ab49193a-f232-4153-b81d-a4c008605fc7/5-0
00:00:03.439 --> 00:00:06.081
Gaurav Agarwal,
the Chief Wellness Executive and Vice

ab49193a-f232-4153-b81d-a4c008605fc7/5-1
00:00:06.081 --> 00:00:09.799
President of Northwestern Medicine.
Gaurav, thank you for joining us today.

ab49193a-f232-4153-b81d-a4c008605fc7/6-0
00:00:09.679 --> 00:00:11.199
Thanks for having me, Jordan.

ab49193a-f232-4153-b81d-a4c008605fc7/8-0
00:00:11.079 --> 00:00:15.099
For those who don't know,
Northwestern Medicine is a health system

ab49193a-f232-4153-b81d-a4c008605fc7/8-1
00:00:15.099 --> 00:00:18.639
headquartered in Chicago,
IL with 2700 inpatient beds, 11,

ab49193a-f232-4153-b81d-a4c008605fc7/8-2
00:00:18.639 --> 00:00:22.059
000 providers, 41,
000 employees across 11 hospitals and

ab49193a-f232-4153-b81d-a4c008605fc7/8-3
00:00:22.059 --> 00:00:26.019
other ancillary facilities.
Appreciate you joining us on the show

ab49193a-f232-4153-b81d-a4c008605fc7/8-4
00:00:26.019 --> 00:00:28.659
today.
We have a lot to talk about now as a

ab49193a-f232-4153-b81d-a4c008605fc7/8-5
00:00:28.659 --> 00:00:30.399
Chief Wellness Executive and.

ab49193a-f232-4153-b81d-a4c008605fc7/9-0
00:00:30.479 --> 00:00:35.366
E You have a lot of work to do to hit
some key metrics that the organization is

ab49193a-f232-4153-b81d-a4c008605fc7/9-1
00:00:35.366 --> 00:00:39.459
looking at hitting. Now,
many of our listeners also are interested

ab49193a-f232-4153-b81d-a4c008605fc7/9-2
00:00:39.459 --> 00:00:43.247
in reducing burnout, improving retention,
reducing attrition,

ab49193a-f232-4153-b81d-a4c008605fc7/9-3
00:00:43.247 --> 00:00:45.079
improving recruitment efforts.

ab49193a-f232-4153-b81d-a4c008605fc7/10-0
00:00:45.839 --> 00:00:51.110
So I'd like you to give us an overview of
both your Scholars of Wellness program

ab49193a-f232-4153-b81d-a4c008605fc7/10-1
00:00:51.110 --> 00:00:55.534
and explain what that is.
And then we're going to dive into how you

ab49193a-f232-4153-b81d-a4c008605fc7/10-2
00:00:55.534 --> 00:01:00.805
actually manage your interventions with
your data analytics platform. So Gaurav,

ab49193a-f232-4153-b81d-a4c008605fc7/10-3
00:01:00.805 --> 00:01:05.359
as many people know you, G,
would you please enlighten our listeners?

ab49193a-f232-4153-b81d-a4c008605fc7/11-0
00:01:05.359 --> 00:01:08.319
What are you up to?
How are you reducing burnout?

ab49193a-f232-4153-b81d-a4c008605fc7/12-0
00:01:08.439 --> 00:01:12.504
Thanks, Jordan. You know, for us,
there's a framework that all of this fits

ab49193a-f232-4153-b81d-a4c008605fc7/12-1
00:01:12.504 --> 00:01:16.730
into and I think that's important to know.
The idea for us is how do we create

ab49193a-f232-4153-b81d-a4c008605fc7/12-2
00:01:16.730 --> 00:01:19.564
better work environments so that promote
well-being.

ab49193a-f232-4153-b81d-a4c008605fc7/12-3
00:01:19.564 --> 00:01:23.362
And and that's really important because
when we talk about well-being,

ab49193a-f232-4153-b81d-a4c008605fc7/12-4
00:01:23.362 --> 00:01:27.159
there's lots of different avenues.
People talk a lot about resilience.

ab49193a-f232-4153-b81d-a4c008605fc7/14-0
00:01:28.039 --> 00:01:32.823
Et cetera. But our singular focus,
our laser focus is on how do we create

ab49193a-f232-4153-b81d-a4c008605fc7/14-1
00:01:32.823 --> 00:01:37.865
better work environments so that people
do not experience distress in the 1st

ab49193a-f232-4153-b81d-a4c008605fc7/14-2
00:01:37.865 --> 00:01:42.712
place. And so with that premise in mind,
we have to understand what design

ab49193a-f232-4153-b81d-a4c008605fc7/14-3
00:01:42.712 --> 00:01:45.039
elements, what operational elements.

ab49193a-f232-4153-b81d-a4c008605fc7/16-0
00:01:45.239 --> 00:01:50.234
Are responsible for to either promote
health or promote burnout or other poor

ab49193a-f232-4153-b81d-a4c008605fc7/16-1
00:01:50.234 --> 00:01:53.436
well-being.
And we call that work determinants of

ab49193a-f232-4153-b81d-a4c008605fc7/16-2
00:01:53.436 --> 00:01:56.894
well-being.
Those design elements that either promote

ab49193a-f232-4153-b81d-a4c008605fc7/16-3
00:01:56.894 --> 00:02:00.479
well-being are we call work determinants
of well-being.

ab49193a-f232-4153-b81d-a4c008605fc7/18-0
00:02:00.919 --> 00:02:04.414
And so because my scope is spans the
entire workforce,

ab49193a-f232-4153-b81d-a4c008605fc7/15-0
00:02:01.039 --> 00:02:01.519
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/18-1
00:02:04.414 --> 00:02:09.053
both clinical and non-clinical,
the idea was what sorts of things matter

ab49193a-f232-4153-b81d-a4c008605fc7/18-2
00:02:09.053 --> 00:02:13.438
to various different job families.
So for instance, I used to start,

ab49193a-f232-4153-b81d-a4c008605fc7/18-3
00:02:13.438 --> 00:02:18.839
my focus was only in physicians initially
and then it expanded and when it expanded.

ab49193a-f232-4153-b81d-a4c008605fc7/17-0
00:02:16.679 --> 00:02:17.159
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/19-0
00:02:18.919 --> 00:02:21.882
It wasn't.
I didn't think it was going to be right

ab49193a-f232-4153-b81d-a4c008605fc7/19-1
00:02:21.882 --> 00:02:25.948
to just take our physician playbook and
apply it to now nurses or our

ab49193a-f232-4153-b81d-a4c008605fc7/19-2
00:02:25.948 --> 00:02:30.130
environmental service workers.
I didn't feel like the the interventions

ab49193a-f232-4153-b81d-a4c008605fc7/19-3
00:02:30.130 --> 00:02:33.673
would land correctly.
And so for us we had to figure out how

ab49193a-f232-4153-b81d-a4c008605fc7/19-4
00:02:33.673 --> 00:02:36.519
what was our going to be our mechanism to
scale.

ab49193a-f232-4153-b81d-a4c008605fc7/20-0
00:02:36.519 --> 00:02:40.114
Interventions that mattered to individual
work families.

ab49193a-f232-4153-b81d-a4c008605fc7/20-1
00:02:40.114 --> 00:02:44.718
And so our flagship program that you
mentioned is called the Scholars of

ab49193a-f232-4153-b81d-a4c008605fc7/20-2
00:02:44.718 --> 00:02:47.808
Wellness.
The idea behind this is that the folks

ab49193a-f232-4153-b81d-a4c008605fc7/20-3
00:02:47.808 --> 00:02:51.529
that do the work,
the frontline folks know how to do their

ab49193a-f232-4153-b81d-a4c008605fc7/20-4
00:02:51.529 --> 00:02:52.159
work best.

ab49193a-f232-4153-b81d-a4c008605fc7/22-0
00:02:52.359 --> 00:02:56.924
And know what the pain points are,
what know what those work determinants of

ab49193a-f232-4153-b81d-a4c008605fc7/22-1
00:02:56.924 --> 00:02:59.710
well-being are and how you might improve
them.

ab49193a-f232-4153-b81d-a4c008605fc7/22-2
00:02:59.710 --> 00:03:03.681
What they don't know is sort of how
organizational change happens.

ab49193a-f232-4153-b81d-a4c008605fc7/22-3
00:03:03.681 --> 00:03:08.364
They don't know the well-being science
very well when they go into the program

ab49193a-f232-4153-b81d-a4c008605fc7/21-0
00:03:06.239 --> 00:03:06.399
2.

ab49193a-f232-4153-b81d-a4c008605fc7/22-4
00:03:08.364 --> 00:03:12.039
and they don't know how project
management occurs at a local.

ab49193a-f232-4153-b81d-a4c008605fc7/25-0
00:03:12.039 --> 00:03:14.656
Institution.
And so a lot of times you'll hear

ab49193a-f232-4153-b81d-a4c008605fc7/25-1
00:03:14.656 --> 00:03:18.107
clinicians say, oh,
I don't know why they just don't fix this

ab49193a-f232-4153-b81d-a4c008605fc7/25-2
00:03:18.107 --> 00:03:22.059
one thing. I would fix this like that.
And the truth is that's not how

ab49193a-f232-4153-b81d-a4c008605fc7/23-0
00:03:18.479 --> 00:03:19.719
Mm-hmm.

