It sounds really obvious, but hospitals aren’t for healthy people. The world’s entire health system is really there to react once people get ill. If doctors are able to catch an illness at stage one that’s great, but if it reaches stage three or four there’s often not that much that can be done. So what if we could treat patients at stage zero and predict the likelihood of contracting diseases? We could then get treatment to people who need it much earlier and take preventative steps to avoid illness altogether.

Currently, when we think of monitoring in healthcare we’re usually referring to monitoring patients’ reactions to drugs or treatments, but this is changing. No amateur runner’s uniform is complete these days without a Fitbit or some kind of analytics tool to monitor progress, so the idea of monitoring the healthy is becoming ingrained in the public’s consciousness. But Fitbits only scrape the surface of what we can do. What if the data from fitness trackers could be combined with medical records, census data and the details of supermarket loyalty cards to predict the likelihood of contracting a particular disease?

With big data we can move from reacting to predicting, but how do we move beyond just making predictions; how do we prevent disease from occurring altogether? Up until now all of our monitoring technology has been located outside of the body, but nano-sized entities made of DNA could one day patrol the body, only acting when they come into contact with specific cells – cancer cells, for example. The technology that would turn tiny machines – roughly the size of a virus – into molecular delivery trucks that transport medication is already being worked on by bioengineers. If this kind of technology can be used to treat cancer, without needing to release toxic agents into the body, can the same technology be inserted into a healthy person and lie in wait for the opportunity to fight disease on its host’s behalf?

Data-driven healthcare

It’s likely that healthcare providers will mine their own data and start to predict the wellbeing of patients

Medication and surgery won’t always be our first port of call when treating diseases. Some of the biggest companies in the world, such as Apple and IBM, are exploring how the litany of data we produce can be used to gain an insight into our health. Another company that is sees the opportunity data presents is Big Data Partnership. The data-science startup is currently working with Innovate UK and Outcome Based Healthcare to predict who will suffer from complications associated with type 2 diabetes.

Myocardial infarction is a complication that will affect one in 16 diabetes sufferers. To treat the complication doctors prescribe metformin, but unfortunately that drug has some pretty awful side effects that includes nausea and diarrhea. By using machine learning to sift through health data – both primary and secondary care data as well as non-health data, such as ONS, census and survey data – Big Data Partnership is able to predict who is likely to suffer from myocardial infarction. This could have massive implications for people who are prescribed metformin and have to deal with its side effects, and it could save the NHS £1.2bn a year in the cost of drugs and dealing with the consequences of metformin prescription.

While that will come as welcome news to type 2 diabetes patients and their families, the real test of machine learning will come when it is tasked with predicting the likelihood of all diseases. Big Data Partnership chief strategy officer Mike Merritt-Holmes thinks that the same principles that the startup has developed to predict complications associated with type 2 diabetes could be used to predict other illnesses.

“The data we’re using is actually very relevant for a lot of diseases, and there’s no reason why we can’t use the same process to do the same thing for other diseases as well,” says Merritt-Holmes. “We know when people get diseases there are a number of factors, so we know that where people work, how they travel, what they eat, the amount they drink, all those things are contributory to a disease occurring as well as people’s genetic makeup. What we don’t know is how non-health factors directly impact diseases, so the techniques we’re using and the data we’re bringing in will definitely help to start to predict how diseases and their complications occur. The challenge is getting the data into something that you can work with and then the possibilities are endless.”

So will the day ever come when people are handing over data to their doctor for analysis? The idea of you heading to your doctors surgery armed with you shopping history might not be as silly as it sounds. “We need a lot of data to start to build the model, but once you’ve built the model you just need an individual’s data and the relevant variables that drive that model to be able to make the prediction,” explains Merritt-Holmes.

Far more likely, however, is that healthcare providers will mine their own data and start to predict the wellbeing of patients. This practice will be far easier in the UK, where medical records are centralised, than in the US, where health data is split between private organisations.

“The NHS is almost unique in the fact that it has access to a lot of data that sits across the whole country, but it doesn’t use it in a combined way. What I mean by that is we don’t take genetics data, medical records, or realtime data coming from ECG units, and bring that all that together to have a combined view,” says Merritt-Holmes. “One of the reasons for that is the cost, but the other reason for that was because there wasn’t really the skill set within the NHS to exploit it, not to mention the challenges in information governance regulations.”

