How Data Can Improve Patient Outcomes with Allyson Jacobsen, Global Marketing Director of Artificial Intelligence and Analytic Solutions at GE HealthCare was automatically transcribed by Sonix with the latest audio-to-text algorithms. This transcript may contain errors. Sonix is the best audio automated transcription service in 2020. Our automated transcription algorithms works with many of the popular audio file formats.
Saul Marquez:
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Saul Marquez:
Welcome back to the Outcomes Rocket. Saul Marquez is here and today I have the privilege of hosting Allyson Jacobsen. She’s a Global Marketing Director of Artificial Intelligence and Analytic solutions at GE Health Care. In her role, she is responsible for the strategic direction and execution of all global marketing functions. She’s a highly adaptable marketing executive with over 15 years experience. Allyson has worked internationally with successful global brands at IBM. She directed Global Influence Teams providing integrated marketing programs with strategies to increase awareness and revenue. With three 60 training that B2B and B2C training marketplace, Allyson led a global marketing and e-commerce team focusing on brand awareness and growth campaigns exceeding annual targets by two hundred percent in 2015, most recently at an information technology consulting firm known as Triad’s. Allyson developed and implemented a global omni channel, marketing strategies responsible for exceeding revenues by over twenty two percent. She holds her bachelor’s from Christopher Newport University, as well as an MBA from Texas A&M. I’m really excited to dig into the topics of A.I. and analytics within health care with Allison and such a privilege to have her join us here today. So, Allyson, welcome to the podcast.
Allyson Jacobsen:
Thank you. I’m glad to be here. I’m glad you talk about kind of my transition from pure technology based industry to the health care industry and how I see AI playing a role in the next few years.
Saul Marquez:
And that’s certainly going to be an interesting chat today. And and before we dig into that transition and do some horizon viewing, I want to dig into a little bit more about what inspires you and your work in health care.
Allyson Jacobsen:
Yeah, for me personally, I left the pure technology sector, I’ll call it for health care, because I really want to play a role in improving the future health care of my children and my grandchildren. I want to be a part of a team that can do anything to help a clinician see more patients, have better visibility, have better diagnostic tools, and to be able to save more lives every day. I mean, we’ve all had that one person in our life who died unexpectedly or died needlessly, whether it was because they couldn’t get to care fast enough, they were misdiagnosed or any other myriad of reasons. And I really am driven to try to change that dynamic for the future.
Saul Marquez:
Well, that’s a wonderful motivation, Allyson, and one that I think drives a lot of us in health care. Tell us a little bit about how you and health care are adding value to the health care ecosystem, specifically around these areas of A.I. and analytics?
Allyson Jacobsen:
Yeah, I think we’re helping that ecosystem by developing AIL model of applications solutions what have you that are really going to help our customers reduce cost It’s going to help them increase the access of care that they can provide to all patients. It’s going to help improve the quality of care that they can provide because it enables improvements and efficiencies within their workflows every day. And I think GE health care is uniquely positioned to do that because we’re able to leverage over one hundred and twenty five years within the health care industry with the medical devices that we create and provide to our hospital systems every day and to the clinical partnerships that we have where we can harness those new technologies that aren’t just focused on technology themselves, but are helping improve patient outcomes every step of the way. For example, right., let’s talk about self-driving cars. When we talk about self-driving cars, we’re not really excited about the technology in the car. We’re excited more about it, reducing commute times, about it being safer about it, eliminating fuel and the environment. It’s those things that really benefit us that get us excited. It’s not about the technology in the car itself. If that makes sense.
Saul Marquez:
It makes a lot of sense. Yeah. So you’re basically saying, hey, there’s a ton of technology out here. We’re focused on the benefits that our customers can obtain through this. And by way of that, the patients, US Right. receiving the care. The outcomes are better. They’re more satisfied. So the driverless cars, the clinical decision support, the interoperability, forget about all that. It’s about what the benefits are.
Allyson Jacobsen:
Exactly and about the patient’s experience as well. Right..
Saul Marquez:
Yeah, I love that. And, you know, and we could forget that, you know, and even so, you’re listening to this and you’re thinking, hmm, yeah, that’s a good point. And you probably have a solution in your bag thinking, OK, let me just not focus on this text so much. Let me focus on these benefits. It’s easy to do, but it’s also easy not to do. And so I’d love to hear more about what you believe makes what you guys are doing different better than what’s available today.
