Speaker 0: I started it.
Speaker 0: Good afternoon, everybody.
Speaker 0: My name is Robert Lovato.
Speaker 0: I'm one of the members of the Center for Intelligent
Speaker 0: Health Care, and I'm here today to introduce Dr. Chris
Speaker 0: Maloney.
Speaker 0: Dr. Maloney earned his medical doctorate at the University of
Speaker 0: Missouri.
Speaker 0: He then pursued a residency in pediatrics and a fellowship
Speaker 0: in pediatric critical care at the University of Utah.
Speaker 0: While at the University of Utah, he also undertook a
Speaker 0: Ph.D. in bioinformatics.
Speaker 0: He's currently the Executive Vice president, chief clinical officer and
Speaker 0: physician in chief at Children's Hospital Medical Center here in
Speaker 0: Omaha.
Speaker 0: His research work centers on almost real time data transformation
Speaker 0: to reduce variation in decision making.
Speaker 0: I'm absolutely I'm still very excited to hear what he
Speaker 0: has to say about his talk today.
Speaker 0: And I thank you very much for being with us.
Speaker 0: Dr. Maloney, welcome to Grand Rounds.
Speaker 1: Well, thank you for having me.
Speaker 1: I've been excited to present this because it's it's really
Speaker 1: a showcase of what our team has done over the
Speaker 1: last couple of years.
Speaker 1: And this is the first time that we put it
Speaker 1: together and shared it with with anybody.
Speaker 1: So we're going to take off.
Speaker 1: I have to ask the proverbial question, Can you see
Speaker 1: my screen?
Speaker 1: And I hope the answer is yes.
Speaker 2: Good.
Speaker 1: All right.
Speaker 1: So I am the chief quality and clinical transformation officer.
Speaker 1: I got this new title in January to concentrate on
Speaker 1: our safety, quality and experience journey.
Speaker 1: And it's really freed me up to spend a lot
Speaker 1: more time in the last five weeks understanding where we
Speaker 1: can improve and how do we use data, convert it
Speaker 1: to information, make that into knowledge, and eventually give the
Speaker 1: organization much more wisdom.
Speaker 0: So.
Speaker 1: Did I just go past this?
Speaker 1: So here are the objectives.
Speaker 1: I'm going to define our journey to move our organization
Speaker 1: from siloed data repositories to a single source of truth.
Speaker 1: I will demonstrate a process to automate manual reporting.
Speaker 1: We're going to just sort of do some eye candy
Speaker 1: here, which is kind of fun from showcasing what the
Speaker 1: team is doing and then talk about the process to
Speaker 1: collect almost real time data to provide decision support at
Speaker 1: the point of care.
Speaker 1: I think it's important should to for you to understand
Speaker 1: that I did a lot of number three back in
Speaker 1: 2003 at Intermountain Health Care when I was doing my
Speaker 1: Ph.D. work.
Speaker 1: My dissertation was a computerized weaning of a pediatric acute
Speaker 1: respiratory failure, and we assimilated data from the monitors, the
Speaker 1: ventilator lab pharmacy, put it through a rules engine, and
Speaker 1: sent an alert to the clinicians on which knob to
Speaker 1: turn on the ventilator.
Speaker 1: And what we found is that following the computerized process
Speaker 1: versus leaving the doctors and nurses to their own discretion,
Speaker 1: we were able to get a child off of the
Speaker 1: ventilator about a day faster.
Speaker 1: And so I've been in search of how we could
Speaker 1: do that here and more robustly in in Omaha since
Speaker 1: I arrived 278 Wednesdays ago.
Speaker 1: Here's the agenda.
Speaker 1: And I found this picture.
Speaker 1: I thought it was cooler a black hole devours the
Speaker 1: bypassing.
Speaker 1: Star came out last week.
Speaker 1: I'm going to define the problem.
Speaker 1: I'm going to talk about a solution to walk you
Speaker 1: through how we developed that solution and implemented it, show
Speaker 1: you some of the successes, and then really talk about
Speaker 1: where we're going in the future.
Speaker 1: So strap on.
Speaker 1: We're going to tell a story here.
Speaker 1: I arrived in October of 2017, and by November I
Speaker 1: was presented with our problem.
Speaker 1: And I don't think this is unique to a lot
Speaker 1: of organizations, but we had people and mainly I was
Speaker 1: working with the doctors go to could go to four
Speaker 1: different entities a department called performance improvement another one called
Speaker 1: Information Technology, Strategic Planning and Finance.
Speaker 1: And they would ask them for.
Speaker 1: They'd ask my question, How do you give me some
Speaker 1: clinical, clinical data?
Speaker 1: And the results differed from each entity.
Speaker 1: Yet every one of them said, My date is right.
Speaker 1: Strategic planning said finance data is wrong, the performance improvement
Speaker 1: data is wrong.
Speaker 1: And so that really left us there with multiple independent
Speaker 1: data sets that we couldn't join into a single query.
Speaker 1: We had a major problem in our organization without having
Speaker 1: a single source of truth.
Speaker 1: So after working with the organization and finally convincing them
Speaker 1: over the over the course of months that we needed
Speaker 1: a better solution, then, well, my it is right.
Speaker 1: I said we need to create a different department that
Speaker 1: just concentrates on analytics.
Speaker 1: We need to hire people into that.
Speaker 1: We need to make sure we know what our portfolio
Speaker 1: of databases are.
Speaker 1: Where did the data live?
Speaker 1: Transfer our business intelligence analysts into that department?
Speaker 1: And then let's go look for a vendor to be
Speaker 1: our analytics engine.
Speaker 1: And so by doing that, that journey there, we knew
Speaker 1: we were going to get best of breed.
Speaker 1: When I was in graduate school at Intermountain Healthcare, one
Speaker 1: of the things that they just pushed into us, it
Speaker 1: was actually at the University of Utah, but the teachers
Speaker 1: all worked at Intermountain is if you can buy it,
Speaker 1: you should not build it.
