In a recent episode of the Becker’s Healthcare Podcast, Senior Analyst Jody Ranck, DrPH, was featured aside Sanjeev Agrawal, President and COO of LeanTaaS – recently recognized as a Flagship Vendor in our AI for Healthcare Operations report. Tune in as they discuss the latest trends on adoption and innovation related to AI in healthcare – specifically more low-risk, high-impact operational use cases. Listen with the embedded player below, or click here to view on Becker’s (originally aired on January 11th).
View the [AI-derived] transcript of this podcast below:
Scott Becker: [00:00:00] This is Scott Becker with the Becker’s Health Care podcast series, I’m Scott Becker, the founder and publisher of Becker’s Health Care and Becker’s Hospital Review. Today, I’m thrilled to be joined by two great leaders, Dr. Jody Ranck and Sanjeev Agrawal. Jody is a senior analyst at Chilmark Research, and we’ll talk more about that. Sanjeev Agrawal is the president and Chief Operating Officer of LeanTaaS. We’re going to talk today about AI and health care operations and a new report from Chilmark Research. The 2021 Augmented Intelligence for Health Care Operations Market Trends Report. This report was released in mid-December and highlights key trends, financial forecast, use cases and vendors in the AI for operations space. We’re pleased to welcome Dr. Ranck, senior analyst of AI and Social Determinants of Health at Chilmark Research, and Sanjeev Agrawal, President and CEO of LeanTaaS. Dr. Ranck is a Senior Analyst at Chilmark Research. I’ll just give a quick bio on both of our speakers, and then we’ll dive into the podcast itself. Dr. Ranck is a senior analyst at Chilmark Research, the global research and advisory firm that’s solely focused on the market to health care IT solutions. Jody has 30 years of experience working in the global health arena. He’s got a magnificent background and resume. He’s worked exclusively on mobile innovations, extensively on mobile innovations, the Internet of Things, wearables, blockchain and the analytics market and health care. And he’s written two books on digital health. He has a doctorate in public health, M.A. in international relations and economics, and a B.A. in Biology. A very, very bright person.
Scott Becker: [00:01:36] We also have the pleasure of visiting today with Sanjeev Agrawal. I had a chance to visit with Sanjeev not long ago. He’s, as we said, the president, CEO of LeanTaaS and a brilliant, brilliant healthcare leader and thinker. LeanTaaS itself is leading health care predictive analytics company based in Silicon Valley. Sanjeev was Google’s first head of product marketing. He’s also led three successful startups, acquired by Motorola, Tell Me Networks acquired by Microsoft and College Feed acquired by After College. He’s written a fascinating book, Better Health Care Through Math, that was published by Forbes in September 2020. He was named by Becker’s Hospital Review as one of the top entrepreneurs innovating health care. Truly a brilliant person and a pleasure to visit with.
Scott Becker: [00:02:20] So, Dr. Ranck. Well, thank you both for joining us. Jody, before we get started, you just want to take a moment and introduce yourself just so people hear your voice as we get started. And then, Sanjeev, I’ll do the same with yourself.
Jody Ranck: [00:02:33] Thank you, Scott. I’m Jody Ranck. I’m a senior analyst who covers analytics and AI and social determinants for Chilmark Research. And Chilmark Research is an analyst firm based out of Boston that covers the overall health I.T. market.
Scott Becker: [00:02:50] Thank you very, very much, and Sanjeev, just a moment from yourself, and then we’ll get it started.
Sanjeev Agrawal: [00:02:55] Sure thing it’s great to be here, Scott. I’m Sanjeev Agrawal and the president and chief operating officer of LeanTaaS. LeanTaaS is a health care predictive analytics company based in Silicon Valley and in Charlotte. And I’ve been here six years. It’s a pleasure to be here, and I’m looking forward to this.
Scott Becker: [00:03:13] Thank you very much, both of you, for joining us. Dr. Ranck, let me ask you this question. This latest surge, which is we’re talking now early January, Omicron is everywhere. It seems like it’s top of mind for everyone right now. As you noted in the report, the pandemic created an opportunity for A.I. to demonstrate return on investment in operations at a time of great need. Staffing challenges: you can’t get off the phone or be on the phone with any health care leader and not talk about staffing challenges. Staffing challenges, workforce challenges are all time high for hospitals and health systems, and experiencing systems are experiencing tight margins, increasing financial pressures from patient loads and decreases in in elective procedures. What other challenges are causing hospitals health systems to tap into A.I. tools? Dr. Ranck, what are you seeing there?
