Technology and Humanity Are Fueling Advances in Health Equity

Brian Urban (00:22):

Yes, this is the Healthcare Rethink Podcast. I am your host, Brian Urban, and today we are joined by two legends of health innovation across our ecosystem. Today on our show, we are joined by Dr. Karen Murphy, the executive vice president, chief innovation officer, and by the way, founding director of the Steel Institute for Health Innovation at Geisinger. Karen, welcome to our show.

Karen Murphy (00:47):

Thank you. It's a pleasure to be here.

Brian Urban (00:50):

This is going to be fun. And alongside you, Karen, we have president of Care Journey and former US White House chief technology officer for multiple presidential terms, Aneesh Chopra. Aneesh, it's great to see you again. Thank you for joining us.

Aneesh Chopra (01:07):

Thanks for having me, but mostly to have me join my friend Karen. It's going to be a good discussion.

Brian Urban (01:12):

Indeed, it will. And we've both gotten to know each other a little bit here and there before our show, which always makes it a lot of fun because we can really expand our dialogue here. So with every episode, we like to have our audience get to know our guests at the personal level and what took you into your leadership role. So Karen, let's start with you. I got some interesting facts, but I want you to take me back before these things happen. So you have been ranked what Modern Healthcare has noted Top Women Leaders in 2023 and then also in 2021 going back a little bit, Clinical Executive Top Influencers. So this doesn't just happen. So take me back. How did you get to this place being chief innovation officer for one of the most creative healthcare and health plan organizations in the US?

Karen Murphy (02:11):

Well, thanks very much for your kind words. My journey has been one that I characterize as I've been very fortunate, very lucky, which has enabled me to get to the place that I'm at today. So started my career out as a registered nurse. I worked 10 years as an intensive care unit nurse and then went into hospital administration, health system administration. So I was president and CEO of a community hospital in Northeastern Pennsylvania. Then I took a magnificent turn to public service, which Aneesh and I share, and very rewarding experience at CMMI where I led the state innovation models initiative and then had the tremendous opportunity to be appointed by Governor Wolf as his Secretary of Health in his first term. Just all of that foundation has brought me to six years at Geisinger and had the tremendous honor of innovating at Geisinger.

Brian Urban (03:26):

That's amazing because your humble beginnings as frontline in healthcare gave you probably a very interesting perspective. And then everything that you've been involved in project level, strategic level, it really shows that your healthcare background, your healthcare orientation to serving lives. So very, very cool. And Aneesh, let's get to some Aneesh Chopra now. So you lead care journey as a president, but back to your US White House chief technology officer days, you yourself were ranked the Most Influential Person by Healthcare Means by Modern Healthcare, so very high regard. And here you are now, leading a lot of analytic work for payers and providers. Take us through your early beginnings and how you've become now a president, a very dynamic health technology organization.

Aneesh Chopra (04:27):

Well, like Karen, life is about serendipity and luck. I was an analyst at Morgan Stanley, fresh out of college. I was on the healthcare team, but my colleagues were on the tech team and they took a little company public called Netscape, and I was like, "What is that?" The stock went through the roof and I was convinced from that moment on that the internet would have a big impact on society. Now I'm a public servant at heart more so than a capitalist at heart. Of course, I am respectful of our market system, but I care deeply about scale and change. So I committed myself to thinking about how the internet would be a force for good and health and energy and education sectors.

(05:10):

Like Karen, I was lucky enough to be in state government. I was Virginia's secretary of technology under Governor Tim Kane. And state government, Karen may have kind of alluded to this, but you get your hands on the ground because you get to see the impact in a way that maybe when you're in the White House or you're at CMS, you have a bit more of a broader view. So I had the pleasure of having that frontline perspective before I moved into the Obama administration.

(05:38):

And while my role was broad, meaning it was in health, energy, transportation, the environment, it was something where both Governor Kane and President Obama said, "I love all of my children, but healthcare is one of those that I want to do a bit more in." So I ended up becoming a bit more focused on healthcare and of course I had a career in healthcare at the advisory board for a decade, so I was aware of this unique public private partnership nature of our healthcare system. So you just fast forward, you think about the last 20 years, the internet economy has been so impactful. We've moved to digitization. Karen, as you know, has won the AI innovation challenge at CMS, so her team has been right up there top ranked in applying these new technologies to solve meaningful problems.

