Data Access and the Evolution of Healthcare with Dr. David Earle

What I just did was the way the Mass Medical Society works is we create a series of resolutions which are like Mass Medical Policy. And so we just had our annual meeting last weekend and the resolution about getting access to all data for quality improvement purposes went through without even any discussion. - You're listening to data nerds in the OR, a search and journey toward value-based care. The podcast focused on data and data science has the keys to a better healthcare system. Ideas brought to life by the vision and experience of host Dr. Bruce Ramsha. Each week Dr. Ramsha sits down with different players in the healthcare system to discuss how data is used today and what it could do if it were used effectively. How it could improve surgical quality, education, outcomes, and drive better patient care. Let's dive into the latest episode. Here's your host Dr. Bruce Ramsha. - Hi, everybody, welcome to data nerds in the OR. I'm Bruce Ramsha, chief medical from X, officer at Care Centax. And thank you for joining us. With me today is Tiki Esham, who's from Care Centax as well, Tiki. - Yeah, thanks everybody, Tiki Esham here. I am a data nerd as well, biomedical engineering undergrad, a business training. And my job is to listen to you both and try to keep up with everything you're saying. Look up any data or any facts you might want to check while we're talking live. But otherwise, we're really looking forward to just being part of this. Thanks. - Thanks, Tiki. And our first guest is David Earl. David's been a friend for a long time, so it's a real pleasure to have him join us today. David? - Gosh, I don't know where to start. I'll start at the beginning, how's that? So I was born and raised in Arizona. I went all the way through medical school. I went and did my general surgery training in New York City at Downstate Medical Center in Kings County in the early 90s when it was basically a war there. I think there was 2,400 murderers my first year there. One year. So it was a great place to train. Maybe not so great to be a patient though. In any case, after my training, I worked for a year in Queens and then did a minimally invasive surgery fellowship with Felicia Station in Westchester County Medical Center. - What year was that? - That was in 1996 to 1997, yeah. So the early days of laparoscopy for sure. And even just to give you a perspective, when I was a resident, we made a lot of colostomies on the trauma team and we started doing laparoscopic colostomy closures and as residents operating without the attendings who didn't even show any interest. Not only did the natural interest in the laparoscopic approach, there were some attendings that were outright hostile to doing laparoscopy for that. So it was a really tough environment. So in addition to it being super busy clinically, we were trying to innovate. There was nobody really to turn to, except for sages. And sages was just getting started and I will. I'll never forget going to my first sages meeting when I was chief resident. And just approaching all of the leaders of the society and they were so open and engaged and these are people that were writing all the papers and they were just talking to me and I was nobody. And so that was a really big part, I think, of coming around. And then I got into sort of an interest in hernias also during my residency when George Wants came and gave us a lecture about the pre-paratoneal and guineal hernia repair that he learned from Renee Stopa in France and he was trying to propagate that and it made all kinds of sense to me. And laparosc could be coming out. He didn't need a big incision to do the same great operation. So, and I think that's when you and I got acquainted with one another was in this whole revolution or evolution, I guess, of the laparoscopic ventral and inguinal hernia repair with Guy Valor and you and Todd and it was, that was fun. - I'm sure that's what brought us together 'cause similarly in our program at Georgia Baptist, it was a residence that drove the innovation of minimally invasive surgery. And the model for us was the fourth year resident would go learn the new technique. And just by chance of my fourth year, the pre-paratoneal laparoscopic enuinal hernia repair was the technique. Barry McCurnen was the first in the world to do it and he was in Atlanta. So, I got to spend a day with Barry and watched four or five cases. And then during my chief year, I scrubbed on every enuinal hernia to teach tap enuinal hernia into all the attending and other reticent. So David, go on with your story. You're in the early days of minimally invasive surgery and it was crazy times back then. People, I mean, then he started to work like you said. We're very much against it. You challenged the status quo and that was uncomfortable to them. And I think stages, you know, took off with that because other societies didn't embrace it yet. Well, that was a big step for the evolution of minimally invasive surgery. - It certainly was. And Sages was creating quote unquote guidelines that said laparoscopic appendectomy is not experimental because there were so many haters basically. - It was a challenge for probably that first decade in the 1990s until it's really clear that it was good for the patient. I think the early pioneers, their intentions were do it because it's better for the patient. And