Thank you again for everybody for joining us here. So the, what the reasoning behind the webinar was just to, a lot of the times that when we're doing presentations or we're discussing health equity projects, people want to understand how to get started or what's the best approach to do this and the best. So we thought that it would be a great idea to create a panel of experts that have, bring in different areas of their experience and can share here. So whether you're doing bench research or you're doing implementation work or you're doing epidemiology work in any area of radiology, you can get involved in this space just to ensure that everybody benefiting from this. So what we're going to do is we're going to get us started. Dr. Spoluto said, yes, we want to encourage you to participate via the chat or the Q&A session just to submit questions along the presentations and we'll address them towards the end. We'll try to answer some of the questions at the presentation keep going. And without further ado, we're going to start with Dr. Ruth Carlos, who will be discussing mapping the biology of racism, social genomics and health equity. Thank you so much, Dr. Scott, Flores and Spoluto for facilitating this conversation. These are my disclosures. These are additional disclosures. If you don't recognize this person, this is Ralph Nader who once told a friend of mine, if people aren't shooting at you, you aren't doing your job right. So let's hopefully do our jobs right. When we talk about conditions of system change, these are the six general areas that facilitate true change, both structural and transformative. And at its base to lead to transformative care, we need to change our mental models. And that includes some of our research models that we are using. So how do we do that? Ultimately, what we want is the right test, the right patient to get the right test, the right treatment at the right time and for the right price. And many of us work at the precision and diagnostics, precision diagnostics and treatment component, really pushing the science. But what do you do when the evidence does support practice where individuals who participate in these clinical trials are often healthier, wealthier and whiter than many of the populations that we tend to see in our daily clinical practice? When we talk about disparities, we often feel really good if we are able to include race information or ethnicity information so that we can summarize there are racial or ethnic differences in outcomes. When we try to operationalize race and health equity, however, it is a flawed proxy for genetic predisposition. This study looked at 4,000 alleles, 92% of the regions shared, 92% of these alleles were shared in two or more regions and 50% of these alleles were present in all seven regions. And when you look at intergroup similarities versus intragroup similarities, similarities across groups are much greater than they are even within a single group. Therefore, we're using the term ancestry to account for the genetic variation reflecting geographic origin. But then that leads to genotype as a small component of contributing to outcomes where genotype would be denoted by ancestry and phenotype and in quotes race contribute potentially larger effects to outcomes often mediated by structural racism and discrimination. And these are some of the structural and social determinants of health. We look at the built environment that looks at food deserts or high density housing. There can be neighborhood service access issues such as crowding or transportation access. And then there are social relationships where segregation and interpersonal racism as well as social support and social isolation can impact one's outcomes. So we really want to look at a society to cells to outcomes approach where the social context, social policies and institutions that lead to biological and clinical outcomes gaps are often mediated by the exposure to the social context which leads then to physiologic stress and epigenomic and genomic regulation and dysregulation. So when we look at structural racism and we're talking about segregation measures or being diagnosed in states that had Jim Crow laws in the past these lead to increased mortality in black women who live in highly segregated neighborhoods compared to lower disease specific mortality and all cause mortality among black residents who live in neighborhoods with at least 20% black residents. When we look at racial animus this is a cultural measure of racism looking at Google searches for the N word. This measure of metropolitan areas or communities was closely associated with the rate of deaths among black individuals. In fact, it was the higher predictor compared to traditional individual level metrics like poverty or socioeconomic status. And then in our own work, we found that women who responded that they had been they had experienced everyday discrimination for example, not being thought as smart as they were because of various metrics of discrimination had lower self-reported health as well as higher rates of system care utilization. I wanna take a step back and talk about the physiology of scarcity. Think about the worst day that you've had in the last couple of weeks. Lists were out of control. You couldn't get in contact with a clinician. You go home that day, really glad you pour yourself a glass of wine or a scoop of ice cream. And then before you know it, that bottle of wine is empty and you've gone through the entire tub of Ben and Jerry's even though just this morning you said, I'm gonna start my fitness routine today. And when we live under conditions of scarcity whether you're talking about time scarcity, resource scarcity, food scarcity, that induces a limit it induces a cognitive event where it becomes difficult to make just one more decision. And I think that's why President Obama had 20 versions of the exact same suit because it's one less decision that they need to make that day. Think about it from an Amazon care worker or an Amazon fulfillment center worker who works hourly wages with a zero hour guarantee contract. And she's down to her last $5 and she has to decide whether she puts gas in her car her asthma medication, or whether she has to pay that co-pay for a lung cancer CT that needs to be done because her lung cancer screening, which was free all of a sudden had found something. And this leads to decisions that may not advance what we want out of our, that may not advance our values but in the moment actually is the most logical thing one can do. So if you think about this ongoing stress it induces overproduction of stress related hormones and biomarkers through the hypothalamic cascade and the adrenal cascade, which then leads to increased inflammatory markers, increased inflammation which influence cell death, leakiness of the endothelium cell deformity and mutation, which then leads to how cancer develops and how we can treat cancer. And there are mechanistic pathways of the effects of racism on health. And some of