(06:24):

And we're at a similar inflection point, I think, in the advent of generative AI. And so the same feels that I had about the Netscape IPO and the internet, I have the same set of feelings about what this capability will do to improve productivity, accelerate the move to value-based care, and hopefully give people a better healthcare system that we want for our loved ones in our private lives. So that's been my life. I live at the intersection of public and private, the release of public data for private use and for broad public use. That's what I do at Care Journey on helping to figure out who the best performing physicians, networks, facilities are. And that's been a blast for the last half a dozen years.

Brian Urban (07:11):

I love, Aneesh, how you described not only your experiences, but this big S curve of the milestones and the next big peak that you're seeing, is generative AI in healthcare, and so many cool things that will help alleviate administrative burden, help access the care, improve quality of care, affordability, et cetera. So we're definitely going to get into that side of it to see your perspective going forward. It's going to be really exciting.

(07:40):

And I love that you both have the public and private intersections in a lot of the different innovations at ground level. So Karen, thinking about that, you've seen a lot of development from the Steel Institute of Health Innovation the last, oh my goodness, just even two years, let alone the last five years. MyCode, Neighborly, 65 Forward, Fresh Food Pharmacies, I love that one, and Free To Be Mom, what we were just talking about before the show here. It's just so amazing. And this portfolio is growing of these health innovations at the community level. How do you see this evolving over the next three plus years? Do you think there's going to be more of these programs, or are they going to become deeper and even more impactful in their current application?

Karen Murphy (08:33):

That's a great question. And I agree with Aneesh, we are at a pivotal point with not only generative AI, but I would say all digital technology that will enable true transformation. So I think the next three years are going to be all about digital data, and as Aneesh said, generative AI. This job is the hardest job I ever had in my life, by the way, because to affect meaningful change that you're really solving problems and transforming and being really transformational, it's hard to have a meaningful impact. It's easy to talk about innovation, shiny new nickels, but it's much harder to really get something done that is meaningful. And I think now we're at a new inflection point where data digital will really enable us to be far reaching.

(09:40):

As you talk about community, Geisinger is a statewide Medicaid plan now. We have clinical assets in [inaudible 00:09:52] one counties, but very hard to reach those other counties, the members outside of our clinical asset areas. So we can digitally, I mean we can leveraging the tools that we have, and really excited, really excited about the future. And I think the other point about generative AI, healthcare post pandemic, I think we thought that when the virus calmed down, everything would be okay. And I think for the next decade we're experiencing challenges in healthcare due to the pandemic that really we have to leverage all the tools in our toolbox, and as Aneesh said, generative AI in order to allow us to be more efficient and also support our clinical teams is going to be critically important based on not only workforce shortages but also our financial models. So I think the next three years is going to be focused in those areas, really.

Brian Urban (11:01):

And I love where you were going with that, Karen, using digital means to close divides, improve access, and that makes me immediately think of the National Wireless Initiative and Startup America, a niche that you led many moons ago, but I want to get into tech and humanity because that's something you let off with in our earlier part of our conversation. And you've touched so many projects. Would you say some of those examples, the National Wireless Initiative, Startup America, that that was really a foundation for building health equity advancing programs on top of, or that has maybe fueled some government-based programs to address social determinants of health barriers? Take me back to those early days. Do you think that's contributed?

Aneesh Chopra (11:55):

Well, I think the pandemic and the Biden administration helped us focus on the health equity issue. I feel guilty that I was personally not as aware or focused on disaggregating performance by these measures to even know how bad the gaps were. So let me acknowledge, this is not a straight line of policymaking, this is a little bit of a leap. But to set the stage, when the president came in and said, "I wanted to create this role of the US CTO." The objective was to really advance the notion of what he saw in his campaign, which is that was a bottom up change. He's a bottom up change guy. And so the internet allowed individuals to take control over their own little communities, organize and make a difference. So much of Washington is top down and it's counterintuitive to think how do you take advantage of the new scale of emerging technologies like the internet to give you that bottom up approach.

