Thank you all for joining us today. Our discussion today will be on leveraging laboratory database for health equity. My name is Monique Dodd. I am one of the pharmacists and my colleague here today presenting with me is Teo Burunda. Both of us are pharmacists that work for Rhodes Group, who is a fully owned subsidiary of Tri-Core Reference Laboratories in Albuquerque, New Mexico. Before we really dive into this presentation, I really wanted to frame our discussion today around value-based healthcare. Several of you might be very familiar with the triple aim as well as the quadruple aim. The triple aim are all the components that you see here except for provider satisfaction. The quadruple aim was developed to include provider satisfaction because it became known that patient experience, reducing healthcare costs, and improving our patient outcomes could not be possible without the wellness of our providers, hence quadruple aim was developed. Now, we are starting to look at the quadruple aim and really look into our healthcare system to identify what continues to be missing to achieve value-based healthcare. This is really where health equity comes in. It is now shown in some of the literature that is out there that the quadruple aim cannot exist without health equity. Throughout this presentation, we will really be looking at applying the lens of health equity and how the laboratory has an integral role in being able to help this movement and not be only an ancillary service but be an integral role in the overall healthcare system. Let's start looking at how healthcare is in general throughout our nation. First of all, unfortunately, our healthcare in our nation is one of the most inequitable. We have the greatest healthcare spend per capita, which isn't a good thing based on our current rankings that are here below. We rank 28th in 34 countries in life expectancy. We are 33rd in infant mortality, and unfortunately, we rank first in poverty. Knowing this information across our nation, and even though we are one of the most wealthiest nations, our healthcare system definitely can use some improvements and some help. How do we begin doing this and how do we continue moving forward as all healthcare professionals and as a whole healthcare system? Now we're looking at really the role of the laboratory and how we can have that information really to produce insights through the lens of health equity. Let's define health equity before we get into this. Health equity is when every person has the opportunity to attain his or her full health potential and no one is disadvantaged from achieving this potential because of social position or other socially determined circumstances. How can this be applied to social determinants of health? Now we can begin bucketing this information from social determinants of health and figuring out how can we begin looking at our data differently from a laboratory standpoint, from a healthcare standpoint to begin looking at value-based care. Here are the five domains of social determinants of health. Education access and quality, healthcare and the quality of healthcare, I'll also put healthcare access into that, the neighborhood and the environment in which an individual dwells, their social community, and ultimately their economic or financial stability. We'll take a look at a few of these as we particularly focus in on the goal of Healthy People 2030. Their goal is really to promote attaining the full potential for health and well-being for all by focusing on the following environments. Again, similar to what we had just shown on the domains, the five domains of social determinants of health, they are looking at social, physical, and economic. From a social standpoint, what is the environment of our patients? Where do they live? What is their neighborhood? From a data standpoint, what is their zip code? Are they living in an environment where there's more poverty? These are all questions to start considering. Even from a physical location, again, where do they live? Based off of the closest hospital, even off of the closest testing site. This creates some of those points when we're considering access to care and ultimately the economic standpoint. How financially stable are our patients? Do they have to choose between putting food on the table for their family and not paying for their medication that is needed for their own health condition? Again, these are all things as we're starting to look at the quadruple aim and putting health equity at the target. Let's start or at least continue framing these disparities and move in deeper into how we can look at some of these data points. To take a step back in framing the disparity, health disparities account for greater than $200 billion in direct medical costs. Combining health equities with premature deaths have totaled $1.24 trillion from 2003 to 2006. To put this into perspective, in 2019, the overall health care spend for the United States was nearly $4 trillion. Even a quarter of that over the course of three years was due to health equities and deaths that could have potentially been prevented. Now as we start to look into the Healthy People 2030 goals, looking over the social, the physical location as well as the financial, let's look at our uninsured population. In 2019, focusing on the economic part of that, about 26 million individuals were uninsured at that time, so about 8% of the population. Of that population, the Hispanic and Native Hawaiians and other Pacific Islanders have the largest increases or were the largest increases in that uninsured population. We also have the 16% of