A Health Catalyst Overview An Introduction to Healthcare Data Warehousing and Analytics

Transcription

1 A Health Catalyst Overview An Introduction to Healthcare Data Warehousing and Analytics Introducing Health Catalyst November 20, 2014 [Jared Crapo] Hey, thanks for joining us today. I just have to tell you a quick story before we get started. One of my co- workers was in Buffalo. And so, it s been kind of interesting to see some of the pictures that he sent. If any of you are from Western New York, know that we re thinking about you and we hope you are safe and well. Today, we are going to talk a little bit about Health Catalyst and our approach to data warehousing and analytics.

2 Medicare Knee Replacements To begin, I want to share some data with you that we got from a data set that s published by CMS. And we went and looked at some data for Medicare knee replacements. So in Sun City, Arizona, which is a nice sunny retirement community, the average cost for Medicare knee replacement is about $50, miles up the road in Las Vegas, also a sunny retirement community, at Spring Valley Hospital, it s more than twice as much for the average price of a knee replacement. And I don t know why that is. The data that CMS provides doesn t allow us to really drill into that any further, but that s a pretty wide range of variation in cost for knee replacements. Wouldn t it be great if we, as an industry, to do things right and narrow that variation, so that a knee replacement costs about the same for wherever you went? Another interesting tidbit, in Sun City, Arizona, they do about 12 knee replacements per 1,000 enrollees per year. And in Las Vegas, they only do 7. I don t know if the knees in Sun City are twice as bad as they are in Las Vegas but wouldn t it be great if we, as an industry, could do the right things? If we had a more uniform way of determining whether someone did need a knee replacement.

3 At Health Catalyst, we believe that if you do things right, and that you do the right things, that yields quality and affordable healthcare. The data correlation between quality and cost is very clear. High quality cares usually cost less than low quality care. What does Health Catalyst offer? So, what does Health Catalyst do? We have three key components to our solution. First, we have our data warehousing platform which brings data in from the far corners of your organization and integrated together so that you can then apply various analytic applications to that data, and those applications are very diverse. Some of those applications might be dashboards to give broader exposure to key data metrics throughout your organization. We have other applications that are focused on improving outcomes for specific clinical conditions and we re going to demonstrate a couple of these applications for you later in this presentation. And finally, we have clinical improvement services which we offer to our clients to help them figure out how they can become a data- driven improvement organization and use data to improve the quality of the care that they deliver, so that our customers can deliver the best possible care for the lowest possible price.

4 A couple of examples from our customers, things that they have been able to accomplish using our tools. I ll just give a highlight of a few of these and I will point out that we have a success story section on our website where we have a couple of dozen of these case studies. I m just summarizing a few of them here, one that I ll point out, our customer MultiCare in the pacific northwest, they in a 12- month period are working on sepsis, were able to reduce sepsis mortality by 22% and yield themselves, their own organization, a $1.3 million cost savings.

5 Success Stories (continued) These next case studies, there s four that we re showing here, these all came from a single customer. Sometimes when we talk about these case studies, we often get asked, Well that s great. Did you have a customer that did one or two of these? Do you have a customer that s been able to do to replicate these kinds of results in many different areas within their organization? And these success stories all came from our customer Texas Children s Hospital. I ll just give you a sense of the breadth of our analytic offerings that they ve been able to incorporate and yield meaningful improvements in savings in many different areas within their organization.

6 Roadblocks to Success As we ve worked with our customers, we ve noticed that there are three common roadblocks to success or hurdles that our customers need to get over to become a more data- driven organization. One of those challenges is that analysts spend a lot of time collecting and collating data instead of interpreting data. Another one of those roadblocks is that often we hear from our customers that they have access to some pretty good information but they re not able to figure out the best way to use that information to affect change within their organization. And finally, we noticed that there are a lot of health systems that are able to put a team on a problem and make noticeable improvements but as soon as that team moves on to the next project, it s difficult to sustain those improvements.

7 Poll Question Which common roadblock resonates the most with you? So, I would like to ask you, which of these roadblocks do you think you face in your organization? [Tyler Morgan] Alright. We ve got that poll up for you. So question, what common roadblock resonates the most with you? The analysts spend too much time gathering data; the reports and dashboards are not showing relevant data, or you have difficulty sustaining improvements. We ll leave this up there for a moment to give you a chance to answer that. Also, I would like to remind everyone that, yes, we will be providing the slides for this presentation afterwards. And also, if you do have questions, you can enter those in your questions pane on your control panel. We ll go ahead and close this poll now and let s share the results. Okay. Jared, it looks like 39% of our audience answered the analysts spend too much time gathering data, 33% say reports and dashboards are not showing relevant data, and 28% difficulty sustaining improvements. [Jared Crapo]