(12:50):

So to give you a little bit of a window, our objective was to create a framework, and the framework, which by the way turns out to be a bipartisan framework, the Trump administration embraced this framework, so I'm just grateful for it. The framework was the role of government historically has been investments in infrastructure. And in this internet based era, now it's no longer roadways, railways and runways, but it's broadband, it's cloud computing, it's human capital like R&D. So you think about what does an investment in infrastructure look like in the modern era? It's the same role of government, but on a new set of things to focus on or an expanded set of things.

(13:31):

Then you need to have rules of the road. And in the internet economy, the challenge, of course, is you over-regulate something that's emerging. You might get like Europeans do, you get it wrong and you reduce the capacity to benefit from the innovation, you stifle it because it's so complicated, it becomes almost impossible to get an enterprise off the ground. So we just tried to embrace a more public private partnership approach, and that had a lot more to do with industry standards, protections of security rules, thinking about privacy frameworks, but really allowing the private sector to work within that construct to reach consensus to be more agile.

(14:11):

Last but not least, the president had a few areas where he said, "I want to have an all hands on deck approach. I call on the country to solve." In this case, what Karen was doing on the state innovation model is a natural and obvious extension of the all hands on deck approach because it was like, look, Washington's going to have some ideas, we came up with MSSP ACOs and things that came from the top, but that the states might want to have their own perspective, local governments too and private sector stakeholders. So how do we create that all hands on deck approach? My view was open data and standards would allow for a lot of organizations to experiment, but not with apples, pears, bananas and grapes, but in a way that we could test, validate and scale what works.

(14:54):

So moving forward, the social determinants of health story did have an Obama administration experiment. I had nothing to do with it, that was Darshak Sanghavi, a colleague of Karen's at the Innovation Center. Darshak said, "I wanted to do screening for purposes of social needs, and if someone had two or more social needs, that they should be given a case manager." And so the experiment called Accountable Health Communities went forward. Now, in that evaluation, they didn't show an economic return on investment, but what did show people were getting their issues addressed, they were screened and addressing these social needs. But there was a lot of friction, it wasn't integrated into the EHR, it was a separate IT system.

(15:36):

Fast forward, we now have interoperability standards. We worked on this program called Sync for Social Needs in the last 12 months, all voluntary, no money, and we got Epic, Cerner, Meditech, and a bunch of other health systems to say, Geisinger of course at the table, to make sure that we get a common language for how to communicate, "This patient, I believe, is food insecure." So now we have a common data standard for this and we hope that that'll be a part of the infrastructure going into 2024 when hospitals have to do universal screening and we can begin with that scalability in mind. So it's not like a one-off decision at each individual site that doesn't really give us the answer to the question, "What's the best way to surface and address social needs?"

Brian Urban (16:23):

That last part of your explanation blows me away. And it's actually something I'm not aware of, so we definitely want to dive in deeper to that perhaps in the rest of our conversation here. So Aneesh, it's now you're coming to, and we call this health technology driving health equity forward arc, in which you're getting these very large organizations to either contribute or I would say donate their resources, technology, time to being able to put a model together that can be refined and future applied for the better. And it seems like it's a universal model. It's not siloed up, which I commend you on that. That is amazing. That takes moving mountains. And I think-

Aneesh Chopra (17:14):

No, it took the government regulating and mandating something in the future, but there wasn't a technical path to doing that. You can't deny the fact that this wasn't all goodwill for the help of the world. This was the organization saying, "CMS just mandated a 1124 universal screening requirement for hospitals." The joint commission, the NCQA, the NQF, all our eyes on this. And we didn't really have the brain power of the quality, I call them the troika, to communicate what the tech has to do. So we just took advantage of the moment that there was a timeline, there was a quality troika demand signal to aggregate. And then there are willing participants like the Geisinger team and the EHR systems to say, "Look, we're going to have to do this anyway. Let's try to do this in a way that doesn't make it be custom manual entry."

(18:05):

And that would allow for Karen to do her Fresh Food Pharmacy scale so that you could say, "You know what? For folks that have this criteria, this is a good program." And it has an economic return. So you're not scaling through charity, you're scaling through ROI on the foundation of the plan. I mean, Karen, you're welcome to comment, but I think the Fresh Food Pharmacy demonstrated in its world an ROI and having a data foundation may make it easier to scale. But I'll defer to you, Karen, since I haven't gotten the latest on your thinking on it.