LGBTQ population that is uninsured as of June 2020. Again, these are starting to identify our populations that we need to be more cognizant of when we're looking at health equity and providing better health care for all. So now let's look at our rural America. About 60 million people live in rural America. That's one out of every five Americans. And about 25% of those Americans live on average 34 minutes away from the nearest hospital. The last point I want to make here around framing disparity is around implicit bias in health care. And what I mean by implicit bias is the act of stereotyping or perhaps prejudice that is unintentional. And this can be usually from our providers and our health care clinicians that our patients interact with. And this is by no means the fault of the provider, but when you think about the provider on a daily basis who is seeing maybe 15, 20 patients throughout their day, maybe one of them is experiencing homelessness. Maybe two of them have uncontrolled diabetes that they've been working on for several years, but perhaps they are women of color. Perhaps another 50% of their patients, they have a history of substance use disorder. So even though that the clinician is preparing to see these patients before they actually walk into that room, perhaps objectively through the chart, they're already producing an image or a way of being able to approach that patient when they enter the room. So it's difficult to say from a personal clinician standpoint, how do we remain objective as we enter into these patient rooms and really be cognizant of the experience that our patients are going through beyond what we even see in front of us or even beyond what they're wanting to share with us. I'm not saying that we need to dig a little deeper, but it's part of that patient-physician relationship that I think is really the start of this and creating better health equity for all. So to dig in a little deeper on the implicit bias in medicine, I just wanted to share with you some of the papers that are out there around this and really that are starting to frame some of the initiatives of how we can better prepare our physicians and our clinicians for these patient interactions. So it is shown that physicians are more likely to underestimate the pain of Black patients compared to non-Black patients. And it is also shown in another paper that physicians' attitudes and behaviors are different between non-White and their White patients. One paper that I thought was very influential, it was titled One of Us, and there's a handful of personal stories in here from women of color that believe they have been a victim of implicit bias through some of their medical stories. Unfortunately, some of them are not with us today. The story that I'm going to share with you with Dr. Shanice Wallace, she is currently here and her case was investigated. At the time of this event, she was a pediatric chief resident at the Indiana University Hospital North. She was pregnant with her first child she delivered early at about 36 weeks due to preeclampsia and had to undergo emergency surgery. What was found in the investigation is that she had symptoms such as high blood pressure, kidney insufficiency, and unfortunately, she even had a ruptured liver that were found to not be monitored during those later weeks of pregnancy. We also know within the guidelines and medical literature that women of color develop preeclampsia at a 60% higher rate. So kind of bringing in the objective standpoint, even from a laboratory standpoint, these are some of the things that my colleague, Dale, will share with you, some of those laboratory data points that we can begin using to identify high risk within our population and to identify care gaps within our population. So at least maybe there can be a step forward where objectively we can show that she was at a higher risk, potentially using the objective data from the laboratory. Now of course, it always ends up in the action of the clinician. How much can we act on as a clinician within the hospital or the clinic setting? What is our timeframe? There are so many variables, but to start off with delivering better information to our clinicians so that we know that within that 15 minutes that they have with the patient, that it can be the best 15 minutes and it can be the most informed 15 minutes that they have with that patient. So now let's look into the role of the clinical laboratory and health equity and starting to bring this information together. So throughout our nation, the clinical labs produce more than 7 billion clinical test results per year. These test results are essentially real time and longitudinal, which is a huge benefit in our data and that we will show you as well in our story. The longitudinal aspect of the data is what creates the stories behind our patients. We're able to say that we saw them at this standpoint, maybe two years ago, they were controlled diabetic. Now three years from now, they're uncontrolled. And why is that? We can start creating more pictures or more questions from this information. As mentioned before, the laboratory data is objective. We start to kind of bite a little bit off from that implicit bias and present the objective and factual information of our patients and identify them as being high risk or potentially having gaps in care. I think another unique potential of the lab is that we are at the position to be able to help break down some of those data silos. We have several entities, healthcare entities that we are servicing that we do lab tests for and we can begin getting more of that information together