In recent years, Big Data has been hailed as the messiah that will deliver us from all the evils of the current healthcare system. Scores of articles have been written on its ability to transform healthcare.
Dr. Martin Kohn, who worked on IBM’s Watson project and is currently chief medical scientist at a startup based on the promise of Big Data, was on hand Tuesday to dispel that notion.
“It makes me very uncomfortable to hear that Big Data will transform healthcare,” Kohn told an audience gathered at the 13th annual Design of Medical Devices Conference at the University of Minnesota. “Big Data is an enabler of transforming healthcare. It is not the driver.”
Why is that?
The main reason for that being that just being able to gather vast amounts of information is not going to be enough. Making that data meaningful so that it can support clinical decision support and knowing hot to use that data is what is required. Most importantly it needs to be able to prove that it can improve clinical outcomes.
“Big Data is very good at detecting correlations but it doesn’t have a direct way to show causal links,” Kohn said.
While Vinod Khosla, the Silicon Valley technologist and venture capitalist, believes that in the future most of what patients need from healthcare would be managed by algorithms, Kohn begs to differ, he said.
"He told me that his iPhone [that has a device snapped on the back] can send his EKG to his doctor 100 times a day," Kohn said of a conversation with Khosla. "I said, 'So what?'"
Kohn thinks it’s vital for people to address the gap left by Big Data and this ability to collect vast amounts of information. Humans need to develop the tools that can make sense of that volume of data to make sense of the patterns that Big Data presents us.
“Watson is very good at understanding English, but he cannot tell when a person is lying,” Kohn said of IBM’s supercomputer that made headlines when it won Jeopardy in 2011.
Improving clinical outcomes by providing better clinical decision support through analytics is then the promise of Big Data. That will help develop a system where healthcare is personalized, a system that can predict who is at risk for developing chronic diseases and to prevent them from actually developing those conditions.
But all of this assumes that Big Data has surmounted a primary challenge – that of seamless sharing of information from multiple stakeholders in healthcare. That hasn't happened so far.
“Many times people don’t want to diminish their power by sharing information,” Kohn said.
Aside from such social challenges, there are political challenges too, possibly fueled by fears of medical data security in the 21st Century.
Kohn gave the example of a Virginia State Senator Stephen Martin who authored a bill last year that would prohibit “any person that regularly stores medical data in an electronic or digital format from … performing any analytic or statistical processing with regard to any medical records from multiple patients for purposes of medical diagnosis or treatment, including population health management.”
The measure ultimately was voted down in committee but two people voted for it, Kohn told the audience.
Nevertheless the ultimate goal of Big Data is something that will likely develop a more robust healthcare delivery system.
“The goal is to save money and improve outcomes,” he said.
And perhaps if these dual goals are achieved, America’s standing in the world in terms of healthcare might also improve.
In 1975, an analysis of the chances of a 45 year-old reaching the age of 60 was conducted comparing the U.S. to 11 other developed nations. The U.S. came in 12th and it was spending roughly the same amount as the other nations to come in last, Kohn said. In 2005, the U.S. still came in last but this time it was spending more.
“We are spending two to three times as other nations to maintain last place,” Kohn said.
Leveraging Big Data and knowing how to use it can help to change this dynamic.
[Photo Credit: iStockphoto.com user Alexsl]