Speaker 1: And so this was our goal is to buy something.
Speaker 1: So I hired a manager.
Speaker 1: We created the care transformation department and moved to the
Speaker 1: business intelligence analyst under this manager.
Speaker 1: And the first.
Speaker 1: The first problem or the first issue that I asked
Speaker 1: our manager to do is to go out and get
Speaker 1: a portfolio of our databases.
Speaker 1: And he came back at 110 and I said, That's
Speaker 1: enough.
Speaker 1: You can just stop.
Speaker 1: But the important thing of this is that Oracle was
Speaker 1: just one.
Speaker 1: Epic was just one.
Speaker 1: There are people that were using SQL spreadsheets, access databases.
Speaker 1: And so we knew that we had a major problem
Speaker 1: because we had these one off data sets.
Speaker 1: And where they even where are they the truth?
Speaker 1: Or did we have more than four versions of the
Speaker 1: truth?
Speaker 1: Then we were as I mentioned, we were going out
Speaker 1: and trying to look for an analytics engine.
Speaker 1: And by the summer of 2019, we had found two
Speaker 1: health catalysts and epic.
Speaker 1: So Health Catalyst is out of Salt Lake City, Utah.
Speaker 1: Epic is out of Verona, Wisconsin.
Speaker 1: They came and did demos for us.
Speaker 1: We had we had developed a data governance steering committee
Speaker 1: by that time, which were basically all of the major
Speaker 1: decision makers in the children's organization.
Speaker 1: So we knew we had the right people at the
Speaker 1: table that could make this decision.
Speaker 1: And in August, after we debriefed the team, believe that
Speaker 1: the commercials solutions were too expensive.
Speaker 1: They were asking for 5 to $7 million over the
Speaker 1: course of five years.
Speaker 1: And so they said, why don't you guys just build
Speaker 1: it internally?
Speaker 1: So that's what we set out to do.
Speaker 1: We we put together a goal of building this electronic
Speaker 1: data warehouse and W that can consume multiple independent data
Speaker 1: feeds, join them when appropriate and create visualizations.
Speaker 1: So we were starting and we used the hims adoption
Speaker 1: model for analytics maturity as our roadmap of how we
Speaker 1: were going to move through this process.
Speaker 1: And you'll see that that started with a data governance
Speaker 1: process.
Speaker 1: Here's the analytics maturity roadmap.
Speaker 1: There are eight levels or stages.
Speaker 1: The we were at stage zero.
Speaker 1: We had fragmented point solutions.
Speaker 1: And so we needed to start moving up around data
Speaker 1: aggregation and data governance.
Speaker 1: Data governance and preaching to the choir here is that
Speaker 1: formal authority, processes and procedures to optimize your data, manage
Speaker 1: it moving from silos to something that resembles the truth
Speaker 1: that people trust.
Speaker 1: That you need to have a trusted source of your
Speaker 1: data, and it has to be trusted across the organization.
Speaker 1: And our goals were all about quality, use and literacy.
Speaker 1: And what we've uncovered in the last six month is
Speaker 1: that literacy is not nearly as high across the organization
Speaker 1: as we would want it to be.
Speaker 1: And so we have a solution for remedying that in
Speaker 1: 2023.
Speaker 1: So I had to sell this.
Speaker 1: Why do we need data governance?
Speaker 1: And the big one was this consistent version of the
Speaker 1: truth.
Speaker 1: We needed to have data quality policies.
Speaker 1: We needed to have processes that we cleaned up the
Speaker 1: data.
Speaker 1: We needed to have data, dictionaries, metadata so that people
Speaker 1: knew what are the numerators, what are the denominators, what's
Speaker 1: the source of this data element value that we're using
Speaker 1: to make calculations?
Speaker 1: We needed a centralized hub for everything and communication and
Speaker 1: training that was consistent of how to use those sets.
Speaker 1: Data owners.
Speaker 1: So we have data stewards throughout the different areas inside
Speaker 1: of our organization that they're actually the owners of the
Speaker 1: data.
Speaker 1: They're the ones that audit it and make sure that
Speaker 1: it is clean and that we can use it in
Speaker 1: different processes.
Speaker 1: And then the whole system had to be scalable and
Speaker 1: relatively cost effective.
Speaker 1: And we built it all around use cases.
Speaker 1: And so there's nothing to take away from this slide
Speaker 1: other than that was the that was the basis of
Speaker 1: building.
Speaker 1: The report is, okay, we have a single server instance
Speaker 1: and we need to be able to join multiple different
Speaker 1: data sources.
Speaker 1: How do we do that?
Speaker 1: And the first one was around Epic and working with
Speaker 1: epic dashboards.
Speaker 1: And with that, I'm going to start showing you some
Speaker 1: of our information and projects that we worked on.
Speaker 1: We don't have everything in Epic that is inside of
Speaker 1: our CDW, but we have all of the information that
Speaker 1: we need to build reports and to help people make
Speaker 1: decisions.
Speaker 1: We have a daily safety breathing briefing where many of
Speaker 1: our leaders get together at 835 in the morning, Monday
Speaker 1: through Sunday, and they go over concerns.
Speaker 1: Some of this is about bed numbers that are available
Speaker 1: for admission.
Speaker 1: So what's our staffing like in and for respiratory pharmacy?
Speaker 1: What are other safety concerns that we need to share
Speaker 1: either that occurred in the last 24 hours or that
Speaker 1: are occurring in that we're worried about in the next
Speaker 1: 24 hours so that we can have that shared vision
Speaker 1: of situational awareness.
Speaker 1: This was very manual both at the process and in
Speaker 1: consolidation.
Speaker 1: It was never updated.
Speaker 1: It was done and it was manually uploaded to our
Speaker 1: children's portal, which we call my children's.
