 We're back live here at the Cassandra Summit. I'm John Furrier, the founder of SiliconANGLE. I'm joined by my co-host. I'm Jeff Kelly, lead big data analyst from Wikibon, and we're joined here with Terrell Depp from Healthcare Anytime, CTO there. Welcome to theCUBE. We'll be your first time on theCUBE, and we promise it'll be an enjoyable experience, right, John? Yeah, so our goal here is really going to get to the events and extract the signal from the noise. People want to know what's going on with Cassandra in the big data space. It's being debates that range from proper language to use, no sequel versus sequel, relational databases, the latest shiny new toy, whatever that is, people like to talk about it, but really what we've found is, big data is really changing business with mobile and with dashboards like the Nexus 7 and the iPad, real-time analytics data, fast data to the edge of the network is really viable, and I'm excited to talk to you about that because what better place than the medical profession if you want to have information at your fingertips because it's life and death, it's not just business, doctors are out in the field, so healthcare is a really viable space, so is big data a reality? That's my first question for you in the hospital space. Obviously tablets are relevant, but big data, so what's the state of the union for big data there? Yeah, big data unfortunately is getting some pretty slow adoption, and I attribute that primarily to the kind of inside-the-box thinking. Healthcare is not known for its rapid technology adoption. As a result, new technologies are even slower to catch on, and when we start looking at solving problems in healthcare, we have to think very much outside the box, and you cannot take an individual patient and put them in a situation where the information you collect on them is the same as the patient that you saw previously. Every patient, every episode of care is going to be absolutely different. Even for the same problem, you know, your heart attack and my heart attack may be completely different. Your follow-up visit is going to be different from my follow-up to visit. How do you take that data and put it into something that can be mined successfully so that you get decent analytics out of it? You simply can't put it into a traditional two-dimensional data model. And also, not all the records are digitized too, right? I mean, that's another problem, right? That's a completely separate problem. That's a problem, that's more of a cultural problem. Again, it goes back to the adoption of technology being very slow. So one of the debates, we won't talk about the whole digitization. I know Obama's got this big money pot out there trying to get people to, you know, two billion dollars to digitized content. Yeah, it costs millions to get it going so you don't get any money to invest on it. So that's a whole nother disaster. We'll talk about that later. But really the issue at stake here is this debate about schema and schema lists, right? So in the hospital business, there's a lot of database work and with HIPAA regulations around data, it's a factor. Is that a factor or is it not a factor? Or is my, I don't know much about it, but that seems like it could be a factor. Well, anytime you're talking about regulations and compliance, it's going to be a factor. And for a company like healthcare anytime, it's a big factor. And it goes beyond just the clinical aspect. It goes into the financial aspect because we have to not only be HIPAA compliant, we also have to be PCI compliant. And, you know, getting that data, keeping that data secure, accessing that data in a timely manner, that's what big data is all about. So, you know, for our viewers out there who might not be that familiar with healthcare or anything, why don't you tell us about kind of what you guys do, your core product. I'm doing this patient portal and kind of bringing in data from multiple sources. So, tell our audience a little bit about what you guys do. And then maybe we can talk a little bit about kind of how you do it. Well, the core purpose for healthcare anytime's existence is for the purpose of satisfying the patient need. It's patient facing healthcare. That's a market that has been grossly underutilized, underleveraged, mostly because there's no money in it. And part of the emphasis that the Obama administration has put on healthcare is to get that into the hands of the patient and make them more accountable. And that's where we come into play. Now, we're not newcomers to this environment by any means. We actually have 30 years of experience in the healthcare space as one of the top healthcare information service providers, systems vendors in the small and medium community hospital market. So, this is something that is kind of a natural progression to us as we started looking at where is the gap? Where is the opportunity? Where is the outlier potential here? And so we look at it from a standpoint of patient engagement, which obviously starts with collecting information about the patient, getting them to pay their bill, and even engaging the patient's extended family and friends to get them more involved in accountability efforts and things like that. So, the whole thing gets wrapped up into what's commonly called a patient portal. So, definitely you're going to encounter a big data problem there. You've got multiple data sources. We were talking earlier a little bit about some of the, a lot of the software that's developed around the medical community is proprietary, different