ab49193a-f232-4153-b81d-a4c008605fc7/25-3
00:03:22.059 --> 00:03:26.512
organizations get fixed. And so for us,
what we had to do was make sure that we

ab49193a-f232-4153-b81d-a4c008605fc7/25-4
00:03:26.512 --> 00:03:29.239
train these individuals in the Wellness
science.

ab49193a-f232-4153-b81d-a4c008605fc7/24-0
00:03:26.559 --> 00:03:27.799
Mm-hmm.

ab49193a-f232-4153-b81d-a4c008605fc7/28-0
00:03:29.399 --> 00:03:33.870
The process improvement,
project management science and the change

ab49193a-f232-4153-b81d-a4c008605fc7/26-0
00:03:33.799 --> 00:03:34.359
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/28-1
00:03:33.870 --> 00:03:39.275
management science so that they can then
take ideas that they have that are pain

ab49193a-f232-4153-b81d-a4c008605fc7/28-2
00:03:39.275 --> 00:03:44.012
points for folks in their local
environments and start to improve them

ab49193a-f232-4153-b81d-a4c008605fc7/27-0
00:03:41.119 --> 00:03:41.679
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/30-0
00:03:43.279 --> 00:03:47.721
I think so. I think Gee,
that what's most innovative and perhaps

ab49193a-f232-4153-b81d-a4c008605fc7/28-3
00:03:44.012 --> 00:03:44.479
and so.

ab49193a-f232-4153-b81d-a4c008605fc7/29-0
00:03:44.679 --> 00:03:45.199
Yeah, good.

ab49193a-f232-4153-b81d-a4c008605fc7/30-1
00:03:47.721 --> 00:03:53.050
most relevant to healthy data podcasts is
the way you kind of abstract custom

ab49193a-f232-4153-b81d-a4c008605fc7/30-2
00:03:53.050 --> 00:03:57.833
metrics per specialty area.
It seems like you have a federated method

ab49193a-f232-4153-b81d-a4c008605fc7/30-3
00:03:57.833 --> 00:04:02.479
of solving issues locally within the
organization and you actually.

ab49193a-f232-4153-b81d-a4c008605fc7/31-0
00:04:02.919 --> 00:04:07.533
Force local individuals in radiology and
pathology and ulmonology and the hospital

ab49193a-f232-4153-b81d-a4c008605fc7/31-1
00:04:07.533 --> 00:04:12.035
is for and say what are the metrics that
most matter to your particular secialty

ab49193a-f232-4153-b81d-a4c008605fc7/31-2
00:04:12.035 --> 00:04:15.759
and that's you're generating your own
data sources, is that right?

ab49193a-f232-4153-b81d-a4c008605fc7/34-0
00:04:15.719 --> 00:04:19.470
That's that's right.
And the other thing I would add is we're

ab49193a-f232-4153-b81d-a4c008605fc7/34-1
00:04:19.470 --> 00:04:22.917
we're generating complete data sources.
So for instance,

ab49193a-f232-4153-b81d-a4c008605fc7/32-0
00:04:21.199 --> 00:04:21.719
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/34-2
00:04:22.917 --> 00:04:26.970
if I send someone an e-mail or the
pathology department an e-mail,

ab49193a-f232-4153-b81d-a4c008605fc7/34-3
00:04:26.970 --> 00:04:30.357
I at best I'm going to get a 30% response
rate at best,

ab49193a-f232-4153-b81d-a4c008605fc7/33-0
00:04:28.719 --> 00:04:29.239
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/34-4
00:04:30.357 --> 00:04:35.076
but when one of their fellow colleagues
sends out a survey or a a focus group

ab49193a-f232-4153-b81d-a4c008605fc7/34-5
00:04:35.076 --> 00:04:35.559
request.

ab49193a-f232-4153-b81d-a4c008605fc7/36-0
00:04:36.239 --> 00:04:41.015
Or any other sort of data request.
Some of the response rates we get are

ab49193a-f232-4153-b81d-a4c008605fc7/36-1
00:04:41.015 --> 00:04:45.855
708090% response rates and so we can
trust the data far more because it's

ab49193a-f232-4153-b81d-a4c008605fc7/35-0
00:04:42.559 --> 00:04:43.079
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/36-2
00:04:45.855 --> 00:04:49.518
complete data.
Otherwise there's a real risk that a lot

ab49193a-f232-4153-b81d-a4c008605fc7/36-3
00:04:49.518 --> 00:04:52.985
of the data,
the well-being folks around the country

ab49193a-f232-4153-b81d-a4c008605fc7/36-4
00:04:52.985 --> 00:04:53.639
are using.

ab49193a-f232-4153-b81d-a4c008605fc7/37-0
00:04:53.959 --> 00:04:59.188
Are incomplete 4050% response rates. Well,
what are the other 50% feeling and

ab49193a-f232-4153-b81d-a4c008605fc7/37-1
00:04:59.188 --> 00:05:02.539
thinking?
Are we sure we know and can extrapolate

ab49193a-f232-4153-b81d-a4c008605fc7/37-2
00:05:02.539 --> 00:05:06.359
based on the the 50% that responded to
our data sources?

ab49193a-f232-4153-b81d-a4c008605fc7/38-0
00:05:06.319 --> 00:05:08.796
So Gee,
I think what I hear you saying is that

ab49193a-f232-4153-b81d-a4c008605fc7/38-1
00:05:08.796 --> 00:05:12.169
it's not necessarily,
at least in this example, a tech problem.

ab49193a-f232-4153-b81d-a4c008605fc7/38-2
00:05:12.169 --> 00:05:16.174
It's a social engineering problem.
And the idea is you need in order to get

ab49193a-f232-4153-b81d-a4c008605fc7/38-3
00:05:16.174 --> 00:05:19.494
valid data that people trust,
you need to get clinical buy in.

ab49193a-f232-4153-b81d-a4c008605fc7/38-4
00:05:19.494 --> 00:05:23.446
In order to get clinical buy in,
it's all about leveraging the right kinds

ab49193a-f232-4153-b81d-a4c008605fc7/38-5
00:05:23.446 --> 00:05:26.239
of organization,
the people within the organization.

ab49193a-f232-4153-b81d-a4c008605fc7/39-0
00:05:26.319 --> 00:05:30.760
In order to get that response rate so
that you have valid data that clinical

ab49193a-f232-4153-b81d-a4c008605fc7/39-1
00:05:30.760 --> 00:05:33.759
leaders trust from their colleagues.
Is that right?

ab49193a-f232-4153-b81d-a4c008605fc7/40-0
00:05:33.479 --> 00:05:35.799
That's exactly right.
That's exactly right.

ab49193a-f232-4153-b81d-a4c008605fc7/41-0
00:05:35.919 --> 00:05:38.786
Excellent.
And so you know you spoke about one

ab49193a-f232-4153-b81d-a4c008605fc7/41-1
00:05:38.786 --> 00:05:42.933
getting the actually, you know what,
let's take a concrete example.

ab49193a-f232-4153-b81d-a4c008605fc7/41-2
00:05:42.933 --> 00:05:47.934
Can you speak about one particular area
of the organization could be radiology or

ab49193a-f232-4153-b81d-a4c008605fc7/41-3
00:05:47.934 --> 00:05:52.569
another specialty and just give us a
three-minute anecdote about particular

ab49193a-f232-4153-b81d-a4c008605fc7/41-4
00:05:52.569 --> 00:05:55.679
metrics that you received that was that
our value.

ab49193a-f232-4153-b81d-a4c008605fc7/42-0
00:05:55.679 --> 00:05:57.919
To that to that area.

ab49193a-f232-4153-b81d-a4c008605fc7/45-0
00:05:58.639 --> 00:06:01.687
Sure.
I'll give you an anesthesiology project,

ab49193a-f232-4153-b81d-a4c008605fc7/45-1
00:06:01.687 --> 00:06:06.227
a project that we just worked on.
You know, anesthesiology is a very,

ab49193a-f232-4153-b81d-a4c008605fc7/43-0
00:06:01.759 --> 00:06:02.279
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/45-2
00:06:06.227 --> 00:06:09.988
very tough field right now from a
recruitment, retention,

ab49193a-f232-4153-b81d-a4c008605fc7/45-3
00:06:09.988 --> 00:06:13.814
satisfaction standpoint.
And our scholar highlighted that,

ab49193a-f232-4153-b81d-a4c008605fc7/45-4
00:06:13.814 --> 00:06:17.187
you know,
they signed up for anesthesiology knowing

ab49193a-f232-4153-b81d-a4c008605fc7/45-5
00:06:17.187 --> 00:06:18.159
that the hours.

ab49193a-f232-4153-b81d-a4c008605fc7/47-0
00:06:18.359 --> 00:06:21.841
Hours are tough.
It's not like they they expected a nine

ab49193a-f232-4153-b81d-a4c008605fc7/44-0
00:06:18.559 --> 00:06:19.759
Mm-hmm.