Speaker 1: So our first solution was to build this inside of
Speaker 1: Epic.
Speaker 1: And so this is now a tool that shows us
Speaker 1: all of our units what the capacity is, how many
Speaker 1: are occupied.
Speaker 1: And then the report is more around what's our staffing,
Speaker 1: because we can't that that's we can't we don't have
Speaker 1: that information inside of Epic, but it tells us about
Speaker 1: what was going on in the emergency room, what was
Speaker 1: going on in the hours, how many admissions do we
Speaker 1: have coming from the hours that day.
Speaker 1: So we automated that process.
Speaker 1: It's dynamic.
Speaker 1: It updates as frequently as stuff changes or you log
Speaker 1: in.
Speaker 1: It's easy, easy access because we gave it to everybody
Speaker 1: that needed it for our daily safety brief.
Speaker 1: Consolidated view of the data.
Speaker 1: You can't manipulate it, you can't drill down.
Speaker 1: And that was an intentional governance decision and then minimal
Speaker 1: maintenance.
Speaker 1: So what were our gains?
Speaker 1: We reduced that manual intervention by 90%, increase the data
Speaker 1: accuracy because that manual report was outdated.
Speaker 1: Basically when we when we published it to the web.
Speaker 1: The data was consistent and how we were looking at
Speaker 1: it, we had a single source of truth.
Speaker 1: That single source of truth is epic, and it was
Speaker 1: easy to use and understand.
Speaker 1: It was using the same visualizations as many of the
Speaker 1: other epic dashboards.
Speaker 1: And so it was easy to access.
Speaker 1: And so now it allows us to use our daily
Speaker 1: safety briefing to really concentrate on what are the concerns
Speaker 1: that are somatic, that it may have happened in one
Speaker 1: unit, but it could have happened in other areas.
Speaker 1: And so we talk about those much more than we
Speaker 1: talk about the numbers.
Speaker 1: So we did the data aggregation and initial data governance.
Speaker 1: We needed to create our centralized data repository, an electronic
Speaker 1: data repository.
Speaker 1: And so what we had, what our old state was,
Speaker 1: is just independent data sources that really connected and nothing.
Speaker 1: We tried to use slicer dicer to bring in to
Speaker 1: connect to these external sources.
Speaker 1: And it really wasn't that that easy to do.
Speaker 1: And and so we started seeking other things.
Speaker 1: We had many customized reports or it was hard to
Speaker 1: customize reports in either Epic or or in Excel.
Speaker 1: We used a lot of Excel.
Speaker 1: Difficult to bring all that together so we could see
Speaker 1: things and we had different ways that we reported data.
Speaker 1: Some was done in slice or dice or an epic,
Speaker 1: some was done with wabi sap solution, some were done
Speaker 1: in Excel, and we would send out PDFs of Excel
Speaker 1: spreadsheets, which always frustrated me.
Speaker 1: And so we built a new set, which is this
Speaker 1: single source of truth.
Speaker 1: We called it Liberator because we're liberating the data.
Speaker 1: We have consolidated to data subject matters inside of that.
Speaker 1: So several data marts that have explicit data governance principles,
Speaker 1: and then we have a single reporting tool and that's
Speaker 1: Power BI is what we used.
Speaker 1: So we automated all of that through the SAS process
Speaker 1: of moving files where we can format that data, apply
Speaker 1: our data governance principles, including data dictionary, so that we
Speaker 1: know the metadata, we do calculations outside of the data
Speaker 1: structure.
Speaker 1: We don't need to save those as can be done
Speaker 1: with the visualization tool.
Speaker 1: So we have data, consistency, accuracy, a single source of
Speaker 1: truth, and then a single reporting tool that is part
Speaker 1: of Microsoft Office 365, which is what we use in
Speaker 1: our organization.
Speaker 1: So everybody has has access to it, not our users,
Speaker 1: but they have access to view.
Speaker 1: And I like this one slightly better.
Speaker 1: But this is just a small list of all of
Speaker 1: the different sources that we bring in.
Speaker 1: So Epic are our meeting attendance, our patient experience score
Speaker 1: that's in our C Oracle red cap.
Speaker 1: So we have some services that we can easily bring
Speaker 1: in.
Speaker 1: Bio digital is our automated hand hygiene process.
Speaker 1: Cority is is from our employee health.
Speaker 1: And then we have a tool called geometry, which brings
Speaker 1: in vital sign data from the monitors in the three
Speaker 1: accused cardiac ICU, pediatric ICU and newborn ICU.
Speaker 1: And then one visualization tool of power by.
Speaker 1: So we have our centralized data repository, then we call
Speaker 1: it Liberator.
Speaker 1: And we need it to now start to move up
Speaker 1: this this road to get efficient, consistent, internal and external
Speaker 1: reports.
Speaker 1: The big one that we started with was in our
Speaker 1: medical staff office for ongoing professional practice evaluation.
Speaker 1: The Joint Commission, who accredits us for Medicare and Medicaid
Speaker 1: Services, says that we need to evaluate every member of
Speaker 1: our organized medical staff at least every 24 months to
Speaker 1: see how they're doing for their professional practice.
Speaker 1: We had a very manual process that everything had to
Speaker 1: go to these different databases in order to put it
Speaker 1: together.
Speaker 1: It was an individual that was their full time job.
Speaker 1: Well, as to process that, to consolidate it, to make
Speaker 1: the reports.
Speaker 1: And then the reports were very segmented.
Speaker 1: It wasn't easy to see.
Speaker 1: You can see in this this report here that there
Speaker 1: was just a list of the different procedures that was
Speaker 1: done in this in in this place.
Speaker 1: So what we did, we we used Liberator, we connected
Speaker 1: many other data sources to that, and we made it
Speaker 1: very automatic.
Speaker 1: So what we have now is you can bring in
Speaker 1: our peer review cases.
Speaker 1: You can bring in the CMA hours.