Jody Ranck: [00:04:04] Well, when we decided to do this report is actually the summer of 2020. And as hospitals at that point were starting to reopen and some of these elective procedures back online, we knew that in and of itself would be a challenge. So just for scheduling issues and so forth. You have such a big backlog from this unprecedented crisis that was a major issue. But we also saw throughout the early stages of the pandemic and then to a great extent, into the present you see supply chain challenges as well. So when we were doing the research for the report, we spoke to companies in addition to LeanTaaS we spoke to, like Premier, with the GPOs on supply chains and how they responded to these shortages of PPE and a wide range of other supplies that hospitals need. And so you have the staffing issue in human resources dimensions, but also drugs, PPE and a whole host of things that need to be, you need to respond to these fluctuations that may happen in waves across the country and so forth.
Jody Ranck: [00:05:19] So when you bring all of that together, then you also have this broader sort of situational awareness of, you know, people, things. And then beyond your hospital walls, the predictive analytics to anticipate future waves. So just this morning, I read an article about how hospitals in Miami were tracking. They’re able to get data on the number of cell phone calls and other activities that the consumers, citizens were doing to anticipate surges in demand at their hospitals down in Miami. And I’ve spoken to a vendor in Norway, for example, that does a lot of tracking weather and historical data for like when nurses and doctors call in sick and so forth. And then creating the predictive capabilities to understand when they’re going to be short on manpower as well as other commodities and so forth in the hospital. That’s in a nutshell, those are some of the main challenges to sort of LeanTaaS was doing with asset optimizations.
Scott Becker: [00:06:24] It’s fascinating the things that the staffing and workforce challenges, supply chain challenges and fascinating discussions with premier and CEOs about that situational awareness and then a whole new level of involvement at a whole different level of predictive analytics and fascinating. And Sanjeev, you’ve talked a little bit about background on what LeanTaaS does and how COVID-19 in this latest surge factor into the problems that LeanTaaS tries to solve and solves.
Sanjeev Agrawal: [00:06:56] So LeanTaaS provides software solutions for capacity management, and we are laser focused on enabling hospitals to maximize throughput and optimize the use of their expensive assets, so be it operating rooms, inpatient beds, infusion chairs within the constraints of staffing, the availability of equipment and other key resources. And as you can imagine, that problem statement has always been of importance to health systems, but it’s really come to the forefront in the last couple of years. And just going back to what Jody said, the way we do it is by predicting the future state of what their operations are going to be, like using machine learning based techniques, asset by asset and by predicting the right actions frontline staff should take to maximize access lower cost. And I’ll give you a few examples to make this real.
Sanjeev Agrawal: [00:07:51] If you think about the O.R. being able to predict during peak times which block owners wouldn’t be using their time well and getting them to release it proactively in a surgeon friendly way, so others could use that time and more elective cases could be done during business hours. If you take that example, that’s really come to the forefront as elective cases have been postponed. And so when they came back, this idea of saying I cannot be a slave to my old block allocation, I need to be much more flexible to be able to get folks to release their time and others could use it. As you can imagine, this solution is almost tailor made for a solution like this. Another example is on inpatient capacity, which has forever been managed in what I’ll call, you know, using tools that are not very sophisticated. Morning huddles are based on Excel spreadsheets have taken count unit by unit, which patients in which units are going to have the most issues.
Sanjeev Agrawal: [00:08:50] Now imagine if that changes into one where you can predict hour by hour the expected inflow and outflow of patients. So to be able to enable patient placement, how supervisors to make discharge decisions and transfer decisions in a way that they can actually proactively plan for, as opposed to, in many cases, just reacting in a highly chaotic environment. And the list goes on.
