Bill Russell: 00:00:09 Welcome to This Week in health IT where we discuss the news information and emerging thought with leaders from across the healthcare industry. This is episode number 51. Today we’re going to do something a little different. I’m introducing a case study episode our first one and this episode we’re going to take a health system from chaos to an effective data and analytics program. This podcast is brought to you by health lyrics health systems are moving to the cloud to gain agility efficiency and new capabilities. Work with a trusted partner that has been moving health systems to the cloud since 2010. Visit healthlyrics.com To schedule your free consultation. My name is Bill Russell recovering CIO writer and adviser with the previously mentioned health lyrics. Over the next two weeks we’re going to be airing the best of this week in health I.T. episodes. I’ve gathered some of the most commented on liked and discussed short videos from our YouTube channel. I brought them together with our production staff to highlight some of the great content over this past year. It also gives me a chance to take two weeks off and have and the people I usually have on the show to take two weeks off over the holidays and just let you enjoy some of the great commentary from this past year so I hope. Hope you hope you enjoy that and hope you share it with your staff as well. So as our last full episode that I would be doing this calendar year. I wanted to do something a little different in a digital world the foundation for a transformation is really data and this is where our guest lives on a daily basis the application of data to solving some of the healthcare biggest challenges. Dale Sanders is the president of technology for health catalyst and a former guest of the show. Good morning Dale and welcome back to the show.
Dale Sanders: 00:01:45 Hey Bill how are you. Thank you. Good to be back
Bill Russell: 00:01:48 Yeah I’m looking forward to our conversation. But let me let me share a couple of stories. So these are things that came up on on my feed this morning and I saw a Bekkers just highlight some of the headlines that are going on. So the first one was in a move to expand its presence in health care space. Amazon is selling software that mines patient health records for information that helps physicians improve treatments and hospital cut costs. The other one was Apple and the Department of Veterans of the V.A. are reportedly in talks to allow veterans to store their health records on their iPhone’s quest diagnostics. Now Supports Apple Health Records feature making it the second clinical testing laboratory to join the project. Microsoft released an open source project called Fire server very creative the fire server for Azure to assist developers in exchanging and managing healthcare data stored on the company’s cloud. The NIH is partnering with technology company Navidia to create artificial intelligence tools to support clinical trials. So the big hitters in data are circling the space yet in health care we really have fits and starts on the data front. What are some of the things you want. What are some of the reasons you think we’ve had you know we’ve had some successes but you know we’ve we’ve really I don’t know we can’t get out of our way in some cases so why do you think we haven’t been as successful with data as maybe we could be.
Dale Sanders: 00:03:15 Well I you know one thing lately, in the last ten years or so came along.since meaningful use came along We’ve been so consumed with meeting the compulsory measures. Who’s got time to do anything creative with data really. I mean right. It’s an ocean of compulsory measures it is also undermining clinician’s satisfaction and contributing to their burnout. So it’s the it’s a classic case of distraction. Now for one thing we just don’t have time. You know you go back to the early days you know when I started my career at Intermountain the compulsory measures at that time for the most part were limited to joint commission and they were pretty small numbers. And so what it gave us as an analytics team and a culture was the time to do cool things with data which put Intermountain on the map and and literally there’s no bandwidth left. So I’m really actually encouraged in spite of all the craziness of the Trump administration Steve Averna and others. I’m starting to see some commonsense return to those compulsory measures. But we need. We need to slash and burn compulsory measures and give the industries some time to perform analytic ingenuity and creativity on their own.
Bill Russell: 00:04:32 We need a chance to breathe. I had a similar when I became the CIO for health system our internal auditor kept doing security audits on our team and when I went to our team and I’m like alright we need to get ahead of this they just looked at me he said we can’t. Yeah. There’s just there’s too much here so I had to go to our internal auditor and say I need a reprieve. You need to give me a reprieve for six months like like no audits for the next six months a year would be better. So our team can actually put some things in place and you can come back and do as many audits as you want but we just need some time to to really get our house in order and that’s essentially what we’re getting from the federal government just continually changing. But I agree with you I’m seeing some positive things there I guess. So we’re going to divert our normal show format and explore a case study so I share this case study with you I’ll share with our audience and then we’re just going to dive into it so here goes and this is if any by any chance this reflects your situation and your health system this is not based on your health system I’m just
Dale Sanders: 00:05:35 the names have been changed to protect the innocent.
Bill Russell: 00:05:39 So this health system has 18 acute care hospitals on two different EHRs with regional customizations operating in four states. The health system participates in two ACOs that utilize point to point integrations between disparate and EHRs and separate systems to produce reports and house registries. The health system has several medical groups on differ on different but a single EHR platform and a clinically integrated network that has 50 distinct EHRs represented. The medical group utilizes their own EDW to generate generate metrics for the clinically integrated network and for their other constituents the health system participates in three different regional health information exchanges that transfer data with CCDs obviously across 60 DA format system level ED, at the system level. They also have an EDW that was created five years back and the demands on the team are really overwhelming the team spends a majority of their time cleaning the data in order to produce a set of reports for their constituents. Every year the data team makes requests to grow the staff by about 20 percent. The executive team is asking for a set of reports across the system to measure clinical and operational performance. Today the variability of the data and that the definition of the terms makes comparison very challenging for the executive team. There there’s a feeling amongst leadership team that their investment in systems making their investment in systems in analytics and the EHR that they should be getting a lot more value from the program for the money that they’ve invested. Their plan is to hire a hotshot data CIO or seat, chief data officer to save the day and Dale. Congratulations you’ve just been offered and accepted the job so we appreciate you joining the organization to help us sort of move through this. I’m excited.
Dale Sanders: 00:07:33 Yeah this is a great scenario. Nicely done.