Karen Murphy (18:35):

I think that's a fair statement. I think the other piece that I would say that is so necessary and the value of screening is really to evaluate the work, Aneesh, when you say we need to have data, very difficult, very difficult because of the complexities of when you're talking about social determinant work, you're talking about community benefit organizations, you're talking about really the lifeblood of the infrastructure that addresses social determinants of health. So I think the screening is going to be so tremendous to guide us, not only to Fresh Food Pharmacy programs, but also those partnerships that how do we connect using data with our community benefit organizations, our Medicaid partnership with the state. I think we will advance the work in a much more comprehensive way.

Brian Urban (19:48):

I love where you both went with this. This is a real world example. There are two Fresh Food Pharmacy locations in Pennsylvania hosted and supported by Geisinger. And Aneesh, it's interesting that you talked about that there's the timeline to meet because there's going to be an expectation of screening for social needs. I think now it's amazing that CMS has become so tight with NCQA to adopt a lot of their measures and their suggestions with the social needs screening in particular. And then how do we apply that data, not just gathering it, but how do we apply it and the steps forward you've taken in all the partnerships you've developed for putting a model together is quite amazing.

(20:31):

So I love the Fresh Food Pharmacy. I've actually been to one in passing in Scranton, Pennsylvania. So it's amazing to see it alive and scaling, not just at a cohort level, but at a personal level too. So I think it's magnificent and it just shows the real world application. And Aneesh, you said something before that you said there's the federal level and the state level of how different state agencies and bodies and organizations anchor systems once you have their impact to advancing health equity and addressing social determinants of health barriers. And I'm thinking about the CMS announcement launch of the 1115 waivers with provisions related to SDH programs and pilots. That's a mouthful right there.

Aneesh Chopra (21:24):

All eyes on CalAIM, baby. Yes.

Brian Urban (21:27):

And it's amazing because I think there's been, we're talking nine plus figures, maybe 10 figure mark that we're into, and CMS giving a monetary amount of support to programs, but also there's a follow through part of that. So I wanted to get both of your perspectives on Section 1115 here. I'm not a policy person by background, but I wanted to understand, is this the next big leap that's going to help adoption of models from state level to private health plans to Medicare advantage focused health plans? I'm curious of your perspectives. Karen, if we could start with you.

Aneesh Chopra (22:08):

For sure. This is her job, man. You got to put her on the spot first.

Brian Urban (22:12):

I want to get your take on Section 1115 and the provisions you've seen being executed, and I think there's probably a dozen still pending, but great progress. What's your take on this?

Karen Murphy (22:24):

I think 1115 waivers are a heavy lift for a state. Obviously Medicaid is both a state and a federal program. I think the fact that we have 1115 waivers that really encourages particularly progressive states that are really thinking of being transformative. I think for SDOH to be included in the 1115s will advance the work. I think the second value of the 1115s is that it really moves the thinking into value-based care. So to be successful at really advancing work in the social determinants, it really has to be grounded in value-based care. So not the fee for service because if you're straight on a fee for service, it's very difficult to achieve good work in SDOH. So the fact that they've included 1115s, including social determinants, has been great. So a lot of states have already, over the past 10 years, leveraged 1115s and have done some remarkable work.

(23:45):

So, very interesting to see how this is going to progress. I think the part that states have challenges, Aneesh, and maybe you could comment on this too, the part that states have challenges is really getting down to the providers and working with the providers because a lot of policy, we are lucky enough and fortunate enough to have healthcare clinical background, but that's not always the case in terms of partnerships. So I think the more we do this, the more we can really get the transformation on the ground because states will learn how to engage with providers. And that was a big part of SIM, the State Innovation Models initiative, is really your denominator is your state population, and that includes all your health systems. So I think it just will be tremendous in accelerating the work. And I hope that we'd love to see more states take advantage of 1115 waivers that now include the social determinants piece.

Aneesh Chopra (24:58):

Yeah. And Brian, I think the spillover benefits of 1115 hinge on the following, how transferable are the workflow investments and the efforts needed to be successful in the model. If it's siloed, if it's a noun, I did a thing funded and subsidized by the 1115 waiver at a very non-competitive price and that when the money goes away or there's some shift, then the whole thing goes. That's not a recipe for success. If it's a seed investment to change the workflows, to change the standards, the data sharing, the operating model, and it can do so at scale across all lines of business, then it becomes the beginning of a new way where it's the role of government to be the innovative tip of the spear to test and then validate and then scale what works. Landing that proverbial triple axle has been very difficult.

