Executive summary
Skills shortages in the health workforce are not a new phenomenon. Over the past two decades, there has been considerable strain on health workforces, both in terms of the numbers of workers and the skills they need to work with new technologies and adapt to new tasks. Skills shortages in the health workforce are a global issue, affecting countries across regions and income levels, and affecting low and middle-income countries (LMIC) in particular. The COVID-19 pandemic has further aggravated these shortages and emphasised the importance of resilient and well-skilled healthcare workforces. Equipping health workforces with the right skills is essential to responding to future health crises, and to preparing for increasing use of digital technologies and demographic change, among other trends.
This report reviews approaches that a selected group of countries are currently taking to anticipate skills needs in the health workforce. It covers 16 OECD and low and medium-income countries: Argentina, Australia, Bangladesh, Canada, Colombia, Ethiopia, Finland, Germany, Ghana, Ireland, Korea, the Netherlands, Norway, South Africa, Sweden, and the United States. The report identifies the types of methodologies that are applied to anticipate skill needs in the health workforce in different countries and examines the ways in which this information is used to shape education, labour, and migration policies as well as collective bargaining processes. The aim of the report is to facilitate knowledge transfer between countries and to assist countries in developing skills anticipation exercises for the health workforce.
This report finds that there is no “one size fits all” approach to anticipating skill needs in the health workforce. The choice of method depends primarily on policy objectives, availability of data and resources, and existing governance structures. Quantitative forecasts project numbers of health workers needed, while qualitative methods, including surveys and Delphi methods, may develop a richer picture of future skills needs within health occupations. In combining the strengths of both, mixed method approaches are considered best practices. They are also the most costly and data intensive, which are major barriers for some countries, and particularly for LMICs.
The way skills anticipation exercises are designed also matters. With health professionals taking between 7-10 years to train, skill anticipation exercises that take long time horizons can better inform the updating of academic curricula and enrolments in education and training programmes. Exercises which take a sectoral approach allow for a more detailed analysis of the specific skill needs of the health workforce than those which take a whole-of-labour-market approach.
To be useful for policymaking, skills needs anticipation exercises for the health sector should be clear about their policy objectives from the beginning. Strong governance mechanisms are needed to validate results and to translate skills intelligence into policy recommendations. Skills anticipation exercises should be user-oriented, involve key social partners and stakeholders, and be well co-ordinated at every level.
The information produced by skills anticipation exercises for the health workforce is used by governments, hospitals, and trade unions for a variety of policy purposes. Quantitative outputs at occupation or qualification level are often used to determine student intake in health education programmes or migrant inflows. Qualitative findings that describe the types of skills that a given occupation will require can be used to define education and training course content and to inform changes to the way tasks are allocated across occupations. Regulations limiting the scope of tasks a person in a given occupation is legally allowed to perform can act as a barrier to more effective use of this information. Other important barriers include lack of funding, coordination, stakeholder involvement, and poor alignment between the skills intelligence and the desired policy purpose. These barriers may be especially challenging to overcome in LMICs.
The five policy recommendations below can guide countries in developing skills anticipation exercises for the health workforce. Chapter 4 of this report also includes a table summarizing the advantages and disadvantages of different methods for anticipating skill needs in the health workforce, as well as a flowchart summarizing the key decisions that need to be considered when establishing a skills anticipation exercise for the health workforce.
Design exercises with specific policy objectives in mind, and consider how the choice of method, scope, skills definition, frequency, and time horizon contribute to achieve those objectives.
Use a combination of qualitative and quantitative methods to achieve the most robust and reliable projections about future skills needs in the health workforce. While costly, investment in mixed method approaches leverages the strengths of both qualitative and quantitative methods.
Focus on skills needs rather than, or in addition to, proxies for skills, such as occupations and qualifications. Exercises that focus specifically on skills needs are rare, but they facilitate a dynamic approach to health workforce planning that considers which skills are needed to prepare health workers for the demands of new technologies and sectoral drivers of change. More investment is needed in a number of countries to conduct these types of exercises.
Involvement of social partners is essential to ensure that skills intelligence is fit for policy use and to promote buy-in to the policy response among stakeholders.
International cooperation is needed to contribute to addressing health workforce shortages. Shortages in the health workforce are widespread. To ensure that migration flows achieve win-win outcomes for both origin and destination countries, international cooperation in both the identification of skill needs and the policy response (such as bilateral agreements, knowledge transfer and development cooperation) is needed.