that's what I think got them through the naysayers 'cause I remember some of our friends who were the true pioneers, which you and I kind of rude their co-tails. They got green and yellow that on the podium. It was pretty bad. The talk a little bit about where you are now. What's your role now? I know you run the New England Hernia Center and a little bit about your current position and then we'll get into the talking more about data nerd stuff. - Yeah, so, you know, I've been now in practice for, I don't know, 30 years or something like that. I can't believe it. And I run the New England Hernia Center in North Chelms for Massachusetts, which is essentially the only comprehensive Hernia practice in all of New England, shockingly. I have an academic affiliation with Tufts, teaching medical students and PA students primarily. I am the current president of the Massachusetts Medical Society North Middle Sex District and I chair the Committee on Information Technology for the MMS. And yeah, that's who I am, that's what I'm doing. - Yeah, thanks, David. You had a wonderful career. We've crossed paths many times and we're good friends. Let's get into talking about data and some other topics related because we've had this discussion many times as a surgery in general, where we are, where we need to be. What is your view on how we use data today in surgery? - I will say that they don't use the term practice medicine for nothing, right? And essentially all of the data that exists, I don't feel is really being used appropriately to guide practice. And in fact, I don't think most surgeons actually practice based on the data that's out there. They use it as a guide, but that's, again, going back to the practice of medicine. That's an age old idiom to say that you practice based on your last complication and then you change your entire practice on essentially your own case report, which I think is important. And I would say of anybody that's 10 years out, is anybody practicing the way they did 10 years ago? Are they doing the same techniques? And the answer is no for most things. - You know, the challenging thing with data is we're not collecting our outcomes. We're not looking at our outcomes, right? We don't have a system in place in healthcare where we see what actually, other than immediate, we see immediate outcomes, but that's not what's important long term for most patients, for most surgical procedures, but without good quality outcomes data. And we're certainly not putting financial outcomes data with patient outcomes data to actually measure true value. And we think that it's almost like we're talking cookbook. If this diagnosis, then you do this. And you're always gonna have without them. And that's not reality. You do the same exact thing to different patients and they're gonna have different outcomes. You need to understand complex biological systems and healthcare isn't set up for that very well. So maybe talk about how you have responded and how you're thinking has changed after being my friend for 20 years and watching me go through that process of learning data science. - Yeah, I'll tell you, if you just look at one of the most common diseases treated in the world, right? Let's just look at hypertension. When you talk about the system, so when we know that there can be problems from hypertension, we don't really know the incidence of it, we don't have good data for that because we treat everybody. So now we come up with these medicine and they go to the FDA and they say, "Oh, we're gonna sell a medicine to lower blood pressure." So all I care about is if it lowers the blood pressure. That's it, but that's not why we treat hypertension. We don't treat it to lower blood pressure. We treat it to prevent a stroke. And now we have a panel of patients, as a doctor says, your primary care doctor and a lot of them have hypertension and they have no idea what their stroke rate is, none. And how could you do that? The variables are tremendous, the variables are variable and we can't do that kind of data analysis. And I would imagine that if somebody did know their stroke rate, they could then access the EHR for all of their patients and find out which ones had strokes and which ones didn't. And then do some fancy data analysis that somebody smarter than me can do and look for trends. You could say, you found out that everyone that had a stroke was in the same tail. There's a direction to go in or what if they were all on the same medication or what if they all had the same constellation of BMI diabetes and something else? Like, I don't even know. Some kind of correlation that you could then use your medical training and experience and say, hey, maybe it's this. - Well, the science is really clear, like you're saying. The science will be able to identify weighted correlations and patterns and then algorithms will allow us to identify which sub-populations of patients are gonna benefit which are gonna be harmed and which sub-population doesn't need a medication at all 'cause they're never gonna have a stroke because of this factor, that factor, or whatever. So the science is really clear what's missing is the data and analytics infrastructure. We have fragmented data because of how the electronic medical records were designed into the fragments of care rather than around the patient process which is a major design flaw. Now, you talked a little bit about it in that