(26:03):

So to give you a little bit of history, one of the first assignments in the Obama years was to land the meaningful use definition when we were putting out the $30 billion subsidy for electronic health record adoption. And Brian, you can analogize what the current 1115 looks like, nouns and verbs. The political pressure was to get the money out yesterday, because it was an economic recovery package. The policy prescription was to use the dollars as capital down payment towards value-based care. So we had the following leaps of faith. We would front load the cash in the early years of the program, but then call that the carrot phase, and then it would transition into a stick phase over time. The hope of the stick phase was that the stick would look like a carrot because if you move to value-based care, you would want to do these things, not be forced to do them.

(27:09):

Like for example, recording blood pressure if you're a specialist, not a cardiologist, that may not be how your clinic operates, but keeping track of blood pressure because you're the most recent clinician visit for someone who happens to also have hypertension, even if they haven't come to see you because of their hypertension, that feedback loop would get someone like the primary care doctor to know, "Oh, I need to adjust the meds or the cardiologist because there's something happening here secondarily to the reason I was going into the orthopedic surgeon for back pain or whatever." So you would want that to be not a drag, like a stick. You have to record the blood pressure, that's annoying. You'd want that to be, "Of course I would do that because it's going to make the whole healthcare system better and the avoided admission tied to that condition getting exacerbated, we'll fund a greater pot of money to the network."

(28:03):

So this idea of 1115, going back to meaningful use, the injection of dollars, the triple axle is it's got to move towards a demand signal where we want to move to value anyway. We need people like Karen at Geisinger to experiment with Fresh Food to say, "Look, this intervention works for this group of people, but not for that group of people." The government's not going to micromanage that or know that level of detail, but it wants to create the learning lab where this sort of experiment will take place.

(28:35):

So we're watching this movie in real time. We're excited that CalAIM was the first out of the gate with this SDOH infusion. I am worried, as a technical matter, it was implemented just at the cusp of the last crappy data infrastructure system. So the actual operations of it today look, from the outside, a little bit like Frankenstein, technically. The hope is now that the CURES Act is live and we've got modern and regulated data standards, maybe the second and third state, maybe if New York goes next, they could build on the California model, but on this more scalable stack so it could be easy for everyone to download and use the workflows that are needed to thrive in these 1115 waiver states.

Brian Urban (29:27):

I love the analogy path that you just gave there, because I think a lot of our listeners, their background is tech, their background is not being able to understand what is being done from a top down approach, and the tip of the spear in terms of the innovation analogy you gave for the government now trying to inject not just the monies but the hopeful, not just a point in time of a study or a program, but then that seed money that you were describing, it actually is taken up as a new program or a new process, a new change to a clinical workflow or a licensed clinical outreach care management scenario as well. And that is the hope. I love that analogy. That makes it so darn simple for not only myself, but a lot of listeners that aren't familiar with how this has been rolled out and in the context you provided in terms of investment, very, very helpful as well.

(30:26):

And it leads me down a path of thinking data. And I love California and New York, high volume Medicaid geographies and the data that is at their disposal, not just census, but eligibility files, things that are of public nature. I'm thinking about looking at that whole person and I'm thinking about socioeconomic data, and we've talked about what the industry is now dubbing as SDOH data in a lot of regards, is taking that data not just into a workflow, but at the industry level into EHRs. Do you think that's the next big leap? We don't have the hard timelines and regulation of saying, "Hey, you have to put third party verifiable data in EHR." We don't have that yet, but do you think that's the next big thing? Are there conversations that are happening around that in the industry, Aneesh?

Aneesh Chopra (31:24):

I want to hear Karen go first because she's on the front lines and I want to react to how she thinks about this, because I would reflect a little bit of the demand signal from folks who have to manage the purchasing and integration of data against the investments in workflow and improvement. But Karen, I would be very keen to hear your thoughts on this and I'll be happy to react.

