EP263: How Population Health Leaders Use Artificial Intelligence Right Now, With Andrew Eye From ClosedLoop

Here’s the thing: All the top-performing Medicare Advantage plans are using, today, right now, some form of advanced analytics and artificial intelligence (AI) to risk-stratify their populations and predict which members will, without intervention, become high cost in the near term. The idea is then to intervene to mitigate risk and stop bad things from happening—bad things that stink if you’re the patient and also cost a lot if you’re the plan. That’s what population health management is all about, after all.

Others using AI, right now, to do the kind of predictive analytics that you need to excel at pop health include PCP groups and other providers, mainly those at risk to manage populations or readmissions.

In this health care podcast, I talk with Andrew Eye about AI. Andrew is CEO over at ClosedLoop. 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 2017, Andrew founded his fourth technology company, ClosedLoop.ai. ClosedLoop.ai is a next-generation predictive analytics platform provider leveraging the latest in artificial intelligence and machine learning technologies to rapidly create predictive models from diverse sources of raw, messy, real-world health care data.

Prior to founding ClosedLoop, 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, with his two daughters and champion “Dock Dog” Sophie.

01:50 Artificial intelligence in health care, and the different things that this means to the health care community.
02:06 Image analysis, also known as replacing doctors with robots.
02:25 Chatbots for health care.
02:43 Predictive analytics.
04:39 “What they really care about is, How can this impact our business? How can this improve patient lives?”
04:51 “For us, this is all just better math.”
08:13 What exactly predictive analytics is.
08:40 The use cases of predictive analytics value.
11:33 The oversimplification of how people think about risk.
13:13 “Did you have an impact or not?”
13:27 The public scorecard for predictive analytics.
18:16 “Explainability is a real hot topic in artificial intelligence, specifically in health care.”
19:46 Data shaming—what’s wrong with it, and why incomplete data are still important.
21:53 The possibilities that machine learning allows for in patient care in health care.
28:08 “Our health care system can’t afford for that level of inefficiency.”
29:21 “It’s not a question of if; it’s a question of when.”
30:37 The diminishing returns of interoperability and more data for machine learning.
33:54 “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.”
34:34 Andrew’s advice: Get started now.