The previous chapters have made clear that a foresight study is not an activity you do just like that. It requires a way of working and thinking that is systematic, abstract and not fearful of uncertainty. This is not always present in all researchers. Most researchers have been trained to look for good evidence rather than embracing uncertainty. Especially in the field of public health such a strong orientation on evidence-informed policy making may not be helpful in stimulating the application of foresight. It requires therefore further strengthening of foresight capacity.
Other aspects that are not in favour of a strong public health foresight tradition in comparison with other research fields might be the importance of (health) behaviour which is still at its infancy of being understood, the fact that public health remains largely a national competence within the (European Union), which does not encourage the exchange of public health foresight knowledge and information, the complexity of health and health care (though other research fields might have similar justifiably claim as well). Other research fields such as climate change and technology might have had the same barriers and current day foresight practices are now common practices in these fields. So there is hope for a bright future of public health foresight studies.
Foresight is a discipline that is also strongly evolving. New methods and tools might arise, with AI as one of the most impactful. RIVM is investing in exploring the potential use of new, innovative methods. AI, group model building and storytelling are the three focal points of the INFORM project (innovation in foresight methods) as part of RIVM’s strategic research programme. RIVM is open to learn, apply, exchange these experience, with this handbook as one of the expressions of these ambitions.