When Billy Beane decided to employ a recent Harvard graduate to use advanced statistical analysis to build a championship major league baseball team, he changed the game forever. While Beane’s famous early 2000s team never won a World Series, multiple 100-win seasons and a new record for the longest winning streak got the attention of teams across the MLB, all while on one of the league’s lowest payrolls. Most people know Beane’s story as it was popularized in the book—and later in the movie—Moneyball.
In healthcare, we are overdue for a “Moneyball” revolution. The shift towards value-based payment has made it clear that our system needs to do a better job generating outcomes that matter to patients — a positive health-care experience, improved health, and good quality of life. The machine learning techniques that were used to algorithmically determine a player’s value were light-years ahead of the archaic methods that had been used in baseball up to that point. Similarly, many of our conventions in delivering care come from an era when healthcare was delivered primarily by doctors and nurses with elite training whose success depended mostly on content expertise. A key component to value-based transformation in healthcare will be artificial intelligence. Without AI, medicine will never advance to a state where the totality of a patient’s data can be used to find predictive signals that will lead to enhanced treatment and population health interventions that improve outcomes.
Our guest this week is Andrew Eye, the founder and CEO of ClosedLoop.ai, the recently announced winner of the CMS Artificial Intelligence Health Outcomes Challenge. Listen and find out why Andrew and ClosedLoop are exemplars in the race to value!
02:00 The Billy Beane story and how, in healthcare, we are overdue for a “Moneyball” revolution
03:00 A key component to value-based transformation in healthcare is artificial intelligence
06:45 Partnership with Dave DeCaprio following his work with the Human Genome Project
07:30 How Andrew’s daughter’s medical condition provided “WHY” inspiration to build a next-gen predictive analytics platform
09:20 How ClosedLoop.ai beat out the world’s leading technology and healthcare organizations to win the CMS AI Health Outcomes Challenge!
11:25 “Physician trust in AI is crucial. Algorithms never saved anybody’s life. We predict the future so that you can change it.”
12:50 Creating an open source, AI-based predictive model for predicting COVID-19 Vulnerability
13:00 Andrew discusses what it was like to submit the winning submission for the CMS AI Challenge without electricity in the Texas Snowpocalypse!
14:00 CMS’ focus on AI Explainability and how ClosedLoop was “born to win”
19:00 Early AI firms were reluctant to share “secret sauce” of proprietary algorithms and the impact on physician trust and external validation of bias
20:00 “We’re not building models. We are building a machine that builds models.”
20:20 “The idea that there is one algorithm that is best for every healthcare organization in the country is a total fallacy.”
21:00 “Explainability in AI is absolutely critical to helping care teams have more effective interventions in population health.”
22:30 Physician paranoia about “machines taking over” where there work will be eventually outsourced to algorithms and other artificial tools of clinical reasoning
23:45 The impact of AI on Radiology and how that scenario differs from other instances in medicine where AI is applied to population health
25:20 The opportunity to augment clinician pattern recognition with AI that goes far beyond manual chart review for surface insights
26:15 “There is going to be a point in time where patients choose a doctor based on whether or not they are using all of the available information.”
30:30 Label choice in ML algorithms where costs are used as a proxy for health
31:30 Differentiation between algorithmic bias (based on math) and algorithmic policy (based on policy)
33:30 The inferiority complex that healthcare organizations have with “data shaming” and AI can pull predictive signals out of messy data
34:30 The Medical Home Network AI case study that focuses on Social Determinants of Health
35:30 Augmenting claims data with ADTs, Rx data, and Health Risk Assessments
37:00 “Until you squeeze all of the predictive signal from the data that you have, you shouldn’t be shopping for data that you don’t have.”
38:50 Andrew explains (in layman’s terms) the Receiver Operating Characteristic (ROC) curve used in statistical validation
39:50 Why a really accurate model for the entire population is not as important as the accuracy within the 3-5% of patients that actually drive up costs
40:45 Focusing on “percent capture” is more important that ROC curves
42:00 Building the right population health AI model by asking the right questions
42:30 Ensuring successful integration of predictive modeling in the workflow of the provider and pop health team
44:45 Feature drift that occurs in algorithms when datasets change and input values are affected
46:00 MLOps as a process of taking an experimental Machine Learning model into a production system
48:20 Andrew explains the FDA regulatory environment for AI in healthcare and its impact on health equity
51:10 Andrew shares his thoughts on Big Data futurism and the future of AI as “the next Moneyball” opportunity
52:30 The need for lowering cost to predictive models in prediction of rare diseases
53:00 How CMS is leading the charge in data liquidity with Blue Button 2.0 APIs
54:00 Organizations leading in value-based care are the ones investing in AI/ML