Bill Russell: 00:07:38 So do you see elements of this across the industry as you go out there right.
Dale Sanders: 00:07:44 Oh yeah yeah I mean this is the norm now as complicated as it sounds it literally is the norm
Bill Russell: 00:07:49 and that’s, Alright so the stated goal from the executive team and the clinical executives and whatnot is that they would like to utilise data to rapidly and continuously improve clinical operational and financial performance for the health system and the care providers. So for our organization we’re going to organize it into really six topics we have to start with Discovery you’re starting your first day. We’re going to look at discovery what you need to discover. Second thing is preparing the organization for change. The third being the components of a healthy data utilizer. Forth Being operationalizing the program fifth being the technology and six being ongoing care and feeding of the program. It’s interesting because so many times people want to start with the technology. If I just bring this in all these problems will be solved. But but we know that that’s not the case so let’s start with Discovery where where are you going to start. What are you looking for. What data do you need to gather who you need to talk to.
Dale Sanders: 00:08:46 You know literally putting my hat on as if this were my first day. I would start in the usual way which is you’ve got to get out and meet all of the major influencers and constituents and start building relationships. I always say that there’s nothing more political especially in healthcare than data and it’s utilization. People think that deploying an EHR is brutal culturally but you know you’ll eventually get past the pain of deploying an EHR. But the politics of data and the politics of utilization of data and the light that it can shine both accurately and inaccurately on people and their careers and processes is what makes it so political. So you know for the most part we’ve almost commoditized the technology right the public cloud has made the infrastructure pretty easy. You know I’m I’m humbled by the progress we’ve made with health Catalist other vendors have you know technology that sits on top of public cloud infrastructure that makes the tech almost a commodity. So I would plan on spending about 80 percent of my time in marketing the value of data to the organization and also making people feel trustworthy and trusted and and building those relationships. I gave a lecture out in L.A. yesterday to a group of folks that are interested in collaborating sharing data there. They’re brought together under a common mission but a disparate governance structure and I shared with them that I spend most of my time now advocating the soft side of data and the human side of data. And I’m going to reference an old Harvard Business Review article I read years ago so long ago I can’t remember now when it came out. But it talked about the ways that the characteristics that are important when you’re trying to influence and collaborate with people and it boils down to three characteristics. The first characteristic is the person on the other side of the table has to feel that you have overlapping values in some areas that in essence that you’re honest and trustworthy and that you have their interests in mind. So they have to feel good about overlapping values and of course if you’re working across cultures and countries and things like that it’s sometimes hard to find those overlapping values. But for the most part honesty and trustworthiness and having a genuine empathy for the person across the table is a pretty common way to establish overlapping trust in that first characteristic. So there has to be genuine. It can’t be manipulated right. The second thing is you have to have a direct sense of empathy for the role that the person occupies. You know the classic example is an I.T. person talking to a physician about how we’re going to improve their lives with technology. It’s not unusual for us as I.T. people to alienate those clinicians because we’ve never been in their shoes. So if you can’t engage in a conversation with a clinician with a sense of direct first order empathy with them then at least bring a partner with you bring a physician partner with you to those conversations or bring a CFO or someone from the finance team with you. But the person sitting across the table from you has to feel that you got first order empathy with the situation that they occupy. And so there has to be a big overlap in that capacity in addition to the values. But then the last thing the last of the three traits that’s really important is the person on the other side of the table has to feel like you have expertise and skills that they don’t have that they value. So you have to bring something to them that’s common in the first two characteristics but you have to bring something to them that’s unique and valuable to them. And so that’s how that’s the first thing I would start doing is engaging with people in the organization in those positions of influence. Reminding myself constantly of those three characteristics that are so important to effect change.
Bill Russell: 00:13:23 So I’m gonna take you through some groups. So first group you’re going to meet with is the head of the clinically integrated network and what they’re concerned about is hey we have an EDW. It’s different than the system EDW we like what we’re doing our teams doing a good job it’s outside of I.T. we have our own little analytics. You’re not going to you’re not going to impact my program are you. You’re not going to take that away from me are you.
Dale Sanders: 00:13:50 And likely answer that would be no. And I had this conversation with one of our most important clients the other day we’re kind of moving into the next generation relationship with them and trying to figure out how to expand the the impact they have. They still have multiple data warehouses in the organization. We’ve managed to reduce from five down to three including ours. But the reality is you can’t convince people to give up the data they have unless you can add value to them that they appreciate. So and usually that value is the sense of control over the data and agility with the data. Those are the two most important things. The third is the cost of the data. butt people will actually hold onto their expenses. If you don’t show value in those other two areas around control and agility so You’re never going to win with a mandate to say we’re going to go in we’re going to for the sake of I.T. consolidation and reduced expenses and licensing. We’re going to take your data warehouse away. That is absolutely losing strategy, going in and offer them something that’s more appealing and attractive than what they have. And until you can do that you’re not going to win.
Bill Russell: 00:15:06 So now we take into the CFOs office and the CFO is saying you know look we’ve we’ve got so many different systems out there I can’t even. You know I’m having trouble closing the books and getting the kind of reports I need out of this thing. What are you going to. What are you going to do. And by the way don’t don’t ask me for 5 million dollars don’t ask me for 3 million dollars. What are you going to do to sort of streamline this so that first of all that I’m getting you know what I think I should get for the money. But second of all that you know we’re not sitting in these board meetings and these executive meetings anymore having arguments over the definition of the data. I mean we need to be comparing these 18 acute care hospitals across their performance and I can’t even do that with the disparate systems that you have so what are you going to say to the CFO around those things.