to create a stronger data lake, a stronger database of patient information. The last two data points, not data points, the last two points here on the slide are demographic data and metadata. These are two points that I think each laboratory has potentially, but perhaps they're maybe our weakest point. For an example, for us in Tricor, our demographic data, we do have the address of the patient. We do have zip code, but our ethnicity data is very spotty. It is not required to have ethnicity data as a laboratory data point for us. It might be for other labs, but again, I think that would be very beneficial to have when we start to identify risks. Now the metadata, a good example of the metadata is we do not have medication data. For an example, if we had blood pressure medications, we have diabetic medications, perhaps thyroid, even the dose and the frequency of that medication, we can begin understanding at what point in the healthcare treatment and monitoring that patient is at and be able to provide our clinicians, again, better information, have them be better informed so that when they go into a patient's room, they don't necessarily have to navigate through all of the EMR data, but there is a way for us to consolidate the information to say right off the bat, this patient is high risk of preeclampsia because of these results. Another aspect of the lab is access to testing. Not only considering how close is that patient, so looking at location, one of those three environments that Healthy People 2030 is looking at, how close are they to a testing site? This is looking at another paper. In April of 2020, the median travel time to a testing site was 20 minutes. This is average across the nation. But looking at Texas in general, it was shown that there was a higher percent of the minority that was associated with an increase in travel time to a testing site. Also looking at the uninsured, higher percent of uninsured that were associated with an increase in travel time to a testing site. When we combined or when they combined the rule, those individuals living in a rural community and were uninsured, they had one of the highest incidents of increased travel time to a testing site. Those are marked in purple, see here on the left-hand side of the state here. Ultimately, what they concluded is those individuals that had decreased access to testing sites, that ultimately there was an increased demand for that testing and primarily by those communities of color. So how are we going to apply this now to New Mexico? To start off, Tricor Reference Laboratories is the main reference laboratory for the state of New Mexico. Though we do not do all the testing in the state of New Mexico, we do perform a majority of the service. Just to provide you some New Mexico demographics before I hand it off to my colleague to share with you our story of through the health equity lens and what we've been able to do with our targeted interventions, I just wanted to set the stage for you on how our population is distributed across the state. So New Mexico is the fifth largest state in the nation. We are 37 out of 56 U.S. states and territories for population. And our population density is 17.2 people per square mile. So to put that a little bit in perspective, California has a population density of about 254 people per square mile, and they are the third largest state in the nation. So though we are a very big state, we definitely have a lot of our patients that live in rural communities and therefore live further away from health care as well as testing sites. To put our social determinants of health around this rural state, and to give you some of those rankings, we are 33rd in health care. We are 44th in our economy. We are definitely at the very bottom, unfortunately, for our education. We are 23rd in preterm delivery and 10th in diabetes mortality. So essentially, even our highest rankings are unfortunately not very good. But this does provide us really a really strong foundation for why we are presenting this to you today is really the role of the laboratory. And we wanna share our story with you, the role that Tricor Lab has taken in value-based healthcare through the lens of health equity. Fahil, I'll hand it to you. Thank you, Monique, very much for going over health inequity and especially setting up this stage on Mexico. And many of the health disparities and inequities that the people from New Mexico face every day. As Monique mentioned, we're gonna go over, kind of tell you our story and the role that we have played in population health and in addressing this health inequity. What you see now is the state of New Mexico. And as you can see, Tricor is always in a very unique place as we're the largest laboratory in the state of New Mexico. We do provide most of the testing around the state and we have a large footprint around the state. This allows us kind of have a rough idea of what's going on in different populations. And by using the clinical laboratory data points that are used for the diagnosis or monitoring of different diseases, we can start creating these, what we call targeted intervention modules to start tracking certain conditions, different actors. Although we have more than just diabetes and prenatal, for this presentation, we're just gonna focus on those two. I wanted to pay attention to the right part of the screen where we kind of break down these different conditions on how we use laboratory data, bucket these patients for different categories. The first category you'll see is a green one which says optimal. That means that the patient has been identified with