Healthcare Unbound_Ines Vigil: this mp3 audio file was automatically transcribed by Sonix with the best speech-to-text algorithms. This transcript may contain errors.
Intro:
Welcome to Healthcare Unbound, a podcast powered by Clarify Health, where healthcare's changemakers discuss ways to advance care outcomes, cost and affordability.
Saul Marquez:
Hey, everybody. It's Saul Marquez, CEO of Outcomes Rocket here hosting the Healthcare Unbound podcast powered by Clarify. I'm so excited to have an extraordinary guest today. Her name is Dr. Ines Maria Vigil. She is the Senior Vice President of Transformation at Clarify Health, a physician of the American College of Preventative Medicine and co-author of a first-of-its-kind textbook called Population Health Analytics, published in 2021. Dr. Vigil has over 17 years of leadership experience in healthcare and population health, working with providers and organizations in the areas of value-based care delivery, social behavior, determinants, data analytics and many other things focused on improving care and efficiency in the way that we run our operations. Dr. Vigil has served to support Clarify Health as both the medical lead and a senior vice president and general manager of Clarify Health's provider business. Prior to Clarify, she served as Vice President of Advanced Analytics at Priority Health and also VP at CareFirst Blue Cross and Blue Shield. With that introduction, I want to welcome you to the podcast, Dr. Vigil. Thanks for joining.
Dr. Ines Maria Vigil:
Thank you. So I'm excited to be here.
Saul Marquez:
Yeah, it's exciting to have you here. Is there anything that I may have left out of your bio that maybe you wanted to share with the listeners?
Dr. Ines Maria Vigil:
I think probably first and foremost, one of the things that people know about me right away is that I'm a complete data nerd, so healthcare and data and analytics are what I've been doing for 20-plus years. It's my passion to help use data and help organizations figure out how to leverage it to improve the health of populations across the US.
Saul Marquez:
That's fantastic. And that love for data is really, I think, at the core of the great work you've done as organizations face challenges to adopt, whether it's value-based care programs or population health. What challenges would you highlight are the biggest challenges to clinicians as they look to adopt those types of programs?
Dr. Ines Maria Vigil:
Well, first and foremost, it's important to recognize that clinicians are not inherently trained to work with data. That's not something that is across medical school curriculums. And even though clinicians are very used to relying on the scientific evidence base that's out there, data is multiplying 32-fold. And healthcare data is now probably the fastest-growing type of data in the United States, trailing behind financial data banking and industry, and even the even media. And so one of the most challenging things I think, for physicians today is how to keep up with it, how to leverage it, how to understand it, when to pay attention to what and when, not to what's noise in the data. And even though data can be very helpful in a lot of ways, there's a lot of it. So weeding through it is not a skill that most clinicians have learned in medical school or even afterwards. And that's one of the challenges I think that they face today, is how to keep up with it and then how to interpret it.
Saul Marquez:
Yeah, it's a great call out. And from your perspective as a clinician, right, you understand this firsthand. What would you say is the best way for them to get better with data?
Dr. Ines Maria Vigil:
Well, it's not by accident that it took me three degrees to figure out how to work across the line and bridge the gaps that exist between being a clinician, utilizing data, and then really architecting data in a way that can be helpful for patient care. But what data can do is really act almost as a GPS for healthcare, and it can help navigate really the complex landscape of what is today public health, how to look at health outcomes from a population perspective, and how to take a data-driven insight and use it to guide better outcomes across populations that clinicians serve. One of the things that I think is the most important for clinicians is to really think more proactively. How do you utilize data in a proactive way? How do you move away from taking reactive data or reactive clinical medicine and applying it to patients in a way that can actually improve their health overall?
Saul Marquez:
That's great. Yeah, let's be more proactive with it. There's a lot of opportunities there. How can clinicians and business leaders and analysts collaborate effectively to achieve value-based care operations?
Dr. Ines Maria Vigil:
This is one of the most important things that I think I've learned over the years. The hard way that I can save clinicians across the country a lot of heartache time and point them in a direction that really helps them. It's to collaborate, collaborate, collaborate, collaborate. And in the population health textbook that I co-authored with a former colleague of mine from Johns Hopkins, we have come to the conclusion that no clinician can do this alone. It really requires a clinician who can bring forward all of the clinical know-how, understand and incorporate social determinants into the way that they proactively make changes around the way they deliver care to patients. Then it requires behind-the-scenes a data scientists analysts who are really the backbone that work in the data and the analytic tools and software set up and interpret analyses, devise algorithms and identify trends and outcomes. Then the third, probably what we call in the textbook, the Troika, or what the power is that points everybody in the right direction, is that business person that is tying that information, that's being gathered clinically and analytically into real business results, whether that's an improvement in patient outcomes, an improvement in the financial capabilities of the organization and the ability to serve more populations over time or in the ability to make sense for what matters, what's really going to work, what doesn't work, and do more of what works for the patients that they serve. And it really is that combination of three people every single time I think that we've seen works most effectively and it is to collaborate.
