Healthcare is a ripe target for machine learning to both optimize processes and greatly improve care delivery.
Take Mercy Health, for instance. When the health system was considering ways to improve care delivery, hospital executives looked at one of the most successful initiatives it had undertaken in the last decade: supply chain management.
“We have a lot of experience with operational efficiency,” said Todd Stewart, MD, vice president of clinical integrated solutions at Mercy. Using the operative suite as an example, he noted that all the supplies that go into and through it are very expensive. And there were many base concerns that needed to be addressed, including issues that seem obvious but are not, such as block time for a surgical case, including how you define start and stop times.
Likewise with Mercy’s care pathways project. The hospital had a lot of data related to patient care that it can use to determine the best ways to manage patients.
That’s where machine learning comes into play. Stewart, who is both an informaticist and a practicing physician, said the tool Mercy uses, from Ayasdi, can pinpoint variations to determine optimal care as measured by quality, mortality, morbidity and length of stay.
“When you see a lot of variation in process, there’s opportunity for standardization and efficiencies,” Stewart said. “That is a classic problem for machine learning — taking a large complex diverse data set and using the machine learning tool across those broad metrics allows us to get insight in ways we couldn’t with any other technological manner.