A method of identifying and correcting miscoding, misclassification and misdiagnosis in diabetes: a pilot and validation study of routinely collected data

Lusignan, Simon de; Khunti, Kamlesh; Belsey, Jonathan; Hattersley, Andrew T.; Vlymen, Jeremy van; Gallagher, Hugh; Millett, Christopher; Hague, N; Tomson, Charles; Harris, Kevin; Majeed, Azeem

doi:10.1111/j.1464-5491.2009.02917.x

Cited by 114 publications

(108 citation statements)

References 26 publications

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“…Thus people with type 2 diabetes aged 36 -70 yrs were included in 2005, with a sequentially ageing cohort so that people with type 2 diabetes were included if aged 40-74 years in 2009. People under 35 years were excluded to reduce misclassification with type 1 diabetes, 19 and the over 75's because more intensive treatment is often precluded by polypharmacy and other considerations. 20 Patients only contributed information each year that they received a treatment prescription.…”

Section: Methodsmentioning

confidence: 99%

Ethnic and social disparity in glycaemic control in type 2 diabetes; cohort study in general practice 2004–9

et al. 2012

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SummaryObjective To determine whether ethnic group differences in glycated haemoglobin (HbA1c) changed over a 5-year period in people on medication for type 2 diabetes. Design Open cohort in 2004 -9.Setting Electronic records of 100 of the 101 general practices in two inner London boroughs.Participants People aged 35 to 74 years on medication for type 2 diabetes.Main outcome measures Mean HbA1c and proportion with HbA1c controlled to ≤7.5%.Results In this cohort of 24,111 people, 22% were White, 58% South Asian and 17% Black African/Caribbean. From 2004 to 2009 mean HbA1c improved from 8.2% to 7.8% for White, from 8.5% to 8.0% for Black African/Caribbean and from 8.5% to 8.0% for South Asian people. The proportion with HbA1c controlled to 7.5% or less, increased from 44% to 56% in White, 38% to 53% in Black African/ Caribbean and 34% to 48% in South Asian people. Ethnic group and social deprivation were independently associated with HbA1c. South Asian and Black African/Caribbean people were treated more intensively than White people.Conclusion HbA1c control improved for all ethnic groups between 2004-9. However, South Asian and Black African/Caribbean people had persistently worse control despite more intensive treatment and significantly more improvement than White people. Higher social deprivation was independently associated with worse control.

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Section: Methodsmentioning

confidence: 99%

Ethnic and social disparity in glycaemic control in type 2 diabetes; cohort study in general practice 2004–9

et al. 2012

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“…There was no change in diabetes prevalence between these collection points. Although these data were extracted for a cluster randomized trial they are from ordinary general practices taking part in a quality improvement trial in chronic kidney disease, and should reflect standard routinely collected primary care data.We designed and tested a simple structured search engine employing Boolean logic, which might be used to improve data quality and highlight cases requiring further investigation [5]. We used a simple binary algorithm to sort diagnostic codes into definite, probable or possible Type 1 diabetes or Type 2 diabetes based on the specificity of the diagnostic term.…”

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confidence: 99%

Automated identification of miscoded and misclassified cases of diabetes from computer records

Sadek¹,

Vlymen²,

Khunti³

et al. 2012

Diabetic Medicine

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Aims To develop a computer processable algorithm, capable of running automated searches of routine data that flag miscoded and misclassified cases of diabetes for subsequent clinical review. Method Anonymized computer data from the Quality Improvement in Chronic Kidney Disease (QICKD) trial (n = 942 031) were analysed using a binary method to assess the accuracy of data on diabetes diagnosis. Diagnostic codes were processed and stratified into: definite, probable and possible diagnosis of Type 1 or Type 2 diabetes. Diagnostic accuracy was improved by using prescription compatibility and temporally sequenced anthropomorphic and biochemical data. Bayesian false detection rate analysis was used to compare findings with those of an entirely independent and more complex manual sort of the first round QICKD study data (n = 760 588). Results The prevalence of definite diagnosis of Type 1 diabetes and Type 2 diabetes were 0.32% and 3.27% respectively when using the binary search method. Up to 35% of Type 1 diabetes and 0.1% of Type 2 diabetes were miscoded or misclassified on the basis of age/BMI and coding. False detection rate analysis demonstrated a close correlation between the new method and the published hand-crafted sort. Both methods had the highest false detection rate values when coding, therapeutic, anthropomorphic and biochemical filters were used (up to 90% for the new and 75% for the handcrafted search method). Conclusions A simple computerized algorithm achieves very similar results to more complex search strategies to identify miscoded and misclassified cases of both Type 1 diabetes and Type 2 diabetes. It has the potential to be used as an automated audit instrument to improve quality of diabetes diagnosis.Keywords diabetes mellitus, diagnostic errors, computerized medical informatics, medical records systems. IntroductionPrimary care records are largely computerised with most primary care clinicians entering data at the point of care. Electronic patient records facilitate improved management of chronic diseases including diabetes [1]. However, a combination of factors may reduce data quality, including: time pressures that limit what is recorded in the electronic patient record; changes in diagnostic criteria and classification rules for diabetes [2]; sampling labelling problems (it may not be obvious if a sample is fasted) [3]; and idiosyncrasy in the coding interface. Furthermore, the various brands of EPR offer different coding choices for the same search term [4].We have demonstrated that a pragmatic search strategy can identify classification problems in diabetes. Many people labelled as having Type 1 diabetes (Type 1 diabetes) are misclassified and actually have Type 2 diabetes mellitus (Type 2 diabetes) and some with Type 2 diabetes may not have diabetes at all [5]. Coding of diabetes diagnostic data is both complex [6] and in need of refinement [5,7] and current practice probably overestimates the prevalence of diabetes [5]. Recognition of these gaps has led NHS Diabetes [8] and the Royal Coll...

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“…24 Techniques like data quality probes to develop internal diagnostic algorithms to identify cases could avoid the problem of the misclassification of diagnostic codes and provide a valuable method for monitoring data accuracy. 25,26 Moreover, procedures such as participating feedback and audits have shown their usefulness in improving data quality. 27 Besides, control charts and cumulative-sum charts can be a good approach for monitoring the cumulative performance of recorded medical information over time.…”

Section: Limitationsmentioning

confidence: 99%

Construction and validation of a scoring system for the selection of high-quality data in a Spanish population primary care database (SIDIAP)

García-Gil¹,

Hermosilla²,

Prieto‐Alhambra³

et al. 2011

Journal of Innovation in Health Informatics

164

189

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A method of identifying and correcting miscoding, misclassification and misdiagnosis in diabetes: a pilot and validation study of routinely collected data

Abstract: 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.

Cited by 114 publications

References 26 publications

Ethnic and social disparity in glycaemic control in type 2 diabetes; cohort study in general practice 2004–9

Ethnic and social disparity in glycaemic control in type 2 diabetes; cohort study in general practice 2004–9

Automated identification of miscoded and misclassified cases of diabetes from computer records

Construction and validation of a scoring system for the selection of high-quality data in a Spanish population primary care database (SIDIAP)

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