The topics in this series were developed by New Scientist in conjunction with Philips, which paid for them to be produced
Take a moment to think about the data associated with your health. Over the years, you’ve probably had X-rays and scans of various kinds, including perhaps an ultrasound or two. Then there are blood tests, body weight and height measurements… The list is long.
This already amounts to a large amount of information about your body and your state of health. But it will soon be dwarfed by the data generated by genetic tests, chemical tests and other blood-marker tests, in addition to the advanced scans and activity trackers that are becoming available.
It is easy to imagine that adding this wealth of information to your electronic patient record will lead to some kind of medical nirvana in which doctors run a predictive analytics algorithm over your “big data” to forecast your future health issues.
Easy to imagine – hard to do. One of the key challenges this raises is planning: the technology that will store and analyse this data has to be right from the start. The UK’s National Health Service – which is poised to embrace the big data revolution over the next five years – admits in its Five Year Forward View that its approach to IT so far has left it with “systems that don’t talk to each other” and which “fail to harness the benefits of interoperable systems”. In future, big data standards will have to be nationally specified, the NHS says.
The technology that makes this possible has rapidly emerged in recent years: always-on, available-everywhere cloud computing services. Cloud computing allows data to be made available to authorised users anywhere, allowing analytics algorithms that predict and help prevent sickness to be developed and run on those data. “You couldn’t do this at all without the cloud,” says John Huffman, chief technology officer of Philips Healthcare in San Francisco, California. “It’s the cloud that will provide the ubiquitous access to health data.”
But exploiting big data is about much more than making systems compatible. “This will need to work like Google,” says Mahiben Maruthappu, a doctor and a leading light in the NHS’s Innovation Accelerator programme. “Google factored in how to scale up its search service from day one and has successfully scaled it up to work for over a billion users,” he told a health innovation conference in Manchester in September. Health systems have to be similarly scalable, he says.
Why? Because such a system will be used by tens of millions of users: it needs to be available at GP practices, walk-in centres and hospitals across the nation, too, so that falling ill 200 kilometres from home will no longer mean a local doctor cannot call up your patient record. Indeed, the NHS is planning to make medical records available nationally – online and via smartphone – by 2018.
Making meaningful analytical use of data that has come from devices such as gene sequencers, diabetic glucose meters and heart-rate sensors in smart watches is no simple matter. These kinds of devices generate significant amounts of data. More than 250 million patients around the world are tracked with Philips patient monitors – the company makes one out of every two patient monitors in the world. It also manages 18 petabytes of data for healthcare providers.
Root of the problem
But crucially, the data that represents health parameters is unstructured and varies enormously. “Unstructured data is the root of all our problems with electronic medical records. Analytics is not the problem – for the most part, we are refining methods that are already mature,” says Huffman.
“The main challenge is getting data into the system in a meaningful form so that you can analyse it,” he says. For instance, a simple record of your daily step counts from a smartphone app is entirely different to the results of a genetic test, since genes interact with each other and a test result can in fact have multiple meanings depending, for instance, on the patient’s environment.
“The main challenge is getting health data into the system in a meaningful form”
So Philips and its partners are working out how to best extract meaning from medical big data in what is turning out to be a burgeoning business of healthcare-algorithm development.
The essential challenge, Huffman says, is that conventional electronic medical records use a very rigid database structure whereas Philips’ approach is to cut the data some slack and use a flexible format that allows, say, a verbal symptom description, as well as numerical data, to be input. “You need to be able to embed things like a surgeon’s description of a tumour, or a nurse’s note on adverse events like a patient having difficulty sleeping,” he says.
Once that unstructured data is in the system the idea then is to apply machine-learning algorithms or neural networks to seek out patterns that predict from historic data what your future health outcomes are likely to be, but also to diagnose existing conditions earlier and thereby improve outcomes for patients.
Predict the future
But does it work? To find out, Banner Health of Phoenix, Arizona, in partnership with Philips, decided to put these algorithmic diagnostics to the test on patients with chronic conditions like congestive heart failure, chronic obstructive pulmonary disease and type 2 diabetes.
With 5 per cent of people with such chronic diseases accounting for about half of all healthcare spending, due to their frequent readmissions to hospital, Banner Health decided to take proactive action to try to reduce the need to readmit.
“We’re able to help individuals get better care in the comfort of their own homes”
To do this, it equipped 135 patients with telehealth monitoring technology at home. And a mobile intensive care team was equipped to respond to wireless alerts from the sensors – allowing them to visit patients’ homes and intervene at the first sign of trouble.
The results have been described as “dramatic” by Banner Health’s senior medical director, Hargobind Khurana. The predictive, algorithm-fuelled approach reduced hospitalisations by 45 per cent, care costs by 27 per cent and acute response costs by 32 per cent. The trial, started in May, is ongoing and now has over 500 patients enrolled.
But it’s about more than hard-nosed cost savings, says Huffman, who believes that the important point is the benefit to patients: “We’re able to help these individuals get better care in the comfort of their own homes.”
This story is part of a series exploring the way innovation is improving health. For more, visit newscientist.com/living_health
(Image: Simon Fraser/Science Photo Library)
This article appeared in print under the headline “Big data, better health”