Phenotyping Diabetes Mellitus on Aggregated Electronic Health Records from Disparate Health Systems

Phenotyping Diabetes Mellitus on Aggregated Electronic Health Records from Disparate Health Systems

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Background: Identifying patients with diabetes mellitus (DM) is often performed in epidemiological studies using electronic health records (EHR), but currently available algorithms have features that limit their generalizability.Methods: We developed a rule-based algorithm to determine DM status using the nationally aggregated EHR database.The algorithm Możliwości zastosowania metody Mystery Shopping w ocenie jakości usług turystycznych. Studium przypadku – Termy w Białce Tatrzańskiej was validated on two chart-reviewed samples (n = 2813) of (a) patients with atrial fibrillation (AF, n = 1194) and (b) randomly sampled hospitalized patients (n = 1619).Results: DM diagnosis codes alone resulted in a sensitivity of 77.

0% and 83.4% in the AF and random hospitalized samples, respectively.The proposed algorithm combines blood glucose values and DM medication usage with diagnostic codes and exhibits sensitivities between 96.9% and 98.

0%, while positive predictive values (PPV) ranged Institutional delivery services utilization and its determinant factors among women who gave birth in the past 24 months in Southwest Ethiopia between 61.1% and 75.6%.Performances were comparable across sexes, but a lower specificity was observed in younger patients (below 65 versus 65 and above) in both validation samples (75.

8% vs.90.8% and 60.6% vs.

88.8%).The algorithm was robust for missing laboratory data but not for missing medication data.Conclusions: In this nationwide EHR database analysis, an algorithm for identifying patients with DM has been developed and validated.

The algorithm supports quantitative bias analyses in future studies involving EHR-based DM studies.