“We’re now moving to a big data world where the technology is there, and we can start to join this data together. The NHS is in a perfect place to exploit it because of all the data it has. If you look to the US all the data is siloed because everyone has private organisations; it’s very hard to get a cross-country view. I think if the NHS can partner with both data providers, particularly around the non-health datasets, but also with the implementers, like Big Data Partnership, who can then take that data and start to mine it for them we’re going to really speed up what insights we can get. I think eventually you’ll find the NHS, whether it’s on a country level or on a more local level, will start to have analytic teams that are doing this, with much more focus around machine learning and starting to drive insights from the data they have.”

Nanotech breakthroughs

The work being done by Big Data Partnership and others like them is moving us a step closer to being able to predict the likelihood of diseases occurring. With that kind of information, the idea of treating someone at stage four will be a long-forgotten anachronism. But can nanotechnology engineer devices that prevent the need for predicting disease altogether? Can nanoscience provide technology that kills disease as soon as it occurs?

Teams of scientists from around the world are working on designing molecules that deliver treatment to targeted cancel cells. University of Cambridge nanotechnology professor Mark Welland is one scientist working on this kind of technology. He explains that his team is currently designing a particle that can get into a cancer cell and then be triggered to release a therapy that destroys the cell or the DNA in the cell so that it stops replicating.

To get to a point where autonomous nanobots patrol the body, there are a number of factors to overcome

Although Welland is optimistic about the success of his and others’ work in using nanotechnology to fight cancer, he’s not convinced that we’re likely to see DNA nanobots that patrol and police the body so we no longer have to worry about our health. “Everything I’ve described is a sort of passive particle. It has no intelligence; it has no ability to determine where it’s going or how it’s going to behave. It’s a passive particle. It sort of sounds [like] when you hear robots that there’s some autonomous behaviour, but we’re a long, long way from that,” says Welland.

To get to a point where autonomous nanobots patrol the body in a Fantastic Voyage kind of way, there are a number of factors to overcome. First of all, you would have to find a way of steering and powering the particle and keeping track of it once it’s inside the human body. Once you’ve done that, and it’s on the inside, the particle would have to be functional enough and have enough intelligence to determine between a good and bad cell. And even if it manages to achieve all of that, it could still be destroyed by the body’s natural immune response mechanisms. Far more realistic is that, similarly to big data, nanotechnology will be used to catch disease at an early stage.

“I think the major impact in the shorter term is going to be early diagnostics. If you think of the biggest impact nanotechnology has had to date it’s in your mobile phone,” says Welland. “We’ve got all this incredible processing power in a tiny platform. If you added then some diagnostic capability, where you put a drop of blood on a sensor that was attached, perhaps, to your phone, then you can in principle do a full blood assay on a tiny drop of blood in a fraction of a second.

“If you compare that with going to a doctor, having blood taken out with a syringe, sending it off to hospital, having it tested and getting the result back, you could have the results back immediately. I think diagnostics is a very obvious [area of development for nanotechnology] and is already happening, so that will have a huge impact – and not just in developed worlds; it’ll have a huge impact in developing countries as well.”

Making predictions

Welland points out that in healthcare research, there’s no advantage to be gained from making large claims that “might take twenty years to back up”. But it’s not hyperbole to say that with big data and early diagnostics made possible by nanotechnology, doctors will be able to treat diseases earlier and earlier, perhaps even at stage zero.

Being able to treat diseases earlier would represent a massive paradigm shift in healthcare as practitioners move from reacting to disease once it occurs to treating diseases before symptoms appear. Because this would represent such a big change to how we treat people, and the results doctors are able to achieve, it’s important to temper our optimism. After all, doing this stuff in the context of real people with real medical challenges and getting all the approvals required is a completely different – and in many ways far more difficult – problem than just developing the technology.

Healthcare, like every other industry, will be disrupted by improvements in data processing power and technology, but Welland is keen to stress that “we have to be proportionate and cautious in our claims, so that we can take the research forward in a sensible and measured way”.