these are genomic or epigenomic changes. Some of these are peritumoral inflammation and the stress response. So this is one potential biological model for the impact of structural racism and discrimination on cancer development. You start with chronic stress from racism or other environmental exposures which then increases the production of stress hormones using the hypothalamic and adrenergic activation mechanism which then leads to the production of inflammatory markers stress hormone secretion, as well as stress related transcriptome expression, histone modification and telomere shortening, which may account for why black women and Hispanic women tend to get cancers and worse cancers at an earlier age. This cascade is termed the allostatic load or the cumulative physiologic effects of chronic stress which then lead to mutational burden neo-antigenicity and cancer. In short, environmental stress leads to cancer formation. And when we look at the multi-ethnic study of atherosclerosis or the MESA study we found that measures of allostatic load were significantly higher among black individuals and Latino individuals compared to Asian individuals and non-Hispanic white participants. When we look at correlates of new cancer development in a population predominantly followed for development of atherosclerotic disease what we found was in addition to race allostatic load was also a significant predictor of new incident cancers. Another way to conceptualize some of the biological effects of racism and other social exposures is through a conserved transcriptional response to adversity. This is a 53 gene transcriptome initially described in HIV patients with social isolation and subsequent poor outcomes. It was initially dubbed the loneliness gene signature. It contains pro-inflammatory genes and antiviral genes and environmental exposure such as childhood trauma or neighborhood violence was associated with increased expression of pro-inflammatory genes and decreased expression of anti-inflammatory genes or protective genes. What is important is that this is modifiable and then may serve for interventions to mitigate the effects of social exposure and social content. Now, social conditions and solid tumor gene expression social conditions increase inflammation, decrease the rate of wound healing. It increases blood vessel growth which then leads to neovascularity. And it also increases the resistance to apoptosis which leads to chemotherapy resistance. So the social condition, the social context is a currently understudied covariate of disparities that we traditionally ascribe to race. And why should radiologists be interested in social genomics and health equity using such a biologically based model? Because we have seen that racial discrimination is associated with increased fMRI signal in the amygdala thalamus, which has a potential mechanism for the embodiment of racism related disease. Epigenetic age from DNA methylation has been shown to be related to poor social determinants of health. And there is a potential mechanism for breast cancer at a younger chronologic age among black and Latina women. We are in our own group looking at false positive screening rates and perilesional environment and linking that to the stress transcriptome CTRA as well as with allostatic load. For those of you who work in abdomen the microbiome and metabolomics has been shown to be susceptible to the environment. And there is great potential to link this with fat quantification and cardiovascular disease. So in essence, we are back to where we started the right patient, the right test, the right treatment the right time and for the right price. And our goal truly is equitable evidence-based care. And we need to start looking at the social context and social genomics to fully understand how we can mitigate inequities that are currently ascribed simply to race and ethnicity. Thank you. The next speaker I would like to introduce Dr. Anand Narayan at the University of Wisconsin who will be speaking on research tools to bridge barriers to radiology care. Dr. Narayan. Thank you so much. And let's see, here we go. Okay. So thank you so much for kind introduction. Thank you for the invitation as well to be here too. It's a great honor and pleasure to be here with this amazing group of individuals. I'm here to talk to you today about research tools to bridge barriers to imaging care. And so we all know the COVID-19 pandemic has really highlighted some of the immense racial and ethnic inequalities that we see in healthcare. And a lot of academic medical centers and health centers have responded to this with various statements and pledges to do better. And this, for example, the NIH statement recently but how do we prevent this from becoming a performative check the box exercise? Well, the CDC has provided some really great tools to really get a sense for how we can actually pay the road towards health equity. And I wanna highlight one of the particular elements of that that they cited and that's measurement. And this idea was popularized by individuals like Peter Drucker, for example, who said that if you really wanna make improvements in things that you really have to measure those things. And so this gets the question of measurement and data and really rigorous synthesis of evidence to really drive policy forward. And so this gets to the question about research and where this sort of fits in with this. Typically in a research process while we're sitting in our reading rooms in our areas in our clinical environments, we identify problems and issues and we then come up with research questions and we try to structure and format them in rigorous scientific ways to be testable in terms of hypotheses. And we oftentimes go to our local data sets and our hospitals to answer those particular questions. But we live in a diverse country in which they have different circumstances and environments in different places. You've got individuals in Portland, for example, who are doing some interesting things over there. I've got a few individuals in Boston as well who speak in this particular manner. And you have things going on in Florida, for example, a different wildlife and species and things occurring in that fashion. And then of course, in the state of Wisconsin, if the movie Scarface were set in the state of Wisconsin, this is how this epic scene probably would have looked like without Pacino. So we live in this diverse country and despite the diversity that we see in our clinical research that we have very, very limited representation of individuals of minority populations within our clinical research. And so how do we improve that representation of clinical research to drive our clinical decision-making? Well, there are a variety of strategies in the published literature that we have employed and individuals have employed to improve representation. And I just wanna throw a really quick plug in for community-based participatory research for those of you RSNA last year, there's a terrific educational