8 So that s pretty interesting. That s more evenly balanced that I would maybe have expected. I think one thing, oftentimes when our customers are trying to figure out what the return on their analytic investment is, they often neglect to measure how much time the analysts spend gathering data. And if we can help those analysts to be more efficient in their work, so that they can spend more time interpreting data and helping working with clinicians to figure out what the data means, that s a meaningful return on investment for an organization. So thanks for those poll responses. Three Critical Elements of Success So from these three challenges or hurdles that we often see in healthcare institutions leads us to one of the core components of Health Catalyst s approach to data warehousing analytics and clinical improvement. We believe that you need three critical elements in order to successfully overcome those three hurdles that we just discussed.

9 Three Critical Elements of Success The first critical element, you need a robust analytic system that can efficiently aggregate data, can help you distribute back data within your organization, and that can help you discover patterns in data and apply sophisticated algorithms to help you figure out what s likely to happen in the future and what actions you can take to impact the care of your patients. So you need a strong analytic system.

10 Three Critical Elements of Success Secondly, the analytics by itself is just isn t sufficient. You need two other core capabilities. One of those, we believe, is what we call a content system or a clinical content system. You need a way to define a registry of patients with this particular disease condition. You need a way to incorporate evidence from the literature in your care of these patients and you need a way to identify high risk patients and rising risk patients so that you can apply effective interventions.

11 Three Critical Elements of Success The third system that is critical, we call it deployment system. Knowing what to do and having that based on evidence is great. But if you can t get a meaningful percentage of the care that you provide to incorporate that evidence, then your return is relatively small. And so when we talk about a deployment system, we re talking about a way to organize permanent clinical improvement teams that can use data and evidence to change the way that they deliver care for patients. So we believe you need all three of these core capabilities to really get to high quality, low cost healthcare.

12 Agenda So, from that brief introduction, we re going to talk about a few more things in our time together. I ll give a brief overview of our analytic and data warehousing platform, then we ll do demonstrations of two of our analytic applications, so you can get a flavor for what those are. We ll briefly talk about a few more applications and then we ll have plenty of time for questions at the end.

13 Late-Binding- Data Warehouse Platform.....,..... c...

14 Late- Binding Date Warehouse Platform Fast- tracking Analytics and Content So, the Health Catalyst Data Warehousing Platform begins with the acquisition and aggregation of data from a broad range of data sources. And the Catalyst platform can pretty much take data from any system that you want to throw at it, and in across our customer base, we have a very broad range and diverse range of data sources that we ve incorporated into our data warehousing platform. And that platform really provides that foundation for your analytic system. Secondly, within the data warehouse, we have a repository of clinical content and vocabulary that help us to make sense of that data and to apply business rules and logic to that data. For example, there are many analytic use cases where you want to know how many comorbid conditions a particular patient has. Well it turns out that s not super easy to figure out. But if we can figure it out once, then we have now another building block that we can leverage in many different analytic applications. And so, this content or these business rules or business logic that s part of the warehouse really supports that content area. When we say clinical content, again, we mean things like the metric definitions for CMS quality measures. Those are a 3 to 4- page definition that has inclusion criteria and exclusion criteria, and by adding those into your data warehouse, you can now leverage those for many different analytic use cases.

15 And then finally within the data warehousing platform, we are able to materialize what we call a subject area data mart, and this is really a puddle of data that you can use to measure and drive improvement in a very specific area, and that area might be a clinical condition like heart failure, that area might be a financial management application that data mart could support a claim data set to help you measure your performance on an accountable care contract. And those data marts were able to leverage the data from the various source systems and the content and the business logic that we ve applied in the data warehouse to build a data tool that can support clinical improvement for a specific use case. Where Do We Start? Key Process Analysis So, where do we start? What I d like to do now is have my friend, Sri, give you some demonstrations of tools or applications, so that you can get a flavor for how Health Catalyst applications can be used and what they look like. Sri?

