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Healthcare Spatial Analytics: Addressing the First Mile
Episode 571

Hillit Meidar-Alfi, Founder & CEO at Spatially Health

Healthcare Spatial Analytics: Addressing the First Mile

This episode features Hillit Meidar-Alfi, founder and CEO at Spatially Health. Hillit discusses how Spatially helps health organizations make data-driven decisions that align with both the interests of the company and the population they serve. She also talks about real-world analysis, testing, location data, spatial data, and social determinants of health. Learn how Hillit and her team are optimizing patterns that can accelerate real-world insights into health care! Tune in to this podcast for more information.

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Healthcare Spatial Analytics: Addressing the First Mile

Episode 571

About Hillit Meidar-Alfi

Hillit is the founder and CEO of Spatially Health. Hillit is a trained architect with over a decade of extensive experience working in housing, urban design, and the health care industry. In 2019 she founded Spatially Health, a technology company focused on pioneering new methods in advancing analytics, location intelligence, and data management for solutions in health care markets. Hillit holds a Masters of city planning and a Ph.D. from the University of Pennsylvania and a Bachelor of Architecture from Carnegie Mellon University.

 

Healthcare Spatial Analytics: Addressing the First Mile with Hillit Meidar-Alfi, Founder & CEO at Spatially Health transcript powered by Sonix—easily convert your audio to text with Sonix.

Healthcare Spatial Analytics: Addressing the First Mile with Hillit Meidar-Alfi, Founder & CEO at Spatially Health was automatically transcribed by Sonix with the latest audio-to-text algorithms. This transcript may contain errors. Sonix is the best audio automated transcription service in 2020. Our automated transcription algorithms works with many of the popular audio file formats.

Saul Marquez:
Welcome back to the Outcomes Rocket. Saul Marquez is here and today I have the privilege of hosting Hillit Meidar-Alfi.

Saul Marquez:
She’s the founder and CEO at Spatially Health. Hillit is a trained architect with over a decade of extensive experience working in housing, urban design and the health care industry. In 2019 she founded Spatially Health, a technology company focused on pioneering new methods in advancing analytics, location intelligence, and data management for solutions in health care markets. Hillit holds a Masters of city planning and a Ph.D. from the University of Pennsylvania and a Bachelor of Architecture from Carnegie Mellon University. We’re going to be diving into some fascinating work that they’re up to and what you could be doing to improve your business models and workflow with data. So Hillit, it’s such a pleasure to have you here on the podcast with us.

Hillit Meidar-Alfi:
Hi Saul. It’s nice to talk with you today.

Saul Marquez:
Yeah, it’s a pleasure. And so you guys have a ton of really interesting work that you’re working on. And so before we dive into Spatially health and how you guys are helping customers in the health care ecosystem, I love to hear more about what inspires your work in health care.

Hillit Meidar-Alfi:
Sure. So at Spatially health, our models are focused on the aspects of human needs and bringing about the ability to fulfill those needs. This is what we call the human spatial relationship. And this is really at the heart of everything that we do. At our core, we believe that people have the right to live in dignity, and that’s what really inspires our work. And by bringing in the human element into the equation and modeling the real world phenomenal businesses can really see and measure problems, but also opportunities and align the interests of both the company and the market of the population that they’re targeting and then be able to make decisions that are truly data-driven. And through my past experience, I’ve seen time and time again, whether in health care and housing, planning and product development or other businesses, decisions are often made in somewhat of a vacuum, really because of the lack of other data sources and equally important ways of integrating them. There are definite challenges in the market, but missing still is fundamental data that is location-driven and scalable across different geographies. And that’s really important, the ability to scale them. So you’re always comparing apples to apples. In health care, this provides unique insights by integrating clinical and non-clinical data and developing strategies to capture market share and ultimately tailor solutions for different local markets, because we know that health care is local.

Hillit Meidar-Alfi:
So the kind of maybe throw in there are tired statistics, but the reality is that we know that less than 50 percent of health outcomes are determined by medical interventions. But what is the other half? And the kicker is that we end up paying for all of it, but arguably only using half of the data and the information that we can make available. So if health care is local for us, location data is essentially health care data. And with that in mind, now we can think about being proactive and actually design solutions. And by developing and delivering the real-world analysis, provide the analytics that drive population evaluations both at larger scales and at smaller scales and at much, much more granular scales. And what people are accustomed to using by identifying the opportunities for innovative care delivery models, which a lot of markets are hungry for modeling strategic initiatives and interventions for better outcomes, and ultimately take this and be able to test these assumptions before costly implementations. This is something that I learned a lot in my past and design, especially in product development. You always want to test before implementing, and unfortunately, that’s sorely lacking in health care and it’s costing us a fortune.

