title: A method of identifying and correcting miscoding, misclassification and misdiagnosis in diabetes: a pilot and validation study of routinely collected data.
abstract: OBJECTIVE
  Incorrect classification, diagnosis and coding of the type of diabetes may have implications for patient management and limit our ability to measure quality. The aim of the study was to measure the accuracy of diabetes diagnostic data and explore the scope for identifying errors.
  
  
  METHODS
  We used two sets of anonymized routinely collected computer data: the pilot used Cutting out Needless Deaths Using Information Technology (CONDUIT) study data (n = 221 958), which we then validated using 100 practices from the Quality Improvement in Chronic Kidney Disease (QICKD) study (n = 760,588). We searched for contradictory diagnostic codes and also compatibility with prescription, demographic and laboratory test data. We classified errors as: misclassified-incorrect type of diabetes; misdiagnosed-where there was no evidence of diabetes; or miscoded-cases where it was difficult to infer the type of diabetes.
  
  
  RESULTS
  The standardized prevalence of diabetes was 5.0 and 4.0% in the CONDUIT and the QICKD data, respectively: 13.1% (n = 930) of CONDUIT and 14.8% (n = 4363) QICKD are incorrectly coded; 10.3% (n = 96) in CONDUIT and 26.2% (n = 1143) in QICKD are misclassified; nearly all of these cases are people classified with Type 1 diabetes who should be classified as Type 2. Approximately 5% of T2DM in both samples have no objective evidence to support a diagnosis of diabetes. Miscoding was present in approximately 7.8% of the CONDUIT and 6.1% of QICKD diabetes records.
  
  
  CONCLUSIONS
  The prevalence of miscoding, misclassification and misdiagnosis of diabetes is high and there is substantial scope for further improvement in diagnosis and data quality. Algorithms which identify likely misdiagnosis, misclassification and miscoding could be used to flag cases for review.
authors.affiliation: St George's - University of London, London, UK. slusigna@sgul.ac.uk
authors.forename: S
authors.initials: S
authors.lastname: de Lusignan
authors.forename: K
authors.initials: K
authors.lastname: Khunti
authors.forename: J
authors.initials: J
authors.lastname: Belsey
authors.forename: A
authors.initials: A
authors.lastname: Hattersley
authors.forename: J
authors.initials: J
authors.lastname: van Vlymen
authors.forename: H
authors.initials: H
authors.lastname: Gallagher
authors.forename: C
authors.initials: C
authors.lastname: Millett
authors.forename: N J
authors.initials: NJ
authors.lastname: Hague
authors.forename: C
authors.initials: C
authors.lastname: Tomson
authors.forename: K
authors.initials: K
authors.lastname: Harris
authors.forename: A
authors.initials: A
authors.lastname: Majeed
mesh_terms: D000328:Adult; D000465:Algorithms; D003625:Data Collection / Q000592:standards*; D003922:Diabetes Mellitus, Type 1 / Q000175:diagnosis* / Q000453:epidemiology; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis* / Q000453:epidemiology; D003951:Diagnostic Errors; D005260:Female; D006801:Humans; D008297:Male; D016347:Medical Records Systems, Computerized; D010865:Pilot Projects