exhibit on community-based participatory research from Arissa Milton who's now a second year medical student at the University of Wisconsin and it's gonna be a radiographics article soon. So check that out if you haven't seen the educational exhibit or the upcoming radiographics article. Now, but I'm gonna talk about using existing data sources here and the benefits of using that. And one of the benefits is that many of these datasets are publicly available and free for individuals to access. So for example, Google itself has a dataset searcher so you can go and see if a particular dataset may answer your particular research question. In addition to that, the CDC has its own set of databases that are publicly available for individuals right here. And so I wanna highlight a few of those things and how they might be useful to help answer radiology research questions. So you have population-based surveys, vital records and provider surveys here. Just gonna do a couple of examples of those things where they can be used to answer imaging-based research questions. And this one by my colleague, Dr. Drew Ross at the University of Wisconsin here used the National Hospital Ambulatory Medical Care Survey here. And what he did was he found in this particular survey that white patients are more likely to get medical imaging compared with non-white patients. But I just wanna draw your attention to some of these incredible sample sizes here that can be gained by using one of these surveys. Tens of millions of patient visits in racially diverse patient populations here that can be used to answer imaging-related research questions and other surveys, for example, like the Behavioral Risk Factor Surveillance System, which is a telephone survey here, about 400,000 interviews every single year. Recently, we used this to look at the effects of the change in eligibility associated with lung cancer screening guidelines from the U.S. Preventive Services Task Force. And we found that despite the change in guidelines from the U.S. Preventive Services Task Force, that the African-American and Hispanic individuals were still less likely to be eligible for lung cancer screening. And so other surveys as well, like the National Health Interview Survey here that oversamples for Black, Hispanic and Asian participants in their surveys as well with large sample sizes. One of the great strengths about using some of these public datasets is the ability to have publicly available data sources with source code as well. So many of these datasets have freely available source code here. So for your favorite statistical programming, you have code that can allow you to use some of these different population-based data sources here to answer some of your questions. But I do want to provide a note of caution here that it's really important to read the instructions right here. And I say read the instructions here because many of these datasets and the source code have really detailed information for them, a complex survey waiting here. So if you've never used some of these data sets before, I would just suggest touching base with somebody at your institution or some other institution who has some expertise using some of these data sets to really help guide you forward, make sure you're on the right path. So let's say you go through your project and you publish a nice research paper, research article here, and so then you're done. Mission accomplished, right? So you've done, so we've done a check, we've achieved our health equity objective. Well, this is one of the challenges I think in academia here, and I think Dr. Carlos has mentioned this, we're going to see a lot of this in later presentations as well too. So much of the work in academia is publications and documenting things, but the next step is really just to really have that work that we do in health equity and research really have an impact on the patients that we serve. So Dr. Ross published this in a recent article, a systematic review, that I thought really highlighted the challenges quantitatively quite well. And what they found is in our literature here, looking at imaging-based health equity research, what he found was that only a small percentage of studies actually addressed interventions that actually mitigate disparities, whereas the vast majority of studies are descriptive. And that's a really important step here, but I think to really have an impact on health equity and health disparities and inequities, we really have to go beyond that. And going from descriptions to interventions here, I want to highlight a couple things. One is the use of quality improvement and implementation science. Dr. Spoodo and Dr. Neal have terrific presentations lined up after me to talk about implementation science, so I'm going to defer that to them. But I'm going to briefly walk through a case example of where quality improvement can really be used to advance health equity objectives here. An example I'm going to use is the Metropolitan Chicago Breast Cancer Task Force here. And what they did is to use a quality improvement approach towards addressing breast cancer disparities, and they listed a wide variety and measured a wide variety of quality metrics related to the performance of mammography screening programs here. And what they did is, once they documented those initiatives, what they did is followed through with a series of quality improvements and public health initiatives to really improve the performance of mammography in all these different domains. And they actually were able to achieve a decrease in breast cancer mortality disparities, which is in contrast to several other major metropolitan areas in the area which saw either unchanged or even worsening breast cancer disparities during the same time frame. And this is the approach we're applying here in the state of Wisconsin here to measure our disparities using a quality improvement approach, and we're going to be talking about this in San Antonio for those of you who will be there in a couple of months. And so to summarize things, high quality data collection and measurement and the research process are really important to improve health equity. And some of these public databases here can really improve the representativeness of our clinical research. Things like quality improvement and implementation science, these are rigorous methods to really both drive data collection and, most importantly, improve outcomes for our patients. So I want to thank you all so much for your attention. And with this, I'd like to turn things over to Dr. Spoluto, who is Vice Chair of Equity at Vanderbilt, and she's also co-chair of the RS&A Health Equity Committee, and she's going to talk to you about a topic which is really important in terms of not just describing and documenting things, but moving things forward, and that's implementation science and design thinking. Thank you. Thank you, Dr. Narayan and Dr. Carlos for those great presentations. I've really been looking forward to this discussion