Data Driven Leadership
Achieving 10X Savings with a Digital Transformation in Health Care
Guest: Michael Schwarz, SVP of Professional Services, Resultant
Patient experience receives 88% of health care’s investment in digital transformation, according to a recent study by Deloitte. Why? Because data has incredible potential to drive phenomenal patient experience.
Patient experience receives 88% of health care’s investment in digital transformation, according to a recent study by Deloitte. Why? Because data has incredible potential to drive phenomenal patient experience.
In the healthcare world, numerous hurdles complicate achieving an environment that meets both industry requirements and organizational goals. We’re bringing in healthcare data experts to share their experience in digital transformation.
In this episode, you’ll hear from:
- Michael Schwarz, SVP of Professional Services at Resultant
- Will Grey, VP of Data Services at Resultant
- CJ Oordt, former Account Executive at HVR
These gurus examine the common challenges, considerations, and outcomes for data transformation projects through the lens of the healthcare industry.
In this episode, you will learn:
In this podcast:
As Senior Vice President of Professional Services at Resultant, Michael is responsible for leading teams of exceptional consulting professionals to solve some of the most complex challenges faced by companies and governments. His primary responsibilities are focused on leading building, deploying, and maintaining of scalable digital solutions.
Michael is an experienced technology leader motivated by driving continuous and transformational improvements with data. He is passionate about applying frameworks and innovative problem-solving strategies to complicated challenges.
Outside of the office, Michael enjoys hiking, golfing, running, photography, and cheering on his children in a variety of sports.
Jess Carter: The power of data is undeniable, and unharnessed, it's nothing but chaos.
Speaker 2: The amount of data was crazy.
Speaker 3: Can I trust it?
Speaker 4: You will waste money.
Jess Carter: Held together with duct tape.
Speaker 3: Doomed to failure.
Jess Carter: This season, we're solving problems in real time to reveal the art of the possible, making data your ally, using it to lead with confidence and clarity, helping communities and people thrive. This is Data Driven Leadership, a show by Resultant.
Hey, I'm Jess Carter, and on this episode of Data Driven Leadership, we're going to take a closer look at the efficacy of data in the healthcare industry. More specifically, we're exploring the potential of data to drive phenomenal patient experience from square one. We'll kick off this episode with our Solution On The Spot segment where we bring in a thought leader, give them a scenario and watch them give us real solutions to complex problems. To help me Solution On The Spot is Michael Schwarz, our Senior Vice President of Professional Services at Resultant. So for people who see and hear your title, Senior Vice President of Professional Services in a consulting firm, they may not think, oh, healthcare. Can you say just a quick snippet of your experience in healthcare?
Michael Schwarz: We service a number of different industries. And healthcare just ends up at a lot of the outcomes focus work that we end up working on. And I came here by way of working in the healthcare space. So I spent a number of years on the facility side working within health systems to look for operational efficiencies. And really just understanding how stressful it can be to go into a hospital, to go into a doctor's office where you're really waiting on a diagnosis and a solution to your problem. And when you think about the patient and all of those emotions that go with it, you can start to pinpoint improvements all along that operational part of the experience to lower stress and to just create a better outcome for your community.
Jess Carter: I'm sure many of us can relate to that, points where we've been either on the receiving end or walking in to support a loved one, right? It's awesome. So we are going to go ahead and take on this Solution On The Spot challenge. Are you ready?
Michael Schwarz: Let's do it.
Jess Carter: All right. Let's say that we get a call from a new CDO, Chief Data Officer of a healthcare facility. They want a thought partner from us to explore where to begin building the organizational's maturity around how to drive phenomenal patient outcomes using the data they have at their disposal. They share that they know the industry, they understand the familiarity of data insights and signals that are changing in a moment's notice in the industry. They feel like they can't keep up. And they're trying to figure out where to begin. Where do you start charging towards a data driven, phenomenal patient outcome?
