Using general practice clinical information system data for research: the case in Australia

Health map for HealthGap: Defining a geographical catchment to examine cardiovascular risk in Victoria, Australia

The HealthGap study aimed to understand cardiovascular risk among Indigenous Australians in Victoria using linked administrative data. A key challenge was differing spatial coverages of sources: state-level data for risk factors but cardiovascular outcomes for three hospitals. Catchments were defined based on hospital postcodes to estimate denominator populations for risk modelling: first- and second-order neighbours, and spatial distribution of outcomes ('spatial event distribution'). Catchment coverage was assessed through proportions of patients presenting to study hospitals from catchment postcodes. The spatial event distribution performed best, capturing 82% events overall (first-order:40%; second-order:64%) and 65% Indigenous (27% and 45%). No approach excluded proximal non-study hospitals. Spatial event distributions could help define denominator populations when geographic information on outcome data is available but may not avoid potential misclassification.

Measuring what counts in Aboriginal and Torres Strait Islander care: a review of general practice datasets available for assessing chronic disease care

Background Large datasets exist in Australia that make de-identified primary healthcare data extracted from clinical information systems available for research use. This study reviews these datasets for their capacity to provide insight into chronic disease care for Aboriginal and Torres Strait Islander peoples, and the extent to which the principles of Indigenous Data Sovereignty are reflected in data collection and governance arrangements. Methods Datasets were included if they collect primary healthcare clinical information system data, collect data nationally, and capture Aboriginal and Torres Strait Islander peoples. We searched PubMed and the public Internet for data providers meeting the inclusion criteria. We developed a framework to assess data providers across domains, including representativeness, usability, data quality, adherence with Indigenous Data Sovereignty and their capacity to provide insights into chronic disease. Datasets were assessed against the framework based on email interviews and publicly available information. Results We identified seven datasets. Only two datasets reported on chronic disease, collected data nationally and captured a substantial number of Aboriginal and Torres Strait Islander patients. No dataset was identified that captured a significant number of both mainstream general practice clinics and Aboriginal Community Controlled Health Organisations. Conclusions It is critical that more accurate, comprehensive and culturally meaningful Aboriginal and Torres Strait Islander healthcare data are collected. These improvements must be guided by the principles of Indigenous Data Sovereignty and Governance. Validated and appropriate chronic disease indicators for Aboriginal and Torres Strait Islander peoples must be developed, including indicators of social and cultural determinants of health.

Undercounting diagnoses in Australian general practice: a data quality study with implications for population health reporting

BACKGROUND

Diagnosis can often be recorded in electronic medical records (EMRs) as free-text or using a term with a diagnosis code. Researchers, governments, and agencies, including organisations that deliver incentivised primary care quality improvement programs, frequently utilise coded data only and often ignore free-text entries. Diagnosis data are reported for population healthcare planning including resource allocation for patient care. This study sought to determine if diagnosis counts based on coded diagnosis data only, led to under-reporting of disease prevalence and if so, to what extent for six common or important chronic diseases.

METHODS

This cross-sectional data quality study used de-identified EMR data from 84 general practices in Victoria, Australia. Data represented 456,125 patients who attended one of the general practices three or more times in two years between January 2021 and December 2022. We reviewed the percentage and proportional difference between patient counts of coded diagnosis entries alone and patient counts of clinically validated free-text entries for asthma, chronic kidney disease, chronic obstructive pulmonary disease, dementia, type 1 diabetes and type 2 diabetes.

RESULTS

Undercounts were evident in all six diagnoses when using coded diagnoses alone (2.57-36.72% undercount), of these, five were statistically significant. Overall, 26.4% of all patient diagnoses had not been coded. There was high variation between practices in recording of coded diagnoses, but coding for type 2 diabetes was well captured by most practices.

