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

Makam AN, Nguyen OK, Moore B, Ma Y, Amarasingham R - BMC Med Inform Decis Mak (2013)

Bottom Line: The date when accumulated points reached a specified threshold equated to the diagnosis date.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.The real-time capability may enable proactive disease management.

View Article: PubMed Central - HTML - PubMed

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

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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Related in: MedlinePlus

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.
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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.

Mentions: The kappa score between e-model and physician on the date of diagnosis was 0.94 with agreement on the exact date in 76% of the cases. Among the cases where both the physician and e-model made a diagnosis of diabetes, only 4 observations (2.6%) were diagnosed by the e-model more than 3 months after the correct date (Figure 3).


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

Makam AN, Nguyen OK, Moore B, Ma Y, Amarasingham R - BMC Med Inform Decis Mak (2013)

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.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3733983&req=5

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.
Mentions: The kappa score between e-model and physician on the date of diagnosis was 0.94 with agreement on the exact date in 76% of the cases. Among the cases where both the physician and e-model made a diagnosis of diabetes, only 4 observations (2.6%) were diagnosed by the e-model more than 3 months after the correct date (Figure 3).

Bottom Line: The date when accumulated points reached a specified threshold equated to the diagnosis date.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.The real-time capability may enable proactive disease management.

View Article: PubMed Central - HTML - PubMed

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

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.

Show MeSH
Related in: MedlinePlus