Saul Marquez:
Well, super interesting. This idea of health care is local. And what are we doing with location-driven data and how are we integrating it? I’m thinking about what are we doing with this data and what data is it and what can we do with it. So tell us a little bit more about Spatially and exactly how you’re helping the ecosystem. Maybe some examples of a particular sector and how the work you guys are doing has helped.

Hillit Meidar-Alfi:
Sure. So let me start by distinguishing between location, data, and spatial data, because this is often missed just because of the nature of the field. So location intelligence, which is something that we’re more familiar with, really is a combination of location data and business intelligence. So the ability of taking data that has an address, the LAT long, some specific location and perhaps some market activity, and then bringing it into a business intelligence dashboard or some platform that then you can see the summaries, you can see some of the graphs and charts that are associated with that data, spatial analysis. Whoever takes that data and is actually a process, mathematical, statistical or geographic, a lot of database heavy lifting, but spatial analysis is used to identify the patterns and the trends that we’re looking for. And spatial data unline location data, which is really an observation. Spatial data has to undergo some type of analytical transformation in order to exist. So spatial data is a lot more robust and a lot more directional. So for health care, you can’t change what you’re not measuring right. and this we know. So at Spatially Health, we identify and measure the nonclinical risk factors within health care. Going beyond the clinical and financial data readily available. So does the ability to incorporate the clinical and the financial data and whatever the data sources are available. But we actually introduce into the picture all those nonclinical risk factors. And to each project, we bring proprietary derivative data sets both from our own models, but then customize the new data models to create the tailored outcomes for the stakeholders, for the market, for the problem at hand.

Hillit Meidar-Alfi:
Some of our tools are proprietary tools, our spatial analytics platform and our spatial risks. For these are two of our signature tools for evaluating location dynamics and market opportunities at a hyper-local scale. So the spatial analytics platform expands the analytical capabilities and inserts new and unique data sources to complete the data-driven strategy. Again, this goes from hyper-local to national. It’s also very, very important because we want to be able to compare apples to apples. The way that we’re going about doing it today is really too clunky. And essentially the spatial analytics platform is to a large extent our database. That’s what we’ve developed using a bunch of different data sources that passes all sorts of different transformations. And then we have our spatial analytics platform. From that, we’ve developed a spatial risk score, which is our proprietary metric that scales a spatial data to deliver measurable insights on how external factors affect health outcomes and the costs for both individual and population levels. And this is also very important that because of the way that we work and conceptually if you can think about it, it’s kind of like a continuous surface. It’s very easy to go from an individual to population level. So you can almost think about it as personalized medicine and population levels, right.or population medicine, personalized levels.

Hillit Meidar-Alfi:
So the spatial risk score really came about as an inspiration from social determinants of health, which I’m sure most of your listeners have heard of. Social determinants of health has gotten a very big push recently, but the concept has been around for quite a while. And the concept actually I remember it back in when I was in school for city and regional planning. The idea that context has an effect and the ability to measure that context is very important. So social determinants came about from a policy perspective, meaning the big government or larger governments that have vast amounts of area to manage and control and very varying populations, their needs, the ability to understand where services are missing and where essentially there are gaps in services. And ultimately this creates vulnerabilities, right. and different populations. The problem is that the way that social determinants of health is built is highly conceptual and not operational. And this is what we’ve done to the concept of social determinants of health. Basically we broke it down, build it within our models in order to create a fully operational model. And we’re able to find a lot of different patterns and reasons and granularity so that you can really identify a handful of blocks or areas are very customized outline of an area that has certain vulnerabilities right, that either have too much exposure to negative influences and perhaps not enough with positive influences. So spatial patterns, what we’ve seen also from the work using our spatial and spatial analytics platform, the spatial patterns really are our greatest source of data and they’re also the most underutilized because they are difficult to get to. By leveraging our data with our clients data, we’re able to uncover the connections, the spatial patterns that are unique within each market. And this ultimately accelerates the real-world insights in health care delivery systems.

Saul Marquez:
Hayzlett I think it’s super interesting because the market has ideas about how to address social determinants of health. Health systems have experts and ways of doing things and payors, they have their way of doing things. There’s really no standardized way of doing it, maybe even data-driven way. It sounds like what you have put together is an opportunity to get there. And so I love if you could share with us how the platform has improved outcomes or made business better, maybe calling out something in particular.