Michael Schwarz: There's certain parts of an interaction within a facility in a doctor's office that you would expect and have been around for a while. That is welcoming a patient, that is looking up a record, that is documenting the experience and what the physicians and clinicians see and hear with the patient. What I would do as the CDO is go in and be a patient for a day. How does that experience look? So how did they create an appointment? How long did it take to create an appointment? Did they get to see their preferred physician? If they're coming from a different system or a different doctor, did those records make it in to the conversation or are we starting from scratch in that first interaction? And on the flip side, even afterwards, is there follow up? So if there's prescriptions, is it easy for the patient to get prescriptions? Do they have a right care plan and a care package to take care of themselves afterwards?
Michael Schwarz: So when you think about the patient experience, there's just a number of different interactions. So I'd start with getting in and doing a little immersion and journey map exercise for what it's like to be a patient. So much is on the soft skill side of things when creating a total patient experience. Part of it is logging that data. But a lot of it is so personal when you get into healthcare that on the data side we can absolutely help make that experience easier for the clinician to facilitate, by helping to make sure that we can look at the patient record, and it's clean and it's clear and there's quality to it. And we also have different types of either recommendations or outcome care plans that can help make that a good experience.
Jess Carter: Are there best practices on how we can collect better data around that X factor of the experience?
Michael Schwarz: Absolutely, and many systems will gather information about the patient experience. That's through survey. It's also through holding each other accountable as well in that setting, and making sure that the operating procedures of care are standardized and we're watching over each other. But backing into it from the data side, there's so many elements that we can capture and collect together to help clinicians and patients set the right experience. A lot of times there's so much uncertainty when going into a healthcare encounter and seeing a physician. The more clarity and the more expectation that we can help set based on the information we have and the baselines we can collect across the system, the better experience it's going to be. It's the surprises that are scary and cause uncertainty. And I think the data that we capture and we can present in just help that experience and make it so that expectations are easier to set, absorb.
Jess Carter: What about the pressure of pace? So are there quick wins that we can establish?
Michael Schwarz: One of the good things I guess when looking at quick wins is more often than not volume exists that you can try early and fail fast. And there's so many opportunities to try new procedures and present new sets of data in a trial format. It's being okay with letting go and saying, "That didn't work," or, "We need to try something else without penalty." And I think we get there early and quicker. I don't think there's a perfect way to say that all caretakers and clinicians and hospitals should try one thing. We're getting into specialized care where different facilities see different types of populations, different types of payers, different demographics, and they need to focus on quick wins that make it better for their specific population.
Jess Carter: Have you seen someone get into this kind of a role and do it poorly? If you were to think about the things that you should advise someone to not go do, do you have those recommendations?
Michael Schwarz: I'll go one step further than emerging yourself as a patient. It's not connecting with the clinicians and caretakers in the system. I mean, when you are CDO in healthcare, you are a supporting role to those that are taking care of patients and they're making those health outcomes better. You're just in a supporting role. And so the servant mindset has to be really at the front of everything. To directly answer your question, to not have that empathetic heart and side to really understand your patient population and your clinicians I think is going to be a short run, and probably a failed operation of the CDO, because you just have to be connected. And if you don't focus on those types of things and iterate through, it's going to be probably not the best experience and probably not the best outcome, what you're trying to achieve for the system.
Jess Carter: So I've got one more question for you. What about my tech stack? How do I know that it's capable of starting to do some of these things? How do I benchmark that? Is that something I ought to do?
Michael Schwarz: Your physicians are going to tell you pretty early if it works. Being able to look across different stacks that exist within a system and within healthcare for continuity and governance across the data that's flowing within those systems. And the physicians at the forefront will know very quickly and provide feedback on what's going on within the patient record, and if they're talking to the right folks. And on the billing side, you'll know pretty quickly on if the information's getting to the right spot and we're sending the right bills and really closing those encounters. But I think the tech stack within an individual department usually is pretty functional. It's the interoperability across the whole continuum of that patient experience. And so it's the interoperability components that really become into play, and that use case for why you're connecting those data points across different systems and stacks to help support that system.