CONCLUSION

In Australia clinical decision support and the reporting of aggregated patient diagnosis data to government that relies on coded diagnoses can lead to significant underreporting of diagnoses compared to counts that also incorporate clinically validated free-text diagnoses. Diagnosis underreporting can impact on population health, healthcare planning, resource allocation, and patient care. We propose the use of phenotypes derived from clinically validated text entries to enhance the accuracy of diagnosis and disease reporting. There are existing technologies and collaborations from which to build trusted mechanisms to provide greater reliability of general practice EMR data used for secondary purposes.

Public preference on sharing health data to inform research, health policy and clinical practice in Australia: A stated preference experiment

OBJECTIVE

To investigate public willingness to share sensitive health information for research, health policy and clinical practice.

METHODS

A total of 1,003 Australian respondents answered an online, attribute-driven, survey in which participants were asked to accept or reject hypothetical choice sets based on a willingness to share their health data for research and frontline-medical support as part of an integrated health system. The survey consisted of 5 attributes: Stakeholder access for analysis (Analysing group); Type of information collected; Purpose of data collection; Information governance; and Anticipated benefit; the results of which were analysed using logistic regression.

RESULTS

When asked about their preference for sharing their health data, respondents had no preference between data collection for the purposes of clinical practice, health policy or research, with a slight preference for having government organisations manage, govern and curate the integrated datasets from which the analysis was being conducted. The least preferred option was for personal health records to be integrated with insurance records or for their data collected by privately owned corporate organisations. Individuals preferred their data to be analysed by a public healthcare provider or government staff and expressed a dislike for any private company involvement.

CONCLUSIONS

The findings from this study suggest that Australian consumers prefer to share their health data when there is government oversight, and have concerns about sharing their anonymised health data for clinical practice, health policy or research purposes unless clarity is provided pertaining to its intended purpose, limitations of use and restrictions to access. Similar findings have been observed in the limited set of existing international studies utilising a stated preference approach. Evident from this study, and supported by national and international research, is that the establishment and preservation of a social license for data linkage in health research will require routine public engagement as a result of continuously evolving technological advancements and fluctuating risk tolerance. Without more work to understand and address stakeholder concerns, consumers risk being reluctant to participate in data-sharing and linkage programmes.

Comparing secondary prevention for patients with coronary heart disease and stroke attending Australian general practices: a cross-sectional study using nationwide electronic database

OBJECTIVES

To compare secondary prevention care for patients with coronary heart disease (CHD) and stroke, exploring particularly the influences due to frequency and regularity of primary care visits.

SETTING

Secondary prevention for patients (≥18 years) in the National Prescription Service administrative electronic health record database collated from 458 Australian general practice sites across all states and territories.

DESIGN

Retrospective cross-sectional and panel study. Patient and care-level characteristics were compared for differing CHD/stroke diagnoses. Associations between the type of cardiovascular diagnosis and medication prescription as well as risk factor assessment were examined using multivariable logistic regression.

PARTICIPANTS

Patients with three or more general practice encounters within 2 years of their latest visit during 2016-2020.

OUTCOME MEASURES

Proportions and odds ratios (ORs) for (1) prescription of antihypertensives, antilipidaemics and antiplatelets and (2) assessment of blood pressure (BP) and low-density lipoprotein cholesterol (LDL-C) in patients with stroke only compared against those with CHD only and those with both conditions.

RESULTS

There were 111 892 patients with CHD only, 27 863 with stroke only and 9791 with both conditions. Relative to patients with CHD, patients with stroke were underprescribed antihypertensives (70.8% vs 82.8%), antilipidaemics (63.1% vs 78.7%) and antiplatelets (42.2% vs 45.7%). With sociodemographic factors, comorbidities and level of care considered as covariates, the odds of non-prescription of any recommended secondary prevention medications were higher in patients with stroke only (adjusted OR 1.37; 95% CI (1.31, 1.44)) compared with patients with CHD only. Patients with stroke only were also more likely to have neither BP nor LDL-C monitored (adjusted OR 1.26; 95% CI (1.18, 1.34)). Frequent and regular general practitioner encounters were independently associated with the prescription of secondary prevention medications (p<0.001).

