What will make a difference? Assessing the impact of policy and non-policy scenarios on estimations of the future GP workforce.

BACKGROUND: Health workforce planning is based on estimates of future needs for and supply of health care services. Given the pipeline time lag for the training of health professionals, inappropriate workforce planning or policies can lead to extended periods of over- or under-supply of health care providers. Often these policy interventions focus on one determinant of supply and do not incorporate other determinants such as changes in population health which impact the need for services. The aim of this study is to examine the effect of the implementation of various workforce policies on the estimated future requirements of the GP workforce, using South Australia as a case study. This is examined in terms of the impact on the workforce gap (excess or shortage), the cost of these workforce policies, and their role in addressing potential non-policy-related future scenarios. METHODS: An integrated simulation model for the general practice workforce in South Australia was developed, which determines the supply and level of services required based on the health of the population over a projection period 2013-2033. The published model is used to assess the effects of various policy and workforce scenarios. For each policy scenario, associated costs were estimated and compared to baseline costs with a 5% discount rate applied. RESULTS: The baseline scenario estimated an excess supply of GPs of 236 full-time equivalent (FTE) in 2013 but this surplus decreased to 28 FTE by 2033. The estimates based on single policy scenarios of role substitution and increased training positions continue the surplus, while a scenario that reduces the number of international medical graduates (IMGs) recruited estimated a move from surplus to shortage by 2033. The best-case outcome where the workforce achieves balance by 2023 and remains balanced to 2033, arose when GP participation rates (a non-policy scenario) were combined with the policy levers of increased GP training positions and reduced IMG recruitment. The cost of each policy varied, with increased role substitution and reduced IMG recruitment resulting in savings (AUD$752,946,586 and AUD$3,783,291 respectively) when compared to baseline costs. Increasing GP training costs over the projection period would cost the government an additional AUD$12,719,798. CONCLUSIONS: Over the next 20 years, South Australia's GP workforce is predicted to remain fairly balanced. However, exogenous changes, such as increased demand for GP services may require policy intervention to address associated workforce shortfalls. The workforce model presented in this paper should be updated at regular intervals to inform the need for policy intervention.

Improving the planning of the GP workforce in Australia: a simulation model incorporating work transitions, health need and service usage.

BACKGROUND: In Australia, the approach to health workforce planning has been supply-led and resource-driven rather than need-based. The result has been cycles of shortages and oversupply. These approaches have tended to use age and sex projections as a measure of need or demand for health care. Less attention has been given to more complex aspects of the population, such as the increasing proportion of the ageing population and increasing levels of chronic diseases or changes in the mix of health care providers or their productivity levels. These are difficult measures to get right and so are often avoided. This study aims to develop a simulation model for planning the general practice workforce in South Australia that incorporates work transitions, health need and service usage. METHODS: A simulation model was developed with two sub-models--a supply sub-model and a need sub-model. The supply sub-model comprised three components--training, supply and productivity--and the need sub-model described population size, health needs, service utilisation rates and productivity. A state transition cohort model is used to estimate the future supply of GPs, accounting for entries and exits from the workforce and changes in location and work status. In estimating the required number of GPs, the model used incidence and prevalence data, combined with age, gender and condition-specific utilisation rates. The model was run under alternative assumptions reflecting potential changes in need and utilisation rates over time. RESULTS: The supply sub-model estimated the number of full-time equivalent (FTE) GP stock in SA for the period 2004-2011 and was similar to the observed data, although it had a tendency to overestimate the GP stock. The three scenarios presented for the demand sub-model resulted in different outcomes for the estimated required number of GPs. For scenario one, where utilisation rates in 2003 were assumed optimal, the model predicted fewer FTE GPs were required than was observed. In scenario 2, where utilisation rates in 2013 were assumed optimal, the model matched observed data, and in scenario 3, which assumed increasing age- and gender-specific needs over time, the model predicted more FTE GPs were required than was observed. CONCLUSIONS: This study provides a robust methodology for determining supply and demand for one professional group at a state level. The supply sub-model was fitted to accurately represent workforce behaviours. In terms of demand, the scenario analysis showed variation in the estimations under different assumptions that demonstrates the value of monitoring population-based need over time. In the meantime, expert opinion might identify the most relevant scenario to be used in projecting workforce requirements.

To teach or not to teach? A cost-benefit analysis of teaching in private general practice.

OBJECTIVE: To identify the financial costs and benefits associated with teaching in private general practice. DESIGN: Cost-benefit analysis of teaching in private general practice across three levels of training--undergraduate medical training, prevocational training and general practice vocational training--using data from a 2007 survey of general practitioners in South Australia. SETTING AND PARTICIPANTS: GPs and practices teaching in association with the Adelaide to Outback GP Training Program or the Discipline of General Practice at the University of Adelaide. MAIN OUTCOME MEASURE: Net financial outcome per week. RESULTS: The net financial outcome of teaching varied across the training levels. Practices incurred a net financial cost from teaching medical students that was statistically significantly different from zero. With respect to vocational training and teaching junior doctors, there were small net financial benefits to practices, although the mean estimates were not statistically significantly different from zero. CONCLUSIONS: This study shows a net financial cost for practices teaching medical students, while at the prevocational and vocational training levels, adequate levels of subsidies and income generated by the trainees help offset the costs of teaching. Our results suggest that a review of subsidies for undergraduate teaching is necessary, particularly as the demand for teaching practices will increase substantially over the next 5 years.