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Microlearning 1: 3 insights on the impact of AI on elderly care

By Henk Herman Nap & Dirk Lukkien, Vilans

Every quarter we will share with you a microlearning: a short and quick learning activity with insights from our project partners. The micro-learnings will cover lessons from AI, care practice, design insights and more.

This first microlearning was provided by Vilans’ Henk Herman Nap and Dirk Lukkien, and tells us the three key insights for our QoLEAD community following from their recently published whitepaper ‘Wat gaat AI betekenen voor de ouderenzorg’.


1. Humans in the context of care are irreplaceable.

While AI applications offer more and more options and can increasingly perform complex tasks more independently, the role of humans in the context of healthcare is certainly indispensable in AI applications. For example, AI is less capable of caring, and unlike humans, AI has less capabilities such as being able to respond to nuances in the social and emotional context. Human healthcare providers will have to continue to interpret the outcomes of AI with their knowledge, skills and experience and remain in charge of providing care and support.

2. Meaningful and responsible use of AI does not come naturally.

Although technological progress seems unstoppable, the meaningful and responsible use of AI applications in elderly care does not come naturally. The conversation about ethical and social implications of technology is not yet commonplace. It is important that during the development, implementation and use of AI applications, people are aware that the use of AI not only entails opportunities but also risks. Opportunities include predictive, preventive, personalized and participatory care, while risks include dehumanization of care, stigmatization of old age and infringement of privacy and autonomy.

Meaningful use of AI can start small, yet scale is of importance to create actual impact

Valuable applications of AI can start small, from ideas or problem statements for which solutions are iteratively developed and tested. At the same time, scale is often important for successful AI applications, for example in terms of innovation power (time, money, etc.) and available data as input for AI. A uniform approach and large-scale implementation of AI applications in long-term care is complicated by, among other things, a wide diversity of needs from healthcare organizations and other parties involved, data availability and interoperability between systems.

Authors of the whitepaper are Luca van Breda, Jolanda Dircks, Bob Hofstede, Henk Herman Nap and Dirk Lukkien.

Want to read more of their work? You can find the entire whitepaper here.