Allyson Jacobsen:
Yeah, I think it’s because while we’re building the A.I. Solutions into our devices and for our clients, what you said, we’re not thinking of A.I. as a technology, but it’s a solution to solve a patient’s problem and to create better outcomes. You know, we’re building those models with a very strict set of AI principles that ensure we build the models that will be trusted, that won’t be biased and will be generalized versus brittle. And I think that’s really important. We have three V’s that we hold ourselves accountable to when we build models here at health care. That first one is volume. It’s important, but it’s not sufficient right. and a volume of data. It’s hard to come by with this health care today. So that’s kind of the first vs the second is variety. If we’re building a model that works in the US and Europe, we better have the data that’s located in the US and Europe. That’s part of why we’ve built the Edison platform is it helps us bring data from disparate sources to use not only as we build those models, but as we train those models and those models continue to work. And then the last V is veracity. Even if you’ve got the volume and you have variety, how do you add political knowledge and ground truth to that data so that the data science model is learning what is important and it’s not being fed and false positives just in the data itself. So volume, variety and veracity. We found that this leads us to really generalized, unbiased data science. But again, it can’t be about the technology. It has to be about that outcome, about the clinical workflow. We can never forget that right. It’s got to be deployed in the workflow in an invisible way that doesn’t add depth or processes for the clinician. And I really think that’s how we’re doing it differently. And I think that difference matters.
Saul Marquez:
Very well said. Love the volume, variety and veracity, I guess trifecta. And you guys are very well positioned to leverage these factors. And when you talk about volume and the presence of health care within the global health care market, the variety that comes with that global footprint and really the veracity, you guys are well-positioned. I love to hear more about how you guys are improving outcomes or improving business models with some of the work you’re doing in the space.
Allyson Jacobsen:
Yeah, sure. Before I answer that, go back to your comment about variety and our global reach. That’s a very important aspect that I think a lot of people don’t realize. But if you take a chest x ray, for example, to try to determine the name of thorax, the way we take an x ray here in the US, it’s very different than the way that it might be taken in India or in China or in a small, remote island right? The equipment might be aged differently. The technicians skills are different. Some people, some regions will focus very finitely on the lungs. Others are chest x ray. Might be the entire torso. Some technicians might have you hold your arms down, while some might have you put your arms on a hip and then you have an open air spot. It is that variety and GE’s ability to have devices and data around the world that again makes those models better every single time. It’s a good point, but how we’re again, doing all of this to improve outcomes is by never forgetting we exist to help clinicians improve lives at the moments that matter, by completely living within the workflow and understand what is hurting the patients and using that technology to help. Let me give you a perfect example. One of the biggest pain points we’ve learned within the E.R. in the ICU is that critical findings in an x ray are often missed or could often be missed. Let’s say it’s early morning and you’re ordering x rays on some critical patients within the ICU. Those x rays go into a queue and can take up to six hours to be read. That first in, first out mentality. It doesn’t matter necessarily what’s in the file at that point. So know, we heard from many clinicians, I just lost a life because of this. There was a finding there that had I have known to intervene, I could have saved a life, but it got buried in the queue. So one of our very first AI cases was to identify those critical conditions that collapsed lung before that misplaced feeding tube right at the point of image acquisition, and then to be able to prioritize that image for the radiologist to the top of this queue so that he could read it, he could identify it and he could save that life. I think that’s a perfect example of how living within the workflow every minute of our existence and understanding what’s broken, what’s hurting our patients and then solving that problem, I think that’s how that’s great.
Saul Marquez:
So they go in, they go get their image, and there’s a set of things that happen probably with some frequency and the machine learning or the eye finds it, points it out to the radiologists and gives them the option Right. of a you know, this may be the case. You might want to double check this and a life is saved.
Allyson Jacobsen:
Absolutely not doing the diagnosis itself. You’re absolutely right. But just about prioritizing. We’re seeing these markers that we really want you to hone in on and not get distracted, maybe by all of the outside noise as we like, you know, as we say that we say, but do so much to look at in so much data to analyze that. If we can just pinpoint some critical markers, it makes all the difference.
