrated clinical prediction rule tool in 57.5% of visits. Providers in the intervention group were significantly less likely to order antibiotics than the control group (age-adjusted relative risk, 0.74; 95% CI, 0.60-0.92). The absolute risk of the intervention was 9.2%, and the number needed to treat was 10.8. The intervention group was significantly less likely to order rapid streptococcal tests compared with the control group (relative risk, 0.75; 95% CI, 0.58-0.97; P= .03).
  
  
  CONCLUSIONS
  The integrated clinical prediction rule process for integrating complex evidence-based clinical decision report tools is of relevant importance for national initiatives, such as Meaningful Use. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT01386047.
authors.affiliation: Division of Internal Medicine, Department of Medicine, Hofstra North Shore-LIJ School of Medicine, Manhasset, New York.
authors.forename: Thomas G
authors.initials: TG
authors.lastname: McGinn
authors.forename: Lauren
authors.initials: L
authors.lastname: McCullagh
authors.forename: Joseph
authors.initials: J
authors.lastname: Kannry
authors.forename: Megan
authors.initials: M
authors.lastname: Knaus
authors.forename: Anastasia
authors.initials: A
authors.lastname: Sofianou
authors.forename: Juan P
authors.initials: JP
authors.lastname: Wisnivesky
authors.forename: Devin M
authors.initials: DM
authors.lastname: Mann
mesh_terms: D000328:Adult; D000900:Anti-Bacterial Agents / Q000627:therapeutic use*; D003657:Decision Making; D003661:Decision Support Techniques; D019468:Disease Management; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011320:Primary Health Care / Q000458:organization & administration*; D011785:Quality Assurance, Health Care; D012141:Respiratory Tract Infections / Q000628:therapy*
chemical_list: D000900:Anti-Bacterial Agents