PubMedDate not supplied
RATIONALE: There is well-founded concern about the current and future availability of Health Human Resources (HHR). Demographic trends are magnifying this concern -- an ageing population will require more medical interventions at a time when the HHR workforce itself is ageing. The lengthy and costly training period for most health care workers, especially physicians, poses a real challenge that requires planning these activities well in advance. Hence, there is definite need for a good HHR forecasting model. OBJECTIVES: To present a physician forecasting model that projects the Full-Time Equivalent (FTE) demand for and supply of physicians in Nova Scotia to the year 2020 for three specialties: general practitioners, medical, and surgical. The model enables gap analysis and assessment of alternative policy options designed to close the gaps. METHODOLOGY: The methodology for estimating demand fo physician services involves three steps: (i) Establishing the FT for each physician. To this end we calculate the income of each physician using Physician Billings Data and then identify the 40th and 60th percentile income levels for each of the 40 specialties. The income levels are then used to calculate the FTE using a formula developed at Health Canada; (ii) Calculating the FTE for each service by distributing the FTE of each physician at the service level (i.e., by patient age, sex, most responsible diagnosis, and hospital status group); and (iii) Using Statistics Canada's population projections to project future demand for three broad medical disciplines: general practitioners, medical specialist, and surgical specialists. The supply side of the model employs a stock/flow approach and exploits time-series and other data for variables, such as emigration, international medical graduates (IMGs), medical school entrants, retirements, mortality, and so on, which in turn allow us to access a host of policy parameters. RESULTS: Under the status quo assumption, demand for physician services will outstrip the growth in supply for all three specialties. CONCLUSIONS: The model can simulate supply-side policy changes (e.g. more IMGs, delayed retirements) and can also reflect changes in demand (e.g. a cure for leukemia; different work intensities for physicians). The model is highly parameterized so that it can accommodate shocks that may influence the future requirements for physicians. Once a future requirement is determined, the supply model can identify the policy levers (new entrants, immigration, emigration, retirement) necessary to close the gap between demand and supply. The model is a user-friendly tool made for policy makers to formulate appropriate physician workforce planning.