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Workforce modelling
Workforce supply and demand modelling are presented below using ⬤ [LIVE] data, updated as new releases become available. These outputs are intended as an open, interpretable resource for stakeholders, ranging from general insight and individual relocation decisions to larger-scale planning by clinics, universities, and policymakers.
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Oversupply
Oversupply
Oversupply can be defined several ways, but a practical measure is whether estimated supply capacity (optometrists’ clinical hours worked) exceeds observed service utilisation (Medicare-billed hours). Specifically, the following service utilisation model is derived from a 2025 workforce report and updated with:
Historical data, location (state/territory) data, and additional Medicare items
Weekly-accurate conversions (4.33 wks/mth vs 4), with adjustments for annual leave, personal/sick and carer's leave, and administrative time
Clinical hours calibrated by year and state/territory (vs a static national average), derived from active practising clinicians only
Note that this approach does not fully capture latent oversupply, including optometrists unable to secure their desired hours, nor private or Medicare under-billing.
Clinical capacity substantially exceeds Medicare-billed service demand
Excess clinician numbers continue to grow by about 3–4% per year
∴ Oversupply is worsening over time
Maldistribution
Maldistribution
Maldistribution can be assessed by whether optometrist supply is aligned to population need across geography, expressed here as population coverage – the number of people each full-time-equivalent (FTE) optometrist services. Results are presented by rurality (Modified Monash Model [MMM]; MM1 metropolitan vs MM2–MM7 non-metropolitan).
A benchmark of 1-clinician:10,000-people is used as a practical indicator of service access. Areas above this threshold have sparse and insufficient coverage (clinician shortage), while areas below it have dense and sufficient coverage (clinician oversupply).
The metro–non-metro clinician distribution gap is modest
Metro–non-metro access to eyecare differences are narrowing over time
Rural clinician shortfalls are small (in the tens, vs thousands in oversupply)
∴ Maldistribution is modest and improving;
rural clinician shortfalls are small
What if...
What if...
Scenario/simulation models estimating how the workforce changes under different conditions
clinicians redistributed rurally?
clinicians redistributed rurally?
Only a fraction of excess clinicians (oversupply) could be redistributed rurally
∴ Rural redistribution may improve access, but cannot resolve oversupply
clinicians moved out of corporates?
clinicians moved out of corporates?
Only a fraction of available jobs are non-corporate; redistribution would take decades
∴ Moving out of corporate practice at scale is not feasible under current job availability
clinicians all switched to 20 min appointments?
clinicians all switched to 20 min appointments?
Moving from traditional 30 min to 20 min appointments increases theoretical throughput by 50% (3 patients/hour vs 2). The model below estimates the resulting increase in oversupply using Medicare service data
∴ Shorter appointments increase oversupply by reducing the workforce needed to meet the same service demand
Rebalancing the workforce
Rebalancing the workforce
No more optometry schools
No more optometry schools
For many years, Australia had three optometry schools: UNSW (NSW), QUT (QLD), and the University of Melbourne (VIC). Since ~2010, four new programs have entered the pipeline with varying dates of when graduates first entered the workforce:
Flinders University (SA), 02/2015
Deakin University (VIC), 08/2015
University of Canberra (ACT), 08/2022
University of Western Australia (WA), 02/2024
Although the effect of any individual program cannot be isolated, historical trends show whether expanding training capacity has coincided with improved population access.
Most ruralities and all PHNs already meet the 1-clinician:10,000 access benchmark. Remaining rural clinician shortfalls are small (in the tens, vs thousands in oversupply)
Relatedly, population access to eyecare has improved over time. Even the sparsest PHNs are trending toward metropolitan-like saturated coverage
The main exception is the NT, where population access to eyecare is not improving over time. Recent proposals for an NT optometry school have argued that training locally increases the likelihood graduates remain in, or return to, the region.
To test this premise, the model below uses Flinders University (SA) as a practical analogue: a new program established in a state without an existing optometry school and outside the eastern-state training corridor. The analysis uses clinician supply density (FTE clinicians per 10,000 people), the inverse of population coverage. Higher values mean more clinicians relative to population, where 1-clinician:10,000-people = sufficient supply density and 2:10,000 = saturated.
We first compare non-metropolitan SA trends before vs after Flinders graduates entered the workforce in early 2015
We then apply the observed SA change to the NT (entirely non-metropolitan) from 2035, the earliest a new school could realistically produce graduates
Accordingly, improving NT clinician supply density via a new school would take decades. This assumes the NT experiences the full SA effect (an optimistic scenario given its smaller population and slower population growth) and that metropolitan-like saturation is the desired endpoint.
Historical patterns also indicate that increases in local non-metropolitan supply are accompanied by larger increases elsewhere. For every 1 FTE clinician added to non-metropolitan areas, approximately 1.5 FTE clinicians are added to metropolitan areas. As the NT is entirely non-metropolitan, this suggests most additional graduates would ultimately increase supply outside the NT, further compounding national oversupply.
Alternatively, the NT's absolute workforce shortfall is small. With a population of ~249,000, the NT already meets the sufficient (1-clinician:10,000-people) benchmark, and only ~16 additional FTE clinicians are required for saturated coverage.
These numbers are very achievable through targeted actions, such as strengthened rural placement pipelines, direct rural incentives, and telehealth-supported models of eyecare, rather than expanding training capacity and exacerbating oversupply across Australia.
∴ New schools are inefficient for improving population access to eyecare
Data provenance and use
Supply:
Demand:
Geography and adjustments:
Trend estimation:
Scenario modelling:
Data availability and updates:
- Supply is estimated using Optometry Board of Australia quarterly general registration counts and National Health Workforce Dataset (NHWDS) allied health workforce annual releases
- NHWDS estimates are restricted to active, practising clinicians and are converted to clinical full-time equivalent (FTE) using average weekly clinical hours (updated each year)
Demand:
- Demand is estimated using Phoropter Free Fridays (PFF) job-advertisement data (weekly), Services Australia Medicare Item Reports (annual service volumes), and Australian Bureau of Statistics (ABS) population estimates (annual)
- Medicare services are converted to hours billed using consult-time assumptions from a 2025 workforce modelling report (28.5 minutes comprehensive, 15 minutes short, 60 minutes contact lens, 10 minutes short telehealth)
Geography and adjustments:
- All analyses are stratified and, where specified by the source data, adjusted by year and geography (state/territory, Primary Health Network [PHN], and/or Modified Monash Model [MMM])
- ABS population is provided at state level; PHN and MMM population denominators are derived by concording ABS SA2 geography to PHN/MMM using Australian Statistical Geography Standard (ASGS) correspondence files
- Where relevant (e.g., Medicare hours billed), estimates are normalised to local workforce participation by benchmarking to 38 hours/week (full-time) and subtracting leave allowances expressed in weeks of local average clinical hours (annual leave: 4× weekly hours; personal/sick and carer’s leave: 2× weekly hours)
Trend estimation:
- Yearly change is estimated using a 3-year rolling log-linear regression of the relevant series
- Regression relationships are assumed linear for parsimony unless otherwise specified
Scenario modelling:
- Hypothetical workforce reallocation models shift workforce distributions to equate supply and demand proportions across geographies
- Years of workforce relief is modelled using a logit drift framework parameterised by observed yearly change rates
Data availability and updates:
- All datasets are publicly available; Phoropter Free Fridays (PFF) job-advertisement data are available upon reasonable request
- Unauthorised reuse of PFF data is prohibited; external use requires prior written permission
- Outputs (including all graphs) are refreshed as new public releases become available
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