Risk & Resolve
The Risk & Resolve Podcast is your go-to resource for insightful conversations at the intersection of leadership, business ownership, and the insurance industry. Hosted by Ben Conner and Todd Hufford, this podcast dives deep into the challenges and opportunities that leaders face in an ever-changing world.
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Risk & Resolve
Steve Santangelo: NextGen Healthcare Summit 2025 Recording Series
In this NextGen Healthcare Summit episode with Steve Santangelo of Garner Health, he delves into healthcare costs heading toward an unsustainable threshold where nearly 9% of company revenue will go toward benefits packages—the same percentage that contributed to GM's financial crisis in 2008. Data analysis reveals that which individual doctor a patient sees has the greatest impact on healthcare costs, with significant variations in quality and expense that don't necessarily correlate with each other.
• Top-performing doctors maintain complication rates around 5% for major surgeries while bottom-performing doctors approach 20%
• Approximately one-third of all healthcare procedures are deemed medically unnecessary
• Basic office visits cost around $100 with top doctors versus $250 with lower performers
• Traditional methods of evaluating doctor performance are highly inaccurate
• More granular assessment of doctor performance includes factors like tendency to jump to surgery, techniques used, complication rates, and setting choices
• Employers can save approximately 27% by directing employees to higher-quality providers
• Current provider directories are only about 27% accurate, creating barriers to finding good doctors
• AI and machine learning can help clean provider directories and deliver real-time information
• Using incentives to encourage patients to see high-quality doctors creates better outcomes while reducing costs
• Implementing these approaches can reduce employer healthcare spend by approximately 12%
You're listening to Risk and Resolve. And now for your hosts, ben Conner and Todd Hufford. I'm Steve Santangelo. As mentioned, I got in late last night from New Jersey where I live. I do have a toddler and I've done the cookie battle many a time, so I can resonate with that. But basically last night got to my hotel, was thinking about healthcare, which is really where I'm focused. I know Ben showed that awesome video highlighting kind of some of the struggles that we're seeing in healthcare today and then thinking about AI technology in the future and kind of how to bring that all together in my presentation here today.
Speaker 1:And so what did I do in my hotel room late last night? And I went to chat GPT and I said how do you give a good presentation? And so there's basically two things you got to do. The first is introduce yourself and you made me sound way more important than I am. And then two, you start with something thought provoking, maybe a question or something like that.
Speaker 1:So for the audience, going back to 2008, huge financial crisis, gm at the forefront of that. Does anyone know what really caused that for GM? What was the real impact to their business that caused that massive financial pressure? The real impact to their business that caused that massive financial pressure, didn't say if no one answers what to do, so I'm going to go forward. Anyway, basically this is really the problem. So GM got to a point where their revenue percent of their revenue 9% was attributed to their pensions and benefit offerings. So think about that 9% going just towards that. So that basically led Warren Buffett to come out and basically say GM's really not a business. What they are is a huge annuity, an insurance company with basically a major auto company attached, and so that is pretty powerful to see there.
Speaker 1:Warren Buffett has also been quoted as saying basically the American healthcare system is kind of a tapeworm on the American economy. It gets more expensive every year. You kind of saw that in Ben's video earlier. It's just costs are continuing to go up and unfortunately incentives are very misaligned across the entire industry to really impact that. And so what's going on in the world and again, I've been in healthcare now over 15 years we're kind of all headed in that direction. That GM was, and so latest estimates basically say that by 2031, all employers in the entire United States are basically going to be at or exceed that 9% of revenue going towards benefit packages. So we're kind of headed on a crash course.
Speaker 1:We've been on that journey for quite some time. Ben can speak very intelligently to that, but when I started at UnitedHealthcare over 15 years ago, we always said if a premium got to $500, the whole system's going to shut down. Many of you in here are probably paying double, if not triple, that, depending upon if you're enrolled with families and things of that nature. So it's really an unsustainable course and unfortunately we got to start to think differently if we're going to make an impact on that. And that's where I think all of these great things data technology, ai can really help us kind of tackle that problem. So the thing that we've done here at Garner is basically looked at over 75% of all of the medical claims in the entire country and that's a big data set. That requires a lot of technology machine readable files, ai, things of that nature and what we said is well, what is it that really impacts, cost the most? And so what we found by using the data is really there's one thing and it's really which individual doctor a patient sees has the greatest impact on the cost of care, and if we can get a lot of people to start seeing really high quality doctors, we can actually reverse that course and start to bring down the expected trend of healthcare. And so what we've done is written over basically 500 different metrics, both quality and cost focused. But just wanted to highlight some of the things that we see in the data around.
