Evaluating the accuracy of data extracted from electronic health records into MedicineInsight, a national Australian general practice database.

Introduction: MedicineInsight 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. Objectives: To examine the accuracy of the extraction and transformation of EHR fields for selected demographics, observations, diagnoses, prescriptions, and tests into MedicineInsight. Methods: We 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. Results: A 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. Conclusions: Overall, 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.

Characteristics of older Australian community aged care clients who fall: Incidents reported by care staff.

Falls are the leading cause of injury and hospitalisation for older adults (aged 65 years or older) worldwide. Data collected by community aged care providers are an underutilised source of information about precipitating risk factors and consequences of falls for older adults living in the community. The objective of this longitudinal, observational study was to describe and compare the characteristics of older Australians who did and did not have falls reported by community aged care staff. We analysed 19 months of routinely collected care management and incident data for 1,596 older clients from a large Australian community care provider. Differences in sociodemographic characteristics, care needs and community care service use were compared between those who had one or more reported falls and those who had none. Fall-related outcomes (injuries, hospitalisations, relocation to residential aged care) were examined. The average age of clients was 82 years and most were women (66%). Seventy-seven (4.8%) clients had one or more reported falls over the study period (total falls = 92). Clients who had falls reported by care staff were more likely to be older adults, male and use more hours of community care services per week. There were 38 falls-related injuries, 5 falls-related hospitalisations and 20 clients relocated to residential aged care after a reported fall. This study demonstrates the potential for using routinely collected community aged care data to understand risk factors and monitor longitudinal outcomes for a population at high risk of falls.

A cluster randomized controlled trial of a MedicineInsight Educational Quality Improvement Programme to improve the diagnosis and treatment of chronic hepatitis C in general practice (the EQUIP-HEPC trial).

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. Australian New Zealand Clinical Trial Registry (ANZCTR) Registration Number: ACTRN12619000508178p.

Exploring the physical health of patients with severe or long-term mental illness using routinely collected general practice data from MedicineInsight.

BACKGROUND AND OBJECTIVES: Australian information on the physical health of general practice patients with a mental illness is limited. The aim of this study was to explore the physical health of patients with a severe and/or long-term mental illness (SMI). METHOD: Analysis was performed of routinely collected data from patients visiting one of 452 general practice sites participating in the national MedicineInsight program during 2017-18. RESULTS: Of the 173,861 participants, 9.1% had recorded SMI. Almost three-quarters had a record of the selected long-term physical health conditions, compared with half of patients without recorded SMI (adjusted odds ratio: 2.2, 95% confidence interval: 2.1, 2.3). Patients with SMI were also more likely to have a history of smoking or moderate-to-heavy drinking. DISCUSSION: More patients with SMI had records of the investigated health conditions than those without SMI. They also had higher rates of modifiable risk factors. As patients with SMI are likely to visit their general practitioners often, this presents an opportunity for intervention that may improve patient outcomes.

Validity of algorithms for identifying five chronic conditions in MedicineInsight, an Australian national general practice database.

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.

Sustained low consumption of fruit and vegetables in Australian children: Findings from the Australian National Health Surveys.

ISSUE ADDRESSED: Intakes of fruit and vegetables in children are inadequate. Our purpose was to examine national data on the proportion of Australian children meeting the fruit and vegetable recommendations in 2011-2012 and 2014-2015, assessing changes over time and differences by age, sex and socio-economic status (SES). METHODS: Secondary analysis of 2011-2012 and 2014-2015 Australian National Health Surveys of Australian children aged 2-18 years. Percentages of children meeting fruit and vegetable recommendations by survey year, age group, sex and SES tertile were calculated using population weights supplied by the Australian Bureau of Statistics (ABS). Chi-squared tests and logistic regression were used to test for the relative influence of each factor. RESULTS: In 2011-2012, 64.6%, 5.1% and 4.6% of children met the recommended intake for fruit, vegetable and fruit-vegetable combined, respectively. In 2014-2015, 68.2%, 5.3% and 5.1% of all children met the recommended intake for fruit, vegetable and fruit-vegetable combined, respectively. There was a large reduction in proportions of children meeting both the fruit and vegetable recommendations between 3 and 4 years of age, which coincides with when most Australian children start pre-school. There were consistent differences by sex for both fruit and vegetables, but we found little evidence that SES is a significant factor predicting the difference in meeting the vegetable recommendations. CONCLUSION: 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.