with all of the speakers on today's panel, and thank you to the RS&A for hosting and putting together such a meaningful webinar. So as Dr. Narayan mentioned, I'll be talking about leveraging design thinking and implementation science. He talked a lot about the big databases that we can look at to examine some of the existing disparities that we sort of know already exist, but how can we get out and start to address some of these things through research and clinical practice? So I have no relevant financial disclosures, so I would like to thank some of the funding sources that have supported our work. So I'm going to talk a little bit about design thinking and implementation science, which are really two very different concepts, but what they share is the concept of making a process or an evidence-based practice more applicable to the real world, the real world where people live, where diverse populations of people live. So design thinking focuses really on understanding a process through the end user experience, that person who is the end user of the experience, the end user in the real world, whereas implementation science focuses on bringing an evidence-based practice to the real world. So both are very important to health equity research and community engaged research because to achieve health equity, we have to focus on this real world, on diverse populations in the real world, and how we can offer these populations in the real world the opportunity to achieve their best health. So what is design thinking? Design thinking is, it's a problem-solving method. It's an iterative process that puts the user's experience at the center. The goal is to develop effective solutions by incorporating the ideas and thoughts of diverse teams of people, and then to iterate and test and improve these ideas. So it uses both divergent thinking, gathering ideas from diverse populations, and convergent thinking with emerging and categorying of these ideas that we've developed together, and helps us to iterate freely, consider logistical restraints, and then to efficiently evaluate the ideas that you've developed. Then usually incorporate some type of empathy mapping with thinking about the people that you're working with. So this might be, if you're looking at a research project, this might be your research subjects and how you're going to engage them and keep them engaged with your research project. If it's clinical, it might be patients. But you want to keep that person in mind and generate ideas for what that person or persona would experience in a given situation, what that person might say about it or think about it, what it might make them do or how it might make them feel. And then once you've taken that empathy mapping, kind of work together and talk about things as a group. So this is an example of a design thinking session that we held to help to improve our health equity curriculum in our department. So we brought together a really diverse group of people. It involved students and residents and fellows and community members and people from other departments and researchers and non-researchers and patients. And together, we generated ideas on what our trainees should be learning about. And through this process, we ensured that all of our participants had a voice in what is heard. Then the group can work together to generate big ideas. Remember, no idea is too big or too small, but big ideas are the solutions to the problem that you are designing for. They're generated based on the insights from this group that you've worked with. So in the example that I presented, the ideas generated were that our institution and our trainees need to better understand the patient population that we serve and to emphasize the importance of empathy. We need to really have a shared patient experience to work with our patients to understand what it's like to be a patient in our medical center. This curriculum needs to be accessible and we need to include experts from outside of radiology so that we understand where we fit in the bigger health system. And we need to understand the learning needs of our trainees. Once these ideas are generated, you can prioritize things so that you can go ahead and proceed with those things that might be less hard to do, but have a lot of value and save those things that are a lot harder for later. Go ahead and kind of take take advantage of the low-hanging fruit first. So that's just an idea of how design thinking might work. And it can really be applied in a lot of settings, in the research setting, in the clinical setting, in the quality improvement setting. But it's that idea of understanding who your end user is and incorporating that end user's experience into how you design your research project or your quality improvement projects or the clinical experience. To shift to implementation science, which is a little bit different, it's more of a type of research, where does implementation science fit in the research spectrum? When we think of the spectrum, sort of on the far left, we have efficacy studies, which focus on, does a certain practice work in a very tightly controlled research environment? So in the perfect setting, does a certain evidence-based practice work? Whereas effectiveness studies focus more on, does it work in the real world? So once you get out of that very tightly controlled research environment, does your evidence-based practice still work in the real world settings? Whereas implementation research focuses on, how do we really get these evidence- based practices that we know work through efficacy and effectiveness studies, how do we get them out into the real world in the right way? And my personal interest is how this implementation research, how it has an effect on equity in outcomes and access. How do we get broad communities in the real world that serve diverse populations to implement evidence-based practices in equitable manners? So that all the populations across our country and across the world really have opportunities to be as healthy as possible. So implementation outcomes look a little bit different than standard research outcomes that we talk about. So Brian Weiner, a renowned implementation scientist, said that the sweet spot for implementation science is taking what works, we know a practice works, and then figuring out how much we can adapt it to different settings without losing its effectiveness, and then scaling it up to reach large numbers of people who can benefit from it. And so the types of outcomes that we look at in implementation science might be acceptability, does an end-user find their level of satisfaction with various parts of an intervention to be acceptable? Adoption, are people really actually doing it appropriately? Cost, feasibility, can you actually carry out an evidence-based practice in a particular setting within a particular organization? So these might be different than the standard clinical outcomes that we think of. So what does implementation science look like in the world? It's