Abstract
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.
Highlights
Effective population management of patients with diabetes requires timely recognition
The National Committee for Quality Assurance (NCQA) requires practices to use patient tracking, disease registries and certified electronic health records (EHR) in order to qualify for patient-centered medical home (PCMH) and accountable care organization (ACO) accreditation [1,2]
This study aims to derive and validate an electronic case-finding model (e-model) that could be used in real-time to identify patients who meet criteria for diabetes at the earliest possible date based on EHR data within a healthcare system
Summary
Effective population management of patients with diabetes requires timely recognition. Practice redesign efforts are shifting the paradigm from volume to value in healthcare in part by emphasizing care coordination, population health, and performance reporting. To this end, the National Committee for Quality Assurance (NCQA) requires practices to use patient tracking, disease registries and certified electronic health records (EHR) in order to qualify for patient-centered medical home (PCMH) and accountable care organization (ACO) accreditation [1,2]. There may be a lag time between when a patient receives a diagnosis of diabetes in the clinical setting compared to when the patient is identified as a diabetic by a case-finding algorithm for the purpose of population management. Because preventing complications of diabetes depends critically on timely intervention, [18] this lag impedes the potential for case-finding algorithms to significantly affect prevention of such complications across diabetic populations
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