ab49193a-f232-4153-b81d-a4c008605fc7/47-1
00:06:21.841 --> 00:06:26.483
to five Monday through Friday job.
But what's also true is that those hours

ab49193a-f232-4153-b81d-a4c008605fc7/47-2
00:06:26.483 --> 00:06:29.232
have started to become far less
predictable.

ab49193a-f232-4153-b81d-a4c008605fc7/47-3
00:06:29.232 --> 00:06:32.347
And so she said you can't just look at
our shifts,

ab49193a-f232-4153-b81d-a4c008605fc7/46-0
00:06:29.679 --> 00:06:30.359
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/47-4
00:06:32.347 --> 00:06:36.439
you have to look at when the shift was
supposed to start and stop.

ab49193a-f232-4153-b81d-a4c008605fc7/50-0
00:06:36.439 --> 00:06:39.342
And then when,
when do we actually leave the hospital?

ab49193a-f232-4153-b81d-a4c008605fc7/50-1
00:06:39.342 --> 00:06:43.089
And the reason that's important is
because the the the time card isn't

ab49193a-f232-4153-b81d-a4c008605fc7/50-2
00:06:43.089 --> 00:06:46.151
getting it right.
We're not able to leave when we when we

ab49193a-f232-4153-b81d-a4c008605fc7/50-3
00:06:46.151 --> 00:06:50.056
thought we were going to leave,
which has huge downstream implications in

ab49193a-f232-4153-b81d-a4c008605fc7/48-0
00:06:47.559 --> 00:06:48.039
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/50-4
00:06:50.056 --> 00:06:52.801
terms of the conflict that's being
created at home.

ab49193a-f232-4153-b81d-a4c008605fc7/50-5
00:06:52.801 --> 00:06:55.439
If I told my kid I was going to come home
at six.

ab49193a-f232-4153-b81d-a4c008605fc7/49-0
00:06:53.839 --> 00:06:53.959
OK.

ab49193a-f232-4153-b81d-a4c008605fc7/54-0
00:06:55.719 --> 00:06:58.859
To go to their game and now I'm not there.
You know,

ab49193a-f232-4153-b81d-a4c008605fc7/54-1
00:06:58.859 --> 00:07:03.657
that's a whole lot of a lot of conflict.
And what we found was that we were able

ab49193a-f232-4153-b81d-a4c008605fc7/51-0
00:07:02.079 --> 00:07:02.599
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/54-2
00:07:03.657 --> 00:07:08.219
to see who was actually leaving on time.
And when they didn't leave on time,

ab49193a-f232-4153-b81d-a4c008605fc7/52-0
00:07:07.519 --> 00:07:08.279
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/54-3
00:07:08.219 --> 00:07:12.839
it was causing the exact conflicts this
scholar had hypothesized would occur.

ab49193a-f232-4153-b81d-a4c008605fc7/55-0
00:07:13.799 --> 00:07:17.673
And then when they have those conflicts,
those were the exact people that were

ab49193a-f232-4153-b81d-a4c008605fc7/53-0
00:07:13.839 --> 00:07:14.359
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/55-1
00:07:17.673 --> 00:07:21.155
experiencing the most burnout,
were telling us that they were going to

ab49193a-f232-4153-b81d-a4c008605fc7/55-2
00:07:21.155 --> 00:07:24.685
reduce their hours or telling us that
they were frankly going to leave.

ab49193a-f232-4153-b81d-a4c008605fc7/55-3
00:07:24.685 --> 00:07:28.412
And so it was this very elegant,
beautiful data point that someone like me,

ab49193a-f232-4153-b81d-a4c008605fc7/55-4
00:07:28.412 --> 00:07:31.550
sitting from where I do,
would have no idea I should be where I

ab49193a-f232-4153-b81d-a4c008605fc7/55-5
00:07:31.550 --> 00:07:33.119
should be looking for that data.

ab49193a-f232-4153-b81d-a4c008605fc7/56-0
00:07:33.399 --> 00:07:37.827
Where she was able to pinpoint that this
is actually the data point that matters

ab49193a-f232-4153-b81d-a4c008605fc7/56-1
00:07:37.827 --> 00:07:38.319
the most.

ab49193a-f232-4153-b81d-a4c008605fc7/58-0
00:07:38.079 --> 00:07:41.607
I really appreciate you delving deep into
that anesthesiology project.

ab49193a-f232-4153-b81d-a4c008605fc7/57-0
00:07:39.079 --> 00:07:39.439
Yeah.

ab49193a-f232-4153-b81d-a4c008605fc7/58-1
00:07:41.607 --> 00:07:44.886
I think that may resonate with many
listeners across the country.

ab49193a-f232-4153-b81d-a4c008605fc7/58-2
00:07:44.886 --> 00:07:47.768
Just real quick,
someone listening to this show right now

ab49193a-f232-4153-b81d-a4c008605fc7/58-3
00:07:47.768 --> 00:07:50.997
say that's amazing. You know,
you validated that hypothesis that

ab49193a-f232-4153-b81d-a4c008605fc7/58-4
00:07:50.997 --> 00:07:53.183
resonates.
I've heard that maybe a CMIO was

ab49193a-f232-4153-b81d-a4c008605fc7/58-5
00:07:53.183 --> 00:07:56.463
listening and saying, yes,
my clinicians have also said something

ab49193a-f232-4153-b81d-a4c008605fc7/58-6
00:07:56.463 --> 00:07:56.959
like this.

ab49193a-f232-4153-b81d-a4c008605fc7/59-0
00:07:57.039 --> 00:08:01.016
Have you been able to present the
intervention and how do you,

ab49193a-f232-4153-b81d-a4c008605fc7/59-1
00:08:01.016 --> 00:08:05.940
now that you know this is a problem,
how do you reduce burnout and help those

ab49193a-f232-4153-b81d-a4c008605fc7/59-2
00:08:05.940 --> 00:08:07.959
anesthesiologists leave on time?

ab49193a-f232-4153-b81d-a4c008605fc7/62-0
00:08:07.879 --> 00:08:12.513
The power of the program is is in most
organizations, pilots are doable.

ab49193a-f232-4153-b81d-a4c008605fc7/62-1
00:08:12.513 --> 00:08:16.449
When you just say, hey,
I'm going to change something and you

ab49193a-f232-4153-b81d-a4c008605fc7/62-2
00:08:16.449 --> 00:08:19.876
don't really know how it's going to work,
it's tough.

ab49193a-f232-4153-b81d-a4c008605fc7/60-0
00:08:16.519 --> 00:08:16.999
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/62-3
00:08:19.876 --> 00:08:24.319
And so the power of this was once we
presented the data very clearly.

ab49193a-f232-4153-b81d-a4c008605fc7/61-0
00:08:23.919 --> 00:08:24.399
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/64-0
00:08:25.079 --> 00:08:29.870
That this is a very causal link you're
seeing. Then people were open to saying,

ab49193a-f232-4153-b81d-a4c008605fc7/64-1
00:08:29.870 --> 00:08:34.360
well, let's do a two-month pilot.
Let's do a two-month pilot where we have

ab49193a-f232-4153-b81d-a4c008605fc7/64-2
00:08:34.360 --> 00:08:38.911
what's called a relief attending.
A relief attending comes in at a set time

ab49193a-f232-4153-b81d-a4c008605fc7/63-0
00:08:36.399 --> 00:08:36.879
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/64-3
00:08:38.911 --> 00:08:43.282
when you were supposed to get off shift
and relieves you of your duties.

ab49193a-f232-4153-b81d-a4c008605fc7/64-4
00:08:43.282 --> 00:08:44.599
So you know that when.

ab49193a-f232-4153-b81d-a4c008605fc7/66-0
00:08:44.599 --> 00:08:48.958
If this shift was supposed to end,
you are going to leave because someone

ab49193a-f232-4153-b81d-a4c008605fc7/66-1
00:08:48.958 --> 00:08:52.550
can take over your case.
And so they implemented this relief

ab49193a-f232-4153-b81d-a4c008605fc7/66-2
00:08:52.550 --> 00:08:55.671
attending.
And what was interesting is that when you

ab49193a-f232-4153-b81d-a4c008605fc7/66-3
00:08:55.671 --> 00:09:00.559
asked the anesthesiologist how many days
a week would you like a relief attending?

ab49193a-f232-4153-b81d-a4c008605fc7/65-0
00:09:00.679 --> 00:09:01.159
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/69-0
00:09:00.679 --> 00:09:03.898
Probably to no surprise,
everyone said every day.

ab49193a-f232-4153-b81d-a4c008605fc7/69-1
00:09:03.898 --> 00:09:09.112
But when we looked at the data and we
said how many days a week does it take to,

ab49193a-f232-4153-b81d-a4c008605fc7/67-0
00:09:04.359 --> 00:09:04.519
Yeah.

ab49193a-f232-4153-b81d-a4c008605fc7/69-2
00:09:09.112 --> 00:09:12.653
at least on the well-being front,
make the difference?