a condition, but according to the laboratory results, the patient does not have any increased risk or target. How do we look for risk? But with that, again, looking at laboratory data, we can find out if patients are experiencing additional comorbidity or other risk factors that may cause the disease to complicate, such as lipidemia, lipid panel. Could also look at kidney function because we know there's a relationship with diabetes and if they have any of these labs that are abnormal or continue to get abnormal, then these patients are bucketed into a risk population. When looking into the care gaps, we leverage the nature of the laboratory data being longitudinal. As Monique was mentioning, we could follow the patients through their continuum of care and for certain conditions, such as diabetes, the guidelines are very specific on how often patients get certain laboratories for their disease management. Therefore, we use this and if the patient has not met that timeline that is recommended by guidelines, that's when we assess, okay, the patients have care gaps and that's when we put the patients in that yellow box. And then on top of it is the red box, which is risk and gaps in care. That's just a combination of your blue and yellow box. These things are probably the patients that need to be in the fastest as they not only have risk, but they also have gaps and they're not sure how long it's been that they have seen a provider. That's really what we try to do first. Right, with that further ado, let's dive in into a little more detail about what these targeted interventions are looking at. For prenatal, we're looking into specifically for Medicaid population. And that's something also that we take advantage of the data because we can break it down into different groups depending on who came for the test or where these patients are from. We can break the population to different cohorts to analyze if there's disparities or there is high risk patients or more gaps in the network to address. I'll also have you pay your attention over to this box that you'll see. This is really the backbone of our targeted intervention. When it comes to prenatal, just like diabetes, break them down into different categories in order to see where these patients are in their prenatal care. Using laboratory data, we can also estimate how far along these patients are, first trimester, second trimester, third trimester, and so on. Additionally, we can look at what risks the patients may have. I don't want to spend too much more time in the analytics, but I just want you to have an idea of how powerful data and insights you can draw just for using laboratory data. There's so much information that can be drawn from a patient. Now, what we have here in the screen is real patients that were shared with us with one of our partners that is actually the partner that helped us bring this targeted intervention to fruition. And they're currently using it with very successful results. As I think this targeted intervention addresses a very important health equity or social determinants of health that many people, but the way it worked before or targeted intervention, there was a woman who was in the rural New Mexico and she accessed the R&D. As you all may already know, is that when a woman of childbearing age access care, whether in a clinic or the hospital, you have to do a pregnancy test, right? Before you initiate a protocol or initiate a mission. At that point, the woman was already pregnant and she did get some prenatal workup. However, there was just no follow-up after that regarding her prenatal care. In March, she accessed the ER again and still no prenatal care was done whatsoever. Comes August, the baby is born. And unfortunately, this baby was admitted to an ICU and the mom was hospitalized and they were discharged early until October. Not only is this a traumatizing experience for the mother, but it also is a very costly and preventable issue. When we look at another woman, this one's from the urban area of New Mexico, this woman was suffering from a substance use disorder for which she had continuous care. In January, she was identified as pregnant, but nothing was done to address that. She still continued to have her drug monitoring as she was supposed, but there was nobody kind of like to connect her. In August, the baby was born and there was another case that was also admitted. So when we apply the health equity lens, as Manu was saying, it's easy to see that one of the factors, although there's many, really comes down to probably there was a lack of access to care and connection to care. But what our retargeting intervention is doing is helping with that. So let's look at these other two cases where our intervention has played a role throughout. So this woman is, they are different women. This one's from rural New Mexico. Again, also access care in September. And at that point, we're identifying her as pregnant. We told the care coordinators right away, then they connected to her care under the RUG plan. Then the baby was born. In April, we had no problems whatsoever. Looking at another one, that also could have started as a high pregnancy with high risk because the patient also accessed the ER. But at that point, taking advantage of the real-time nature of the data, we can tell the care coordinators right away, which was done in this case also. And once the care coordinator called the patient, connected her, the patient received a proper prenatal care. And the baby was born without any... This is just one piece using the laboratory. So we just went over about the laboratory results