In this episode, Dr. David Stoffel, Chief Business Officer at RapidAI, here to discuss combating negative margins with advanced clinical AI.
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Learn how to listen to The Hospital Finance Podcast® on your mobile device.Highlights of this episode include:
- About RapidAI.
- Why some healthcare AI solutions are missing the mark.
- What makes an effective and valuable clinical AI tool versus one that isn’t.
- Why it is important for hospitals’ healthcare AI tools to provide financial value in addition to clinical value.
- How RapidAI’s advanced clinical AI has positively impacted hospital finances.
Kelly Wisness: Hi, this is Kelly Wisness. Welcome back to the award-winning Hospital Finance Podcast. We’re pleased to welcome Dr. David Stoffel. He has spent more than 20 years developing and commercializing innovative technologies and services in the medical device industry and has an extensive track record of success in scaling healthcare businesses. He currently serves as RapidAI’s Chief Business Officer, where he leads a variety of commercial and operational teams, including clinical affairs, education, marketing, clinical success, corporate development, and finance. Prior to RapidAI, David helped launch and lead the mobile cardiac telemetry business at iRhythm Technologies, one of the fastest-growing digital health companies, and was Chief Business Officer at Ceribell, maker of an innovative point-of-care EEG solution. He also led marketing and corporate development at Intuitive Surgical, contributing significantly to establishing the Da Vinci Surgical Robotic System as a new surgical standard of care. He has a BA in economics from Stanford University and received an MD and MBA from the University of Chicago. In this episode, we’re discussing combating negative margins with advanced clinical AI. Welcome, and thank you for joining us, David.
Dr. David Stoffel: Thank you for having me, Kelly. Thrilled to be here.
Kelly: Awesome. Well, let’s go ahead and jump in. So can you tell us a little bit more about RapidAI and your role as Chief Business Officer?
David: So yeah, happy to introduce Rapid. So Rapid is the largest AI focusing on healthcare by install base. We have over 2,200 hospitals worldwide. We’re in over 100 countries. And we’ve done over 14 million scans to date. RapidAI was actually spun out of Stanford University. It was focused originally on stroke and was one of the pioneering AI algorithms in determining which patients should be eligible for mechanical thrombectomy. It was published in the DAWN and DEFUSE trials, which were seminal trials published in the New England Journal of Medicine, that really changed the face of stroke care. In fact, after those studies were published, the guidelines for how stroke should be treated were changed within 30 days. Which, if you know, in healthcare was actually– it’s pretty astounding. And so since we launched in 2018 and 2019, we’ve really gone and been adopted. We’re in about 75% of the comprehensive stroke centers in the country. And since then, we’ve also expanded the number of disease states that we go after into hemorrhagic stroke, aneurysm, pulmonary embolism, and a variety of other disease states that are pretty near-term.
Kelly: Wow, that is impressive. So, let’s start by setting the scene. We hear so much about the tough financial position and challenges that hospitals are facing right now. We also hear a lot about the potential of advanced technology and AI to change things for them, though implementing those tools obviously comes at a cost. Can you talk about the situation that many hospitals are finding themselves in now?
David: Sure. Yeah, it’s a tough situation for hospitals because the demand for imaging, like CT imaging, MRI imaging, is growing exponentially. And the supply of radiologists is only growing linearly. And so you’re getting this huge divide between the demand and the number of scans that a radiologist has to read, and the actual number of radiologists that are available to read them. And so that’s kind of one of the problems is that there’s a productivity problem. And so, the radiologists need a technology, and really the only way to bridge that gap is with technology. And so, you need something that’s going to help them triage things, allow them to perform really at the top of their license by flagging the items that need to be paid attention to, and so that they can pay attention to the things that actually need their expertise and be able to triage the things away that maybe don’t need as much as their expertise. So that’s one big role for AI is bridging that productivity gap between radiology and the ever-growing demand for more and more imaging.
The second part of AI is that there is a huge demand for information sharing throughout the entire hospital. And so many times when we think about the traditional way of doing medicine, the patient goes to the CT, the MRI, the images go over to radiology, radiology calls down to the ER doc, the ER doc calls to the interventionalists, the stroke team is coordinated, but it’s a very serial way of processing the information. When we think about AI, we think about algorithms, but we also think about how that workflow is impacted throughout the entire hospital. And what we try to do is install parallel communication as opposed to serial. And what I mean by that is this. Imagine that once the CT is done or the MRI is done, the radiologist, the ER doc, and the neurointerventionalist is getting the information at the same time. And then they can coordinate and discuss the care of that patient in real time over their phones, no matter where they are, whether they’re in the hospital or they’re at their homes. That’s really the power of what Rapid can bring, is that it not only diagnoses the diseases upfront, it enhances that communication. It’s embedded in the workflows so that we take friction out of the system so that people are communicating effortlessly and efficiently so that they can coordinate and deliver better care for their patient.