example. So let's pivot a little bit because I wanna get your perspective on the FDA. FDA is maligned a lot in healthcare and you work as a surgeon at the FDA. And as we talk and you have that experience, just like a lot of organizations, most of the people there trying their best, it's just a very difficult job in a lot of ways. So maybe give your experience from the perspective of the FDA regulatory process, the surgeons who work there and the evolving kind of understanding of the move to real world data. Talk about some of that. - Yeah, it's a medical officer in the medical device section and we primarily focused on robotics at the time and I was pleasantly surprised at the quality of the individuals, no one's out to get anybody. They want stuff to be out there. They want it to get through as quickly and efficiently as possible, but also as safely as possible. The one thing that struck me about the design of the FDA process was that it's not really, it's not legal, a set of laws. And that makes it nimble and they're able to adjust based on as they learn. And we just saw a big example of that with robotics that one of the robots that, well, in 2000 got FDA cleared using a laparisco as the predicate. Well, obviously it's a little more complicated than a laparisco. And then once that happens, then everybody uses that robot for the approval of the next one and so on and so forth. It's like the campfire game a little bit, you know, by the time you're 20 years down the road, the devices don't even look anything like that. So along the way they created something in between what's called pre-market application, which is typically a more rigorous process compared to the 510K process and that's the de novo process. And they said, you know what, we're just gonna do that. And if it was a legal system set up, you know, they'd say, well, there's precedent and that's so that's now the law. And they don't do it like that. They just do what they think they can do to get things through safely and efficiently. And I really liked that about the agency. - Where do you think they are with this evolution toward real world data away from prospect randomized control trials? I know the comfort level is still in controlled trials, but as we've discussed many times, the future is real world evidence because that's where the science is. The science of data and systems is in understanding real world data and using data science principles to quickly, much more efficiently, much faster, much safer, understand the safety and value of a product of robot or a drug or device. Where do you think they are in that evolution? - I can tell you in devices, and that's still sort of headed up with by Benita Asher, she gets it. And I think everybody in that division gets it. The problem is we don't have good real world data. They'll look at published studies as that. They'll look at data registries and they are using real world data for that. In fact, in the device world, I am working with a device right now where they're going through a PMA process. It's going through a usability trial. It's looking at the published data from Europe because it's already cleared in Europe. And that is unusual, but they realize, hey, this really doesn't need a prospect of randomized trial. And frankly, it doesn't. And it shouldn't have the time to plan, execute, and analyze the data for a prospective randomized trial from inception to publication. It has got to be 10 years. - I'm not sure the average, but it's many years. And the thing is, by the time that those results are done, not only does it not apply the real world because of exclusion criteria, it's probably archaic in the information because things change over 10 years. And things aren't the same as when the protocol was designed a decade ago. So things are different. That's encouraging. That's very encouraging. - The 10 years, when we would ask people in the audience when they were teaching these hernia classes, who practices like they did 10 years ago? - Right. - No one raises their hand. Now we're going to come out with the best level evidence with the prospect of randomized trial. And it's 10 years old. The day it's published. - That's a big reason why this movement is happening that a concept of prospective randomized controlled trial being the golden truth. It's a facade. It doesn't work. And when you understand the science, you know, it's not even valid because for a prospective randomized controlled trial to be valid, there's at least three assumptions that have to be met. Nothing can change. You have to know and control all variables. And whatever's produced has to be generalizable to all people in all local environments. None of those are true. So I think the fact that we're learning that that science is not valid is a big step. And that kind of brings me to something relatively recent in your role with Massachusetts Medical Society. You invited me to give a couple of talks. One to the IT committee, which you chair. And then the spring meeting, did a 40 minute session with one of the Harvard ophthalmologists. And I was pleasantly surprised 'cause you heard me during that talk. I've gotten to the point where I'd say flat out. It's not a valid science that we're using in healthcare. And here's a valid science, and I'll give examples and show what we learned. And also remind people, I didn't learn this overnight. This took me over a decade for my brain