From the September 2017 issue of HealthLeaders magazine.
Todd Stewart, MD, an internist and vice president of clinical integrated solutions at St. Louis–based Mercy, a Catholic health system with 43 acute care and specialty hospitals and more than 700 physician practices and outpatient facilities, says Mercy began its AI journey three years ago, with a focus on nettlesome procedural challenges around standardizing care pathways.
"We started with some of the highest-volume, high-dollar procedures, like total hip/total knee. Across Mercy we do thousands of those," Stewart says. "The idea was that if we standardize the process, limit care variation, there is strong evidence that we could extract financial value and also patient value in terms of lower mortality, lower length of stay, better outcomes."
Like most health systems since the advent of electronic health records, Mercy had at its disposal mountains of data around outcomes, use, and supply chain. It was time to put that data to work.
"Can we use machine learning algorithms to lower length of stay? What cohort of patients had really good length of stay compared with others? Can we use our existing data to help guide some of these best practices?" Stewart says. "That type of approach with machine learning is precisely what you can get. It can show you relationships that are almost impossible to find except by luck with just humans. What we found was that it absolutely added to the value process."