Jess Carter: Awesome. You've done an excellent job in the hot seat and I appreciate you solution'ing on the spot with me.
Michael Schwarz: Hey, it was great. Appreciate the time and the questions. It was a lot of fun.
Jess Carter: It's critical when you're going through a digital transformation to come in and benchmark where you're at today, build a roadmap of where you want to go, and then think through the incremental improvements you can make along the way. You want to keep it simple and keep it clear. Now for the deep dive on what digital transformation looks like in healthcare, the experts you'll hear from are Meredith, our global account executive at Resultant, Will Grey, managing director of client success at Resultant, and CJ from HVR. As you'll hear shortly, Michael's thoughts around healthcare and data are directly aligned to some of the thematic information they share. In their conversation, they're going to touch on the common challenges, considerations and outcomes for data transformation projects through the lens of the healthcare industry.
Meredith Wylie: First, we're going to talk about challenges. What are common challenges that need to be considered in the healthcare industry? Will, would you like to kick this off and talk a little bit about what you're seeing and some of the customer challenges you're seeing in the market?
Will Grey: I would love to, and I think one of the important ways to start this off is healthcare is so broad and it can be very big. And so when we really think about it in five key areas, we think about our providers, and so hospitals who are in the front lines and helping patients get the better outcomes that they need and desire. We talk about payers, or health insurance organizations who are helping patients afford the healthcare that's in the day. We have councils or data aggregators who are collecting all this information and handling state reporting, or doing comparison reporting. And providing that back to the hospitals and providers to make that data inside better. We have DME or durable medical good companies who are providing beds and equipment out to the services. And we have pharma. And at the core I think it's all about making our healthcare system work better and making patients have better outcomes.
Will Grey: But when you really pull back, there's many different problems. But some key challenges that we see across the board are many different sources of data. A lot of times that data is not collected well or is very unclean. And that can result in things like patient duplication. So I have a John Smith in my database and I have a J. Smith in my database. And how do I know if that's the same person, or if that's two separate patients that I'm dealing with? Population health is big, so how is this medical community working together to increase the overall health of this zip code? Or are there any medical deserts or food deserts and what's the impact on overall healthcare to that population? And then how do you get this data to be actionable into the front lines? And these are all challenges and questions that were being asked about that. And then also dealing with things like patient deduplication, and helping make sure that we're targeting the right patient and knowing that those outcomes are well.
Meredith Wylie: What are you seeing in working with HVR and in your half of [inaudible 00:11:36]? What are some common challenges that you're seeing in the healthcare space?
CJ Oordt: So my experience is more specifically around hospital systems. There's a specific hospital system in Virginia that we're working with right now, and as you can imagine, it's been a pretty interesting project as COVID came out of nowhere, right in the middle of engaging with these guys. What we were seeing with them, even before COVID happened was the challenges that no only hospital systems, but just business across the board are taking when it comes to the analytics and data warehousing. They have this data warehouse project that has been going on for a decade and it's kind of this hodgepodge thing that's been built by a number of different teams over the years. And it becomes kind of a tangled mess. I mean, data warehousing is the highest risk project in IT.
CJ Oordt: But these guys kind brought in a team and the goal of modernizing their data warehouse environment and specifically building a data warehouse of the cloud was they wanted to better predict their inpatient and outpatient numbers was the core of it so they could provide better care to their patient and then set up the hospital well financially to stay in the black and keep themselves in a good spot. And that was pre COVID. So as we started to talk to these guys, they decided they wanted invest in Snowflake as their modern data platform to kind of build this predictive modeling platform. And that presented a challenge of, okay, not only do we have to modernize our data warehouse, but we have to figure out what's the modern way to get this data into the data warehouse? We can't use a free ETL tool anymore. We can't build a homegrown EBT tool. So kind of what's out there that integrates well together that's not going to give us a bunch of headaches that we kind of build this seamless modern stack? So the challenge that I've seen out there is how do we bring our analytics environment into the 21st Century so we can better care for our patients?