Sanjeev Agrawal: [00:09:14] So in infusion centers, for example, predicting the volume and mix of patients that will come in the future and having them come in a specific order that respects your staffing level, the number of nurses you have. So you can maximize the use of your chairs throughout the day instead of having PKI afternoons and slow mornings and evenings. So providing these solutions to frontline staff that are making hundreds of these really hard decisions, they’re playing supercomputer chests in their heads instead of a supercomputer would play that game of chess and give them the results in the moment to make those decisions. That’s pretty important. And frankly, during COVID, especially given the staffing shortage, which has further exacerbated the issue of high demand with existing supply, the industry has found that traditional methods that they’ve relied on: EHR reports, process improvement, lean Six Sigma done on the backs of Excel spreadsheets, consulting projects that leave behind PowerPoint process maps that don’t actually solve the problem. So the need for these much more scientific, much more mathematically sophisticated tools has never been as important as it has been in the last couple of years.
Scott Becker: [00:10:29] Your point in sort of the metaphor of playing chess in the head or super chess in the head or grandmaster chess in one’s head as they try and figure out these different things and doing it at huddles and doing the spreadsheets when it’s so fluid and so movement. And that movement of that towards advanced analytics, whether for block time, inpatient capacity, discharge and transfers, infusion management, a whole host of areas is really well stated. I mean, it really is this complete evolution of applying analytics and they are to these things. And Jody, let me follow up with you on that question, because your reports on this really sort of almost an emerging category of AI for operations–you’ve seen explosive growth in this. And I think that the stat I’ve been given is annualized growth rate of 40 percent plus of AI for operations forecast for the next few years. How did you assess this category and its vendors and products? Dr. Ranck, how did you how did you get going in this? How did you start to assess this category and the vendors and products in the AI for operations category?
Jody Ranck: [00:11:34] Sure. Well, in addition to the sort of niches that we just signed over with sort of hospital beds, assets and so forth, we also include revenue cycle management and a great deal of that. These processes around eligibility calls, prior authorizations and so forth. So, you know, those are very large markets, especially when you look at RCM, the revenue cycle management and there’s a great deal of room for process improvements and automation. When you look at those by sort of granularly and then when it comes to AI itself, some of the surveys we saw estimate that only about twenty five to 30 percent of hospitals, health care organizations in general actually have an AI strategy for at the moment. So that means there’s, you know, quite a bit of room for growth. And then when you look at the last two years, what we were really impressed, you know, Chilmark, we have a reputation for being pretty hard nosed and critical of health I.T. and this report, in contrast to a lot of the reports we’ve done, you’re quite impressed with the overall sort of outcomes of the vendors we looked at and the impact that they had.
Jody Ranck: [00:12:52] So we thought given the way these vendors sort of came through in the clutch during the pandemic, while the world’s kind of really focused on virtual care, AI for Ops, sort of for us felt like sort of the unsung hero here in this whole pandemic story. And so we think that’ll give a big boost to growth given the demonstrated impact on the market. Then just when you look at the room for impact on these processes and there’s a great deal of room for, you know, cutting administrative waste and so forth that consumes a very large chunk of health care dollars in the U.S. So when you bring all of those together, I mean, most analyst firms view that overall AI market, you see growth rates expected to be thirty five to 40 percent growth rate for the next five or 10 years and we think AI for Ops is largely on par with that. And there may be some pieces that grow even faster, drilling down into the smaller hospitals and things like that. That’s going to be sort of the tail end of that five years. As you know, price points and tools become easier to use for small physician practices like that. But right now, a big chunk of larger hospital systems will really find a great deal of benefit.
Scott Becker: [00:14:17] And take a moment, Jody, and yes, Chilmark’s got this magnificent reputation for unbiased research, for doing a great objective job. You have this thing called the Flagship Vendor. What does that mean and talk about how LeanTaaS selected as a flagship vendor for operational excellence and asset optimization? Why was that? What does this mean and why was LeanTaaS selected?
Jody Ranck: [00:14:41] It simply means when we find vendors are at the peak of vendors within a particular niche that we’re looking at and they have, they have demonstrated impact, meaning there are clients of theirs who have spoken about the substantial impact they have had, whether it’s on the bottom line or processes and efficiencies and so forth. But we see a great deal of evidence. It’s one thing to have marketing materials and another thing to have demonstrated impact and with LeanTaaS it’s actually quite easy to get a lot of information on the impact they’ve had and the breadth of the impact they’ve had at the time. When writing the report, I believe LeanTaas had already been in at least 40 states in the country, and AI in general, having that breadth and then demonstrated impact on the bottom line and so forth, that was quite impressive. We thought that was worthy of recognition.