formats, and there's some issues around interoperability there. Talk a little bit about how kind of the proliferation of data sources is impacting your business. That is significant. Healthcare has standards of sorts. There's an organization called HL7 that kind of standardizes what the data can be and how it can be formatted. But because we're all so different, there is no rubber stamp kind of data format each episode of care is different, like I said. So as a result, everything is optional. All of the data that can go into a CCD is essentially optional because, like I said, you may not get your cholesterol checked on every visit, but take that a step further. Not only is that dynamic, but you also have some limited standards and limited, especially limited adoption of standards among different vendors. So whether it's a larger GE system or a McKesson or a Medatech or whatever, each of these systems has some limitations of their own. And let's face it, there's a little bit of territory there and there's some turf warfare. Nobody really wants to have to play with their competitor. Most recent legislation is kind of forcing the issue. Right, that's interesting, because we were talking earlier, John, about kind of the need and big data environments for the vendors need to balance, of course they want to build their business, but there has to be interoperability and you have to be able to play nice together in a sense because that's really what makes big data big data, the ability to bring data together for multiple sources, for multiple systems that might not really have anything to do with each other, but when you combine data sources, you get some new insights that you might not have thought of before. So let's talk about some of the real use cases. I mean, both your platform and just kind of healthcare, big data analytics and applications in general. I mean, what are we talking about? We hear from the Obama administration, we see it from in the press about how big data can really help lower costs in the healthcare world and improve outcomes, which obviously is what everybody wants. So what are some of the real use cases that you think are a real practical amount of practical reality based on what you guys are doing and what you're seeing in the industry? I think you have to take a look at what are the different stakeholders in healthcare? Obviously we have the patient and that's probably the most atomic level of care, a single episode of care being, or visit to the doctor's office being the smallest most atomic component. The patient is generally more interested in knowing, okay, what were the results of my most recent lab test? They're not as interested in the long-term perspective or the long-term view of their care like a doctor would be. The doctor's going to be looking more at what is your cholesterol doing over time? What is your fluid intake after having congested heart failure? What's happening with your blood pressure monitor, blood glucose? It just goes on and on and on. But the doctor's looking at more of a continuum of care. Then you take another step out and you have a completely different perspective on the data in that you have a hospital or even a region that is interested in things that are peculiar to that region. Some areas have a higher rate of diabetes than other areas. Obesity is more common in certain areas than in other areas. Those regions want to look at the data in a very different way. Step it out a little bit further and you start looking at a national and international level and we have public health and immunization concerns and we want to know what's this next flu outbreak going to do and how do we project where the flu is headed next? How do we stop these things before they become financial catastrophes? Right, so very much depends on your perspective. Exactly. And where you're coming from, interesting. So we're here at Cassandra Summit. So talk a little bit about what you guys are doing with Cassandra under the covers and how did you come to use Cassandra? We've been talking a lot today about the horse race between the different NoSQL databases and just kind of walk us through your experience with Cassandra. Well, when we started approaching the issue of looking at patient data and gathering a tremendous amount of patient data into a single source, there was no question from the very beginning that a typical relational database was not going to solve the problem. You simply can't take something as fluid as an individual's health and put it into a two-dimensional structure. So the next step was, well, how do we get this information? We looked at it and said, well, you know what? We get this in a document, an XML document, and we could, you know, let's treat this like document management. We'll just take all these documents, we'll stick them in and we'll, you know, we'll run through these things and index them and we'll just do, you know, free text search and pretend we're Google. After a couple of failed attempts at that, we realized that wasn't going to work but we came pretty close. The biggest problem we found in our implementation was that we had a threshold of about 20,000 new patients per hour and that's where it kind of peaked out and in order to go any bigger, we were going to have to upscale tremendously. We were going to have to go, you know, bigger instead of broader and that's when we kind of hit the brakes and said, okay, what are our options here? We looked very quickly at Cassandra and several of the other technologies out, big data technologies out there