that can give us some information, not only on the condition, but also in health equities. But what are other data points that we can use that can also give us more information on the condition and also on the health inequities that our patients... As you can see here at the top, we have substance use disorder. And although that's a condition that we get the information for, at this point, we're not putting our health interventions on you. Because as you know, many of these patients, they are protected and it's very sensitive information. At this point, we're not sharing that, but perhaps there could be something that we could do in the future. There's also other things that we could add, such as hepatitis C. As we know, many of these patients who have hepatitis C usually may have other problems or other barriers to their care that may be meaningful to add it too. Also, if we are sharing that PARA that tells us what the past of the woman has been in her past, how many life experiences has she had. And if we use that in combination with zip code, which is something that we currently have, but we're not using it or targeting intervention, then we can all not only add the ones that I previously mentioned, but we can look at education, we can look at the city mentality. Because once we have all those points and then we assign it to the zip codes, perhaps we can identify hot areas where there's more issues than others. But not only that, but trying to figure out what's happening, right? By applying that health experts. What is affecting these patients and hopefully address it. Moving on to care gaps. You're not thinking about the case that many percent of Dr. Wallace. Some of the things that we currently don't have is blood pressure or vaccine. And that's just because we're a lab. However, we're thinking about, you know, repositioning the lab at some point. Perhaps that's a service that could be included in the future by using the type of pharmacist patients and also that spectrum. But another thing that we could add currently are, for instance, like function tests and kidney functions that could also give us more information and use the objectivity of the data to identify these concerns. And I defined them early so we can avoid complications. Now, let's dive into diabetes and chronic kidney disease. Again, looking at diabetes, it should start looking familiar to you. Just like prenatal, these patients are bucketed just the same way. And I don't want to spend too much time on the analysis, but I just want to show you how much insights can be drawn from a population solely with laboratory data, such as care gaps, risks, by using different laboratories. The next one that we have here is chronic kidney disease. And this is a condition that I really like. And I think the laboratory can play a huge role with this condition too, because if you look at the clinical guidelines, chronic kidney disease, the majority of how it's monitored is through laboratory data. And also in diagnosis, if you look at the table on the right, you're really being able to identify what stage every patient's on, solely using laboratory data. With this, we can help providers know well how the population is not only bad, but what are the risks that they have. And are there any issues that may be having, so thinking back about the access to care, maybe they're just not able to get to care and that's why their kidney is not being monitored. But these are all the things that can start to be looking into, to see what inequities are gonna be addressed. And again, everything solely with laboratory. Let's look at an example where we look at some health inequities. When it comes to diabetes, thinking about our target intervention that I just showed you, we did a very simple analysis by dividing the diabetes population into different cohorts. We looked at one that was seen, what it was classified as a higher income zip code versus one that was a lower income zip code in the state of New Mexico. When we're looking at patients' risk and care gaps, looking at a higher zip code population, we identified that from the patients who had diabetes, 485 had risk and care gaps. When we're looking at their lower income, there was 453. And although this number may be looking similar, well, we have to take into consideration that the population of the zip code significantly. When the higher zip code is 18,000, the lower income is only 2,800. Not only that, but we also went a little bit further and see, well, let's see how many patients access Tricor. We can maybe compare how many patients have diabetes and what percentage manage. We look at the higher income, we saw 561 patients. And when we look at the patients with the lower income, it was 463, meaning that really only 10 patients who had diabetes from all that zip code were well-managed. But I still don't want you to forget about the higher income because this is important also. Because although their diabetes virtually seems like it's better managed, I would say that there's a huge disparity here also. And this is what I want to drive home with everybody who's in this. Although zip code and economy play huge roles for determinants of health and health equity, we have to remember that health equity is multifactorial and very complex. It's not just dependent on the factors, but there's many other things that are affecting our patients. What it is here, what's here, I mean, we don't know, right? But we just know that there's an issue happening. And really the only way to find out what's happening is by applying the health equity lens and figure out what is happening. There is