If I had a nickel for every guest on this show who went on to achieve wild success … TJ Parker from PillPack three years before they were bought by Amazon. Anyway, let me introduce this show with a clip from the recent podcast (EP325) with Dr. Mai Pham. We were talking about the rampant and very open secret of excessive upcoding in Medicare Advantage (MA) that is costing American taxpayers a fortune and is very not correlated with actual spend. Here we go with Dr. Mai Pham:
If I had a nickel for every guest on this show who went on to achieve wild success … TJ Parker from PillPack three years before they were bought by Amazon. Anyway, let me introduce this show with a clip from the recent podcast (EP325) with Dr. Mai Pham. We were talking about the rampant and very open secret of excessive upcoding in Medicare Advantage (MA) that is costing American taxpayers a fortune and is very not correlated with actual spend. Here we go with Dr. Mai Pham:
Stacey: Do you have any thoughts relative to how you ensure that these MA plans that are becoming vast are still accountable to not game the system? How do you plug loopholes in a way that doesn’t invite additional and more nefarious gaming?
Dr. Pham: My fantasy has always been that CMS can develop, or somebody can develop, a black box machine learning–driven, risk-adjustment algorithm that no one can see into—not even the payer. It would very much level the playing field, assuming that it was developed correctly, appropriately, and you used unbiased data; but that’s the kind of system and extreme solution that I think starts to sound almost necessary given the state of things and the rate of acceleration in upcoding.
So, people may not have noticed that CMS had put out a request for — I think it was a challenge grant, maybe? And they recently announced a couple of winners. They were asking for artificial intelligence–driven approaches to predicting health outcomes, which I believe is just the first shadow approach, the first step that you take in thinking about artificial intelligence–driven risk adjustment.
I also want the audience to understand, it’s not like we’re talking about replacing a really superlative gold standard, right? The majority of the most commonly used risk-adjustment approaches today produce a correlation with actual spend of only like 0.2. This is the best we can do? This is how we’re deciding how we’re going to spend a trillion dollars each year? Surely, we can do better.
And, by the way, the winner of that CMS AI contest was ClosedLoop.ai; and Andrew Eye from ClosedLoop.ai was on the show. Cue Encore Episode here!
In the original version of this show, there was a whole prelude about whether AI is or is not anything beyond an overused marketing pitch; but I think, in the time-space continuum, we’re beyond that conversation now. Don’t get me wrong, everybody still has AI in their cloud analytics platforms. And some of them are still, as they say, programmed in PowerPoint (that was a joke); but real deals are emerging from the fray.
As mentioned, in this health care podcast I talk with Andrew Eye about AI. (He was born for this job.) Andrew is CEO over at ClosedLoop.ai. ClosedLoop.ai beat out over 300 rivals with their system that forecasts adverse health events and then plops warnings even in the EHR with action steps for clinicians to avoid the calamity in the making.
You can imagine many things that CMS might be contemplating using this tool for, including as a control for false upcoding and all of the financial toxicity that goes along with that. By the way, keep in mind all the top-performing Medicare Advantage plans are using today, right now, some form of advanced analytics and artificial intelligence to risk stratify their populations and predict which members will, without intervention, become high cost in the near term. Others are using AI right now to do the kind of predictive analytics that you need to excel at population health.
I get to ask Andrew some of the hard questions that have been bothering me about all the AI hype, and he set me straight a couple of times. Love it when that happens.
You can learn more at closedloop.ai or by following Andrew (@andreweye) on Twitter. Andrew Eye’s executive and entrepreneurial experience spans over 20 years in business to consumer and business to business for start-ups and Fortune 500 companies. Andrew founded and sold three technology companies and today is the CEO and founder of ClosedLoop.ai.
In 2012, Andrew cofounded the mobile software company Boxer. Boxer developed mobile productivity software for individuals and large corporations. Boxer’s flagship email product was downloaded by millions of users and received significant industry praise for its exceptional user interface, including a 2015 Webby Nomination as one of the top 5 productivity applications in the world. Boxer was purchased by VMWare (one of the top 10 largest software companies in the world) in 2015.
