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Open Access Highly Accessed Research article

Identifying patients with diabetes and the earliest date of diagnosis in real time: an electronic health record case-finding algorithm

Anil N Makam1*, Oanh K Nguyen1, Billy Moore2, Ying Ma2 and Ruben Amarasingham23

Author Affiliations

1 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

2 Parkland Center for Clinical Innovation, Dallas, TX, USA

3 Division of General Internal Medicine, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390, USA

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BMC Medical Informatics and Decision Making 2013, 13:81  doi:10.1186/1472-6947-13-81

Published: 1 August 2013



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.


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 (nā€‰=ā€‰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.


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.


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.