Speaker 1:First, complication rates from major surgery. If you look at the top 25% of doctors, they average about a 5% or better complication rate. If you look at the bottom 25% of providers, you have almost a 20% complication rate, and again, that's just after a major surgery. But that applies basically to any healthcare needs you could ever have. And then the other thing that we're hearing out there. A lot of people have talked about fraud, waste, abuse in the healthcare system. The reality is is that about a third of all healthcare out there is deemed unnecessary, meaning it's a procedure, it's a test, it's a drug, it's whatever. That should have never actually happened.
Speaker 1:And imagine that if we could eliminate 30% of all healthcare spend in the country again really starting to bring down that cost of care and so highlighting kind of inappropriate replacements of joints again unnecessary care you get people to the top 25% of doctors. That's sub 10%, unfortunately, if you end up seeing a bottom 25% provider, that's near 40%. And so that's it from kind of a quality perspective. There's also huge cost implications. Obviously, a surgery that's unnecessary is a huge cost burden. But you can see here, you know, just the basic cost of an office visit. So you get people to the top 25% of doctors somewhere around 100 bucks. Bottom 25, you're approaching to $250. And then if you think about costs of knee replacements, there's, you know, huge cost variations just on the negotiated rate between the payers and the providers and you see a staggering difference there. Top 25% of doctors you know in the ballpark of $13,000, almost $25,000 if you go to the bottom performing doctors.
Speaker 1:So what I wanted to do and hopefully these are no one's relatives here, but we're gonna start naming names and talking about kind of what's been done in the world as it relates to identifying high quality performers and kind of how Garner's taking a different approach using the technology and some of that machine learning, and so historically all doctors have been scored on this thing called episode groupers. You basically take a whole bunch of patients attributed to that doctor, you divide up the average cost per patient for a specific set of codes, whatever the condition might be, and you basically get a total cost of care. Again, I think we're all aligned here that having lower cost is a good outcome, and so in that traditional approach, you get one doctor who averages $13,000. You have another doctor who averages $14,000. Everyone wants to get over to the left-hand side Lower cost is better.
Speaker 1:The unfortunate reality, though, of how we've done data and analytics in identifying doctors with this episode grouper approach is it's highly inaccurate. So you could have one or two patients who maybe the doctor did all the clinically appropriate things. They prescribed them in a low-cost generic medication, but all of a sudden that patient has an allergic reaction to that generic drug and needs to go on to a higher cost brand name medication. When you do things like episode groupers, it doesn't really understand that and equate that. And now you've got a doctor who's done all the right things maybe the doctor on the right here and they're getting dinged or a higher cost score for that patient, and it's kind of skewing them. And then, if you imagine, maybe they only have 20, 30 patients worth of patients in that data, you could see how this could get skewed year over year.
Speaker 1:And so what we found is it's highly inaccurate and you're going to give recognition to one doctor, but maybe that's not the best approach, and so what we've done here is kind of again, use data, use technology. We use a lot of technology to actually grab the latest peer-reviewed medical literature out there to help inform our team on how to accurately identify top performing doctors, and so what you can do is actually start to go in a much more granular fashion to understand doctor performance. How likely are you just to jump right to surgery before trying physical therapy? Again, about a third of all healthcare out there, those surgeries are unnecessary, and so if you can get more people to try physical therapy first, alleviate pain, alleviate pressure and maybe they're back on their feet, that's obviously a great outcome, not only from a cost perspective. But I don't think anyone in this room wants to get surgery when you could go for six, eight PT sessions and avoid that procedure, and so you can see a stark difference here between the doctors on the left and the right. We're then going to look at what types of techniques do they actually use when they're performing their surgeries? What are the complication rates after you have that particular procedure? Revision rates, what percent of the surgeries are going to be done in an inpatient setting, really high cost, versus maybe an outpatient ambulatory setting. What are the costs associated with those negotiated rates between the providers, the facilities that they utilize and the payers?
Speaker 1:And what we've done is kind of come up with this new approach to really understand which doctor you actually want to see. And you could see here a stark difference in a rating when you go kind of a bottoms up methodology versus a traditional episode grouper approach. And so obviously it's pretty clear, I think here, looking at the data, that you want to go to the right instead of the left, whereas as a nation we've kind of always operated if you could even get access to information with the model up on the right. And so what we've done is actually started to show the results for employers that we've worked with. Again, we are fortunate enough to actually get the data and be able to mine that with our technology, and so before kind of utilizing that approach that I showed before, you can kind of see the results. So they had five people go to the doctor on the left because again it was deemed high quality.
