BACKGROUND: The prevalence of diabetes is increasing with growing levels of obesity and an aging population. New practical guidelines for diabetes provide an applicable classification. Inconsistent coding of diabetes hampers the use of computerised disease registers for quality improvement, and limits the monitoring of disease trends. OBJECTIVE: To develop a consensus set of codes that should be used when recording diabetes diagnostic data. METHODS: The consensus approach was hierarchical, with a preference for diagnostic/disorder codes, to define each type of diabetes and non-diabetic hyperglycaemia, which were listed as being completely, partially or not readily mapped to available codes. The practical classification divides diabetes into...
Abstract Background Electronic medical records contai...
To conduct a systematic review to identify types and implications of incorrect or incomplete coding ...
BACKGROUND: Electronic medical records contain valuable clinical information not readily available e...
Background The prevalence of diabetes is increasing with growing levels of obesity and an aging popu...
<strong>Background</strong> Diabetes mellitus (DM) is a serious, chronic condition affecting 2.3 mil...
Background: Research into diabetes mellitus (DM) often requires a reproducible method for identifyin...
Differentiating between type 1 and type 2 diabetes is fundamental to ensuring appropriate management...
Read codes that relate to diagnoses of diabetes, based on codes developed originally for Eastwood SV...
BACKGROUND: Differentiating between type 1 and type 2 diabetes is fundamental to ensuring appropriat...
Diabetes mellitus (DM) is a serious, chronic condition affecting 2.3 million people in the UK and co...
BACKGROUND: An algorithm that detects errors in diagnosis, classification or coding of diabetes in p...
Objective: To assess the effect of coding quality on estimates of the incidence of diabetes in the U...
Abstract Background Validated algorithms to classify type 1 and 2 diabetes (T1D, T2D) are mostly li...
Background: Multiple laboratory tests are used to diagnose and manage patients with diabetes mellitu...
Background: Electronic diabetes registers promote structured care and enable identification of undia...
Abstract Background Electronic medical records contai...
To conduct a systematic review to identify types and implications of incorrect or incomplete coding ...
BACKGROUND: Electronic medical records contain valuable clinical information not readily available e...
Background The prevalence of diabetes is increasing with growing levels of obesity and an aging popu...
<strong>Background</strong> Diabetes mellitus (DM) is a serious, chronic condition affecting 2.3 mil...
Background: Research into diabetes mellitus (DM) often requires a reproducible method for identifyin...
Differentiating between type 1 and type 2 diabetes is fundamental to ensuring appropriate management...
Read codes that relate to diagnoses of diabetes, based on codes developed originally for Eastwood SV...
BACKGROUND: Differentiating between type 1 and type 2 diabetes is fundamental to ensuring appropriat...
Diabetes mellitus (DM) is a serious, chronic condition affecting 2.3 million people in the UK and co...
BACKGROUND: An algorithm that detects errors in diagnosis, classification or coding of diabetes in p...
Objective: To assess the effect of coding quality on estimates of the incidence of diabetes in the U...
Abstract Background Validated algorithms to classify type 1 and 2 diabetes (T1D, T2D) are mostly li...
Background: Multiple laboratory tests are used to diagnose and manage patients with diabetes mellitu...
Background: Electronic diabetes registers promote structured care and enable identification of undia...
Abstract Background Electronic medical records contai...
To conduct a systematic review to identify types and implications of incorrect or incomplete coding ...
BACKGROUND: Electronic medical records contain valuable clinical information not readily available e...