16 [Sriraman Rajamani] Hey Jared. Thank you so much. I hope everyone can hear me now. So, as we look at Jared s platform discussion, so when we work with organizations, where do we start in terms of improving your care process delivery? So when we look at the improvements, one way is to look at the variations in the care process delivery. So this is one of our applications called Key Process Analysis that helps the organization. So, one way is you can look at what s the dollar being consumed or what s the resources being consumed. And then you can look at what opportunities do I have for improvement. So before I get into the demo of the applications, I want to introduce a couple of terminologies that we would be looking at. Clinical Hierarchy Organize codes into a clinically meaningful hierarchy So, clinical hierarchy is one of our pre- built content that we have in our platform. So as we saw, Jared talked about the three systems approach, the clinical elements of success. Clinical hierarchy is one way we tie all these three systems together during the implementation. In

17 fact, this hierarchy was developed by one of our clinical leaderships teams and that was led by Dr. Burton. So what we mean by hierarchy is in a hospital system, we see there are 12 clinical programs, for example cardiovascular, and they can be broken down into multiple care process families. For example, cardiovascular can be broken down into heart failure, rhythmic disorders, pulmonary disorders, etc. So there are 92 different care process families and each of them can be broken down into multiple care processes. For example, heart failure can be broken down into valve disorders, CHF or cardiomyopathy, etc. So we have these codes that come in various data source systems in the healthcare system and tens of thousands of codes. So what we do is we roll up those codes and then those of hierarchy. KPA: Measuring Opportunity So the next terminology I want to introduce is measuring opportunities. So what we mean by that is, for example, let s look at this case where Dr. J does vascular procedures and Dr. J s cost per case is $15,000 on average. Across the institution, we see that the mean cost per case for vascular procedure is about $10,000. So that gives us immediately an opportunity of $5,000 in increment just for Dr. J. And if you count that for 15 cases, we are looking at about $75,000. So similarly, as we see there, if we bring Dr. J closer to the mean and other providers in the organization, that s another example where the cost per case is about the opportunity of

18 $4,000. And if you think about the cases of 25, you re looking at $100,000 opportunity. So as we improve our process and bring the cost per case closer to the mean, we are looking at a total opportunity of about $10,000. Demo: Key Process Analysis So we are looking for this demo and see how we can identify those opportunities.

19 Demo starts at 18:04 KPA Pareto Analysis [18:05] I am going to shift toward to my browser and look at this particular application. So this is our essential application called Key Process Analysis and what we look here is Pareto Analysis. And what we mean by that is there are few care processes in the system that consume maximum dollars. Before I get into the details, I want to introduce you to the filters on the left- hand side, the clinical program broken down into care process family and further broken down into care processes. So these are the clinical hierarchy that we just saw on the slide. So here we see in the center, I m looking at variable direct cost as a default measure. [Tyler Morgan] Sri, so we re getting a few request if you could zoom in a bit on this particular view so that we could get a really good look at what you re showing us. Thank you. [Sriraman Rajamani] Tyler, I hope it s better now. [Tyler Morgan] Yes, thank you.

20 [Sriraman Rajamani] Alright. So, what we are looking at here is we are measuring the care process family across a specific parameter. And in the center, what we see is the variable direct cost. So, this cost, what you see on the X- axis, the number of care process families, each individual blue dot represents each care process family. On the Y- axis, it says about the percentage of variable direct costs. So these red dots represent a cumulative percentage of variable direct cost at the running percentage. So this is what we typically see in our customers, that there are about 10 care process families that consume about 50% of the internal healthcare system s cost. So below here on the table we see that heart failure is number one in terms of variable direct cost, followed by pregnancy, ischemic heart disease and etc. And again, this data is specific to the organization. So what I would like to do is to understand from your perspective, what is that clinical program that consumes maximum dollars in the healthcare system? So let s do a quick poll and see what is your perspective. Poll Question

21 What is the care process that you think consumes the majority of healthcare dollars in your system? [Tyler Morgan] Alright. I ve got the poll up for you. What is the care process that you think consumes the majority of healthcare dollars in your system? Select one of the following diabetes, mental health, heart failure, or cancer treatment? Again, we ll leave this poll up for a few moments and then we will share the results. And while you re taking this, I would like to remind everyone that you can enter your questions in to the questions pane on your control panel, then we will address questions during our questions and answers time. Okay. We ll go ahead and close this poll now and let s share the results. Okay. Sri, it looks like our audience, 47% answered diabetes, 16% answered mental health, 23% heart failure, and 15% answered cancer treatment. [Sriraman Rajamani] Great. And thanks, Tyler. So that represents a significant perspective of what we see that the care process consuming the dollars in the system. KPA Pareto Analysis [21:34]

22 So let s get back to demo and see what are the care processes and how can we identify these variations. Tyler, I hope you can see my screen. [Tyler Morgan] Yes, we are at your screen now. [Sriraman Rajamani] So, what I m going to show is we look at the variations in terms of care processes. KPA Bubble Chart [21:57] Now let s see how we can compare them against each other. So this is a bubble chart here that shows the care processes. And as you can see there are more bubbles here because we are looking at the granular level of care process and not the care process family. So, for example, the X axis represents the total variable direct cost, the Y axis represents the coefficient of variation, and what I mean by this, we are looking at care processes to the coefficient so that we can normalize the scale and we are looking at the severity of just the variation.

