The US healthcare sector has struggled to put innovations like AI into practice. Mara Cairo explains the advantages of applying machine learning and AI for hospitals to Jim Cagliostro. Episode Introduction Mara explains why the first step towards successfully embracing AI is literacy, the challenges hospitals face in system integration and why AI isn’t intended to replace humanity in patient care. She also illustrates the benefits of AI for healthcare, including predicting patient no-shows, effectively managing inventory, and reducing costs, and explains why successful leadership means getting out of the way. Show Topics Taking the first step towards AI literacy The challenges of AI in healthcare Applying AI across industry sectors Anticipating patient no-shows The impact of AI on cost reduction initiatives Leadership tip: Hire the right people and get out of their way 03:48 Taking the first step towards AI literacy Mara said AI literacy helps to overcome resistance to AI. ‘’Really the most important thing is AI literacy. It's just like learning what AI is, what it isn't, the types of problems it's really great at, the types of problems you shouldn't use it for. On the earlier side of the spectrum, we have lots of training and education really meant to get industry partners, but also the general public. We're working even with K-12 teachers and students now ... to make sure that everyone has that literacy because it's just becoming more and more important to kind of arm yourself with the information because we're being inundated with information and news articles and scary stories. So it starts with literacy, that's the first part, and then kind of evolves from there hopefully.’’ 05:46 The challenges of AI in healthcare Mara said the complex needs of healthcare mean hospitals struggle with system integration. ‘’There are different disciplines. Each maybe has their own labor agreements, regulation and whatnot. So when we think of human resources as a piece of inventory, that gets quite complicated quickly. Another thing, supply level. Inventory levels are complex. We kind of all saw it in COVID. The demand can spike really, really quickly. And you don't necessarily know when that's going to happen, right? So these surges can catch everyone off guard. And maybe traditionally it's been harder to anticipate when these surges might appear. Luckily, maybe machine learning is a tool that can help us with that. Also, just I think the shelf life of different supplies is unique to healthcare. You have to be really, really careful about storage and transportation requirements. And all of that is compounded by distance and transportation costs. Especially in Canada, in the far north, those care locations, they're really dependent on certain supplies, but if there's a road closure or a snowstorm or something, it's further complicated. The inventory supplies and healthcare are potentially life changing, right? So it's just so much more important that that is managed properly. And that complicates things. I think overall, in general, we've just seen that healthcare systems can struggle with system integration.’’ 08:30 Applying AI across industry sectors Mara gave examples of how AI helps with demand forecasting and warehouse management. ‘’Some of the really cool projects we've worked on with our industry partners in the supply chain space, but more in the kind of consumer goods area are things like demand forecasting. So helping them better predict what items they're going to need and when. What's really great again about working with our supply chain partners is they have a ton of data, historical data. And that's really, really important. When we start looking to build machine learning solutions, we often rely heavily on that historical data to be able to make those predictions about the future. So the demand forecasting problem is really ripe for innovation and for machine learning because usually there is a large amount of data and we can start making predictions based off of what's happened in the past about what supplies will be needed and when. Another cool thing we worked on with one of our warehousing companies was pick route optimization. So when you're picking items from an order, what's the most efficient way to pick the items to start fulfilling orders? And then to that even more so is how do you build your warehouse up from nothing? How do you make sure that the space is optimized the best way that it can be so that you're optimizing your pick route, but also so that maybe commonly used supplies aren't blocked in. So we're able to, again, use some really cool machine learning techniques and historical data to help just those ground level initial planning things to make sure that we're setting up these warehouses to be really, really efficient.’’ 10:41 Anticipating patient ‘’no-shows’’ Mara said machine learning can help hospitals...