Dale Sanders: 00:15:58 Well it sounds like there’s two battlefronts there right. One is the the the disparate data and data governance and the other is just the plain old expense of having all those disparate systems right. And you know you can go back out to the organization and the folks that are in the field and appeal to them from a perspective of cost and appeal to their larger sense of belonging to the organization. Most of the time these disparate source systems are funded not centrally by I.T. but by the business units and departments. So if you can show them a way to save money on those expenses that they have in those local I.T. disparate systems without giving up their sense of agility autonomy and you know self governance then you have a chance of making progress. But the CFO, you kind of have to wear the rank of the CFO. But hopefully not in a way that takes away a person’s sense of control and autonomy around the data that they control.
Bill Russell: 00:17:10 So let’s talk about the clinical leadership, so the clinical leaderships going to look at you and say you know what we have some reports that we’ve been requesting again these are retrospective reports we’ve been requesting for the better part of three to three to four to five months and we still don’t have those reports. Some of this data that’s coming in I don’t even want to see it anymore. Other than the data I would rather not have it come in that way I’d like to see it in my workflow. I don’t need a separate system. What are you going to do. What do you say to that person. I mean obviously we’re just in this discovery phase we’re not in the creating the solution phase. And so I imagine you’re going to show empathy for that person but what are you going to say to the clinical leader.
Dale Sanders: 00:17:51 Well yeah I mean the first conversation that I have with clinicians nowadays is just a simple acknowledgement that your you’re over measured about things that don’t matter. And I get that as an I.T. guy I totally get it. I totally empathize. We’re over measuring all your processes. We’re under measuring and detracting from your ability to achieve outcomes and be personal with care you provide. So here’s what I’m gonna do as an I.T. guy. I’m going to try to relieve as much of that compulsory burden from your shoulders as I possibly can. I’m going to try to optimize the way the data is collected in the EHR. I’m going to try to optimize and present the data back to you in a way that’s helpful to you and not oppressive to you. And I’m going to find bandwidths in what we do and how we work together so that I can actually give you data that you really want in the form that you really want. But the first thing we have to do is automate as much of these internal external KPIs that are burning you out and get as much of that off your shoulders as we possibly can. And then let’s give you some time to work with me and my staff to give you the analytics and the decision support that you need. And then going back to your you know the strategy for getting things into the workflow. I always break down my decision support strategy in three tiers. The first tier is working at the population level. The middle tier is working at the protocol level and the third tier is working at the patient level. And I. And so you have to have an analytics and decisions support strategy that operates at all three of those. Right. So there’s different kinds of analytics when you’re dealing with the population of a community. Different skill set it looks more like epidemiology applied to chronic disease. Then you’re working in timelines that extend you know eight to 10 years. Right. The temporal dimension is very long at the protocol level now you’re starting to do analytics and decisions support in a subset of that population. You’re saying for these kinds of patients these are the analytics that we need to better understand the care that we’re delivering the outcomes and the costs associated with these cohorts and patients. And that’s typically you know now you’re dealing with hundreds of thousands maybe tens of thousands of patients instead of millions and in general what you’re saying is the patterns for care for these patients need to look kind of like this. And by the way let me comment that what we’re doing at docs right now is we’re trying to force all patients into these evidence based care guidelines. And we’re taking away from the ability to personally treat patients. So we’re putting too much emphasis on evidence based care as if every patient should be treated exactly the same way. And what we should be encouraging is sort of the protocol level minimized reduction variation or minimized variation in reduction variation but within that there should be a lot of variability on a micro level about how we’re treating patients. And right now we’re not doing that with that with evidence based care and the in the data strateg. Yeah.
Bill Russell: 00:20:59 Interesting.
Dale Sanders: 00:21:00 Finally the last the last loop which is you know ultimately might be the most important is what data are we going to present to help you as a clinician engage with a specific patient that’s sitting right in front of you and how are we going to do that within your workflow whether the workflow is on your your mobile device when you’re on the treadmill at the gym or if it’s in your car or if it’s at the EHR wherever your workflow and your decision making can be supported. Let’s give you the data about this patient that can optimize the conversation that you have with that patient about their care and their outcomes. You know the I referenced the study that can calm MoDOT at University of Utah did a number of years ago in which he concluded that and found that clinicians are 15 times more likely to adapt their treatment towards a patient if you give them the substantiating data. To do that at the point of care as opposed to giving that to them in a conference room someplace right it’s what I call the folly of conference room analytics. You can only go so far at the population of the protocol level you have to push decisions meaningful decisions support into the workflow of the clinician and it’s still hard to do. You know frankly it’s hard to do. I’ve been in this you know I’ve been passionately pursuing all three of those loops for my whole career and it’s still hard to do. Impart, You know and I’ll put a little pressure on the EHR vendors the EHR software was not designed to support dynamic intelligent user interfaces. And so I think you know fire is helping get to that last loop and that last mile of decision support but I think long term I think the EHR vendors have to bulldoze their software and build it in modern software that’s more indicative of intelligent user interfaces that we see as consumers now and it’s enabled by good software in the background. It’s I quite often say modifying old software is like trying to modify a house with with concrete walls. It just concrete walls were just not meant to be adaptable like we’re accustomed to as consumers today.
Bill Russell: 00:23:27 The EHR needs to be much more modular in terms of its architecture so that we can. Let these are the things that well wow that’s discovery we go five more to go. But I had a side question of my side question is I it’s not in this case study but if in this case study you had let’s say they were operating as a payer as well. Is that a whole different analytics mindset and approach or is that pretty similar in terms of how they would view the data and go after the data.
Dale Sanders: 00:24:00 No I think it’s a great thing to have a payer as part of the environment right that’s what finally closes the economic model back onto itself. Instead of this open model that we have right now where everybody is spending everybody else’s money.
Bill Russell: 00:24:17 But can I take. Hospital acute care analytics team and just say hey you’re also handling the pear side of it or is it a different different thought process or skill set that I’m going to need.
