“Unlocking Accountable Care” Podcast
Learn about challenges and successes Medicaid ACOs are facing and what critical lessons can be learned for people in all aspects of health care as value based care continues to grow.
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Unlocking Accountable Care
Turning Data into Patient Health w/ Christina Severin (Ep.4)
Comprehensively collecting and analyzing patient data can give providers a more complete picture of their patient’s health and allow them to better direct care and resources. In this episode, we will hear how one innovative MassHealth Medicaid ACO is using data and highlight best practices for all organizations transitioning to value-based care.
Max: 00:13 Hello and welcome to Day Health Strategies podcast, Unlocking Accountable Care. The healthcare podcast where we talk everything value based care with the top experts in the field. We at Day Health Strategies are thrilled to welcome you back to our podcast. I'm Max Blumenthal and as always I'm joined by Sarah Bliss Matousek, Senior Consultant at Day Health Strategies. Sarah, are you ready to crunch some numbers today?
Sarah: 00:44 Absolutely.
Max: 00:44 Good, because today on Unlocking Accountable Care, we are going to be talking everything data and analytics. Okay, so here's my first question for you to kick things off. Being able to use data and analytics are obviously important for organizations in all industries. It can really help them understand their customers and be ahead of the curve and their market. We hear a lot about how it is becoming really important in the healthcare field too, but data's becoming especially essential for organizations transitioning to value based care. Can you tell our listeners why having robust data and analytic capabilities is an absolute must for organizations to be successful in the transition to value based care?
Sarah: 01:25 Great. So first of all, thanks everyone for joining us today. As Max said, we are talking everything data and analytics. So to answer the question, let me first explain the kind of care that we want to deliver to improve health and then we can talk about how data is critical for actually doing that well. So to be successful in a value based care world, it's important for healthcare organizations to deliver care more specifically. And by that I mean, that they need to provide the right care for the right person at the right time. So for example, you might offer preventative healthcare to the well, disease management education for people with chronic illnesses, and home care support with intensive case management for someone who's fairly sick or at risk of regularly visiting the hospital. In order to actually know what people need and then effectively manage their health with them, we need to be able to track patients outside the walls of their primary care office and then integrate that information to get a complete picture of each patient's health state and the comprehensive range of the care that they are receiving at different places.
Sarah: 02:35 So the term population health is often used to describe this because the data is used to stratify the population into a series of categories ranging from healthy to sick or high risk. Then you tailor your intervention specific to each of those categories. On their own, electronic health record data and claims data, emergency use information doesn't paint a full picture, but more sophisticated organizations like ACOs have the ability to actually pull data in from those disparate sources across the healthcare system into something called an electronic data warehouse or EDW. An EDW is, you can think of it like a brain or like the building where it puzzle pieces come together. So each data stream entering is a single puzzle piece and then when they're all put together, they form a picture that makes much more sense than the pieces randomly placed on the table.
Max: 03:34 That really makes so much more sense. But can you talk a little bit more specifically about what kind of data is coming together into the EDW?
Sarah: 03:42 Yeah, yeah, absolutely. So let me give you a couple of examples. So I already mentioned electronic health record data, so you might have health record data from a primary care office that has things like diagnostic history and prescriptions and separately you might have a case management documentation feed that could add some information about social determinants of health information like housing status and nutrition challenges. This might also provide health goals for example that the person has come up with a case manager. And then separate from that, you could have something like claims data from insurers or a health plan, that would show all the different places where this person has sought care over the last few months. They might also have hospital admissions data that would tell you how many times they've been admitted and what for. And so if you put all this information together, and I'm sure you can imagine other types of data, you can get a much better picture of a person's health status in real time than with one data feed alone. And companies are using this type of information to perform some pretty advanced predictive analytics to make educated guesses about who in the population is at greatest risk for an adverse event and should be looked after more carefully. So in this way organizations can identify those patients who could benefit from things like case management or simply who's just late for their colon cancer screening or their flu vaccine. I'm sure you can see how powerful this information could be for providers who care about providing value based care. This type of information is actually actionable, so they can be more proactive about the care that they give to their patients rather than simply treating what walks in the door.
