Using AI to Enhance the Hospital Supply Chain | E. 104

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 to predict individual patient no-shows. 

‘’And then maybe speaking more specifically about healthcare, one project we worked on was really cool. It was about managing staff inventory and patient load. So healthcare patient no-shows are a bit of a problem sometimes. My dad is a retired dentist, and that would just pain him every time there was a no-show. I know firsthand how frustrating that can be. And that can also lead to an oversupply of clinicians, right? It throws the whole system off. So we worked with one of our partners to develop a model that actually predicts the likelihood of the patient being a no-show. And if the model is saying, "This patient is likely to not show up," maybe we send them an additional notification. Maybe there's a bit of an overload of bookings in that anticipation that no shows are coming. So that is a really cool application of machine learning to hopefully alleviate a little bit of the load and the stress of the healthcare system.’’

 

14:27 The impact of AI on cost reduction initiatives

Mara said AI can help to improve the flow of inventory from the outset and help with HR resource planning. 

‘’…machine learning can be really great with helping inventory management get closer to more accurate just in time delivery. So again, that sort of demand forecasting. I think currently the mitigation strategy for that is just to stockpile more supplies than you need, but then that leads to spoilage, especially if there's a shelf life and we're sort of back to square one. But machine learning is a really great tool for that demand forecasting, right? Also, these models can help us locate supplies where they're most likely required. So instead of last minute shuffling around supplies to an area that really needs them, which is increasing our costs, you're making sure that they're getting to the right care sites from the beginning so that it's just a more efficient flow of inventory from the very beginning. And then again, on the human resources side, even forecasting the future need for skilled professionals….if we just had a better line of sight into the future, which again, machine learning is a great tool to make those predictions, I see that as decreasing costs across the board and just making it easier to make decisions ahead of time, again, through data, but vetted by an expert that can also bring that domain expertise and that perspective to the solution too.’’

 

22:07 Leadership tip: Hire the right people and get out of their way 

Mara also added that soft skills can be harder to teach in a technical environment. 

‘’I lead a team of very technical machine learning scientists. I don't necessarily have the exact same background that they do, but what I've found when I'm building my team, I'm really focused on hiring the right people, making sure from the beginning that you're investing the time and finding the right person and then sort of getting out of their way, but being there to support them if and when they need you. So I really want to make my team's jobs as easy as they can be and not have to worry about things that they shouldn't be worrying about. I think that's what I've learned through my leadership experience. And because I'm hiring often highly technical people, those roles can be hard to hire for. And I think, of course, technical skills are really, really important and valuable, but there's also a lot of room for growth. And when you're hiring early career professionals, they should be given the benefit of the doubt in some cases that they can continue to learn and grow in the role. So I am usually looking for those softer skills that are maybe a little bit harder to coach or teach someone. And because we're putting our young scientists right in front of a client like their first day basically, it's the softer skills that are much, much more important. I think when you're hiring technical people, it's really important to be aware of their understanding of the business side of things and if they're able to translate their fundamental technical knowledge into something that non-technical people can understand.’’

 

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Connect with Jim Cagliostro on LinkedIn

Connect with Mara Cairo on LinkedIn 

 

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You’ll also hear: 

 

AI is a journey:  ‘’It's not necessarily something that happens overnight, and we often refer to our AI adoption spectrum in a way to assess where our partners are at and where they want to go. …my team works with companies who are really ready to get some hands-on support to start building out these predictive models.’’

 

Overcoming the challenges of system integration in healthcare: ‘’…. Just… managing all of the information that's spread across different platforms, that can be really difficult to pull together and start understanding the bigger picture in real time. .. that understanding is really important and it leads us to solutions, but bringing all of that information together, …we've seen it being a bit of a challenging project to take on.’’

 

Maintaining the humanity in healthcare: ‘’We don't want a machine to be telling you that you have cancer, right? So whatever the solution is, we also want to make sure that there's a human in the loop maybe validating these decisions…..It's a tool for clinicians or anyone to use. And it's not meant to replace anyone. It's really meant to just kind of enhance our decision-making abilities with some ….data-driven insights.’’

 

How Aimii meets organizations where they are: ‘’…we have services where we work with our industry partners just to help them start brainstorming ideas. "So let's learn about your business. What are all of your problems? What data have you historically been collecting, and where can we maybe tie that data into the solution of some of these problems that we've kind of helped you brainstorm?" So that's just a way to help them start feeling comfortable that they know how to identify a machine learning solution to a problem.’’

 

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