Dale Sanders: 00:24:33 Well it depends right if you’re utilizing claims data to support a better understanding of clinical operations cost of care what’s happening outside the four walls of the healthcare delivery system and it’s rounding out your understanding of the the patients health then then your traditional analytics team you know hospital centric organizations can handle that kind of thing. If you’re talking about the analytics associated with risk management risk projection more at that looks more like an actuarial function than I think you’re you’re more than likely going to stretch if not exceed the capabilities of most analytics teams in a hospital. I will say that there is an opportunity for those folks in the hospitals those analytics teams in hospitals to bring new data science techniques into risk management projections. So you know the short story is I think we can actually disrupt traditional actuarial techniques with some of our new machine learning capabilities and an algorithms. So there is a chance for those analytics teams in the hospitals and traditional delivery systems to actually be better at predicting risks and managing not only risks clinically but risk financially than what the actuarial folks do in a traditional payer you know if you ever. And I got I had the opportunity to peek behind the scenes you know at Intermountain and in the Cayman Islands where we had this integrated delivery network with insurance and care delivery under the same leadership. And I’ve got to tell you man the actuarial techniques that are being used by payers is way outdated. And then we pay a premium for that as patients. So there’s a lot of opportunity to disrupt the actuarial techniques are currently in use today.
Bill Russell: 00:26:32 Awesome. Our last. So we have five things. Next thing we need to do is prepare the organization for change and that is create a sense of urgency build your coalition of the willing and a vision. So one of the things you said is you to go out there and educate people on the value of data. I guess we’re going to start now. How are you going to do that. So what does the vision potentially the vision like one or two talking points on the power of data. How are you going to create urgency and how are you going to build a coalition of the willing.
Dale Sanders: 00:27:05 Well yeah it’s a great question. And you have to paint a vision that is compelling and motivating and that’s part I think if I do anything in the industry right now I think that’s what I spend most of my time on. And you know if you describe the use of data to enable better digital conversations between a physician and a patient right I’ve got one. I’ve got my aspirational statement hanging on my wall right here. I take should off and read it to you. You read those aspirational statements to a clinician and to a patient and to an administrator and they go yep that’s exactly what we need to do.
Bill Russell: 00:27:45 What what what is your aspirational statement.
Dale Sanders: 00:27:48 OK hold on. I wish I were smart enough to have it memorized but I still don’t.
Bill Russell: 00:27:54 Well your well we’ll just admire the beautiful picture of your family back there by the lake.
Dale Sanders: 00:28:02 That’s up in Canada a couple years ago. OK. Here it is. So this is the aspirational statement that we have here at health catalyst. But I’ve had this hanging in my office before I was a CIO we provide the software data and professional services that enable physicians to extend this commitment to their patients. I can make a health optimization recommendation for you informed not only by the latest clinical trials but also by local and regional data about patients like you. The real world health outcomes over time of every patient like you and the level of your interest and ability to engage in your own care in turn. I can tell you within a specified range of confidence which treatment or health management plan is best suited for patients specifically like you and how much that will cost. So you know we literally parse this statement and we look at and we go OK what’s the technology and what’s the data that we have to have to achieve and enable that conversation. And when you read that to physicians and administrators they go Yeah that’s exactly what it is and when you read that to patients they nod their head boy I’d love to have that kind of conversation with my physician
Bill Russell: 00:29:22 and I think we all we all get this right. So what we’re saying is we’re going to we’re enable you to be a better CFO with the data we’re going to enable you to be a better CEO with the data we’re going to enable you to be a better clinician with the data we’re even going to enable you to be a better patient or consumer of health with the data. It is the enabler for all these things. And that’s that’s the vision we’re sort of painting but how do you build the sense of urgency around hey we need to we need to start doing things now because things I mean generally what people do is they go in there and say hey things are dramatically changing in this space you feel it you feel with you know how you paint the picture of a Blockbuster Amazon or whatever and they’ll say look all of these things are changing. Health care is changing but you know my experience I came in from outside of health care. My experience was hey things are changing rapidly and and when I came into health care I said I think health care should change in three years. And the things I was saying six years ago that were going to change in three years haven’t changed yet.
Dale Sanders: 00:30:27 Yeah. Well yeah. And I said the same thing I wrote a paper for HIMMS 12 years ago that said analytics and decision supported were standing on the brink of a revolution that was 12 years ago and we still are a long way from what I described within that paper I thought we were within a year or two of it. You know it’s funny right Bill. Everyone has a sense of urgency. I mean all except the most passive and apathetic people in health care and there aren’t very many of those. Everybody has a sense of urgency but I think we’re trapped on a treadmill that makes it incredibly difficult. To implement the kinds of changes that we’d like to this system makes it very difficult. Which makes me sort of pessimistic about whether the existing system can change fast enough or whether it’s going to be an Amazon or a Google or an apple or an ABC consortium that pulls it off. You know there are always the old guard in every culture who feel like they’re either surrendering to the fact that we can’t change or they’re not convinced that we’re not good enough. And you know quite often as part of change management you just have to get the right people on the bus. So if there are key people that are holding back the organization’s progression towards being data driven in a smart way and being more digital. I think you have to get rid of those people quite frankly. Yeah. And you know I had lots and lots of turnover and it. It’s not something you go into with a light heart at all it’s brutal it’s horrible. But if you don’t get the right people on the bus then certainly your sense of urgency is going to suffer. Now whether good people on the bus can still operate within the constraints we have is still yet to be seen. Actually
Bill Russell: 00:32:29 yeah but building the coalition of the willing isn’t as hard as it once was because because we’ve had successes right. So people are out there going, I’ve seen what you know this health system I’ve seen what Asantes done, I’ve seen what Intermountains done I’ve seen what Geisingers done and they’re able to say hey why can’t we do those thing. And you find enough for those people that are saying we want to do those things that’s your coalition of the willing. Right there
Dale Sanders: 00:32:53 totally yet find the early. And when you go out and you’re building these relationships find the folks that are passionate about change that are passionate about doing it from a data perspective and that have influence in the organization. Look for those three qualities and then attach yourself as an I.T. person to those folks. You know I always reference back to Don Alipate head of cardiology in a cardiovascular clinical program at Intermountain. He was my first clinically influential anchor at Intermountain. When I came in I didn’t know anything about health care but I knew a lot about analytics and data warehousing and I just found him and his staff. They were data driven and they were research oriented. And I said you know I think I can make your life easier with the technology that I understand. And Don became the seed for influencing the rest of the company and clinicians when they saw all the cool things that he was doing around data. Then a lot of folks followed
Bill Russell: 00:33:51 yeah that is such a huge principle for CIOs. The CIO shouldn’t shouldn’t be influencing as much as it sort of coming alongside the influencers. So let’s talk about the next thing components of a healthy day to utilizers so you have some clients you can name them or not name them doesn’t really matter to me on the show. But you know components of healthy utilizers they have certain roles in place certain principles which they operate on and certain types of governance. I mean one of the reasons we’re having this conversation is I read a paper that you wrote three years ago on data governance and I thought Man I wish I had read this six years ago or maybe maybe it is that old and I just didn’t but. But yeah there are some principles that good healthy data utilizers sort of operate with in some roles they put in place. Why don’t you talk about some of those things.