Max: 05:33 So I see getting the right information and putting it together in the right way can give a healthcare organization more actionable data. But is this new haven't groups been trying to do this population health and manage care for decades?
Sarah: 05:46 Well, yes, and some groups are actually doing a really great job at it. Frankly, health plans have been doing just this for a really long time and they're really good at it. They're really good at the data that they're collecting and the way that they're using it. And so were some provider groups. Now that we're seeing health plans and provider organizations partner more, I think this is one of the ways that they're stronger together. But the information available and the power to analyze it has improved considerably over the last several decades. So if you think about the HMO failures in the eighties and nineties, the will to do all these things was there, but organizations really didn't have the IT or data capacity to actually deliver what they needed in time for those effective interventions. So right now what we have is increased capabilities around analytics and really powerful computing systems to pull all these different data sources together and provide actionable conclusions for providers and other groups to intervene.
Max: 06:53 Yeah, I mean, it's not surprising when we think about how far computers have come in the past several decades, but what else can healthcare organizations do with data? You know, what is new about our ability to synthesize all this information about the population beyond just individual patients,
Sarah: 07:09 Well, data is increasingly enabling organizations to clearly see the value of the care that patients are receiving and what health outcomes are being achieved relative to the volume of care that they get across the entire health system. So, you know, the value based care definition. This can also highlight where the inefficiencies or breakdowns and referrals are, or where there are opportunities to reduce spending or unnecessary healthcare utilization. Analyzing patient data can also illustrate the areas where health outcomes are especially poor or the rate of screenings is not meeting quality goals, for example. The exciting thing is that we're now much better able to measure and report on actual health outcomes instead of just reporting what is being done or what we consider process measures.
Max: 07:58 This sounds really great Sarah, but what do companies need to do to actually be able to do this?
Sarah: 08:04 So, a lot of things. To start they need wide reaching data infrastructure, which is something that doesn't make sense on its own but I'll try to explain. They also need the capacity to examine the data that they're pulling in. And then the, the ability to extract meaningful results from that information and draw conclusions in a very complex context and then they need to be able to evaluate the success of those value based efforts, which is a constant challenge. That just brushes the surface. Those are a few high level things.
Max: 08:42 Yeah. Wow. I really see how data has the potential to be a really powerful tool for healthcare providers. But I think next, I can feel like you're hinting at this, you're going to tell me to pump my brakes. Why is everyone not doing this?
Sarah: 08:56 There are a number of challenges here. First off, getting robust data collection and analytics programming started for many organization requires a significant monetary investment. It's extremely expensive. I'll put this into perspective. Many of our Massachusetts based or New England based audience will know Partners Healthcare. It's the partnership of the Brigham and Women's Hospital and Mass General Hospitals here amongst some other hospitals, and it's one of the larger health systems. They recently undertook an effort to transition their entire electronic health record system onto a new system and the project not only span multiple years but cost over a billion dollars to do and that was just the EHR system. So imagine wanting to feed in multiple other data streams and provide powerful analytics on top of or beneath that. So building the foundation is a great start, but it isn't sufficient. Then second, collecting the right data from a lot of different sources is really hard, especially when you're dealing with sensitive patient information that's protected with federal privacy laws. You need legal agreements for sharing and the right protections against theft and leakage and you have to do that in the right way. Then you need to actually translate the data that you're getting from all those different sources, which is in all different formats so that it's in the correct and same language. We call that interoperability. Then finally you need to analyze that information together to draw conclusions. So many organizations have the data but not the analytical power to turn that into information that their frontline clinical staff can actually act on,
Max: 10:41 But despite all these challenges, today on our podcast we're going to be hearing from someone who is succeeding in building an ACO with extensive data and analytic capabilities that is really a model for other organizations. So before we jump into interview with Christina Severin, the CEO of the Community Care Cooperative, or as you will hear us refer to it, C3, which is an ACO here in Massachusetts. What do our listeners need to know about her organization?
