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. 2013 Aug 1:13:81.
doi: 10.1186/1472-6947-13-81.

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

Anil N Makam  1 , Oanh K Nguyen, Billy Moore, Ying Ma, Ruben Amarasingham
Affiliations

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

Anil N Makam et al. BMC Med Inform Decis Mak. .

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.

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Figures

Figure 1
Figure 1
Electronic diabetes case-finding model derivation and validation flowchart. * Charts used in derivation were excluded from the validation cohort.
Figure 2
Figure 2
Receiver operating characteristic curve for the electronic diabetes case-finding model identification of diabetes compared to physician review by different point thresholds (C statistic 0.98).
Figure 3
Figure 3
Comparison of the date of diagnosis of diabetes within a healthcare system as ascertained by the electronic diabetes case-finding model and physician reviewer. Observations below and to the right of the dashed line (shaded area) are within the allowed 3-month window for agreement.
See this image and copyright information in PMC

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