basically how do we take an evidence-based practice, something like screening monography, this is what I do every day in clinical practice, how do we take this practice and integrate it into routine, sustained clinical care out in the real world, not just in our ivory tower academic settings. So why is implementation science important for health equity-focused research or community-engaged research? Well, this is the state I live in, Tennessee, the pink state on this map. We know that in Tennessee, Tennessee has the seventh worst breast cancer mortality rate of all the states in the country, so this is a little bit embarrassing. We're way down there at the bottom, we know we have problems to address. But within Tennessee, if we look a little bit closer, breast cancer mortality rates vary by county and by community. So here the darker pink counties have higher mortality rates than the lighter pink counties. So we know that really there are small, subtle differences in communities across our state that need to be addressed. And if we only focus on the bigger, more academic centers, and here these symbols are on where our larger academic centers are, so Nashville, where Vanderbilt is, or in Memphis, or in Chattanooga, but that's not where all of our disparities are. We need to get out into a broader real-life setting. We need to get out into these communities across the state that have breast cancer mortality disparities that we know we need to address. And how can we do that? We can do that by understanding our end users and by understanding how we can get the evidence-based practices that we know work. How can we get these out into the real world, into these other healthcare settings and other general population settings? And we can do that by working with our communities and our community partners to do this. So to recap, design thinking and implementation science share the concept of making a process or evidence-based practice more applicable in the real world. By applying a health equity lens to design thinking and implementation science, we have an opportunity to address existing health and healthcare disparities through a focus on the real world and on diverse populations in the world and how we can offer these populations of the real world the opportunity to achieve their best health. So thank you so much. I'm now going to turn it over to Dr. Neal. Dr. Neal is an assistant professor at the University of Oklahoma Health Promotion Research Center. He specializes in translational cancer communication science, and he has particular interest in disseminating tailored navigation strategies to meet the needs of rural and underserved populations. Dr. Neal will be discussing advancing equity through user-centered intervention design and implementation science. Turn it over to you, Dr. Neal. Okay, thank you very much for that introduction and also the opportunity to present today. Obviously, as the topic is health equity, I think it's important to talk about, you know, who I am as a researcher and frankly my point of privilege to work in this area. As a white male, I obviously carry with me biases and privilege that many of the participants that I try to help are not fortunate enough to have, but I'm also grounded in diverse collaborations as well as stakeholder groups that inform my work and obviously lift my work in many different ways, and I'm hoping to be able to sort of touch on very briefly some of that work today and really use it as a point to jump off from so that we can have a really great discussion. If I just move forward, I'll let you know that I have no financial disclosures. So for the topic today, after I saw what Dr. Scaluto was going to present on, I thought that there's a little bit of an overlap with design, thinking, implementation science and user-centered design and implementation science, and I really wanted to focus on something slightly more or offer a more unique contribution rather than just trying to do a poor impression of her work, and that is actually to talk about cancer communication science. So I was trained as a translational cancer communication science at the University of Florida. My PhD was primarily within our CTSI. As part of that CTSI, we were one of the few, if not the only, CTSI that actually had communication as one of its pillars, and the goal of our translational cancer communication program was really to take, yes, bench-to-bedside medicine, but also bedside-to-community medicine. So what we knew worked, could we get it into the community, and could we do that using evidence-based communication practices? So whenever I saw this quote from the former CDC director, it really spoke to me, both in terms of the benefits of translational cancer communication, but also in its relevance to help disparities work, and it says, the gap between what we know and what we do is far greater than the gap between what we know and what we don't know. So when I think of translational cancer communication science, I really think that it sits in sort of three primary areas. Of course, our first priority is always to try and reduce disparities within the realm, across the cancer continuum. I am a mixed methods researcher, and I'm very fortunate that another area that guides my work and the work of cancer communication science is direct engagement with our community, understanding what we believe is, understanding what they believe is priority, and also understanding what are feasible and acceptable approaches to try and address that priority. And then what I hope is the unique contribution that I bring to the table is a training in communication science. So whenever we think about how we're going to deliver interventions, and I work primarily with mobile health or scalable digital interventions, how do we ensure that those interventions are grounded in communication science and best practice? And to give you an example, there are many, many different communication theories and models that we can look at. But one that guides my work, but is certainly more simplistic, is a linear communication model. So whenever we develop an intervention, we often think, you know, who should this intervention be communicated from? What is the source that has most credibility? What is the source that has most identification with our target population? Next, we think about, well, what kind of message do we want to create? How do we embed messages that are meaningful and culturally sensitive to the communities that we try to intervene upon? And of course, how do we make sure that we get these messages to those individuals that we're trying to intervene upon? And that's where obviously the medium and the channel has to be important and effective for communicating our message. And then we try to understand the receiver. What are their needs? Who is it that we think is most in need of receiving this message, whether it's to try and promote cancer screening or other health behaviors? And then obviously we measure in effect. Now, this is a simplistic model. I