Abstract Background The increasing burden of type 2 diabetes mellitus makes the continuous surveillance of its prevalence and incidence advisable. Electronic health records (EHRs) have great potential for research and surveillance purposes; however the quality of their data must first be evaluated for fitness for use. The aim of this study was to assess the validity of type 2 diabetes diagnosis in a primary care EHR database covering more than half a million inhabitants, 97% of the population in Navarra, Spain. Methods In the Navarra EPIC-InterAct study, the validity of the T90 code from the International Classification of Primary Care, Second Edition was studied in a primary care EHR database to identify incident cases of type 2 diabetes, ...
Background: The Institute of Medicine framework defines six dimensions of quality for healthcare sys...
OBJECTIVEdTo create surveillance algorithms to detect diabetes and classify type 1 versus type 2 dia...
OBJECTIVE: Quality indicators for the treatment of type 2 diabetes are often retrieved from a chroni...
Abstract Background Electronic medical records contai...
Background: In North Karelia, Finland, the regional electronic health records (EHRs) enable flexible...
BACKGROUND: Electronic diabetes registers promote structured care and enable identification of undia...
Background: Electronic diabetes registers promote structured care and enable identification of undia...
BACKGROUND: Electronic medical records contain valuable clinical information not readily available e...
Introduction: The increasing prevalence of type 2 diabetes (T2DM) presents a significant burden on a...
Background: Data routinely collected in electronic health records (EHRs) offer a unique opportunity ...
ObjectivesAn estimated 25% of type two diabetes mellitus (DM2) patients in the United States are und...
International audienceObjectivesIn the French national health insurance information system (SNDS) th...
Abstract Background Health care data allow for the study and surveillance of chronic diseases such a...
Introduction We aimed to develop algorithms distinguishing type 1 diabetes (T1D) from type 2 diabete...
Background and aims: Diabetes can often remain undiagnosed or unregistered in administrative databas...
Background: The Institute of Medicine framework defines six dimensions of quality for healthcare sys...
OBJECTIVEdTo create surveillance algorithms to detect diabetes and classify type 1 versus type 2 dia...
OBJECTIVE: Quality indicators for the treatment of type 2 diabetes are often retrieved from a chroni...
Abstract Background Electronic medical records contai...
Background: In North Karelia, Finland, the regional electronic health records (EHRs) enable flexible...
BACKGROUND: Electronic diabetes registers promote structured care and enable identification of undia...
Background: Electronic diabetes registers promote structured care and enable identification of undia...
BACKGROUND: Electronic medical records contain valuable clinical information not readily available e...
Introduction: The increasing prevalence of type 2 diabetes (T2DM) presents a significant burden on a...
Background: Data routinely collected in electronic health records (EHRs) offer a unique opportunity ...
ObjectivesAn estimated 25% of type two diabetes mellitus (DM2) patients in the United States are und...
International audienceObjectivesIn the French national health insurance information system (SNDS) th...
Abstract Background Health care data allow for the study and surveillance of chronic diseases such a...
Introduction We aimed to develop algorithms distinguishing type 1 diabetes (T1D) from type 2 diabete...
Background and aims: Diabetes can often remain undiagnosed or unregistered in administrative databas...
Background: The Institute of Medicine framework defines six dimensions of quality for healthcare sys...
OBJECTIVEdTo create surveillance algorithms to detect diabetes and classify type 1 versus type 2 dia...
OBJECTIVE: Quality indicators for the treatment of type 2 diabetes are often retrieved from a chroni...