Sarah: 11:05 Right. I'm really excited about our guest today. So under Christina Severin, C3 has brought together a wide range of independent community health centers from across Massachusetts to form an ACO that is responsible for the care of patients, in all those geographically dispersed health centers. The community health centers are integral parts of many different ACO, but C3 is really the first ACO that we're aware of that is exclusively led by community health centers. It's certainly the only health center based ACO in Massachusetts. This unique model enables C3 to leverage the things that community health centers already do really well, like integrating their care into the communities that they serve and providing personalized and culturally appropriate services. In addition, different than most of the other ACOs we've already talked about, C3 is what we call a MassHealth model b ACO or sometimes that's referred to as a primary care ACO. This means that they don't have a managed care organization partner. So MassHealth is basically their insurance company or Medicaid. Their direct relationship with MassHealth has been key because they're able to access their data warehouse with all the patient claim information in a little bit more timely manner than others.
Max: 12:27 Okay. Wow. Now that you've gotten me really excited, why don't we shift gears and jump into your interview with Christina.
Sarah: 12:38 So I am thrilled to be sitting down with Christina Severin of the C3 ACO. Christina has extensive experience working with providers, most recently running the Beth Israel Deaconess Care Organization and with insurers as the president of Network Health and later at Tufts Health Plan. Christina launched C3 out of her house in 2016 and today it's one of the most innovative ACOs in the country. So Christina, thank you so much for joining us. To start us off, can you tell us a little bit more about how you came to be involved with C3 and what that journey to start the ACO has been like?
Christina: 13:14 I have a history of working with federally qualified health centers, FQHC's which are 330 organizations with HRSA. And when I saw what was happening with MassHealth's interest in reorganizing the MassHealth program from an MCO program into an ACO program, I got together with several community health centers who were thinking exactly the same thing. Together we formed a group of interested health center people. And other than me, they were all health centers CEOs in having exploratory conversations about whether a group of health centers could come together and found a new organization, a mass health ACO that would be FQHC owned and controlled. They're an amazing group of people. Fortunately, you know, one thing led to another and we were able to sort of identify what the barriers or impediments would be. And we were able to figure out ways to solve those barriers and impediments sometimes just amongst ourselves. Also in forging partnerships in the market and also through a great collaboration with Secretary Sutters and her administration who also very much wanted health centers to be part of this program, support health center autonomy and are fiercely committed to the success of this ACO program.
Sarah: 14:47 Awesome. So how many health centers do you have in that group?
Christina: 14:52 For what is known as performance year one, which is also a calendar year 2018. But we actually went live in March. So performance year one is actually March 1st through the end of calendar year 2018. We have 15 health centers. We've had some new health centers express interest and that is under development and under review with MassHealth right now.
Sarah: 15:17 Okay. So you expect probably to grow over the next couple of years?
Christina: 15:22 Yeah, we're thinking that we'll probably experience some growth over the next couple of years.
Sarah: 15:28 So we brought you in today because we happened to know that C3 is particularly strong in the data. In using data, collecting it, analyzing it, and managing to it. We know that it's unique. We also know that you consider data valuable and you've been proactive and prioritizing it. So can you tell us a little bit about how C3 specifically uses data to support your providers and enhance what we call population health?
Christina: 15:59 Yes. So my professional experience is sort of 50 percent on the provider side and 50 percent on the manage care side. And so I've had a good view into what the information assets are in both of those environments and what the information deficits are in both of those environments. A really easy example would be health plan data is very clean data. It's very structured data and in many ways is very robust data. However, when you look at the value of EHR, electronic health record data, it is much more clinically robust, although sometimes there are data assets within EHR data that are less structured than what you see at health plans and claim space data. So my idea had been, well, what if you marry the best of those two worlds where you're bringing in all of that robust health plan data, paid claims data with EHR data. That was the basic principle under which we started architecting our EDW.
Sarah: 17:11 So you've got health plan data, you've got provider level data, marrying them together. How is it that you take those two pieces, major buckets of information and then make it usable for folks in organizations?