Saul Marquez:
That's great advice. Dr. Hill. And if somebody out there is listening to this and just thinking, wow, I just really wish that I could enlist my business leader or I could get the funding for an analyst, what would your advice be to them if they don't currently have that collaborative environment capability?
Dr. Ines Maria Vigil:
Well, you know, it's interesting, actually, because one of the things that I do at Clarify is provide that as a service. So a lot of times the natural tendency is to build out your teams, build out your capabilities, and that's something that can come over time. One of the most difficult things is to properly direct an analyst or a data scientist in all of the work that they can do, because not only do you have to have a command of data governance, cleaning data, understanding what data is, the correct data to use, how to apply it, how to ask the right questions of data. But it's also very difficult on the output end to know how to use data to develop an insight that can really be impactful to populations in positive ways. And so one of the things that we've done at Clarify, we have a tremendous amount of data that we house. We have analysts in-house and we have folks like me and my team that can do all of the things that organizations who may be starting out to learn how to use data can actually leverage somebody who can ask the right questions of data, hand it off to a team of people who can build the data asset that you need to answer all of the questions in a way that's impactful and the business know-how of what's going to really make an impact to the population's health or the financial bottom line, or even understanding how social determinants impact populations over time with the services that an organization provides. Another way to do it is if they want to build it out themselves. Don't start by amassing a huge team and doing it all at once. Really think through the stepwise approach of what data you need, what data you have, how you would utilize it, what are the real business questions or health population-based questions that you're really trying to answer? What is the impact that you think they can have? And then think through what are the skills or the types of roles that you want to build out in your organization over time. And organizations like Clarify Health and people like me who do this for a living and whose passion and who totally nerd out on the data over and over again over time. We're really good building blocks for building out your own capabilities and organizations across the US hospitals and health systems payers, all of them, as they work with data and want to do it in new and innovative ways. It certainly helps to have folks that have the expertise and the patience and the know-how to do it.
Saul Marquez:
That's great. No, thank you, Dr. Vigil. And also with the exposure that you guys have across the market, there's patterns that you see, there's opportunities that you may be able to identify that, hey, you don't need to start from scratch. So I think that's a really great call out. You mentioned skills and I think I'd love to just double-click on this one with you. Dr. Vigil, what skills do analysts need to bridge clinical expertise and data insights?
Dr. Ines Maria Vigil:
Yeah. Well, again, I'm going to start with my comment. It's important to collaborate, but when I think about all of the ways in which I use my different skill sets when I'm applying them to not only select the right data, build it out in a way that's purposeful and then glean meaning from it. I'm really utilizing skills that are looking at whether or not there's meaning in what the data is telling us, What is the context within which the data is being applied? Who are the populations or patients that the information would be applied to? What's the action that would be taken that's different than what's happening today? And what's the both intended and unintended consequences of pulling or taking that action as a clinician or as a care team member? So I encourage clinicians and in our textbook Population Health Analytics, we actually guide analysts in not only understanding the data that they're working with, but what the context is of the ways in which they're applying it, and really the mindfulness to be aware of the best practices and pitfalls to avoid when working with data, especially when applying it to patients who may have may be vulnerable or may come from disparate or different backgrounds. And the importance of kind of bringing in and identifying health risks and social determinants very early on. So the interventions can really be impactful and not escalate or not have unintended consequences or harm.
Saul Marquez:
It sounds like an amazing amount of context that you give folks in your book. So, ladies and gentlemen, we'll make sure to include a link to Dr. Vigil's book in the show notes, because it's a great asset to really guide your teams if you have teams doing this. But much like she said, when you think about data for your organization, it's important that you consider outsourcing some of it to do what you can and do it best. I'm interested in hearing examples because examples really kind of help resonate with all of us. Can you share an example of a clinician using data for better patient care?