Speaker 1: The meetings are hand hygiene compliance.
Speaker 1: You can then bring in some epic information that's there,
Speaker 1: as well as our peer review information so that you
Speaker 1: can have that as well as other activity.
Speaker 1: So we automated all of that stuff, which was great
Speaker 1: for the organization.
Speaker 1: And there's very little maintenance is needed for our OPI
Speaker 1: reports for organized medical staff.
Speaker 1: So we reduced the time from a full time FTE
Speaker 1: to about 30 minutes a month.
Speaker 1: We run it in batch files and so it can
Speaker 1: just spit out.
Speaker 1: We use pagination.
Speaker 1: We added additional external sources so that we have better
Speaker 1: information.
Speaker 1: And then we have many more metrics that are available.
Speaker 1: So we were able to repurpose that FTE into into
Speaker 1: the organization.
Speaker 1: We didn't fire anybody.
Speaker 1: And so that was really good.
Speaker 1: We're bringing in additional information now.
Speaker 1: And we moved that FTE into our medical staff office
Speaker 1: from a different department so that there's much more coordination
Speaker 1: with our organized medical staff.
Speaker 1: So this is information from our clinics.
Speaker 1: We have now 15 clinics in the organization.
Speaker 1: And this was a click view report that an individual
Speaker 1: built one person knew how to use click view in
Speaker 1: the organization, and they subsequently left.
Speaker 1: And so we knew we had to replace this thing.
Speaker 1: It was it was dying.
Speaker 1: But this is what the doctors and nurses use to
Speaker 1: look at how they're doing with visits on a daily
Speaker 1: basis, a monthly basis, how it rolls up in a
Speaker 1: yearly basis, how we're doing relative to our budget, so
Speaker 1: that it gives them an idea of of their progression.
Speaker 1: It's a it's a static report delivered in the month.
Speaker 1: Pardon me was in the mornings there weren't many people
Speaker 1: that had access to it and it was really hard
Speaker 1: to update and clip you.
Speaker 1: So instead we used our Liberator tool.
Speaker 1: This is mainly just epic.
Speaker 1: We automated the process and you still have the same
Speaker 1: information.
Speaker 1: It's presented in a slightly different graphic, but you can
Speaker 1: really get your eyes around what's going on.
Speaker 1: It's consolidated views and very minimal maintenance is needed in
Speaker 1: order to get that same information that we had in
Speaker 1: that other report.
Speaker 1: So we had enhanced functionality.
Speaker 1: We brought budget in from the urgent care centers.
Speaker 1: So that was good.
Speaker 1: We started urgent Care virtual visits because of COVID.
Speaker 1: You could see trending over time.
Speaker 1: Obviously easy to use and we no longer had to
Speaker 1: pay for Click View.
Speaker 1: So that was about $60,000 a year.
Speaker 1: And we just continue to enhancement based on what our
Speaker 1: end users need.
Speaker 1: All right.
Speaker 1: We're moving up the stage for measuring and managing evidence
Speaker 1: based care.
Speaker 1: Care visibility and waste reduction.
Speaker 1: We had an asthma pathway in our emergency department, and
Speaker 1: the goal in asthma management in the ED is to
Speaker 1: not get a whole bunch of x rays.
Speaker 1: And so as part of that solution, this is the
Speaker 1: information that we would we would give to our providers.
Speaker 1: We would give them the patient encounter and the description
Speaker 1: of what they ordered one viewer to view.
Speaker 1: But we didn't have any other information because this was
Speaker 1: just an epic.
Speaker 1: And so we did have it was automatic, but it
Speaker 1: was relatively static.
Speaker 1: And we had we didn't get it's not it's not
Speaker 1: much information, so we didn't convert too much data into
Speaker 1: information.
Speaker 1: And instead we built a solution inside of power.
Speaker 1: BI that looked at the dynamic reporting with drill down
Speaker 1: capabilities for each of each of the folks, we automated
Speaker 1: that data processing.
Speaker 1: Obviously, again, easy access because it's online, you have the
Speaker 1: consolidated views and you can see how we're doing trending
Speaker 1: over over time with with this one metric of chest
Speaker 1: x rays in the in EDI.
Speaker 1: And we have many other key processes, key performance indicators
Speaker 1: that we use within Power BI Is that that tool
Speaker 1: that you can see on other areas of the asthma
Speaker 1: pathway?
Speaker 1: So we started with three.
Speaker 1: We we built now a little more than seven.
Speaker 1: We have added dynamic capabilities, much more visualization and interact
Speaker 1: and they have dead trending.
Speaker 1: It's easy to use.
Speaker 1: And now it's just a matter of making sure that
Speaker 1: the key stakeholders get at get at activity with it.
Speaker 1: And and we're replacing some of our current tools that
Speaker 1: we used to create pathways.
Speaker 1: And in fact, we've contracted with a company called Agile,
Speaker 1: M.D, which integrates pathways into epic.
Speaker 1: And so in creates orders.
Speaker 1: So we're looking to implement that here in 2023.
Speaker 1: What about security?
Speaker 1: So with our power I-Reports, we have quite a bit
Speaker 1: of security.
Speaker 1: We have it at the DOE workspace area.
Speaker 1: So you have to log in and you have to
Speaker 1: use duo in order to access any of the any
Speaker 1: of the reports that we have.
Speaker 1: At the report level, you can access it by your
Speaker 1: role.
Speaker 1: So we have really role based security, but we also
Speaker 1: have row based security.
Speaker 1: So the role level security lets just to allow somebody
Speaker 1: to see or not see specific examples.
Speaker 1: And so that's been built into the data governance process.
Speaker 1: And then we're at level at stage five, enhancing quality
Speaker 1: of care, Understanding the economics of care.
Speaker 1: This is a work in progress.
Speaker 1: This is where we started some of this work last
Speaker 1: year.