Christina: 17:29 I would say that, yeah, you also have to start with a philosophy about data and my philosophy about data is that data is a shared asset. You want to get as much as you can and have it be the most sort of clean and normalize and harmonized and then as a way to share it back the community that you're working with. So what we did is. Also in addition to the claims data which we ingest from MassHealth, our partner in paying these claims, we get EHR data from our participating health centers. We get additional data assets as well. For example, another robust source of data that we get and store in our EDW is real time information on patients presenting to emergency departments and being admitted and discharged and transferred from acute care hospitals and freestanding behavioral health facilities. In the industry you know, there's so much jargon in the industry, so here's some more jargon. These are known as ADTs or ENS ADTs. So ADT is admission discharge transfer, which is applicable to an inpatient setting in terms of an inpatient bed or an emergency department admin and emergency department discharge. An ENS is essentially is an electronic notification system and could colloquially be known as a ping. So we get ADT ENS's when our members present to emergency departments and other care settings where those care settings participate in the system of a shared ADT notifications. We're fortunate in Massachusetts we have a nonprofit, a 501c3 The Mass E-Health Collaborative, otherwise known as MAeHC who served as this clearinghouse. So acute care facilities like the Beth Israel Deaconess Medical Center, who was a very early adopter of this. In fact, they were submitting to MAeHC before C3 was even formed as a company. We now as an ACO, can ingest these data assets that the BIDMC has been submitting. We now have many, many hospitals participating in the system and any ACO is able to ingest this information. We put a lot of effort, Micky Tirpathi, who's the CEO of MAeHC put a lot of effort into creating contracts with data use agreements and very strong a HIPAA agreements and very strong security in his organization to protect this information to make sure that information is only shared by companies that have proven that they have the right covenance in place and written agreement to be able to ingest this information.
Christina: 20:41 So, so back to the story about streaming data assets. That was a long way of saying we ingest these ADT feeds, that gives us real time information about what is happening in acute care settings. We've also been able to work with our behavioral health partner and BHP, the Mass Behavioral Health Partnership, who is a third party administrator for MassHealth for behavioral health services. They send a prior authorization. So a prior authorization notice for an individual who is being admitted for a mental health state with the leadership of MAeHC, we're able to take that prior authorization notice and metamorphosize it into a hospital admission transaction. And that's the way we ingest it. So at any given time, because of the partnership with MBPH in MAeHC, we have an inpatient setting for mental health. We also ingest labs that are performed in a freestanding facility by Lab Corp or Quest, Those are also in our data enterprise. So if you close your eyes for a moment and you think about our data enterprise, you know, think about a storage unit where you would walk up and open the door and walk in to the storage facility. And the storage facility would have a lot of shelves or you could also use the analogy of a supermarket where you walk in down an aisle and there's the cereal aisle and the soup aisle and the oil aisle, etc. And these are sort of structured elements that have a label, um, may have ingredients, right? That would be the data dictionary that supports that data element. And so all of these data elements stream into the EDW, they're all normalized and harmonized. So cereal is with cereal and oil is with oil and detergent is with detergents. That allows us to have a tidy environment under which we then really do all of the work that happens in the course of the day to day at our Aco.
Sarah: 22:45 That is so exciting. I mean, I get really, you know, we talked earlier about how excited we both are about data. But the idea of pulling all of these different feeds together and actually, you know, the combined usage of these things is so much more powerful than any one of them on their own.
Christina: 23:01 I'll give you a really easy example of that. Health plan information is really good at knowing about filled medication, they pay the claims for pharmacy and that happens with a lot of rapidity. So within a couple of days a health plan can have that information. Electronic health records are really good at knowing what a doctor or a clinician prescribed. When you combine the two, you know, the difference between what was prescribed and what was filled.
Sarah: 23:31 Yeah, I mean that's really powerful. So we talked a little bit about health plan data about provider level data. The ADT feeds, getting the behavioral health information. What about the complex management information? Do you have a way to add that to the fold?