think it does help guide our interventions. I think when we think about what actually communication is, it's much more transactional, a lot more complicated. So what we find is that very rarely is communication a linear process. Very rarely does it go from one entity to another and there's no back and forth by directionality. And in fact, actually, there's an argument that that linear process from academia or the ivory tower that Dr. Ezra mentioned earlier, I think that is actually the problem. We need to really listen to our communities and we need to be able to be identifying ways that we can create interventions that are supported by evidence-based communication theory and that can translate effectively to those communities. And when we sort of think about these transactional models of communication, how do we encode or create a message in our environment that's meaningful in their environment? How do those individuals in that environment decode or understand the message that was created for them? Who is the communicator of our message? Who's the communicator in our community that can relay information on its effectiveness or non-effectiveness? And while it's depicted here as noise, this is really all the other structural and perceived barriers that affect inequities and structural racism, for example, that actually causes these disparities in the first place. And we really need to cut across that noise as best we can with structural-based interventions, as well as individual and community-based interventions that are tailored to our community using communication science. I'll give a couple of brief examples. I was fortunate that while my time at the University of Florida and thereafter, we worked on an R01 that was to try and bridge access to evidence-based recommendations for colorectal cancer screening for rural and underrepresented minorities in North Florida to get access to understanding about colorectal screening through FIT tests. Understandably, we know from the literature that many patients here, many minority patients, don't ever see a doctor who looks like them, sounds like them, and comes from their same cultural or racial background. So we developed racially concordant and binarized gender-concordant virtual humans through extensive user-centered design work to try and take what are interactions that would normally happen in the clinic space that could actually be done in rural settings at home and be delivered through a representative provider to try and increase willingness to engage in this information among our rural and racially ethnic minority populations. And that R01 is coming to a close, but we've learned so much about the delivery of this information and what does and doesn't work for different groups. Likewise, when I did my postdoc in Boston and working primarily with Dr. Flores, we've worked on different ways in which we can try and improve uptake of lung cancer screening and the integration of tobacco cessation. We've used rigorous message design theories and tests to try and improve how we communicate meaningful messages to smokers about the benefits of lung cancer screening from when they're initially presented with the idea right the way through to when the results are communicated to them and how that can motivate behavior change within those teachable moment interventions. We've provided this in both English and Spanish based interventions. Likewise, when we were told by our stakeholders that the idea of lung cancer screening and the process was hard to understand or conceptualize, we tried to create low health literacy animations that helped exemplify what an individual would do while they undergo the lung cancer screening process. And likewise, whenever we were told that the guidelines were hard to understand when they were communicated non-visually, we presented those in our digital outreach strategies with visuals, as well as understanding who should be conveying this message. And we found that both the combination of a provider, a radiologist, and Dr. Flores, as well as a patient brought together both expertise and trustworthiness to provide a credible source for our communities. I just want to sort of finish off, because I know we're over time, is that, you know, while moving through my graduate work, my postdoc work, and then now my current position at the University of Oklahoma and the Stevenson Cancer Center, we have understandably huge challenges in the state of Oklahoma, but we're also buoyed by the fact that we have unique opportunities. So we have the only mobile health resource called Insight that's integrated into our cancer center. So we can build an app within two weeks. We have five dedicated coders and we have expertise in the creation and dissemination of those apps. The goal of these apps is to provide tailored digital navigation. So we bring in our understanding of what messages should be tailored on to address unique as well as population level barriers to care. And I don't want to take too much time because I have a few slides that can discuss this if we'd like to in the discussion section, but I'm certainly happy to answer any questions because this platform is available not only to investigators at the Stevenson Cancer Center, but also to investigators outside the Stevenson Cancer Center. So with that in mind, I think we're meant to transition to a discussion portion of the presentation and I'm happy to contribute to that. Dr. Neal, Dr. Paluto, Dr. Narae and Dr. Scott for incredible introduction and also phenomenal journey of all the different aspects of health equity research and how to truly bend to bedside from like social genomics to the implementation component and not only the implementation, but also gathering, understanding the epidemiology focus at the population level, but also in gathering that community input to ensure that the implementation reflects the voice of the community. I mean, we can probably keep going and on and on and on here, I would just like each presentation as I was listening to, it could be like its own webinar because it's so rich in terms of like what is covered and you can really go in depth in it. So while we get some questions from the audience that you want to get us started with, I could ask all questions here, but Dr. Carlos, I would like to start with you in your presentation talking about the biology of racism and social genomics and understanding, as I was listening to your presentation, a lot of things came to mind, for example, the advancements of radiomics, theranostics, and when you touch base on how these social stressors influence cancer biology, and I'm curious to see, you know, what are your thoughts on perhaps some ideas of potential ways to incorporate some of these factors into new risk models that will reflect these stressors among high priority populations, such as racial ethnic minorities in the realm of cancer screening? Great question. At minimum, some of the predictor models that we have, for example, the Gale model or the coronary artery