Miscoding, misclassification and misdiagnosis of diabetes in primary care
Article first published online: 11 JAN 2012
© 2012 The Authors. Diabetic Medicine © 2012 Diabetes UK
Volume 29, Issue 2, pages 181–189, February 2012
How to Cite
de Lusignan, S., Sadek, N., Mulnier, H., Tahir, A., Russell-Jones, D. and Khunti, K. (2012), Miscoding, misclassification and misdiagnosis of diabetes in primary care. Diabetic Medicine, 29: 181–189. doi: 10.1111/j.1464-5491.2011.03419.x
- Issue published online: 11 JAN 2012
- Article first published online: 11 JAN 2012
- Accepted manuscript online: 26 AUG 2011 09:31PM EST
- Accepted 19 August 2011
- clinical audit;
- computing methodologies;
- diagnostic errors;
- medical records systems;
- quality of health care
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.