to learn. But when I presented this concept, so I was expecting some pushback. But actually it was very positive response. So maybe talk about your role in the Massachusetts Medical Society, some of the response you saw from my talk. And then what you're doing within the society to try to free up data so that we can apply these data science principles in healthcare. - Yeah, in fact, I realized that because we don't have our long-term outcomes, like I'm a regional tertiary referral for hernias. And I get people that will come from sometimes far away with the very straightforward, not complicated, inguinal hernia. I'll say, well, you know, you have surgeons. You know, they'll drive seven, 10 hours, spend the night maybe. And I'm like, yeah, there's surgeons over there. They can do a good hernia repair for you. And they'll say, oh, well, you're the best. That's why I came here. I said, oh, really? I said, do you know what my hernia recurrence rate is? And they're like, no, I guess I bet it's pretty low. And I said, guess what? I don't know either. Neither does anybody else. And they're like, yeah, but that's okay. I know you're good. And I realized, wait a minute, okay. You've been talking about this for 20 years. I've been practicing like this for the vast majority of my career and realized that we are, we're practicing medicine. And in order to affect that kind of change, what are the barriers? Why can't we do it? All the data is out there, right? We now have EHRs. It's all there. Guess what? We can't get it. The companies are hoarding the data. And they claim to not be hoarding it, right? But they are hoarding it and knowledge is power and knowledge is also valuable. And so I thought, well, mass medical society is essentially like a political action committee, right? They're a special interest group, but they're a special interest group of doctors who have patients at the forefront of their entire mission, right? So then I thought, if I can get some momentum within the society, we can help to push legislation through at least in Massachusetts to get access, to allow access to all of the records for all legal purposes. And one of those purposes under the HIPAA is quality improvement. And what will happen in my mind is that we will, a bunch of companies that do data analysis will spring up. And insurance companies will hire them and health systems will hire them. And they'll find out, okay, what is the stroke rate? You brought up a couple of things here. One, the HIPAA, I talked about this a lot because it blew me away when I learned this. And in the HIPAA, there are two exemptions. One is you can use patient protected data and send it to anybody in the country. You don't even know who they are to get paid. Everybody does that. But the second exemption, like he says, is called healthcare operations and includes getting data for hospital marketing, doing care coordination, doing what we should all be doing, quality improvement activities. That's an exemption from HIPAA. And it's been so misinterpreted by hospitals and physician groups that I know at least two full day congressional hearings have been had to basically say, look, hospitals and doctors have misinterpreted HIPAA and it's harming patients and still nothing's changed. So I think that what you're doing to make awareness of that to legislation and interoperability, good luck. That could be another step to where we need to be with data science and healthcare. That's a big step. - What I just did was the Wade Mass Medical Society works as we create a series of resolutions which are like mass medical policy. Because we spend a lot of time writing it so that we can do that. So that's step one. The next step in that is going to be to try and get legislation within the Commonwealth of Massachusetts and to get to the legislators. And I think we can do that. And even the federal government recognizes this. There's a organization called Tefka. And I don't remember what the acronym stands for. - It's the trusted exchange framework and common agreement, Tefka. - Thanks. - So they are trying to make this, that concept of outcomes data come to reality, but they're not. But they've spent a lot of time coming up with extraordinary definitions, a definition of like everything. And that's the wrong mindset. It's a controlling thing, right? And we have to get away from the notion that we can control our environment. We can't. And that's what complex data science is. It's the realization that we can't control it. So let's just look at the data and then see where it takes us. Not the other way around. - Yeah. All I was gonna say, it brings up one of the really important principles of data science. That is top down one size fits all definitions. We'll never work. You have to do it decentralized in each local environment. And one example is quality measures that have been top down defined by CMS and other entities. And we're measuring things like SSI, infections, catheter infections. And guess what? Nothing has improved in 20 years. We've been reporting all those things. You don't define things as one size fits all and tell everybody to measure the same thing. To your point, you have to have each local environment apply measurements that make sense for their environment and then continuously improve it. And when you do that with feedback loops of good data and good data analytics, you'll get better and better results. But the