This transcription is provided by artificial intelligence. We believe in technology but understand that even the smartest robots can sometimes get speech recognition wrong.

 Welcome to this week in Health IT News, where we take a look at the news that will impact health it. This is another field report where we talk to leaders from health systems on the front lines. My name is Bill Russell Healthcare cio, coach and creator of this week in Health. It a set of podcast videos and collaboration events dedicated to developing the next generation of health leaders.

As you know, we've been producing a lot of shows over the last three weeks and series. Healthcare has stepped up to sponsor and support this week in Health It, and I want to thank them for, uh, giving this the opportunity to, to capture and share the experience, stories, and wisdom of the industry during this crisis.

If your system would like to participate in the field reports, it's really easy. Just shoot me an email at Bill at this week, health it.com. Now on to today's show. Today's conversation is with Lee Pierce, healthcare Chief Data Officer, formerly with Intermountain. Now with Sirius Healthcare. Good afternoon, Lee, and welcome to.

Uh, thank you Bill. Appreciate the opportunity. We are all remote working and, uh, and I really do appreciate you spending some time. I know you've been really busy. Uh, so let's just get right to it. How, you know, how are you doing? What kind of things have you been working on? You know, uh, doing pretty well.

Um, it's been an extra busy time. It seems like, uh, more work from home has led to. Uh, the schedule filling up with, uh, conference calls and, and, uh, events that, uh, that we're, uh, trying to support our customers with. And so, um, it's been good. Um, my family and, uh, uh, is all healthy and have 'em all here together.

And, uh, so from a personal perspective, uh, things are going well, enjoying time with the family, but, uh, professionally, um, it's as busy as ever, I'd say. Yeah, it's, it's been interesting to be this with my family for this long, uh, because I'm sure like you, we called around the country. We fly around a lot. We talk to lot of people.

And, uh, I, I'm not sure I've spent this much dedicated time, uh, but we're gonna talk, we're.

Data and analytics teams and efforts, uh, supporting health systems response to covid-19. Yeah. Data, data and analytics teams that, uh, particularly provider organizations, uh, they are being thrown into the fire it seems like, uh, ready or not. Um, you know, it's, it's been interesting that they have, um. Many of the leaders of these data and analytic teams have been pulled into the response teams for covid 19, uh, organizations recognizing the importance of good data and good analysis to actually help them in their response.

And so I think that's, you know, that's one good thing that I've seen is that health systems really. Um, are, are recognizing this as, as an opportunity to, um, you know, to double down on the value of data and analytics, even though it's focused right now on the Covid 19 response. Uh, teams are having to come together.

We have to identify leaders and specifically what decisions they're trying to make and what data can support those decisions. And so, um, they've, they're really very busy, uh, with their. Um, with their responses, uh, with various, I I've probably spoken with a dozen different health systems, uh, data and analytic leaders over the last couple of weeks.

Uh, friends, uh, clients of ours, and, uh, everybody is, um. All hands on. All hands on deck, for sure. Yeah. What kind, what kind of analytics are they being asked for? I assume they're creating dashboards and whatnot, but what, what, what specifically are they being asked for? Yeah, you know what's, what's interesting is, you know, I think, uh.

The dashboards, it's not as much about the dashboards as it is about getting the data into the hands of those that are asking for it. It's not as much about the, the visualization right now as it is focusing on, on, on the data and the accuracy of that data. And so, you know, it's, it's been around a couple of key themes.

You know, first and foremost I would say is the patient, um, identification. Just simply knowing who the patients are, you know, what conditions that they present with in addition to, you know, identifying them as, as, uh, positive for C Ovid 19. Um, you know, basic statistics have actually been in quite a challenge for, uh, for most health systems, both large and small.

And some of that is. Um, just because they're either their data capabilities, um, are not, um, you know, to the level that they need to be, or even if they are, it's more, uh, retrospective analysis and not as much real time analysis. And so patient identification is kind of the first, you know, questions it seems that have come to these data and analytic teams.

