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Imagine a smartphone app that predicts what medical conditions you're likely to suffer from and tells you how to avoid them. You may be able to download one sooner than you think.
Such apps would tell you what to eat, how much to exercise and when to visit the doctor based on the analysis of medical research, your medical history, family medical records and medical records of strangers of the same sex, weight, age and ethnicity.
"The quality and insight into our health will be dramatically improved by the quality of data and the ability to bring it all together," said Craig Wentworth, principal analyst for MWD Advisors.
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Telehealth is a fast developing industry. Existing apps allow users to monitor blood pressure and blood sugar, while apps like Fitbits and Nike+ measure different aspects of fitness. Together such apps can provide insight to users' overall health.
Privacy concerns still rife
However, the growth of big data in the health industry will only take place once privacy concerns are addressed.
In February, privacy concerns led the U.K. National Health Service to postpone the implementation care.data. The database aimed to collate anonymous information from patients to help doctors and health organizations better spot emerging health trends and provide treatment suggestions.
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Even though care.data would have remained anonymous, the program met significant resistance amid concerns about how personal data would be shared and handled.
"Health data is not the same as sharing marketing data, like where you live or what washing powder you use. It's a lot more personal," said Wentworth.
"Before we can use big data in health, we need to build trust and to do that we need an open conversation. Companies who are brokering the big data conversation need to be crystal clear about what patient data will be used for. The use of big data in health needs to be gradual, allowing people to choose between opting in and opting out."
Additionally, the sharing of self-collected data would require another major cultural shift, Wentworth said.
"It's one thing for you to know how many steps you walked in a day, and what your blood pressure is, but it's another thing to want to share that data," said Wentworth.
"Maybe someday sharing that data will mean your health insurance costs less, but even if this is the case, there will still be people who won't want to trust insurance providers with their data," he added.
Future of big data in health
Big data analytics could save the American health care system $300 billion per year and the European public sector €250 billion, according to a 2011 report by the McKinsey Global Institute.
Big data analysis is already being used to make diagnoses in some hospitals. In Canada, Toronto Hospital uses big data to detect blood infections in premature babies and uses IBM's analytics technology to monitor changes in heart rate, breathing and blood pressure to help predict potential changes in a baby's condition.
"The next step is prescriptive analytics, where substances in a drip would automatically be altered to medicate a patient without any human involvement. But this is far off yet - people are bound to feel a bit uncomfortable about the idea," said Wentworth.
However, James Norman, healthcare development director for big data company EMC, said big data usage in local hospitals is limited, while less than 1 percent of private healthcare providers use it.
"There's still a way to go before big data is used in wider community projects at a national level, like to reduce emergency readmissions to hospital, or proactively address chronic diseases like diabetes, cancer or kidney problems," said Norman.
Working with machines
IBM's supercomputer, Watson, is perhaps the most famous big data technology in the market. The system hit the headlines in 2011 when its capabilities were used to play the game show Jeopardy and beat former champions to 'win' a $1 million jackpot for IBM.
Doctors are using Watson to keep up with health research and to leverage the latest breakthroughs.
"Trainee doctors could learn with machines like Watson," said Wentworth, noting individuals wouldn't need to see specialists, as general practitioners could advise with Watson's assistance, reaching a diagnosis and starting treatment faster.
Additionally big data analytics could be used to follow epidemic outbreaks. This was seen with the Swine Flu epidemic in 2009 where Twitter and other social networks were used as early warning systems because of their ability to deliver "real-time" information.
"What we can do today is start performing complex tasks such as pattern matching and recognition, as well as machine learning for trends and patterns that occur in our societies," said Ben Woo, principal analyst for Neuralytix.
"Big Data enabled doctors and scientists to learn so much about the Severe Acute Respiratory Syndrome (SARS), and how quickly it spread, within weeks of the World Health Organization's initial warnings. Also Google, in conjunction with the U.S. Centers for Disease Control and Prevention (CDC), was able to arrest and contain the fast spreading H1N1 flu virus in a matter of weeks."
Now, as the Ebola virus threatens to spread across African borders, public health authorities may look to social networks and mobile data to ensure the virus is contained and health supplies are available where necessary.