There’s a hypothetical scenario I’ve been pondering for a while. I’ve actually been trying to write a short story about it, framing it from different perspectives. But that’s taking too long, and reality is fast catching up.
In the scenario, the British government has decided that the only way of making universal free health care affordable is by compelling citizens to have data on their bodily health and lifestyle tracked, with behavioural changes recommended to individuals by artificially intelligent “healthware” to keep them from from falling ill. The healthware learns how best to persuade people to act differently, fitting itself to individuals’ personalities to ensure maximum compliance. If people are consistently non-compliant, they have their access to free healthcare revoked.
Naturally, hospital visits are still required for genetic and particularly complex conditions, and in the wake of accidents or unexpected emergencies. But there are no more queues in GP surgeries or A&E. The number of people on medication drops to levels not seen for decades. The physical and mental health of the population soars, with higher productivity, longer life expectancy, and wellbeing to match (or better) the Scandinavians.
On the one hand, this sounds wonderful. On the other, it would herald the arrival of the sort of big state that socialist governments of the past could hardly dream of (their dreams looked more like this). The level of social control that would become possible – with our every behaviour monitored and, ultimately, made to fit a “healthy” norm – is intensely disquieting. Even more perturbing is the fact that, at least to me, this doesn’t seem particularly far-fetched.
In reality, healthware this sophisticated would come from a big tech firm before any government had even properly thought about it. I’ve posted a piece on Medium that comes at the possibility more from this angle. But I also wanted to take a few minutes to expand on why I think such a scenario is feasible, and offer a list of related things to read.
I’ve actually written before about the difficulties of applying a data-driven approach to a biological system as endlessly curious as the human body. That, though, was in the context of elite performance, and keeping someone within the bounds of reasonably good health ought to be more straightforward than turning them into an Olympian.
Naturally, it could take years for a system to be successfully trained with the sort of capacity outlined here. This is particularly the case given that the learning process would likely require real-time participants, and accordingly move only as fast as the rate at which people live and fall ill. Historical health records, along with some expert knowledge, could be used to speed up the process, but both may prove to be sub-optimal and useful only as a starting point. The concurrent analysis of the data of many, many individuals, and the pooling of the resulting knowledge (as has happened for the training of autonomous vehicles) will likely prove crucial – the more participants the better.
Eventually, a system should be sufficiently accurate for commercial roll-out. And over time it would just get better: optimising to take into account the individual quirks of your body, and benefitting from the more general findings from everyone else’s systems (perhaps attributing greater weight to data from family members and those physiologically similar to you). It could also keep abreast of the latest medical research findings (as IBM’s Watson does) in a way that would be impossible for a human, incorporating these into its predictions and recommendations to boost performance even further.
The bigger problem will be that there is currently far too much missing data on almost everyone to accurately predict health outcomes. Making wearable technology as ubiquitous as phones, and developing more ways of collecting health and lifestyle data automatically so you don’t need to rely on useless humans to input it manually, will be key (Apple is attempting to do both).
From the perspective of business, developing healthware at this level of sophistication could make some of the most powerful companies in the world even more money. If Apple could even get close to it, the Apple Watch would become a must-have – which seems ample motivation for pushing on with it as smartphone sales stagnate. Health insurers would happily make use of all that data to aid their own predictive models, and big pharma’s displeasure at a possible decline in medication levels could be offset by having healthware recommend, and automatically deliver their drugs.
Government would also likely be supportive given the scope for relieving strain on health services. It might be that government – or health providers more generally – come to endorse, or even require the use of this sort of technology (hence the scenario painted above). Besides, the British government seems supportive of pretty much any new way to better track people and invade their privacy, so there should be no problem on that front.
Another force that might drive the development of this sort of technology is the longevity hype that’s apparently consuming Silicon Valley. You could almost imagine this sort of healthware being pursued as a vanity project by one of any number of tech-entrepreneurs-turned-billionaires looking for a data-driven approach to living forever, regardless of whether or not it would end up being profitable.
What about, well, normal people? As a starting point, a 2016 survey of American healthcare “consumers” found that a quarter own wearable tech, 88% have used some sort of “digital health tool”, and 77% are willing to share their health data with their doctor to improve care – with 60% happy to give that data to Google.
The number of people buying wearables will continue to grow (likely driven more by marketing campaigns and the waning allure of near-identical mobile phones than anything else), as will the adoption of digital health tools as they become ever more useful. The figures on willingness to share health data may not sound especially high, but they’re ample for an initial phase of developing sophisticated predictive healthware – and if any system proved to be effective, they’d likely go up.
Americans are, admittedly, much further down this road than the rest of the world. But given that so many of our recent technological trends (e.g. personal computers, smartphones) have come from the US, and have been driven by American companies, it wouldn’t be an enormous surprise if the rest of the “developed” world soon caught up.
OK, enough. A few things to read / listen to that haven’t been linked to in either this or the Medium piece:
2017 Internet Trends Report – Kleiner Perkins (Mary Meeker) > See slides 288-319 for a range of pointers as to where healthcare might be going. (The rest is interesting, too.)
Self-regulation in Sensor Society – Natasha Schüll > Cool talk, available as a podcast from Data & Society, on the softer, fuzzier form of tracking represented by wearable tech (“little mother”, as opposed to the “big brother” of CCTV etc.) and its implications for individual autonomy and selfhood.
Some decent long-ish reads from a range of publications: this from the FT (paywalled), which is from a while ago and probably the first thing I remember reading on the subject, focusing mainly on Babylon; this from the Atlantic, which is even older (2013!) and concentrates on IBM’s Watson (which is still going strong in the healthcare game); and this from Newsweek International last Friday, which has more of an American bent but covers a load of interesting startups I haven’t really discussed here.
Networks of Control – Wolfie Christl and Sarah Spiekermann > A longer, broader work focusing on the collection and use of personal data by businesses working in a range of areas. Considers whether this corporate surveillance can enable businesses to control consumer behaviour – which is relevant here.
Intervention Symposium: “Algorithmic Governance” – org. Jeremy Crampton and Andrea Miller > A bit academic, but some interesting thoughts here and in the collected essays giving some background to the notion of algorithmic control and its implications.
As usual, I’m always keen for cool new stuff to read, so hmu if anything jumps to mind!