Digital Case Manager—A Data-Driven Tool to Support Family Caregivers with Initial Guidance

Paul Wunderlich (), Frauke Wiegräbe and Helene Dörksen
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Paul Wunderlich: inIT—Institute Industrial IT, OWL University of Applied Sciences and Arts, Campusallee 6, 32657 Lemgo, Germany
Frauke Wiegräbe: inIT—Institute Industrial IT, OWL University of Applied Sciences and Arts, Campusallee 6, 32657 Lemgo, Germany
Helene Dörksen: inIT—Institute Industrial IT, OWL University of Applied Sciences and Arts, Campusallee 6, 32657 Lemgo, Germany

IJERPH, 2023, vol. 20, issue 2, 1-17

Abstract: Due to the demographic aging of society, the demand for skilled caregiving is increasing. However, the already existing shortage of professional caregivers will exacerbate in the future. As a result, family caregivers must shoulder a heavier share of the care burden. To ease the burden and promote a better work-life balance, we developed the Digital Case Manager. This tool uses machine learning algorithms to learn the relationship between a care situation and the next care steps and helps family caregivers balance their professional and private lives so that they are able to continue caring for their family members without sacrificing their own jobs and personal ambitions. The data for the machine learning model are generated by means of a questionnaire based on professional assessment instruments. We implemented a proof-of-concept of the Digital Case Manager and initial tests show promising results. It offers a quick and easy-to-use tool for family caregivers in the early stages of a care situation.

Keywords: machine learning; healthcare; case management; caring; multi-label classification (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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