Karen Murphy (31:45):

So I think when we talk about taking multiple data sets into generate meaningful data, I think this is where generative AI is going to be tremendously helpful, because we are going to be able to do data aggregation and then test on that data. It's not so much, I don't think, that we're worried about ingesting socioeconomic data into an EHR, but it is, "Let's identify a way that we can ingest data from multiple sources, including the EHR and then aggregating that a way that produces meaningful guidance for us." And I think generative AI will be able to advance our work. I mean, we ingest multiple data sources now, but it's clunky to be quite honest. It's pretty clunky to deal with. And in order to do this SDOH work, we have to have this data.

Aneesh Chopra (33:00):

Brian, I think the building up from where Karen is going, the highest and best use of generative AI, in my view, may be to equip everybody with their superpower data analyst. And so what Karen is basically saying, and I'm going to infer from that, we've increased the supply of data from a liquidity standpoint, but we haven't necessarily equated that to the maximal use of said data. I can't imagine how many doctors have complained to me. Yes, I can access the medical records from a doctor down the street and I can read their 75 page PDF to prepare for my visit, but are you kidding me? I'm not reading a 75 page PDF, a scanned in fax or some other kind of odd dynamic.

(33:54):

So to the extent that we think of this as both the supply of data about the individual growing, that's a given. It is growing, whether it includes third party SDOH data or just external claims history or external EHR data from the market or survey data that may come in a more computable format. The challenge is the use. And so I think it wasn't a secret or it wasn't a surprise that when Microsoft and Epic announced their partnership, of course the sizzle is we're going to reduce the friction in the doctor's inbox that gets a lot of the headline. Or to summarize this medical records at the point of care, so to speak, to reduce the friction I just alluded to. But the real magic, in my view, was the announcement that Epic and Microsoft were going to train LLMs to ask questions of the data to help inform the kind of thinking that Karen has to do every day in her innovation capacity at Geisinger, which is what subset of the population has the highest need for an intervention that we either have tested or are ready to scale.

(35:17):

And so there's no formula. No one goes to Karen and says, "Give free groceries to 80% of people in this zip code." That's too blunt an instrument. She'll overspend on folks that may not need it and she'll underspend on those who might that are not in that zip code. So we need a little bit more precision science and learning and the combination of the supply of data growing and the analytics capacity at scale and LLM and my copilot data analyst. That feels like the moment we're in. To answer your question, yes, always more data, but that is not the solution, that is a step on the journey.

Brian Urban (36:03):

Yeah. I love how you said that. I immediately thought of an AI superhero when you were first describing what you're seeing and where we're at now. And I think it's really interesting you both bring this up. It comes down to the provider level. How would you sow a meaningful social health view of a patient or a member that isn't a laundry list of, "Hey, they just had this criminal activity, they just had a spouse pass away, they just had their debt to income ratio change drastically. They're losing assets." No one could even begin to understand how do you address those challenges that are definitely impacting their health and even their trust in receiving certain types of healthcare. So it's not just the data, but it is the appropriateness of the view of that and then the application of, "Well, what do you do from here?"

(36:56):

Do you prescribe food? Do you prescribe a transportation service? Do you prescribe in-home care? So we're getting there and I think the generative AI generation that we're going into will help clean that up and give more precision. I love that. You took that very basic question and you took us into the future, so thank you both for that question. And it makes me think of some things that are outside technology. So Karen, your Geisinger At Home program has been around for multiple years. It's been very successful in reaching a lot of hard to reach populations in rural Pennsylvania and even the hinterlands in Pennsylvania as well. So I'm curious, it feels like a very much offshoot of being able to see and potentially address social determinants in the home, having clinicians in the home. Can you help me understand the adoption of your patients with this in-home care model? Has it grown successfully because of the trust as currency with your patients or word of mouth or just simply the great clinicians you have going in homes? How has this transformed over the last several years?

Karen Murphy (38:15):

So we have really refined the referral process and analyzed what patients belong in Geisinger At Home, what patients benefit the most from Geisinger At Home. And I think when there are patients with multiple chronic conditions, what we are always trying to do is to maintain our members and our patients in the home. It is purely value-based care in that we are doing the right thing at the right time with the right patients. So we have identified who benefits the most in terms of experience. I always start with experience because if we're not doing something that is not positively viewed by a member or patient, then that's not the right thing to do.