maybe an access, maybe education or something else. And now we're going to go over COVID, a COVID story that we also have. Because let's face it, we cannot have a seminar this year without talking about COVID. But for this, it is also another important collaboration that we had. So it was, this project was a collaboration between the state laboratory for capacity balance. In these collaboration, there were daily phone calls to determine the need of overflowing testing. And there was also a collaboration between the Care System Tricor and the National Guard, state to distribute supplies and samples for appropriate testing patients. As we know from the pandemic, testing is critical to be able to apply appropriate measures. And also there were an activation of mobile testing units, a local hotspots for mass testing. When looking at the map of New Mexico and where the testing was being done, then applying the health equity lens, what you can see here is M's and H's. M just stands for incapacity testing and H is high capacity testing for COVID. Very easily you see that there's a huge gap here, which right, it comes out across very fast, but the other thing that I was talking about earlier is there's more in social determinants of health than just economy, right? Or this, there's a lot more because if we look at Las Cruces, Las Cruces is a city in New Mexico, which is the second largest state in the whole, the third, pardon me, the second largest city state of New Mexico. So maybe that was overlooked at some point, right? It's a large city, wasn't getting enough resources, but we're looking at the data, it was very clear that they were facing a huge disparity, that being the access to care. With this, with the proper partnerships, the Tricor Saturn Core Laboratory was born. So this is a partnership with the New Mexico State University that Tricor was part of, and they used some of the old laboratory space that the university had, and they turned it into COVID testing. And this has been a very successful partnership as they continue to expand testing, and they are testing on just COVID. And with that partnership and continued work, testing continues to be delivered across the state. As you can see, now there's not really that many holes, although there's still some areas that may still lack testing, as you can see, testing has greatly expanded. That was thanks to the analytics through the laboratory and strategic partnerships. Thank you, Teo, for sharing our story. We are very excited that we have been able to come this far and share our insights as a laboratory with our partnering clinicians, our payers, and really the delivery health system that we have partnered with for so many years. Tricor has really changed the culture of our organization to begin looking through the health equity lens and really to achieve value-based care. This has become our business strategy. And the pros that we wanna leave with you is that health equity, number one, is attaining full health potential despite the social determinants of health. What we've been able to share with you today is definitely not a huge step in how we can use the data for health equity, but I think it is our vow as a laboratory, and we believe that other laboratories, in order to move further in this direction of health equity is to really begin taking a look at the data that we have. And though we haven't been able to dip really deep into data describing health equity, through our partnerships, we can achieve so much more and being able to move in the direction of the quadruple aim with health equity at its center. We have been able to show that the clinical laboratory can apply a health equity lens objectively. And this was really when we were discussing implicit bias and hopefully we're able to help or be more of an integral health partner for our clinicians and our payers to be able to provide this information in a different way. Again, we can't do this without our strategic partners. They have a lot of data as well. I think every healthcare partner has their own database, and this is the beginning of breaking down those data silos so that we can provide better care across the health delivery system. Ultimately, it is a cultural shift for all labs to not be an ancillary service anymore across our healthcare system and to be an integral role and to be an integral health partner and to be a team member within that healthcare system. We can begin showing our value as a lab through the health equity lens by strengthening the data that we have to provide our clinical decision support across the healthcare system. Not only strengthening our demographics, the metadata, but ultimately, again, those strategic partnerships to break down the data silos. Dale, I will let you bring it all to a close. All right, for the ending, we just wanted to thank you, everybody, for your time and allowing us to talk to you about this very important subject. I wanna leave you with a quote that really resonated with us from Dr. Martin Luther King. Of all the forms of inequality, justice in healthcare is the most shocking and inhumane. We wanna just tell you that I think we want this to be a call of action to all of us, all the medical care professionals, laboratorians, and everybody who plays a role in healthcare that health disparities and addressing health equity is the responsibility of all of us. And hopefully, these inspire you to start taking a stand in addressing health disparities in our country.

health equity

value-based healthcare

quadruple aim

social determinants of health

laboratory data

healthcare disparities

patient outcomes