The second is questions related to workforce. Um, you know. Who's re working remotely? You know, how many people at any given time, you know, we've been looking at, at system data like, uh, for VPN connections at one time, you know, a number of laptops that they have to even have people work at home. Uh, we're helping a children's hospital with this very issue right now, ACIO that is just, you know, struggling to try and understand how, um.

How to keep track of and better enable from a technology perspective, the workers that need to work from home to keep the lights on for their healthcare system and really requires a lot of. You know, HR data, asset tracking, data system monitoring data. So that's been quite a, uh, you know, uh, a second theme that I've seen around analytics.

Um, another one is resource, uh, utilization. And this is both personnel and equipment. Um, you know, if it be ventilators, we've heard a lot about in the news, every health system is trying to figure out just the number of ventilators that they have. You know, how many, how many. Uh, clinical professionals do they have of various, various kinds, you know, so staffing has been.

Um, critical. Um, you know, tying that to hospital census has become really important and, um, and even the, the personal protective equipment, uh, real time data around that has, has become important. Um, you know, ICU bed utilization, of course, we all know those, you know, we've seen those counts of ICU beds and potential patient volumes, but within a given facility.

Within any given health system by hospital, having those counts, you know, and then even historically how you've counted 'em, which, uh, which beds can actually be modified in a surgical unit to an ICU bed. And, and so just definitions around all of that have been, have been challenging. Uh, last one I'll mention, bill is just

Then the financial and operational impact. I think that's a whole category of analytics that, um, that health systems are being hit with. So, uh, you know, canceling of elective inductions, just knowing what the impact of that is, uh, how many cancellations, being able to forecast, you know, potential impact. Um, on how many of those might be rescheduled in the future.

You know, just lots of analytics coming from operations. Also, analytic requests coming from operations, uh, personnel within the health systems. So those are, those are some of the categories that I've been, uh, that I've been seeing requests come from, which, which health systems are best able to respond. I.

Um, but you know, we, we progressed and we did master data management. We did a bunch of other things. We put in, uh, some other tools. Uh mm-Hmm. , what have you found? I mean, are, are, are there some organizations that are just stifled by the complexity of healthcare data and while others, uh, maybe have prepared somewhat their data environment for this kind of agility?

Yeah. You know, it's, it's quite, quite a mix. Uh, those that are best prepared certainly are those that have. Already invested early on in their, uh, in their data and analytics capabilities. Um, do they have, um, a data warehouse where they have most of their, you know, as you described, the, uh, data sources ingested and organized and, and available to be able to draw from, uh, to do the various analysis.

So that's one aspect of it. But, but what I've heard is. Well, that's good for some questions, but what most are lacking is then the more real-time data that's, uh, that's necessary to really be able to respond to it. We need to know an hour by hour, you know, sense census and count of, of, of positive C 19 patients, of, of, uh, staff currently.

So, so it's more the. The more timely analytics that, uh, that has been a challenge even for those organizations that have done a lot with their retrospective analytic capabilities. Um, yeah, so it's, it's quite a mix, but, uh, but I. Um, those are the ones that are, that are certainly better prepared than, than others.

You know, it's interesting. So the, the operational, the, the clinical operational reports typically come outta the ERI mean, we're even, we're putting the lab data in there, we're putting all that. So that's gonna be the source for a lot of that clinical data. Mm-Hmm. , I.

I can't imagine moving this fast with trying to projections around the impact around the financials. Now we had, again, we were, uh, what about six organization? So we had a team just dedicated to financials. Um, but even, even then, I mean, the complexity of building out those models that you're talking about, okay, we just lost all elective surgeries.

Let's, let's build out those models. That would be pretty, pretty challenging to do, uh, quickly, wouldn't it? Yeah. Um, absolutely. And again, whatever work has been done to understand what is available. Because each question, you know, the, the, the EMR certainly is going to be a key source for, you know, for some of the analysis.

But what we found is there's a lot of, you know, questions that also touch supply chain data, that touch HR data that, that want to take into account external data sources. Um, so that they can, they can be looking at data that is, uh, you know, for their. Other health systems within the, their, their region or, or state.

