April 9, 2020

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April 9, 2020 We got a chance to catch up and talk data and analytics with Lee Pierce, Healthcare Chief Data Officer with Sirius Healthcare for today’s field report. In our conversation, we talk about the asset data analytics has been to COVID response teams during the crisis as well as some challenges and proud moments teams are experiencing as they scale. We cover the different kinds of data requests that Lee is seeing and the hard task it has been to find ways of firstly digging into different data pools with sometimes limited technology and secondly, merging those insights. Unforeseen collaboration crops up as a major theme for today, and Lee talks about the pace at which significant strides have been made to solve data problems thanks to systems working together. One of the great successes we hear about is the decision to include data analysts as members of response teams. He shares a few other specific problems that have been skillfully resolved, such as a testing issue that involved difficulty accessing LOINC codes. Some of the challenges we talk about are the issues that even the most prepared systems are having accessing real-time data, and how data governance and standardizing definitions are slowing down processes. Catch us today as we get another glimpse at the situation on the ground with Lee Pierce.  

Key Points From This Episode:

  • The important role data and analytics teams are playing in COVID response teams.
  • What types of information the data teams are providing health systems with.
  • Which health systems are coping with the complex task of searching their data.
  • One type of data even the most prepared systems are having trouble monitoring.
  • The difficulty of gathering from multiple unrelated pools of financial, health, and HR data.
  • How key the practice of collaboration has been for accurately searching data.
  • A challenging testing problem involving LOINC codes that was solved by data teams.
  • Challenges around finding standardized definitions of data types that are slowing processes.
  • Why an emergency data governance organization was set up.
  • Otherwise insurmountable barriers to data sharing between providers that are open now.
  • Lee’s thoughts that analytics will be more a part of future health systems after this.

Analytics at the Pace of Crisis with Lee Pierce of Sirius Healthcare

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Analytics at the Pace of Crisis with Lee Pierce of Sirius Healthcare

Episode 222 – Transcript – April 9, 2020:

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

[0:00:04.5] BR: Welcome to This Week in Health IT news where we look at the news which will impact health IT. This is another field report where we talk to leaders from health systems on the frontlines. 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 Sirius Healthcare has stepped up to sponsor and support This Week in Health IT and I want to thank them for giving us the opportunity 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 [email protected] Now, on to today’s show.


[0:00:55.3] BR: Today’s conversation is with Lee Pierce Healthcare Chief Data Officer formally with Intermountain and now with Sirius Healthcare, good afternoon Lee and welcome to the show.


[0:01:05.3] LP: Thank you Bill, I appreciate the opportunity.


[0:01:07.4] BR: We are all remote working and I really do appreciate you spending some time. I know you’ve been really busy. Let’s just get right to it. How are you doing, what kind of things have you been working on?


[0:01:20.3] LP: You know, doing pretty well. It’s been an extra busy time but seems like more work from home has led to the schedule filling up with conference calls and events that we’re trying to support our customers with and so it’s been good. My family is all healthy and have them all here together and so from a personal perspective, things are going well. Enjoying time with the family but professionally, it’s as busy as ever, I’d say.


[0:01:59.4] BR: Yeah, it’s been interesting to be this connected with my family for this long. I’m sure like you, we get called around the country, we fly around a lot, we talk to a lot of people. I’m not sure I’ve spent this much dedicated time, but we’re going to talk data and analytics. What have you seen, how are data analytics teams and efforts supporting health systems’ response to go with COVID-19.


[0:02:28.9] LP: Yeah, data and analytics teams, particularly provider organizations, they are being thrown into the fire it seems like, ready or not. You know, it’s been interesting that they have – many of the leaders of these data and analytic teams have been pulled into the response teams for COVID-19.


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 are recognizing this as an opportunity to double down on the value of data and analytics. Even though it’s focused right now on the COVID-19 response, 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. 


They’re really very busy with their responses, with various – I’ve probably spoken with a dozen different health systems, data and analytic leaders over the last couple of weeks. Friends, clients of ours and everybody is all hands on deck for sure.


[0:03:56.6] BR: Yeah, what kind of analytics are they being asked for? I assume they’re creating dashboards and what not but what specifically are they being asked for?


[0:04:05.5] LP: Yeah, you know what’s interesting is, you know, I think 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 visualization right now as it is focussing on the data and the accuracy of that data.


You know, it’s been around a couple of key themes. You know, first and foremost, I would say is the patient identification. Just simply knowing who the patients are, you know, what conditions that they present with in addition to identifying them as positive for COVID-19. You know, basic statistics have actually been quite a challenge for most health systems, both large and small, and some of that is just because they’re either their beta capabilities are not – you know, to the level that they need to be or even if they are, it’s more retrospective analysis and not as much real time analysis and so, patient identification is kind of the first questions that seems that have come to these data and analytic teams.