Speaker 1:After realizing the supplying the information, we were able to actually change behavior to provider on the right, and so this is kind of what happened. So you can see here they had five members see that doctor on the left. You can see jumping to surgery, revision rates, all of the things that I just mentioned, and you could actually see the total paid claims that that employer had attributed to just one doctor, and you can imagine many people seeing many doctors like this on the left, and so what they also had was huge member cost share. So not only are employers being squeezed again as we approach that 9% revenue threshold, we also have an affordability problem in this country where most folks can't actually cover the deductible because the average cash on hand is actually lower than their deductible. So, even though you've got health insurance, you're virtually uninsured.
Speaker 1:And so what we did is again, use the data, use the technology. Let's get people over to the doctor on the right-hand side, and here's what actually can happen. So if you can leverage data, you can leverage technology. You can give people, at time of need, useful information on where they should be going for their care. You can make a major impact, saving over $100,000 just for this one particular employer, and you can see all of the great clinical outcomes. But the best part about it is, if you can share that information and have people make optimal decisions, you can eliminate the affordability problem as well. So any employers in the audience that are covering or sponsoring health benefits, you could see easy trade. I'll pick up the $18,000 for you and we'll save $100,000 on the top line, and that's how we can really start to use data technology to leverage win-win situations for employers.
Speaker 1:So the other thing that I wanted to talk about is how we should all think about this stuff differently. So what's interesting is I live in New Jersey but right outside of New York City a lot of folks in here if you do any traveling for work, your company kind of gives you a per diem for your food when you're traveling, and so what I thought I would do is kind of highlight here if we're looking at the price of a restaurant in New York City, there's obviously huge variations in the cost. You can, you know, go to Chipotle, or you can go to a you know five star restaurant, but the company say, hey, basically we're gonna give you a flat rate. You can choose to go to the $1,000 dinner if that's what you want to do, or you can choose to stay below budget. That's really up to you, but we're going to cap our exposure for your meals and that's going to kind of be how you have to go purchase that.
Speaker 1:Well, what's interesting is people think, well, I've given health insurance benefits, so as long as you go in the network, we'll pay whatever it is. But what's very clear is that there's huge costs, as I just mentioned, associated with which doctors you see, and so we have to start to think about this stuff a little bit differently. Why is it okay to limit, kind of the cost of a meal and make sure people want to go and have a thousand dollar dinner at the five-star restaurant? Great. But we as the employers are not going to sponsor that. Whereas with health insurance it's kind of well, go on the network, we don't care what it actually costs, and you can see that how that's starting to create a big problem there. So the other thing is a lot of folks think, well, the $1,000 dinner, it's a great experience, the food is really good, the service is great, so you get what you pay for.
Speaker 1:The unfortunate reality is, in healthcare that's actually not the same. So sometimes when you're ending up at those higher cost doctors that I showed on the right hand side, you actually are paying more and getting less, meaning there's doctors unfortunately out there who are just you know, they own the imaging center across the street, so anyone that walks in, you're going across the street and you're getting that image, whether right or wrong, and so there's huge impacts to cost and quality, again by the doctor and it's not really correlated. So, just highlighting here complication rates this is in New York City and you can see that you can get really low complication rates for a fair cost, where there's also doctors that are really high cost with high complication rates and there's no rhyme or reason to why members are choosing one or the other. The other thing is that there's a lot of emerging technology. Obviously we're learning about AI, but also in healthcare. So over the last couple of years, glp-1s have been a huge hot topic of debate everyone talking about that and you can see there's been explosive growth on GLP-1s, but particularly for low value care. And what we found is the same idea applies to prescriptions. Right, there are certain doctors that are going to just write patients a script because they walk into the office and I want my Wegovi or whatever it is that they want, and then there's going to be doctors who are really going to follow clinically best practices and only prescribe medication that's truly going to help and is warranted for that specific patient. And so you can see here I showed it earlier on complication rates, revision rates, but this is something that we're constantly doing and reviewing the data on is really GLP-1s, and you can see by doctor. There's basically a 6x rate in terms of how much wasteful care, meaning they're up-coding patients that aren't clinically necessarily appropriate for that prescription. They're obviously not making sure that the patients are adhering to it and that meeting with them and checking in. So huge again cost quality implications. And the reality is, if you don't have the data and you're not looking at it, there's not really a way to make an impact. So how can we actually use technology and data to help with this problem?
