Using surrogate markers in primary electronic patient record
systems to confirm or refute the diagnosis of diabetes

Bagheri, Amir; A, Sadek; Chan, Tyk; Khunti, Kamlesh; Lusignan, Simon de

doi:10.14236/jhi.v17i2.724

Cited by 12 publications

(14 citation statements)

References 22 publications

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“…Other codes have no clear mapping, and it is impossible to tell what type of diabetes, though most of these are likely to represent possible T2DM 16. Our mapping was based on the work completed as part of the NHS Diabetes⁄Royal College of General Practitioners (RCGP) practical classification of diabetes 17,18…”

Section: Methodsmentioning

confidence: 99%

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An algorithm to improve diagnostic accuracy in diabetes in computerised problem orientated medical records (POMR) compared with an established algorithm developed in episode orientated records (EOMR)

Lusignan

Liaw

Dedman

et al. 2015

jhi

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Background An algorithm that detects errors in diagnosis, classification or coding of diabetes in primary care computerised medial record (CMR) systems is currently available. However, this was developed on CMR systems that are episode orientated medical records (EOMR); and do not force the user to always code a problem or link data to an existing one. More strictly problem orientated medical record (POMR) systems mandate recording a problem and linking consultation data to them. Objective To compare the rates of detection of diagnostic accuracy using an algorithm developed in EOMR with a new POMR specific algorithm. Method We used data from The Health Improvement Network (THIN) database (N = 2,466,364) to identify a population of 100,513 (4.08%) patients considered likely to have diabetes. We recalibrated algorithms designed to classify cases of diabetes to take account of that POMR enforced coding consistency in the computerised medical record systems [In Practice Systems (InPS) Vision] that contribute data to THIN. We explored the different proportions of people classified as having type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) and with diabetes unclassifiable as either T1DM or T2DM. We compared proportions using chi-square tests and used Tukey's test to compare the characteristics of the people in each group. Results The prevalence of T1DM using the original EOMR algorithm was 0.38% (9,264/2,466,364), and for T2DM 3.22% (79,417/2,466,364). The prevalence using the new POMR algorithm was 0.31% (7,750/2,466,364) T1DM and 3.65% (89,990/2,466,364) T2DM. The EOMR algorithms also left more people unclassified

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Section: Methodsmentioning

confidence: 99%

“…type 2 and maturity onset). Additional steps included checking therapy and blood tests were compatible with the diagnosis; these were the same steps used in 5,6,16,18. The details of these steps were as follows:…”

Section: Methodsmentioning

confidence: 99%

An algorithm to improve diagnostic accuracy in diabetes in computerised problem orientated medical records (POMR) compared with an established algorithm developed in episode orientated records (EOMR)

Lusignan

Liaw

Dedman

et al. 2015

jhi

Self Cite

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“…More patients may also be identified if medication records 20 and other non-diagnostic data 21 are used in the search strategy.…”

Section: Further Researchmentioning

confidence: 99%

Identification of patients with neuropathic pain using electronic primary care records

Gajria¹,

Murray²,

Birger³

et al. 2011

Journal of Innovation in Health Informatics

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Background Chronic neuropathic pain is a common condition which is challenging to treat. Many people with neuropathic pain are managed in the community, so primary care records may allow more appropriate subjects to be recruited for clinical studies. Objective We investigated whether primary care records can be used to identify patients with diseases associated with neuropathic pain. Method We analysed demographic, diagnostic and prescribing data from over 100 000 primary care electronic patient records in one part of London, UK. Results The prevalence of diagnoses associated with chronic neuropathic pain was 13 per 1000,

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“…age, obesity and ethnicity). 13 These markers both individually and in combination can predict a diagnosis of diabetes. This offers the allure of creating algorithms to validate or refute diagnostic labels within GP clinical records.…”

Section: Fundamental Issues In Diabetesmentioning

confidence: 99%

In this issue of Informatics in Primary Care: ethnicity, learning and diabetes

Lusignan¹

2009

Journal of Innovation in Health Informatics

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Using surrogate markers in primary electronic patient record systems to confirm or refute the diagnosis of diabetes

Cited by 12 publications

References 22 publications

An algorithm to improve diagnostic accuracy in diabetes in computerised problem orientated medical records (POMR) compared with an established algorithm developed in episode orientated records (EOMR)

An algorithm to improve diagnostic accuracy in diabetes in computerised problem orientated medical records (POMR) compared with an established algorithm developed in episode orientated records (EOMR)

Identification of patients with neuropathic pain using electronic primary care records

In this issue of Informatics in Primary Care: ethnicity, learning and diabetes

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