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Analysis

Healthcare turns to big data analytics to gain insight and awareness for improved patient care
Dana Gardner By: Dana Gardner, Principal Analyst, Interarbor Solutions
Published: 13th December 2013
Copyright Interarbor Solutions © 2013
Logo for Interarbor Solutions

Analytics platforms and new healthcare-specific solutions together are offering far greater insight and intelligence into how healthcare providers are managing patient care, cost, and outcomes.

Based on a number of offerings announced this week at the HP Discover Conference in Barcelona, an ecosystem of solutions are emerging to give hospitals and care providers new data-driven advantages as they seek to transform their organizations.

To learn how, BriefingsDirect sat down with Patrick Kelly, Senior Practice Manager at the Avnet Services Healthcare Practice, and Paul Muller, Chief Software Evangelist at HP, to examine the impact that big data technologies and solutions are having on the highly dynamic healthcare industry. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions. [Disclosure: HP is a sponsor of BriefingsDirect podcasts.]

Here are some excerpts:

Gardner: How closely are you seeing an intersection between big data and the need for analytics in healthcare?

Muller: It's undoubtedly a global trend, Dana. One statistic that sticks in my mind is that in 2012 what was estimated was approximately 500 petabytes of digital healthcare data across the globe. That’s expected to reach 25,000 petabytes by the year 2020. So, that’s a 50-times increase in the amount of digital healthcare data that we expect to be retaining.

The reasons for that is simply that having better data helps us drive better healthcare outcomes. And we can do it in a number of different ways. We move to what we call most evidence-based medicines, rather than subjecting people to a battery of tests, or following a script, if you like.

The test or the activities that are undertaken with each individual are more clearly tailored, based on the symptoms that they’re presenting with, and data helps us make some of those decisions.

The other element of it is that we’re now starting to bring in more people and engage more people in basic medical research. For example, in the US, the Veterans Administration has a voluntary program that’s using blood sample and health information from various military veterans. Over 150,000 have enrolled to help give us a better understanding of healthcare.

We’ve had similar programs in Iceland and other countries where we were using long-term healthcare and statistical data from the population to help us spot and address healthcare challenges before they become real problems.

The other, of course, is how we better manage healthcare data. A lot of our listeners, I’m sure, live in countries where electronic healthcare records (EHR) are a hot topic. Either there is a project under way or you may already have them, but that whole process of establishing them and making sure that those records are interchangeable is absolutely critical.

Then, of course, we have the opportunity of utilizing publicly available data. We’ve all heard of Google being utilized to identify the outbreaks of flu in various countries based on the frequency of which people search for flu symptoms.

So, there’s definitely a huge number of opportunities coming from data. The challenge that we’ll find so frequently is that when we talk about big data, it's critical not just to talk about the size of the data we collect, but the variety of data. You’ve got things like structured EHR. You have unstructured clinical notes. If you’ve ever seen a doctor’s scribble, you know what I’m talking about.

You have medical imaging data, genetic data, and epidemiological data. There’s a huge array of data that you need to bring together, in addition to just thinking what is the size of it. Of course, overarching all of these are the regulatory and privacy issues that we have to deal with. It's a rich and fascinating topic.

Gardner: Patrick Kelly, tell us a little bit about what you see as the driving need technically to get a handle on this vast ocean of healthcare data and the huge potential for making good use of it.

Kelly: It really is a problem of how to deal with a deluge of data. Also, there’s a great change that’s being undertaken because of the Affordable Care Act (ACA) legislation and that’s impacting not only the business model, but also the need to switch to an electronic medical record.

From an EHR perspective to date, IT is focused on capturing that data. They take and then transpose what’s on a medical record into an electronic format. Unfortunately, where we’ve fallen short in helping the business is taking that data that’s captured and making it useful and meaningful in analytics and helping the business to gain visibility and be able to pivot and change as the need to change the business model is being brought to bear on the industry.

Gardner: For those of our audience who are not familiar with Avnet, please describe your organization. You’ve been involved with a number of different activities, but healthcare seems to be pretty prominent in the group now. [Learn more about Avnet's Healthcare Analytics Practice.]

Kelly: Avnet has made a pretty significant investment over the last 24 months to bolster the services side of the world. We’ve brought numbers up to around 2,000 new personnel on board to focus on everything in the ecosystem, from—as we’re talking about today—healthcare all the way up to hardware, educational services, and supporting partners like HP. We happen to be HP’s largest enterprise distributor. We also have a number of critical channel partners.

In the last eight months, we came together and brought on board a number of individuals who have deep expertise in healthcare and security. They work to focus on building out healthcare practice that not only provides services, but is also developing kind of a healthcare analytics platform.

Gardner: Paul Muller, you can’t buy healthcare analytics in a box. This is really a team sport; an ecosystem approach. Tell me a little bit about what Avnet is, how important they are in HP’s role, and, of course, there are going to be more players as well.

Muller: The listeners would have heard from the HP Discover announcements over the last couple of days that Avnet and HP have come together around what we call the HAVEn platform. HAVEn, as we might have talked about previously, on the show stands for Hadoop, Autonomy, Vertica, Enterprise Security, with the “n” being any number of apps.