Speaker 1:What's interesting is there's a lot of new technologies coming forward for healthcare, particularly alternative-driven health plans. So these are new approaches to healthcare. What they are? Narrow networks or a select network of doctors who want to drive as many people there. That'll hopefully help the cost problem.
Speaker 1:The interesting thing, though, is how many people have tried to go out to their carrier directory and find a doctor and had success in making sure that doctor's phone number's right, the address is right, they're actually in the network, they're accepting new patients, there's not an eight-month wait time to actually go and see that provider. It's pretty problematic and I'm going to show you some stats. But reality is most directories are not very accurate and what's happening is, as these new technologies and data sharing is occurring out in the world, you're starting to see more and more people rely on directories, particularly with their carriers or their health plan, to find top doctors that, again, are going to drive high quality outcomes. And so what we're seeing is the previous state only about 11% of people went to a carrier website to find doctors. What happened now is, with these value driven health plans, is you got about 64% of folks looking at those directories to try and find providers? The reality is it's only 27% accurate. So you got a ton of demand now going towards these directories to try and find high quality care, but the reality is again are they in the network? Are they actually accepting new patients? Do they have reasonable appointment availability? All of those things kind of factor into the experience of that member, and the reality is it's not really gonna help, and so, again, odds of being able to successfully call one of those first five phone numbers you have about a 28% likelihood of that actually happening. And so what happens is a lot of members out there try and engage, try and go to do the right thing. They check with one of these tools and unfortunately you can't actually go and see those providers, whereas if you can use technology and artificial intelligence to clean up provider directories, you really can make a meaningful impact and actually have only about a 2% issue.
Speaker 1:And so what I want to talk about here and this will kind of play off to the side here is actually leveraging AI and machine learning to help eliminate some of that. So what we've actually done is built proprietary tools internally where we could say, hey, go out to this specific hospital system, this specific doctor, I'm interested in understanding when the next appointment availability will be, I'm looking for an in-person visit, I'm a new patient, etc. And what you could see here is that the technology actually is smart enough to go out, find this specific doctor. They have online appointment booking technology. You can go in, pick the location, and, again, this is not being done by a human, this is all computer. And what you could see is it can actually help and listen to the prompt that you put into there and then it's going to populate. Hey, here's actually the next appointment for this person, and you can imagine using artificial intelligence to actually help enhance healthcare experiences with something simple like that. Now you know that this doctor if we're going to recommend Dr Lin here has appointment availability coming up. This was a couple weeks ago, but it was February 25th at 11 am, and you can share that real time with members and that creates a really awesome experience when you're trying to figure out what doctor to go to. And so what we found is, by leveraging artificial intelligence and basically utilizing tools and technology to help clean up something so simple that's been around and plagued forever provider accuracy you can actually use that stuff to actually get about a 90% accuracy rating that the doctors are in your network, they have appointment availability, they're accepting new patients, so on and so forth. And so, again, really leveraging this technology to help do it.
Speaker 1:And so, in closing, I just want to talk about kind of how we're leveraging all of this stuff to really help make an impact for those of you interested in curbing healthcare costs. And so, again, what we found is there's a lot of things out there, but they're not really moving the needle as it relates to cost and quality. It's really hard to get people to engage with information again, particularly if it's going to be inaccurate and not provide a great experience. And the other thing is healthcare is personal and it can't be really disruptive, and so a lot of things that have come out. They're good in theory, but folks don't really want to engage with it, whereas what we found is you can use the data, you can use technology, you can use artificial intelligence. You can really help support members by adding to your benefit plan something that's going to accurately identify rankings, and we brought back Dr Graf here.
Speaker 1:We actually have found that you can utilize incentives.
Speaker 1:Again, I showed you the ability to drastically lower costs and offset that by helping people pay their out-of-pocket expenses while also keeping your existing network, and so what that does is it generates significant savings across all of the information we have access to.
Speaker 1:The doctor on the left versus the doctor on the right that I showed you can save about 27% every time you move people over to the right. And again, as we think about climbing towards that 9% revenue number, the more we can get that 27% savings, the better you can get in lowering healthcare costs. Utilizing incentives to actually get people to engage with the data and find higher quality care is super important around generating engagement, and then what that will actually lead to is overall reduction in plan costs. So, again, as we creep towards 9%, imagine immediately being able to impact your personal spend by 12%, and so what that really does is creates a unique experience where you can leverage data, you can leverage technology, a great user experience to really provide quality outcomes to hopefully help this entire room kind of avoid the march to the 9% revenue number that I talked about earlier. Thanks for tuning in to Risk and Resolve. See you next time.