Saul Marquez:
Wow, that’s a great application. And I’m sure one of many across the variety of devices and therapies you guys offer. What would you say is, is a setback you guys have found? You know, I’m sure the three V’s and some of the things that you guys have seen in the market have the knowledge has been informed by some setbacks. So what types of setbacks have you seen and what was the key learning?
Allyson Jacobsen:
Yeah, I don’t know that we’re seeing setbacks in that way. I think one of the biggest challenges we see today is when an I.T. department and the clinicians within that organization aren’t aligned. Yeah, we see a lot of requests come from that aren’t necessarily, again, aligned or coupled to achieve the results that the organization really wants to achieve. And that’s why some of our early adopters of A.I. are being focused on operational use cases. How do I take a twenty five minute mama and make it 20 minutes? How do I take an ultrasound exam and create a 70 percent time saving these? These are operational things that both reduce costs. They help the clinicians and they help the patient. Quite frankly, one of the apps that we have in particular is our Imar Eric Solutions, where we where we do automated slice placements for neuro exams. That takes minutes out of the exam, but it also improves the patient’s experience. Right that customer, if you misplace the spice plane, may go home and then two days later get called back to the hospital because the radiologist can’t necessarily compare that image versus an image taken a month ago. And that immediately is felt by the patient and the doctor and not providing that speed of care so that you immediately felt the impact of A.I. that’s both improving operations, reducing costs, and it’s increasing that patient’s experience in a way that really matters. So I think that that’s a challenge. And that’s what we’re learning, is when you can vary the operational benefits with the clinical benefits, you’re really getting better solutions for everyone.
Saul Marquez:
That’s a great example. Again, Allison, thank you for that one. And you’re right. You know, this cross-functional alignment is a challenge. And finding ways to to speak to both of those stakeholders within the health care system is critical. I mean, you know, I had a guest recently that said, you know, technology is kind of like a utility. Now, you’re not going to be able to do anything without it anymore. And if we’re not working to find solutions like Allison and her team is at GE Health Care to find solutions for I.T., for clinicians, in this case, operational use cases that Mary, with clinical use cases, then your technology is not going to succeed and you’re not going to be a leading firm like likely to provide solutions. So what a great example, Allyson. And shedding some light into how you guys are really adding value to your customer and ultimately the patient. What makes you most excited today?
Allyson Jacobsen:
I think it’s just about the endless opportunities ahead of us know health care. We can’t do this in a vacuum. We’ve developed an amazing Edison platform, which is a really robust set of services and technologies and business partners and clinical partnerships. And we’re leveraging this in a way to provide and build solutions at speed and at scale. Things that are amazing data scientists. And believe me, we have some fantastic ones that I work with, but that we’re able to leverage others outside, some directly within the hospital systems, some at amazing universities that specialize in these things. And we can really get more and more of these solutions embedded invisibly in devices, in workflows to remove the burden on our clinicians and let them spend time doing what they really need to do to give us better health care versus I’ve got to go to this computer program and then switch over to. This device doesn’t go here, there’s so much bogging them down that I’m excited about relieving that burden on them and letting them use their skill and their capabilities again to really pay attention to those I love and to give the best health care that they possibly can without limitations. That makes me excited.
Saul Marquez:
Certainly does. And the opportunities huge to be able to do that for those that are caring for all of us. And so kudos to you and the team, Alison, for the great work and the great thinking that is going on over at health care. We’re getting here close to the end before we do get to your concluding thoughts. Love to hear what’s on your mind reading wise. Any books you recommend to the listeners?
Allyson Jacobsen:
Yeah, I think which one of my favorites that I’ve not read it in a couple of years. I’ve read it several times and should probably read again is Outliers by Malcolm Gladwell. It’s you know, it’s a really interesting book to me. We often think we in the business world need to fit into a certain definition or behave a certain way or we have limited tasks available to us. And that book, every time I read it, reminds me that greatness comes from differentiation and to embrace that and to operate in a way that sets you apart. Does it make you swim with all the fish in the right direction, if that makes sense?
