Prevalence and characteristics in coding, classification and diagnosis of diabetes in primary care

Introduction Approximately 366 million people worldwide live with diabetes and this figure is expected to rise. Among the correct diagnosis, there will be errors in the diagnosis, classification and coding, resulting in adverse health and financial implications. Aim To determine the prevalence and characteristics of diagnostic errors in people with diabetes managed in primary care settings. Methods We conducted a cross-sectional study in nine general practices in Leicester, UK, from May to August 2011, using a validated electronic toolkit. Searches identified cases with potential errors which were manually checked for accuracy. Results There were 54 088 patients and 2434 (4.5%) diagnosed with diabetes. Out of 316 people identified with potential errors with the toolkit, 180 (57%) had confirmed errors after manually reviewing the records, resulting in an error prevalence of 7.4%. Correctly coded people on registers had significantly greater glycated haemoglobin (HbA1c) reductions. There were no significant differences between patients with and without errors in their HbA1C, body mass index, age and size of practice. There was also no significant association of the errors with pay-for-performance initiatives; however, those patients not on disease register had worse glycaemic control. Conclusions A high prevalence of diabetic diagnostic errors was confirmed using medication, biochemical and demographic data. Larger studies are needed to more accurately assess the scale of this problem. Automation of these processes might be possible, which would allow searches to be even more user friendly.