Identifying patients with diabetes and the earliest date of diagnosis in real time

An electronic health record case-finding algorithm

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

Research output: Contribution to journalArticle

16 Citations (Scopus)

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.

Original languageEnglish (US)
Article number81
JournalBMC Medical Informatics and Decision Making
Volume13
Issue number1
DOIs
StatePublished - 2013

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Electronic Health Records
Early Diagnosis
Physicians
Public Hospitals
Disease Management
Population
Delivery of Health Care
Sensitivity and Specificity

ASJC Scopus subject areas

  • Health Informatics
  • Health Policy
  • Medicine(all)

Cite this

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title = "Identifying patients with diabetes and the earliest date of diagnosis in real time: An electronic health record case-finding algorithm",
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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