Speaker 1: This is from our heart center.
Speaker 1: We built a dashboard.
Speaker 1: And there's a calculation that looks, use, looks at variable
Speaker 1: life, adjusts the display and its risk adjusted survival.
Speaker 1: What you see here is by stat level.
Speaker 1: So that's basically acuity level stat one being the lowest
Speaker 1: acuity and five being the highest.
Speaker 1: And for every case it's a dot.
Speaker 1: And if there's a death, then there's a decrease.
Speaker 1: If there's a survival, that increases.
Speaker 1: And what you can see from this is and we
Speaker 1: saw this in retrospect, is that back there in December
Speaker 1: of 21, in January of 22.
Speaker 1: We were we had a lot of moral distress in
Speaker 1: our organization, and it was because we saw all of
Speaker 1: those deaths.
Speaker 1: We saw a little bit of that again in March
Speaker 1: and April of 22.
Speaker 1: And so now we can start to be more proactive
Speaker 1: to bring resources to our teams that are witnessing and
Speaker 1: are being part of this caring process.
Speaker 1: So we can predict that if we're going to have
Speaker 1: one death, what that's going to do to their moral
Speaker 1: distress and if there's any more.
Speaker 1: And and so we use this to really understand what's
Speaker 1: going on in our organization around mortality, in our cardiac
Speaker 1: care units.
Speaker 1: And this is where I have a video, and let's
Speaker 1: hope that it works.
Speaker 0: When a provider orders heat and high flow in Epic,
Speaker 0: they are prompted to answer a question as to whether
Speaker 0: or not the patient is on the heated high flow
Speaker 0: pathway.
Speaker 0: If they click yes, they must answer another question, this
Speaker 0: time to certify that they have completed the heated high
Speaker 0: flow pause prior to initiation of the high flow.
Speaker 0: If they click yes, no further action is needed and
Speaker 0: the order can be placed.
Speaker 0: However, if they click no, they must cite a reason
Speaker 0: that they are deviating from the pathway.
Speaker 1: So there's just demonstrates that we're trying to get upstream
Speaker 1: of why people don't want to follow a recommendation in
Speaker 1: a pathway.
Speaker 1: So if if they're going to follow it, we don't
Speaker 1: really need them to answer any more questions.
Speaker 1: But if they're not going to follow the recommendation that's
Speaker 1: in the pathway, we want to capture that not to
Speaker 1: be punitive, but to learn.
Speaker 1: They may have they may know things or are do
Speaker 1: things that we didn't we didn't know about.
Speaker 1: And so you're able to to do this is either
Speaker 1: free text or we have some dropdown menus and this
Speaker 1: is just implemented in this in this single pathway.
Speaker 1: However, we're making it very easy for the clinicians to
Speaker 1: follow the pathway.
Speaker 1: So this is where we've ended up in really December
Speaker 1: of 2023.
Speaker 1: Continuing on this journey to move to level six, our
Speaker 1: goal when we started this project, this process in 2020
Speaker 1: was to get to level six by 2025.
Speaker 1: We will be getting there for sure by 2025 with
Speaker 1: predictive analytics and clinical risk interventions, because our goal is
Speaker 1: to get further upstream and get into the areas where
Speaker 1: people are making decisions at the point of care.
Speaker 1: So what does that look like?
Speaker 1: Well, one of the things is we want to make
Speaker 1: sure that people see value.
Speaker 1: So we're bringing our activity based costing into our pathways
Speaker 1: to understand the value of standardization.
Speaker 1: Years ago, I standardized the way that we took care
Speaker 1: of patients with bronchiolitis in the inpatient unit, and I
Speaker 1: drove costs up and it was good to know that
Speaker 1: we did that.
Speaker 1: And then we were able to identify the leverage points
Speaker 1: to bring it way down, which which was really the
Speaker 1: goal of the project.
Speaker 1: Want to collect almost real time data.
Speaker 1: So we partnered with Philips to get the vitals from
Speaker 1: their monitor, and we're specifically using a use case around
Speaker 1: epinephrine infusion management in our cardiac ICU.
Speaker 1: And so we compare the orders and you know what
Speaker 1: the parameters are supposed to be with whatever the value
Speaker 1: is coming from the monitor.
Speaker 1: And either we store that as most recent data or
Speaker 1: we send an instruction on what to do, change the
Speaker 1: infusion rate, give a bolus with the goal to maintain
Speaker 1: appropriate parameters for the blood pressure and compare that to
Speaker 1: patients that are having their blood pressure manage.
Speaker 1: Just the old fashioned way.
Speaker 1: Then we'll expand to other entities, including other modalities such
Speaker 1: as feeding pumps are our near-infrared spectroscopy that we use
Speaker 1: for monitoring blood flow to the brain and belly, as
Speaker 1: well as ventilators and other things.
Speaker 1: And we're in the process of building a command center.
Speaker 1: We're going to call it the watchful eye program to
Speaker 1: capture external inputs.
Speaker 1: So some of those things that we just talked about
Speaker 1: here for inpatients in the ICU and then moved to
Speaker 1: the to the floors and also use information from patients
Speaker 1: that we're monitoring outside.
Speaker 1: We have some of our patients that have a single
Speaker 1: ventricle.
Speaker 1: We need to monitor their fluid status and their weight
Speaker 1: on a routine basis.
Speaker 1: And can we capture that from the Internet of Things
Speaker 1: or wearables that we can get that information in and
Speaker 1: be able to manage these patients in a timely fashion
Speaker 1: to keep them out of our emergency departments and especially
Speaker 1: out of our hospitals?
Speaker 1: With that.
Speaker 1: I'd like to acknowledge a lot of the team.
Speaker 1: So the Care Transformation department or Garcia has done yeoman's
Speaker 1: work in building most of this.
Speaker 1: Amber Els, our data scientist, she's really helped with a
Speaker 1: lot of the more detailed statistical work the.