Meredith Wylie: So we have Will talking about data duplication, deserts, and then you're talking about this tangled mess, right? So Will, it sounds like a lot of challenges. And then on top of this we come to the next part of this discussion is the consideration. So you've talked with financial services companies, right? And we've talked about data security. But then in healthcare I'm sure with privacy and whatnot, there's a lot of the other things beyond just overcoming these challenges that need to be considered. Will, do you want to talk about additional considerations that are more specific to the healthcare industry?
Will Grey: There's a lot of unique considerations in the healthcare community, and a lot across the different pillars that we talked about and under challenges. But the big one is HIPAA and PHI. And as stewards of this data, all the providers and payers and everybody who touches it has a huge weight on their shoulders and dealing with PHI. And in fact, and the dark web PHI information's one of the number one most expensive pieces of information that transacts. And so there's a big community that goes after this information because you can cause a lot. So making sure that the data is secure and controlled, and the right people have the right access at the right time. And a lot of digital transformation really revolves around that.
Will Grey: And I think others as you're considering is what type of source systems are you dealing with? Are we dealing with flat files or are we dealing with different EMRs? Or are we having to integrate with APIs? And there's many disparate systems that you have to deal with as you consolidate when you're defining where you want to go in this digital transformation. At the end result, it's about getting the insight into the hands of the doctors and nurses who can impact patients' lives, or make sure you can process claims faster for those patients so you can get the bills paid.
Will Grey: The last kind of two things go hand in hand is governance around the data. How do you make sure that data flows as seamlessly as possible but that you can find those data assets very quickly? And so governance is something that we're seeing as healthcare organizations take that leap into this new digital transformation that they're cataloging and defining how data assets are used in that organization. And that's been a very big hot button. And part of that is also the audience.
Will Grey: And what we oftentimes see is different departments have different needs of that data and have different business rules that need to be defined. So I may define a certain claim one way if I'm a payer and I'm doing actuarial science on it. And I may look at it a different way if I am in sales and I may be trying to say, how do I define the success or profitability? So different business rules can come off different ways. And so making sure that you align your organization and it's okay to define things different ways, but you have to know what you're looking at and what filters are applied in that data. So when we're helping organizations go through a digital transformation and move to the cloud, such as platforms like Snowflake, we really try to take those into consideration and do the deep dive up front to make sure that we are aligning the different facets of the business so we can get it done correctly on that first shot.
Meredith Wylie: At what point when an organization is embarking on this, should they be thinking about these rules and how they're going to define the data flows?
Will Grey: So many organizations already have thousands of data flows taken place that are occurring in Excel files or being manually manipulated in somebody's SQL drive under their desk or in access database. And it's really trying to extract those different facets. And it sometimes feels like we're an investigator and we have kind of that board up and we're tying strings figuring out how data's flowing in an organization. And looking for opportunities to leverage code and technology platforms like HVR or Alteryx, or APIs to really streamline that data flow through the organization and make sure that we understand who's touching it and when, providing that secure overlay. And ultimately digital transformation is not, and governance is not about applying additional rules, but it's making sure the right rules are in the right place. But also streamlining how access is occurring so that it's not in the way, but it's enabling the organization to ultimately make the right decisions faster.
Meredith Wylie: I'd like to take this question to CJ, and working with this customer, the one you just spoke about, were there specific considerations and needs they had to address when thinking about how do we modernize our business? How do we get our data to Snowflake?
CJ Oordt: Will really hit the nail on the head, as far as governments insecurity being huge. And the thing with a hospital system, as they're feeding this data into basically their decision making engine, they have to ensure it's accurate and they have to ensure it gets there fast. Specifically around the COVID use case, which kind of made everything a lot more intense. This became really important to them because they're making decisions that they've never had to make before, like turning an entire wing of the hospital into a makeshift ICU. They want to be able to decisions like that, and they need to able make like with [inaudible 00:18:41] data and the most recent data. So having data coming in in real time was huge.