Hillit Meidar-Alfi:
Yes. So data is everywhere, right? And between financial clinical EHRS, different apps, third parties, Internet, the data generation has absolutely exploded. And the big question is, what do you do with it? And we have a very different approach. So Spatially health is actually forging a new vertical in health care spatial analytics, where analytics are built for direct cost savings and from market growth. So take social determinants of health, for example. This is a problem and we don’t focus solely on that. It’s just one of those really big, interesting parts of what we hear very often.

Saul Marquez:
We’re honing in right now on social determinants of health. You guys do other things like analytics for research and development, product development, et cetera.

Hillit Meidar-Alfi:
Yes, we actually we have a lot of exciting things happening. The solutions that we see today for concepts around social determinants of health really is enigmatic of the tools and the data that we have. Often what we see are solutions that take care of what we call the last mile. So for the entity, it’s really not a cost savings, but it’s a known added cost at the end. Not only is it finding the additional services to provide for the member for the patient, but by the time the patient has actually gotten there, they’ve gone to the entire patient journey. And we’ve missed several opportunities to improve the outcomes by targeting that person earlier on or being able to identify the needs so that the services can be aligned with the actual needs in that area. We call that the last mile. It’s a very needed service today. You hear a lot about it. But our focus is really taking the data, the information that we have and the power of actually producing models, the ability to test and to bring in all sorts of different data sources into it. Our focus is really on the first mile and that allows us to set the tone and to work with different stakeholders to solve different problems and to actually identify cost savings ahead of time and identify the opportunities to build out innovative care delivery models, provide different plans or different services that are very targeted to specific locations. So from an outcomes perspective, we work with our stakeholders to match individuals more accurately with their true health care needs. And again, this is something that’s done before. We’re able to implement real-world modeling for targeted population insights. So often enough, we talk in the realm of zip codes or in counties. That’s very, very difficult to understand what the real needs are and then connect those needs and align it with the correct network and with the most operable network. We can highlight successful programs that align with value-based care. There’s a big push now for value based care and a lot of entities want to be able to get on to that. But that takes some strategy to going from volume to value is not easy. And because the alignments are not completely there, there is a struggle in order to get onto more value based care. Plus, the problem is a lot more complex because now you really need to understand the population that you are serving and the population that you want to expand it to in order to really make the equation make sense. Employing predictive profiling, to scale up member acquisition rates, that you have a population that’s working really well and you want to expand that population, want to find out where more of them are or if you have a population that’s really hard to manage, you want to understand what’s the difficulty there, what’s causing this and what can we do as a provider? What can we do to improve the situation? Again, looking at it always from that first mile, the preemptive side visualizing service needs and usage patterns with greater clarity. So get out of the spreadsheets, but really understand what’s happening where and why are these decisions being made? You want to be able to gauge trends and both the outperformance and the underperformance of different networks effectively build out those networks and efficiently drive down the costs so that you know where the leakages and you know what’s going on, on the ground. And there’s a lot more for different companies and stakeholders to work for. The bottom line is that we hear a lot of fatigue in the industry and there’s also a lot of frustration. And it’s really because you don’t see the needle moving and there is constant pressure to reduce the costs and improve outcomes, adjust to the regulatory changes and now we have a pandemic. So there always seems to be some surprise around the corner, but we take a deep look at the spatial patterns. And again, they are really our greatest source of data, especially new data. And by evaluating and seeing any market in a variety of different from a variety of perspectives, and by doing this, we can cover those connections that are needed to accelerate the real-world insights and Health care delivery systems.

Saul Marquez:
Tt makes a lot of sense to we’re dealing with a pandemic for the longest time we’ve had issues with even before COVID chronic conditions and how do we manage them, the largest expenditure being allocated in chronic condition management. How do you understand the best ways to help these populations? And the answer is, first mile get the analytics upfront and put together a strategy, a program that can help you manage and take care of these people in a more adequate way. So as you think about the models you guys have developed and how you’re approaching the market, what would you say is one of the biggest setbacks you’ve experienced and a key learning that came out of that.