top down one size fits all will never work. And we've been proving that for decades now in healthcare. - You know what? That reminds me too, is I used to say what we need is a single medical record so that we can have access to all the data. And you know, it's exciting to think that, oh, let's have one record. So first of all, you can't have, right? Someone's always gonna write something down on a piece of paper or have a parallel record. That's what these data analytic companies are gonna do. And that's how they're gonna make their money. They're gonna go get the data, wash it, clean it, do whatever they want, aggregate it and analyze it. - Like you said, it's just not realistic. If it was in theory, yeah, if we had one agnostic medical record, that would be great. It's not, yeah, it's not reality. What is reality is what we were talking about. Getting the data, freeing up the data, bleeding it, which is a huge need in healthcare. And then learning how to apply the principles of data science, very much like what Major League Baseball learned to do 25 years ago with Moneyball. There is a science, there's a way to do it. You can decentralize the data. You can minimize what data you collect by focusing on what really matters. That impacts value-based outcomes. You don't need all the data. We have way too much data that we document in healthcare. We need probably less than 5% of it for each patient process that really impacts value-based outcomes. And when we learn how to do that, eventually healthcare will do what Major League Baseball has done. However, the big difference is in baseball, it's competing teams. So once every team learned data science, there was no advantage anymore. I think the most exciting thing about the data science principles is that when we apply this and we're doing this in each local environment, the ultimate ability to predict the best value treatment for the right patient sub-obulations when we network our learning and network our algorithms across other local environments around the world. So basically to have the full extent of data science, we all need each other. And we need to be in a collaborative environment, which I think we've all learned is important for learning and healthcare, but we need to do it with the data appropriately too, right? And that's just the point, right? The data is its own thing. We don't create it, right? We make the data and we have to change that perspective and for anybody that may be watching this that's not familiar with Howard Mosquets, search for the TED talk of Malcolm Gladwell about spaghetti sauce. He is a experimental psychologist that got involved in the food industry and is the reason why we have all these different flavors of pickles and types of orange juice and spaghetti sauce and all of that. I mean, he wasn't the only one doing it, but he was the one that looked at it initially and came up with this idea of surveying people in a variety of ways and then reacting to the results of the survey. Not coming up with a hypothesis that spaghetti sauce A is better than B, right? Let's just throw them out there and see what people like. And Howard is now getting into using that kind of complex data analytics, which involves clustering and looking at lusters of data and then figuring that out instead of trying to prove a hypothesis. He's starting to get into healthcare. And here's a great little example. I was moderating session at the ACS last October about ventral hernia. And I connected with Howard and created a survey with like 10 questions or 20 questions or something like that that would take somebody about two minutes to fill out. I then took those questions and emailed them to every single one of my patients, 40 or 50 responses, enough such that we could see there were three clusters of mindsets around what people thought about hernias. Some people were most concerned about it getting worse. Some people were most concerned about doing whatever their doctor told them to do. And some people were most concerned just about their symptoms. Oh, and by the way, that took like four days. Imagine if I went through the IRB and I got a quote unquote validated tool and blah, blah, blah, I mean, it would take a year. And I can publish that because it's just a question. I'm not proving or disproving anything. I don't need to validate anything. I just need to look at what the data say. And we could utilize that concept. What if they answered these questions and we knew what was important to them? You could address it, right? You could say, oh, I really care about X. The doctor can talk about X and analyze X and whatever it is. I just think it could make us more productive. And we can actually listen to people in a more efficient manner by finding out what they want. I highly recommend that Ted talk. It really helps you understand the shift in thinking away from best practice or benchmark or anything like that. That's not valid. It's not reality. It goes totally against the data science. In the real world, these clusters are real. And you can match the best treatment or avoid the complication from the wrong treatment if we have a data and analytics infrastructure. I use Netflix as an example. They send that sets of movies and shows to what they call taste clusters. And they do that through analytics and understanding is not perfect. But it's pretty good, it's better than guessing. So it's a change in