AI-supported qualitative analysis of free-text responses on home care burden and support needs in Saxony

Abstract The number of people in Germany requiring care has risen steadily, increasing the importance of informal care. This form of care is often associated with considerable psychological and physical strain. The aim of this study is to systematically categorize and qualitatively analyze the free-text responses from a survey on home care using an artificial intelligence-based approach in order to identify key challenges and support needs in home care from the perspective of informal caregivers and non-caregiving relatives. The study used data from a 2019 survey on home care in Saxony. Free-text responses were categorized and analyzed using GPT-4 Turbo within a hybrid human-AI workflow. All AI outputs were subsequently validated and corrected by researchers. Respondents reported substantial financial burdens for both care recipients and informal caregivers. They also highlighted structural barriers to accessing services and insufficient support from the care system. Improving home care requires structural measures, including the expansion of low-threshold counseling services, more flexible leave regulations, stronger financial security for informal caregivers, and the sustainable strengthening of care infrastructures. Given an AI error rate of 36.45%, the study emphasizes the need for human post-processing to ensure analytical accuracy.