Speaker 1: By analysts Caitlin Lee and Cindy.
Speaker 1: Kat We have a couple of engineers, data engineers, Nick,
Speaker 1: Kendra and Ben, and then the physicians that have helped
Speaker 1: put this together on their pathway Steering committee.
Speaker 1: Doctors Maskin, Naughton and Delta.
Speaker 1: And then Dr. Kearns really has made a lot of
Speaker 1: the visualizations happen.
Speaker 1: And then our clinical effectiveness team that has really been
Speaker 1: pushing our pathways.
Speaker 1: And that standardization led by Dr. Norton along with Kelsey
Speaker 1: and Abby.
Speaker 1: And with that, I'm going to end my presentation.
Speaker 1: And happy to answer any questions or take your comments
Speaker 1: or whatever you think.
Speaker 1: Stop sharing.
Speaker 2: Thank you very much, Chris.
Speaker 2: I realize how much work that is and it's impressive
Speaker 2: that those five years go by pretty fast.
Speaker 2: What do you see as the opportunities moving forward to
Speaker 2: get into the predictive analytics?
Speaker 2: Because I know quite a few people on this.
Speaker 2: Call on this meeting.
Speaker 2: Are interested in artificial intelligence and predictive analytics.
Speaker 1: Yeah.
Speaker 1: That's really getting into the data and building models.
Speaker 1: Predictive models.
Speaker 1: Initially, we'll do it from retrospective data.
Speaker 1: One of the things that we're looking at is if
Speaker 1: we can predict when patients are going to deteriorate based
Speaker 1: upon the information that we already have in our in
Speaker 1: our data sources and that we can send up in
Speaker 1: an alert, that's that those are some of the things
Speaker 1: that we're looking at doing.
Speaker 1: We're not there yet.
Speaker 1: I can't get the data in real time.
Speaker 0: So there are questions out there.
Speaker 0: Oh, sorry, Doctor.
Speaker 2: Yeah, No, Chris, just the first comment is congratulations on
Speaker 2: the work of your team.
Speaker 2: You and your team in a remarkable journey.
Speaker 2: The Hermes framework, obviously is helping provide some degree of
Speaker 2: direction, but I'm sure that a fair amount of this
Speaker 2: is the brainchild of you thinking about, okay, where are
Speaker 2: we when you arrived and where would we like to
Speaker 2: go?
Speaker 2: And again, appreciate the vision that you have shared with
Speaker 2: us and the pathway to get there.
Speaker 2: Your presentation actually brings up a number of questions for
Speaker 2: me.
Speaker 2: I guess the first one I would start was you
Speaker 2: already mentioned that you started off with 110 or so
Speaker 2: different data sources of information that was in databases and
Speaker 2: that the job, number one was a bit of, if
Speaker 2: you will.
Speaker 2: I think you use the term governance.
Speaker 2: I would likely guess that there was more wrangling of
Speaker 2: personalities and ownership and a lot of the, if you
Speaker 2: will, political components thereof.
Speaker 2: What did it take to accomplish that part of it?
Speaker 2: You know, this is back to step zero, step one.
Speaker 2: But how did you go from multiple disparate and sometimes
Speaker 2: incongruous sources of information to a more unified set of
Speaker 2: data platforms?
Speaker 2: The direct question, by the way, is because the Center
Speaker 2: for Adults and Health Care is one of the foundational
Speaker 2: pillars of the center is good data.
Speaker 2: And so any perspectives you have on what does it
Speaker 2: take to achieve good data, unified, uniform, consistent, high quality
Speaker 2: computable data.
Speaker 2: When you are starting off in an environment where you
Speaker 2: have lots of very well-intentioned individuals, but they aren't collected
Speaker 2: and aligned in a consistent way.
Speaker 1: Yeah.
Speaker 1: So the big one, one, I brought credibility because of
Speaker 1: my background.
Speaker 1: I could talk the talk.
Speaker 1: And the second one is that I didn't have a
Speaker 1: dog in the hunt.
Speaker 1: I wasn't in any of those four areas.
Speaker 1: And so I wasn't a threat to anybody.
Speaker 1: If if we were going to try to put it
Speaker 1: all into information system or performance improvement, that wouldn't have
Speaker 1: gone over well at all.
Speaker 1: So we needed to create an entity that was Switzerland
Speaker 1: that would work with the other four entities and build
Speaker 1: that trust that what we can do is make things
Speaker 1: better for the organization overall.
Speaker 1: And it's still a work in progress.
Speaker 1: I've been able to assimilate most all of the data
Speaker 1: sources in the organization except my finance team.
Speaker 1: And that's why that's my goal for this year is
Speaker 1: to bring at least some of the finance data into
Speaker 1: into our electronic data warehouse so that we can really
Speaker 1: understand what our work does onto costs.
Speaker 1: Once we got going, then it was just a matter
Speaker 1: of showing the wins.
Speaker 1: You know, when you start reducing FTE fees, I didn't
Speaker 1: even show our very first Pride project, which was we
Speaker 1: had somebody that was collecting positive viral tests and putting
Speaker 1: them into a table, summing that table and sending out
Speaker 1: a PDF of the of the table that says this
Speaker 1: week, this is how many adeno virus we had ancient
Speaker 1: human animal virus.
Speaker 1: And it's like, Well, what do you want me to
Speaker 1: do with this?
Speaker 1: I can't.
Speaker 1: I can sort of visually trend it in my head.
Speaker 1: So that was taking them 4 to 6 hours every
Speaker 1: week, depending on how many viruses there were.
Speaker 1: We automated that.
Speaker 1: And so it went away.
Speaker 1: That person was able to do something else.
Speaker 1: The PPE, when we're able to automate and save an
Speaker 1: entire FTE to repurpose.
Speaker 1: And so then our h.r.
Speaker 1: Group really got excited at what can we do there?