thinking that we have to go through. So I want to finish this session. Thanks so much again for your time and your insights. I want to talk about the change in thinking we have to go through. And you introduced me to a book called Deep Survival. And they really analyze people who were in life-threatening situations. They're lost in the woods. They're on a mountain. They've been mapping, climbing an avalanche happens. Those are massive storm happens. And they analyze who survived. And what were the characteristics? If you could talk about it, because I think it relates to the change in thinking we have to go through in healthcare and then data science and understanding data science. So talk a little bit about that book. I thought it was fascinating. Yeah, that book is a great example. And it's Deep Survival by Lawrence Gonzalez. And it's an explanation, basically, for why people do what they do. So if somebody does something stupid and you say, what were they thinking? The real answer is they weren't thinking, right? And essentially, the way our minds work is that we make mental maps of our environment based on our experiences, which is why sometimes if you're thinking about something else and you drive home, sometimes you don't remember the drive home. Well, how did you know what street to turn on? Because your brain used your past experience to take that information and put it into the front of your consciousness and say, you're just going to follow this blindly like a GPS, right? And it turns out that can be good for things and it turns out that it can be bad for some things. And we do the same thing in medicine. In fact, there was a great study about why people get common biodech injuries. And it's a problem of perception. Yes. And we talk about this all the time. In fact, there's a video that you used to show a lot of somebody in Europe doing a live case, during a conference, doing a adhesiolysis and they cut right into the bow. But the surgeon didn't see it. And yes, that concept is so fundamental to how our brains are hardwired. They say that people, only about 20% of people naturally can update their mental maps in real time as their environment changes. And that can improve with training. But in that they talk about there was a guy on a fire pilot landing on an aircraft carrier that obviously wasn't going to make it. And completely ignored all the warning signs and crashed the airplane. Highly trained individual surgeons, highly trained at the common biodech. And if somebody else was watching the video, they'd say that was the common biodech. But they didn't see it. When the environment changed, they couldn't update their mental map in real time. And as I learned this, there's a lot of evidence around the ability to rewire our brains to the higher brain. A lot of it is driven by the lower brain where you do what you've done in the past. You want to do the same thing or you're driven by fear or other lower brain emotions. And I think it parallel to understanding data science that we can't just do what we always done. We can't just think there's one right way to do things. We have to use our higher brain and be agnostic. We don't have drugs and devices that are good or bad. It depends on how they perform in different subpopulations and clusters. And we have to think differently. Great. I feel as a patient, I feel like we get a lot of guidance and a lot of rules that are based on worst case scenarios or based on averages. How do you see that changing and what do we need to do to change so that we can actually get, as you said, more tailored recommendations based on who I am as a patient or who the next person in coming in as a patient. I'll tell you how I think it's going to change and how it has to change actually. And that is by surveying patients with that same tools that Howard Moskowicz can use and finding out what is important to them. He actually did a study 10 years ago where they took hospitalized patients with CHF They sent out the survey to see what their mindset was with doctors. And one of them was, I do what the doctor says, I can't remember what they all were. But then they gave the patients a message, like a refrigerator magnet with a message that says, take your medicine. But the message was tailored to the mindset. And then they didn't do that with another group of patients. The readmission rate for the group of patients that did not get directed messaging was like, I want to say it was 17% something like that. And the readmission rate for the group with directed messaging was like 2%. And so that's what's going to happen in the future. But for now, you know, I tell people about complication rates and oh, it's 1% what difference does it make? And explicitly say, those percentages are based on the average values of a bunch of people that aren't you, you will either have the complication or you will not, it's 0 or 100. So none of that is predictive and you and everyone gets that everyone understands. They just want to know the probabilities, right? So they can make an informed decision about where they want to take their risks. Yeah, I think we have to change our thinking in how we teach health care from I think still today, a dominant way we assess medical students and doctors is a multiple choice test with one right and how silly is that to think that there's one right answer. But that's how we're trained. That's how we're conditioned. So