CONCLUSIONS

Secondary prevention management is suboptimal in cardiovascular disease patients and worse post-stroke compared with post-CHD. More frequent and regular primary care encounters were associated with improved secondary prevention.

Advancing cross-sectoral data linkage to understand and address the health impacts of social exclusion: Challenges and potential solutions

The use of administrative health data for research, monitoring, and quality improvement has proliferated in recent decades, leading to improvements in health across many disease areas and across the life course. However, not all populations are equally visible in administrative health data, and those that are less visible may be excluded from the benefits of associated research. Socially excluded populations - including the homeless, people with substance dependence, people involved in sex work, migrants or asylum seekers, and people with a history of incarceration - are typically characterised by health inequity. Yet people who experience social exclusion are often invisible within routinely collected administrative health data because information on their markers of social exclusion are not routinely recorded by healthcare providers. These circumstances make it difficult to understand the often complex health needs of socially excluded populations, evaluate and improve the quality of health services that they interact with, provide more accessible and appropriate health services, and develop effective and integrated responses to reduce health inequity. In this commentary we discuss how linking data from multiple sectors with administrative health data, often called cross-sectoral data linkage, is a key method for systematically identifying socially excluded populations in administrative health data and addressing other issues related to data quality and representativeness. We discuss how cross-sectoral data linkage can improve the representation of socially excluded populations in research, monitoring, and quality improvement initiatives, which can in turn inform coordinated responses across multiple sectors of service delivery. Finally, we articulate key challenges and potential solutions for advancing the use of cross-sectoral data linkage to improve the health of socially excluded populations, using international examples.

Agreement of acute serious events recorded across datasets using linked Australian general practice, hospital, emergency department and death data: implications for research and surveillance

Introduction

Understanding the level of recording of acute serious events in general practice electronic health records (EHRs) is critical for making decisions about the suitability of general practice datasets to address research questions and requirements for linking general practice EHRs with other datasets.

Objectives

To examine data source agreement of five serious acute events (myocardial infarction, stroke, venous thromboembolism (VTE), pancreatitis and suicide) recorded in general practice EHRs compared with hospital, emergency department (ED) and mortality data.

Methods

Data from 61 general practices routinely contributing data to the MedicineInsight database was linked with New South Wales administrative hospital, ED and mortality data. The study population comprised patients with at least three clinical encounters at participating general practices between 2019 and 2020 and at least one record in hospital, ED or mortality data between 2010 and 2020. Agreement was assessed between MedicineInsight diagnostic algorithms for the five events of interest and coded diagnoses in the administrative data. Dates of concordant events were compared.

Results

The study included 274,420 general practice patients with at least one record in the administrative data between 2010 and 2020. Across the five acute events, specificity and NPV were excellent (>98%) but sensitivity (13%-51%) and PPV (30%-75%) were low. Sensitivity and PPV were highest for VTE (50.9%) and acute pancreatitis (75.2%), respectively. The majority (roughly 70-80%) of true positive cases were recorded in the EHR within 30 days of administrative records.

Conclusion

Large proportions of events identified from administrative data were not detected by diagnostic algorithms applied to general practice EHRs within the specific time period. EHR data extraction and study design only partly explain the low sensitivities/PPVs. Our findings support the use of Australian general practice EHRs linked to hospital, ED and mortality data for robust research on the selected serious acute conditions.

Australian general practitioner perceptions to sharing clinical data for secondary use: a mixed method approach

OBJECTIVE

The potential for data collected in general practice to be linked and used to address health system challenges of maintaining quality care, accessibility and safety, including pandemic support, has led to an increased interest in public acceptability of data sharing, however practitioners have rarely been asked to share their opinions on the topic. This paper attempts to gain an understanding of general practitioner's perceptions on sharing routinely collected data for the purposes of healthcare planning and research. It also compares findings with data sharing perceptions in an international context.  MATERIALS AND METHODS: A mixed methods approach combining an initial online survey followed by face-to-face interviews (before and during COVID-19), designed to identify the barriers and facilitators to sharing data, were conducted on a cross sectional convenience sample of general practitioners across Western Australia (WA).

