How Data is Solving Some of the World’s Biggest Problems
WHY YOU SHOULD CARE
The health cloud is making it rain with solutions to world problems.
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Ending world hunger. Reducing crime. Halting deadly outbreaks. Before you get excited, let’s be clear that we haven’t figured all that out. Yet. Data innovation, however, is on the case, especially thanks to innovations within the health cloud. Which may not sound as exciting as Pokémon Go, but it damn well should.
Take algorithms. During West Africa’s Ebola outbreak in 2014, for example, algorithms perusing open data sources honed in on an unofficial report that mentioned 23 deaths due to hemorrhagic fever in a region where bushmeat was regularly consumed. This unglamorous gem of info helped epidemiologists and data scientists from Boston-based HealthMap identify the outbreak — four days before the World Health Organization called it. Data also did some heavy lifting when a computer program created by Epidemico revealed that a reformulated, abuse-deterrent OxyContin was driving down street prices — confirming a step in the right direction. And let’s not forget everyone’s favorite eyes in the sky: Nonprofit Flowminder compiled data from satellite imagery, measuring things like light emission, environmental conditions, roof distributions and anonymous data from cellphone operators, to pinpoint some of the world’s poorest areas, down to one square kilometer. Boom: suggestions on the most ideal spots for new roads, health clinics, vaccinations and subsidized food.
When combined with the powers of the health cloud, which is a scalable and secure, integrated analytics and visualization system that manages healthcare data, all of this has the potential to speed up how quickly doctors give a diagnosis — and help stem major disease outbreaks. A veritable arsenal of valuable information is being stored and analyzed in electronic health records and uploaded to the cloud to help solve major global problems. Go, data! And while there’s certainly no price tag on saving human lives, there’s a definite economic benefit to this technology.
For example, $300 billion to $450 billion could be saved by using data to better predict disease outbreaks in the U.S. alone, according to McKinsey Global Institute.
Larry Madoff, editor at ProMed, a global electronic reporting system for emerging infectious diseases, says the potential for data analysis and biosurveillance to reveal big-picture predictions can be compared to the trajectory of weather forecasting over the years, which has strengthened due to “an ability to model based on lots and lots of data points.” Apply this weatherman idea to disease outbreaks, he says, and we might someday be able to predict them — ESP for Ebola? “Of course, we’re not there yet, but it’s an exciting possibility,” Madoff says.
To be sure, the sheer volume of data available today — specifically when factoring in unconventional data sources like tweets or iPhone usage — presents its own sets of challenges for leaders in this burgeoning field of data analysis and prediction. “We’re rapidly approaching this world of having too much data and needing to dwindle that down to a subset that’s meaningful,” says Chi Bahk, Epidemico’s vice president of operations. There’s also the risk of broken links in the chain leading to a loss of information, Madoff explains, but these vulnerabilities could be mitigated by solutions like industrial-strength operating systems and platforms and more efficient processes.
As big data and technological innovation soldiers forth, it “could really revolutionize how we can practically engage with major challenges,” says Linus Bengtsson, co-founder and executive director of Flowminder. While data innovation won’t provide the solution itself in most cases, it could present an extremely fundamental piece of information that just might be the key that unlocks new answers.
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