Scott Becker: [00:15:45] Fantastic. It’s fascinating how you’ve looked at this category compared to other health I.T. categories as well. Thank you. And Sanjeev, let me ask you a question. So Chilmark Research rated LeanTaaS as the leader amongst all rated health vendors in health care. What are you seeing as sort of health system trends? And how is your AI-based software being used? I mean, you must be quite pleased to be ranked by like that by Chilmark. But what are you seeing in health system trends and how is your AI software being used?
Sanjeev Agrawal: [00:16:18] Um, you know, it’s no new news, and it’s no surprise that the pandemic has caused a shift in thinking. And as Jody mentioned, one of the most obvious places is the use of telemedicine, even though that’s not necessarily an AI-based solution. But just this adaptability to open up your arms and embrace something that took 10 years to get there. But COVID made more progress in three months in the use of telemedicine, as many of my colleagues in the health care systems tell me.
Sanjeev Agrawal: [00:16:50] Now what is perhaps more surprising, but not so much, if you think about it deeply, is even the use of AI. So from where we sit, you know, our products have accelerated through the pandemic because they’re great products, but also because they saw key capacity management problems for health systems. So we have a front and center road to what’s going on in health systems. And in the last two years, we’ve almost doubled in size 40 percent growth year over year in each of the last two years, both 2020 and 2021. So we now work with about 125 health systems, almost four hundred and seventy hospitals. We’re now in 42 states, over 10,000 surgeons, 10,000 infusion chairs, 4,000 beds being optimized daily based on the work we do.
Sanjeev Agrawal: [00:17:41] But just to give you one of the examples of what Jody mentioned and around the ROI of these solutions really coming through during the pandemic, which has accelerated their use and adoption, Novant Health stands out to me. They are not an academic medical center. They’re a community health system, large health systems, 15 hospitals, almost 140 operating rooms.
Sanjeev Agrawal: [00:18:04] And if you think about their leadership being forward-thinking enough in the early days of the pandemic to embrace the kind of AI we do and say, “Look, yes, we’re postponing elective surgery, but when it comes back, surgery being the the economic backbone of our hospital system, we want it back as quickly as possible and provide access to patient care as quickly as we can.” And so they adopted our older tools. They also adopted our infusion tools and they were able to recover their backlog, which was thousands of cases within a space of less than two months.
Sanjeev Agrawal: [00:18:39] There are many other examples, for example, the University of Colorado Health that adopted the inpatient beds product. Being able to place patients faster in the ICU admit incoming patients faster, which was one of the biggest issues given the length of stay actually got went up because of COVID. So culturally, we’re seeing a thawing in what I call the sacred cows that have prevented innovation historically that health systems and we can talk about them later in terms of obstacles. But you know, the usual ones like “we’re different from anybody else and what works for UPS and the airlines and and other asset intensive systems will not work for us” or “my EHR can do it all or my IT team can do it all.” Those kinds of things are changing faster in some cases than others, but I think COVID is kind of precipitating that change in a fairly rapid way.
Scott Becker: [00:19:34] And take a moment–you had mentioned EHRs. Let me ask Jody about this, Jody, one of the things that’s fascinating about your report is that EHRs been just the key to digitization efforts in health care. We all know that we won’t be anywhere near where we are today without EHR. When people say return to paper, we know that they’re crazy. But in your talk about sort of A.I. for Ops and that category, you really didn’t talk about any of the major EHR vendors. Can you talk about how this has happened and how that’s evolved?
Jody Ranck: [00:20:05] Well, if you look at, let’s say, tech revenue cycle management, for example, Epic is the more robust players in that space or it has as a solution in that space. But then when we put AI and RCM together, you take a look at the market and we see that. So there are a number of other vendors from, you know, a lot of that sort of startups or more mature startups Change Healthcare, Waystar, Health Catalyst, for example.
Jody Ranck: [00:20:39] If you look at the number of claims and so forth that they’re managing per year, it’s an extremely high percentage of overall claims. I think just two or three of those together, you can get to 75, 80 percent of all transactions in the United States. So when it comes to the AI piece, you just think that these other vendors outside of the EHR ecosystem, where a lot of the innovation is happening and, you know, if they integrate into the major EHR vendors in many ways.
