Are GP visits being fairly counted?

Data gaps as high as 20% have been found when comparing GP visits via two trusted Australian health measures, and it could have significant consequences for healthcare planning and resourcing.
 
Research from the University of South Australia (UniSA) shows that the way the population is counted could distort how health services are planned and funded, with some regions potentially being over- or under-invested by more than 20%.
 
Comparing GP visits using two of Australia’s trusted population health measures – the Australian Bureau of Statistics’ (ABS) Estimated Resident Population (ERP) and Medicare enrolment data – researchers found that while national doctor attendance rates appeared similar overall (about a 2% difference), there were discrepancies at local and demographic levels.
 
For example, in the ACT, GP attendance rates for young people aged 15–24 were found to be 16% lower when calculated with ABS data instead of Medicare data.
 
In the Northern Territory, rates for women aged 85 and over were 21% higher when based on the ABS figures.
 
Lead researcher from the UniSA Dr Imaina Widagdo says the findings show how small differences in the way we count people can have big consequences for healthcare planning.
 
‘Most people assume health statistics are objective, but who we count and how we count them can significantly skew the story,’ she said.
 
‘Health planners rely on data to decide where to place doctors, clinics, and services, and to prioritise health interventions.
 
‘If the population figures are off – even slightly – we risk misallocating resources, which could mean too few doctors in some communities and too many in others.’
 
The study highlights how ERP data (26 million people based on Census, birth, death, and migration data) and Medicare enrolment data (26.2 million people eligible for publicly funded care) capture different populations.
 
The researchers emphasised that data inconsistencies are common, particularly among First Nations peoples who are often under-represented in datasets, contributing to equity gaps.
 
Workforce planning is complex, Dr Widagdo told newsGP, as GPs don’t always see Medicare-funded patients and services to international students or tourists, for example, aren’t captured in Medicare data.  
 
‘Some Medicare-funded services or health interventions may be misallocated to population subgroups that appear to have high or even low usage rates.’
 
Speaking from the Rural Medicine Australia conference in Perth, RACGP Rural Chair Associate Professor Michael Clements said the study highlights where statistics don’t always tell the best story.
 
‘We certainly know that the quality and the usefulness of data decreases as we move out to the rural areas for a few reasons, and one aspect that the study shows quite well is [this can occur] because of how people use Medicare – the MBS services can be different from how they report the census data,’ he told newsGP.
 
‘But we also know that rural and remote communities aren’t the best places to [source] Medicare data from, because often they will be using hospital or health services that don’t bill Medicare. In many cases, for example the Royal Flying Doctors Service (RFDS), many of their clinics do not access Medicare depending on the funding stream.
 
‘PBS data also isn’t an accurate dataset because in some rural and remote communities in particular, they’ve got dispensing mechanisms and S100 systems which don’t adequately report back through the PBS to show what people are using.’
 
Associate Professor Clements said another data challenge is understanding doctor to patient ratios when comparing country and city counterparts.
 
‘The other challenging thing when we look at population – population spread and population numbers – and how many doctors we need for a particular population is that it’s all fine and well for urban areas and urban models to look at the hundreds of doctors and the hundreds of thousands of patients in a small catchment area,’ he said.
 
‘But once you go to the rural communities, you need a threshold – a rural hospital needs a certain minimum number of doctors to make the hospital viable, regardless of how many patients live in that community.
 
‘We certainly support the findings of this study, which is that we have to be very careful when we use the data in rural and remote areas.’
 
Dr Widagdo said failing to account for data differences can unintentionally bias results, especially in areas with high mobility, migration, or non-Medicare populations.
 
‘In regions with small or shifting populations – like the NT or ACT – the impact of counting differences can be significant,’ she said.
 
‘Ultimately, if we count people differently, we may end up putting doctors, pharmacists or health services in the wrong place – and that could have serious consequences for minority groups or vulnerable communities.
 
‘While there’s no single perfect population measure, at the very least, we should be reporting which dataset is used and understanding its limitations. This is essential for fair and accurate health planning.’
 
Dr Widagdo added that her message to GPs is to be aware that apparent differences in service-use rates can reflect how populations are counted, not just how care is delivered.
 
‘When interpreting performance data or planning workload, check the denominator source and local context (for example, areas with many international students or tourist hotspots.’
 
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