Dale Sanders: 00:34:43 Okay well I will mention a couple of role model clients and of course I’ll offend a lot of by not mentioning them. Mission Healthcare for example. John Brown the CIO there in
Bill Russell: 00:35:00 North Carolina right.
Dale Sanders: 00:35:05 Yep. Alina the culture there at ALynna. Jonathan Shoemaker is the CIO up there. Those are two good examples. We have more than that but those are two good long term examples of clients who take advantage of data. They’re good utilizers and they they have a great combination of defining their data driven goals from the top down sort of the intermountain model frankly which is the board is going to approve some significant financial and clinical goals for the next year. And those goals have to be backed by by the evidence of data that we’ve achieved. So there’s always Intermountain, Geisinger’s the same way mission they have really good top down clinical and financial goals that then they line up analytics resources underneath those in the end as well as change agents underneath those. But they also leave room in the analytics capabilities and capacity of the organization to allow things to bubble up from the ground up recognizing that innovation the true innovation right around data happens at the edges of the organization. So to do that you have to have a good centralized governance but not too heavy can’t be too top down. And you also have to have a culture that is I would say more risk tolerant than the norm when it comes to data utilization. So you have to knock down barriers to data access you have to have approvals when there are needs for data you have to knock down those barriers for approval so that people feel empowered to get to the data as quickly as they can when they need it. And then you have to trust people with data. You have to push it out to the edges of the organization you have to give each person in the organization that wants data the data they need as opposed to some organizations who still have a very authoritarian governance structure a very authoritarian security environment and a very authoritarian utilization environment. And you know I still hear to this day we can’t trust this person with that data because they’ll misuse it. We can’t trust them with their own ignorance. Well what. That’s not the right attitude for heaven’s sakes give them the data then teach them how to use the data in a proper way. You know so those are some of the components the the the data governance structure at some of these high utilizers are is a very good combination of balance is what it boils down to right. I always paint the extremes of data governance as authoritarian or anarchy right. There is either very heavy handed governance which holds back progress or there’s no data governance which has all sorts of impacts on data quality utilization consistency and the best organizations hit the sweet spot in the middle and they prefer a data as an asset.
Bill Russell: 00:38:06 So it’s interesting. So you’re saying from a risk posture standpoint err on the side of more transparency allowing people to utilize the data. I mean you can pull it back later. I mean you’re not obviously you’re. You’re saying it’s somewhere in the middle it’s not hey we’re just gonna let everybody in here. But one of the things one of the things I found interesting was we had a an organization we were working with to share data with patients and through our digital tools and our internally they were concerned about sharing it externally because they were like hey we’re not sure how clean that data is. And the the partner said I’ll give them the data they’ll tell you how clean it exactly.
Dale Sanders: 00:38:53 Exactly. There’s no better way to clean up data than to expose its ugliness. And you’ve got to have a thick enough skin as a CIO and you know Chief analytics officer to recognize that data is never going to be perfect and as long as you release the data in a smart risk management way explaining the possibility of and the limitations of data quality the best way to improve data quality is to expose it. There’s no doubt about it but that takes a little bit of courage. Frankly I think a lot of organizations don’t have. They’re worried too much about their careers.
Bill Russell: 00:39:29 So good governance programs. You know I’m sure people are going to want more detail in order to be able to give them 45 minutes but when you think about the best governance you know what. What people are you know how did they meet. What do they discuss when they get together. Those kinds of things.
Dale Sanders: 00:39:49 Yeah. The best data governance function has the weight and authority of the Supreme Court. But not every case goes to the Supreme Court. So you want a top down endorsement about data as an asset data governance is important data quality is important literally from the CEO. So I helped I helped craft you know e-mails for CEOs to express their their commitment to data governance and data as an asset establish that tone and announce the data governance function as just another one of our governance bodies in the organization announced the membership which should include a good chunk of the “C” levels on the CEO’s staff. So the CMO needs to be there. The CFO needs to be there. The COO the CIO but then what I’ve always managed to do and what I advocate is a delegate a lot of the day to day decision making to the CIO. So he that’s that he or she becomes the what amounts to the day to day court system and when the CIO needs to elevate and resolve this situation around data governance and data strategies then you have the body the Supreme Court to appeal to and support them.
