Smart Healthcare Systems and Social In-/Justice

• In Event: Innovation and Inequality in Health and Medicine - III

Abstract

AI-based health technologies emerge from stochastic analyses of big health data (insurance status, medical record, lifestyle data) and are based on the algorithmic processing of these data, which simultaneously can be turned into assets on global health markets (Birch et al. 2020; Vezyridis/ Timmons 2021).
We ask to what extent health equity is affected by algorithm-based services. In addition to data-based bias against subgroups, unequal representation and lack of explicability and validation of AI-based intelligent decision-support systems, vertical disparities in health care (access to, utilization, and quality of services) have consequences for potential discrimination. Health disparities particularly affect people of low socioeconomic status, and the elderly, groups that are more likely to have health-risking lifestyles and higher risks of disease and mortality.
Studies from STS draw our attention to both, the risks of algorithmic decision making and the embedding of AI in global health markets (Beer 2017; Fourcade & Johns 2020; Lupton 2017; Mackenzie 2005 Martin 2015). We discuss the promises of AI-based health technologies and its implications into the existing German care system. Our findings from a literature review and expert interviews support assumptions about shifts in key differentiations (health-disease, prevention-therapy, physician-medical informatics, care-product), a fundamental redefinition of concepts of health care, and point to problems of interoperability between different technologies and providers. These challenges are discussed against the goal of health equity for all, esp. sensitive subgroups. First contours of necessary debates on social inequality and AI medicine are outlined.

Authors

• Cordula Kropp, University of Stuttgart

• Karolin Tampe-Mai, University of Stuttgart