Discover Performance

HP Software's community for IT leaders // March 2014

From Big Data to better health

When it comes to healthcare, Big Data insights promise a lot, but the challenges are many. Find out what it will take to turn disconnected data into longer, happier human lives.

Wherever it’s used, Big Data analytics changes lives for the better, but nowhere is this more literally true than in healthcare. When applied to healthcare, advanced analytics tools provide the insight and awareness to help give patients the right treatment at the right time, dramatically improving the quality of care and patient outcomes. But what will it take for provider organizations to get there?

That question was the subject of a recent HP Discover podcast, in which Patrick Kelly, senior practice manager at the Avnet Services Healthcare Practice, and Paul Muller, HP Software’s VP of strategic marketing, discussed the potential of Big Data to improve healthcare delivery.

In this excerpt, host Dana Gardner, principal analyst at Interarbor Solutions, attempts to get to the bottom of industry-specific challenges in information management, as well as an understanding of how greater cost efficiency and data visualization ultimately saves lives.

Dana Gardner: Tell me a bit about what you think are the top problems that need to be solved in order to get healthcare information and analytics to the right people in a speedy fashion. What are our hurdles to overcome here?

Patrick Kelly: One of them is that it’s a pretty big cultural change. Right now, we have an overtaxed IT department that’s struggling to bring electronic medical records online, and also deal with a lot of different compliance issues. That’s a pretty significant load on those guys.

Now they’re being asked to look at delivering information to the business side of the world. And there’s not a good understanding, from an enterprise-wide view, of how to use analytics in healthcare. So part of the challenge is governance and strategy, and looking at an enterprise-wide roadmap for how you get there.

From a technology perspective, there’s a problem with industry readiness. There are a lot of legacy systems floating around that can range from 30-year-old mainframes to more modern systems. There’s a great deal of work that has to [be done] to modernize systems and tie them together. That all leads to problems with data logistics and fragmentation, which equals cost and complexity.

Paul Muller: These sound conceptual at a high level, but the impact on patient outcomes is pretty dramatic. One statistic that sticks in my head is that hospitalizations in the U.S. are estimated to account for about 30 percent of the trillions of dollars in the annual cost of healthcare, with around 20 percent of all hospital admissions occurring within 30 days of a previous discharge.

In other words, we’re potentially letting people go without having completely resolved their issues. Better utilization of Big Data technology can have a very real impact, for example, on the healthcare outcomes of your loved ones.

PK: There’s a lot of data and a lot of elements to look into just to identify patients who have been readmitted and to track those. But the more exciting and interesting part is the predictive: looking forward and seeing patients’ conditions, comorbidity, how sick they are, what kind of treatment they receive, what kind of education they receive, what follow-up care, as well as how they behave in the outside world.

Then, it’s bringing all that together and building a model to be able to determine whether a person is at risk to readmit. And if so, how do we target care to help reduce that risk?

DG: We certainly have some technology issues to resolve and some cultural shifts to make, but what are the goals in the medical field—in the provider organizations themselves?

PM: I was reading a report today, and it was kind of shocking. There is a tremendous amount of waste in the system, as we know. It said that in the U.S., $600 billion—17.6 percent of the nation’s GDP—that is focused on healthcare is potentially being misspent. A lot of that is due to unnecessary procedures and tests, as well as operational inefficiency.

Those lead to a lot of unnecessary deaths, in addition to driving up cost not only for the hospital but for the payers of the insurance. These are areas in which they will get visibility to understand where variation is happening and eliminate that.

Finally, a new aspect is customer experience. Somehow, reimbursements are going to be tied to—and this is new for the medical field—how I, as a patient, enjoy, for lack of a better term, my experience at the hospital or with my provider, and how engaged I have become in my own care. Those are critical measures that analytics will help provide.

DG: When you create a different culture to make the data analysis available fast, you start to move toward that predictive—rather than reactive—approach. Do you have some sense, or even examples, of what good can come of this? Are there some tangible benefits, some soft benefits, to get as a payback?

PK: Any first step with this is about visibility. It opens eyes around processes in the organization that are problematic. A very quick win is to understand why your patients seem to be continually having problems and staying in the bed longer than they should be.

As we start attacking some of these problems with hospital-based infections—help the provider make sure they are covering all their bases, following best practices, and eliminating the variations between each physician and care provider—we start seeing some real, tangible improvements and outcomes in saving people’s lives.

DG: Do you have any early-adopter examples you can provide for us, so that we have a sense of what types of organizations are putting this into place, what they’ve done first, and what the outcomes have been?

PK: We’re partnering with a 12-hospital healthcare system, dealing with some blocking and tackling around understanding better how to utilize their physician network. A challenge for a hospital that has acquired a number of physicians is how to get visibility into those physicians’ practices. How do you understand the kinds of things we’ve talked about—cost, patient experience, outcomes—out in the wild, in the primary care offices, and in the specialty offices? That data has traditionally just been completely segmented from the hospital systems.

The challenge is to build tools that are going to be leveraged by the physicians themselves—as well as the hospitals and the executive level—and to use that information to help optimize how those practices are run. It’s kind of a basic problem for most businesses, but it’s something very real for hospitals to deal with.

DG: What sort of opportunity is this, and how is HP going at it?

PM: … From our perspective, it’s about providing the underlying Big Data platform technology, HAVEn. The great partner ecosystem that we’ve developed in Avnet is a wonderful example of an organization that’s taken this powerful platform and very quickly turned it into something that can help not only save money but, as we just talked about, save lives.

Get the full audio podcast or transcript from BriefingsDirect, or find the audio via iTunes. For more on Big Data strategy, visit


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