Bill Russell: 00:41:09 But are there some roles. So let’s let’s go back to your data and your analytics team. Are there some roles that are morphing or changing. So we had a we had a bunch of ETL people we had a bunch of analysts we had a bunch of people doing data cleansing. I mean are there are there roles that are changing with the technology as it’s as it’s evolving and are health system. You know it’s interesting. So I’ll just air my mistake. So I hire I advertise and hire three data scientists. They get snatched up in the organization. I go back six months later and they’re writing reports and I’m like, you know. And so that’s you know what. What are the what are the roles look like and are they changing I guess.
Dale Sanders: 00:41:56 Yeah they are changing and we’re starting to move up the stack of commoditization which is great. So in the old days. Right. A simple thing like a storage engineer for data warehouse data warehousing storage engineering was really complicated. You don’t need that anymore. Right the public cloud has made the infrastructure the storage server configuration especially in analytics and data warehousing essentially a commodity. So the old the old style data warehouse DVA systems engineer systems admin storage engineer those roles are starting to become commoditized replaced by by the public cloud. One of the things that I do see that isn’t moving fast enough is too many resources in the analytics teams spending their time on ETL. And that’s a combination of a couple of things right over time. The number of jobs the number of data objects that you have to manage in a data warehouse and a data platform just increases. So that’s just a necessity or not a necessity, but just sort of a reality. The other is you know you can file two extremes of data modeling that continue to drag things down enterprise data models have their own ETL problems late binding has its own data problems for ETL in-between. There ought to be some structures that should be more commoditized where you locked down the information models and data models around things that are persistent and comprehensively agreed upon. And by the way that’s the that’s the mantra I use is persistent and comprehensive agreement about data bindings and data logic ought to be locked down so that you’re not continuously maintaining that you can lock that down and then you can move your analytic staff UTL staff on to other things. So a big part of what I see with analytic teams right now they’re still spending too much time on ETL because of this extreme of either late binding data modeling or enterprise data models we need to start facilitating these intermediate data structures that offload some of that work. Yeah. You want me to comment on the data analyst versus data scientist thing there Bill.
Bill Russell: 00:44:14 Oh yeah absolutely. That would be Great.
Dale Sanders: 00:44:17 So what quite often happens right is because we’re inundated with these compulsory measures. You look around the organization for anybody that can write a report to fulfill the need. You just have to resist that. There’s a great book called essentialism everybody oughta read essentialism and and quite often it’s too easy to say yes to every report that comes along. And if you say yes as an analytics team. Every report requests that comes in and there’s no first order economic pain from the requestor of that report you’re going to spin yourself right into the ground. So you have to have a data governance structure and a leader of the analytics function that can knows when to say yes and when to say no that’s really important and as mundane as it sounds I see it all the time where analytics teams get consumed by individual requests funnelling in and their requests Que never ends. And so they never get a chance to do cool and innovative things. So you’ve got to be able to say no. You have to have the air cover from the governance committee’s to support No occasionally. And then you have to carve out a team in it. I think right now I would absolutely carve out a data science team that was focusing on what amounts to next generation analytics. You’ve got to have that core that addresses the internal and external KPIs that are required by compulsory measures right now that’s inescapable. Try to make it as efficient as you can with these intermediate data structures but then carve out a dedicated team and don’t let anyone touch that team without the significant oversight of the leader of the analytics function and put those data scientists on an innovative opportunities and ideas that we’ve never had a chance to address before.
Bill Russell: 00:46:04 Are you hiring those people are you potentially outsourcing or just out staffing that.
Dale Sanders: 00:46:11 Well I’ll tell you a great lesson that I learned actually. I think there’s I think the path from data analyst to data science is easier than it’s ever been because the models the algorithms and such are being commoditized by the open source community of data science Machinlery. So I went in to this role about three years ago thinking that I was going to have to hire you know million dollar a year data scientists to take advantage of this accept that we have and I’d probably have had that same mindset as a CIO if I were still practicing. But what I found is that over the last three years the rate of improvement for machine learning and data science has is far outpacing Moore’s Law. You know Moore’s Law , you know doubling in capacity every 18 months we’re doubling in machine learning capacity in like every three to four months. And that’s I mean that quite literally. So you don’t need the Ph.D. data scientists like you used to. What you need are solid feature engineering skills which data analysts are perfectly suited for. And then what you really need is knowledge around what amounts to experimental design in data science so that you make sure that what you’re producing from data science makes sense operationally and clinically. So it’s the feature engineering and the data analysis skills of traditional data analysts. They’ve got a great career path going forward. The models the algorithm and things are easily accessible to the citizen data scientist. I can be a data scientist today in ways they never could in the past. And then if you add that experimental design to your skillset knowing how to take advantage of and thoughtfully apply machine learning and AI then you’re you’ve got a really bright future. So I don’t think I don’t think organizations have to hire outside anymore for the data science skills I think they ought to build the data analyst skills or the data analyst from data analyst in data scientist.
Bill Russell: 00:48:13 So operationalizing the program I had two things under that handling the backlog and quick wins. I do want to go into quick wins that make the most sense for a new program. But before we get there handling the backlog so let’s assume I have requests that are you know 9 months nine months kind of backlog. Do you essentially put a stake in the ground and say All right here’s what we’re do we’re to take all of these requests back through governance or… You would, Okay.
Dale Sanders: 00:48:45 Absolutely. Yeah. And you know you’ve got to essentially set aside the economics to support the request and say Look we’re going to dedicate and this is a number I quite often advocate 60 percent of the centrally funded data analysts resources will be assigned to things that we decide as a governance structure, top down. These are important initiatives departments projects that we’re going to support from the top down. Then I’m going to carve out 40 percent of my team to handle opportunities that pop up that we didn’t foresee from the top down as a governance body. So I’m a strong advocate of that 60 40 split and essentially keeping those two teams almost separate. Because if you try to split a person 60 40 towards what amounts to compulsory measures and new opportunities they always get overtaken by the compulsory measures.