And so yes, it is, it, it is a real challenge. Um, but, but it's amazing how much can actually be done with the data that's available as long as you have the right, you know, the right people. Um, that you're learning from each other. Um, there's been a lot of collaboration that's been happening across health systems in, in sharing what the challenges they're having.

You know, just to give you an example, one of the key, one of the key issues early on was, okay, how do we actually identify positive, positive, uh, covid 19 patients, the, you know, the system of, of. Lab tests. The codes that are used as, as you may remember from your day as ACIO, are called loin codes. Well, the Loit codes didn't even exist on the loin.org website, let alone in each of the health systems.

For the specific tests and then the results associated with C Ovid 19. So even if you go out to Loit today, which is again the standard data that's used for many of these, uh, you know, many of the labs that are returning results and being able to then order tests. They, they just, they're, they're in a pre-release status even today.

And so just being able to share amongst health systems what, um, you know, just that initial list of loin codes that somebody had to, you know, go find and make available and then work with their. Both on the technology side as well as the clinical laboratory side to be able to coordinate those efforts to be able to then just a fundamental data issue that, um, that every health system has had to go through to then be able to respond and do the analysis and the data polls necessary.

Um, so, so that's been really interesting to watch just something so basic that we hadn't even thought about how quickly we can turn those things around, let alone everything that you talked about, which is then, you know, all of the other sources of data. So yes, it's a challenge. But I have seen more collaboration amongst TE systems around this topic than I have maybe ever, which, um, may be one good thing coming outta this, if, if any.

Yeah, it's, you know, I was wondering, so you're describing some of the challenges. Is it more of a challenge that some, uh, aspects of the organization or even organizations outside of, uh, your organization? You talked about the coach not even being available. Um, is it, is that more of a problem that some aspects of healthcare doesn't move fast enough or, I, I remember the data definitions being so important that we would spend literally meetings, hours and days talking about the definitions of, of data.

That, or is that more challenging because we're, we're using data so fast. Potentially the definition's getting lost in the process. Um, I, I would say Abso absolutely both. Um, and, and actually one is a problem. So, so the broader issue that is, you know, a theme that I've been seeing is just. The, I refer to them as data governance issues.

So as you, as you mentioned, definitions, um, and, and, and metadata for these data elements. So even, even around, you know, the tests and the loin codes and which codes are going to be included with what results. In order to say that it's a positive, you know, a patient positive for. Data governance, um, I believe is, is coming to the forefront as you have these, these, um, response teams as you have these central teams that have been brought together to respond to the covid, um, you know, situation that every health system's in.

Basic data governance principles and practices around data quality, data definitions, data stewardship around who can even make decisions on how to calculate these things. It's, it, it's almost like, uh, you know, one organization I talked to actually said they even stood up what they're calling an emergency data governance structure and process IT governance team just in.

Just to get the definitions around count of patients Correct. So that they can, and the quality checks and processes as well as then who can actually release the data to external entities. You know, the count it comes from a health system. So, you know, I, it's a, it's a real challenge that, um, that most healthcare organizations don't have.

Um. You know, nailed down as well as they would like. And, and I think it really, I, I guess I see it as a half glass full. I see it as an opportunity to come out of this and that health systems are gonna realize, holy cow, we did, we were not prepared. And something so simple as how we're gonna get a count of patients that have a certain

Condition. Why is that so difficult? And how are we gonna do this better in the future? Do we have a glossary that we can go to and look up certain definitions that we have agreed upon? How are we gonna agree on data quality checks? All of those kinds of things. So, answer your question, I think, I really think it's both, but you've hit on a, uh, topic that I'm very passionate about, which is, you know, data governance where I roll those, um, you know, those issues into.

It's interesting, you, you cited a lot of lessons learned there and that's, that, that's fantastic. I mean, I, I think one of the things maybe I, I didn't really imagine when I was ACIO was . We had a data governance process and it moved very slow and I never imagined it in the time of a, a pandemic or a crisis where it.

