Jones, Julia L ORCID: 0000-0002-1266-4731, Lumsden, Natalie G, Simons, Koen, Ta'eed, Anis, de Courten, Maximilian ORCID: 0000-0001-9997-9359, Wijeratne, Tissa, Cox, Nicholas, Neil, Christopher, Manski-Nankervis, Jo-Anne, Hamblin, Peter Shane, Janus, Edward D and Nelson, Craig L (2022) Using electronic medical record data to assess chronic kidney disease, type 2 diabetes and cardiovascular disease testing, recognition and management as documented in Australian general practice: a cross-sectional analysis. Family Medicine and Community Health, 10 (1). ISSN 2305-6983

Abstract

Objectives To evaluate the capacity of general practice (GP) electronic medical record (EMR) data to assess risk factor detection, disease diagnostic testing, diagnosis, monitoring and pharmacotherapy for the interrelated chronic vascular diseases-chronic kidney disease (CKD), type 2 diabetes (T2D) and cardiovascular disease. Design Cross-sectional analysis of data extracted on a single date for each practice between 12 April 2017 and 18 April 2017 incorporating data from any time on or before data extraction, using baseline data from the Chronic Disease early detection and Improved Management in PrimAry Care ProjecT. Deidentified data were extracted from GP EMRs using the Pen Computer Systems Clinical Audit Tool and descriptive statistics used to describe the study population. Setting Eight GPs in Victoria, Australia. Participants Patients were ≥18 years and attended GP ≥3 times within 24 months. 37 946 patients were included. Results Risk factor and disease testing/monitoring/treatment were assessed as per Australian guidelines (or US guidelines if none available), with guidelines simplified due to limitations in data availability where required. Risk factor assessment in those requiring it: 30% of patients had body mass index and 46% blood pressure within guideline recommended timeframes. Diagnostic testing in at-risk population: 17% had diagnostic testing as per recommendations for CKD and 37% for T2D. Possible undiagnosed disease: Pathology tests indicating possible disease with no diagnosis already coded were present in 6.7% for CKD, 1.6% for T2D and 0.33% familial hypercholesterolaemia. Overall prevalence: Coded diagnoses were recorded in 3.8% for CKD, 6.6% for T2D, 4.2% for ischaemic heart disease, 1% for heart failure, 1.7% for ischaemic stroke, 0.46% for peripheral vascular disease, 0.06% for familial hypercholesterolaemia and 2% for atrial fibrillation. Pharmaceutical prescriptions: the proportion of patients prescribed guideline-recommended medications ranged from 44% (beta blockers for patients with ischaemic heart disease) to 78% (antiplatelets or anticoagulants for patients with ischaemic stroke). Conclusions Using GP EMR data, this study identified recorded diagnoses of chronic vascular diseases generally similar to, or higher than, reported national prevalence. It suggested low levels of extractable documented risk factor assessments, diagnostic testing in those at risk and prescription of guideline-recommended pharmacotherapy for some conditions. These baseline data highlight the utility of GP EMR data for potential use in epidemiological studies and by individual practices to guide targeted quality improvement. It also highlighted some of the challenges of using GP EMR data.

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