Simon de Lusignan1, Siaw-Teng Liaw2, Daniel Dedman3, Kamlesh Khunti4, Khaled Sadek5, Simon Jones5. 1. University of Surrey, Guildford, UK. s.lusignan@surrey.ac.uk. 2. University of New South Wales, Australia. 3. Clinical Practice Research Datalink, London, UK. 4. University of Leicester, Leicester, UK. 5. University of Surrey, Guildford, UK.
Abstract
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 11,439 (12%), as to their type of diabetes compared with 2,380 (2.4%), for the new algorithm. Those people who were only classified by the EOMR system differed in terms of older age, and apparently better glycaemic control, despite not being prescribed medication for their diabetes (p < 0.005). CONCLUSION: Increasing the degree of problem orientation of the medical record system can improve the accuracy of recording of diagnoses and, therefore, the accuracy of using routinely collected data from CMRs to determine the prevalence of diabetes mellitus; data processing strategies should reflect the degree of problem orientation.
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 11,439 (12%), as to their type of diabetes compared with 2,380 (2.4%), for the new algorithm. Those people who were only classified by the EOMR system differed in terms of older age, and apparently better glycaemic control, despite not being prescribed medication for their diabetes (p < 0.005). CONCLUSION: Increasing the degree of problem orientation of the medical record system can improve the accuracy of recording of diagnoses and, therefore, the accuracy of using routinely collected data from CMRs to determine the prevalence of diabetes mellitus; data processing strategies should reflect the degree of problem orientation.
Entities:
Keywords:
computerized; diabetes mellitus; epidemiology; medical record systems; medical records; problem-oriented; records as topic
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