The second is questions related to work force. You know, who is working remotely, how many people at any given time, we’ve been looking at system data like for VPN connections at one time, you know, a number of laptops that they have to even have people work at home, we’re helping a children’s hospital with this very issue right now.


A CIO that is just, you know, struggling to try and understand 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. 


Really requires a lot of HR data, asset tracking data, system monitoring data, that’s been quite a second them that I’ve seen around analytics. Another one’s resource utilization and this is both personnel and equipment. 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.


How many clinical professionals do they have of various kinds, you know, staffing has been critical. You know, tugging that to hospital census has become really important and even the personal protective equipment. Real time data around that has become important.


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 with any given facility, within a given health system by hospital, having those counts, you know, and then even historically how you’ve counted on which beds can actually be modified and in a searchable unit too and ICU bed and so just definitions around all of that had been challenging.


Last one I’ll mention Bill is just, then the financial and operational impact. I think that’s a whole category of analytics that health systems are being hit with. You know, canceling of elective inductions just knowing what the impact of that is, how many cancellations, being able to forecast and whole potential impact on how many of those might be rescheduled in the future. You know, just lots of analytics coming from operations also, analytic request coming from operations personnel within the health systems. Those are some of the categories that I’ve been seing request come from.


[0:08:13.1] BR: Which health systems are best able to respond? I assume, when I became a CIO back in 2011, I had a spaghetti factory of data, right? Now we add a lot of – you know, we have a lot of connectors going in and we had really smart SQL ingestion people and they were moving the data around and they were massaging the data to make it ready for report. I would imagine if I was back in that timeframe, trying to respond to this would be almost impossible but you know, we progressed and we did [inaudible 0:08:48] demand and we did a bunch of other things.


We put in some other tools. What have you found? I mean, are there some organizations that are just stifled by the complexity of healthcare data and while others maybe have prepared somewhat, their data environment for this kind of agility?


[0:09:05.6] LP: Yeah, you know, it’s quite a mix. Those that are best prepared, certainly are those that have already invested early on in their data and analytics capabilities. Do they have a data warehouse where they have the most of their as you described the data sources ingested and organized and available to be able to draw from – to do the various analysis.


That’s one aspect of it 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 necessary to really be able to respond to it. We need to know an hour by hour, you know, census and count of positive COVID-19 patients of staff currently.


It’s more the more timely analytics that has been a challenge, even for those organizations that have done a lot with their retrospective analytic capabilities. Yeah, it’s quite a mix but those are the ones that are certainly better prepared than others.


[0:10:23.3] BR: You know, it’s interesting. The operational, the clinical operational reports typically come out of the EHR. We’re putting the lab data in there. We’re putting all that. That’s going to be the source for a lot of that clinical data, I would think. The financial data to be honest with you, I can’t imagine moving this fast with trying to do the projections around the impact, around the financials.


Now, we had – where were we? About six and a half, seven-billion-dollar organization. We had a team just dedicated to financials. But even then, the complexity of building out those models that you’re talking about, “Okay, we just lost all active surgeries. Let’s build out those models.” That would be pretty challenging to do quickly, wouldn’t it?


[0:11:14.3] LP: Yeah, absolutely. Again, whatever work has been done to understand what is available – because each question, you know, the EMR certainly is going to be a key source for some of the analysis but what we found is there’s a lot of questions that also touch supply chain data. The touch HR data that want to take into an account external data sources. So that they can be looking at data that is all for their other health systems within the region or state.


Yes, it is a real challenge but it’s amazing how much can actually be done with the data that’s available as long as you have the right people that you are learning from each other. There has been a lot of collaboration that’s been happening across health systems in sharing what the challenges they’re having. You know just to give you an example, one of the key issues early on was, “Okay, how do we actually identify a positive COVID-19 patients?”


The system of lab tests, the codes that are used as you remember from your days as CIO are called LOINC codes. Well the LOINC codes didn’t even exists on the loinc.org website, let alone in each of the health systems for this specific tests and then the results associated with COVID-19. So even if you go out to LOINC today, which is again the standard data that’s used for many of these, you know many of the labs that are returning results and being to then order tests. 


They’re in a pre-release status even today and so just being able to share amongst health systems what – you know just that initial list of LOINC codes that somebody had to 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 the fundamental data issue that every health system has had to go through to then be able to respond and do the analysis and the data pools necessary. 


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 all of the other sources of data. So yes it is a challenge but I have seen more collaboration amongst health systems around this topic than I have maybe ever, which maybe one good thing coming out of this if any. 


[0:14:15.5] BR: Yeah, you know I was wondering so you are describing some of the challenges, is it more of a challenge that some aspects of the organization or even organizations outside of your organization – you talked about the LOINC codes not even being available, is that more of a problem that some aspects of health care doesn’t move fast enough or I remember the data definitions being so important that we would spend literally meetings, hours and days talking about the definitions of data or is that more challenging because we are using data is so fast that potentially the definition is getting lost in the process? 


