Abstract Background Disease surveillance of diabetes among youth has relied mainly upon manual chart review. However, increasingly available structured electronic health record (EHR) data have been shown to yield accurate determinations of diabetes status and type. Validated algorithms to determine date of diabetes diagnosis are lacking. The objective of this work is to validate two EHR-based algorithms to determine date of diagnosis of diabetes. Methods A rule-based ICD-10 algorithm identified youth with diabetes from structured EHR data over the period of 2009 through 2017 within three children’s hospitals that participate in the SEARCH for Diabetes in Youth Study: Cincinnati Children’s Hospital, Cincinnati, OH, Seattle Children’s Hospita...
BACKGROUND: Electronic diabetes registers promote structured care and enable identification of undia...
In epidemiology studies, identification of diabetes type (type 1 vs. type 2) among study participant...
Introduction We aimed to develop algorithms distinguishing type 1 diabetes (T1D) from type 2 diabete...
Background: Effective population management of patients with diabetes requires timely recognition. C...
Abstract Background Effective population management of patients with diabetes requires timely recogn...
Objective To develop an efficient surveillance approach for childhood diabetes by type across 2 larg...
The performance of automated algorithms for childhood diabetes case ascertainment and type classific...
Abstract Background Validated algorithms to classify type 1 and 2 diabetes (T1D, T2D) are mostly li...
Abstract Background Electronic medical records contai...
OBJECTIVEdTo create surveillance algorithms to detect diabetes and classify type 1 versus type 2 dia...
We aimed to develop an efficient surveillance approach for childhood diabetes. We analyzed EHR data ...
Background: Electronic diabetes registers promote structured care and enable identification of undia...
Abstract Background Health care data allow for the study and surveillance of chronic diseases such a...
BACKGROUND: Electronic medical records contain valuable clinical information not readily available e...
Objective: To develop and validate a phenotyping algorithm for the identification of patients with t...
BACKGROUND: Electronic diabetes registers promote structured care and enable identification of undia...
In epidemiology studies, identification of diabetes type (type 1 vs. type 2) among study participant...
Introduction We aimed to develop algorithms distinguishing type 1 diabetes (T1D) from type 2 diabete...
Background: Effective population management of patients with diabetes requires timely recognition. C...
Abstract Background Effective population management of patients with diabetes requires timely recogn...
Objective To develop an efficient surveillance approach for childhood diabetes by type across 2 larg...
The performance of automated algorithms for childhood diabetes case ascertainment and type classific...
Abstract Background Validated algorithms to classify type 1 and 2 diabetes (T1D, T2D) are mostly li...
Abstract Background Electronic medical records contai...
OBJECTIVEdTo create surveillance algorithms to detect diabetes and classify type 1 versus type 2 dia...
We aimed to develop an efficient surveillance approach for childhood diabetes. We analyzed EHR data ...
Background: Electronic diabetes registers promote structured care and enable identification of undia...
Abstract Background Health care data allow for the study and surveillance of chronic diseases such a...
BACKGROUND: Electronic medical records contain valuable clinical information not readily available e...
Objective: To develop and validate a phenotyping algorithm for the identification of patients with t...
BACKGROUND: Electronic diabetes registers promote structured care and enable identification of undia...
In epidemiology studies, identification of diabetes type (type 1 vs. type 2) among study participant...
Introduction We aimed to develop algorithms distinguishing type 1 diabetes (T1D) from type 2 diabete...