As an expert in developing artificial intelligence tools to improve health care, Ross Mitchell has always viewed the research process a little differently.
For a long time, he explains, the standard approach involved starting with a hypothesis, conducting experiments to collect data and then determining whether the data confirmed or denied that hypothesis.
Instead, Mitchell was approaching issues from a data-first perspective — much like the way the physicians he worked with approached the problems they saw.
"It's what happens every day in hospitals around the world. When somebody comes in with symptoms, you don't know what it is, so you do tests and imaging to look for things. You literally start by looking at the data and then form a hypothesis," says Mitchell, a professor in the Department of Medicine, adjunct professor in the Department of Computing Science and one of two University of Alberta researchers recently named Canada CIFAR AI Chairs.
Harnessing health data to benefit patients
One area Mitchell has focused on involves something called unstructured data. As he explains, about 80 per cent of health-care data is stored in a format that isn't searchable, such as images. The current standard requires trained professionals to go through this data manually, a time-consuming process that creates lags in care delivery.
A past project of his involved creating a system that would process tumour pathology reports to identify which patients may be a good fit to enrol in a clinical trial.
"It did a really good job of extracting the tumour site and the type of tumour it was, and we could store that in a database immediately and the next day it's searchable," says Mitchell, who is also the inaugural Alberta Health Services Chair in AI in Health as well as senior program director of AI adoption with AHS.
"That now opens up all kinds of clinical trials to patients immediately instead of this slow, laborious, expensive process of having to read through these documents manually," he explains.
