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.

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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?

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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.

Cybersecurity analytics plays a vital role in safeguarding sensitive healthcare data and protecting against the growing threat of cyberattacks in the healthcare industry. With the increasing digitization of patient records, the adoption of interconnected medical devices, and the rise of telemedicine, healthcare organizations are facing unprecedented cybersecurity challenges. Cybersecurity analytics involves the continuous monitoring, detection, and response to cyber threats by analyzing vast amounts of data generated from network traffic, system logs, and user behavior. By leveraging advanced analytics techniques such as machine learning, anomaly detection, and threat intelligence, healthcare organizations can proactively identify and mitigate security vulnerabilities before they can be exploited by malicious actors.

One of the key benefits of cybersecurity analytics in healthcare is its ability to provide real-time threat detection and response, enabling organizations to swiftly mitigate security incidents and minimize the impact on patient care. Through the analysis of network traffic patterns, system logs, and user behavior, cybersecurity analytics can identify suspicious activities indicative of unauthorized access, malware infections, or other security breaches. By correlating and contextualizing these disparate data sources, cybersecurity analytics platforms can prioritize security alerts based on their severity and relevance, allowing security teams to focus their efforts on the most critical threats. Moreover, by automating routine tasks such as incident triage, investigation, and response, cybersecurity analytics solutions can help healthcare organizations improve operational efficiency and reduce incident response times, thereby enhancing their overall security posture.

Furthermore, cybersecurity analytics enables healthcare organizations to achieve greater visibility and control over their digital infrastructure, empowering them to proactively identify and address security weaknesses across their networks, applications, and endpoints. By conducting comprehensive risk assessments and vulnerability scans, healthcare organizations can identify potential security gaps and prioritize remediation efforts based on their potential impact on patient safety and regulatory compliance. Additionally, by monitoring and analyzing user access patterns and authentication logs, cybersecurity analytics can help organizations detect and prevent insider threats, unauthorized access attempts, and credential misuse. Ultimately, by investing in cybersecurity analytics, healthcare organizations can enhance their cybersecurity posture, protect patient data, and safeguard the integrity and availability of critical healthcare services.