At like 10 x the speed that it was currently moving. And you, you talked about that organization standing up an emergency data governance group and uh, and that thought had never really, that's an interesting lesson or that's an interesting, um, model, uh, that they've stood up because the data is so.

Was like, look, we, we've gotta move. Are there other things that people are doing to move faster?

Yeah. Good. Good question. Um. Yeah. You know, one, I normally send all the questions over ahead of time and give you time to think about it, but, but we're, we're doing a lot of this stuff on the fly. I'm, I'm just curious. I mean, because it's, it is so interesting to me that, um, a lot of the parts of it have had to move at huge speed, increase increases.

You know, telehealth obviously work from home, obviously, and now we're sort of exploring how does data move faster? How does, how does data governance. So, so one thing that I've seen from healthcare organizations, uh, in, in order to really move, um, at the speed that the, that the organization needs in response to covid 19 is analytics professionals are actually being embedded right?

Within the covid response teams and command centers. Yeah. So because, because this is something that we don't understand. You know, there's so much of, of this, of this virus that we don't understand and so many factors that are. You, you have to be able to ask a question, have almost a, a, you know, a first response, you know, a, a very fast response to provide data back.

That having, you know, your analytic professionals actually within those teams becomes really critical and that allows then immediately the discussions around. Okay, well how are we gonna, where are we gonna pull this from? How are we gonna calculate this? Who are the leaders, you know, that, that have accountability for these various things?

So I've also seen to speed things up, it's breaking down barriers that naturally exist in health systems, maybe quicker than, uh, than has happened previously. So there may be barriers to access to data just fundamentally within a health system around supply chain data or HR data. Well, this is a shared emergency that we as an organization have to respond to.

We need the data. And, and so, so I have seen some very positive movement to say, this is a shared problem. These are shared data assets. Let's, let's go after this. Let's define things, let's move forward because we really don't have a choice. And, um, and some days I wish that, uh, the day-to-day means of doing data management and analytics and data governance work.

Um, you know, that we, we'd have some of that same passion, some of that same drive that, that I'm seeing right now for just, you know, good quality improvements and application of data to other problems that we know can add tremendous values. So, yep. Um, yeah. Those are my thoughts on that. You know what, Lee, I've already gone over my time.

I'll close this, which I think is maybe an obvious question, but I wanna discuss, which is, do you think this gonna increase of data science, not analytics per se, but data science and healthcare organizations? Um, uh, uh, I think without question, the answer is yes. Um, for the reasons that we have, you know, some that we've talked about, but, um.

I, I think coming out of this, there is going to be real discussions around how can we improve our capabilities so that we're better ready for, uh, uh, providing data. Uh. In times of need like this in the future. So I, I sure hope that it, uh, that it prompts maybe some organizations that have not yet put, you know, a lot of time and effort into, uh, maturing their data and analytics capabilities or those that have, you know, to kick it into gear and take it to the next level because, you know, I, I, data truly is an asset in this situation and fighting this, uh, fighting this pandemic and helping all of us through it.

And, you know, we, we need to be prepared for whatever comes next. Yeah. Um, so I, I really believe that adoption will increase, as, you know, as a result of this. Well, Lee, thank you very much for taking some time. I, I appreciate it and I appreciate all the work that you're doing for, you know, the various clients that you're working with.

Thank you, bill, for your time. Really appreciate the opportunity to, to talk to you about some of this. Thanks for all you do. Thanks. Take care. That's all for this show. Special thanks to our channel sponsors VMware Starbridge Advisors, Galen Healthcare Health lyrics and pro talent advisors for choosing to invest in developing the next generation of health leaders.

If you wanna support the fastest growing podcast in the health IT space, the best way to do that is to share it with a peer. Send an email dmm, whatever you do. You can also follow us on social media. Uh, you know, subscribe to our YouTube channel. There's a lot of different ways you can support us, but sharing it with peers is the best.

Uh, please chip back often as we'll be dropping many more shows, uh, until we flatten the curve across the country. Thanks for listening. That's all for now.