23 KPA Bubble Chart [22:44] So, as we look at the chart, we are more interested on the top right quadrant here. So each bubble represents a care process and the size of the bubble represents the number of cases involved. So, I go and see this red bubble here that says heart failure has a maximum variation because it has shifted and has a high level of severity. So, what I would like to do is to see what are some of the variations within heart failure.

24 KPA Bubble Chart [23:12] So I just did a double click and do a drill- down into heart failure and see here. So this chart shows the various severity indexes that are available in the heart failure. So in terms of the number of cases which particularly are based on their severity, we see four levels as represented here. The X axis represents the variable direct costs and the Y axis the severity indexes. So at this point it may seem that what s happening with my bubbles. But here, each bubble represents a specific physician. The size of the bubble represents the number of cases. So this is very similar to the slide we saw on measuring opportunities.

25 [23:55] So if they can bring all these bubbles closer, what I mean is if they can bring the cost per case of all these physicians closer to the mean, what is my opportunity to improve? [24:15]

26 So you can look at that by selecting a specific level of severity and then drilling it further and see here, I have this particular provider with the lowest deviation. Whereas this provider on the right hand side has a higher deviation. And again, you can look at this variation as a direct cost or as a length of stay or as a revenue. Another interesting perspective, you can look at this heart failure variation by location. [24:37] In a multi- location facility, how does my heart failure variation compare against the true location? But here I can see that the location of 1000 has a huge variation compared to the other one. So, this application, what it does is it gives us an opportunity to compare the care processes and identify through your four areas how you can start focusing on and that s where our customers start getting other perspectives in terms of subjective and their work with their physicians and see what are the areas that they can focus on improving.

27 Key Process Analysis So what I d like to do is a quick summary of what we just saw here. So from a three systems perspective, as Jared introduced, key process analysis is an analytics tool as part of an essential layer that gives us an opportunity to look at the quality improvement and the cost reduction. From a content perspective, we saw the clinical hierarchy, it showed how we are able to stratify the care processes, and we start off the calculations to determine the variations and the opportunities. From a deployment perspective, this is where we start working with the customers in identifying the four care processes and start prioritizing them, so that we can come up with an improvement plan. So that s where we saw how one of our essential applications can help our customers.

28 Heart Failure Readmissions Introduction Now what I would like to do is get into an advanced application that is used in the actual care process delivery. So in the demo, what we saw with heart failure as the number one care process is consume maximum dollars and have maximum opportunities in the system. So, from a deployment perspective, when we work with our customers, this is where we start working with the clinical team, the technical team, and identifying the specific areas of improvement. In this case, let s say you want to improve heart failure readmission. So how do we go about that? So one work we do is we work with the customers and form a specific objective that, okay, by 16 months I have to reduce my heart failure readmission by X percentage for my low- risk patient. So this is a very specific statement our customers usually had and what we call as an AIM statement. So once we have the AIM statements agreed upon, we would start building the content for you. So how should I look at my population, what are the specific definitions that I need to have, and what are some of the intervention plans for my heart failure that I need to put forth.

29 So Health Catalyst brings predefined content in terms of hierarchies. So we usually work with our customers and customizing these pre- defined content and making it specific to their care improvement process. So with this introduction, I want to show you a demo of our advanced application. Demo starts at 28:02 KPA Bubble Chart [28:02] So as we get into this heart failure readmission, what we see here is metrics in terms of readmission.

30 [28:24] I see the four listed here. So what we see here is the four metrics in terms of 30- day readmission, 90- day readmission and we see how the targets are being mapped as these bars and then you can measure various types. Typically, what we understand and what we believe is if an organization starts focusing more on using 30- day or 90- day readmits, their ER utilization or observation stay is going to go up. So from a comprehensive perspective, we find that a customer should look at all the four metrics and then measure their readmission. So below this we see the four bars that represent the intervention plan, like medication reconciliation, follow- up phone call, and discharge appointment. Now, I want to share an interesting story here. One of our customers, they had one more step of intervention, they called it as a teach back processes. What this means is when a patient was discharged, physicians provide the discharge instructions. So the patient starts speaking back to the physician what they understand. It s an essential step but a very powerful step to make sure that you have the right instructions being provided for the patient. So they were able to use this as an application, customize their particular step and start tracking. For a period of 16 months, this customer was able to track and improve the complaints by 80%. That s a significant achievement how that advanced application can offer towards the improvement of heart failure. So here on the bottom, we see the readmission. This is a trending graph. Each bar represents a specific month and the number of discharges. The yellow line trends about 30- day readmission over each month and the blue lines represent the 90- day readmission. So this helps you to look

