Health data analytics is shaping up to become a global issue. But for that day to come, patients have to consent to sharing their data with providers and, likewise, providers must agree to do the same with their competitors.

While the politics are already proving difficult, the technology is stepping forward. IBM distinguished engineer Scott Schumacher and global healthcare ambassador Lorraine Fernandes spoke with Government Health IT Editor Tom Sullivan about the challenges of health data analytics, what it will take for physicians to actually be able to use IT systems as robust as Watson, and how the days of nations functioning as islands are disappearing.

Q: Reports circulated last week about the University of Tennessee’s Nautilus supercomputer, which a researcher claims applied sentiment analysis algorithms to determine the hit on Osama bin Laden and the conflict in Egypt, in arrears. Is that sort of predictive analytics happening in healthcare?
Schumacher: They’re not there yet. The underlying infrastructure is not there to do that. I think they would like to do that but the only industry where we’re hearing that is law enforcement and policing. It doesn’t seem that healthcare, from what I’ve seen, has been jumping on that bandwagon.

Fernandes: The point I’d add to that is that traditionally healthcare has underinvested in IT. Now finally we’re seeing providers, payers and the other components investing more, so there’s still a lot of putting their house in order, saying ‘Let’s make sure we have the pieces we need in order to do predictive analytics.’ There’s still a big segment of healthcare providers, the bricks-and-mortar set that are just starting to work toward attaining meaningful use, ensuring their patient and provider identities, get the clinical data out there to demonstrate they’re delivering better care for ACOs and other incentive programs. But there is a segment of the provider community that’s beyond that foundation and beginning to embrace predictive analytics and other heavy lifting.

Q: What sort of predictive analytics are they running?
Fernandes: When they decide to do that, they look at where the cost and quality issues are in healthcare. It’s the old 80/20 rule, so it’s your chronic illness patients, diabetics, congestive heart failure patients, hypertension patients. It’s where you can really do some analytics based on the data available today and impact care models, care delivery processes, drugs coming to market, look at the effectiveness of both quality and costs.

Q: That’s timely considering the study published this week estimating some 366 million diabetics globally and others saying that by 2020 more than half of Americans will be diabetic or pre-diabetic, though I have to be somewhat skeptical …
Schumacher: I do share skepticism with you on the predictive analytics stories. It’s hard to do predictive analytics right. They often report things they could have found but don't always include the number of false reports. We need to be able to know the acuracy of that prediction and the type of errors that come out of that. Still, it's hard to predict future trends but even harder without good analytic techniques.

Q: There’s a lot of buzz about Watson in healthcare but we don’t actually hear all that much in the way of detail, so what are some practical real-world examples?
Schumacher: Watson is sort of the showcase and it’s interesting to see how to bring this query capability to healthcare providers to decide if I have somebody in front of me with a particular set of diseases, what’s the literature on that set? And that’s where Watson works. There are a couple of plots that are going to go around Watson. The main practical use of Watson within healthcare is as a way to make that vast literature available in a usable way to the physician. That’s one of the key things. Healthcare providers don’t have time to keep up on all of the literature out there and having a way, not just Google where you type something in and what you want is on page 10, but here you could put in a complex query and get targeted information back. So the key to Watson in healthcare is the ability to ingest a large corpus of medical information, then it does the parsing, the natural language processing, and then respond at the point of care.

Q: Now, do you physicians at the point of care really know how to conduct complex queries?
Schumacher: That was one of the things if you watched Watson on "Jeopardy," it was being fed human questions. Who’s the president of Mexico? That’s one problem. But when a doctor has a patient with heart failure plus diabetes, plus this and plus that, what is out there about him? There’s a big difference between those two. There’s going to need to be a UI developed that will help the physician go through that. But the basic ability to do a snippet of human language is one of the key components.