ab49193a-f232-4153-b81d-a4c008605fc7/69-3
00:09:12.653 --> 00:09:16.644
It was four days a week.
The folks that had had were a relief

ab49193a-f232-4153-b81d-a4c008605fc7/69-4
00:09:16.644 --> 00:09:18.639
attending for four days a week.

ab49193a-f232-4153-b81d-a4c008605fc7/71-0
00:09:18.839 --> 00:09:23.466
Or more were not having those work home
conflicts at a rate that was sufficient

ab49193a-f232-4153-b81d-a4c008605fc7/68-0
00:09:19.879 --> 00:09:20.519
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/71-1
00:09:23.466 --> 00:09:27.687
enough to cause enough conflict,
enough burnout. So if we had to choose,

ab49193a-f232-4153-b81d-a4c008605fc7/71-2
00:09:27.687 --> 00:09:30.810
and sometimes you have to make these
choices in life,

ab49193a-f232-4153-b81d-a4c008605fc7/71-3
00:09:30.810 --> 00:09:33.470
can't four days was going to give you
enough,

ab49193a-f232-4153-b81d-a4c008605fc7/70-0
00:09:31.399 --> 00:09:31.959
And again.

ab49193a-f232-4153-b81d-a4c008605fc7/71-4
00:09:33.470 --> 00:09:38.155
even though obviously everybody wanted
somebody to relieve them every single day

ab49193a-f232-4153-b81d-a4c008605fc7/71-5
00:09:38.155 --> 00:09:38.559
and so.

ab49193a-f232-4153-b81d-a4c008605fc7/72-0
00:09:38.679 --> 00:09:41.902
That's the kind of thing that really
resonates with organizations,

ab49193a-f232-4153-b81d-a4c008605fc7/72-1
00:09:41.902 --> 00:09:44.885
that we're being efficient and thoughtful
with our resources,

ab49193a-f232-4153-b81d-a4c008605fc7/72-2
00:09:44.885 --> 00:09:46.039
not just more is better.

ab49193a-f232-4153-b81d-a4c008605fc7/73-0
00:09:45.639 --> 00:09:48.260
Gee,
I appreciate your answer to my question

ab49193a-f232-4153-b81d-a4c008605fc7/73-1
00:09:48.260 --> 00:09:52.044
about that intervention with the pilot
and the relief attending.

ab49193a-f232-4153-b81d-a4c008605fc7/73-2
00:09:52.044 --> 00:09:56.527
I do want to get to the data analytics
platform portion of the conversation,

ab49193a-f232-4153-b81d-a4c008605fc7/73-3
00:09:56.527 --> 00:10:00.720
but one more question before we do.
Some listeners may who are from the

ab49193a-f232-4153-b81d-a4c008605fc7/73-4
00:10:00.720 --> 00:10:05.319
clinical space may say you know that
sounds fantastic, you know my anesthesia.

ab49193a-f232-4153-b81d-a4c008605fc7/74-0
00:10:05.359 --> 00:10:09.649
Aesthesiologist is supposed to get off at
7:00 AM and should be home to get the

ab49193a-f232-4153-b81d-a4c008605fc7/74-1
00:10:09.649 --> 00:10:13.832
kids ready for school. And that's great.
But what if they're in the middle of

ab49193a-f232-4153-b81d-a4c008605fc7/74-2
00:10:13.832 --> 00:10:17.585
surgery or they're in the OR, you know?
Or what if there's a handoff?

ab49193a-f232-4153-b81d-a4c008605fc7/74-3
00:10:17.585 --> 00:10:20.159
Like maybe the relief attending starts at
7:00,

ab49193a-f232-4153-b81d-a4c008605fc7/74-4
00:10:20.159 --> 00:10:24.341
but it takes 2540 minutes for me to do a
handoff because there's some complex

ab49193a-f232-4153-b81d-a4c008605fc7/74-5
00:10:24.341 --> 00:10:25.199
cases coming up.

ab49193a-f232-4153-b81d-a4c008605fc7/76-0
00:10:25.239 --> 00:10:29.825
You know, I still can't get out on time.
So do you have the relief attending come

ab49193a-f232-4153-b81d-a4c008605fc7/76-1
00:10:29.825 --> 00:10:32.956
at like 6:00 AM?
How do you handle the handoff and also

ab49193a-f232-4153-b81d-a4c008605fc7/76-2
00:10:32.956 --> 00:10:35.919
like I'm in the OR up to my and I can't
do anything?

ab49193a-f232-4153-b81d-a4c008605fc7/80-0
00:10:37.119 --> 00:10:41.634
I would say that I'm probably not the
expert on on the the ways the

ab49193a-f232-4153-b81d-a4c008605fc7/80-1
00:10:41.634 --> 00:10:45.684
anesthesiologists have operationalized
the relief attending,

ab49193a-f232-4153-b81d-a4c008605fc7/77-0
00:10:41.839 --> 00:10:45.599
Yeah, yeah. Mm-hmm.

ab49193a-f232-4153-b81d-a4c008605fc7/80-2
00:10:45.684 --> 00:10:51.062
but I would say that we all know what a
handoff looks like and you sort of build

ab49193a-f232-4153-b81d-a4c008605fc7/78-0
00:10:49.639 --> 00:10:49.919
Hmm.

ab49193a-f232-4153-b81d-a4c008605fc7/80-3
00:10:51.062 --> 00:10:56.108
that in to what it's going to take to
have a clean handoff and or you build

ab49193a-f232-4153-b81d-a4c008605fc7/79-0
00:10:52.399 --> 00:10:53.719
Got it.

ab49193a-f232-4153-b81d-a4c008605fc7/80-4
00:10:56.108 --> 00:10:56.639
that in.

ab49193a-f232-4153-b81d-a4c008605fc7/85-0
00:10:56.719 --> 00:10:59.929
Into the predictability of when I'm going
to get home. If I, you know,

ab49193a-f232-4153-b81d-a4c008605fc7/85-1
00:10:59.929 --> 00:11:03.139
you sort of know that the handoff might
take 30 minutes and so now I'm

ab49193a-f232-4153-b81d-a4c008605fc7/85-2
00:11:03.139 --> 00:11:06.710
comfortable telling my family I'm going
to add that 30 minutes in and and I'll

ab49193a-f232-4153-b81d-a4c008605fc7/82-0
00:11:05.159 --> 00:11:05.639
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/85-3
00:11:06.710 --> 00:11:09.920
know what time I'll get home.
That is far more predictable than I have

ab49193a-f232-4153-b81d-a4c008605fc7/85-4
00:11:09.920 --> 00:11:13.492
no idea when the surgery is going to end
and I obviously can't leave until the

ab49193a-f232-4153-b81d-a4c008605fc7/83-0
00:11:09.999 --> 00:11:10.199
Yeah.

ab49193a-f232-4153-b81d-a4c008605fc7/84-0
00:11:11.919 --> 00:11:12.039
Uh huh.

ab49193a-f232-4153-b81d-a4c008605fc7/85-5
00:11:13.492 --> 00:11:14.079
surgery ends.

ab49193a-f232-4153-b81d-a4c008605fc7/86-0
00:11:13.919 --> 00:11:17.002
Reliability.
That's a real big take away for our

ab49193a-f232-4153-b81d-a4c008605fc7/86-1
00:11:17.002 --> 00:11:19.770
listeners.
Now let's transition to the data

ab49193a-f232-4153-b81d-a4c008605fc7/86-2
00:11:19.770 --> 00:11:22.915
analytics platform. First,
I'd like to allow you,

ab49193a-f232-4153-b81d-a4c008605fc7/86-3
00:11:22.915 --> 00:11:27.759
can you mention the coal mine metaphor,
please? I think that would resonate.

ab49193a-f232-4153-b81d-a4c008605fc7/90-0
00:11:25.479 --> 00:11:30.156
So my dad was a coal miner,
and so the idea was that you hear a lot

ab49193a-f232-4153-b81d-a4c008605fc7/90-1
00:11:30.156 --> 00:11:33.801
of us are familiar with the Canary in the
coal mine.

ab49193a-f232-4153-b81d-a4c008605fc7/90-2
00:11:33.801 --> 00:11:37.721
They took the Canaries down there.
If the air was toxic,

ab49193a-f232-4153-b81d-a4c008605fc7/90-3
00:11:37.721 --> 00:11:42.329
the Canary would die first.
That would be the warning sign for the

ab49193a-f232-4153-b81d-a4c008605fc7/87-0
00:11:37.839 --> 00:11:38.079
1.

ab49193a-f232-4153-b81d-a4c008605fc7/88-0
00:11:40.359 --> 00:11:40.839
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/90-4
00:11:42.329 --> 00:11:45.079
coal miners to get out of the coal mine.