and kind of narrowed in on Cassandra as being a good, really the ideal choice for us. We liked the Hadoop integration. All of this looked really good. The one missing piece that we had trouble finding was the kind of free text search capabilities that we needed. The kind of thing that a document management system we thought might help us out with. That wasn't available and we need that because doctors' notes are freehand. They're not codified. So how do you get into the doctor's notes and try and do some intelligent analytics and try and find every reference to congestive heart failure because it could be CHF, it could be misspelled. There could be any number of symptoms in the doctor's notes that we need to get to as well. Getting that out of big data was a concern of ours and we looked very hard at solar and what was going to integrate solar when data stacks came out with the solar integration. That's when we said, okay, we think we might have something here and so far it's been very successful for us. So talk about using, it is an open source technology. There's a kind of community associated with it. Tell us a little bit about your kind of, well your thoughts on this show and kind of the attendees here and what more generally the Cassandra community and in terms of your decision to go with Cassandra, how much did the community play into that? In other words, obviously you want to drive in community, it's building new and innovative ways to use the platform and how much did that kind of come into your thinking and we're looking at different technologies. The very first thing we look at is how active is the community when it comes to open source because the last thing you want to do is hedge your bet on something that is floundering or headed in the wrong direction. The Cassandra community has been very much alive and vibrant for a considerable period of time. So that immediately took care of our concerns. The next concern was particularly with regards to solar integration, we didn't want to have to roll our own. We're solving healthcare problems. We don't want to have to solve big data problems. We don't want to have to write the integration of solar into a technology stack that includes Cassandra. So finding a company like DataStacks to kind of provide that for us really pushed us over the edge and the support has just been phenomenal from the community and from the company. So here's a hard question. Let's solve the problem. What is it going to take to increase adoption of patient portals and other kind of big data sponsored technologies to really kind of create the kind of environment where we really can take advantage of data and analytics to improve healthcare in terms of both the price, lowering prices and improving outcomes at the same time because obviously it's a huge issue on the national level. I'll talk in the election about it. What is it going to take to really get to that next step and actually see some significant adoption? It starts with education, obviously. People have to learn to take a look at the problem a little differently and they have to look at it from a, a more, a thinner, more purpose built application, not a more general purpose like a relational database. The second thing it's going to take is some success stories and we're not the only ones doing big data in healthcare. There are a few others, but there's not a lot. When you consider the size of the industry and the number of players that are taking a big data approach, it's surprisingly light, but, you know, big data is still, still ramping up. It's still in its early stages of adoption, I think, in more than just our industry. And that's really the reason why we're here. You know, we brought our power hitters out here to do a little cross training. You know, we want them to be able to spend some time rubbing elbows with people in other industries solving similar problems and see if we can't find some creative implementations and best practices of what those people are doing and make it apply to us as well. Yeah, I mean, well, that's one of the real benefits of kind of the open source community and it's really a collaborative, that you don't necessarily get in another type of technology approach. It's a more proprietary approach. That's right. Carl and Cheryl, thanks for coming on the queue. We really appreciate it. You guys are in a growing market. Obviously it needs to be some transformation in healthcare and you guys are doing some good work there. Appreciate the time. The goal is to make the transformation as painless as possible. You know, a lot of people are attempting to shoehorn the industry into a new way of doing business, a new business model and change workflows. And I think that's part of the reason why there's been so much of a resistance to the adoption of new technologies and new ideas. Well, we've seen the iPad certainly great, cold executives at levels and other organizations in business, like SAP we've heard, you know, the CEO, I wanted on this, you know, so with the iPad, you know, you think the tablets are great tools for healthcare workers, doctors, et cetera. So maybe that'll be the catalyst to it. Bust down that rock in the river that's holding all the water back. We're seeing a tremendous amount of interest in our mobile devices. Mobile cloud, great, perfect storm for innovation. Terrell, thanks for coming to the Cube. We'll be right back with our next guest after this break here inside the Cube, live at Cassandra Summit 2012 data stacks event. This is Silicon Angle, Wikibon's theCUBE.