Perhaps it was a fuel for me as I grew in leadership and process transformation, and things like that, to sort of think about what ultimately became known as value-based care in later years. I was lucky enough to be involved nationally at the advocacy level for ACOs via NAACOS. I met a colleague Denise Prince there who recruited me to come to Mount Sinai as the CMO for Pop Health and was promoted last year to become the Chief Population Health Officer.

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JL: Within the ACO context and perhaps the broader value umbrella, how is Mount Sinai thinking about equity in its strategic plan and approach?

 

RF: To some degree, it’s hard to separate out the way Mount Sinai thinks about equity on the value side, relative to the rest of the system. I think like other systems that have been reenergized around this work here in the last several years, it comes on the backs of a long history of trying to approach it in lots of ways that have been variably effective.

 

We are in a community that is literally straddled by the most expensive real estate in America on one side, and some of the poorest per capita income communities on the other side. And we’ve tried to deal with all the things that come with that disparity, both socioeconomically but also racially and ethnically, and have struggled with it for years not always effective but sometimes have been more effective; just very variable. I think in recent years with the reenergized, refocused approach is trying to organize ourselves in terms of what our goals are as a system, what are the overarching strategies to achieving equity, and then getting down into the tactical sorts of things.

 

When you get into the tactics, we realized that there are gaps in data. Many of our medical records had no value written in there for race or ethnicity. We struggled with how to ask the question, how to have conversations with folks about how they identify, and then what categories to put them in, because there’s always a need to put people in categories. We struggled with all of those strategic and tactical things in the beginning of this newly formed effort.

 

On the value side, one of the things that became crystal clear is that we measure a lot of outcomes. But because we lacked that data infrastructure, we struggled really to measure those outcomes along racial and ethnic lines. So, we weren’t sure if the outcomes we were driving held true across those lines at all, that’s one problem. Secondly, realizing that if you look at where our downside financial risk is, it is predominantly in payers that have a high proportion of racial and ethnic minority patients. So that’s where a huge chunk of our global risk is.

 

I’ve been changing my language, honestly, to say: if the moral argument were good enough, we’d be doing it already. It needs to be a strategic imperative. That’s one. And number two: it is a strategic imperative because there is no path to success on the value-based care side without driving towards equitable outcomes because it turns out that the patients that make up the predominate share of our value-based population comes from racial and ethnic minorities that have been disenfranchised and suffer from the disparities in health care that exist today.

 

JL: You touched on data infrastructure. Are there a few examples that you can share with regard to investments that Mount Sinai has made to do that and to measure population health outcomes in a different or better way?

 

RF: I’ll focus more on the population health side rather than the system at large. But just keep in mind in the background there are a huge number of efforts on the system side that help improve our processes there.

 

We started at our baseline where about 60% of the value-based population that entered our system had, as I mentioned, blank fields for race or ethnicity. So that’s where we started. And while we are actively participating as a global system on the hard work to ask those questions differently and gather that information, we needed something to do now to fill that gap.

 

So we actually purchased privately available data that you can buy today along all sorts of domains, in terms of social determinants, but also the particular choice we made in terms of vendors, supplied us with person level information on race or ethnicity. And to be clear, we don’t push that into the medical record. We believe that a patient has the right to identify how she or he believes they should identify.

 

But we do use it for analytics purposes. We’ve done some validation on it and feel that it’s of high enough quality, in terms of what we know to be true from our own records, that we’ve used it for analytics so we can supplement that gap. And that 60% comes down to less than 5% of a gap with that externally purchased data. So that we can start to measure, for example, cancer screenings or inpatient per thousand data, total cost of care data, vaccination data, etc., using that purchased data to supplement our internal data so that we can have a good picture of how well we’re performing among various groups. That’s one concrete example.

 

If we talk about remote monitoring and other efforts, we’ve made investments there. But initially what we started with was just trying to fill the gap on that piece. 

 

JL: Let’s talk about remote monitoring because I think it’s another opportunity to contribute to data and data infrastructure. Share a little bit more about Mount Sinai’s remote monitoring program, in general but also how that is driving impact on equity.

