TY - JOUR
T1 - Identifying patients with diabetes and the earliest date of diagnosis in real time
T2 - An electronic health record case-finding algorithm
AU - Makam, Anil N.
AU - Nguyen, Oanh K.
AU - Moore, Billy
AU - Ma, Ying
AU - Amarasingham, Ruben
N1 - Funding Information:
The authors would like to acknowledge Dr. Chang Rhee, a board-certified endocrinologist at UT Southwestern, who provided content expertise in selecting and weighting candidate variables. This study was supported by the grant, “Dallas Social-Health Information Exchange Project,” from the W.W. Caruth Jr. Foundation Fund of the Communities Foundation of Texas. AM had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. AM and ON were supported by a federal training grant from the National Research Service Award (NRSA T32HP19025-07-00).
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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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U2 - 10.1186/1472-6947-13-81
DO - 10.1186/1472-6947-13-81
M3 - Article
C2 - 23915139
AN - SCOPUS:84880872976
SN - 1472-6947
VL - 13
JO - BMC Medical Informatics and Decision Making
JF - BMC Medical Informatics and Decision Making
IS - 1
M1 - 81
ER -