calcification model for cardiovascular disease need to be revisited using zip code information, such as NSES, and you can even layer on top of that segregation measures, such as neighborhood isolation or distance from city center, which is a marker of access. Second, I think it's also important to look at potential mediator models. So, for example, if we find that there are disparities due to race and ethnicity, understanding some of the biological linkages can potentially help us identify targets for intervention. I also jokingly say that what we need is a lab test for racism, because as physicians, sometimes we don't believe something is true in the social context until we have a test for it. So, I see we have a, well, first of all, thank you, Ruth. Always an excellent response from you. Always impressed with your ideas and knowledge. We do have a few additional questions in the chat box here. So I'm going to start with one that was directed for Dr. Neal, and I'm actually interested in hearing more about it, because I know we got a little bit cut off in your presentation and I was looking forward to hearing the rest. So it says, Dr. Neal, do you believe that even with such a competent and considerate communication method, noise will continue to be present even as we move forward with equitable research? Meaning, as we continue to conduct meaningful research to gain the important data points that Dr. Narayan referenced, with the considerations for the community that Dr. Spilloo mentioned, do you believe the noise in communication will slowly die down logarithmically? That was an extremely well-worded question and 10 times better than my response. I can already tell you that. I think it's important to think of effective communication, while it's both universal in its application, I think it's also still only a tool in our toolkit to try and reduce inequities. I think across our talks, we reference structural inequities, and while effective communication can try and promote, for example, willingness or greater motivation to engage in a preventive behavior, if those structural inequities are so great that you can't overcome them, there's only so much noise that good communication can really provide. That said, I do think that there are many opportunities to cut through the noise. And again, I actually think noise, it minimizes inequities. It's just part of this theoretical model. So I appreciate that we're using that as a construct, but it's a somewhat minimizing term. But communication can obviously help promote not just the end user, in this case, the patient, but I also think that it can cut across at a multi-level. So when we do our community outreach awareness, when we do our provider academic detailing with effective communication to try and increase understanding of who is and who isn't being presented with the opportunity to screen for cancer, as well as at our caregiver level and also at our community level, effective communication is universal. And I think the incremental and sort of cumulative effect of good communication can somewhat cut through the noise as best as best possible and slowly move or hopefully reduce inequities. Thank you, Dr. Neal, for your answer and to the audience for that incredible question. Let me think. We have another question from the audience that talks about, you know, thank you for the presentation. You said a lot of effective interventions involve community engagement. Can we discuss specifically who these are and what sort of information is passed between the researchers and stakeholders? I'll definitely start. I'll address these ones first to Dr. Spoluto and Dr. Narayan. We'll talk about the implementation and then also the community-based participatory research. But everybody, definitely, Dr. Neal, I'm going to say Jordan, please feel free to chime in the same thing, Dr. Carlisle, whoever. Go ahead. So I'll share a little bit and then I definitely want to hand it over to Dr. Narayan because he has that great RSNA presentation that's coming out in Radiographics, which highlights a lot of this. But community engagement can cover a lot of different sort of entities. There's a lot of ways to incorporate community engagement into research projects. One, in study design, we could work with a community advisory board or a community engagement studio to gain the perspectives of the community around you, which community can be really defined as a lot of different things. Is it a geographic region? Is it a certain racial or ethnic population that you're looking to work with? Is it a gender-based population? So community, it can be defined differently. So first of all, think about what is the community you're trying to work with and how do you want to engage them? Is it in your study design? Is it in how you want to roll out your study? Is it in how you want to disseminate your study? So as I said, there are opportunities through community engagement studios, community advisory boards, building community academic partnerships, and that can take a long time as a researcher to get to know your community, to build those partnerships, to work together to figure out what kinds of research are important to the community, what is meaningful, what people want to participate in, and what they want to drive change towards and how they want to learn about those results. So there's a whole spectrum of ways that you can work with the community. But I'm definitely interested in hearing Anand's thoughts also. Yeah, I wholeheartedly agree with that. I think the spectrum of involvement with the community, looking at it from the perspective of the immense sort of power differential that exists within academic medical institutions and hospital systems versus surrounding communities, we have to ask ourselves, like, who is driving the agenda and who is asking the questions and for whose perspective is the thing most important to? And this is the spectrum in which community-based participatory research resides upon. And the idea behind the community-based participatory research model is that the questions and then the concerns and the things that are really driving things are derived from the community. And when you say community, that looks very differently in different places. So it's different in Nashville versus Ann Arbor versus Boston versus different places in Oklahoma versus all sorts of sites in Wisconsin. And how that looks like means different things for different people. I can say, like, in Wisconsin, we have urban sites. And the conversation in the urban site is a very different conversation versus some of our rural sites about what is community, what does it actually look like in terms of deliver effective and equitable patient care for those things. But I think the big issue about community, I think, is really taking a look at the surroundings that you live in your hospital systems and really shifting that power differential to having your care models and the way in which you deliver care be shifted in terms of this sort of top-down