As the health-data ecosystem expands to include novel types of digital data and new methods of capture and analysis, consultants and data analytics companies are enthusiastic about the revolutionary future of health care. Customers can be profiled based on their use of medication, social media networks, or exercise patterns. Daily purchases or fast food orders can be analysed to segment consumers according to how likely they are to suffer from life-style related diseases. Predictive models can be used to calculate whether schoolchildren will take up smoking, drop out of school, or become obese.
In an article published in the journal Biosocieties, Linda Hogle, an expert on medical history and bioethics, provides a convincing portrayal of the current datafication of health. In the worst-case scenario, data analytics combined with a market-based healthcare system accelerate health inequalities. When power is transferred to data and algorithms, calculative apparatuses result in judgements and decisions that would be inhumane if put into practice. Individuals could be categorised and scored in ways that irreversibly affect their lives without being aware of the grounds for such outcomes or their possible consequences, while those with a low ‘medical adherence score’ could be left to their own devices.
Yet there is also a more optimistic way to promote the future of health care with the aid of algorithms. Medical anthropologist Rayna Rapp makes the case that medical research can chart imaginative and liberating paths with big data research. She argues that the turn to data analytics in the field of paediatric neuroscientific research has the potential not only to pathologize but also to normalize variation, or ‘neurodiversity’. In this account, data analytics reveals new linkages between diverse conditions, opening diagnostic categories such as ADHD and autism to reconsideration. Parents expect a great deal from this research: when more is understood about their children’s atypical neurological processes, perhaps better care and support can be provided.
Harnessing the healing powers of data requires new kinds of research and healthcare environments, and cooperation between patients and professionals. Communications researchers, sociologists, philosophers, and anthropologists need to work alongside engineers and health professionals. The division of labour between humans and machines will have to be fine-tuned. Ethical issues should be considered in relation to the politics of algorithms and alternative future scenarios. Societal success needs to be a goal alongside data interoperability.
The more open and explorative data world will not bind analytics to the cost-efficiency ethos, but rather be one in which careful thought is given to the longer-terms goals of health care. If the possibilities of data analytics are approached more as ‘an unknown’ than a pre-defined set of segmentation tools and risk assessment frameworks, big data approaches can contribute to the range of possibilities for rethinking health and health promotion. This will, however, require imagination as well as algorithms. Human intuition is still superior to machine intuition and should be used as a map when going where nobody has been before.