ArticleBMC health services research2021
Validity of algorithms for identifying five chronic conditions in MedicineInsight, an Australian national general practice database.
Article in BMC health services research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Incidence of GLP-1 receptor agonist use by women of reproductive age attending general practices in Australia, 2011-2022: a retrospective open cohort study.The Medical journal of Australia · 2025Article
- Prevalence of SGLT2 inhibitor and GLP1 receptor agonist prescriptions in type 2 diabetes patients with and without chronic kidney disease: Analysis of an Australian primary care dataset.Diabetes, obesity & metabolism · 2025Article
- Cardiometabolic monitoring in children and adolescents prescribed antipsychotics in Australian primary care, 2011 to 2017.The Australian and New Zealand journal of psychiatry · 2025Article
- Article
- Assessing patterns of chronic kidney disease care in Australian primary care: a retrospective cohort study of a national general practice dataset.The Lancet regional health. Western Pacific · 2025Article
- Sex disparities in the prevalence, incidence, and management of diabetes mellitus: an Australian retrospective primary healthcare study involving 668,891 individuals.BMC medicine · 2024Article
- Management of physical and psychological trauma resulting from motor vehicle crashes in Australian general practice: a mixed-methods approach.BMC primary care · 2024Article
- Antipsychotic prescribing patterns in children and adolescents attending Australian general practice in 2011 and 2017.JCPP advances · 2024Article
- The Prevalence of CKD in Australian Primary Care: Analysis of a National General Practice Dataset.Kidney international reports · 2024Article
- Oral Anticoagulant Use in Patients with Atrial Fibrillation at Low Risk of Stroke and Associated Bleeding Complications.Journal of clinical medicine · 2023Article
- Predicted cardiovascular disease risk and prescribing of antihypertensive therapy among patients with hypertension in Australia using MedicineInsight.Journal of human hypertension · 2023Article
- The Cost of Uncontrolled Blood Pressure in Australian General Practice: A Modelling Study Using Electronic Health Records (MedicineInsight).PharmacoEconomics · 2023Article
- Diabetes mellitus monitoring and control among adults in Australian general practice: a national retrospective cohort study.BMJ open · 2023Article
- Agreement of acute serious events recorded across datasets using linked Australian general practice, hospital, emergency department and death data: implications for research and surveillance.International journal of population data science · 2023Article
- General practice management of depression among patients with coronary heart disease in Australia.BMC primary care · 2022Article
- Incidence of Osteoporosis in Primary Care Patients with Atrial Fibrillation Receiving Different Oral Anticoagulants.Journal of clinical medicine · 2022Article
- Development and validation of algorithms to identify patients with chronic kidney disease and related chronic diseases across the Northern Territory, Australia.BMC nephrology · 2022Article
- Diabetes Mellitus Diagnosis and Screening in Australian General Practice: A National Study.Journal of diabetes research · 2022Article
- Evaluating the accuracy of data extracted from electronic health records into MedicineInsight, a national Australian general practice database.International journal of population data science · 2022Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMedicineInsight is a database containing de-identified electronic health records (EHRs) from over 700 Australian general practices. It is one of the largest and most widely used primary health care EHR databases in Australia. This study examined the validity of algorithms that use information from various fields in the MedicineInsight data to indicate whether patients have specific health conditions. This study examined the validity of MedicineInsight algorithms for five common chronic conditions: anxiety, asthma, depression, osteoporosis and type 2 diabetes.
methodsPatients' disease status according to MedicineInsight algorithms was benchmarked against the recording of diagnoses in the original EHRs. Fifty general practices contributing data to MedicineInsight met the eligibility criteria regarding patient load and location. Five were randomly selected and four agreed to participate. Within each practice, 250 patients aged ≥ 40 years were randomly selected from the MedicineInsight database. Trained staff reviewed the original EHR for as many of the selected patients as possible within the time available for data collection in each practice.
resultsA total of 475 patients were included in the analysis. All the evaluated MedicineInsight algorithms had excellent specificity, positive predictive value, and negative predictive value (above 0.9) when benchmarked against the recording of diagnoses in the original EHR. The asthma and osteoporosis algorithms also had excellent sensitivity, while the algorithms for anxiety, depression and type 2 diabetes yielded sensitivities of 0.85, 0.89 and 0.89 respectively.
conclusionsThe MedicineInsight algorithms for asthma and osteoporosis have excellent accuracy and the algorithms for anxiety, depression and type 2 diabetes have good accuracy. This study provides support for the use of these algorithms when using MedicineInsight data for primary health care quality improvement activities, research and health system policymaking and planning.
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