when we see a patient, if we're a good doctor, we're supposed to know the right answer. That's just a facade. That doesn't, that's not reality. So we have to help change how we think, how we teach clinicians, nurses, doctors, we have to teach the fact that there is no one right answer. And the best we can do today is a shared decision process as David has talked about. But tomorrow in the future, we better be using data appropriately and generating algorithms so we can help guide patients to David's point. I'll never be perfect, but it'll get better and better and it'll certainly be better than just educated guessing, which is the best we can do today. And so I think it's changing how we think and not feeling like it's on us as the clinician to make the right answer choice. No, we should be helping the patient make the best answer for them in their situation with their goals, their fears, and eventually based on the predictive algorithms that we will be able to generate in health care that will help match the best value treatment with the right patients' population. Well, I didn't think of this until just now, but I was always big into making patients informed. And I used to say, well, I don't know if I want to get my hernia, so like, well, you have to decide. I'm not deciding for you. And there's a group of patients out there that want me to decide. Yeah. So I, maybe I should decide for them and I'll tell them how I arrived at that decision, but they're the ultimate ones that have to sign a consent form. So that's a perfect segue into the last question and thank you very much for both your time here. Doctor, while you point it out, you don't even know your own hernia recurrence rate, right? You see a problem, a systemic problem. What's limiting us here? What can we do as a system so that physicians can at least know their own data? It's got to be access to the data all over the place and who owns it, right? Does the patient know it? I think the way it's ultimately going to pan out is with legislation that says you have to share your data. If you're going to operate a medical record in the United States, we're going to try to make that happen to Massachusetts. Or we go to the patient and somehow get patients to access their data. And then the data analytics company accesses all the data via the patient consent process to say, okay, we get access to all your data. Here's your consent for that or the purposes of quality improvement. Yeah. We already have that in the consent to treat. We consent to treat every patient's signs if you're using that data for quality improvement. So we've been doing follow up since I started learning the data signs around 2010, 2011, but I think we can to your point, David, I think when we let patients know that we're going to access their data, not to sell it, not to do something self-serving, we're accessing that data to learn from it. So we can do better for the next patient, 90 plus percent of patients are super happy with that. So I think I think when we get the mindset of data is important for learning and improving and that patients can participate in that by sharing how they're actually doing a year later, two years, whatever the process needs in terms of follow up. I think then we'll change the system and it'll be just normal part of health care to access the patient's data throughout their whole cycle of care. The science is there, we just got to learn how to apply and then that'll lead to a sustainable health care system, I think. So thanks again. Thanks for your time. Anything else? Thank you. Any other questions, Tyke? Well, there's plenty of questions here, but I think I as a patient, and is that just to listen to you, I really enjoyed this conversation, Dr. Earl. Thank you so much. Always good to see you. David, I just want to thank you very much, really fascinating life that you have. And I really value our friendship and Tyke, thanks very much for this podcast, and we'll see you next time. Yeah, thanks for having me. I love talking about this stuff, I think it's fascinating, and obviously the work that you have done back in the days when it was really difficult to articulate, and people got it. They knew you weren't crazy, but they didn't get it. And I think people are starting to get it. Thanks for joining us for another episode of Data Nerds in the OR, a surgeons journey toward value-based care. You'll find links in the show notes to any resources mentioned in today's show. If you're enjoying our podcast, please subscribe so you never miss an episode, and if you want more content like this, you can always sign up for our smart surgery blog via the link in the show notes. Or, if you want to ask Dr. Remshaw or the team a question directly, please send an email to [email protected]. This episode is brought to you by Carrisontax, the leading vendor-neutral surgical intelligence platform. The Carrisontax platform can ingest and analyze data throughout the surgical work, including clinical, operational, financial, and outcomes data to capture a complete picture of the surgical pattern. That data can produce actionable insights from a hands-efficiency and innovation for surgical teams, hospital administrators, and medtech developers, driving innovation for the future of healthcare delivery. Carrisontax impacts over 3,000 galars and more than 3 million annual procedures across the globe. Learn more at www.carrisontax.com.