Hillit Meidar-Alfi:
Setbacks? They’re kind of varying. Setbacks also comes from also some frustration. Right. So there’s a lot of interest and desire to incorporate data driven insights and advanced analytics into the workflows into decision making. But the reality is that it’s a far cry from happening, and it’s largely because of the legacy systems that a lot of these companies are sitting on. And it makes it very difficult for different departments and different teams to actually create the analytics that are also consistent right from one department to another. And we see that time and time again. The fact that data siloed the way that it is makes it very difficult. And even in conversation, you know, this whole idea of data and analytics is really often tossed with very maybe a shallow understanding of what it really is. And then the interpretation of the implementations are difficult. But we take these setbacks and they really become our opportunities. Right. So you spoke about chronic conditions before I can ask your listeners, do you know what the chronic conditions are in your market? Meaning do you know exactly where they are? Do you know who the people are? Are you able to identify your membership with those chronic conditions? Do you know where they live and where they work? And then if you’re in the Medicare field, do you know where the Medicare agents are? You want to try to take care of them before they go into Medicaid and Medicare excuse me, equally is the uninsured, which is also a very large expense. So with chronic conditions, for example, we’ve actually taken that and mapped out literally the chronic conditions, understanding that at a high resolution where the propensity is for these chronic conditions so that our clients can really model out their markets and build customized solutions, both from a network perspective or from a plant perspective, whatever tools they have to be able to target those people and really give them the services that they need. And at the end of the day, it really starts treating the person completely. But it does require having a much broader understanding of the data that’s available and beyond the data that we have actually within building walls. But what’s happening out there in the real world.

Hillit Meidar-Alfi:
Yes, it’s the combination of both. And do you understand your patient based you understand your community base of and where are those chronic conditions? There’s a great opportunity to better understand those if you have the right tools. And so as you think about what you’re most excited about today, what would you say that is?

Hillit Meidar-Alfi:
There’s a lot actually to be excited about, even with the difficult situation that we’re in right now, socially and medically with the pandemic. But for us at Spatially Health we are looking at a couple of very exciting projects coming on board. One is working with behavioral and mental health, which is also a very, very big problem, and also working with some large health plans. So really being able to take on modeling and our tools and take it across a much broader geography and much deeper problems and issues. What’s happening right now that we’re very excited about is actually the implementation of our covid-19 Local Vulnerabilities map. This is a map that we published back in April, which is probably one of the first maps that were out in the market looking at local vulnerabilities, but at a very, very granular scale, not just zip codes and counties, but really down to almost rooftop levels. And the goal with the map was to work with what we know and working off of the risk factors that were identified by the World Health Organization. We decided to develop a map that actually identifies the areas that are most vulnerable to outbreaks. And then, of course, if an outbreak does happen, they will be in a much dire straits situation than some of the other populations. So that map was published and got a lot of conversations here in South Florida. But recently we were commissioned by a large non-profit in South Florida to create customized vulnerability map for their particular mission, which was. Vulnerabilities due to health disparities, and it’s being used right now to direct the interventions and measure the impact. So we’re very excited about that project going forward and seeing how with a set amount of time and resources, you can actually have the greatest amount of impact if you are data driven, if you’re measured, and then you’re also able to know what the impacts were afterwards.

Saul Marquez:
That’s pretty cool. Yeah. And folks, the map is actually on their website. If you go to Spacially Dotcom’s, you’ll see a little link there where you could check out the COVID-19 map and pretty neat stuff here. Sounds like you guys are doing some great work with that. This is so interesting and I always wish we had more time when we have discussions like this, but we’re here at the end. And so no one just folks, I definitely encourage everybody to go to Spatially dot com to learn more about the spatial intelligence, spatial analysis that they’re doing there to help you with your efforts. But, hey, tell us something that we should be thinking about as we end here and then where the listeners could get in touch with you and your company after this is over.

Hillit Meidar-Alfi:
Sure. So, as I said before, data is everywhere. And every business today really is a data business. The question is, what do you do with that data? What we do at Spacially health is highly visual. And if you have any questions about data, if you’re interested to learn more about the first mile, about spatial analytics platform and spatial risk score and anything else that I’ve covered, I’d be happy to take you, your listeners through a demo. You can visit our site, as Saul said, at spatially dot com, a lie dot com or feel free to email me. At least that’s h i l l i t at spacially dot com. Thank you, Saul.

Saul Marquez:
My pleasure. I appreciate you sharing the insights you guys have there. Again, listeners, just check them out. You can also get all of the links as well as the email for Hillit the website, the covid-19 map. All those links are going to be there along with the transcript that outcomes rocket, that help in the search for bar type in Spatially and you’ll find it. Hillit, thanks again for for spending some time with us.

Hillit Meidar-Alfi:
My pleasure. Thank you.

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Things You’ll Learn

  • Bringing the human element into the equation and modeling the real-world phenomena, businesses can see and measure problems, but also opportunities and align the interests of both the company and the market of the population that they’re targeting and then be able to make truly data-driven decisions.
  • Location data is essentially health care data.
  • You always want to test before implementing, and unfortunately, that’s sorely lacking in health care and it’s costing us a fortune.

 

Reference
https://spatiallyhealth.com/