12 - Devin Singh: How can AI improve patient outcomes?

Even before COVID-19 overwhelmed hospitals, many healthcare workers were feeling engulfed by their workload, never having enough time to properly focus on their patients. Automating key tasks in the patient journey could help change this, so patients receive the best possible care.


Dr. Devin Singh, a pediatric emergency physician in Toronto, Canada, as well as the founder of Hero AI, a startup dedicated to empowering patients and healthcare providers with AI, came in to chat about how he was doing just this. Devin discusses applications of AI in healthcare, navigating the institutions that compose the healthcare system, his collaborative approach to bringing people from different fields together in order to cut the red tape surrounding the implementation of a brand new solution, and the future of healthcare.


Read on for the transcript!


Monte 0:07


Hi, I’m Monte Zweben, CEO of Splice Machine. You’re listening to ML Minutes, where cutting-edge thought leaders discuss Machine Learning in one minute or less.


This episode, our guest is Dr. Devin Singh. Devin is a pediatric emergency physician in Toronto, Canada, as well as the founder of hero AI, an innovative startup lab dedicated to empowering patients and healthcare providers with AI. His research focuses on using machine learning to solve some of healthcare's largest problems. Welcome, Devin.


Devin 0:43


Hey, Monte, thanks for having me. It's such a pleasure to be here. And I'm really excited to chat and, you know, connect with the audience, really looking forward to it.


Monte 0:52


Well, tell us about your journey. How did you get to where you are now?


Devin 0:55


Yeah, so like you said, I'm a pediatric emergency doctor working in Toronto. But the journey has been a bit of a exciting convoluted one. I did my undergraduate degree at Western University, and then worked for government for a few years as a business analyst there with the Ministry of Finance and revenue, actually, and then after that, jumped over to Australia to do med school at the University of Sydney, and then did my pediatric residency training and pediatric emergency medicine fellowship at sickkids in Toronto, and then I also did a, an AI fellowship at the University of Toronto as well with a real clinical focus on figuring out how to translate AI into healthcare practice and workflows. And then just because I love to learn too much so and punish my wife with school tuition fees, I did a Master's of computer science as well at the University of Toronto. So that's just wrapped up now. I'm really excited to like, have the oldest experience and education behind me looking forward to it.


Monte 1:58


Well, that's awesome. You're, you're a real slacker. You have your master's in computer science. Boy, I can't believe you've been so lazy.


Devin 2:07

It's all my wife, Monte, I gotta say she's gotten me through it for sure.


Monte 2:13

That's cool. Well, tell us more about your work with Hero AI. What's the problem you're trying to solve?


Devin 2:20


Yeah, that's a it's a great question. What I've realized through med school residency, and now throughout my master's of computer science is that we're sitting on this incredible technology, and infrastructure, we've got a ton of data being generated from EHR systems, we have a patient population that's highly motivated to engage and utilize technology, and a physician group and a nursing group and healthcare system as a whole. That is really overrun, putting COVID aside, you know, the burden that sits on health care systems, particularly in Canada, a publicly funded system is quite large. And so we've got this setup of technology data and motivated stakeholders, where now is really the time to figure out how to connect the dots together. And I really feel that machine learning and artificial intelligence can sit perfectly in that triangle, and allow for these types of technologies to be translated and create impact at scale in a way that's never been done before. And so that's what we're trying to solve is how do we translate that into the hands of patients and providers?


Monte 3:27


Thanks, Devin. And just for clarity, EHR, means electronic health record, the system that doctors use to store all their notes and all the information on all of their patience. Can you give me an example of how AI can be used in order to deal with that burden for the staff in this environment?


Devin 3:50


Yeah, totally. I mean, it's all about, you know, building capacity. And I think machine learning allows us to build capacity into healthcare workflows, in a really unique way because of its ability to facilitate aspects of automation. So let's take a simple example, kiddo comes in to an emergency department. Unfortunately, you know, ice skating, playing hockey broke his wrist common problem right now in the winter, right? When he or she comes into our emergency department, we assess pain, we treat that pain, but then we need to circle back and reassess pain. But you can imagine that there's a human element there. You know, the emergency department gets busy. We've, you know, not forgotten but have been pulled away because of different priorities to circle back to that kiddo. Right. And our ability then to adequately address someone's pain in an emergency department gets limited based on capacity issues. You can use basic machine learning algorithms, like NLP models, to understand when a patient is having pain to trigger an assessment for pain for that person through the interaction of a front end device like a mobile app, and then feed that back to providers. And that's just one very simple idea. have, you know the types of technologies we're building out?


Monte 5:04


Yeah, that's great. So first and foremost, we were talking about the staff a moment ago in terms of helping them deal with the capacity limitations. But what you just told a story about was really looking at patient outcomes and helping the patients in addition to helping the staff, is that a fair assessment?


Devin 5:26


Absolutely. All of this is very much rooted in this human element, right? It all comes down to we work in a healthcare system, that's very challenging one to get into to be able to have the privilege to work there, but to to even, you know, be functioning and providing this level of care to patients. And what are we motivated by is patient outcomes, patient care? That's really the heart of the type of innovation we're focusing on at hiroi. And we're trying to enable healthcare providers to do that for their patients.


Monte 5:57


I bet it's pretty cool for kids to to be talking to an AI and helping and having the AI help them feel better to.


Devin 6:06


Absolutely. And there's so much cool opportunity to think of like, how do you customize user interfaces based on the developmental age of a child? Right? So whether you're a five year old, and we're asking you about pain, or gathering symptoms, versus a 14 year old, there's a lot of nuances to how an interface needs to communicate despite the end goal being exactly the same. And despite the back end model being the same, the way it engages, needs to be done in an intelligent way. That's a really unique technical challenge. But it's super fun to work on.


Monte 6:37


Excellent. Well, let's transition to some more technical talk, what are some of the tools that you're using for your work?


Devin 6:45


Yeah, so I mean, really, as I alluded to, before, we've got these silos of opportunity, you've got a large EHR database that is then broken up amongst different institutions. And so one of the very simple objectives is getting all the right data into the right spot. And that involves, you know, partnering with community clinics partnering with patients in their homes, allowing them the power to share certain levels of wearable information or home data with the right providers at the right time. And then building a back end infrastructure that can ingest all of this synthesize this data and turn it into action. And so, you know, a lot of it is a I mean, splice machine is great at doing this, you know, a lot of it is pushing, pulling together data in the right space. But then our challenge with hero AI is building the machine learning models, to then integrate seamlessly into a clinical workflow, keeping the patient journey in mind, and making sure that we delivered the right alerts the right automation at the right time, never wanting to be obsessive about it, but also not wanting to miss the opportunities for efficiency.


Monte 7:54


Fantastic. So what we're talking about here is ingestion, getting the data from the disparate sources, and being able to consolidate it and make it useful. So moving to your journey in the implementation of AI into the clinical workflow, what's one specific challenge that you faced along the way you'd like to share with our listeners,


Devin 8:18


the first is actually just being able to translate solutions into clinical practice. And at a large institution, there are many, many barriers in place. And it's not because stakeholders are intentionally sort of road blocking you Everyone is very motivated to see these technologies translate has been my experience, like extraordinary enthusiasm. But the systems in place to allow for the approval and the movement forward of these types of new technologies weren't designed to vet machine learning. And so each step of the way, I find that not only are we translating new technology, but we're also helping an institution invent the process to vet the technology that then needs to be translated. And I found that that was really interesting and not anticipated right from the get go.