Prior to Boxer, Andrew cofounded the cybersecurity firm Ciphent in 2007. Ciphent grew to nearly 100 employees with 1000 customers by 2010 before being acquired by Accuvant (now Optiv). With a three-year growth rate of 8900%, Ciphent was recognized by Inc. magazine as the 16th fastest-growing private company in the United States. During his tenure as SVP of services at Accuvant, Andrew oversaw a $50-million, 200-person organization and was responsible for doubling revenues in 18 months.
Andrew also served as CEO of Bodkin Consulting Group, where he worked with Fortune 500 brands and technology companies to define their interactive marketing strategies. Andrew began his career as a software architect working with NASA, i2 technologies, and the US Marine Corps.
Andrew graduated summa cum laude from Virginia Tech with a degree in management information technology. Andrew lives in Austin, Texas.
04:34 What exactly predictive analytics is.
05:05 The use cases of predictive analytics value.
07:23 The oversimplification of how people think about risk.
09:03 “Did you have an impact or not?”
09:17 The public scorecard for predictive analytics.
13:59 “Explainability is a real hot topic in artificial intelligence, specifically in health care.”
15:24 Data shaming—what’s wrong with it, and why incomplete data are still important.
17:34 The possibilities that machine learning allows for in patient care in health care.
23:45 “Our health care system can’t afford for that level of inefficiency.”
24:57 “It’s not a question of if; it’s a question of when.”
26:04 The diminishing returns of interoperability and more data for machine learning.
29:21 “You’re running your business today, and whatever data you’re using to run your business … you can use it to provide better patient care.”
30:01 Andrew’s advice: Get started now.
You can learn more at closedloop.ai or by following Andrew (@andreweye) on Twitter. Check out our newest #healthcarepodcast with @andreweye of @ClosedLoopai as he discusses #populationhealth and #artificialintelligence. #healthcare #podcast #ai #pophealth #digitalhealth
What is #predictiveanalytics value to health care? @andreweye of @ClosedLoopai discusses. #healthcarepodcast #populationhealth #podcast #ai #pophealth #digitalhealth #artificialintelligence
Oversimplifying risk. @andreweye of @ClosedLoopai discusses. #healthcarepodcast #populationhealth #podcast #ai #pophealth #digitalhealth #artificialintelligence
“Did you have an impact or not?” @andreweye of @ClosedLoopai discusses. #healthcarepodcast #populationhealth #podcast #ai #pophealth #digitalhealth #artificialintelligence
“Explainability is a real hot topic in artificial intelligence, specifically in health care.” @andreweye of @ClosedLoopai discusses. #healthcarepodcast #populationhealth #podcast #ai #pophealth #digitalhealth #artificialintelligence
What is data shaming, and why is it an issue? @andreweye of @ClosedLoopai discusses. #healthcarepodcast #populationhealth #podcast #ai #pophealth #digitalhealth #artificialintelligence
#Machinelearning in #patientcare. @andreweye of @ClosedLoopai discusses. #healthcarepodcast #populationhealth #podcast #ai #pophealth #digitalhealth #artificialintelligence
“Our health care system can’t afford for that level of inefficiency.” @andreweye of @ClosedLoopai discusses. #healthcarepodcast #populationhealth #podcast #ai #pophealth #digitalhealth #artificialintelligence
“It’s not a question of if; it’s a question of when.” @andreweye of @ClosedLoopai discusses. #healthcarepodcast #populationhealth #podcast #ai #pophealth #digitalhealth #artificialintelligence
“You’re running your business today, and whatever data you’re using to run your business … you can use it to provide better patient care.” @andreweye of @ClosedLoopai discusses. #healthcarepodcast #populationhealth #podcast #ai #pophealth #digitalhealth #artificialintelligence
