The proportion of Australian children meeting fruit and vegetable recommendations are sub-optimal across all SES groups which suggests that a national approach across demographic strata is warranted. SO WHAT?: Future health promotion interventions should have a refocus on vegetables instead of "fruit and vegetables," particularly in the key transition period when children start pre-school.
Background MedicineInsight 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. Methods Patients’ 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. Results A 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. Conclusions The 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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The availability of direct-acting antivirals (DAAs) sparked efforts to eliminate hepatitis C virus (HCV) in Australia. We evaluated whether an educational intervention of a 1-h discussion among staff using audit and feedback data from the MedicineInsight GP programme would improve DAA uptake. Of 296 eligible general practices in MedicineInsight, 11% opted out. Randomization stratified by practice caseload allocated 130 practices to the intervention arm and 129 to control. The primary outcome was the number of patients started on DAAs over 6 months using the negative binomial regression model adjusted for DAA prescription history and clustering by practice. Data for analysis were available for 78% of practices, which included 101 practices and 2469 DAA-naive patients with confirmed/possible HCV in the intervention arm, and 100 practices and 2466 patients in the control arm. At baseline, 49.5% of practices had prescribed ≥1 DAA in the past year; 18.9% of HCV patients had already been treated with DAAs; the mean age of DAA-naive HCV patients was 43 years old, and 57% were men. Over 6 months, 43 patients in the intervention arm and 36 in the control arm started DAAs (adjusted IRR 1.19; 95% CI 0.67-2.11, p = 0.55). In the first 3 months, 27 vs 16 patients started DAAs (adjusted IRR 1.77, 0.88-3.58; p = 0.111).Few patients were started on DAAs, and a facilitated discussion in HCV management did not lead to a significant increase. Alternative measures, such as incentivizing GP initiations or patients, are suggested to address remaining barriers to DAA uptake in Australian primary care.
IntroductionMedicineInsight is a database containing de-identified electronic health records (EHRs) from over 700 Australian general practices. Previous research validated algorithms used to derive medical condition flags in MedicineInsight, but the accuracy of data fields following EHR extractions from clinical practices and data warehouse transformation processes have not been formally validated. ObjectivesTo examine the accuracy of the extraction and transformation of EHR fields for selected demographics, observations, diagnoses, prescriptions, and tests into MedicineInsight. MethodsWe benchmarked MedicineInsight values against those recorded in original EHRs. Forty-six general practices contributing data to MedicineInsight met our eligibility criteria, eight were randomly selected, and four agreed to participate. We randomly selected 200 patients ≥ 18 years of age within each participating practice from MedicineInsight. Trained staff reviewed the original EHRs for the selected patients and recorded data from the relevant fields. We calculated the percentage of agreement (POA) between MedicineInsight and EHR data for all fields; Cohen's Kappa for categorical and intra-class correlation (ICC) for continuous measures; and sensitivity, specificity, and positive and negative predictive values (PPV/NPV) for diagnoses. ResultsA total of 796 patients were included in our analysis. All demographic characteristics, observations, diagnoses, prescriptions and random pathology test results had excellent (> 90%) POA, Kappa, and ICC. POA for most recent pathology/imaging test was moderate (81%, [95% CI: 78% to 84%]). Sensitivity, specificity, PPV, and NPV were excellent (> 90%) for all but one of the examined diagnoses which had a poor PPV. ConclusionsOverall, our study shows good agreement between the majority of MedicineInsight data and those from original EHRs, suggesting MedicineInsight data extraction and warehousing procedures accurately conserve the data in these key fields. Discrepancies between test data may have arisen due to how data from pathology, radiology and other imaging providers are stored in EHRs and MedicineInsight and this requires further investigation.
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