ab49193a-f232-4153-b81d-a4c008605fc7/89-0
00:11:45.159 --> 00:11:45.639
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/93-0
00:11:45.199 --> 00:11:49.390
And the the story I had heard was
actually that the Canaries were viewed as

ab49193a-f232-4153-b81d-a4c008605fc7/93-1
00:11:49.390 --> 00:11:52.753
pets by the coal miners.
So they created this thing called a

ab49193a-f232-4153-b81d-a4c008605fc7/91-0
00:11:52.479 --> 00:11:53.319
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/93-2
00:11:52.753 --> 00:11:55.896
Canary resuscitator,
where if the the Canary passed out,

ab49193a-f232-4153-b81d-a4c008605fc7/93-3
00:11:55.896 --> 00:11:59.755
they would put it in the Canary
resuscitator, pump it full of oxygen,

ab49193a-f232-4153-b81d-a4c008605fc7/93-4
00:11:59.755 --> 00:12:03.559
the Canary would live and everyone would
get to leave the coal mine.

ab49193a-f232-4153-b81d-a4c008605fc7/94-0
00:12:03.679 --> 00:12:07.826
And that resonated with me a lot later
because I'm a psychiatrist and I take

ab49193a-f232-4153-b81d-a4c008605fc7/92-0
00:12:03.879 --> 00:12:03.999
Huh.

ab49193a-f232-4153-b81d-a4c008605fc7/94-1
00:12:07.826 --> 00:12:11.866
care of of of impaired physicians
especially and other healthcare workers.

ab49193a-f232-4153-b81d-a4c008605fc7/94-2
00:12:11.866 --> 00:12:15.959
And one of the doctors that I was taking
care of said, you know this, well,

ab49193a-f232-4153-b81d-a4c008605fc7/94-3
00:12:15.959 --> 00:12:18.759
this stuff isn't gonna work unless
someone decides.

ab49193a-f232-4153-b81d-a4c008605fc7/97-0
00:12:19.199 --> 00:12:21.817
That they don't want to just make
stronger Canaries.

ab49193a-f232-4153-b81d-a4c008605fc7/97-1
00:12:21.817 --> 00:12:24.977
Unless someone decides they want to have
a healthier coal mine,

ab49193a-f232-4153-b81d-a4c008605fc7/95-0
00:12:24.719 --> 00:12:25.239
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/97-2
00:12:24.977 --> 00:12:28.977
it's not going to be enough for people to
just try to make stronger and stronger

ab49193a-f232-4153-b81d-a4c008605fc7/97-3
00:12:28.977 --> 00:12:31.446
canaries.
And so that's been our guiding light or

ab49193a-f232-4153-b81d-a4c008605fc7/97-4
00:12:31.446 --> 00:12:33.717
our North Star,
whatever you want to call it,

ab49193a-f232-4153-b81d-a4c008605fc7/97-5
00:12:33.717 --> 00:12:37.519
as to what it is that we do in well-being
as we create healthier coal mines.

ab49193a-f232-4153-b81d-a4c008605fc7/96-0
00:12:33.799 --> 00:12:34.279
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/98-0
00:12:38.239 --> 00:12:41.562
Now, Gee,
you said the data analytics platform has

ab49193a-f232-4153-b81d-a4c008605fc7/98-1
00:12:41.562 --> 00:12:45.081
enabled you to see the entire coal mine
across silos.

ab49193a-f232-4153-b81d-a4c008605fc7/98-2
00:12:45.081 --> 00:12:50.163
Many listeners will know it's no secret
that healthcare across the country at

ab49193a-f232-4153-b81d-a4c008605fc7/98-3
00:12:50.163 --> 00:12:54.593
almost every organization,
it has many different silos by specialty

ab49193a-f232-4153-b81d-a4c008605fc7/98-4
00:12:54.593 --> 00:12:57.199
area, by vertical finance, HR, clinical.

ab49193a-f232-4153-b81d-a4c008605fc7/99-0
00:12:57.679 --> 00:13:03.245
Areas and whereas we just spoke about the
scholars of Wellness who in each specific

ab49193a-f232-4153-b81d-a4c008605fc7/99-1
00:13:03.245 --> 00:13:08.480
specialty area have their own metrics,
you're also interested in leveraging AI

ab49193a-f232-4153-b81d-a4c008605fc7/99-2
00:13:08.480 --> 00:13:13.648
to look across the different silos and
see if there are metrics that nobody's

ab49193a-f232-4153-b81d-a4c008605fc7/99-3
00:13:13.648 --> 00:13:15.039
even really thinking.

ab49193a-f232-4153-b81d-a4c008605fc7/100-0
00:13:15.199 --> 00:13:18.025
Might be worth working on to improve
Wellness.

ab49193a-f232-4153-b81d-a4c008605fc7/100-1
00:13:18.025 --> 00:13:22.655
Can you speak about the data analytics
platform, how it works, what it does,

ab49193a-f232-4153-b81d-a4c008605fc7/100-2
00:13:22.655 --> 00:13:24.879
and some of its data sources, please?

ab49193a-f232-4153-b81d-a4c008605fc7/104-0
00:13:25.639 --> 00:13:28.623
Yep.
So the idea here is that Wellness for us

ab49193a-f232-4153-b81d-a4c008605fc7/104-1
00:13:28.623 --> 00:13:32.645
in in the in a future state has to be an
operations exercise.

ab49193a-f232-4153-b81d-a4c008605fc7/101-0
00:13:31.319 --> 00:13:31.799
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/104-2
00:13:32.645 --> 00:13:35.824
It can't be an initiative that sits in
the side.

ab49193a-f232-4153-b81d-a4c008605fc7/104-3
00:13:35.824 --> 00:13:40.948
It has to be embedded in the operations
because if you're fixing coal mines to

ab49193a-f232-4153-b81d-a4c008605fc7/104-4
00:13:40.948 --> 00:13:45.359
fix a coal mine that the operation of the
coal mine is what you're.

ab49193a-f232-4153-b81d-a4c008605fc7/102-0
00:13:41.359 --> 00:13:42.519
Mm-hmm.

ab49193a-f232-4153-b81d-a4c008605fc7/103-0
00:13:45.359 --> 00:13:46.519
Mm-hmm.

ab49193a-f232-4153-b81d-a4c008605fc7/108-0
00:13:45.359 --> 00:13:48.868
Dressing and what we found is that if if
that's true,

ab49193a-f232-4153-b81d-a4c008605fc7/108-1
00:13:48.868 --> 00:13:54.262
then we Wellness must be at Wellness data
has to be the same kind of data that our

ab49193a-f232-4153-b81d-a4c008605fc7/108-2
00:13:54.262 --> 00:13:58.680
operators are used to seeing.
And so if our operators wanted to fix

ab49193a-f232-4153-b81d-a4c008605fc7/105-0
00:13:54.519 --> 00:13:54.999
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/106-0
00:13:56.319 --> 00:13:56.839
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/108-3
00:13:58.680 --> 00:14:01.279
turnaround times in the operating rooms.

ab49193a-f232-4153-b81d-a4c008605fc7/107-0
00:14:01.999 --> 00:14:02.479
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/112-0
00:14:03.159 --> 00:14:08.349
If our operators want to improve access
to care, for instance,

ab49193a-f232-4153-b81d-a4c008605fc7/112-1
00:14:08.349 --> 00:14:13.127
they can look up at the their data that
day, that moment,

ab49193a-f232-4153-b81d-a4c008605fc7/109-0
00:14:08.479 --> 00:14:09.119
Mm-hmm.

ab49193a-f232-4153-b81d-a4c008605fc7/112-2
00:14:13.127 --> 00:14:19.634
and know exactly how many ORS are empty.
They can know exactly how many people

ab49193a-f232-4153-b81d-a4c008605fc7/110-0
00:14:14.039 --> 00:14:14.599
And.

ab49193a-f232-4153-b81d-a4c008605fc7/111-0
00:14:17.399 --> 00:14:17.959
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/112-3
00:14:19.634 --> 00:14:21.199
discharged by noon.

ab49193a-f232-4153-b81d-a4c008605fc7/115-0
00:14:21.599 --> 00:14:26.045
To to be able to to have all of this
operations data.

ab49193a-f232-4153-b81d-a4c008605fc7/113-0
00:14:26.039 --> 00:14:26.459
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/115-1
00:14:26.045 --> 00:14:31.889
The problem is Wellness data has
historically been a survey done maybe

ab49193a-f232-4153-b81d-a4c008605fc7/114-0
00:14:31.199 --> 00:14:32.279
Mm-hmm.

ab49193a-f232-4153-b81d-a4c008605fc7/115-2
00:14:31.889 --> 00:14:38.639
once a year that is responded to by maybe
4050% of the workforce if you're lucky.

ab49193a-f232-4153-b81d-a4c008605fc7/118-0
00:14:39.119 --> 00:14:42.430
And so now you have this stale data
that's incomplete.

ab49193a-f232-4153-b81d-a4c008605fc7/118-1
00:14:42.430 --> 00:14:46.163
And when I gave the operators that kind
of data and say, hey,

ab49193a-f232-4153-b81d-a4c008605fc7/118-2
00:14:46.163 --> 00:14:49.534
your burnout rate in March was X,
how are things going?

ab49193a-f232-4153-b81d-a4c008605fc7/116-0
00:14:49.159 --> 00:14:49.239
The.

ab49193a-f232-4153-b81d-a4c008605fc7/118-3
00:14:49.534 --> 00:14:53.928
They don't know what to do with that.
Or hey, I, you know, you're right,

ab49193a-f232-4153-b81d-a4c008605fc7/117-0
00:14:53.079 --> 00:14:53.319
Yeah.