RESULTS

Eighty online surveys and ten face-to-face interviews with general practitioners were conducted from November 2020 - May 2021. Although respondents overwhelmingly identified the importance of population health research, their willingness to participate in data sharing programs was determined by a perception of trust associated with the organisation collecting and analysing shared data; a clearly defined purpose and process of collected data; including a governance structure providing confidence in the data sharing initiative simultaneously enabling a process of data sovereignty and autonomy.

DISCUSSION

Results indicate strong agreement around the importance of sharing patient's medical data for population and health research and planning. Concerns pertaining to lack of trust, governance and secondary use of data continue to be a setback to data sharing with implications for primary care business models being raised.

CONCLUSION

To further increase general practitioner's confidence in sharing their clinical data, efforts should be directed towards implementing a robust data governance structure with an emphasis on transparency and representative stakeholder inclusion as well as identifying the role of government and government funded organisations, as well as building trust with the entities collecting and analysing the data.

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.

Identifying primary care datasets and perspectives on their secondary use: a survey of Australian data users and custodians

Background

Most people receive most of their health care in in Australia in primary care, yet researchers and policymakers have limited access to resulting clinical data. Widening access to primary care data and linking it with hospital or other data can contribute to research informing policy and provision of services and care; however, limitations of primary care data and barriers to access curtail its use. The Australian Health Research Alliance (AHRA) is seeking to build capacity in data-driven healthcare improvement; this study formed part of its workplan.

Methods

The study aimed to build capacity for data driven healthcare improvement through identifying primary care datasets in Australia available for secondary use and understand data quality frameworks being applied to them, and factors affecting national capacity for secondary use of primary care data from the perspectives of data custodians and users. Purposive and snowball sampling were used to disseminate a questionnaire and respondents were invited to contribute additional information via semi-structured interviews.

Results

Sixty-two respondents collectively named 106 datasets from eclectic sources, indicating a broad conceptualisation of what a primary care dataset available for secondary use is. The datasets were generated from multiple clinical software systems, using different data extraction tools, resulting in non-standardised data structures. Use of non-standard data quality frameworks were described by two-thirds of data custodians. Building trust between citizens, clinicians, third party data custodians and data end-users was considered by many to be a key enabler to improve primary care data quality and efficiencies related to secondary use. Trust building qualities included meaningful stakeholder engagement, transparency, strong leadership, shared vision, robust data security and data privacy protection. Resources to improve capacity for primary care data access and use were sought for data collection tool improvements, workforce upskilling and education, incentivising data collection and making data access more affordable.

Conclusions

The large number of identified Australian primary care related datasets suggests duplication of labour related to data collection, preparation and utilisation. Benefits of secondary use of primary care data were many, and strong national leadership is required to reach consensus on how to address limitations and barriers, for example accreditation of EMR clinical software systems and the adoption of agreed data and quality standards at all stages of the clinical and research data-use lifecycle. The study informed the workplan of AHRA’s Transformational Data Collaboration to improve partner engagement and use of clinical data for research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01830-9.

General Practice Statistics in Australia: Pushing a Round Peg into a Square Hole

In Australia, general practice forms a core part of the health system, with general practitioners (GPs) having a gatekeeper role for patients to receive care from other health services. GPs manage the care of patients across their lifespan and have roles in preventive health care, chronic condition management, multimorbidity and population health. Most people in Australia see a GP once in any given year. Draft reforms have been released by the Australian Government that may change the model of general practice currently implemented in Australia. In order to quantify the impact and effectiveness of any implemented reforms in the future, reliable and valid data about general practice clinical activity over time, will be needed. In this context, this commentary outlines the historical and current approaches used to obtain general practice statistics in Australia and highlights the benefits and limitations of these approaches. The role of data generated from GP electronic health record extractions is discussed. A methodology to generate high quality statistics from Australian general practice in the future is presented.