31 at any kind of seasonal implications on their readmission, what kind of improvement that I can take from a seasonal perspective. On the left side here, we see the filters. This is an advanced application and what we mean by that is we use a lot of evidence- based principles. For example, I can define my heart failure patient by varying dimensions. I can define by CHF or the core measures or I can define based on the ICD9 by codes, primary, secondary, etc. I want to share another interesting thing here, the risk filters. So when we look at the heart failure patients, we want to know what their risks are for readmission. There are multiple options available. One is a physician s flag. This is something many of our customers have in their EMR system. So the physicians at the point of care provide this particular indicator for the patient. We can look at based on Charleson Index or the comorbidity count. Also, we have our proprietary homegrown risk index called Catalyst Heart Failure Appointments. It is a combination of Charleson and the comorbidity count. So this gives you the ability to look at your readmission based on a specific risk of your patient and start taking those intervention plans. Heart Failure Appointments [32:06] What I would like to do is we have many of the intervention plans and we can look at it, I want to show an example to the follow- up appointment.

32 So as we move into this tab, this tab is going to show what are the follow- up appointments and the trends related to that. So as you re seeing the chart here, the trending chart, I m able to see what are these appointments needed for my discharge patient against what are the appointments already scheduled in terms of the blue lines that trends over it. So if you want to make a specific improvement action, you need to have a focus. What I mean by that is let s say my high- risk patients for readmission, what is their appointment being scheduled like? So if this is there, I can use the predictive algorithm. For example, I m going to use Catalyst Risk Filter. So as you see, I m going to select the high- risk patients and taking greater than 80. [32:58] Now, as we do that, the tool is going to drill down and give me the high risk patients. And on the center, you see this particular table that says appointment not scheduled for 3400 plus patients. So this is where I can make an immediate improvement action. So what I mean by that is I look at these patients, I have their phone numbers, I have their primary care physician s phone numbers. So all I can do is right- click, export to an Excel, and then send it to my scheduling team, and there you have made an action that is helping to improve the readmission. So you ve got your appointments fixed for your high- risk patients.

33 Heart Failure LOS & Costing [33:46] So similarly, I want to show another interesting thing on this application. I want to look at the length of stay and costing. So this is a tab that helps us combine multiple types of data. For example, we are looking at the clinical data, the costing data on the right- hand side and the patient satisfaction data. So, it s not just about combining these data but how can you make your action that helps to improve the readmission. For example, if you re a quality director, I want to see what is the improvement I can make by reducing the length of stay for my low risk- patients. Just for the sake, assume that all the high- risk patients have the right reasons to stay for a longer time.

34 Heart Failure LOS & Costing [34:28] So as we ve just cleared the filters, this particular graph here shows the length of stay based on the risk filters. So, I want to select my low- risk patients. So what I m going to do is in the Catalyst Risk Filter, I m going to select less than 40. So these are my low- risk patients. Heart Failure LOS & Costing

35 [34:50] Now after you do that, on the right hand side you can see the average length of stays, the days and the average cost per case or the variable direct cost is about $53,000. Now, if I have to make an improvement statement, I will say that I will reduce the half a day length of stay. So what I can do is I can go to the target, just times 3.2 from the 3.7 by half a day and then see what is my opportunity. So here you see below the target we have about 52,000 cases and that gives us an opportunity of about $17 million. That s a significant amount to look at the opportunity. So another objective is, this is not an ROI calculator, this is something that shows you the opportunity so that you can make some improvement actions. Also, you can see the patient satisfaction. If I reduce the length of stay by half a day, how are the satisfaction? I see that below target length of stay, the red line, that has significantly improved from before. So this is an example that we can combine the clinical data, costing data, patient data, and then come up with a specific improvement plan for the organization. So we looked at quite a bit of information in this demo. So what I d like to do is to do a quick summary of what we saw.

36 Demo: Heart Readmissions

37 Heart Failure Readmissions Conclusion So from a three systems perspective, we saw the analytic system that s all about collecting those metrics, providing the baseline readmission metrics and helps you to drill down to a very patient level and then make those improvement actions. For example, we saw how we can select the high- risk patients and then see who have not the follow- up appointments and they immediately work with the scheduling team. From a content perspective, it s about the evidence- based content that we have, for example, how we can define the population or the risk stratification models based on comorbidity, etc. From a deployment perspective, our team works with the clinical and the technical staff on how we can follow an agile approach in terms of achieving our end statement objective, that is reducing the readmission for half- day admissions.

