title: User centered clinical decision support tools: adoption across clinician training level.
abstract: BACKGROUND
  Dissemination and adoption of clinical decision support (CDS) tools is a major initiative of the Affordable Care Act's Meaningful Use program. Adoption of CDS tools is multipronged with personal, organizational, and clinical settings factoring into the successful utilization rates. Specifically, the diffusion of innovation theory implies that 'early adopters' are more inclined to use CDS tools and younger physicians tend to be ranked in this category.
  
  
  OBJECTIVE
  This study examined the differences in adoption of CDS tools across providers' training level.
  
  
  METHODS
  From November 2010 to 2011, 168 residents and attendings from an academic medical institution were enrolled into a randomized controlled trial.
  
  
  METHODS
  The intervention arm had access to the CDS tool through the electronic health record (EHR) system during strep and pneumonia patient visits.
  
  
  METHODS
  The EHR system recorded details on how intervention arm interacted with the CDS tool including acceptance of the initial CDS alert, completion of risk-score calculators and the signing of medication order sets. Using the EHR data, the study performed bivariate tests and general estimating equation (GEE) modeling to examine the differences in adoption of the CDS tool across residents and attendings.
  
  
  RESULTS
  The completion rates of the CDS calculator and medication order sets were higher amongst first year residents compared to all other training levels. Attendings were the less likely to accept the initial step of the CDS tool (29.3%) or complete the medication order sets (22.4%) that guided their prescription decisions, resulting in attendings ordering more antibiotics (37.1%) during an CDS encounter compared to residents.
  
  
  CONCLUSIONS
  There is variation in adoption of CDS tools across training levels. Attendings tended to accept the tool less but ordered more medications. CDS tools should be tailored to clinicians' training levels.
authors.affiliation: Department of Medicine, Division of Internal Medicine, Hofstra North Shore-LIJ School of Medicine , Manhasset, NY.
authors.forename: L J
authors.initials: LJ
authors.lastname: McCullagh
authors.affiliation: Department of Medicine, Division of General Internal Medicine, Mount Sinai School of Medicine , NYC, NY.
authors.forename: A
authors.initials: A
authors.lastname: Sofianou
authors.affiliation: Department of Medicine, Division of General Internal Medicine, Mount Sinai School of Medicine , NYC, NY.
authors.forename: J
authors.initials: J
authors.lastname: Kannry
authors.affiliation: Department of Medicine, Section of Preventive Medicine & Epidemiology, Boston University School of Medicine , Boston, MA.
authors.forename: D M
authors.initials: DM
authors.lastname: Mann
authors.affiliation: Department of Medicine, Division of Internal Medicine, Hofstra North Shore-LIJ School of Medicine , Manhasset, NY.
authors.forename: T G
authors.initials: TG
authors.lastname: McGinn
mesh_terms: D000328:Adult; D003625:Data Collection; D020000:Decision Support Systems, Clinical / Q000706:statistics & numerical data*; D005260:Female; D006282:Health Personnel / Q000193:education* / Q000706:statistics & numerical data; D006801:Humans; D008297:Male
keywords: Implementation science; clinical decision support; clinical prediction rules; evidence based medicine; primary care