Totally. No, that’s such a great call. And just thinking outside the box Right. thinking laterally. Yes. So great. Yeah. And we do have that tendency in health care Right.. I mean, look, we kind of were vertical thinkers, we think within the health care silo and often even worse within our own silo in the health care economy. So I think this is a great, great book for you to recommend to all of us. Allyson, I had a chance to read it. I’m going to put it on my list as well so I appreciate you triggering that thought and book recommendation.
Allyson Jacobsen:
Yeah, any time I you know, I would love for you to share with me some of your favorites as well. I love to read. I have to admit, I have not been doing as much of it lately as I should. But any recommendations from you?
Saul Marquez:
Gosh, you know, I there’s a ton of books that I love, but, you know, one that comes to mind that is similar to Outliers. Well, I guess there’s OK, I don’t want to give you a bunch, but Range is a book that comes to mind. Have you checked out Range?
Allyson Jacobsen:
I have not.
Saul Marquez:
OK, so Range is about lateral thinking and it’s about exploring the areas outside of our own domain for greatness. And it just gives you an opportunity to really hone in on things that maybe you would not have associated to come up with insights. So Ranges is certainly up there on the list for me.
Allyson Jacobsen:
Well, I’m going to check that out because I definitely always thinking outside of your box is really important to me. And anything that can help motivate me to continually do that is worth it.
Saul Marquez:
You and me both love it. And I’m sure listeners, you’re like, yeah, count me in. This has been this has been a great conversation, Allyson. Some great ideas and insightful comments that you’ve made. I’m sure a lot of people are taking notes, as I am right now. Before we conclude, it’d be great if you could just leave us with the closing thought and then the best place for the listeners could get in touch with you to continue the conversation.
Allyson Jacobsen:
Yeah, I think my closing thought always when I talk about data and analytics is number one, don’t be afraid of it. It’s not here to replace us as humans. It’s here to make what we do every day better, too, is don’t feel overwhelmed by by analytics, by the infrastructure or the things that you think it will take to implement it in your organization, start small, see some successes and expand from there. And you don’t have to do it all yourself. There are some great businesses out there, Intel, U.S., Microsoft, many small data and A.I. startups that would love to partner with you and to build opportunities together. So I think those are kind of the three things I always want people to feel and embrace when they talk about, hey, I have the opportunity for us tomorrow, great thought to leave us with and and even considering GE health care for opportunities.
Saul Marquez:
So if a customer of yours is currently on your platforms, a great application thinking about how the Edison platform can help. But also, how about if somebody is not using your platforms and does the Edison platform help them or can you maybe touch on that a little bit?
Allyson Jacobsen:
It does. Yes, the technologies that we’re building or what I would call device agnostic technology. So absolutely, our applications and solutions can certainly be leveraged from a pure IP play to help anyone.
Saul Marquez:
Very good to know so folks as you explore your options, check out all of them, including GE Health Care, on what they could do. And so where could the listeners find out more about you, about the Edison platform and team?
Allyson Jacobsen:
Yeah, around Edison and A.I. analytics and the things that we’re doing, if you go to GE Health Care dot com rght. on the home page, there’s a product dropdown products and solutions. And if you click that, Edison is readily available. There are lots of white papers, articles, videos, resources where we talk through our development practices and how we’re enabling precision here. Health care with A.I.. So you can go there and you can feel free to email me at Allyson.jacobsen at ge.com or check me out on LinkedIn. I think any of those three ways we could continue a conversation.
Saul Marquez:
I love it, Allyson. There you have it, folks. Several ways to engage. And Alison, it’s a l l y s e n Jacobsen at GE. We’ll also leave a link to all the resources that she has shared today. The show notes, transcripts. You could find all those outcomes, rocket that help in the search bar, type in GE health care, and you’ll find all those ways to get in touch and read the show, notes Allyson. Such a privilege to connect with you today and definitely looking forward to hearing more about how the Edison platform is working to make health care better and also keeping in touch with you.
Allyson Jacobsen:
Yeah, thank you. Invite me back any time. As you can tell. I love this stuff. I’m very passionate about it, and I like to take any opportunity possible to share.
Saul Marquez:
Well, we certainly do appreciate it. Allyson, thanks again.
Allyson Jacobsen:
All right. Bye.
Saul Marquez:
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