 

RF: I think like others, at the start of the pandemic, everyone got really energized and excited about telehealth and digital health, and it was going to save health care. There are lots of parts about that sentiment that I think are potentially true. There is a tremendous potential to improve access to care using technology.

 

I think from a health systems side, where we see access for the uninsured or for those with Medicaid in particular, but for all government payers more broadly, we see access diminished often because of economic terms. What we get reimbursed on a fee-for-service basis is often not enough to cover the cost for providing that care, so it becomes really difficult. And I think technology offered a possibility of lowering that cost structure so that it made the economics more reasonable, and improved access. So all those things are potentially true.

 

But what we also learned very, very quickly – we were able to prove in our own population using data that we purchased – there’s tremendous risk in worsening existing disparities because the infrastructure for digital care doesn’t exist universally. There are digital deserts, just as there are food deserts. We realized barriers such as adequate broadband, Wi-Fi, data plans even; it wasn’t necessarily lack of access to phones in many cases, but having a data plan that would support the kind of digital experience that we were trying to provide. All of those were effective gaps in care.

 

So, as we were thinking about how to start remote monitoring, we thought really hard about the technology and how it would play a role. The vendor we partnered with had an incredibly simple, yet elegant solution. Essentially, we started with hypertension so when a patient is enrolled in our remote monitoring program, they receive a kit just like many other folks do in a remote monitoring programs around the country. That kit just has a bluetooth blood pressure cuff and a small, cellular enabled hub.

 

So if a patient has electricity and can manually place a blood pressure cuff, they can participate. They plug the data hub in, the synching is done at the factory so as soon as they check the first blood pressure, the integration is already complete and it comes into EPIC. When we were investigating our RPM program, what we heard from very large systems that have tens of thousands of patients enrolled in remote monitoring is that the number one barrier was technology. We don’t have that. Our disenrollment is about 2%, which is exceedingly low, and technology is not a barrier for us. It’s because of that: independent of broadband, Wi-Fi, or smart phone, folks can participate.

 

Not only is our disenrollment lower. We did a bit of a program evaluation [over a three month period] and took matched controls with uncontrolled hypertension and standard care, compared to those in our remote monitoring program – which is the clinical model using clinical pharmacists that are managing the medications and actually prescribing and titrating. So in that model, we found that about 60% of our patients come from households whose per household income is $50,000 or less in New York City, which is very low. And about 70% of our patients come from Black or Latinx backgrounds. So, we’re effectively targeting the populations that have really been hurt the most by the disparities in hypertension: they go undiagnosed more often, more inadequately treated more often, and therefore often have greater downstream complications.

 

So we are targeting patients who are unlikely candidates for a technology driven platform, but we feel particularly proud because those are the populations that most need the help. Going back to the strategy component, [they] are most likely to benefit in some value-based arrangements because they constitute a large part of that population. So by design, we were able to have an approach to RPM that really had equity in mind as we took on the model.

 

JL: Something that I’ve heard from others in the health care community is that even if you have the technology, you have to have a trust and relationship to be able to use that. Tell us about your or Mount Sinai’s experience: have there been any issues in terms of building trust?

 

RF: Honestly, we haven’t had it quite as much with RPM because our referrals come through the primary care physicians, and those physicians generally by definition already have that trust built with those patients.

 

But you’re a thousand percent correct on the issues of trust more globally as a system. We struggle with that. On the pop health side, we’ve tried to really think thoughtfully about that, and how we partner with communities and can talk about an example of it in terms of our approach. But that is a huge issue for systems. Often it goes back hundreds of years at this point. There are many good reasons why communities of color do not trust health systems; there are very good reasons that we all know about. We as big systems can’t just march into communities and say, we acknowledge all that but we’re here to do good, we’re going to “save you” in some way – I know folks don’t say that explicitly, but that’s what it often feels like.

 

And often these initiatives are very project-based or community benefit-based. We have a grant that’s going to start this preventive screening program in this disenfranchised community, or we’re going to start a housing program and build respite housing or de novo housing in this community. Isn’t it great? And we’ll get lots of press out of it.

