Federal Public Service for Public Health
Health & Wellbeing
Philippe Vandenbroeck

Better prediction of medical manpower needs


It is key, ethically and economically, that countries are able to anticipate the needed number of health professionals to cover for future public health needs. This is called ‘medical workforce planning’. 

In Belgium this activity has been delegated to a Planning Commission within the Federal Public Service (FPS) for Public Health. The Commission relies on a predictive model to do its work. In order to improve buy-in for its recommendations, the Commission wanted to organise a broad stakeholder consultation. The ambition was to inventorise perceived strenghts and weaknesses of the model and to collect suggestions for scientifically sound improvements.

The assignment was tied to a very ambitious timeline (elapsed time 4 months). 


This assignment required us to work with a very technical focus, under significant time pressure, in a politically very sensitive setting.   

We designed an electronic survey to enable stakeholders to reflect on each variable of the ‘stock-and-flow’ model used by the Planning Commission. The insights from this survey were validated against best practices from international literature and collated as a set of preliminary recommendations to improve the model. These were then submitted for critical review in a stakeholder workshop. This led to a final set of recommendations discussed in a final report. 

In this project we worked together with researchers from the Federal Health Care Research Institute (KCE) and with supply chain expert Prof. Henk Akkermans from Tilburg University. 


The consultation led to greater confidence in the Belgian approach to medical workforce planning. Some of our report’s recommendations were immediately taken on board by the Commission. As a result, it was decided to run a trial project that embedded the predictive workforce analysis in a process of scenario-based ‘horizon scanning’. This project focused on the workforce assessment for midwifes. shiftN was also invited to contribute to this project (2018).