ORLANDO, FLA. – We know that the usefulness of AI-driven solutions is tied to both the quality and quantity of the data used. It’s not just a matter of garbage in, garbage out. You’ve got to have a massive amount of high-quality data to train the algorithms – that’s how to get results that will work with a broad range of patients. For this reason, the Mayo Clinic is building one of the largest repositories of clinical data in the world.
“Everything starts with data,” asserted Dr. John Halamka, president of the Mayo Clinic Platform, which is focused on transforming healthcare through the use of AI, connected devices and a network of partners.
He explained in a presentation at the recent HIMSS conference that you need accurate data and a lot of it. A colleague at another organization told him that his facility had 5,000 patient records with which they will build AI algorithms. “That’s not enough breadth,” warned Halamka.
For its part, the Mayo Clinic has 11.2 million patients with electronic records. And it’s not stopping there. The hospital chain is building a global, federated network of partner hospitals and patient records that can be drawn upon for building apps.
Already, it has 242 algorithms under development. The goal is to improve the art and science of medicine around the world.
“You need a global network to deliver on a global basis,” said Halamka. So, the Mayo Clinic has been creating alliances with other large hospitals and health organizations to share data. They include Toronto’s University Health Network, along with the Apollo chain of 73 hospitals in India and the Albert Einstein hospital in Brazil.
The data are de-identified and they never actually leave the host site – instead, metrics about the anonymous patients are shared.
That protects patient privacy. And the sharing of data over a wide range of geographies and ethnicities helps avoid bias in the data, as much as possible, when building AI models.
Nevertheless, said Halamka, “every algorithm will have a bias. We create and test the algorithms, recognize the bias, and then adjust.”