Associations between regular GP contact, diabetes monitoring and glucose control: an observational study using general practice data

OBJECTIVE

Continuity and regularity of general practitioner (GP) contacts are associated with reduced hospitalisation in type 2 diabetes (T2DM). We assessed associations of these GP contact patterns with intermediate outcomes reflecting patient monitoring and health.

DESIGN

Observational longitudinal cohort study using general practice data 2011-2017.

SETTING

193 Australian general practices in Western Australia and New South Wales participating in the MedicineInsight programme run by NPS MedicineWise.

PARTICIPANTS

22 791 patients aged 18 and above with T2DM.

INTERVENTIONS

Regularity was assessed based on variation in the number of days between GP visits, with more regular contacts assumed to indicate planned, proactive care. Informational continuity (claims for care planning incentives) and relational continuity (usual provider of care index) were assessed separately.

OUTCOME MEASURES

Process of care indicators were glycosylated haemoglobin (HbA1c) test underuse (8 months without test), estimated glomerular filtration rate (eGFR) underuse (14 months) and HbA1c overuse (two tests within 80 days). The clinical indicator was T2DM control (HbA1c 6.5% (47.5 mmol/mol)-7.5% (58.5 mmol/mol)).

RESULTS

The quintile with most regular contact had reduced odds of HbA1c and eGFR underuse (OR 0.74, 95% CI 0.67 to 0.81 and OR 0.78, 95% CI 0.70 to 0.86, respectively), but increased odds of HbA1c overuse (OR 1.20, 95% CI 1.05 to 1.38). Informational continuity was associated with reduced odds of HbA1c underuse (OR 0.53, 95% CI 0.49 to 0.56), reduced eGFR underuse (OR 0.62, 95% CI 0.58 to 0.67) and higher odds of HbA1c overuse (OR 1.48, 95% CI 1.34 to 1.64). Neither had significant associations with HbA1c level. Results for relational continuity differed.

CONCLUSIONS

This study provides evidence that regularity and continuity influence processes of care in the management of patients with diabetes, though this did not result in the recording of HbA1c within target range. Research should capture these intermediate outcomes to better understand how GP contact patterns may influence health rather than solely assessing associations with hospitalisation outcomes.

Mapping end-of-life and anticipatory medications in palliative care patients using a longitudinal general practice database

OBJECTIVE

End-of-life and anticipatory medications (AMs) have been widely used in various health care settings for people approaching end-of-life. Lack of access to medications at times of need may result in unnecessary hospital admissions and increased patient and family distress in managing palliative care at home. The study aimed to map the use of end-of-life and AM in a cohort of palliative care patients through the use of the Population Level Analysis and Reporting Data Space and to discuss the results through stakeholder consultation of the relevant organizations.

METHODS

A retrospective observational cohort study of 799 palliative care patients in 25 Australian general practice health records with a palliative care referral was undertaken over a period of 10 years. This was followed by stakeholders' consultation with palliative care nurse practitioners and general practitioners who have palliative care patients.

RESULTS

End-of-life and AM prescribing have been increasing over the recent years. Only a small percentage (13.5%) of palliative care patients received medications through general practice. Stakeholders' consultation on AM prescribing showed that there is confusion about identifying patients needing medications for end-of-life and mixed knowledge about palliative care referral pathways.

SIGNIFICANCE OF RESULTS

Improved knowledge and information around referral pathways enabling access to palliative care services for general practice patients and their caregivers are needed. Similarly, the increased utility of screening tools to identify patients with palliative care needs may be useful for health care practitioners to ensure timely care is provided.