Service: Automatic identification of individual risk factors in social care and healthcare

In October 2019, the Japanese multinational IT service provider, Fujitsu, announced that it was developing an AI solution for South Karelia’s social care and healthcare district (known by its Finnish acronym Eksote). The project employs machine learning methods with the aim of helping Eksote identify factors underlying social exclusion of young adults, as well as predicting associated risks. With the predictive model, social and healthcare professionals will be provided an overview of risk factors. According to Fujitsu’s press release, the model identifies some 90% of young adults susceptible to social exclusion. In practical terms, the model that is being used is derived from pseudonymized data taken from the use of Eksote’s services by young adults, and it uses this data to predict social exclusion outcomes defined by Eksote’s professionals. According to Eksote, the legislation on the secondary and combined use of healthcare data makes it possible to use only non-identifiable, pseudonymized data. This means that Fujitsu’s model cannot be used to identify individual young adults considered to be at risk of social exclusion; rather, the model produces a list of risk factors on a general level. The next step in the project is to examine whether it is possible, under the current legislation, to set up a consent-based system: a client’s consent would be asked for before using the predictive model on their individual data when they, for example, have an appointment with a social care or healthcare professional.