Basu Kisalaya, Gupta Anil
Economiste principal, Division de la modélisation par microsimulation de l'analyse des données, Direction de la recherche appliquée et de l'analyse, Ministère de la Santé, Canada.
Cah Sociol Demogr Med. 2005 Apr-Sep;45(2-3):255-85.
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.
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.
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.
Under the status quo assumption, demand for physician services will outstrip the growth in supply for all three specialties.
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.
人们对卫生人力资源（HHR）当前及未来的可获得性有着充分的担忧。人口趋势加剧了这种担忧——在卫生人力资源队伍本身老龄化的同时，人口老龄化将需要更多的医疗干预。大多数医护人员，尤其是医生，漫长且成本高昂的培训期构成了一项切实的挑战，这需要提前对这些活动进行规划。因此，确实需要一个良好的卫生人力资源预测模型。
提出一个医生预测模型，该模型预测到2020年新斯科舍省全科医生、内科医生和外科医生这三个专业的全职等效（FTE）医生需求和供给情况。该模型能够进行差距分析，并评估旨在缩小差距的替代政策选项。
估计医生服务需求的方法包括三个步骤：（i）确定每位医生的全职等效工作量。为此，我们使用医生计费数据计算每位医生的收入，然后确定40个专业中每个专业的第40和第60百分位收入水平。然后使用加拿大卫生部制定的公式，根据这些收入水平计算全职等效工作量；（ii）通过在服务层面分配每位医生的全职等效工作量（即按患者年龄、性别、主要诊断和医院状态组），计算每项服务的全职等效工作量；（iii）利用加拿大统计局的人口预测数据，预测全科医生、内科专科医生和外科专科医生这三大医学领域未来的需求。该模型的供给侧采用存量/流量方法，并利用时间序列数据和其他变量数据，如移民、国际医学毕业生（IMG）、医学院入学人数、退休人数、死亡率等，这些数据进而使我们能够获取一系列政策参数。
在现状假设下，所有三个专业的医生服务需求将超过供给的增长。
该模型可以模拟供给侧政策变化（如增加国际医学毕业生数量、延迟退休），也可以反映需求变化（如白血病的治愈方法；医生不同的工作强度）。该模型高度参数化，以便能够适应可能影响未来医生需求的冲击。一旦确定了未来需求，供给模型就能识别出缩小供需差距所需的政策杠杆（新入职人员、移民、移民、退休）。该模型是一个方便用户使用的工具，供政策制定者制定适当的医生人力规划。