The "n" or any numbers of apps is really where we work together with our partners to utilize the platform, to build better big data enabled applications. That’s really the critical capability our partners have.

What Avnet brings to the table is the understanding of the HAVEn technology, combined with deep expertise in the area of healthcare and analytics. Combining that, we've created this fantastic new capability that we’re here to talk about now.

Gardner: What 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?

Kelly: If we pull back the covers and look at some of the problems or challenges around advancing analytics and modernization into healthcare, it’s really in a couple of areas. 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 to also deal with a lot of different compliance things around ICD-10 and still meet meaningful use. So, 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 right now, there's not a good understanding, from an enterprise-wide view, of how to use analytics in healthcare very well.

So, part of the challenge is governance and strategy and looking at an enterprise-wide road map to how you get there. From a technology perspective, there’s a whole problem around industry readiness. There are a lot of legacy systems floating around that can range from 30-year-old mainframes up to more modern systems. So there’s a great deal of work that has to go around modernizing the systems and then tying them together. That all leads to problems with data logistics and fragmentation and really just equals cost and complexity.

One of the traditional approaches that other industries have followed with enterprise data warehouses and traditional extract, transform, load (ETL) approaches are just too costly, too slow, and too difficult for healthcare system to leverage. Finally, there are a lot of challenges in the process of the workflow.

Muller: 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 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 utilizing big data technology can have a very real impact, for example, on the healthcare outcomes of your loved ones. Any other thoughts around that, Patrick?

Kelly: Paul, you hit a really critical note around re-admissions, something that, as you mentioned, has a real impact on the outcomes of patients. It's also a cost driver. Reimbursement rates are being reduced because of failure. Hospitals would be able to address the shortfalls either in education or follow-up care that end up landing patients back in the ER.

You’re dead on with re-admissions, and from a big data perspective, there are two stages to look at. There’s a retrospective look that is a challenge even though it's not a traditional big data challenge. There’s still lot of data and a lot of elements to look into just to identify patients that have been readmitted and track those.

But the more exciting and interesting part to this is the predictive, looking forward and seeing the patient’s conditions, their co-morbidity, how sick they are, what kind of treatment they receive, what kind of education they received and the 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 this person is at risk to readmit. If so, how do we target care to them to help reduce that risk.

Gardner: 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? I’m thinking of such things as cutting cost, but more than that, things about treatments and experience and even gaining perhaps a holistic view of a patient, regardless of where they are in the spectrum.

Muller: You kind of hit it there, Dana, with the cutting of cost. 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 US, $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.

From a provider perspective, it's getting a handle on those unnecessary procedures. I’ll give you an example. There’s been an increase in the last decade of elective deliveries, where someone comes in and says that they want to have an early delivery for whatever reason. The impact, unfortunately, is an additional time in the neo-natal intensive care unit (NICU) for the baby.

It drives up a lot of cost and is dangerous for both the mother and child. So, getting a handle on where the waste is within their four walls, whether it’s operationally, unnecessary procedures, or tests and being able to apply Lean Six Sigma, and some of these process is necessary to help reduce that.

Then, you mentioned treatments and how to improve outcomes. Another shocking statistic is that medical errors are the third leading cause of death in the US. In addition to that, employers end up paying almost $40,000 every time someone receives a surgical site infection.

Those medical errors can be everything from a sponge left in a patient, to a mis-dose of a medication, to an infection. Those all lead to a lot of unnecessary death as well as driving up cost not only for the hospital but for the payers of the insurance. These are areas that they will get visibility into 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 better term, my experience as the hospital or with my provider, and how engaged I had become in my own care. Those are critical measures that analytics are going to help provide.

Gardner: Now that we have a sense of this massive challenge, what are organizations like Avnet and providers like HP with HAVEn doing that will help us start to get a handle on this?

Kelly: As difficult as it is to reduce complexity in any of these analytic engagements, it's very costly and time consuming to integrate any new system into a hospital. One of the key things is to be able to reduce that time to value from a system that you introduce into the hospital and use to target very specific analytical challenges.

From Avnet’s perspective, we’re bringing a healthcare platform that we’re developing around the HAVEn stack, leveraging some of those great powerful technologies like Vertica and Hadoop, and using those to try to simplify the integration task at the hospitals.

We’re building inputs from HL7, which is just a common data format within the hospital, trying to build some standardized inputs from other clinical systems, in order to reduce the heavy lift of integrating a new analytics package in the environment.

In addition, we’re looking to build a unified view of the patient’s data. We want to extend that beyond the walls of the hospital and build a unified platform. The idea is to put a number of different tools and modular analytics on top of that to have some very quick wins, targeted things like we've already talked about, from readmission all the way into some blocking and tackling operational work. It will be everything from patient flow to understanding capacity management.

It will bring a platform that accelerates the integration and analytics delivery in the organization. In addition, we’re going to wrap that into a number of services that range from early assessment to road map and strategy to help with business integration all the way around continuing to build and support the product with the help system.

The goal is to accelerate delivery around the analytics, get the tools that they need to get visibility into the business, and empower the providers and give them a complete view of the patient.

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy.

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