Christina: 23:49 So what happens is that we use all of these data assets to then do stratification and segmentation. So the counting data element doesn't equal complex care management. But what we do is we take all of those data elements and combine them with other things that we know about individuals like where they live and what some of the characteristics are of that neighborhood. What's the poverty level? What are some unique public health issues like violence, like food, food deserts, etc. And then we combine that with a black box predictive analytic. And the intention of the black box predictive analytic is to stratify or run a complex care management stratification where we identify individuals who are at risk for an upcoming adverse event where an intervention would make a difference. When you think historically about what we've all been taught and shown and build muscle memory around. For example, a traditional care management pyramid where you've got pop health at the bottom, rising risk at the middle, and then that small segment at the top and that small segment is either called high risk or high cost. And an organization, like a health plan, is trying to make a difference on that top x percent, let's say five percent for lack of better precision. This system is a little bit different, right? This is about saying, we may have an interest in high cost and individuals who are or who are incurring a lot of expenditures and healthcare, but we're also equally interested in those who are rising risk and most interested in acting on information that we believe is actionable and impactful. Which I think is, is the unique thing happening here. And I think it's not just unique to C3. I think that there's a lot of movement in the industry around stopped chasing things that are just high costs. A recognition that there's not always something you can do about it and there is usually pretty impressive regression to the mean. But to really start getting involved with individuals in a patient driven way around their own interests and managing issues. They may be health issues, they may be social issues that will support and are impactful and can make a difference on not only healthcare spend, but also quality of life for that individual and perhaps their family as well.
Sarah: 26:35 Great. So I'm going to jump ahead a minute because I think this is a good moment. Is there a specific example like a patient story or an example of what you were just talking about. Where this data feed or this information was able to, or we can just make up an example of what this might look like. Where a clinician was able to get the information almost real time and able to make an impact to prevent an adverse event or maybe to help with something that's just happened.
Christina: 27:03 Yeah. So I can share a story. Bear with me because I don't remember every detail, but I remember the parts of the story that really stuck with me. So we identified an individual through our predictive analytic. And we worked with this person, this gentleman, to understand what was going on. We perform a comprehensive assessment and then develop a care plan. And one thing that became really clear in working with this gentleman was that he was really emotionally suffering due to the loss due to the death of his mother, his elderly mother. And due to some complexity that this gentleman had in his life. His mother was really an important support in addition, of course, all of the love that he had for his mother over the 50 years of his life and he didn't really have a way to manage that grief. And so one thing that we discovered in working with him was that the level of his grief and the pain that the suffering because of his grief was really a key driver in the adversity that he was facing and why he was using a lot of healthcare services and also why he was having a problem taking his medicine. And so when we, we try to change some of the language that we use. For example, I really don't like the term, the individual is noncompliance and taking their medicine or they're not adherent. I don't think that those are. We talk about patient centered care. I don't think that those are patient centered terms. So we work to understand what the barriers are to somebody being successful in taking their medicine. That was where we discovered that the impediment here was was grief. And so we're able to connect this individual with professionals and community supports that could help him in managing his grief and getting through that on a day to day basis in addition to the staff people in complex care management who also became supports. The last report that I got on this gentleman was that he was being successful on taking his medicine on a day to day visit and had not visited the emergency room nor had an inpatient admission.
Sarah: 29:19 That's so great. What a good example of using the information you have to directly impact individual circumstances. So given what you've already told us about the way that you're using data, we know that gaining the organizational capacity to collect, analyze, and then communicate that information is often a huge challenge for other healthcare organizations. What do you think the biggest factors have been for your success at C3?
Christina: 29:47 One of the advantages that we have and the most important core asset that we have is that we are an FQHC owned and operated company. So you can imagine copy and paste C3 into a different environment with with perhaps a very diverse portfolio of provider organizations and you say to them, hey, we want to pull your EHR data. It's not easy. Pulling EHR data isn't easy. It's easier than it used to be because the industry has gotten more sophisticated on the methods to extract that data. With our organization we talk about what the game plan is and when we're all in agreement on what the game plan is, we all act on the game plan. So the level of collegiality and, and fierce cooperation that we have in the organization really makes everything easier. I mean, doing this as hard, right? Managing cost and quality is hard work. The work that ends up trickling down to PCPs in the course of every day is really hard work. But I think that what buoys are momentum and success and our ability to execute and get things through the finish line, fundamentally is the uniqueness of FQHCs and the collaboration that we have in this CFQHC owned and operated company.
Sarah: 31:14 That's great. Alright, so final question, what does success look like over the next five years for this MassHealth endeavor as a whole and then for your organization specifically?