ab49193a-f232-4153-b81d-a4c008605fc7/118-4
00:14:53.928 --> 00:14:57.239
it was that in March.
I've been doing a lot of things.

ab49193a-f232-4153-b81d-a4c008605fc7/122-0
00:14:57.519 --> 00:15:01.870
I don't know if it's making a difference.
Maybe I'll wait 17 months until the next

ab49193a-f232-4153-b81d-a4c008605fc7/122-1
00:15:01.870 --> 00:15:05.016
survey for you to tell me if if the
changes we made worked.

ab49193a-f232-4153-b81d-a4c008605fc7/122-2
00:15:05.016 --> 00:15:07.637
And so that wasn't gonna work to me.
We couldn't.

ab49193a-f232-4153-b81d-a4c008605fc7/122-3
00:15:07.637 --> 00:15:10.834
We could keep talking about Wellness as
an operations thing,

ab49193a-f232-4153-b81d-a4c008605fc7/122-4
00:15:10.834 --> 00:15:14.818
but until we gave them data in the same
form that the operations folks use,

ab49193a-f232-4153-b81d-a4c008605fc7/119-0
00:15:11.119 --> 00:15:11.799
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/122-5
00:15:14.818 --> 00:15:17.439
I didn't think we were going to be able
to do it.

ab49193a-f232-4153-b81d-a4c008605fc7/120-0
00:15:15.439 --> 00:15:15.599
M.

ab49193a-f232-4153-b81d-a4c008605fc7/123-0
00:15:17.479 --> 00:15:21.554
So we have been collating data sources
including the EMR.

ab49193a-f232-4153-b81d-a4c008605fc7/121-0
00:15:18.359 --> 00:15:18.519
Right.

ab49193a-f232-4153-b81d-a4c008605fc7/123-1
00:15:21.554 --> 00:15:26.613
So looking at a lot of signal data,
looking at people's burnout scores,

ab49193a-f232-4153-b81d-a4c008605fc7/123-2
00:15:26.613 --> 00:15:30.828
engagement scores,
other well-being metrics, survey scores,

ab49193a-f232-4153-b81d-a4c008605fc7/123-3
00:15:30.828 --> 00:15:35.956
having a bunch of HR demographic data,
looking at their workload data as

ab49193a-f232-4153-b81d-a4c008605fc7/123-4
00:15:35.956 --> 00:15:36.799
measured by.

ab49193a-f232-4153-b81d-a4c008605fc7/126-0
00:15:37.199 --> 00:15:39.959
RV use looking at their paid time off
data,

ab49193a-f232-4153-b81d-a4c008605fc7/126-1
00:15:39.959 --> 00:15:44.975
what's the rest and recovery look like
and putting that all together in a cloud

ab49193a-f232-4153-b81d-a4c008605fc7/126-2
00:15:44.975 --> 00:15:48.863
to say how are these various things
related? So for instance,

ab49193a-f232-4153-b81d-a4c008605fc7/124-0
00:15:48.439 --> 00:15:48.919
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/126-3
00:15:48.863 --> 00:15:53.504
everyone sort of talks about the EMR,
electronic medical record being the

ab49193a-f232-4153-b81d-a4c008605fc7/126-4
00:15:53.504 --> 00:15:56.639
source of all evil and the source of all
burnout.

ab49193a-f232-4153-b81d-a4c008605fc7/128-0
00:15:56.719 --> 00:16:00.854
And healthcare providers,
it's actually been pretty difficult to

ab49193a-f232-4153-b81d-a4c008605fc7/125-0
00:15:57.039 --> 00:15:58.119
Mm-hmm.

ab49193a-f232-4153-b81d-a4c008605fc7/128-1
00:16:00.854 --> 00:16:05.752
find any signal data that that you can
say this one signal data predicts the

ab49193a-f232-4153-b81d-a4c008605fc7/128-2
00:16:05.752 --> 00:16:11.095
burnout is what's causing all the burnout.
People have these sort of theories about

ab49193a-f232-4153-b81d-a4c008605fc7/127-0
00:16:09.959 --> 00:16:10.559
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/128-3
00:16:11.095 --> 00:16:15.547
it that has been difficult,
but if you add signal data with paid time

ab49193a-f232-4153-b81d-a4c008605fc7/128-4
00:16:15.547 --> 00:16:16.119
off data.

ab49193a-f232-4153-b81d-a4c008605fc7/130-0
00:16:16.359 --> 00:16:20.604
Data with other data,
now you're able to see things emerge,

ab49193a-f232-4153-b81d-a4c008605fc7/130-1
00:16:20.604 --> 00:16:24.636
and particularly for us,
what we see emerge are the work

ab49193a-f232-4153-b81d-a4c008605fc7/130-2
00:16:24.636 --> 00:16:27.820
determinants of well-being.
So for instance,

ab49193a-f232-4153-b81d-a4c008605fc7/129-0
00:16:26.719 --> 00:16:27.879
Mm-hmm.

ab49193a-f232-4153-b81d-a4c008605fc7/130-3
00:16:27.820 --> 00:16:32.559
when we did this with our nurses and this
is all preliminary data.

ab49193a-f232-4153-b81d-a4c008605fc7/133-0
00:16:32.639 --> 00:16:36.624
To be to be sure,
we found that nurses that had a certain

ab49193a-f232-4153-b81d-a4c008605fc7/133-1
00:16:36.624 --> 00:16:40.814
level of PTO accrued,
so meaning that they have all this PTO

ab49193a-f232-4153-b81d-a4c008605fc7/131-0
00:16:40.039 --> 00:16:40.599
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/133-2
00:16:40.814 --> 00:16:44.042
stored,
they haven't been taking their PTO for

ab49193a-f232-4153-b81d-a4c008605fc7/133-3
00:16:44.042 --> 00:16:47.683
various reasons.
But if they have too much PTO hours

ab49193a-f232-4153-b81d-a4c008605fc7/132-0
00:16:44.839 --> 00:16:45.479
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/133-4
00:16:47.683 --> 00:16:52.079
stored, a certain number,
their risk of being burnt out was 2X.

ab49193a-f232-4153-b81d-a4c008605fc7/134-0
00:16:52.239 --> 00:16:57.703
The nurses who had less stored,
meaning they had been taking PTO more

ab49193a-f232-4153-b81d-a4c008605fc7/134-1
00:16:57.703 --> 00:17:01.840
regularly.
So now you can see my ask of an operating

ab49193a-f232-4153-b81d-a4c008605fc7/134-2
00:17:01.840 --> 00:17:06.679
team. The managers on the unit is to say,
hey, you know what?

ab49193a-f232-4153-b81d-a4c008605fc7/137-0
00:17:07.279 --> 00:17:10.719
40% of your nurses have X number of PTO
hours accrued,

ab49193a-f232-4153-b81d-a4c008605fc7/137-1
00:17:10.719 --> 00:17:13.659
which puts them at double the risk of
burnout.

ab49193a-f232-4153-b81d-a4c008605fc7/137-2
00:17:13.659 --> 00:17:18.725
What I'd like from you is to not figure
out or have to deal with what's burnout.

ab49193a-f232-4153-b81d-a4c008605fc7/135-0
00:17:15.289 --> 00:17:15.769
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/137-3
00:17:18.725 --> 00:17:22.039
How do I deal with burnout?
All I'm asking from you.

ab49193a-f232-4153-b81d-a4c008605fc7/136-0
00:17:21.519 --> 00:17:22.679
Mm-hmm.

ab49193a-f232-4153-b81d-a4c008605fc7/140-0
00:17:22.319 --> 00:17:25.405
Is to take that 40% number and reduce it
to 20%.

ab49193a-f232-4153-b81d-a4c008605fc7/140-1
00:17:25.405 --> 00:17:30.505
Let's figure out some interventions that
you know best that can get these nurses

ab49193a-f232-4153-b81d-a4c008605fc7/138-0
00:17:29.639 --> 00:17:29.879
Hmm.

ab49193a-f232-4153-b81d-a4c008605fc7/140-2
00:17:30.505 --> 00:17:35.479
to take their PTO as they understand how
big of an impact it's having on their

ab49193a-f232-4153-b81d-a4c008605fc7/140-3
00:17:35.479 --> 00:17:37.935
health.
That is what our operators are

ab49193a-f232-4153-b81d-a4c008605fc7/140-4
00:17:37.935 --> 00:17:38.879
world-class at.

ab49193a-f232-4153-b81d-a4c008605fc7/141-0
00:17:39.279 --> 00:17:44.239
That is how they know how to to to use
data and effectively move the needle.

ab49193a-f232-4153-b81d-a4c008605fc7/139-0
00:17:39.599 --> 00:17:41.039
I think dance.

ab49193a-f232-4153-b81d-a4c008605fc7/143-0
00:17:44.359 --> 00:17:47.141
You know,
I want to mention a an analog analogy

ab49193a-f232-4153-b81d-a4c008605fc7/143-1
00:17:47.141 --> 00:17:51.372
that that may be a little bit outside the
box for some of our listeners.

