InetSoft Webinar: Healthcare Machine Learning Analytics

Below is the transcript of a Webinar hosted by InetSoft on the topic of Healthcare Machine Learning Analytics. The presenter is Abhishek Gupta, Product Manager at InetSoft, and the guest is Jim Reynolds, CTO at Health Analytica.

Abhishek Gupta: We're here to learn how a leading healthcare analytics software provider and OEM partner of InetSoft's delivers actionable investigative intelligence for healthcare fraud detection using machine learning analytics.

As an analytics industry professional and a social media producer I speak with a lot of consumers of technology to uncover the business value from innovative uses of the latest IT systems and processes, and among the most exciting and interesting intersections of commerce and technology today is the way that machine learning analytics identifies and quantifies risk from massive and previously inaccessible data volumes.

These machine learning case studies have expanded far and wide to include many vertical industries. Healthcare is the focus of today's discussion, with trillions of dollars involved per year in the United States alone, it is no less than imperative to bring improved efficiency, productivity, quality and security to the vast healthcare ecosystem of payers, providers, patients and consumers.

We're going to learn today how this company uses advanced machine learning analytics platforms and methods to identify risk across complex healthcare activities.

#1 Ranking: Read how InetSoft was rated #1 for user adoption in G2's user survey-based index Read More

The payoff is delivery of faster, easier and more actionable findings to among other things advance governance and oversight to often dispersed and unwieldy and even hard to track transactions. To hear how this company addresses massive data volume challenges, to identify risk in healthcare networks and deliver answers instantly to generate more revenue, save wasted costs, and improve patient outcomes, we're pleased to be welcoming to our webcast their CTO. Welcome, Jim.

Jim: Thank you, thank you for having me.

Abhishek: Before we begin I'd like to offer a reminder to our audience to please be part of today's webcast. Add your questions to the online interface where it's indicated and we'll address those toward the end of our presentation. Jim, let's begin with some trends looking at the drivers, what is driving healthcare industry customers to you?

Jim: So the complexity in healthcare is quite large it's a massive spend area for the country, and the data that's available on healthcare is pretty varied and diverse, but harnessing that is one of the hardest that big data challenges in general that's out there. It has its own unique set of problems. Healthcare claims are very complicated, and so that becomes a big challenge for our customers to be able to make sense of that data and then get value out of it, do it cost effectively and quickly and so that's the challenge that we are here to meet.

Abhishek: And Jim, as I understand it, your firm has been around for about ten years, but you didn't begin with the healthcare industry. Tell us a little bit about how you developed your products and technologies and then extended them to the healthcare problem?

view gallery
View live interactive examples in InetSoft's dashboard and visualization gallery.

Jim: Sure, back quite some time ago, and I believe it was a small company that did specialized research for the DoD and intel community, and there was a lot of work done to really understand the nature of complicated data and apply advanced analytics to that data in lots of diverse ways, and overtime as those became more sophisticated and developed, we became more able to start moving this technology towards more commercial applications, and overtime we started applying that to cyber security.

And as we matured that, and we're able to start to deal with data at scale, we then turned our attention towards healthcare and a natural outcropping of the kinds of work that we did in cyber security is to be able to apply those technologies towards detecting events and cases and fraud or waste or abuse inside of healthcare data, and so that's how we have matured our analytics technology overtime.