 

I’m sure in some way it’s all well intentioned. The problem goes back to what I was saying earlier. If there’s no strategy behind it from the systems side, it isn’t sustainable. So those projects tend to go away, and now you’ve just really perpetuated the issue that health systems are just in it for themselves. We haven't really resolved the trust issue. The solutions often that are proposed from large health systems are not really driven by the needs and wants of the community. What does the community want? How are they involved in the process?

 

I think those are ongoing. There’s lots of learnings happening over the years. And I hope folks, folks being health systems in particular, are listening a lot more these days as they’re thinking about their initiatives, and listening to the community specifically about what their needs are and how they want to be involved.

 

JL: We’ve been talking about patient level data. Going back to data infrastructure, individual level data are hard to get, and get accurately and comprehensively. There’s been some discussion across health care about using area level data, focusing on indices that may reflect health opportunity or area deprivation. Could you share a little bit about how Mount Sinai is thinking about using area level data, either alone or along side individual level data, as it thinks about the impact of its payment and care delivery initiatives? 

 

RF: All the markers that we see out there are pretty grossly imperfect. I know the federal government for example is using ADI (Area Deprivation Index) in the new REACH Model. If you actually look on the maps that are available around ADI and how we fare in that model, communities that we know that are struggling come out looking pretty good. Central Harlem, lots of areas in Brooklyn, areas of the Bronx that we know have gaps in almost any way of measuring, whether it’s in terms of per capita income, food deserts, I mean you name it. It’s well intentioned but they’re all grossly imperfect, so it’s a work in progress.

 

But we do use similar data, ideally driven as locally as possible, to do program evaluation. For example, we’ll use transportation information in certain areas to look at where folks may have a gap, or where we might start an initiative in terms of either bringing mobile services to the community or providing transportation in, depending on what the need is. We will use, where available, housing data to get a sense of where our public housing is and then design programs around that need, along with either employment rates or vaccination rates from the local health departments. We’ll use that data in program design, less so at the point of care. We have not gotten to that point here just yet.

 

JL: Unfortunately, sometimes I worry that it’s the so -safety net, or essential, providers and clinicians, that if you put [the work of collecting individual level data] on them, it may be harder for them to [do]. I do sense that there is a role for area-level data, but they are imperfect and require more work.

 

RF: For sure. We have taken on a pretty huge effort to try to collect it on an individual level, as hard as it is, standardize it across the system, collect it in our EPIC instance, and then be able to resurface it in a way that ends up being useful. But to your point, that is a slow effort, that’s years of work to do that at a scale that seems appropriate or really helpful. Again, we did purchase individual data to help supplement, but it’s also not perfect. And then there are the questions that, usually when it does exist, it’s probabilistic. So, it might be, hey we think that there’s a 70% chance that this person may have a housing gap, how helpful is that really when you’re talking to the patient directly. It’s not appropriate, I don’t think, to make assumptions on their housing based on this external data, even if it’s helpful, again, in summation to get a sense of trends or where issues might be. It’s very challenging to use when you’re really face-to-face with an individual and talking about their care. I think it’s still a work in progress to figure out how to use that stuff.

 

JL: Is there anything else that we haven’t talked about yet hat you feel, based on your depth of experience in this area of value, that the collective we in health care need to prioritize in order to meaningful advance equity?

 

RF: I would say that the one thing we need to do is to lead with humility. I know it seems a little out there, more qualitative than quantitative. You’ve got to do something, and there’s lots of ways to tackle it and have an impact, some of which we’ve talked about today. But the one thing I think about is for health systems of any sort is, whatever you do, do it with humility and listen so that you can understand what the communities want of you, and not the other way around.

 

JL: I think that’s a great note to end on, and often from a strategic standpoint what we think about as quality or cost issues are actually rooted in structural inequities. So the humility to see that and to reframe what we’re doing – and then what we’re going to do to address what we’re seeing – is incredibly important.

 

Thank you again for joining today for this conversation. I really enjoyed learning about the work that you’re doing, and thank you for doing it. I know the listeners will really enjoy and learn from our conversation.

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RF: Awesome, thank you so much.