Christina: 31:29 So starting with overall, with MassHealth. CMS is looking at at MassHealth, MassHealth is looking at us. What does success look like? So I think at the highest level of success looked like we've achieved the highest level objectives on effectively managing cost trends and effectively improving quality. And what that looks like when you put them together, that's sort of how this thing gets measured. That's true for us too. But we make very specific annual goals on a total cost of care management and quality improvement. And we're at the beginning of that of course. It's a five year contract. The ACO program, just went live in March. So we are at the beginning. I think for us additionally, and if you were looking at our balanced scorecard and at our highest level objectives. Right above the ones we've just spoken of on cost and quality would be, great we've achieved these things on cost and quality and how has that impacted community health. So it's not just achieving these goals on cost and quality for the sake of achieving those cost and quality. It's about improving community health and improving the lives of the individuals who live in the community served by these health centers. That's our most important objective. We're also looking at, that is something that is hard to measure. It's not as easy decreased cost trends, right. How do we measure that? We are starting to work on that too. And starting to think about, we call it SROI, social return on investments. So we're at the beginning here and we've got some really great partners. We last week we were awarded a grant from the health policy commission that, is about many things including social determinants of health. And within that we do have some planned work on getting started on measuring SROI. And by the end of that project, we really want to not only have some of the metrics, what would we measure, but also how would we have an economic ROI on that measurement. So stay tuned as, as we further develop that.
Sarah: 33:49 Great. Well we look forward to hearing how it is going. So we'd love to have you back and have another discussion. Thank you so much for joining us today, Christina. We know that you're really busy and we appreciate you taking the time out of that schedule. So we will talk to you again next time.
Max: 34:10 Well, I know we are just at the beginning of a long road for this program. It really seems to me that C3 has set itself up to be successful with how they're using data to really think proactively about their patients' health needs and track what kind of services they can provide them. What stuck out to you about your conversation with Christina and how they're getting the most out of their data?
Sarah: 34:30 Right. So, I think that my key takeaways are that group should focus on sourcing data comprehensively, meaning that they gather data from several different sources and shared among all of their organizations. I also noted that building relationships is really critical to accomplish this and they're doing a really good job of it there. And third, that it's important to not just focus on the high utilizers, but finding ways to use the information that you have to prevent high utilization in the first place.
Max: 35:03 So let's say I work at a healthcare organization that's not the same level as C3 and I'm facing a lot of the challenges that you outlined in the introduction. What are some concrete steps that my organization can take to start collecting data and using analytics to improve patient care?
Sarah: 35:20 So a wise colleague once told me that companies should start with what they have. The first step is to identify what data you already have available but might not be using in the right ways. So for example, can you extract information from your EMR as it currently stands? Do you get a monthly or a quarterly report that you aren't looking at but could have useful information? So a word of advice for those just starting out. For ACOs, data should be involved in conversations very early on, especially when you're determining the organizational structure across different entities if you have those coming together. Work to create data sharing agreements early so that you know that you can count on getting what you need when you need it. And then when thInking about bringing new data sources to your data warehouse, you can think of that as the brain or the building where that puzzle gets put together like we talked about earlier. Don't try to do too much too soon. For example, you could start by getting a single ADT feed, ADT is admIssion discharge transfer from lIke a acute care facility like a hospital. So you could start by just getting one single ADT feed from one hospital that patients happen to use a lot and then once you have a workflow to manage that, then add more. So again, work with what you have and start small and don't forget to have a plan for the outputs. This is that next piece. Getting the data in is just step one, what are you going to do about your patients that you've identified as high or rising risk? Do you have a plan or do you have capacity to reach out to them and get them the services that they need? So putting all those things together I think is a great starting point for building a robust analytics and data capability.
Max: 37:10 So you're saying start early, work with what you have, create a plan and then build from there. Get that right?
Sarah: 37:18 Correct.
Max: 37:20 So thank you so much for joining us for another episode of Unlocking Accountable Care. Hope you all have a great week and we'll talk to you next time. If you are interested in learning more about accountable care or how organizations can succeed in today's healthcare system, please visit our website www.dayhealthstrategies.com. Check out our blog, follow us on twitter and join our mailing list. We regularly post content relevant to current healthcare issues and overcoming challenges in delivering value based care.
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