(39:11):

So, always experienced, but also the other indicators such as decreasing emergency department use and decreasing repeated admissions. I never heard anybody say in my whole career, "I'd like to be in the hospital." So patients and members just love the fact that they can have a high level of care in the home. And I think that the team has done a great job at identifying where we can be most impactful with that program. And you're right, we call it windshield time. When you're in rural communities, you have to really work to get to those members and patients. So it has really been a remarkable, remarkable program and very successful.

Brian Urban (40:04):

I love to hear that. And that's the part of the human condition that a lot of our technologies today cannot be able to empathize with or address. So it's definitely a blend of things and emerging of things as well. And we've covered so much in our conversation, humanity and innovation, definitely fueling health equity in the US right now. I wanted to ask you both as some closing remarks. What would you both say to other health innovation healthcare leaders across the US in terms of investing in different programs, whether it's data exchange, different innovations, programs, technologies to be able to better address social determinants of health? Karen, can we start with you? What would your message be across the landscape for those who are hesitant or don't have this as a team maybe or a culture built in their current organization?

Karen Murphy (41:08):

So my first recommendation would be to focus on what's measurable and what's impactful. I would rather much rather be deep in one or two programs and produce results than be a mile long and an inch deep and just say we have a list of these programs, but they're really not at scale. Or there's some programs, I mean, that have not demonstrated clearly that they impact clinical outcomes. So my recommendation would be start with selecting what we know. We know is food is not only medicine, food is health. So there's a national movement. There's plenty of information out there on how you could do it. So I would say start with that. I would say if you have limited resources, don't go after the programs that are easier to implement but don't produce a direct positive outcome.

Aneesh Chopra (42:17):

I come at this from a scale perspective, the tech lens out as much as the program level in, and I would say the following practical questions. Right now we have the screening muscles getting worked out. I ask my doctor friends, "If you were to screen someone for food insecurity, before you go to the list of the programs, mile wide inch deep..." However Karen outlined them. "Before you get to the directory, which of those are paid for by the patient's plan?" How many doctors know that there's 12 meals vouchers offered by the health plan that this particular patient sees? There's no standard for that, Brian. There's no 270, 271 EDI transaction that says, "Get social needs benefits that are triggered by a eligibility criteria." So to me, scalability would mean let's try to solve for if people are funding benefits, let's educate the practitioners on what's available.

(43:30):

And then to Karen's point about the food as medicine, the evidence is overwhelming, it is. But think of all the other interventions, and within food as medicine, a lot of permutations, there's a lot of learning needed to be done. What's the learning database on which this can be understood? If everybody is siloing their little experiments and there's no common method by which we can pool and learn, it'll take that 17 year from bench to bedside timescale, and we don't have the luxury of that. So you may have seen at the White House Hunger Conference until year ago under President Biden, the Rockefeller Foundation and the American Heart Association jointly announced a quarter of a billion dollar investment to put together a data learning lab on all the food as medicine experiments. And my hope is that we can double down and on steroids such a public private asset because we need it.

(44:33):

So we need a screen, we covered that. We need to have some kind of eligibility funded benefit knowledge. And then when it's used, we want a copy of the fact that it was used to be part of a learning lab to say, "Oh, patients of this sort with these doctors and these clinical support systems did far better than the others. What did they do?" Oh, well, they integrated the nutritionist into workflow and they figured out how to do that. Whatever the answer is. I would rather we learn together faster than continue on the path of micro learnings because our system is heavily fragmented and it's not naturally designed for collaboration of this sort.

Brian Urban (45:18):

I love where you both took this. Find something that's measurable, go deep on it, and then be able to share your learnings and make sure that you're connecting the provider into that journey. I think if we start to go down that path, everyone's going to jump in. So I hope everyone listening to this found that not only inspirational, but really a how to path. So those are some deep considerations to take into your organization. So at this point, I got to thank you both. I love your brains and your hearts from everything that you've been a part of and what you're impacting in your life today and in your career. So Karen Murphy, thank you so much for joining us. Aneesh Chopra, thank you so much for joining us. This has been a wonderful conversation.

Karen Murphy (46:02):

Thank you.

Aneesh Chopra (46:03):

Thank you.

Brian Urban (46:04):

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