ab49193a-f232-4153-b81d-a4c008605fc7/143-2
00:17:51.372 --> 00:17:55.951
I come from a political world and some
people may remember that the Speaker of

ab49193a-f232-4153-b81d-a4c008605fc7/143-3
00:17:55.951 --> 00:18:00.530
the House of Representatives in the 1980s
is a man named Tip O'Neill, I think.

ab49193a-f232-4153-b81d-a4c008605fc7/142-0
00:17:59.599 --> 00:18:00.119
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/143-4
00:18:00.530 --> 00:18:04.239
And he had a famous expression that said
all politics is local.

ab49193a-f232-4153-b81d-a4c008605fc7/144-0
00:18:04.319 --> 00:18:07.774
And I feel like that may ring true at
Northwestern Medicine.

ab49193a-f232-4153-b81d-a4c008605fc7/144-1
00:18:07.774 --> 00:18:11.568
You're relying on your metrics locally on
the people on the floor.

ab49193a-f232-4153-b81d-a4c008605fc7/144-2
00:18:11.568 --> 00:18:15.815
What do you think needs to happen to
improve retention and reduce burnout?

ab49193a-f232-4153-b81d-a4c008605fc7/144-3
00:18:15.815 --> 00:18:19.609
And you're also relying locally with your
data analytics platform.

ab49193a-f232-4153-b81d-a4c008605fc7/144-4
00:18:19.609 --> 00:18:23.799
You're putting that data in the cloud and
you're trying to allow them to.

ab49193a-f232-4153-b81d-a4c008605fc7/145-0
00:18:24.159 --> 00:18:27.412
To implement.
So you come up with the insight and then

ab49193a-f232-4153-b81d-a4c008605fc7/145-1
00:18:27.412 --> 00:18:32.085
you go back to the people on the floor,
to the local people in your healthcare

ab49193a-f232-4153-b81d-a4c008605fc7/145-2
00:18:32.085 --> 00:18:36.698
organization and you say this is the
insight, this is the recommended action,

ab49193a-f232-4153-b81d-a4c008605fc7/145-3
00:18:36.698 --> 00:18:41.489
but I'll leave it up to you to figure out
how your team needs to best get there.

ab49193a-f232-4153-b81d-a4c008605fc7/145-4
00:18:41.489 --> 00:18:43.559
So instead of a top down directive.

ab49193a-f232-4153-b81d-a4c008605fc7/146-0
00:18:44.079 --> 00:18:48.943
You give local people on the ground
agency in the solution and not only did

ab49193a-f232-4153-b81d-a4c008605fc7/146-1
00:18:48.943 --> 00:18:53.679
that agency allow you to get better
completion rates when you had a local

ab49193a-f232-4153-b81d-a4c008605fc7/146-2
00:18:53.679 --> 00:18:58.735
colleague asking them for responses,
but now you have more agency and more buy

ab49193a-f232-4153-b81d-a4c008605fc7/146-3
00:18:58.735 --> 00:19:03.919
in when you have local people figuring
out how they're going to get from A to B.

ab49193a-f232-4153-b81d-a4c008605fc7/147-0
00:19:03.999 --> 00:19:06.679
You define the outcome that you want to
see.

ab49193a-f232-4153-b81d-a4c008605fc7/151-0
00:19:06.639 --> 00:19:09.991
That's exactly right.
And you know what we say is we

ab49193a-f232-4153-b81d-a4c008605fc7/151-1
00:19:09.991 --> 00:19:13.786
centralized the approach to decentralize
the action, right?

ab49193a-f232-4153-b81d-a4c008605fc7/148-0
00:19:11.319 --> 00:19:11.879
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/151-2
00:19:13.786 --> 00:19:17.960
Because I do think the top down
heavy-handed approach won't work.

ab49193a-f232-4153-b81d-a4c008605fc7/149-0
00:19:14.399 --> 00:19:15.079
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/151-3
00:19:17.960 --> 00:19:22.639
We'll just get it wrong to be honest with
you. And so this really allows.

ab49193a-f232-4153-b81d-a4c008605fc7/150-0
00:19:20.479 --> 00:19:21.079
Mm-hmm.

ab49193a-f232-4153-b81d-a4c008605fc7/152-0
00:19:22.719 --> 00:19:25.717
People to say, you know,
we have to solve these problems

ab49193a-f232-4153-b81d-a4c008605fc7/152-1
00:19:25.717 --> 00:19:29.976
differently at a small Community Hospital
compared to our large academic Medical

ab49193a-f232-4153-b81d-a4c008605fc7/152-2
00:19:29.976 --> 00:19:32.552
Center.
The solution isn't going to be the same.

ab49193a-f232-4153-b81d-a4c008605fc7/152-3
00:19:32.552 --> 00:19:36.338
Now having said all of that,
if the problem, once they try to solve it,

ab49193a-f232-4153-b81d-a4c008605fc7/152-4
00:19:36.338 --> 00:19:40.071
they can't solve it because there's a,
you know, larger systems issue,

ab49193a-f232-4153-b81d-a4c008605fc7/152-5
00:19:40.071 --> 00:19:42.279
then that's where someone like Mai's role.

ab49193a-f232-4153-b81d-a4c008605fc7/154-0
00:19:42.559 --> 00:19:45.478
Can be important.
We're having a seat at the table at the

ab49193a-f232-4153-b81d-a4c008605fc7/154-1
00:19:45.478 --> 00:19:48.950
executive level can say, OK,
we've tried all of these things to move

ab49193a-f232-4153-b81d-a4c008605fc7/154-2
00:19:48.950 --> 00:19:52.623
the needle on this PTO problem.
But the real reason we can't take PTO is

ab49193a-f232-4153-b81d-a4c008605fc7/154-3
00:19:52.623 --> 00:19:55.893
when our staff asks for PTO and we're not
staffed appropriately,

ab49193a-f232-4153-b81d-a4c008605fc7/154-4
00:19:55.893 --> 00:19:59.818
then I can't give them the time off
because someone's got to take care of our

ab49193a-f232-4153-b81d-a4c008605fc7/154-5
00:19:59.818 --> 00:20:01.679
patients. Now it's a central problem.

ab49193a-f232-4153-b81d-a4c008605fc7/155-0
00:20:01.759 --> 00:20:06.615
Now it's a central problem to say, hey,
what are we doing around our staffing

ab49193a-f232-4153-b81d-a4c008605fc7/153-0
00:20:02.919 --> 00:20:03.999
right

ab49193a-f232-4153-b81d-a4c008605fc7/155-1
00:20:06.615 --> 00:20:11.719
models that isn't allowing people to take
the PTO that is their rightful benefit?

ab49193a-f232-4153-b81d-a4c008605fc7/156-0
00:20:11.759 --> 00:20:14.641
So Gee,
we are somewhat approaching the end of

ab49193a-f232-4153-b81d-a4c008605fc7/156-1
00:20:14.641 --> 00:20:18.381
this podcast episode. Now again,
it is Healthy Data podcast,

ab49193a-f232-4153-b81d-a4c008605fc7/156-2
00:20:18.381 --> 00:20:23.347
so I would be remiss if I didn't raise
the the question of how do you aggregate,

ab49193a-f232-4153-b81d-a4c008605fc7/156-3
00:20:23.347 --> 00:20:26.657
normalize and deduplicate these various
data sources.

ab49193a-f232-4153-b81d-a4c008605fc7/156-4
00:20:26.657 --> 00:20:31.439
You're identifying new data metrics,
you're bringing in data from HR systems.

ab49193a-f232-4153-b81d-a4c008605fc7/157-0
00:20:32.239 --> 00:20:35.751
Putting it in Databricks and Azure.
You're getting data on PTO.

ab49193a-f232-4153-b81d-a4c008605fc7/157-1
00:20:35.751 --> 00:20:39.701
You're getting data on attrition.
You're getting all kinds of data from

ab49193a-f232-4153-b81d-a4c008605fc7/157-2
00:20:39.701 --> 00:20:43.267
across the organization.
How are you reconciling and integrating

ab49193a-f232-4153-b81d-a4c008605fc7/157-3
00:20:43.267 --> 00:20:44.199
all of that data?

ab49193a-f232-4153-b81d-a4c008605fc7/160-0
00:20:44.679 --> 00:20:49.392
So and again, I want to be clear,
this is a very preliminary work and what

ab49193a-f232-4153-b81d-a4c008605fc7/160-1
00:20:49.392 --> 00:20:53.980
we're trying to do is work with our
medical social science teams to help

ab49193a-f232-4153-b81d-a4c008605fc7/158-0
00:20:53.919 --> 00:20:54.439
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/160-2
00:20:53.980 --> 00:20:58.064
validate that the data isn't junk, right.
So our data says, hey,

ab49193a-f232-4153-b81d-a4c008605fc7/159-0
00:20:57.639 --> 00:20:58.119
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/160-3
00:20:58.064 --> 00:21:02.651
this is what we think we're seeing.
Now we're beginning to go into local

ab49193a-f232-4153-b81d-a4c008605fc7/160-4
00:21:02.651 --> 00:21:04.159
areas doing qualitative.

