Call for consistent coding in diabetes mellitus using the Royal College of General Practitioners and NHS pragmatic classification of diabetes

Simon de Lusignan, Khaled Sadek, Helen McDonald, Pete Horsfield, Norah Hassan Sadek, Aumran Tahir, Terry Desombre, Kamlesh Khunti


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 type 1 (T1DM), type 2 (T2DM), genetic, other, unclassified and non-diabetic fasting hyperglycaemia. We mapped the classification to Read version 2, Clinical Terms version 3 and SNOMED CT.

Results T1DMand T2DM were completely mapped to appropriate codes. However, in other areas only partial mapping is possible. Genetics is a fast-moving field and there were considerable gaps in the available labels for genetic conditions; what the classification calls ‘other’ the coding system labels ‘secondary’ diabetes. The biggest gap was the lack of a code for diabetes where the type of diabetes was uncertain. Notwithstanding these limitations we were able to develop a consensus list.

Conclusions It is a challenge to develop codes that readily map to contemporary clinical concepts. However, clinicians should adopt the standard recommended codes; and audit the quality of their existing records.


data quality; diabetes mellitus; medical records systems computerised; records as topic; vocabulary controlled; medical informatics

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Holman N, Forouhi NG, Goyder E and Wild SH. The Association of Public Health Observatories (APHO) Diabetes Prevalence Model: estimates of total diabetes prevalence for England, 2010–2030. Diabetes Medicine 2011;28(5):575–82. PMid:21480968

Hex N, Bartlett C, Wright D, Taylor M and Varley D. Estimating the current and future costs of Type 1 and Type 2 diabetes in the UK, including direct health costs and indirect societal and productivity costs. Diabetes Medicine 2012 Apr 26 [epub].

Clark K. The six critical attributes of the next generation of quality management software systems. Bioanalysis 2011;3(13):1521–30. PMid:21728775

Leatherman S and Sutherland K. The Quest for Quality in the NHS: A mid-term evaluation of the ten-year quality agenda. London; TSO, 2003.

Chalkidou K, Walley T, Culyer A, Littlejohns P and Hoy A. Evidence-informed evidence-making. Journal of Health Service Research Policy 2008;13(3):167–73. PMid:18573766

de Lusignan S and Mimnagh C. Breaking the first law of informatics: the Quality and Outcomes Framework (QOF) in the dock. Informatics in Primary Care 2006;14(3):153–6. PMid:17288700

Boyle S. United Kingdom (England): health system review. Health Systems in Transition 2011;13(1):1–483, xix–xx.

Swindells M and de Lusignan S. Lessons from the English National Programme for IT about structure, process and utility. Stud Health Technol Inform 2012;174:17–22. PMid:22491103

Department of Health. Six Years On: Delivering the Diabetes National Service Framework. London; Department of Health, 2010.

Vamos EP, Pape UJ, Bottle A et al. Association of practice size and pay-for-performance incentives with the quality of diabetes management in primary care. Canadian Medical Association Journal 2011;183(12):E809–16. Healthcare Quality Improvement Partnership (HQIP), Health and Social Care Information Centre (HSCIC), Diabetes UK. National Diabetes Audit (NDA). PMid:21810950 PMCid:PMC3168664

Calvert M, Shankar A, McManus RJ, Lester H and Freemantle N. Effect of the quality and outcomes framework on diabetes care in the United Kingdom: retrospective cohort study. British Medical Journal 2009 May 26;338:b1870. Erratum in: British Medical Journal 2009;339:b2768.

de Lusignan S and Chan T. The development of primary care information technology in the United Kingdom. Journal of Ambulatory Care Management 2008;31(3):201–10. PMid:18574377

Schade CP, Sullivan FM, de Lusignan S and Madeley J. e-Prescribing, efficiency, quality: lessons from the computerization of UK family practice. Journal of the American Medical Informatics Association 2006;13(5):470–5. PMid:16799129 PMCid:PMC1561797

NHS Information Centre. General Practice Extraction Service (GPES).

Gnani S and Majeed A. A User's Guide to Primary Care Collected in England. Cambridge, UK: Eastern Region Public Health Observatory on behalf of the Association of Public Health Observatories, 2006.

de Lusignan S. Codes, classifications, terminologies and nomenclatures: definition, development and application in practice. Informatics in Primary Care 2005;13(1):65–70. PMid:15949178

Britt H and Miller G. Data collection and changing health care systems. 2. New Zealand. Medical Journal of Australia 1993 Oct 4;159(7):476–9. PMid:8412922

de Lusignan S, Chan T and Jones S. Large complex terminologies: more coding choice, but harder to find data—reflections on introduction of SNOMED CT (Systematized Nomenclature of Medicine—Clinical Terms) as an NHS standard. Informatics in Primary Care 2011; 19(1):3–5. PMid:22118330

Protti D, Bowden T and Johansen I. Adoption of information technology in primary care physician offices in New Zealand and Denmark, Part 3: medical record environment comparisons. Informatics in Primary Care 2008;16(4):285–90. PMid:19192330

