title: Can data extraction from general practitioners' electronic records be used to predict clinical outcomes for patients with type 2 diabetes?
abstract: BACKGROUND
  The review of clinical data extraction from electronic records is increasingly being used as a tool to assist general practitioners (GPs) manage their patients in Australia. Type 2 diabetes (T2DM) is a chronic condition cared for primarily in the general practice setting that lends itself to the application of tools in this area.
  
  
  OBJECTIVE
  To assess the feasibility of extracting data from a general practice medical record software package to predict clinically significant outcomes for patients with T2DM.
  
  
  METHODS
  A pilot study was conducted involving two large practices where routinely collected clinical data were extracted and inputted into the United Kingdom Prospective Diabetes Study Outcomes Model to predict life expectancy. An initial assessment of the completeness of data available was performed and then for those patients aged between 45 and 64 years with adequate data life expectancies estimated.
  
  
  RESULTS
  A total of 1019 patients were identified as current patients with T2DM. There were sufficient data available on 40% of patients from one practice and 49% from the other to provide inputs into the UKPDS Outcomes Model. Predicted life expectancy was similar across the practices with women having longer life expectancies than men. Improved compliance with current management guidelines for glycaemic, lipid and blood pressure control was demonstrated to increase life expectancy between 1.0 and 2.4 years dependent on gender and age group.
  
  
  CONCLUSIONS
  This pilot demonstrated that clinical data extraction from electronic records is feasible although there are several limitations chiefly caused by the incompleteness of data for patients with T2DM.
authors.affiliation: University of Sydney, Australia. mstaff@nsccahs.health.nsw.gov.au
authors.forename: Michael
authors.initials: M
authors.lastname: Staff
mesh_terms: D001315:Australia; D057225:Data Mining; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis* / Q000628:therapy; D019468:Disease Management; D057286:Electronic Health Records / Q000458:organization & administration / Q000706:statistics & numerical data*; D005240:Feasibility Studies; D005260:Female; D058006:General Practice / Q000379:methods / Q000706:statistics & numerical data*; D006801:Humans; D008017:Life Expectancy; D008297:Male; D008875:Middle Aged; D009010:Monte Carlo Method; D017063:Outcome Assessment, Health Care / Q000379:methods*; D010865:Pilot Projects; D011379:Prognosis