Wearables, Machine Learning, and Efficiency in Health Care: How Will I know That You're Thinking of Me?

Abstract

In the emerging digital health market place, accounts of the potential for wearable medical devices to dramatically impact global healthcare are overwhelmingly positive. Pursuing a technological innovation narrative, the benefits of wearables and their attendant big data platforms and machine learning applications are predicted to include personalised medicine, improved efficiency and quality of care, the empowering of under-resourced communities, and delivery of health services previously unavailable to the citizens of developing countries. Typically techno-optimist or solutionist in their description, key barriers to this impending inflection point in healthcare are identified as technical issues such as short battery life, unreliable connectivity, poor wearability and a lack of data protection. While these bottlenecks are of legitimate concern, I argue the dominant solutionist paradigm is consistent with problematic ethical, social, and political assumptions regarding how to justly distribute healthcare ‘goods’, how to consider the individual as part of society, and which political frame should provide the scaffolding to future healthcare policy. Ultimately, these assumptions (respectively: an efficiency ethic, as autonomous and independent, and neoliberal), if accepted uncritically risk, one, undermining the real clinical potential of medical technologies (in this instance wearable devices) and, two, designing social inequality and injustice into our interventions in global healthcare.

Presenters

Mark Howard
x, x, Australia

Details

Presentation Type

Paper Presentation in a Themed Session

Theme

Social Realities

KEYWORDS

Wearable medical devices Digital health marketplace Solutionism Efficiency Global health

Digital Media

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