Dana Gardner's BriefingsDirect: Healthcare turns to big data analytics platforms to gain insight and awareness for improved patient outcomes

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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.

Muller

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.

Basic medical research

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.

There’s a huge array of data that you need to bring together, in addition to just thinking about the size of it.

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.

Capturing data

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

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.

What Avnet brings to the table is the understanding of the HAVEn technology, combined with deep expertise in the area of healthcare and analytics.

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. [Learn more about the HAVEn platform.]

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.

Significant load

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.

Better utilizing big-data technology can have a very real impact on the healthcare outcomes of your loved ones.

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 that, things about treatments and experience and even gaining perhaps a holistic view of a patient, regardless of where they are in the spectrum.

Waste in the system

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.

From a provider perspective, it's getting a handle on those unnecessary procedures.

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.

Standardized inputs

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.

About visibility

Kelly: Any first step with this is about visibility. It opens the eyes around processes in the organization that are problematic and that can be very basic around things like scheduling in the operating room and utilization of that time to length of stay of patients.

A very a quick win is to understand why your patients seem to be continually having problems and being in the bed longer then they should be. It’s being able, while they're filling those beds, to redirect care, case workers, medical care, and everything necessary to help them get out of the hospital sooner and improve their outcomes.

A lot of times, we've seen a look of surprise when we've shown, here is the patient who has been in for 10 days for a procedure that should have only been a two-day stay, and really giving visibility there. That’s the first step, though very basic.

As we start attacking some of these problems around hospital-based infection, we help the provider make sure that they are covering all their bases and doing kind of the best practices, and eliminating the variation between each physician and care provider, you start seeing some real tangible improvements and outcomes in saving peoples lives.

When you see that from any population be it stroke, re-admissions -- as we talked about earlier -- with heart failure and being able to make sure those patients are avoiding things like pneumonia, you bring visibility.

A challenge for a hospital that has acquired a number of physicians is how to get visibility into those physician practices.

Then, in predictive models and optimizing how the providers and the caregivers are working is really key. There are some quick wins, and that’s why traditionally we built these master repositories that we then built reports on top of. It’s a year and a half to delivery for any value, and we’re looking to focus on very specific use cases and trying to tackle them very quickly in a 90- to 120-day period.

Massive opportunity

Muller: The opportunity for HP and our partners is to help enable putting the right data at the finger tips of the people with the potential to generate life saving or lifestyle improving insights. That could be developing a new drug, improving the impatient experience, or helping us identify longer-term issues like genetic or other sorts of congenital diseases.

From our perspective, it’s about providing the underlying platform technology, HAVEn, as the big data platform. The great partner ecosystem that we've developed in Avnet is a wonderful example of an organization that’s taken the powerful platform and very quickly turned that into something that can help not only save money, but as we just talked about, save lives which I think is fantastic.

Gardner: We know that mobile devices are becoming more and more common, not only in patient environments, but in the hospitals and the care-provider organizations. We know the cloud and hybrid cloud services are becoming available and can distribute this data and integrate it across so many more types of processes.

It seems to me that you not only get a benefit from getting to a big-data analysis capability now, but it puts you in a position to be ready when we have more types of data -- more speed, more end points, and, therefore, more requirements for what your infrastructure, whether on premises or in a cloud, can do. Tell me a little bit about what you think the Avnet and HP Solution does for setting you up for these future trend?

Kelly: At this point, technology today is just not where it needs to be, especially in healthcare. An EKG spits out 1,000 data points per second. There is no way, at this point, without the right technology, that you can actually deal with that.

If we look to a future where providers do less monitoring, so less vital collection, fewer physicals, and all of that is coming from your mobile device, it's coming from intelligent machines. There really needs to be an infrastructure in place to deal with that.

I spent a lot of time working with Vertica even before Avnet. Vertica, Hadoop, and leveraging economy in the area of unstructured data is a technology that is going to allow the scalability and the growth that’s going to be necessary to leverage the data that we need to make it an asset and much less challenge and allow us to transform healthcare.

The key to that is unlocking this tremendous trove of data. In this industry, as you guys have said, it’s very life and death, versus it's just purely a financial incentive.

Targeting big data

Muller: This is an important point that we can’t lose sight of as well. As I said when you and I hosted the previous show, big data is also a big target.

One of the things that every healthcare professional and regulator, every member of the public needs to be mindful of is a large accumulation of sensitive personally identifiable information (PII).

It's not just a governance issue, but it's a question of morals and making sure that we are doing the right thing by the people who are trusting themselves not just with their physical care, but with how they present in society. Medical information can be sensitive when available not just to criminals but even to prospective employers, members of the family, and others.

The other thing we need to be mindful of is we've got to not just collect the big data, but we've got to secure it. We've got to be really mindful of who’s accessing what, when they are accessing, are they appropriately accessing it, and have they done something like taking a copy or moved it else where that could indicate that they have malicious intent.

It's also critical we think about big data in the context of health from a 360-degree perspective.

It's also critical we think about big data in the context of health from a 360-degree perspective.

Kelly: That’s a great point. And to step back a little bit on that, one of the things that brings me a little comfort around that is there are some very clear guidelines in the way of HIPAA around how this data is managed, and we look at it from baking the security into it, in everything from the encryption to the audit ability.

But it’s also training the staff working in these environments and making sure that all of that training is put in place to ensure the safety of that data. One of the things that always leaves me scratching my head is that I can go down the street into the grocery store and buy a bunch of stuff. By the time I get to register, they seem to know more about me than the hospital does when I go to the hospital.

That’s one of the shocking things that make you say you can’t wait until big data gets here. I have a little comfort too, because there are at least laws in place to try to corral that data and make sure everyone is using it correctly.