Speaker 1: So a lot of it is is early wins.
Speaker 1: And then sharing those wins very broadly.
Speaker 1: And it changed people's minds.
Speaker 2: Great.
Speaker 2: If I might ask a follow on question.
Speaker 2: Many of the things that you are describing are, I
Speaker 2: guess I could call them project oriented.
Speaker 2: There is a need that's been identified typically by a
Speaker 2: combination of clinical, operational and financial axes.
Speaker 2: The dimensions that come together coalesce along the lines of
Speaker 2: saying, Oh yeah, we have ways to provide perspectives about
Speaker 2: that.
Speaker 2: That could potentially change the our approach or improve outcomes,
Speaker 2: etc..
Speaker 2: Could you shed some insight as to how you prioritize
Speaker 2: the work that you're actually trying to accomplish?
Speaker 1: So I've used a model for a couple of decades,
Speaker 1: which is if you want to try to look at
Speaker 1: a process, you need to have several things aligned and
Speaker 1: that is that there's enough volume in the process.
Speaker 1: And so we do asthma, we do bronchiolitis, we have
Speaker 1: a lot of that in given in a yearly basis.
Speaker 1: So that's where we started.
Speaker 1: Do you have the volume to see that there's differences?
Speaker 1: So is there variation then in whatever metric you're you're
Speaker 1: evaluating?
Speaker 1: So with asthma, it's how quick did you get the
Speaker 1: steroids in in the emergency department?
Speaker 1: Did you get a chest x ray?
Speaker 1: How how, how much of the of albuterol do you
Speaker 1: need to get to?
Speaker 1: You get need to give an over what time.
Speaker 1: And there's variation in that data.
Speaker 1: And then does that variation lead to either real like
Speaker 1: I got when I worked at Intermountain Health Care or
Speaker 1: Theoretical, which is what we think variation cost you money.
Speaker 1: There's been a lot written about that.
Speaker 1: And so if if you think that's the case, then
Speaker 1: the Big Trumper is do you have a physician champion?
Speaker 1: And if I have all four of those, it's an
Speaker 1: absolute we're going to do this if I have three
Speaker 1: of them, but one of them's a physician champion, I'm
Speaker 1: going to do it because I need to get the
Speaker 1: physicians on board that working with data and standardizing processes
Speaker 1: improves outcomes.
Speaker 1: He's familiar with that methodology.
Speaker 2: I don't mean to hoard the questions, but I ask
Speaker 2: one more.
Speaker 2: Where do you see your environment in terms of supporting
Speaker 2: research questions?
Speaker 2: How much of the work are you doing already?
Speaker 2: Just proportionately, that, you would say, is much more, if
Speaker 2: you will, pure research as opposed to where it sounds
Speaker 2: like you have most of the emphasis in clinical operations?
Speaker 1: Well, I look at it as quality improvement and I
Speaker 1: consider quality improvement research.
Speaker 1: And so as soon as we feel comfortable that we've
Speaker 1: got a stable process.
Speaker 1: We'll compare pre and post data and if it's something
Speaker 1: novel will present that.
Speaker 1: You know, I got lucky.
Speaker 1: I got into this movement over 20 years ago, and
Speaker 1: so when we found changes, we could publish it.
Speaker 1: It's harder to do that now.
Speaker 1: So now you've got to be a bit more innovative
Speaker 1: of what what you're doing out there, and that's going
Speaker 1: to get into the more predictive stuff and managing much
Speaker 1: closer to where decisions are made.
Speaker 1: So that's where I see us helping out.
Speaker 1: We're also trying to move our epic data into the
Speaker 1: crane structure to be able to participate in the popcorn
Speaker 1: X of a research studies.
Speaker 2: All right.
Speaker 2: Thank you.
Speaker 1: You're welcome.
Speaker 1: Thanks for the questions.
Speaker 2: Chris, a quick question related to P Cornett.
Speaker 2: Isn't there a pediatric network as well?
Speaker 2: And do you participate in that?
Speaker 1: I'm looking into that based on some contacts that I
Speaker 1: got from Dr. Bray just last week.
Speaker 1: Not now.
Speaker 1: We we haven't it's not been a priority for the
Speaker 1: organization.
Speaker 1: Research has not been it.
Speaker 1: It is.
Speaker 1: It is now.
Speaker 1: And so it's been kind of fun to watch that
Speaker 1: that true revolution.
Speaker 1: Scott, did I hear you had a question earlier?
Speaker 0: Know who?
Speaker 0: Me?
Speaker 0: Yeah.
Speaker 0: You know, I always have questions.
Speaker 0: No, I just want to know when our lawyers will
Speaker 0: agree on getting that data for yours into Crane so
Speaker 0: that you can do what you just said to Dr.
Speaker 0: Chang.
Speaker 0: And then we get two of those recorded networks for
Speaker 0: pediatrics.
Speaker 3: So, Chris, maybe I'll jump in.
Speaker 3: This was sort of deja vu all over again to
Speaker 3: just see how you kind of took the stuff we've
Speaker 3: been playing with here in Utah out and about, and
Speaker 3: show us how to do a really nice, efficient version
Speaker 3: of that.
Speaker 3: And just just a few years after, we've been trying
Speaker 3: to do it for a few decades, even starting last
Speaker 3: century.
Speaker 3: But I'm interested in your thoughts about where are the
Speaker 3: gaps in the informatics standards and tools that would be
Speaker 3: the most helpful to to push you further ahead of
Speaker 3: those sort of standards or tool gaps.
Speaker 3: And then the thoughts, as we've discussed, about how to
Speaker 3: scale this to be a multi institutional thing or a
Speaker 3: pediatric adult or, you know, expanding into Coronet and so
Speaker 3: forth.
Speaker 1: Well, I'll take those as as two different questions, though.
Speaker 1: I see the gaps right now in the manual collection
Speaker 1: of data.