38 Health Catalyst Product Lines So those are the two analytical applications that we just saw at the demo. But overall, Health Catalyst has about 100 plus analytical applications. So this is how we see our product lines. So we see the five fillers in terms of financial management, accountable care, population health management, operational and workflow, and patient injury. So the advanced application, what we saw, half- day readmission, is part of population health management. So we have about 100 plus applications that fall under these five fillers. So we saw the essential application, for example, the KPA tool that we looked at. So what it does is it helps us to identify our stratifying the care process delivery based on variations and opportunity and it helps us arrive at a specific improvement plan. For example, we saw how KPA helped us looked at heart failure readmission and then you get into the advanced application. So all these applications are covered by our Late- Binding Data Warehouse Platform. It is all about how we can provide drastic time to value.

39 Population Explorer So what I want to do is give you a sense of couple of examples of our applications. This is the Population Explorer application. This is one of our essential applications. It s about how we can stratify the population based on 800 plus predefined definitions. For example, you can look at your population 30- day readmission trends, you see the length of stay has dropped. If you have a question like what is my patient discharge based on units like emergency or outpatients or how are my high- risk patients visiting their primary care physicians. So some of your operational level questions can be answered by this Population Explorer. You can even look at specific for heart and start comparing between two different locations of your hospital. For example, how are my Medicare patients performing in location A against location B in a 30- day readmission or a length of stay. So in summary, this application helps us look at the population across the continuum of care and identify some of the patterns in the treatment.

40 Community Care So here is one more application, Community Care. This is part of our population health management advanced application. So here we help our customers in measuring their four focus areas of care delivery. For example, we can look at the care delivery and complaints on diabetics, cardio, preventative, or medications management. So we help our customers in measuring their performance against some of the benchmarks. For example, we see the blue bars here, they represent the HEDIS scores. So customers can measure their performance against their internal benchmarks, our external benchmarks. Also, this application helps an organization to look at their providers and start comparing the locations or peer- to- peer provider comparisons at the chart. And even you can go to a patient level and see what is my gaps in care for these areas in terms of diabetes or cardio, etc.

41 Financial Management Explorer So here s one more application, Financial Management Explorer. This application helps you answer many of your financial management questions. For example, what is my reimbursement percentage for Women and Newborn care process or how is my payment trending for a particular insurance carrier. So, many of the financial questions can be answered by this essential application. And again, this is just more set of applications that we have out there. If you want to look into details of these applications, if you can go to our website, under the Knowledge Center, we have the recordings of demos of these applications that can give you more information about each of these applications.

42 Implementation So, from an implementation perspective, our objective is about helping organizations in achieving the value for their specific area of improvement. So we believe rather than affecting the whole problem of EDW in a big bang. Many of our customers have told us that they have spent two years in terms of building a data warehouse and then getting into the care process improvement and they have not gone further. So we believe that from these small achievement levels, for example, our achievement level 1 is all about getting those foundational data into the warehouse and providing those essential applications to the clinical staff and your technical staff, so that they can start analyzing their data, start identifying some variations, and they would be able to come up with some specific improvement plans. So achievement level 2 is about once there is an improvement plan, you will get into the advanced level and say, how we can improve this improvement plan. For example, how is our heart failure is one of our advanced applications. So this is where many of our customers start achieving the outcomes and the success stories start emerging. As we saw, Jared explained about the success stories. So once a customer sees this success, they only take it across their department. So that s where we come in to achievement levels 3 and 4 as we go across the organization and help them improve their care process delivery.

43 So that s a high level where we looked at a couple of demos and our implementation approach. So I will get back to Jared and take us to the conclusion from here. Jared? Three Systems of Care Delivery [Jared Crapo] Thanks Sri. So you ve now seen a couple of demonstrations of applications and how we combine an analytic system with a content system and deployment system to improve the quality of the care that we provide for our patients. And just to summarize, we believe that you need strong capabilities in all three of these areas to have a systemic impact on improving care in many different clinical areas within your organization.

44 Poll Question In which area do you think your organization needs the most improvement? So, for almost final poll question, I want to hear from you how you think about which of those three areas your organization could use the most improvement in. [Tyler Morgan] Alright. We have the poll up, Jared. In which area do you think your organization needs the most improvement? Analytics, content, or deployment? We ll leave this poll question up for a few moments to give you a chance to answer. We will share the results. We would like to remind you that if you have any questions, please type them in to the questions pane of your control panel. Alright. We re going to go ahead and close that poll and let s take a look at the results. Alright. Jared, it looks like 39% answered analytics, 26% of our audience answered content, and 35% answered deployment. [Jared Crapo]

