Are we misunderstanding what health data really means?
Is the most health useful data simply slipping through our fingers? Maxine Macintosh argues that it’s our ‘wellness data’ — which includes everything from our lifestyle to our social networks — that holds the key to effective healthcare in the future, not just our medical records
The seeming nonchalance with which our day-to-day personal data is used continually surprises me, especially when contrasted with public attitude towards the use of healthcare data. This lack of concern represents a melting pot of ambivalence, convenience and ignorance.
Take for example, Pokémon Go, which hit the headlines earlier this summer not least because the app seemingly required ‘full access’ to individuals’ Google accounts. Though this was later revealed as ‘bad’ wording, the request had evidently not deterred millions of Pokémonites. Access to personal information and mobile sensors was perceived to be an appropriate payoff for using this amazing game. A less transient example is Google; as I mentioned at a recent event on the role of data in digital health innovation, our data is the currency by which we buy the services of convenience from Google. This was in response to the question of whether the donation of our health data should be a criterion upon which the NHS is free at point of care. Personal data as a currency for convenience, use and access is now a familiar scenario, so why has this not yet pervaded healthcare?
A Eurobarometer study in 2010 found that 74 per cent of us accept the compromise of personal data privacy for convenience, an ever-growing part of modern life. With most users understanding that ‘free-to-use’ online services take our data as payment.
Compare this to attitudes towards health data and we get a very different picture. According to an Ipsos Mori report, only 16 per cent of us are aware that commercial organisations are involved in NHS data, and only 18 per cent were aware of academic institutions’ involvement. Furthermore, such involvement was treated with mistrust and shock. The key quotation of this report was from a patient who said, ‘It’s a one-way mirror; they know everything about you, but we don’t know what they’re doing with that information’. This view infiltrates all sectors, but seems to affect the healthcare data discussion considerably more.
Defining health data
But when we say health data, what exactly do we mean? For most people, this refers to their electronic health record. A health record can be the most sensitive information digitally captured — a snapshot of one’s lowest, most vulnerable and most intimate moments. It’s not surprising therefore that the debate around data privacy, security and governance has moved beyond that of commercial data.
The UK has had its fair share of health-data blips in the last six months alone, including the scrapping of care.data, an NHS England programme that aimed to bring together health and social-care information to improve understanding of patterns of public health, as well as the inquiry into Google DeepMind’s access to data from the Royal Free NHS Trust in London.
So while multimillion pound national health-data programmes are hitting the headlines, fuelling ever more divisive attitudes towards health data, our non-health data is being liberally shared, extracted and analysed. But herein lies the misnomer — the data we commonly perceive to be ‘health data’ is in fact our sickness data. Our ‘health data’ is information captured which shows just how healthy we are — where we are travelling to, what we are Googling and what we’re spending our money on.
Factors beyond the healthcare system
Anyone with a background in public health will be all too familiar with Dahlgren and Whitehead’s social determinants of health, which maps the relationship between the individual, their environment and their health. In other word, it is factors largely outside the healthcare system that determine your ultimate state of health. These include everything from your lifestyle, social networks and employment, to education, living conditions and culture.
Personally, I don’t suffer from any medical conditions that are either embarrassing or would put me at a professional or societal disadvantage — I’m privileged in this sense. What makes me ‘me’ is not the fact that I had mumps two years ago, keep switching my contraceptive pill or have developed a new allergy. I don’t feel defined by episodic snapshots of ill-health, which all members of the population will encounter at some point. What defines me is what I like doing, where I like eating, the questions I need answering, the music I am listening to, the people I am interacting with — all of which can be easily captured through my digital behaviour. But, maybe this isn’t a concern shared by all, given the freedom with which such information is exchanged and given away.
There is therefore an enormous opportunity to use genuine health data to fulfil one of digital health’s many mantras — of predictive and preventive medicine. Our sickness data shows the effect, our health data — the cause.
Understanding population health
This, of course, doesn’t render the debate around health-data privacy and governance obsolete and, in fact, raises a number of new questions, for example, can internet activity as a indicator of health be used by insurers? But with such vast quantities of commercial data available, those commercial organisations that have an insight into the public’s most ordinary day-to-day activities are in a prime position to truly understand population health. And with great power comes great responsibility.
Our ‘sickness data’ goggles have been blinding us. We must conceptually overcome our misunderstanding of what is health data, and with that, engage the right stakeholders, in the right manner, to drive the next phase of predictive medicine. With a shift from sickness to wellness and reaction to prevention, our sights need to be set on a different pool of data — health data that sits outside the healthcare system.
Originally published at medtechengine.com on September 14, 2016.