Jody Ranck: [00:21:11] So we just decided to go with, you know, really take a look at what we thought would be the bleeding edge of innovation in this space. And our focus on that, and it doesn’t mean that there’s nothing going on in the EHR space. We just think that what these folks that are coming from outside the EHR space mean in terms of A.I. for creative efficiencies, RCM, operational excellence and so forth is much more impressive at this current stage of AI, and there may be a lot of acquisitions and things like that. And going forward, we do expect to see a lot of acquisitions of some of the smaller startups in the future, too. But I think this is where a lot of the innovation is happening right now, the things that will sit on top of the EHRs.
Scott Becker: [00:22:08] And talk about that, this AI for Ops, it’s a fascinating perspective because you highlight that digital transformation is still in the early stages of health care, but that essentially A.I. for operations is becoming foundational. What are some of the critical components when it comes to making this digital transformation successful? And what has that growth been like the past couple of years?
Jody Ranck: [00:22:32] It’s just the one thing that we’re beginning to see is, you know, in the last, prior to the pandemic and even of the last two years, you saw a lot of hospitals and health care organizations doing their first, you know, getting your feet wet in AI and going for a single point solution and seeing how that works. And there are various levels of sort of openness to engaging with AI and then the given the pandemic and as Sanjay was saying, like the need to innovate their way out of this crisis, minds began opening up to other solutions. And so what we began seeing is that, you know, you really need to go beyond these point solutions and think more broadly about platform plays that can do end-to-end applications of AI to really harvest the benefit and whether it’s revenue cycle management or operational excellence or what have you, that that’s where you really see the value add. And just the point solution can be rather extremely limited and you’re not taking advantage of feedback cycles and things like that. And really, you know, forcing your organization to come up with a digital transformation strategy and, you know, some people are talking about AI first.
Jody Ranck: [00:23:54] It’s part of that strategy, but there are various different perspectives on how you proceed with that. But I think the most important sort of foundational component in my mind beyond the technology, is going to be the people and understanding what the various end users and stakeholders, what are their needs and how they’re thinking about this. Because we do see resistance. People fear, you know, losing jobs due to A.I. and so forth.
Jody Ranck: [00:24:24] So really, you know, having a digital transformation strategy that engages with your end users or nurses and other staff very early on in understanding sort of the politics of technology within the organization, the meanings that they give will enable a smoother adoption of the right tools for the right people. In the end, that’s what you know, to work. That’s what you really need to to have. And you know, Sanjeev, I know, has a lot of interesting thoughts too about, you know, some of these sacred cows and health care we need to get over as well.
Scott Becker: [00:25:06] It’s a fascinating perspective in terms of whether it’s piecemeal or an end end solution and so forth or somewhere in between. What we do know from watching the development of technology and health care is that people don’t want 10,000 different solutions on this. They want it most a handful or so forth versus versus so many isolated and fragmented solutions. It’s a fascinating perspective. Jodi, thank you very, very much.
Scott Becker: [00:25:29] Sanjeev, let me ask you this question about staffing. Obviously, you can’t get through a conversation today without hearing somebody talk about how challenging workforce issues are and exhaustion and burnout among nurses and physicians and so forth. There’s discussions. Reuters reports the intention to leave has doubled to 20 to 30 percent, with the current variant now here. How can hospital systems lean on digital tools to help combat nurse staffing issues and challenges and shortages? How do you utilize digital tools, A.I. and so forth to help in this area and this category too?
Sanjeev Agrawal: [00:26:07] Yeah, this is absolutely the biggest crisis facing health care in the short term and the need to be more efficient and maximize productivity, that’s never been higher. And so where digital tools can really help is to ensure that the staffing that is there is optimized and leverage the best it can be to match the demand. And also, it can be used to enable the staff to make higher level decisions and take more of the mundane away to increase staff satisfaction. You will find that large portions of usable time go unused during business hours, but these ORs are still doing elective cases in over time.