Dr. Ines Maria Vigil:
Yeah, absolutely. I have two examples. One that's all about the individual patient and then another that's all about the big picture. On the individual patient side, I was working with a couple of cardiologists who transplant specialists who recognized that the care that they provide is some of the most expensive care across the nation. They wanted to have an impact and contribute in some way and wanted to be able to focus some of their time to avoiding a complication or the development of another condition, patients who have cardiovascular disease. And so utilizing data, we looked across all of their patient base and said, of all of the things that you two clinicians can focus your time on, what would be the most impactful to the patients that you would serve if you were to prevent unintended consequence or prevent unwarranted event from occurring? And across the data, it showed that patients with congestive heart failure who were taking on more fluid than they should or needed to and were controlled on medication at home, but were tipping the scales a little bit. And we're presenting in inpatient settings with a hospital admission. What they really needed was they needed a clinician who could titrate their medications when they needed it. And oftentimes that was right after a vacation or spending time with family or traveling. You know, all the things that we do when we're walking around the planet enjoying ourselves happens to be the one or 2 or 3 factors that coincided with hospitalization and what we call imbalanced fluid control. So we worked with these two cardiologists to develop an action from the insight, which was they started up a fluid, what they called a fluid control clinic, which was basically a colon. Any patient on the list identified to have cardiovascular condition that met the criteria, had access to what we called a nurse navigator could call the nurse navigator when they were feeling a little bit off-kilter or under the weather, the nurse would have them weigh in. They wouldn't have to leave their home and they could titrate those clinicians could titrate their medications from their care setting to the patient at home. And with a little bit of tweaking, we were able to go from a 76% readmission rate for patients with cardiovascular disease and fluid overload to 0% readmission rate.
Saul Marquez:
Huge!
Dr. Ines Maria Vigil:
Over the course of 9 to 12 months.
Saul Marquez:
Zero?
Dr. Ines Maria Vigil:
Yeah, at 76% to zero. And it wasn't a change in the clinical practice. It wasn't a change in the evidence base. There was nothing that the clinicians did differently than what they were doing. It was that there was somebody there to catch the patients in the time that they needed to prevent the hospital admission from even occurring. And it ended up saving the health plan that was associated with these patients because it was the collaboration between the health plan and the hospital-based clinicians. It ended up saving them over $250,000 over the course of that nine months. And paved the way for those clinicians to be more active and feel rewarded about the way they were practicing clinical medicine. And it was a tweak in not so much what it wasn't a tweak in them, what they did. It was a tweak in the how they did it, really. They adjusted their clinical workflow or transformed their clinical workflow in a way that better suited the patients that needed their assistance.
Saul Marquez:
That's brilliant. So essentially the patients going home that started feeling a little bad, they would call in, right? And then at that point they were instructed to call. It was a new workflow for them too. They would call in, then they would titrate, find a good balance, and then prevent those admissions.
Dr. Ines Maria Vigil:
Completely! Yes, absolutely.
Saul Marquez:
That's great. And like you said earlier. Right, Dr. Vigil, it's those questions that we ask and you guys asked some really good questions to them.
Dr. Ines Maria Vigil:
Yeah, well, you know, when working with data, it really is understanding what the clinicians are looking to achieve, what is a good outcome for them, how involved are they willing to be? What is it about the clinical workflow that is creating gaps in care today? And then what is the time to intervention that you can what kind of action can you take that can change the outcome of those patients for the better? And it ties directly to value-based care, because for those patients that were in a value-based care model, a saved or prevented admission or readmission and defined as unwarranted care, they didn't have to go in. They could be triaged in an outpatient setting and their healthcare needs addressed in that setting, avoiding a readmission generating cost savings allowed those clinicians to set it up so they could share in those savings as well. So they lost no revenue. They actually gained more revenue on both sides by preventing the readmissions from occurring at all.
Saul Marquez:
That's awesome. And that's an important point, right? You know, when you look at these things, you got to take a look at the revenue models, too, and understand the economics behind it, which I understand is a big part of your role as well. Taking a look at the economics of things so brilliant that you share that piece with us as well.
