A step forward in supporting home care more effectively: Individually tailored in-home care consultancy utilizing machine learning.

Due to the ongoing demographic change, informal care is even more to be considered as main pillar in caring for the increasing number of care-dependent older people. However, informal caregivers do not feel sufficiently informed about suitable support measures and meeting their care tasks can pose a high burden on them. Therefore, in the nationally funded research project INGE an app was developed to implement quality-assured and outcome-oriented effective in-home care consultancy utilizing machine learning. Yet, data on home care are missing to build machine learning features. So, synthetic data generation was used in the INGE-project as compensation and improved later on by real data collected with the INGE app during in-home care consultancy visits. Outcomes of real data collection and the design of the INGE app will be presented in this paper.