Chris Newmarker

August 25, 2015

4 Min Read
How Big Data Could Reach Its Medtech Potential

Here are three things that need to be overcome when it comes to turning raw medical data into actionable information, courtesy of wireless technologies consultant and entrepreneur Upkar J. S. Dhaliwal.


Upkar Dhaliwal

Upkar J. S. Dhaliwal

Chris Newmarker


Think of how online marketers seem to know your interests better than you do, based on your Internet browsing--or how a major retailer such as Target reportedly creeped out mothers-to-be by determining they were pregnant before that was public knowledge, thanks to analysis of shopping data. Consider how weather forecasts use data to predict whether it is going to rain in a day or even a week.


When it comes to medtech, though, "we're in the infancy of this stuff," says Upkar J. S. Dhaliwal, who is involved in a host of ventures in the space. (See Dhaliwal discuss how to turn raw data into tangible care pathways during a panel discussion at MEDevice San Diego, September 1-2.)


"We're moving from a world of embedded control, memory, and recording little events. We're moving away from a heart monitor or pacemaker just monitoring things. ... The machine and the sensors will stay as they are. It's the insights and intelligence that are improving," Dhaliwal says.


And the benefits could be more than just getting advance deals on baby diapers or saving yourself from a rained-on picnic. The right insights and analytics could predict health events and save lives. (IBM, for example, is spending $1 billion to acquire Merge Healthcare, all so that its Watson supercomputer might be able to draw conclusions from hundreds of thousands of medical images.) 


Dhaliwal, who is system architect, advisor and analyst through Future Wireless Technologies (San Diego), seemed impatient to see more progress. Here are three things that Dhaliwal thinks need to be overcome for medtech to find its big data holy grail:

1. Overcoming the Structural Barriers

"It's very hard to move the needle in this country because of the structure," Dhaliwal says of the U.S. health system with its numerous public and private health providers and insurers. 


With a venture called Ease Interactive that Dhaliwal is involved in, he recalls that it was military generals versus the VA that pushed for Ease's predictive health data analysis to help veterans better manage post-traumatic stress disorder. "They wanted clinical outcomes that could be proven in the research labs," Dhaliwal said. 


Other countries such as the United Kingdom, Germany, and Sweden are much more focused than the U.S. on achieving better healthcare insights through data. 


Still, there needs to be some kind of push to get investment money behind data analytics systems in healthcare. "It's not about the technologies, it's about the business behind them," Dhaliwal said. 

2. Building Better Wi-Fi

A major technological hurdle when it comes to collecting so much data involves Wi-Fi, according to Dhaliwal. 


"We didn't know Wi-Fi would be this popular, Dhaliwal says. "The underlying protocals can't keep up with it." 


Dhaliwal is working with another venture called Cognition Systems that uses COTS Wi-Fi silicon with TDMA mode protocol and a scheduler to prevent data collisions. "We need greater levels of connectivities in hospitals and everywhere."

3. Figuring Out What Data Is Useful

"All data is useful. We just don't know what it's useful for," Dhaliwal said. 


Dhaliwal has been active with a federation of big data seeing what big data could be especially useful in healthcare. He noted that medical knowledge right now is often based on clinical trials data involving subsets much smaller than the overall population. A new venture called Big Data Federation has a Prediction Valley product.


"As we come online and connected with real-time data, the outcomes, the efficacy of the research will change--and business practices."


(See Dhaliwal discuss how to turn raw data into tangible care pathways during a panel discussion at MEDevice San Diego, September 1-2.)

Chris Newmarker is senior editor of Qmed and MPMN. Follow him on Twitter at @newmarker.

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