Sanjeev Agrawal: [00:26:47] Meanwhile, if I’m a surgeon, it’s easier for me to find a table for four for dinner on OpenTable than it is for for me to find time in the O.R.. That’s because the scheduling practices that have been in place are just 50 years old, and they’re archaic and they’re built into the EHR and they never changed. Meanwhile, anesthesiologists and nurses are having huge peaks and valleys during the day, so the way you change that using AI and ML based tools is to get the right open time available to the right surgeon way in advance of auto release, which are these backstops and measures that are put in place because that’s the best technology we have. Another example is, you know, why are nurses and patient placement staff still relying on these? And I’ll just say I’ll call them abacus-level tools in the morning huddles to decide which units need the most attention. Imagine digital tools that actually do much of that work and take the cognitive overload of making all these hundreds of these decisions away from people who are there to save lives.
Sanjeev Agrawal: [00:27:49] They aren’t the ones there to do supercomputer math in their heads, and instead, if that’s augmented, their job is augmented by giving them these tools at their fingertips. Much like, you know, if you look at the life of a UPS truck driver and how that’s changed over the years, it’s gone from, you know, paper based routing to everything is done for them. They need to show up and just execute against a set of instructions that are 99 times out of 100 pretty much the right thing to do. You know, and similarly, if you look at clinics that are still faxing in and calling in surgery orders and requests for four to see a patient, it’s 2022. Why isn’t this happening electronically and automatically? And you know, this notion of staff satisfaction and retention depends so much on the quality of life that nursing anesthesia and the techs and others in hospitals experience. And so if we can free them up from mundane and repeated tasks, so they get nuanced predictions and prescriptions and what they should do, much like Waze gives us in traffic and so many other systems that we use in our everyday lives do. So some of the frustration that arises in addition to shortages in staff comes from having to fly blind and each day having to like Groundhog Day, repeat the chaos of the previous day, and a lot of that can be avoided through AI.
Scott Becker: [00:29:17] And take a moment. Sanjeev, sort of you’ve got so many things pushing the driving of AI in health care and maybe talk about some of those factors, but also talk about, on the flip side, what are some things that are impeding the adoption of A.I. in health care? What are some of the things you see here that driving and and impeding adoption?
Sanjeev Agrawal: [00:29:39] Yes, I think the biggest driver of AI, much like most other technologies, if you think about the adoption of the internet or mobile phones, it’s market economics. You know, if you think about five thousand hospitals in the United States that have sunk in anywhere between $400 to $600 million each of assets in the ground, operating rooms, cancer centers, there’s a lot of capital. There’s trillions of dollars of capital that isn’t being used well because of archaic methods of scheduling. There isn’t that much more capital left to put into the system, so the existing infrastructure will have to be used better because, you know, the hospital economics were suffering even before the pandemic hit them. And this has just done a a worse number on those economics.
Sanjeev Agrawal: [00:30:25] And meanwhile, demand is rising. We have a growing, aging population with more chronic disease. And so the need to figure out how to do more with less is no longer a luxury. Operational efficiency is a must. And if you look at other industries, you know, if you look back at banking in 1985, there were 17,000 or so banks in the United States. Now there are 6,000, and the six thousand that have consolidated and are left are far more efficient because companies like Schwab came in and through a mix of online and offline banking, provided much better service at lower cost to the consumer.
Sanjeev Agrawal: [00:31:00] And so that has to be the case with health care, too. It has to be a when I talk about consumer centric or patient centric health care, it’s “give me access to good health care when I need it, for a cost I can afford.” And the only way that’ll happen is through the use of these tools, whether it’s AI or ML. In some cases, frankly, it’s not even artificial intelligence. Some natural intelligence to do things differently will do as well. That’s a whole other topic. The biggest obstacle is I’ll just build on what Jody said: to me, are the fact that this is not just a technology problem, it’s actually technology-enabled transformation of process and that requires people to adopt it. And that requires leadership; that requires people who have been in positions of power and are making the decisions to viscerally understand that old ways of doing things and the same six-year consulting project and the process improvement stuff we’ve been doing, will only go so far. They have to get over this idea that, yes, they paid hundreds of millions of dollars to put their EHR in, but it doesn’t do what is required. It’s a large luxury yacht, it’s not a speedboat.