CJ Oordt: The thing with the hospital system, as well as all over healthcare, is you can't sacrifice data governance, the accuracy of the data, for security, for that speed. So in working with this customer, one of the big reasons that they chose HVR among all the different options that are out there to integrate data is that they had full control over their data. Their data was encrypted as it was moving across the wire. It never left their environment. We didn't touch their data. So as we're working with these guys and they decided, "Hey, we need this stuff right now," it was much easier than if we had been a staff product that was actually bringing their data and the patient data into our environment. That was a huge piece for them, that they had full control. And then they were able to do a little proof of concept and ensure that everything was accurate, that everything was getting to the target speed and the speed necessary to make decisions on the most accurate data that they possibly could. That was a huge piece of it.
Meredith Wylie: We talk a lot about security, and Will also touched upon a little bit ago about data accuracy, thinking that J and John are the same person. Will, I know HVR as CJ said has a data accuracy, we have our data compare and repair piece. But Will, are there other techniques that you recommend for healthcare organizations that are concerned with, if we're going to deliver this information, how do they govern this data and make sure that it's accurate?
Will Grey: HVR does a really good job and kind of do that source to target matching or making sure that whenever you're moving data from one place to the other that it ends up, I'm bringing over the assets that I need. But once it's in the data warehouse and you're going through the data warehouse transformations to get it to the end result, it's making sure that there's not drift in that accuracy as I'm applying filters or taking and adding transformations to it and it's aggregating up and aggregating down. And so we believe in data QA. In fact we leverage a tool called Validator, that makes sure that as I'm doing these aggregations or summations and creating my star schema or vaults that the data stays accurate over time.
Will Grey: And going back to that pediatric data aggregator I referenced before, they were doing heavy manual QA and having lots of different exceptions. And what we did was we used some different techniques to automate about 70 different QA tests that then outputted only the exceptions where those rules didn't pass, so that I have a lot less humans touching those errors. And once those tests flow through, overall that data gets better and better over time. Because as you learn what creates an exception, then I can implement new rules that can be placed in technologies like Validator, that ultimately ensure my data is improving over time. And so we do believe in measuring data quality and also the overall wellness of a data asset as it flows through the organization. And that especially comes true when you think about patient de-duplication or grouping exercises that end up taking place. And so any place you can apply automation that ultimately saves much time and prevents many errors that can take place.
Meredith Wylie: So let's talk about results. So CJ, you've been taking us through this great story about a customer who has leveraged HVR and Snowflake. In terms of results, what are the results that they can expect as part of deploying these solutions?
CJ Oordt: As I mentioned, these guys suddenly found themselves in quite a hurry to deploy this solution or at least a piece of it. So not only were they investigating HVR, they were looking at Talend for ETL and data governance, they looking at Tableau for reporting. So it was a lot of new for this team. Reason that they went with that stack is that all of those tools integrate really together. They're built to work with the other modern day tools on the market. What they were able to do specifically with HVR, HVR is built for heterogenous replication out of the box. Basically they have these two Oracle sources that ran Epic on one them, on the other one that ERP system. So basically they're bringing in patient data and financial data into Snowflake. And like Will mentioned, it kind of automated within HVR that the data types are automatically mapped over. It's very low touch to get Oracle data into Snowflake and make it all make sense.
CJ Oordt: So, we actually gave them access to the software before they even bought it, because we wanted to help them combat COVID anyway we could. And they were able to get that piece up and running in a matter of weeks. So they had a real time data streaming in from their [inaudible 00:23:39] management system and their ERP data was all coming into Snowflake. On top of that, they ended up going with Talend and Tableau, and are currently finishing up this predictive model specifically around this COVID use case that's going to allow them to really predict what's happening in the hospital in the coming days, in the coming weeks. And the most important metric really is getting test data, positive and negative test results into their analytics environment in real time. Because that's kind of the first domino that kind of hits everything else and causes them to react accordingly.
