Moneyball for Healthcare - Why Hasn’t it Happened?

Moneyball, a book by Michael Lewis (later made into a movie starring Brad Pitt), describes the success of applying the principles of data science to develop a winning strategy in baseball. It’s a transferable skill, you would think if data science can be used to win more games in baseball, it could be used to lower costs and improve outcomes in healthcare – so why hasn’t Moneyball happened in healthcare yet?

For over a century, baseball was using data the same as healthcare is today. At first, baseball statistics were based on the original development of one set of static measurements like batting average, runs, and runs batted in (RBIs). These statistics were invented in 1845 and presented in the “box score” for each game. The more these old statistics were examined, the less sense they made. They were not the best measures of player and team value, so they didn’t give the best insight into how to score more runs and win more games.

It wasn’t until the 1970s when Bill James, a writer and night watchman at a Stokely Van Camp pork and beans cannery, began to question the status quo of baseball statistics. In 1977, James published a periodical called the 1977 Baseball Abstract: Featuring 18 Categories of Statistical Information That You Just Can’t Find Anywhere Else.

James developed new ways to measure baseball success and found that runs scored were highly correlated with wins. He developed weighted correlations which led to a formula that generated what he called “runs created”. As he developed momentum, he met with a small group of friends, including Sports Illustrated writer Dan Okrent, at La Rotisserie Française restaurant in New York City – this is where the concept of “Rotisserie” baseball was born. Eventually, James’ work developed into the fantasy sports industry, which is worth nearly $10 billion annually.

But at that time, the only people interested in these new baseball measurements were the fans. As James continued to develop better measurements, there was one other group that started showing interest – player agents. The agents wanted more statistics that validated the value of their clients, the professional baseball players, to justify negotiating larger salaries. Interestingly, the group of people who showed no interest in these better measurements, and the application of data science to baseball, were the owners and managers of the teams. The people most invested in the outcomes of the games had no interest in changing how they used their data and managed their teams.

James, working with a company called STATS Inc., tried to persuade teams that they should use the new measures he had developed. Teams just weren’t interested. Part of the problem was that baseball already had their data company – Elias Sports Bureau. They had the contract for managing all baseball statistics and like with the current generation of electronic medical records in healthcare, baseball at that time did not think there was any need to change. The company certainly did not want to admit or believe that the statistics that they were paid to collect and publish were poor indicators of player and team value. There was no appetite or incentive for innovation or improvement.

The status quo was not challenged again until two entrepreneurs from the financial industry took what they learned about how to use data science applied to financial derivatives and realized that they could do the same thing in baseball. They started a company called AVM (Advanced Value Matrix) Systems in 1994 and approached teams to see if they could consult and apply their data science methods to baseball.

Change did not come easily. It wasn’t until the Oakland A’s were sold to a more frugal ownership group that there was enough financial pressure to make changes to the status quo. The inequities in baseball budgets raised to the level where some teams could afford the best individual players while others could not. Change usually only occurs when the pain of the status quo raises to a level greater than the discomfort of making a change.

When the new owners of the Oakland A’s refused to match the salary offers for star players who were plucked away by the wealthy teams, like the Yankees, management, with Billy Beane in charge as general manager, felt the pressure to make changes in how they operated. Beane had read every one of Bill James’ Baseball Abstractpublications and he discovered that baseball was not using data appropriately.

But everything changed when Beane hired Paul DePodesta, who was an intern for the Cleveland Indians when they first met. DePodesta graduated from Harvard with a degree in economics, but his real passion was the intersection between economics and psychology, a discipline now called behavioral economics. Coincidentally, DePodesta had recently met the Wall Street traders turned baseball data gurus during an initial sales call and was intrigued, so DePodesta convinced Beane to hire AVM Systems to apply data science to the Oakland A’s – the rest went down in history as Moneyball.

Although, something to note is that there is a major difference in applying data science to baseball and healthcare. Ultimately, baseball is a competitive sport – it’s about winning; beating another team. When other major league teams learned to apply data science to their organizations, the advantage for the Oakland A’s was diminished. In fact, just two years after the A’s had tied the Yankees for the most wins during the 2002 season with one of the lowest budgets in baseball, the Boston Red Sox won their first World Series in almost 100 years using the same principles of data science.

In healthcare, we should not be competing. We should be focused on a goal that aligns all of us – improving the value of care for all patients with any disease or health problem. When we align around the goal of value and work collaboratively to improve value for patients, we can apply one of the most important tools of data science: The Ensemble Model for Learning. If every clinical team in each local environment were to implement a value-based continuous learning model and then network the learnings from each clinical team, we could improve value forever.

Data science is real, but very different from the reductionist science paradigm we’ve been taught and are functioning under in healthcare today. Until we feel that the pain of continuing to suffer in this reductionist status quo is worse than the discomfort of learning and applying a new data science paradigm (like Moneyball did for baseball) we will continue to suffer the consequences.

(A portion of this content was previously published in the July 2020 issue of General Surgery News)