Potential health gains from the digital revolution have been widely heralded, if yet to be fully realised. But who are likely to be the biggest winners and losers of this revolution? In this blog, I suggest that to harness the benefits of Big Data for health equity, we will need democratic as well as technological progress.
The amount of digital data in the world continues to grow exponentially – we now generate 2.5 quintillion bytes a day. As capacity for connecting and interrogating those data expands, so too do expectations of what they can offer. Sociologist David Beer describes this as a powerful data imaginary– the enticing promise of a brighter, more efficient, more connected and more strategic future from advances in data analytics, and the fear of being left behind by the digital revolution.
Progress in health care has become particularly bound up in these promissory visions. Health research funders are investing heavily in Big Data research capacity; DigitalHealth.London proclaims that “Digital innovations have the ability to transform health and care”. One recent commentary listed the huge health advances made possible by data analytics, including better understanding of the effectiveness of therapies, improved patient safety, earlier diagnosis, cost efficiencies, and early epidemic outbreak identification. “Real-time big data analytics is a key requirement in healthcare” the authors note.
Against this optimistic technophilia are ranged more dystopian health concerns about digital futures. Anxieties focus on data privacy, the emergence of new diseases caused by technology addictions, the potential of social media to erode the embodied social ties that have traditionally fostered health, and the anxiety produced by living under the constant surveillance of what David Beer describes as the digital gaze.
If the putative health benefits and risks of the digital society have been widely debated, one area that has attracted less attention is implications for health equity. Who gains, and who loses, from living in a digital world? Our recent review of research on digital technologies and walking and cycling identified rich seams of sociological research exploring devices and apps such as Fitbits and Strava. Existing literature covered topics such as the quantified self, the role of how apps in biomedicalizing walking, and how digital technologies contribute to creating responsible, healthy citizens. However, we found little research on how social divisions are reproduced or disrupted by digital technologies.
One obvious way in which divisions are reproduced is through digital exclusion. A recent EU report identified growing internet access across Europe: but also that those least likely to use the internet regularly were older citizens, those in poorer countries, those on lower incomes, and those in rural areas. These divides increasingly matter for access to the basic determinants of health. With the roll out of Universal Credit in England, for instance, an online account is now required to access benefits. Some 46% of applicants required help with this. This is a real, and health damaging, constraint for many of those in greatest need. In this context, it is not surprising that then Health Secretary Jeremy Hunt’s announcement about the introduction of an app to book GP appointments in England was met with considerable scepticism from GPs, who were quick to point out that an app will hardly improve access to an underfunded and overstretched primary care service. Like many digital interventions, it may well simply improve access for the already advantaged.
But exclusion is not the only way that digital technologies impact on health equity. As Virginia Eubanks, writing from the US perspective, notes, a focus on exclusion assumes that technologies are neutral ‘goods’ which could simply be distributed more fairly. This ignores the ways in which social inequalities are built into many technologies. Stigmatising, racialised, and othering discourses are embedded in many of the platforms and algorithms that increasingly automate decision-making in many areas of US public life. In her book Automating Inequality, Eubanks describes how the already-disadvantaged are most likely to be in work that is constantly surveilled though key stroke counting, swipe cards, and search history monitoring. They are increasingly likely to be on benefits where spending habits are digitally monitored and used to punish, or refused bail because of automated algorithms that say ‘no’ if you are Black or Latino. As she notes, it is hardly surprising that there is a certain ‘critical ambivalence’ about technology from those least likely to be in control of it and more likely to be controlled by it.
These issues of control and agency are key to health equity, as are attending to the social conditions of the production of data. To democratise health gains from data analytics, we need to ensure that first that data sources represent all of us. If those on benefits or in precarious employment are over-represented in some data sets, they are likely to be under-represented in others. If clinical trials are largely done on adult males, digital techniques for synthesising ever larger numbers of trials will not tell us more about women’s health outcomes. If the only data available on cycle journeys come from Strava users, data mining may be a limited way to find out about how and where the less affluent or less confident cycle.
Harnessing the value of Big Data for health equity will rely on its contributors (all of us) having ownership of those data. It will rely on public trust in the data platforms and governance – not just to keep our data secure, but to do the right thing with it. In England, the challenges faced by NHS Digital and its predecessors – particularly over the abandoned care.data programme – suggest that there is some way to go before that trust is earned.
Health equity in the digital age needs more that just equitable digital access and digital competencies. These are important, but not enough. We also need to ensure that benefits, healthcare appointments and other key determinants of health do not rely on digital access: digital technologies should not be barriers to entitlement. We will need trusted, accountable and democratic governance of health data platforms, built on informed citizen participation in decisions about the uses to which they are put. We should demand that aggregated data should be open where possible, to allow broad engagement with using, validating, and questioning the data. In short, realising the potential of digital technologies for health equity will mean a democratic, as well as a digital, revolution.