[0:14:53.8] LP: I would say absolutely both and actually one is a problem. So the broader issue that is a theme that I have been seeing is just that I refer to them as data governance issues. So as you as you mentioned definitions and meta data for these data elements. So even in around the test and the LOINC codes and which codes are going to be included with what results in order to say that it is a positive patient, a patient positive for COVID-19. 


Data governance I believe is coming to the forefront as you have these response teams, as you have the central teams that have been brought together to respond to the COVID situation that every health system is 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 is almost like – one organization I talk to actually said they even stood up what they’re calling an emergency data governance structure and process. 


It is the data governance team just in response to a COVID challenge just to get a 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 that comes from the health system. So you know it is a real challenge that most healthcare organizations don’t have nailed down as well as they would like and I think it really 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 going to realize, holy cow we are not prepared and something so simple as how are we going to get a count of patients that have a certain condition. Why is that so difficult and how are we going to do this better in the future? Do we have a glossary that we can go to and look up certain definitions that we’d agreed upon? How are we going to agree on data quality checks? 


All of those kinds of things so to answer your question I think I really think it’s both but you hit on a topic that I am very passionate about which is data governance where I roll those issues into. 


[0:17:40.1] BR: It’s interesting, you cited a lot of lessons learned there and that’s fantastic. I think one of the things maybe I didn’t really imagine when I was a CIO was we had a data governance process and it moved very slow and I never imagined it in the time of a pandemic or a crisis where we’d have to move at like 10X the speed that it was currently moving and you talked about that organization standing up on emergency data governance group and I thought I’d never really – 


That is some interesting lesson or that is an interesting model of that they stood up because the data is so important I would assume that that organization is like, “Look, we got to move.” Are there other things that people are doing to move faster? 


[0:18:28.5] LP: Yeah, good question – 


[0:18:37.0] BR: Yeah, I normally send all the questions over ahead of time and give you time to think about it but we’re doing a lot of this stuff on the fly. I am just curious I mean because it is so interesting to me that a lot of the parts of IT have had to move at huge speed increases. You know Telehealth, obviously work from home obviously and now we are sort of exploring how does data move faster. How does data governance move faster? 


[0:19:04.1] LP: So one thing that I have seen from health care organizations in order to really move at the speed that the organization needs in response to COVID-19 is analytics professionals are actually being imbedded right within the COVID response teams and command centers. So because this is something that we don’t understand, you know, there is so much of this virus that we don’t understand and so many factors that are new. 


You have to be able to ask a question, have almost a first response, a very fast response to provide data back that having your analytic professionals actually within those teams becomes really critical and that allows them immediately the discussions around, “Okay well where are we going to pull this from? How are we going to calculate this? Who are the leaders that have accountability for these various things? So I have also seen to speed things up it’s breaking down barriers that naturally exist in health systems maybe quicker than what has happened previously.


So there may be barriers to access the 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 so I have seen some very positive movements to say this is a shared problem, these are shared data assets, let’s go after this, let’s define things, let’s move forward because we really don’t have a choice. 


And some days I wish that the day to day means of doing data management and analytics and data governance work that we’d had some of that same passion, some of that same drive that I am seeing right now for just good quality improvement and application of data to other problems that we know can have tremendous value. So yeah those are my thoughts on that. 


[0:21:33.6] BR: You know what Lee? I already got over my time now but I’ll close with this, which I think is maybe an obvious question but I sort of want to discuss, which is do you think this event is going to increase the adoption of data science not analytics per se but data science in the health care organizations? 


[0:21:50.6] LP: I think without question the answer is yes for the reasons that we have – some of what we have talked about but I think coming out of this there is going to be real discussions around how can we improve our capabilities so that we are better ready for providing data in times of need like this in the future. So I sure hope that it prompts maybe some organizations that have not yet put a lot of time and effort into maturing their data and analytics capabilities or those that have to kick it into gear and take it to the next level because you know data truly is an asset in this situation and fighting this pandemic and helping all of us through it and we need to be prepared for whatever comes next. So I really believe that adoption will increase as a result of this. 


[0:22:53.0] BR: Well Lee, thank you very much for taking some time. I appreciate it and I appreciate all the work that you’re doing for the various clients that you are working with. 


[0:23:01.6] LP: Thank you Bill for you time, I really appreciate the opportunity to talk to you about some of this. Thanks for all you do. 


[0:23:08.4] BR: Thanks, take care. 




[0:23:09.5] BR: That is all for this show. Special thanks to channel our 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 want to support the fastest growing podcast in the health IT space, the best way to do that is to share with a peer. Send an email, DM, whatever you do. You could also follow us on social media, subscribe to our YouTube channel. 


There is a lot of different ways you can support us but sharing it with a peer is the best. Please check back often as we would be dropping many more shows until we’ve flattened the curve across the country. Thanks for listening. That is all for now. 




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