thing in which we as medical centers tell you, the community, what you need and what you do, and shift that around to actually, no, you, the community, you know what you know, you have a sense for what your populations are suffering from and they're experiencing and what are the on-the-ground realities that exist in their day-to-day lives. And we exist to help facilitate those in the context of people who have specialized training and care to address some of the specific issues and contexts as they come up. So if a problem pops up that you have specific expertise in, that's where you can be sort of helpful. But the power, the idea is really shifting the power to the communities that you work with. And I would just add to that, that, you know, we're doing our best to serve the community and also work within sort of the academic model at the same time, and particularly working with our American Indian populations. We need to be very upfront about the dissemination of our findings and what they're happy with. So we need approval for every abstract submission. Every presentation has to go through a tribal IRB. And we also have rigorous ways in which to detail where data will be stored and the data sovereignty rules that we agree to as part of our agreements with that community. So if they are a partner in helping us conduct the intervention, they're also a partner in sort of capacity building. So they're able to continue to use the results from that intervention going forward to help support the further development of that community or that population. So I think there is obviously unique by population and community, but there are also some consistencies as well. Jordan, I think that's a great point. And I think often that dissemination piece is forgotten with community engagement that one, you know, we should engage our community partners in developing the dissemination products and approving them. But we can also invite them to be part of our presentations at meetings or whatever community events we're presenting. And I will give a big kudos to the RS&A that this year I'm bringing one of my community partners with me to share the stage of one of our presentations and the RS&A was very welcoming and supportive of that. So I think there's a shift in our major societies to embracing this. And I'm just very thankful that you brought that important point up. No, thank you, everybody. I think one of the last questions, maybe this is for everybody before we wrap up, but I will look at all your presentations, for example, Dr. Poluto talking about the different implementation science model, like hybrid designs, or for example, Dr. Narayan talking about the different data source, or Dr. Neal talking about the different framework for translational cancer communication, or Dr. Carlo discussing like different framework to understand the biological model for structural racism and how this impacts cancer biology. Just wondering, what advice would you give to the audience when they're deciding either what framework to select, or what database they have to curate and select what are the most effective way, just some practical tips or pointers for the audience? I would say, if you're just getting started, don't be afraid to reach out and ask for help. There are a whole slew of great people in this field in radiology and beyond. This is a great field to collaborate across different disciplines and meet new people and learn about how different people have approached different types of research. I found it to be an incredibly friendly field of people who just want to drive change and make things better. Don't be afraid to reach out to anyone on this webinar or anyone whose paper you read and you thought it was great and you want to do something similar. People love to hear that you've read their work and that they can try to help. One way also to increase your network, but also try to get involved in the space is to work with organizations that already have data. For example, ECOG-ACRIN, we've run a ton of therapeutic trials, a ton of symptom trials, and now a lot of those data are being used to using a very specific health equity lens. Many of the models that we talked about here today, and ECOG-ACRIN in particular has a committee dedicated to cancer care delivery research. That means really understanding how we deliver services to everybody that we serve, not just the ones at the fancy academic centers. I think for data and for research, my big take home for the audience is if you're looking to affect change and really make a difference in your local population, you need to be pursuing your research projects and initiatives with the perspective of those stakeholders in mind who actually will be doing the work. What that means is you need to get in close with your clinical operations teams, your quality improvement teams, because if those are the people who actually will be driving the change, and it's so amazing that Dr. Scott has a leadership role in quality and safety now that specifically allows these connections there, because if you really want to drive the change, you can't just have this exist in an academic space and you just publish some paper and it goes out in the ether. If you want it to make a difference, you need to think the way in which how is what I'm going to do and the project that I embark upon actually going to lead to that change. If you act at the very beginning of your research projects with that goal in mind, then you'll find much more success in seeing things happen down the road. I'll just add quickly, because I know we're out of time, that there's been much discussion about health equity tourism. I think we need to be mindful of this is meant to be a career in health equity, and while it's great that you want to contribute to one project, you really have to think about are you pilfering data from a community to help meet your own needs, or are you going to sit at a table and listen to that community and try to provide long-term support with the privileges that you have, presumably at an academic medical centre. I think that conversation needs to be one with humility and one that has foresight and is going to benefit in the long-term as well. Well, I think that brings us to the end of our hour of really terrific discussion. Thank you so much to all of our speakers today, Dr. Carlos, Dr. Narayan, Dr. Neal. Thank you to Dr. Flores for moderating, and a big thank you to the RS&A for putting this together and hosting such a meaningful webinar, and also thank you to all of our audience members for your participation and your great questions. In closing, be sure to copy that link from the resources panel to complete a brief survey to earn CME credit, and please check the RS&A website for upcoming educational activities and resources. So again, thank you all, appreciate your time.

health equity

radiology

social determinants

racial disparities

design thinking

implementation science

community engagement

cancer communication

long-term partnerships