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Miscoding, misclassification and misdiagnosis of diabetes in primary care


Simon de Lusignan, Professor of Primary Care and Clinical Informatics, Department of Health Care Policy and Management, Faculty of Management and Law, University of Surrey, Guildford, Surrey GU2 7X, UK. E-mail:


Diabet. Med. 29, 181–189 (2012)


Aims  To determine the effectiveness of self-audit tools designed to detect miscoding, misclassification and misdiagnosis of diabetes in primary care.

Methods  We developed six searches to identify people with diabetes with potential classification errors. The search results were automatically ranked from most to least likely to have an underlying problem. Eight practices with a combined population of 72 000 and diabetes prevalence 2.9% (n = 2340) completed audit forms to verify whether additional information within the patients’ medical record confirmed or refuted the problems identified.

Results  The searches identified 347 records, mean 42 per practice. Pre-audit 20% (n = 69) had Type 1 diabetes, 70% (n = 241) had Type 2 diabetes, 9% (n = 30) had vague codes that were hard to classify, 2% (n = 6) were not coded and one person was labelled as having gestational diabetes. Of records, 39.2% (n = 136) had important errors: 10% (n = 35) had coding errors; 12.1% (42) were misclassified; and 17.0% (59) misdiagnosed as having diabetes. Thirty-two per cent (n = 22) of people with Type 2 diabetes (n = 69) were misclassified as having Type 1 diabetes; 20% (n = 48) of people with Type 2 diabetes (n = 241) did not have diabetes; of the 30 patients with vague diagnostic terms, 50% had Type 2 diabetes, 20% had Type 1 diabetes and 20% did not have diabetes. Examples of misdiagnosis were found in all practices, misclassification in seven and miscoding in six.

Conclusions  Volunteer practices successfully used these self-audit tools. Approximately 40% of patients identified by computer searches (5.8% of people with diabetes) had errors; misdiagnosis is commonest, misclassification may affect treatment options and miscoding in omission from disease registers and the potential for reduced quality of care.