45 Excellent. So I think from these responses, it looks like many of you feel like a big step forward in analytic capability within your organization would make a big difference. And so when we talk about, you know, this is where data warehousing platform can really help an organization, we have a number of clients who I have pretty strong lean process improvement programs already running, but they need a more robust and comprehensive way and a uniform way to analyze data and it looks like many of you feel like that would be a great benefit to your organizations. So we have some time now for some questions. Thank you

46 Upcoming Educational Opportunities So Tyler, help me out with some of the questions that we ve received so far. [Tyler Morgan] Alright. Well before we get into questions, we do want to mention that we have a free ebook that s available, the Healthcare Data Warehouse: Why an EDW is Critical For Success. You could find that up on the link on the page that you see in front of you. Also, we got the contact information for both Jared and Sri. You can reach out to them. Let s go to our questions now. QUESTIONS AND ANSWERS QUESTIONS How are you defining attribution? Can your platform finance those health payers and hospitals, support, and define the ACA 3Rs reporting requirements? ANSWERS So great, a couple of questions bundled together there. First, let s talk about physician attribution. One very difficult problem in analytics is when we ve looked at for often is a limited data set, how do we decide for a particular patient who their provider is? And this often comes up accountable care kinds of

47 contractual arrangements where we need a way to attribute a particular patient to a single provider. And it s complex because for most patients they have many different providers who are giving them services. So how do we do that? Well, Health Catalyst has several different attribution models that come as an element of content within our data warehouse, and you can choose depending on the specific use case which attribution model you would like to apply, in a way similar in the heart failure demonstration, Sri showed how we have a couple of different predictive models for predicting heart failure readmissions. The similar way, we have several different provider attribution models and that gives you the flexibility to either use one of the ones that Health Catalyst has applied or to create your own new model that meets the specific needs of an accountable care contract that you may have. So this is a pretty good complex topic. We could probably talk about this topic alone for a couple of hours but that s sort of a high level summary of our approach to provider attribution. Second part of that question, how do we help organizations with accountable care reporting requirements? We could extend that to meaningful use or joint commission reporting requirements. We have a couple of different applications that are designed specifically to provide those key metrics. We have one that s focused on the ACO 33 or ACO 37 metrics. We have another tool called the Regulatory Explorer that helps with other CMS or joint commission metrics and in many cases, those metrics are computed by nurses abstracting charts and doing research. And we don t believe that we can make all of that go away but we do believe by supporting those nurses with more sophisticated analytic tools, we can dramatically reduce the chart abstracting time that is required to provide those metrics. So, a couple of different applications and ways that are robust to analytic system can help you reduce the effort in abstracting required for government reporting or meaningful use or joint commission reporting. Are the clinical metrics, are they the CMS- HCCs? Yeah. So good question. Health Catalyst has many different metrics definitions in our platform. As you know, CMS provides many of those definitions and

48 that s part of the content that Health Catalyst includes in our warehouse. So you re not buying an empty analytic shell, you re buying an analytic system that already knows how to compute many different regulatory and quality metrics that are defined by CMS or the National Quality Forum and so on. So yes, we do have many of those metrics. I don t think we can claim to say that we have every CMS metric but we have a pretty broad slough of coverage across those metrics that come as part of our analytic platform. Do you provide tools for All Payers Claims Databases in the states? So I could interpret this question two ways. One of the ways would be if you are operated in All Payer Claims Database in your state, could Health Catalyst Warehousing Platform help you operate that All Payer Claims Database? That s probably not a great use case for us today. The second way I could interpret that question might be if I m a healthcare provider, can Health Catalyst help me incorporate a subset of data for my state s All Payer Claim Database into my warehouse. And we do have a couple of clients who are incorporating pretty broad sets of claims data into their warehouse so they can reconcile and analyze that in comparison with their own internally generated data. What all would you think already needs to be emplaced for taking on a data warehouse venture with clinics on different systems? How can we assess the readiness to deploy? Oh this is a great question. So, many, there are a lot of emerging healthcare delivery organizations that won t be able to aggregate and measure across a pretty far- flung set of sometimes independent physician practices. And we have many customers that fall into the similar situation. So they might have either a medium- sized IDN, they have several hospitals, they employ a number of physicians in their physician group, but then there are many sort of affiliated providers in the community and they won t be able to measure and analyze care delivered across that broad set of providers. And Health Catalyst has the infrastructure as part of our platform to be able to bring in data for many different physician off the CMRs and reconcile it together. There s also another avenue that we can use to acquire data from far- flung physicians and that is in some cases there is an existing health information exchange that has been deployed to facilitate sharing of clinical data across a broad group of physician practices, and we can use that Health Information Exchange as an input into the data warehouse and

