The State of Equity in America

Marion:

This afternoon we're going to continue with Optum, Dr. Ali Shirvani-Mahdavi. He said, "Marion, since you can't pronounce my name, just call me Dr. Ali." So let me just tell you a little bit about him, and then let Dr. Ali really introduce himself. He's been doing 15 years of result driven leadership with extensive and diverse experience in business development, strategic planning, business consulting, product development. He's an expert in transforming concepts to high impact revenue generating products. He's an expert in transforming, and that is what we need to do, really transform the minds of the people, transform our systems, so that we can produce an equitable healthcare system for every single body. So I'm going to bring up Dr. Ali. Dr. Ali will share his work with you. Dr. Ali.

Dr. Ali Shirvani-Mahdavi:

Good afternoon everyone, and thank you for being here. Yeah, my name is Ali and I'm with Optum. And today, this afternoon, for my session, we are going to talk about the kind of work we are doing at Optum in connecting data technology, analytics, and, most importantly, collaboration with different stakeholders in the community, as well as the health system, to really address the health equity issues. And I'm going to start with a little bit of problems still. To quote Winston Churchill, "Never let a good crisis go to waste." And we are clearly in a crisis mode here. But it's not a crisis that's new.

I think everybody who is in this room has known about health disparities and health equity issues in this country forever. And what is new, and what is the crisis, is that for the past three years, we have experienced the worst pandemic in at least a century, and I would say it's even worse than 1918, not only from a mortality rate, but because of the disparity. Because people in 1918 couldn't go work from home. So everybody was in kind of the same boat. As well as the reinvigorated social justice movements that happened as a result of the George Floyd murder, which happened in my backyard.

And I think the combination of those two things didn't necessarily create a health equity problem, it just exposed it. And I think it really brought about an understanding of the fact that this is not something we can ignore, nor is it something that by ignoring it's going to go away. And so we are going to talk about solutions a little bit, but before we get there, I'm going to throw a couple of more stats in there, which was presented in a much more eloquent way by my presenters before me.

But the same trends hold. You have much higher mortality rate among people of color than you do of whites. You have, in terms of experience and access, people of color experience much worse quality of care than white people. And as well as among children, people who have asthma end up in ED, ED rooms, for Black children than they do white, three to six times more. But most importantly, addressing health equity is really cost effective in the sense that it would save the health system and the health industry up to a trillion dollar by 2015 annually.

So it's not just about making things better, although that's a big part of it. It's also really to help the health system function better and save everybody money for better outcomes. So the solution we have been working on, and I will give a couple of examples of the work we are doing, is really this combination of using, again, a holistic set of data, what we call whole person data sets, that connects health set data, clinical, behavioral, RX, to social data, to demographics data, that allows us to have a whole person view of an individual. Which says, what are those relationships between social determinants of health, upstream factors, race, ethnicity, language, gender identification to midstream factors, things like housing security, food security, transportation access, and then finally to outcome, things like disease progression, or opiate progression, or healthy pregnancies.

But that's the beginning of it. Using the data, using those analytics, it can allow us to create a data-driven strategy to identify areas of opportunity where we can make a significant difference by addressing the needs of the population we have identified through our models. And from there, the strategy allows us to start, experiment, develop pilots, and see what works. One of the big things we always emphasize is that for health equity to really be addressed, we have to show me show measurements. We have to show that what we have done and the work that is done is both impacting the populations we want it to impact, but also that people are getting the value. Whoever put up the money for the program is getting some value in return. So measurement becomes a big component of the work we do. And it starts at the pilot level.

So once we go through the pilot, we see the effectiveness, the ones that perform well move on to scalability, and the ones that don't essentially get stopped there. And from there, we scale it to reach more people and repeat, both in terms of improving upon the projects that we have identified, but also identifying new ones. And one of the areas we are going to talk about is around this SDOH dataset that we have right now.

So at Optum, we are working on a platform called Social Data Infrastructure, which it brings in social data regardless of the source. So we have a lot of propensity models which look at housing security and food security. They're predictive model. They are very accurate, they are very broad, and it's at the personal level, so it allows us to connect it to the healthcare data. But there's also data that comes in from Z codes, from assessments, from surveys. We want to also bring those in, and those tend to be more accurate than predictive model. And so that set of data, again, becomes the foundation of the work we do.

And this is just some of the things we see in the work we do, and there's a lot of literature around it, is that, for example, the relationship with housing security and chronic condition progression, housing security and level of utilization, whether it's admission, readmission, ED use, length of stay, food security and higher rates of obesity, and more rates of chronic conditions associated with obesity. Financial security, obviously we have talked a lot about that, deferring care. And obviously now, with inflation, as was mentioned earlier, that has become a bigger problem. I believe in the morning session, someone mentioned that in parts of New York, people are spending up to 50, 60% of their income on rent. That certainly is going to impact how often you go to the doctor. And that results in, again, in worse outcomes.

And finally, one of the things that, again, was mentioned was education. But education obviously pulls people out of poverty in a significant way. But also literacy, both in the sense of health literacy and just general literacy, knowing how the health system works, knowing what benefits are available to people. And we know people who don't have health literacy have lower rates of RX adherence. And again, that can have a significant impact on downstream outcome.