Then on top of that, we also provide a data and analytics system that can provide feedback to the program as to how they’re doing. It’s called our Insights Platform. And what this does is it captures the information and the data as it flows through your system. So, it gives you real-time feedback into how you’re doing, and it helps you identify places where you might not be as efficient. So, it’s not only giving you the information for the algorithms, helping socialize it through the communication platform, but it’s also giving you that feedback to help optimize your processes so you’re continually improving and able to get more and more efficient.
Kelly: Wow, that is super cool. It sounds like a very impressive system. So why are some healthcare AI solutions missing the mark when it comes to providing financial value for hospitals?
David: Well, I think the challenge for a lot of AI is that they think AI is only an algorithm. And what we really try to do is deliver AI as a program. And it’s something that enables you to spread it throughout your entire system. So, if you just dump an algorithm on the hospital, then they have to go and integrate it into their IT systems. They have to figure out how they’re going to figure out the communications platforms. And they’re going to have to figure out how the communication is spread throughout their hospital network. Rapid really has a complete thought and a complete program, which is what I described earlier, which is it’s not just the algorithms, but we’ve designed the workflows to take friction out of the system so that it’s pretty effortless to be able to incorporate all the different teams within your own hospital, but also spread the AI throughout your entire network so that you can almost communicate as one. And so patients who are walking into one of your hospitals, many times, if they don’t have AI, they’re kind of isolated away from the expertise. So, the hub or the academic center or the tertiary care center may have the neurointerventionalists and the neurorads, but they’re isolated from those spoke hospitals who might be more remote.
And so, they don’t have that access to information or the expertise that that hub can provide. With AI and that connectivity between the spoke and the hub, a patient can walk in anywhere in your network and receive that expertise almost instantaneously because that information and the AI algorithms and the communication have socialized it throughout such that every patient is getting the best care possible the minute they walk into any hospital on a network.
Kelly: That makes a lot of sense. So now let’s get into what makes an effective and valuable clinical AI tool versus one that isn’t.
David: Well, if I could, I’d go back to what is the financial value of this? And maybe I’ll back into that. Really, when you start spreading the AI through your network and start utilizing it in all your different spoke hospitals, you’ve enhanced the communication among your physicians, and you’ve enhanced the level of decision-making. So, all of a sudden, take a hemorrhagic stroke, for example. We have a solution that can identify hemorrhagic stroke and then quantify the volume of the blood in the brain. Many times, without this type of– without the connectivity, a hospital in the rural area will see blood on the brain and immediately transfer them over to the tertiary care center. Well, this transfer can take many hours and takes the patient way far away from their family and their site and where they live. They also might not get an intervention at the tertiary care center. So, a lot of times these patients arrive at the hospital and they’re not intervened with, they kind of sit for a few days and then they’re transferred back.
With AI, what the connectivity and the information can do is that once that blood is detected, that detection can then go to the neuroradiologists or the experts in the hub hospital. They can converse with the spoke hospital and decide if this patient is needed to be transferred. So, with that, you’ve not only just made– if the decision is made to keep the patient in the spoke, you’ve kept them closer to their family, you’ve not transferred them unnecessarily and wasted all the time and resources there, and you keep the financial return in that spoke hospital because they’re keeping the patient. If you decide to transfer that patient, that frees up a bed in the spoke, but it also transfers the patient, enabling them to get a procedure– enabling the tertiary care center to get their revenue from doing that procedure. So, you’ve created a financial win throughout the entire system. Clinically, this is really important because all of a sudden, you’re not subjecting patients to unnecessary transfers. You’re not subjecting them to just wait-and-bake type of scenario is what they’re called. You’re sitting there and you’re transferring. You’re going to wait there for 10 days because they decide what to do with you. You’re able to get– you’re able to line up the right patient with the right bed and the right care. And that’s better financially for the hospitals, but it also delivers great care for the patients.
Kelly: That sounds like a win all the way around to me. So why is it important for hospitals’ healthcare AI tools to provide financial value in addition to just clinical value?