49 that really allows us to leverage the work that s already been done in Health Information Exchange to feed data into the warehouse. As to the readiness assessment, Health Catalyst has several readiness assessments on our website that you can use to help determine your readiness assessment and Health Catalyst also can provide your organization with an assessment. We ll conduct an assessment with you to help you figure out where, which areas I need the most focus for you to be able to move forward in a meaningful way with analytics. If you re further interested in that, send me an , and I will be happy to discuss that further. From my understanding, Late- Binding implies that you apply business rules once the use case is identified and the question is well formed. This allows you to keep data into all form which is beneficial because different clients will have very different looking data. However, have you operationalized the creation of business rules that you apply to the raw data? And is it something you have to figure out with each new client? Are some of the rules generalized? It s a great question about our Late- Binding Data Warehouse Architecture. So in fact, the vast majority of the rules generalize. And the way that works, when we bring raw data from the sources into our warehousing platform, one of the activities that we do is we map every single of those, one of those inbound data elements into our Metadata repository, and that repository tells us which source systems have which types of data elements and our metrics are driven from the Metadata layer, not directly from the raw source data. So that means, for example, we have a lot of clients who have Epic at their core inpatient EHR but the metrics that we define, because they re driven from our Metadata repository, they also apply to customers who have Cerner or Meditech or Soarian because the metrics are driven from the Metadata, not from the raw source data. How do the models shown in the demo handle comorbidities? What if someone has heart disease and diabetes? So when we define the comorbidities in our application, that s where, as we just saw, how in specific order an organization wants to stratify that population. For example, we have a specific way to identify if a person has comorbidity at the time of admit or at the time of discharge and then determine the scope. So in terms of comorbidity count, we can look at the specific patient having a type of a disease at the time of admit or is it something that occurred during the inpatient stay. So there are multiple options we have to determine the comorbidity and that can be general based on what Health Catalyst believe there s a definition or it can be specific to some of our organizations that they might have their own definitions.

50 So that s one application that we saw. Another application that we saw was the Population Explorer that helps to look at the patients across their comorbidity conditions. For example, if you want to look at specific for diabetes, what was the comorbidity count for this particular set of patients; or for the heart failure, what was the comorbidity count. So, many of our applications are integrated with this particular concept and it also helps in protocol variable content in the platform. And I hope that answered the questions. I have a question about ACO tools and Health Information Exchange relationship. Can ACO tools sit on federated model Health Information Exchange and extract 33 ACO measures mandated by CMS or HIE s ACO participants? This is a great question, a fairly complex question. I ll see if I can give a somewhat succinct answer. So in this scenario, I have perhaps a far- flung provider network that is participating in an accountable care contract. And the question is, if that provider network is already connected with a federated Health Information Exchange, can I use Health Catalyst platform and Accountable Care tools to extract the ACO 33 or ACO 37 metrics that Medicare Accountable Care Organizations require to report? The soaring answer is it depends and let me explain why I give that. In a federated Health Information Exchange model, you typically have to query for the particular data set or data elements that you are interested in, and most federated HID models are designed to respond to queries for a single patient. And the use case there, so I have a patient who presents in one of the providers in the Accountable Care Organization and had never been seen in that practice before, that provider can query the Health Information Exchange to retrieve a patient chart or a patient health summary of the care that s been provided by other members of the Accountable Care network. And that s great for one patient at a time care. But often, those federated- type Health Information Exchange models are not well- suited to responding to queries for every patient that s been seen at any member of that Accountable Care network, I need to know whether the patients have been counseled about smoking cessation. Most federated HIEs are not able to respond to those kinds of queries. And so, we would see some challenges in implementing the Health Catalyst Accountable Care tools on top of the federated HID model. Now, there are some work around, there are some

51 things you can do to work around that or to make it easier to compute those ACO 33 metrics, but those strategies would involve your federated HID vendor because you re probably going to have to figure out if there s a way for participants in the network to contribute their metrics to the HIE and then to have the HIE actually store and pass those along to an analytic system. So, that s a fairly complex question. answer is helpful. I hope that [Tyler Morgan] Alright. Well we are at a time for us today. Thank you Jared. Thank you Sri so much for your great input. Before we close the webinar, we do have one last poll question. If you are interested in the Health Catalyst deeper- dive demo, please take the time to respond to this last poll question. Shortly after this webinar, you will receive an with links to the recording of this webinar, the presentation slides, and the poll question results. Also, please look forward to the transcript notification we will send you once it is ready. On behalf of Jared Crapo and Sri Rajamani, as well as the folks at Health Catalyst, thank you for joining us today. This webinar is now concluded. [END OF TRANSCRIPT]