title: Identifying patients with diabetes and the earliest date of diagnosis in real time: an electronic health record case-finding algorithm.
abstract: BACKGROUND
  Effective population management of patients with diabetes requires timely recognition. Current case-finding algorithms can accurately detect patients with diabetes, but lack real-time identification. We sought to develop and validate an automated, real-time diabetes case-finding algorithm to identify patients with diabetes at the earliest possible date.
  
  
  METHODS
  The source population included 160,872 unique patients from a large public hospital system between January 2009 and April 2011. A diabetes case-finding algorithm was iteratively derived using chart review and subsequently validated (n = 343) in a stratified random sample of patients, using data extracted from the electronic health records (EHR). A point-based algorithm using encounter diagnoses, clinical history, pharmacy data, and laboratory results was used to identify diabetes cases. The date when accumulated points reached a specified threshold equated to the diagnosis date. Physician chart review served as the gold standard.
  
  
  RESULTS
  The electronic model had a sensitivity of 97%, specificity of 90%, positive predictive value of 90%, and negative predictive value of 96% for the identification of patients with diabetes. The kappa score for agreement between the model and physician for the diagnosis date allowing for a 3-month delay was 0.97, where 78.4% of cases had exact agreement on the precise date.
  
  
  CONCLUSIONS
  A diabetes case-finding algorithm using data exclusively extracted from a comprehensive EHR can accurately identify patients with diabetes at the earliest possible date within a healthcare system. The real-time capability may enable proactive disease management.
authors.affiliation: Division of General Internal Medicine, University of California San Francisco, Box 1211, Laurel Heights Campus, Room 383, 3333 California St., San Francisco, CA 94143, USA. anil.makam@utsouthwestern.edu
authors.forename: Anil N
authors.initials: AN
authors.lastname: Makam
authors.forename: Oanh K
authors.initials: OK
authors.lastname: Nguyen
authors.forename: Billy
authors.initials: B
authors.lastname: Moore
authors.forename: Ying
authors.initials: Y
authors.lastname: Ma
authors.forename: Ruben
authors.initials: R
authors.lastname: Amarasingham
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D016208:Databases, Factual; D003920:Diabetes Mellitus / Q000145:classification / Q000175:diagnosis* / Q000517:prevention & control; D003936:Diagnosis, Computer-Assisted; D019468:Disease Management; D042241:Early Diagnosis; D057286:Electronic Health Records / Q000295:instrumentation / Q000592:standards*; D005260:Female; D006779:Hospitals, Public; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011320:Primary Health Care / Q000191:economics / Q000706:statistics & numerical data; D015203:Reproducibility of Results; D013781:Texas; D019037:Urban Health Services
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