title: The completeness of electronic medical record data for patients with Type 2 Diabetes in primary care and its implications for computer modelling of predicted clinical outcomes.
abstract: To describe the completeness of routinely collected primary care data that could be used by computer models to predict clinical outcomes among patients with Type 2 Diabetes (T2D).
  
  
  
  Data on blood pressure, weight, total cholesterol, HDL-cholesterol and glycated haemoglobin levels for regular patients were electronically extracted from the medical record software of 12 primary care practices in Australia for the period 2000-2012. The data was analysed for temporal trends and for associations between patient characteristics and completeness. General practitioners were surveyed to identify barriers to recording data and strategies to improve its completeness.
  
  
  
  Over the study period data completeness improved up to around 80% complete although the recording of weight remained poorer at 55%. T2D patients with Ischaemic Heart Disease were more likely to have their blood pressure recorded (OR 1.6, p=0.02). Practitioners reported not experiencing any major barriers to using their computer medical record system but did agree with some suggested strategies to improve record completeness.
  
  
  
  The completeness of routinely collected data suitable for input into computerised predictive models is improving although other dimensions of data quality need to be addressed.
authors.affiliation: Public Health Unit, Northern Sydney Local Health District, Sydney, Australia. Electronic address: Michael.Staff@health.nsw.gov.au.
authors.forename: Michael
authors.initials: M
authors.lastname: Staff
authors.affiliation: Northern Clinical School, Hornsby Hospital, University of Sydney, Australia.
authors.forename: Christopher
authors.initials: C
authors.lastname: Roberts
authors.affiliation: Rheumatology and Musculoskeletal Medicine, Northern Clinical School, Royal North Shore Hospital, University of Sydney, Australia.
authors.forename: Lyn
authors.initials: L
authors.lastname: March
mesh_terms: D000368:Aged; D001315:Australia; D015415:Biomarkers / Q000097:blood; D001794:Blood Pressure; D001827:Body Height; D001835:Body Weight; D008076:Cholesterol, HDL / Q000097:blood; D000068598:Data Accuracy; D003625:Data Collection / Q000592:standards; D057225:Data Mining; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000175:diagnosis / Q000503:physiopathology / Q000628:therapy*; D057286:Electronic Health Records / Q000592:standards; D005260:Female; D006442:Glycated Hemoglobin / Q000032:analysis; D006304:Health Status; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D016017:Odds Ratio; D066230:Patient-Specific Modeling; D011320:Primary Health Care; D013997:Time Factors; D016896:Treatment Outcome
chemical_list: D015415:Biomarkers; D008076:Cholesterol, HDL; D006442:Glycated Hemoglobin; C517652:hemoglobin A1c protein, human
keywords: Computer modelling; Data completeness; Electronic records; Primary care