So now I'm going to tell you a little bit about a center that we started literally a year ago tomorrow called Optum Center for Health Equity. It's a center that was financed by Optum to address health equity issues. And our first project in this area is addressing pregnancy, healthy pregnancy, and healthy babies in the Washington DC area. And we literally went through the process that I just described.

We started with data analytics to identify the right area to focus on and the right population to focus on, and based on doing geographical information system work, looking at the data that was available, we landed on Fort DuPont in Ward 7 of Washington DC. And this is an area where there is very limited clinical access to care. There are very high levels of claim, especially in third trimester. There is a very high poverty rate, a very high percentage of people of color, and high housing insecurity issues, as we have seen, that has a significant impact. So using the data analytics and the methodology of using geographical information system allowed us to really target the particular area in DC that we started this center, and we are doing work with our community and health system partners.

And we focused on housing, as I mentioned, because housing impacts so many other things. People with housing security issues obviously don't have stable relationships, don't have stable community resources, and most of the time they live in unsafe or unstable housing environments, which for a pregnant birthing person and their newborn, could create a lot of problems. And we also saw that there was significant housing issues, especially in the third trimester of pregnancy.

And one of the big things that we always see in these cases is that it's so complex, both in terms of the social support that's available to them, but also in terms of health support. So navigating the myriad of bureaucracies that stand in the way to kind of figure out where the support lives. It's obviously housing and nutrition, access to nutritious food, goes hand in hand, and we talked a little bit about how food insecurity itself impacts downstream outcome. And then finally, the voucher program in DC, sometimes the wait is so long that by the time somebody actually gets a voucher, it may be too late to have stable housing during the pregnancy. So all of that allows us, understanding that, to really focus on the housing issue for the birthing parents in Ward 7.

And how we did this, and again, this is what I talked about earlier, in practice. And the main component of what we... A, we worked a lot with the community resources in that area. We worked with health systems to identify opportunities for bringing a clinic to the area. We worked with policymakers and the council members to kind of identify. And the one area we landed on was this idea of essentially creating a health navigator, a care navigator individual, that becomes the go-to person... Individuals, that the birthing parents would go to both understand about the housing resources that may be available, as well as the health resources that are available. And then they go to a DC housing intake, they get access to the housing that's available, and that creates... We worked with FQHCs, we worked with CBOs, and hospitals, then to give them the shelter support and the care that they need. And this got off the ground a couple of months ago, and, again, per my conversation, we are measuring the results very closely to see how effective it is and adjusting as we go along.

But obviously, in terms of when it comes to solutions, there is a lot more going on than what we are doing at Optum, both at the governmental level, at the health system level, by the plans. I know UnitedHealthcare has spent a lot of money on building affordable housing for their memberships. Kaiser has done the same thing. A lot of health plans are providing secure housing. Health systems are doing the same thing. Here in New York State, as part of the Medicaid redesign team, they started the Supporting Housing initiative, which identified very highly vulnerable populations, both in terms of clinical needs, as well as housing needs, to give them stable housing. And if you can see on the numbers down below, this was truly a population that needed a lot of clinical health help. Over half of them had at least one chronic condition, almost a quarter had three chronic conditions, 41% were had a substance use disorder, 5% had HIV, and about 53% had serious mental illness. And it really was a very vulnerable population and majority of them experienced housing security issues.

Again, this becomes, and this was mentioned during the panel discussion, what needs to make sense for health equity to stick is that it needs to make financial sense, both for the actors in the system, health systems, health plans, community organizers, the incentive process needs to work, as well as outcomes need to show that there is value delivered. In the case of New York State, after five years, and they did a very vigorous study of the cost savings, the people who had secure housing, on average, they saved $7,000 per member annually. And for the high utilizers, that number went up to about 46,000. For the people who transitioned from nursing home to stable housing, that number was about $90,000 per year. So it was a very cost effective and measured value that it was delivered. And so that's another example of a solution that's out there.

So to wrap it up, the example that I give for Optum for Center for Health Equity, the example of New York State, the example of myriad of other programs, the big things that we emphasize is that data analytics technology help us start the process, help us identify where to focus, help us measure the outcomes, help us redesign the programs if they're not meeting the needs. But really that's the starting and the ending point. The middle part is all about collaboration. It's all about bringing the right resources at the right time, right partners for the right solution, into the mix so that we don't have to reinvent the wheel. There are lots and lots of support systems out there.

But for that to work, two or three things need to happen. One is incentives. How the money flows in the system really impacts how well the system works. How the plans pay the systems, how the systems work with community organizers, how does the government agencies, and the policymakers, and CMS essentially adjust payment rates based on health equity issues? And CMS is definitely going down that path. So the incentive mechanism in the industry needs to really work. The second piece is the standardization of data. Right now, the same thing that has happened with healthcare, but that's getting better, the social data is all over the place. It's sparse in some cases, it's inaccurate in other cases, it's inadequate, they don't talk to each other. And this creation of a holistic, standardized data set really will go a long way. And then finally, it's about measuring, improving, and showing value. Because, again, at the end of the day, if they come out of it and they don't see value out are, it's not going to go anywhere. On that note, thank you for your time.