ab49193a-f232-4153-b81d-a4c008605fc7/164-0
00:21:04.239 --> 00:21:07.308
Of interviews to say this is what our
data shows.

ab49193a-f232-4153-b81d-a4c008605fc7/164-1
00:21:07.308 --> 00:21:12.094
Is this the real lived experience for
folks? Is this data have face validity?

ab49193a-f232-4153-b81d-a4c008605fc7/161-0
00:21:08.719 --> 00:21:10.519
Mm-hmm, mm-hmm.

ab49193a-f232-4153-b81d-a4c008605fc7/164-2
00:21:12.094 --> 00:21:15.715
And and you know,
because I think your point is well taken

ab49193a-f232-4153-b81d-a4c008605fc7/164-3
00:21:15.715 --> 00:21:20.747
and what we have found frankly over the
last two years is this data isn't as easy

ab49193a-f232-4153-b81d-a4c008605fc7/164-4
00:21:20.747 --> 00:21:23.999
as just putting it together. It's dirty,
it's messy.

ab49193a-f232-4153-b81d-a4c008605fc7/163-0
00:21:22.359 --> 00:21:22.919
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/166-0
00:21:24.079 --> 00:21:28.196
And if you oversimplify the chances that
you, you know garbage in,

ab49193a-f232-4153-b81d-a4c008605fc7/166-1
00:21:28.196 --> 00:21:32.434
garbage out problem is real.
And so we are beginning to see if it if

ab49193a-f232-4153-b81d-a4c008605fc7/165-0
00:21:29.719 --> 00:21:30.199
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/166-2
00:21:32.434 --> 00:21:35.629
it is valid,
valid data and if it's actionable data

ab49193a-f232-4153-b81d-a4c008605fc7/166-3
00:21:35.629 --> 00:21:40.359
and and hopefully so far things have been
looking pretty good on that front.

ab49193a-f232-4153-b81d-a4c008605fc7/167-0
00:21:40.479 --> 00:21:44.053
And you mentioned the phrase,
and this is very popular among health

ab49193a-f232-4153-b81d-a4c008605fc7/167-1
00:21:44.053 --> 00:21:47.364
systems across the country.
I want real-time operational data.

ab49193a-f232-4153-b81d-a4c008605fc7/167-2
00:21:47.364 --> 00:21:50.307
I don't want an 18 month lag,
a 12 month lag, whatever.

ab49193a-f232-4153-b81d-a4c008605fc7/167-3
00:21:50.307 --> 00:21:54.406
So a lot of people are thinking about
FHIR repositories in the cloud in order

ab49193a-f232-4153-b81d-a4c008605fc7/167-4
00:21:54.406 --> 00:21:58.190
to get that data in real time.
Are you leveraging any of those sorts of

ab49193a-f232-4153-b81d-a4c008605fc7/167-5
00:21:58.190 --> 00:22:00.239
technologies in order to get real time?

ab49193a-f232-4153-b81d-a4c008605fc7/168-0
00:22:00.479 --> 00:22:03.684
Data.
How are you facilitating that real time

ab49193a-f232-4153-b81d-a4c008605fc7/168-1
00:22:03.684 --> 00:22:04.519
data access?

ab49193a-f232-4153-b81d-a4c008605fc7/173-0
00:22:05.679 --> 00:22:10.259
I've got really good data analysts that
help me make sure that you know for the

ab49193a-f232-4153-b81d-a4c008605fc7/173-1
00:22:10.259 --> 00:22:14.094
things that are real time.
So certain things you know as I said we

ab49193a-f232-4153-b81d-a4c008605fc7/169-0
00:22:10.559 --> 00:22:10.839
Yeah.

ab49193a-f232-4153-b81d-a4c008605fc7/173-2
00:22:14.094 --> 00:22:18.616
have the survey data in this model that
that is only asked once every whatever

ab49193a-f232-4153-b81d-a4c008605fc7/170-0
00:22:14.359 --> 00:22:14.999
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/171-0
00:22:17.679 --> 00:22:18.639
Mhm, mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/173-3
00:22:18.616 --> 00:22:21.536
year it is.
There's other data that search such as

ab49193a-f232-4153-b81d-a4c008605fc7/173-4
00:22:21.536 --> 00:22:25.199
PTO accrual that the way they've created
this database is live.

ab49193a-f232-4153-b81d-a4c008605fc7/172-0
00:22:24.679 --> 00:22:25.959
Mhm, mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/174-0
00:22:25.199 --> 00:22:28.001
Live and basically updates on a weekly
level,

ab49193a-f232-4153-b81d-a4c008605fc7/174-1
00:22:28.001 --> 00:22:32.934
even a daily level to say what are the
current PTO accruals we're seeing for our

ab49193a-f232-4153-b81d-a4c008605fc7/174-2
00:22:32.934 --> 00:22:37.319
nurses across the system and that
automatically refreshes in real time.

ab49193a-f232-4153-b81d-a4c008605fc7/175-0
00:22:37.519 --> 00:22:40.954
Any final thoughts, Gee, to our listeners?
Any recommendations?

ab49193a-f232-4153-b81d-a4c008605fc7/175-1
00:22:40.954 --> 00:22:45.247
I'm sure many of the issues that you've
raised today resonate with listeners to

ab49193a-f232-4153-b81d-a4c008605fc7/175-2
00:22:45.247 --> 00:22:48.521
Healthy Data podcast.
If they're intrigued and want to learn

ab49193a-f232-4153-b81d-a4c008605fc7/175-3
00:22:48.521 --> 00:22:50.936
more,
maybe they want to kind of put some of

ab49193a-f232-4153-b81d-a4c008605fc7/175-4
00:22:50.936 --> 00:22:55.229
your ideas into practice to their own
organization. What would you advise them?

ab49193a-f232-4153-b81d-a4c008605fc7/175-5
00:22:55.229 --> 00:22:56.839
Or even what would you advise?

ab49193a-f232-4153-b81d-a4c008605fc7/176-0
00:22:57.119 --> 00:23:02.000
Yourself in 2024, you know,
a few years ago when you were earlier in

ab49193a-f232-4153-b81d-a4c008605fc7/179-0
00:23:01.439 --> 00:23:05.665
I will say we've published the our
methodology for how we created this data

ab49193a-f232-4153-b81d-a4c008605fc7/176-1
00:23:02.000 --> 00:23:02.919
this process.

ab49193a-f232-4153-b81d-a4c008605fc7/179-1
00:23:05.665 --> 00:23:08.111
analytics team in the Rhode Island
Journal.

ab49193a-f232-4153-b81d-a4c008605fc7/179-2
00:23:08.111 --> 00:23:11.335
It's worth looking up so you can kind of
see our process.

ab49193a-f232-4153-b81d-a4c008605fc7/179-3
00:23:11.335 --> 00:23:14.838
We've tried to be very transparent on
what we're trying to do.

ab49193a-f232-4153-b81d-a4c008605fc7/179-4
00:23:14.838 --> 00:23:17.784
So if folks are interested in recreating
that model,

ab49193a-f232-4153-b81d-a4c008605fc7/177-0
00:23:15.199 --> 00:23:15.679
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/179-5
00:23:17.784 --> 00:23:20.119
I think that our publication is out there.

ab49193a-f232-4153-b81d-a4c008605fc7/178-0
00:23:18.399 --> 00:23:18.559
And.

ab49193a-f232-4153-b81d-a4c008605fc7/180-0
00:23:20.559 --> 00:23:24.119
I think for me I am very interested in.

ab49193a-f232-4153-b81d-a4c008605fc7/183-0
00:23:25.519 --> 00:23:28.955
Moving towards this model of objectivity
around Wellness,

ab49193a-f232-4153-b81d-a4c008605fc7/183-1
00:23:28.955 --> 00:23:32.806
I think it'll be taken seriously.
Obviously during the pandemic,

ab49193a-f232-4153-b81d-a4c008605fc7/181-0
00:23:29.639 --> 00:23:30.119
Mhm.

ab49193a-f232-4153-b81d-a4c008605fc7/183-2
00:23:32.806 --> 00:23:37.663
everyone cared about Wellness because it
was pretty obvious as we move forward is

ab49193a-f232-4153-b81d-a4c008605fc7/182-0
00:23:34.319 --> 00:23:35.799
Mm-hmm.

ab49193a-f232-4153-b81d-a4c008605fc7/183-3
00:23:37.663 --> 00:23:42.166
going to take data just to remind people
how Wellness is related to safety,

ab49193a-f232-4153-b81d-a4c008605fc7/183-4
00:23:42.166 --> 00:23:42.639
quality.

ab49193a-f232-4153-b81d-a4c008605fc7/184-0
00:23:42.719 --> 00:23:47.609
In all the metrics that we care about in
healthcare and it's our it's incumbent

ab49193a-f232-4153-b81d-a4c008605fc7/184-1
00:23:47.609 --> 00:23:52.194
upon us to use that data to tell those
stories and figure out where we can

ab49193a-f232-4153-b81d-a4c008605fc7/184-2
00:23:52.194 --> 00:23:53.599
improve that coal mine.