Vikstrom A, Nystrom M, Ahlfeldt H, Strender LE and Nilsson GH. Views of diagnosis distribution in primary care in 2.5 million encounters in Stockholm: a comparison between ICD-10 and SNOMED CT. Informatics in Primary Care 2010;18(1):17–29. PMid:20429975

Nadkarni PM and Darer JA. Migrating existing clinical content from ICD-9 to SNOMED. Journal of the American Medical Informatics Association 2010 Sep–Oct;17(5):602–7. PMid:20819871 PMCid:PMC2995664

Bagheri A, Sadek A, Chan T, Khunti K and de Lusignan S. Using surrogate markers in primary electronic patient record systems to confirm or refute the diagnosis of diabetes. Informatics in Primary Care 2009;17(2):121–9. PMid:19807954

NHS Information Authority Terminology Browser. Version 1.04. – UK Terminology Centre.

Dataline software. Snoflake Browser.

Rollason W, Khunti K and de Lusignan S. Variation in the recording of diabetes diagnostic data in primary care computer systems: implications for the quality of care. Informatics in Primary Care 2009;17(2):113–19. PMid:19807953

Winckler W, Weedon MN, Graham RR et al. Evaluation of common variants in the six known maturity-onset diabetes of the young (MODY) genes for association with type 2 diabetes. Diabetes 2007;56(3):685–93. PMid:17327436

Pines Corrales PJ, Lopez Garrido MP, Louhibi Rubio L et al. Importance of clinical variables in the diagnosis of MODY2 and MODY3. Endocrinologıa y nutricion 2011;58(7):341–6. PMid:21737366

de Lusignan S. Flagging fasting plasma glucose specimens: time to routinely label the context in which pathology specimens are recorded. Informatics in Primary Care 2009;17(2):63–4. PMid:19807947

Heianza Y, Hara S, Arase Y et al. HbA 1c 5.7–6.4% and impaired fasting plasma glucose for diagnosis of prediabetes and risk of progression to diabetes in Japan (TOPICS 3): a longitudinal cohort study. Lancet 2011;378(9786):147–55.

World Health Organisation. Use of Glycated Haemoglobin (HbA1c) in the Diagnosis of Diabetes Mellitus Abbreviated Report of a WHO Consultation, 2011. WHO/NMH/CHP/CPM/11.1

Farhan S, Jarai R, Tentzeris I et al. Comparison of HbA1c and oral glucose tolerance test for diagnosis of diabetes in patients with coronary artery disease. Clinical Research in Cardiology 2012 Mar 6 [epub ahead of print]. PMid:22391987

Preckova P, Zvarova J and Zvara K. Measuring diversity in medical reports based on categorized attributes and international classification systems. BMC Medical Informatics and Decision Making 2012 Apr 12;12:31. PMid:22498343 PMCid:PMC3344690

World Health Organisation. ICD-10. Tenth Classification of Disease and Health Related Problems (2e). Geneva: WHO, 2004.

Stone MA, Camosso-Stefinovic J, Wilkinson J, de Lusignan S, Hattersley AT and Khunti K. Incorrect and incomplete coding and classification of diabetes: a systematic review. Diabetes Medicine 2010;27(5):491–7. PMid:20536944

de Lusignan S, Khunti K, Belsey J et al. A method of identifying and correcting miscoding, misclassification and misdiagnosis in diabetes: a pilot and validation study of routinely collected data. Diabetes Medicine 2010 Feb;27(2):203–9. PMid:20546265

de Lusignan S, Sadek N, Mulnier H, Tahir A, Russell-Jones D and Khunti K. Miscoding, misclassification and misdiagnosis of diabetes in primary care. Diabetes Medicine 2012 Feb;29(2):181–9.

The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Report of the expert committee on the diagnosis and classification of diabetes mellitus. Diabetes Care 1997;20:1183–97. PMid:9203460

Bakhshi-Raiez F, Cornet R, Bosman RJ, Joore H and de Keizer NF. Using SNOMED CT to identify a crossmap between two classification systems: a comparison with an expert-based and a data-driven strategy. Studies in Health Technology and Informatics 2010;160(2):1035–9. PMid:20841841

Whitston M, Chung S, Henderson J and Young B. What can be learned about the impact of diabetes on hospital admissions from routinely recorded data? Diabetes Medicine 2011 Dec 12.

Daultrey H, Gooday C and Dhatariya K. Increased length of inpatient stay and poor clinical coding: audit of patients with diabetes. Journal of the Royal Society of Medicine Short Reports 2011;2(11):83. PMid:22140609 PMCid:PMC3227384

Sadek N, Sadek AR, Tahir A, Khunti K, Desombre T and de Lusignan S. Evaluating tools to support a new practical classification of diabetes: excellent control may represent misdiagnosis and omission from disease registers is associated with worse control. International Journal of Clinical Practice 2012;66(9):874–82.



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