Speaker 1: Like.
Speaker 1: So we have we have registries like everybody else has.
Speaker 1: And most of that data entry is manual.
Speaker 1: It's not automatic.
Speaker 1: And yet the computer is really smart, Right?
Speaker 1: We can teach it how to write a legal thesis
Speaker 1: to get into to law school.
Speaker 1: We can.
Speaker 1: So why can't I teach if I can teach a
Speaker 1: person how to find information in a chart, I can
Speaker 1: surely teach a computer to do that.
Speaker 1: And that's a big gap.
Speaker 1: We spend a lot of money and then we get,
Speaker 1: you know, finger problems because we put it into an
Speaker 1: Excel spreadsheet and then we've got to move it into
Speaker 1: a different table.
Speaker 1: So we're actually piloting just this week we started there's
Speaker 1: a tool called Skew Health Spreads and it looks exactly
Speaker 1: like an Excel spreadsheet, but it goes directly the data
Speaker 1: goes directly into our data warehouse so that we can
Speaker 1: use it and we don't have to move it twice.
Speaker 1: But I really want to make that automatic.
Speaker 1: I think that that's what's what's really important as far
Speaker 1: as scaling.
Speaker 1: It can be scaled.
Speaker 1: Right now it's just a matter of joining any sort
Speaker 1: of data source and the team can, you know, they
Speaker 1: go in and they see how data is formatted and
Speaker 1: how do we want it, you know, So we have
Speaker 1: three people that do ETL work and so they just
Speaker 1: go in and move data around and and then, you
Speaker 1: know, build the data dictionary so we know where it
Speaker 1: came from and that it's good strong data.
Speaker 1: So right now it's scalable.
Speaker 3: And so you're using sort of the OMAP kind of
Speaker 3: mapping approach or some of those other sort of interop
Speaker 3: tools to go that direction.
Speaker 1: Yes, that's what we'll need to do.
Speaker 0: You know, Chris, you had made a comment on.
Speaker 0: The the timeliness of the data, Right.
Speaker 0: And, you know, as we've been working together and what
Speaker 0: we're trying to do, I agree.
Speaker 0: And even using the OMA model, which is great, we're
Speaker 0: still the ETL process is very, very long, you know.
Speaker 0: And as we hope and I think you and I
Speaker 0: have discussed using this creating this type of environment for
Speaker 0: a state wide hospital of lack of better or surveillance
Speaker 0: network.
Speaker 0: Right.
Speaker 0: Can we really see that?
Speaker 0: Can we really be surveilling activities that are going on
Speaker 0: in our in our in our health care institutions to
Speaker 0: detect an emerging problem much, much sooner?
Speaker 0: You know, can we learn from COVID that's going to
Speaker 0: require us to get our ETL process under a de.
Speaker 0: Yes.
Speaker 0: And incremental.
Speaker 0: And that's a you know, we're working hard to move
Speaker 0: it that way, but it's it's no small undertaking.
Speaker 1: I agree.
Speaker 0: And to your point, is that the type of.
Speaker 0: With one day late I call it one day late
Speaker 0: be out.
Speaker 0: How helpful would one day late.
Speaker 0: The one day old data be in some of the
Speaker 0: efforts where you're trying to operationalize at children's or Robert
Speaker 0: what you've been trying to do and the A.C in
Speaker 0: the in the PACU environment?
Speaker 1: Well, we use one day of data every day and
Speaker 1: it really does help us.
Speaker 1: You can still do build prediction models.
Speaker 1: You know, one of the things back when bronchiolitis was
Speaker 1: seasonal, we were able to brick build a prediction model
Speaker 1: of when the admissions would start to roll in because
Speaker 1: we had a different staffing model, a nurse staffing model.
Speaker 1: We now have that here.
Speaker 1: And so we we can roll that out.
Speaker 1: The problem that we've had, though, in the last two
Speaker 1: years is that we have a mismatch between the viruses
Speaker 1: are starting to rise and do we have enough staff
Speaker 1: with staff based upon that seasonality rather than real time?
Speaker 1: So I think you can still be informed if you're
Speaker 1: nimble enough and agile enough to react to the data,
Speaker 1: there's going to be a signal in there.
Speaker 0: Okay, I have a question that's a little farther afield.
Speaker 0: Clearly, this was a considerable investment by HMRC.
Speaker 0: Do you have any sense of how long the timeline
Speaker 0: was for your return on investment?
Speaker 0: Was that was it fairly short?
Speaker 0: Are you still somewhere in between?
Speaker 0: Or do you have a sense of how that's going
Speaker 0: to go?
Speaker 1: I would say that right now the return is we've
Speaker 1: already received the return.
Speaker 1: By moving those fees.
Speaker 1: That was a big that was a big player getting
Speaker 1: rid of click view.
Speaker 1: That was a big one.
Speaker 1: So I think we've already recovered on that.
Speaker 1: But I had I had great people working.
Speaker 1: I mean, it was more luck than anything, just with
Speaker 1: the great people that were here.
Speaker 0: Yeah, I would say that even even with the change
Speaker 0: in the moral hazard that you are talking about, that's
Speaker 0: that's a payoff.
Speaker 0: That's almost immeasurable.
Speaker 0: But, you know, I'm thinking about this from like an
Speaker 0: enterprise point of view.
Speaker 0: That's really helpful information.
Speaker 0: Thank you.
Speaker 1: Well, thank you all for attending this afternoon as it's
Speaker 1: getting dark.
I appreciate the questions.
Speaker 1: And if you want more information, I'm happy to have
Speaker 1: a one on one with you for sure.
Speaker 1: Thank you very much.
Speaker 1: All right.
Speaker 1: Have a great evening.
Speaker 0: Take care.
Speaker 0: Thanks, Chris.
Speaker 0: You, too.
Speaker 0: Thank you, Chris.
Speaker 3: Well, a real impressive work.