CJ Oordt: So these guys within a matter of three months of purchasing this entire stack, are getting this full use case into production. It just kind of goes to show that when you automate a lot of it, like Will is saying, you can get things in production, you can have real results pretty quickly. And it's a big game changer for these guys in combating this pandemic.
Meredith Wylie: When you've worked with customers who have deployed solutions like CJ's talking about, how much time are they saving by doing such?
Will Grey: They're saving tremendous amounts of time. In the example of that pediatric data aggregator, the automated process that takes place, you're not only saving by leveraging automated processes and the time that analysts are spending doing QA, they're saving a lot of time moving to a modern system. And when we look at what it took to replace the legacy systems that the automated process replaced, we're looking at something they spent roughly a million dollars plus a year on. And so now they had that investment up front using technologies like WhereScape to do the data warehouse automation and then leveraging QA process.
Will Grey: One thing I didn't touch on in consideration is kind of a QA framework. We do think about something called CUVCAT. It's an acronym that we have where it's we want to make sure that the data is consistent, unique, valid, complete, accurate, and timely. And so when we take that in, it's really hard to calculate the amount of time it saves, but we think it's anywhere from five to 10 X savings over the standard manual legacy frameworks that have been used in the past. And so that's the power of digital transformation. And there's multiple facets that we use to calculate that ROI.
Will Grey: And so just kind of bringing that back to the end, it's about replacing your legacy systems and if you can deprecate those, then there's huge costs and in subscription savings and things like that. Then there's the time that you're saving from doing things from manual QA or manual ETL, or having to constantly change and rewrite code. And then there's the huge amount of value that can be leveraged from it by getting some really smart people focused on the right things and spending time asking questions of, how am I going to provide better insight to the front lines so they can handle patients better, or provide better comparisons back to hospitals so that they can improve the overall outcomes of their systems? And what we see is that value that's added compounds over six, 12, 18 months, and then becomes much more value added than just the dollar value of manual QA and replacing legacy systems.
Meredith Wylie: What should they think about if they are embarking on a digital transformation journey?
CJ Oordt: For every business, for every facet of healthcare is going to be different priorities. And I would just say, figure out what the most important piece of it is for you. Whether it's governance, accuracy, automation, speed, security. Once you have a clear idea of what the biggest game changer would be for the business, look into what the modern technology stack is that's out there that supports other companies that are doing the same thing. Because, there's so much good software out there and so much of it integrates so well together that the time to value from building out a stack like this is so significant that if you can do your due diligence and really figure out what's going to be the game changer for you, getting a couple pieces of software in there to build this modern stack, the ROI on it is just absolutely remarkable.
CJ Oordt: The other piece of it is just getting the right expertise in there as well. So there's a lot of good stuff out there and there's a lot of companies in the healthcare space that are doing some really fantastic things with their analytics environment. So I would just say look to them and investigate a little bit, see what the other folks are doing.
Will Grey: And just to add to it, we believe in starting with the end in mind. And so understanding where you want to go and then working back and breaking it apart. And then taking it back to first principles of understanding, okay, here's the data assets I have, here's the data assets I want. And then building out a strategy and a roadmap to meet those. And oftentimes we walk into organizations and they wanted to be doing AI, or be doing these advanced analytics, and the data can be an absolute mess to get it there. And so we think it comes down to taking a Lego block approach and building up from a foundation, and make it to where you can bring in new technology and replace as it builds.
Will Grey: And so we have a lot of bumps, bruises, headaches, and scars from working with multiple clients across many different industries, especially healthcare. And one thing we really do leverage is strategic services to do that. We work with a lot of organizations to do things like executive workshops, design thinking sessions to help develop best strategy. And then also strategic data assessments to really plan out and create a roadmap, a strategy, maturity model to help point organizations to where they are going and ultimately in healthcare to improve their patient's outcome or the ultimate outcomes of their organization.
Jess Carter: Thank you for listening. I'm your host Jess Carter. And don't forget to follow the Data Driven Leadership podcast wherever you get your podcasts, and rate and review letting us know how these data topics are transforming your business. We can't wait for you to join us for our next episode.
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