Electronic Health Records

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For healthcare analytics, you need to have goals in sight

By Jennifer Bresnick

- Just a few years ago, the Wild West of healthcare was the design, implementation, and use of electronic health records.  But as providers acclimate to the idea of integrating health IT systems into the business and practice of medicine, a new digital frontier is looming, filled with possibilities and pitfalls.  Analytics may be a wide-open market with plenty of vendors offering solutions to problems we haven’t fully defined yet, but some companies have been in the business for long enough to have a good understanding of where the opportunities may lie.

Oracle is one of those companies, and the organization has already laid some strong foundations in the healthcare data space.  Marc Perlman, Global Vice President of Healthcare and Life Sciences, spoke to EHRintelligence during HIMSS14 to explain how data analytics can focus providers on finding solutions to difficult problems – as long as they understand what they’re looking for.

How do you see the analytics landscape at the moment?

If we step back and evaluate where the industry has come from, years ago, we were dealing with departmental systems just trying to automate simple processes.  But when HITECH started, and the American Recovery and Reinvestment Act (ARRA) started funding the digitization of information, I think we changed the tenor and the trajectory.  I was laughing that people used to say, “Well, we are meaningful users” like two weeks after ONC announced they were going to do meaningful use.  Nobody knew what meaningful use really was, but everyone said they were doing it.

But we have almost 80% of the hospitals using EHRs now, and we’re making progress in that area.  I think we’re now really at the starting line where we can really transforming healthcare.  We have a lot of the enabling technology and infrastructure.  And so at Oracle, we’ve divided our solutions into several major areas.  One is connected health, which is the ability to do health information exchange.  And we’re also starting to bring in connected medical devices.  We have to start taking that information and making some sense out of it.

What are some of the biggest challenges the industry needs to address?

How can we bring in medical device information, stream it, correlate it, work on it from another place, and bring together structured and unstructured data? Because that then leads into population health and interventions.  When we have this wealth of data, we have a better opportunity to really start transforming things.  So after we connect, we need to analyze.

But here’s the question.  Do you look at analytics from a big bang point of view, or do you look at just solving specific use cases?  The hard problem with analytics is the data normalization.  When you start moving to the next level of care, you have to take all those indicators, understand what they are, what they mean, and then start figuring out how you really apply it to personalized medicine.

Another challenge that analytics can address is filling clinical trials.  The way it used to be, some researchers would go out there and say, “I want to start recruiting someone with the following 17 characteristics.”  It would be just like firing into a lake and hoping you hit a fish. But when you use a data analytics model, you could probably identify where your patients are in 15 minutes, and start making phone calls.

How will analytics impact the concept of personalized medicine?

You don’t necessarily want real-time healthcare.  You want just-in-time healthcare.  If you have somebody who presents to you as a physician, you want to be able to look for how many other patients had this particular characteristic. How many have had a very similar genetic profile?  What were the things that we were able to use?  What were the results?  And how do you track the outcomes?  We actually can start answering those really hard questions.

Personalized medicine is the ability to bring in large volumes of data and start to look at it from an analytical point of view.  But I’d also argue that it’s not about big data, which I think is an overused buzz word.  It’s about small data, because you need to be able to have that data specific to you.  And that’s going to take bringing together a lot more information over a longer period of time.

The strain on the healthcare delivery system is such that the economic benefits are going to be justified over time.  But if it’s you or your relative, you want everything you can get, everything.  It doesn’t matter how much it costs.  It really doesn’t.  Providers need to remember that, too.

What are your suggestions for providers who may be unsure where to start with analytics?

I would highly encourage people to start around specific high-value use cases.  I think the average hospital or health system has over 400 different interfaces and integration points, but it may take seven of them working together to give you some value out of your data.  Hospitals and health systems need to think about what they’re trying to fix, and then based upon that, what data sources they need.

We had a focus group recently, and the responses were all over the map.  Some said they want to try to do one course of treatment or create one clinical guideline or protocol across their enterprise.  Or they can’t even figure out what the end users want in terms of reporting.  So, there’s a definitely disconnect right now – and I’m just going to give a gross generalization – between expectations of end users and what information technology thinks the needs are.

We think it makes a lot of sense to think about what they are going to try to accomplish, what the methods are that they’re trying to drive, and how they are going to focus on sustainability.  I would say the most important thing is have a vision of your solution, what you’re trying to fix, and know where you’re going.  And then the technology will follow.




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