Sanjeev Agrawal: [00:32:14] So AI provides a bunch of speed boats that are required in addition to the backbone that the EHR provides. This idea that to build scalable, usable software, they have to look at something built in the last five years as opposed to something built 30 years ago. They have to embrace the fact that, yes, a lot of my workforce has been doing the same thing forever, but it’s in my and their best self-interest to actually make change happen. Think about what the experience of someone that manages a check-in counter at an airline at an airport is very different today than it was 20 years ago, because most people are self checking their bags. Most people self checking themselves. So the quality of the problems that someone at a checking counter or in baggage claim is handling at an airport is much more advanced. These are problem solvers. These aren’t people that are doing the mundane, everyday tasks. So leadership that gets the need for this transformation and health care by far will not be the first industry that will go through. It will be the ones that survive and thrive. And I think that’s that’s going to be a big market driver for why AI will be adopted even faster than it’s been so far.
Scott Becker: [00:33:28] Sanjeev, thank you, and Jody, let me ask you, Dr Ranck, if any color commentary you wanted to add to Sanjeev’s thoughts on things that are driving adoption might slow adoption if you wanted to add a moment or two of color commentary there. That’d be great.
Jody Ranck: [00:33:43] For me, my thoughts extend more to, you know, broader AI applications beyond operations. But where we have this issue of trust and trust in algorithms, decision support or, you know, analytics used in a population health management tool and so forth. I mean, we’re we’re hearing a lot of talk about, you know, trust is central, but there’s not a lot of trust yet, given it’s the early days for AI. And I think we need to begin really tackling that head on. But also, you know, understanding what I mean when we talk about trust, are we talking about trust in a system or trust in an algorithm? Where do the human human factors come into over, like in clinical judgment, if a physician decides not to go with what an algorithm tells them to do, you know, how’s that managed and what happens when things go wrong and what’s recourse? What kind of recourse you as a patient have if things don’t turn out all right due to an AI model and so forth.
Jody Ranck: [00:34:49] And so I think we’re getting a lot of issues in the press around bias and things not turning out OK when it came to like the Epic Sepsis model or the Optum disease management model a couple of years ago that was found to be racially biased and then had to be tweaked to get rid of that bias. But these things are in the press a lot. The health systems, you know, not a lot of people trust their insurer. So when it comes to AI, you’re sort of dealing with things beyond AI and so coming up with some conceptual clarity around what is trust, how, who, you know, do you earn it and what do we mean by, you know, trust versus reliability and confidence and all of that? I think we’re, you know, it’s part of that overall digital transformation piece. We have some homework to do on that front to make things a little bit easier for all users and designers, developers involved, especially when it comes to the clinical algorithm.
Scott Becker: [00:35:55] Thank you. It’s a fascinating perspective on some of the concerns that are out there that you might not expect, it certainly wouldn’t be top of mind for someone like myself who follows the space with health care so closely but wouldn’t have thought of those are some of the issues on AI that that are some of the obstacles. Fascinating. Sanjeev, final question. 2021 Augmented Intelligence for Health Care Operations Market Trends Report. Where can people find that? How can people get a copy of that report?
Sanjeev Agrawal: [00:36:25] In a couple of ways. They can go to our website. LeanTaaS.com. That’s leantaas.com/chilmark. So Chilmark with one L.. They can also email me. It’s just Sanjeev (at) leantaas.com. And I believe that when this podcast is published, the link to the report might be included in the description. And if it is, that’s a third way of getting it. So any of those ways which should work.
Jody Ranck: [00:36:58] You know, Chilmark research has a link to it for people who want to purchase the full report. We’ll have that as that’s always available in our website as well.
Scott Becker: [00:37:06] Fantastic. I want to thank both of you. I want to thank LeanTaaS as our sponsor today’s episode, but far more so, just a great pleasure to visit always with Sanjiv Agarwal, President and COO of LeanTaaS. Just brilliant. And again, if you haven’t seen the book “Better Health Care Through Math”, just brilliant and thoughtful. Dr. Jody Ranck, a pleasure to visit with you again. Brilliant, brilliant health care, digital career, senior analyst at Chilmark Research, global research and advisory firm. Thank you both for joining us and thank you for the work that you do. Thank you very much.
Sanjeev Agrawal: [00:37:40] Thanks, Scott.