title: Factors influencing physician use of clinical electronic information technologies after adoption by their medical group practices.
abstract: BACKGROUND
  A major factor limiting efficiency and quality gains from clinical information technologies is the lack of full use by the clinicians.
  
  
  OBJECTIVE
  To identify the practice and physician characteristics that influence the use of e-scripts after adoption.
  
  
  METHODS
  Data were obtained from 27 primary care medical group practices that had e-script technology for 2 years. Physician and practice characteristics were obtained from the clinics, and the proportion of each physician's prescriptions sent electronically was calculated from the prescription records. Practice culture data were obtained from a survey of the physicians in each practice. Data were analyzed using hierarchal regression.
  
  
  RESULTS
  Practice-level variables explain most of the variance in the use of e-scripts by physicians, although there are significant differences in use among specialties as well. General internists have slightly lower use rates and pediatricians have the highest rates. Larger practices and multispecialty practices have higher use rates, and five practice culture dimensions influence these rates; two have a negative influence and three (organizational trust, adaptive, and a business orientation) have a positive influence.
  
  
  CONCLUSIONS
  While previous studies have identified physician characteristics and product deficiencies as factors limiting the use of electronic information technologies in medical practices, our data indicate that the influence of these factors may be highly dependent on the culture of the practice. Consequently, practice administrators can improve physician acceptance and use of these technologies by making sure that there is a culture/technology fit before deciding on a product.
authors.affiliation: Division of Health Policy and Management, University of Minnesota, Minneapolis, USA. krale001@umn
authors.forename: John E
authors.initials: JE
authors.lastname: Kralewski
authors.forename: Bryan E
authors.initials: BE
authors.lastname: Dowd
authors.forename: Titilope
authors.initials: T
authors.lastname: Cole-Adeniyi
authors.forename: Dave
authors.initials: D
authors.lastname: Gans
authors.forename: Lucy
authors.initials: L
authors.lastname: Malakar
authors.forename: Bob
authors.initials: B
authors.lastname: Elson
mesh_terms: D000328:Adult; D000555:Ambulatory Care Information Systems / Q000706:statistics & numerical data*; D001291:Attitude of Health Personnel; D016303:Clinical Pharmacy Information Systems / Q000706:statistics & numerical data*; D004059:Diffusion of Innovation; D005163:Factor Analysis, Statistical; D005260:Female; D006123:Group Practice / Q000458:organization & administration* / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D050316:Medical Order Entry Systems / Q000706:statistics & numerical data*; D008511:Medicine / Q000458:organization & administration* / Q000706:statistics & numerical data; D008875:Middle Aged; D015233:Models, Statistical; D015279:Organizational Culture; D010820:Physicians / Q000523:psychology* / Q000706:statistics & numerical data; D011216:Practice Management, Medical; D013038:Specialization; D014481:United States