Dr. Ines Maria Vigil:
Another way to think about healthcare and the provision of healthcare is really thinking taking a more population or public health-based approach. And another example I can give of how data can really create an insight that can change the way healthcare is delivered is one where I was working with a national payer. I was looking at their data and looking at and we found that there were a number of admissions and readmissions across a population of neurology patients. When we use the data to hone in and look more closely at who those patients were, and this was a national data set. So we're talking big data about as big as you can get. And Clarify Health certainly has all of that data. 300 million plus members across the US is what we're talking about. We were able to hone in on a profile of patients that had a diagnosis of Alzheimer's disease. And when we looked more closely about the timing of when they were admitting how often they were admitting and providing questions for their clinicians to ask them what was going on in the family or the context of which the care that they were receiving. We identified a group of patients across the country that really had a lack of caregiver support, so they didn't have folks at home that could help them care for themselves and really provide them with all of the care that was necessary for them to prevent admitting or readmitting into the hospital. And so as a result of that, we were able to guide the payer to identify some benefits for those patients that were actually not clinical. It wasn't a clinical intervention at all, but rather enhanced caregiver support for those select group of patients allowed them to address the social determinant that was identified to be the barrier to care, creating unnecessary or unwarranted admissions and readmissions, and really a plan to focus on reducing those admissions and readmissions over time and meeting those patients needs in a way that was non-clinical but crucial to really helping them to address all of their care needs at the time that they needed it.
Saul Marquez:
You know, that's a really great example. And I'm just curious, right, if we're going to dig into this one a little bit deeper, how did that patient subset pop out? Because that was the insight, right? So I'm just like, how do I get that?
Dr. Ines Maria Vigil:
Yeah, this is the challenge of working with data. So because I'm a clinician, because I have the business acumen from my business degree and because I'm a trained biostatistician, there are ways in which I look at data that data can tell a story and what I'm looking for or what are the right questions to ask, to arm the clinicians to ask their patients. That will really elucidate a response that identifies what the real barrier to care or the real barrier or the underlying cause that is really causing their challenges to escalate and really overutilizing resources and really preventing them from living longer, healthier lives. And it's. A little bit of art, mostly science and understanding datasets. So I can certainly tell you it's not easy. There are so many of us who are trained to do this for a living, and certainly if you have a problem to solve, you don't necessarily have the clinicians that are trained to look at data in this way. You may feel like you don't have the analytic skill sets that you'd like, or you're having trouble directing your analytic skill sets and folks and or you're lacking the business acumen to understand what the impact is that can be made by addressing patients at a population level or in a value-based care way, then certainly reach out. This is what Clarify does day in and day out. We love it and we'd love to hear from you and the challenges you'd like to solve that you may need help from us to do.
Saul Marquez:
Awesome. Thank you so much for that, Dr. Vigil. Great example and a great invitation for everybody listening to collaborate because that's in essence the theme of today. Collaborate. If you don't have to do it on your own. Dr. Vigil, thank you so much. So how can healthcare organizations promote collaboration? That's the key, right? That's the new currency. How can organizations promote collaboration among different stakeholders?
Dr. Ines Maria Vigil:
Yeah, there are so many different stakeholders that are part of even as simple as developing a clinical workflow and incorporating data into that process. Data is or can be regarded as a fuel, but the humans are the engine and the humans are really what make data work in a way that is most impactful. And so the collaboration really is between policymakers, healthcare providers, communities and patients all doing something a little bit extra, doing their part in a really tailored and precision way so that the patients are getting what they need when they need it in the best way possible. Healthcare providers are guided to what's the most impactful tool, either therapy, medication or protocol to apply to those patients to get the best possible outcome. And policymakers are making it easier for them to be able to do that in either the system in the way that healthcare system operates, or even as simple or straightforward as value-based care where finances incentivize. The financial model incentivizes providers to select the right best choice for the patients that provide the best outcome.
Saul Marquez:
That's great practical advice in how we can all enlist collaboration, and it's something we should all consider. This is an episode would hit rewind on to learn more because it's been value-packed. Dr. Vigil, I can't thank you enough for today. What closing thought would you share with our listeners and what's the best place they could find you and follow the work that you're up to?
Dr. Ines Maria Vigil:
I would say population health data and value-based care is really all about the big picture in healthcare. And taking an analytic approach really looks beyond the individual clinical metrics and really looks to incorporate the social, the economic and the environmental factors that really influence health and is really providing a context for how it all works together and creating that more holistic understanding of what wellness can look like, what good patient outcomes can look like, and what a healthy health care system and the way that they operate can look like is really what I think you can think of when you think population health analytics and value-based care, because it really is about the big picture for all of us. Can we figure out ways to make ourselves healthier, happier and really better picture of what wellness means today?
Saul Marquez:
That's so great. Dr. Vigil And that is truly an example of how we can make healthcare unbound. We can't thank you enough for being on the podcast today and just want to say thanks for taking the time to do it.
Dr. Ines Maria Vigil:
Yeah, this was really fun for me. Saul So thank you. I appreciate it.
Outro:
Thank you for listening to Healthcare Unbound. We hope today's episode was insightful. If you want more information on how Clarify Health can help you, please visit clarifyhealth.com.
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