David: Well, I think we’re in a place where I think the last statistics that I saw were over 50 or almost 60% of the hospitals actually have negative operating margins. So, in other words, almost 60% of hospitals are losing money in the way they operate. And so for them to really adopt new technology, even if it is better care for their patients, they have to find a way to make it financially viable for their institution. And so, when we think about AI solutions, we think about hospital financial impacts in a variety of different ways. It can increase the number of procedures that are done. And that goes back to that example I saw with transfers coming in from spoke hospitals. You can make transfer decisions and say, “Okay, we are going to intervene with this patient.” And it’s a way for that hospital to increase the number of procedures they do. It’s also a way for them to avoid unnecessary transfers. This enables them to keep money out in the spokes– or the patients out in the spokes and keep that money in the spoke hospital as opposed to transferring it unnecessarily.
And then when you deliver better care and have interventions, you also have lower length of stay, lower complication rates, because you’ve lined up that patient with the right side of care and the right type of intervention. And so you get the better outcomes that go along with it. And so for us, it’s really important to always think about delivering clinical value because what’s right for the patient is the technology that we want to deliver. But we also want to make lives for the physicians more efficient and easier so they can do their job faster and more efficiently. But then you also want to have to deliver financial value for the hospital so that they can afford the technology that they need to purchase. And so that’s why, for us, whenever we develop something or an algorithm, we always start with the clinical value, but we translate it back to how is that going to impact the providers’ lives. But also, we want to make sure it’s making a financial impact for our hospital partners so that they can go and afford what it is that we’re offering them and still make a reasonable financial return as well.
Kelly: Yeah, I really like that idea of providing financial value and clinical value. Those two things really go together here. So how has RapidAI’s advanced clinical AI positively impacted hospital finances, both directly and indirectly?
David: Sure. So, our approach to clinical AI is different from other companies. We have an approach we call Deep Clinical AI. And many AI companies just have what they call triage. And triage is very simple. It’s the lowest level of FDA clearance. And what it is, is that the AI detects something on a scan, and it says, hey. It shows up a flag to the radiologist, or whoever’s reading it, and says, “Hey, there’s something here.” But then the radiologist has to go and hunt and peck on that original source imaging and go find what it is. This is sometimes frustrating for the radiologists or the physicians because there are a lot of false positives and false negatives out there. So, if you get an alert and you don’t find something on it, well, was it an error of the AI, or am I just making a mistake? So really, it doesn’t increase the efficiency of the physician. Rapid’s approach is very different. So, we not only do triage, but we do what we call localization, quantification, characterization, visualization, and tracking over time. So that’s a real mouthful. But what that means is that we not only flag potential disease that’s found, we also will localize it for you, we’ll help you visualize it, help you characterize it, and we enable you to track those changes over time.
And those require a higher level of FDA clearance. But we think that that level of investment and that level of clinical information is what’s really important to help change and improve decision-making and enable physicians to arrive at the right decision faster, as opposed to increasing the stress of just saying, “Hey, there might be something here,” and then leaving it on them to go find it. So fundamentally, our approach to clinical AI is very different from the others. And then when you think about all the integrations it has, everything we do is integrated into PAX. Everything we do is integrated into mobile. Everything that we do has that data analytics platform attached to it. That’s when we start thinking about really, really operationalizing that AI, and not only enabling greater decision-making, but also enabling those financial returns for the hospital to keep improving the program, but also see how they’re doing and make changes as needed.
Kelly: Yeah, I love all the positive impacts you’re making there. Well, David, thank you so much for joining us and for sharing your insights on combating negative margins with advanced clinical AI. We really appreciate you being here today.
David: Well, thank you for having me, Kelly. Really enjoyed it.
Kelly: Yeah. And if a listener wants to learn more or contact you to discuss this topic further, how best can they do that?
David: Yeah. Feel free to reach out to me. My email address is Stoffel, S-T-O-F-F-E-L, at rapidai.com. And happy to engage in more conversations or field any questions.
Kelly: Great. Thank you for providing that. And thank you all for joining us for this episode of The Hospital Finance Podcast. Until next time…
[music] This concludes today’s episode of The Hospital Finance Podcast. For show notes and additional resources to help you protect and enhance revenue at your hospital, visit besler.com/podcasts. The Hospital Finance Podcast is a production of BESLER | SMART ABOUT REVENUE, TENACIOUS ABOUT RESULTS.
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