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

BMC Medical Informatics and Decision Making

Research Article

Oanh K Nguyen¹  Anil N Makam¹  Ying Ma²  Billy Moore²  Ruben Amarasingham³ 

[1] Division of General Internal Medicine, University of California San Francisco, Box 1211, Laurel Heights Campus, Room 383, 3333 California St, 94143, San Francisco, CA, USA;Parkland Center for Clinical Innovation, Dallas, TX, USA;Parkland Center for Clinical Innovation, Dallas, TX, USA;Division of General Internal Medicine, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, 75390, Dallas, TX, USA;

关键词: Metformin;    Electronic Health Record;    Validation Cohort;    Problem List;    Diagnosis Date;   

DOI  :  10.1186/1472-6947-13-81

 received in 2013-04-07, accepted in 2013-07-26,  发布年份 2013

来源: Springer

【 摘 要 】

BackgroundEffective 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.MethodsThe 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.ResultsThe 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.ConclusionsA 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.

【 授权许可】

CC BY   
© Makam et al.; licensee BioMed Central Ltd. 2013