In partnership with NHCAA, ClarisHealth is hosting a live, educational webinar on Tuesday, August 11 to talk with health plan fraud teams about how advanced technology – specifically solutions that employ artificial intelligence – can offer value to the SIU. 

We sat down with the experts who will be presenting the webinar – Mark Isbitts, vice president of program integrity for ClarisHealth, and Kyle Cheek, the director at the Center for Applied Analytics and Clinical Associate Professor of Information and Decision Sciences at the College of Business Administration at The University of Illinois-Chicago – to get a quick preview of the session. 

LIVE Webinar Tuesday, August 11 – 1 pm CDT | 2 pm EDT 

Read the Transcript

AB: In partnership with NHCAA, ClarisHealth is hosting a live, educational webinar on Tuesday, August 11 to talk with health plan fraud teams about how advanced technology – specifically solutions that employ artificial intelligence – can offer value to the SIU. 

We sat down with the experts who will be presenting the webinar – Mark Isbitts, vice president of program integrity for ClarisHealth, and Kyle Cheek, the director at the Center for Applied Analytics and Clinical Associate Professor of Information and Decision Sciences at the College of Business Administration at The University of Illinois-Chicago – to get a quick preview of the session. 

Both Mark and Kyle have worked with and in health plan fraud organizations for years. Mark provided insights into three major challenges facing SIU teams: 

04:17-04:29 Mark “The bigger issue is they need better leads. They need to reduce the false positives and get more accuracy around what they’re facing.” 

05:13-05:30 Mark “The whole process of … or a true trend.” 

08:33-08:54 Mark “Classic example is … not getting any more resources.” 

10:54-11:05 Mark “Only addressing the big dollar cases. And that’s really at the heart of it. Spending a lot of time without getting much in return.” -11:10 “That’s why getting the false positives down is so critical.” 

AB: With these seemingly insurmountable challenges of too many false positives, not enough resources to dedicate to hitting escalating targets, and a lot of manual processes to connect the dots on leads and weave together a workflow, investigators have an idea of how their day-to-day could improve. According to Mark, it’s about prioritizing the data and presenting it in context: 

12:00-12:28 Mark “Ideally, they want to be told what to look for: here’s a provider, here’s why they’re abnormal, here’s how big a deal it is, here’s how sure it is a real case.”  

AB: SIU teams are concerned about providing value and proving that value, but with no way to trend the data, they might be overlooking potentially big cases: 

15:05-15:35 Mark “If someone’s really good at gaming the system, they’ll just be upcoding from a level 3 to a level 4 office visit every other patient. What’s that, $50? Not enough to notice over a short period of time, but it really adds up over a year.”         

AB: And, as Kyle reminds us, it’s not just ROI that’s at risk when investigators are making value decisions on whether or not to pursue a case. 

22:20-22:59 Kyle “Health insurance company has a counter-balancing interest in maintaining goodwill in its provider network. Fraud mostly driven from the provider side. SIU challenge is not only do they have to build a case strong enough for law enforcement but strong enough to sell internally and consider how it affects other business units.” 

AB: Healthcare data – both in terms of quality and quantity – has traditionally been a huge hurdle in fraud teams’ abilities to take advantage of advanced technology, which sets it apart from other industries’ advances in nearly hands-off fraud detection. 

23:48-24:05 Mark “Healthcare fraud is so grey where something like banking fraud is black and white. Those grey areas are why you need different detection systems than what they have.” 

26:00-26:22 Kyle “It’s just a function of the way the business works that … If CMS would just put a checkbox on the 1500 form that says is this claim fraud, yes or no, that would make things a lot easier, but it doesn’t exist.” 

26:50-27:23 Kyle “It’s the difference between being able to measure the accuracy of your models and not being able to directly measure … Healthcare falls as far on that spectrum as they can be towards not having labels on the data and having to do unsupervised analysis and really being an exercise of feeling around in the dark as best as possible without having traditional tools to measure analytic performance by.” 

AB: Because healthcare data presents so many challenges with its complexity and lack of consistencyadvanced technology offers distinct advantages, but A.I. is easily misunderstoodWith his expertise in data science, Kyle has some theories as to why. 

32:05-32:33 Kyle “Everyone focuses on the “I” and I think the emphasis should be on the “A.” The key qualifier is artificial. We’re trying to build processes that emulate something, and the thing we’re trying to emulate is intelligence. And most of us don’t have a good definition of intelligence – cognitive scientists don’t have a good definition of intelligence.” 

AB: And it’s not just common definitions that are lacking. The way applications of A.I. work, they’re really good at producing superior results, but they can resist simplistic explanations.  

36:25- Kyle “Where we think of traditional models, we tend to think of those in a linear context. It’s easy to say, ‘If a provider’s use of a certain procedure goes up, the probability they’re committing fraud goes up. In the case of neural nets – of multi-layered, sequential, recursive, iterative processes – we can’t simplify the causal links that way.” 

41:21-41:35 Kyle “It’s pretty easy to make improvements in the output of the analytic engine, but it’s hard to do so in a way that maintains the transparency in why the result was produced.” 

AB: How can models for healthcare fraud detection that take advantage of A.I. overcome the challenges with healthcare data, avoid provider abrasion and offer explainability for investigators so they can focus on their best work, trust the results and prove value for the SIU? Mark and Kyle will be discussing these hot topics and showing some real-world examples in this interactive session. 

Please sign up today to attend the live, educational webinar Demystifying A.I. for Healthcare Fraud Detection: Finding Value for the SIU on Tuesday, August 11 at 1 pm CDT. Find the registration link in the description below.