Bill Russell: 00:49:40 And we all do it. We all sit there and go Sixty percent of your time over here 40 percent and you know what happens is whichever one they like the most that gets 100 percent of their time.
Dale Sanders: 00:49:49 Yep yep. So I’m an advocate of creating two teams within the analytics department one is focused on what amounts to the automation of and the handsfree completion of Top-Down compulsory measures and things that the board is interested in. And then 40 percent of the team is dedicated to opportunities that are going to pop up from the grassroots.
Bill Russell: 00:50:11 So to reinforce the value of this program we need some quick wins. What are some places you’ve seen quick wins quick being defined as within the first three to six months.
Dale Sanders: 00:50:25 Oh gosh. There’s the usual suspects of clinical and financial value that we see all the time in orthopedics. Cost control unnecessary and sort of low value care. Sepsis Qadi clapsy. It’s usually pretty easy to rally people around those kinds of things. So those are some off the top of my head Bill. What I also try to do is is take care of and kind of a bright and shiny object in the quick win. So there’s the commodity kind of analytics around cost and quality that you know there’s roughly 10 to 15 things that make up 80 percent of the opportunity in healthcare. So take care of those things. But the the other side is you’ve got to give people something kind of cool and interesting that keeps them attracted to the data digital strategy. So I would look around for a researcher or maybe an opportunity even in finance. Right. We’ve got a predictive model and that helps identify propensity to pay and get patients lined up with better financial arrangements ahead of time before they get into trouble for example. And those those bright and shiny objects are important they have to have real value but they also have to show the value of what you could do more broadly. So taking care of the commodity stuff is important but you also have to have a little bit of a marketing and a bright and shiny project or two that keeps people motivated.
Bill Russell: 00:52:12 Yeah. And that bright shiny object for us was we created a sort of a front door to our data warehouse that the financial people could play with to look at value based care and they were able to move different levers and see what the impact would be. And my gosh. I mean they could play with that thing for for days but it was it was solving a very real problem which was when and how and what impact is it going to have to go from fee for service to value based care and how do we make this transition so very cool. Yeah it’s very interesting. You’ve already talked a lot about the technology is there anything else you’d say on the technology.
Dale Sanders: 00:52:52 Well the you know the big trend in the technology realm right now is these hybrid architectures. Between sequal no sequel. And all of the cloud vendors Microsoft Google Amazon all offer some version of that.
Bill Russell: 00:53:05 as a CIO you’re not saying this I’m saying this I can’t imagine building my own EDW on prem anymore. That does, the concept, like I don’t understand. I would not build up my own data center anymore. If somebody came to me and said Hey let’s build a five million dollar data center for our health system I would say that’s insane. But I almost feel the same way about EDW at this point it’s like no let’s look I mean there’s this many layers and if we go to a cloud vendor we’re going to cut off you know six to the bottom half of the layer that we’re not going to even have to do. And why would we do it. It’s already out there and it’s already efficient. Is that is that what you’re seeing or is that what you feel at this point?
Dale Sanders: 00:53:48 Well, you know we’re in a weird spot right now right where the EHR vendors are offering their analytics and data warehousing strategy. They’re nascent they’re just emerging frankly, they haven’t been around for very long. You’ve got companies like health catalysts that are deeper. We’ve been around for a longer time. We’re not perfect either. You’ve got the pure technology players like Cloudera. Mongo. And then what the public cloud Amazon Azure and Google offer. And so there is this weird churn going on right now in the market where if you compared health Catalyst for example against a pure technology player you’d go wow those tech guys are way more sophisticated than health catalyst, so I’m going to go that direction. Well then you get over there to the public cloud vendors and you go they don’t know anything about health care data and they don’t have any tools that are specific to health care data. So I’m going to have to build all those data models all those APIs all those applications myself. That’s not very appealing. So then you look at the EHR vendors in the EHR vendors are new to this they offer some tech that’s always centric around the data that they have. They don’t have the experience working in a disparate heterogeneous environment, they don’t have the tools for that. So it’s a if I were a CIO today it would be pretty confusing I mean of course it sounds self-serving I’m I’m you know with health catalyst I’m trying to be that clear answer. But the reality is there’s a convenience in the attractiveness of doing business with your core vendor. There’s the bright and shiny appeal of doing business with Amazon Google and azure and then there’s this sort of pragmatic list to business with health Catalyst. And those are the three. Those are the three options that I see in the industry right now and I think it’s still working itself out you know. I think it’s still yet to be determined which of us is going to survive as the best option.
Bill Russell: 00:55:47 So since I’ve taken up more of your time and I’m already three minutes over the end of our show here I am going to give you a chance to answer. So but doesn’t health Catalyst give me the best of all worlds? Aren’t you built on top of the cloud platforms cloud vendors that are already out there and you give me the ability to bring in all that disparate data so I can I can actually plug into. I would assume I can plug into some of those cloud AI and machine learning things that are out there. Plus you’re you’re you are sucking in a lot of that EHR data as well aren’t you like sort of that middle ground that sort of gives me the best of the cloud vendor and a little. I mean I understand that the EHR vendors are going to be able to do that. That regulatory reporting and the clinical operations probably better than anybody because they could just make it right in there. But but don’t give that sort of middle ground of the best of both worlds.
Dale Sanders: 00:56:45 Well I appreciate you saying that Bill I mean that’s what we’re trying to do. And I you know I’m cautious about using your podcast as a sales channel. But that essentially that’s I’m trying to provide to CIOs and to the healthcare system. The what I always wanted as a CIO and what I think you know we need in the industry is just a commercial version of what we’ve done at Intermountain and northwestern and other places so you know if I were still a CIO I would definitely be looking at health catalyst. I would not be looking at building my own I wouldn’t be looking as the pure tech players in Silicon Valley building my own there’s no way I would do that. I’d be looking carefully at EHR vendors and I what I would probably do is say I’m going to use EHR vendors for part of my analytics and I’m going to use health catalyst for the other part. And I think that’s the best option in the industry right now is that combination
Bill Russell: 00:57:43 and just so I don’t get my other friends mad at me. The guys at heart have a similar platform to what you’re trying to do. Yeah. And the people at Minerva have a similar platform as well. But essentially it’s based on that concept and I think it’s the right concept which is to say you know you’re going to have multiple EMRs. You’re going to have multiple well you’re going to have. I mean we had 1800 applications. So how are you going to bring all that data together and how am I going to plug into how am I going to plug into the machine learning and AI that is going to be developed at the level it’s going to be developed the best is at that Microsoft cloud level and at that Amazon cloud level Google cloud level. And if I can’t figure out a way to plug into theirs and rely on the EHR vendor to build that out for me that that level of of I’m not sure that makes sense. So yes.
Dale Sanders: 00:58:40 Yeah I would add I’ll add one more comment on this it’s not as cheap going to the public cloud as I think all of us former CEOs thought it would be. You know it’s still pretty darn expensive. It’s actually when you look at the costs of our private cloud that we have offered and granted you know we couldn’t offer the flexibility in the infrastructure that Azure Google Amazon offer but it’s still pretty expensive to go to the public cloud. So I hope that I hope that we see those prices come down from the public cloud vendors. I’m sure we will.
Bill Russell: 00:59:15 Well yeah. And yeah, I think about 10 episodes ago I met with Robert Rice and we talked about the five different cloud architectures that you need to look at. And that’s one of the ways that we are able to manage costs where we were at. So ongoing care and feeding, gosh we’re seven minutes I’m probably gonna split this in the two episodes now. But, ongoing care and feeding. Is there anything you do from a culture continuous improvement. So we’ve addressed but you know we’ve sort of stood this up. We put in governance we’ve hired the right people we put the right technology in place. And it sounds like you know from a leadership standpoint they set the right metrics and they. So maybe maybe we’re I’ll take this as you recommend a book, I’ll recommend a book. So the book I just finished reading is measure what matters. Yeah. Which which a friend gave me. And it’s a phenomenal book and I love that the phrase I get out of this is as measured by. So when somebody says hey this is what we want to do and that natural follow up question is as measured by what what’s the measurement. And it’s really I think you talked about this earlier. You know if in health care we would stop measuring things that don’t impact I think we’d be better off. What what reading would you would you recommend.
Dale Sanders: 01:00:36 There’s two books. One is it’s actually written by thank you for the book you gave me by the way. It’s written by the same author Stanley McChrystal and it’s team of teams. Highly recommend that book if you if you peel back the lines underneath the lines of that book it’s really about information sharing. So it’s it’s a summary of what McChrystal did as the Joint Operations commander in Afghanistan and Iraq during the first phase of conflict there when we were you know the U.S. was being outmaneuvered by al Qaeda in Iraq. And despite you know unlimited financial resources and military capability we were being outmaneuvered by this network of terrorists. And he talks about what he did to transform the way the Pentagon and the way his teams operated in that theater. But it’s all it fundamentally boils down to two things knocking down organizational barriers in pursuit and cultural barriers in pursuit of a common goal and knocking down information sharing barriers. It’s really, it really boils down to those two things is what he did. Information sharing became critical to the success of the first phase of that conflict. So that’s one book I highly recommend. The other is a book by an author her name is Tali Sharot. Last name is S H A R O T. Tali Sharot. She’s a Ph.D.. I think she’s a cognitive scientist. She’s at the University of London and more and more you know as we commodititize the technology of data I’m advocating you have to be more in tune with neuro psychology of data and the behavioral side of data going back to those three attributes I describe you know about common overlapping values and common empathy and the unique skills that you bring to the table when you’re engaging somebody for change. The book that she wrote recently that I really enjoyed was called the influential mind and the subtitle is what the brain reveals about our power to change others and it’s if you combine the right technology with the cognitive science described in that book. That’s going to be the key to success with a data driven strategy in health care and engaging people in a different human way than they’ve been engaged traditionally in the past around data. You know I’ve never in my 35 career years in this career of data going all the way back to my military in the NSA days I’ve never seen anything like what I see today which is the more you try to push facts on people to change their opinion or behavior the more entrenched they become in that opinion behavior. It’s not effecting change. So it’s tribalism it’s fake news it’s you know the climate change deniers all of that. The more you try to push change through data on people the less likely they are to change. So you have to engage your data strategy from a different neuro-psychology of data than ever before. I’ve never been, I’ve never seen anything like this in my 35 year career.
Bill Russell: 01:03:59 That’s interesting
Dale Sanders: 01:04:00 yeah.
Bill Russell: 01:04:03 Well awesome, Dale thank you for coming on the show. Best way for people to follow you. I assume is the health catalyst blog. I see your articles out there. I also see you on LinkedIn is there anywhere else people can follow you?
Dale Sanders: 01:04:17 I tweet once in a while Bill and so those are probably the best ways I guess.
Bill Russell: 01:04:23 Those are the best three. Awesome. You can follow me on Twitter @thepatientsCIO the show @thisweekinHIT or website thisweekinhealthit.com and shortcut to the YouTube channel is ThisweekinhealthIT.com/video. Please tune in next couple of weeks for our best of episodes. Everybody have a great holiday and please come back every Friday for more news information and commentary from industry influencers.
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