7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/5-0
00:00:03.538 --> 00:00:07.174
the EVP and CFO and President of
Value-Based Enterprise at Henry Ford

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/5-1
00:00:07.174 --> 00:00:09.978
Health System. Robin,
thank you for joining me today.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/6-0
00:00:10.778 --> 00:00:11.898
It's a pleasure to be here.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/8-0
00:00:12.698 --> 00:00:16.011
So for background,
Henry Ford Health System is a 4,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/8-1
00:00:16.011 --> 00:00:21.106
600 bed system headquartered in Detroit
with 20 hospitals and 10,000 providers.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/8-2
00:00:21.106 --> 00:00:26.202
And Robin sits at the intersection of
finance strategy and care transformation.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/8-3
00:00:26.202 --> 00:00:29.068
Robin,
what I'd like to address today is how

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/8-4
00:00:29.068 --> 00:00:33.973
Henry Ford Health System is using data
integration and AI to make healthcare

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/8-5
00:00:33.973 --> 00:00:36.138
more affordable and accessible. So

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/9-0
00:00:36.618 --> 00:00:40.440
I think we're going to start off with
your data analytics strategy.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/9-1
00:00:40.440 --> 00:00:45.048
I understand that leveraging predictive
analytics to drive value-based care model

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/9-2
00:00:45.048 --> 00:00:47.521
is a big goal for Henry Ford Health
System.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/9-3
00:00:47.521 --> 00:00:50.555
What are some of the use cases you're
tackling first,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/9-4
00:00:50.555 --> 00:00:54.938
and what data infrastructure had to be in
place before those became possible?

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/10-0
00:00:55.738 --> 00:00:58.726
Well,
I would say in general for our data

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/10-1
00:00:58.726 --> 00:01:03.564
strategy that we're thinking about it for
care delivery operations,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/10-2
00:01:03.564 --> 00:01:08.116
the value-based enterprise,
the insurance enterprise, you know,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/10-3
00:01:08.116 --> 00:01:14.092
our home care, every facet of what we do.
So the very basic things and foundational

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/10-4
00:01:14.092 --> 00:01:19.498
pieces we've been working on for two or
three years has really been getting

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/12-0
00:01:20.138 --> 00:01:25.488
the infamous data lake in place and the
ability to make data with permission sort

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/12-1
00:01:25.488 --> 00:01:30.772
of ubiquitous so that you can have the
standards and governance over definitions

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/12-2
00:01:30.772 --> 00:01:35.600
so that you can get onto those big
natural language questions that we all

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/12-3
00:01:35.600 --> 00:01:40.101
envision having Jarvis, right,
and just being able to ask questions.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/12-4
00:01:40.101 --> 00:01:42.058
But until then, you know, it's

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/13-0
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It still remains a little more
complicated,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/13-1
00:01:45.184 --> 00:01:48.311
but with all the amazing tools that are
coming out,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/13-2
00:01:48.311 --> 00:01:52.339
whether it's for your data analyst,
it's for your quality analyst,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/13-3
00:01:52.339 --> 00:01:56.067
your financial analyst,
the folks that we have working in the

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/13-4
00:01:56.067 --> 00:01:59.976
value-based enterprise,
really looking at the cost and trying to

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/13-5
00:01:59.976 --> 00:02:01.178
drive affordability.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/14-0
00:02:01.498 --> 00:02:07.246
How do we make that easy and accessible?
So we actually spent quite a bit of time

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/14-1
00:02:07.246 --> 00:02:12.363
where we had fragmentation in the
value-based enterprise beyond just the

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/14-2
00:02:12.363 --> 00:02:18.041
data, but the tools that we were using,
and really trying to line up how we talk

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/14-3
00:02:18.041 --> 00:02:22.808
about things. And so again,
back to those definitions and standards

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/14-4
00:02:22.808 --> 00:02:23.298
and how

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/15-0
00:02:23.818 --> 00:02:28.450
how reports or dashboards get done.
So that we're working together,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/15-1
00:02:28.450 --> 00:02:32.059
we're communicating together. And,
you know, Jordan,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/15-2
00:02:32.059 --> 00:02:37.167
the data being available, you know,
we talked about it's only good as it's

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/15-3
00:02:37.167 --> 00:02:40.708
curated for, right?
So if you've got junk going in,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/15-4
00:02:40.708 --> 00:02:42.138
it's junk coming out.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/17-0
00:02:42.458 --> 00:02:48.141
And when you're aggregating particularly
claims data in the universe that you are

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/16-0
00:02:46.258 --> 00:02:46.858
Mhm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/17-1
00:02:48.141 --> 00:02:51.398
doing for your clinically integrated
networks,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/17-2
00:02:51.398 --> 00:02:56.942
or you're doing for population healthcare
management, or on the insurance side,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/17-3
00:02:56.942 --> 00:03:02.417
getting that right and filling in the
holes so that you kind of have the whole

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/17-4
00:03:02.417 --> 00:03:04.218
picture becomes important.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/19-0
00:03:05.018 --> 00:03:11.047
So we actually took on a project that
helped us, for the lack of a better word,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/19-1
00:03:11.047 --> 00:03:16.774
build our garden for our claims data,
and then also a methodology for us to

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/19-2
00:03:16.774 --> 00:03:21.295
tend to the garden.
So because new data comes in every day,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/19-3
00:03:21.295 --> 00:03:24.611
right?
And so those become really important

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/18-0
00:03:22.738 --> 00:03:23.218
Mhm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/19-4
00:03:24.611 --> 00:03:25.138
things.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/21-0
00:03:25.218 --> 00:03:28.494
features.
And I was just at Becker's and we were

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/21-1
00:03:28.494 --> 00:03:33.373
talking about how important your
commitment around the base fundamentals

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/21-2
00:03:33.373 --> 00:03:36.248
are.
I think there's plenty of opportunity

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/21-3
00:03:36.248 --> 00:03:41.395
when you start talking about AI to help
in the whole domain and continuum of

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/21-4
00:03:41.395 --> 00:03:45.004
healthcare.
And we put our governance in place around

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/21-5
00:03:45.004 --> 00:03:45.338
that.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/20-0
00:03:45.818 --> 00:03:46.378
So...

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/22-0
00:03:45.818 --> 00:03:49.013
So happy to talk about that,
but I'm going to, it's your podcast,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/22-1
00:03:49.013 --> 00:03:49.738
your questions.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/24-0
00:03:50.138 --> 00:03:52.974
So Robin,
you mentioned a number of incredible

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/24-1
00:03:52.974 --> 00:03:57.196
topics that we're going to try to get to
today. You mentioned Gen AI,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/24-2
00:03:57.196 --> 00:04:01.660
and we're going to get to that in a
moment. You also mentioned your plan.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/24-3
00:04:01.660 --> 00:04:06.244
So for our listeners who don't know,
Henry Ford Health System owns a Health

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/24-4
00:04:06.244 --> 00:04:10.829
Alliance Plan referred to as HAP.
I think you mentioned previously that 50%

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of Health Alliance Plan members

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/25-0
00:04:12.938 --> 00:04:15.408
are actually Henry Ford Health System
patients.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/25-1
00:04:15.408 --> 00:04:19.370
And so you're sitting on a uniquely rich
longitudinal data set spanning both

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/23-0
00:04:15.458 --> 00:04:15.578
Yeah.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/25-2
00:04:19.370 --> 00:04:22.611
clinical and claims data.
You mentioned that you're building a

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/25-3
00:04:22.611 --> 00:04:26.006
garden for your claims data.
How are you managing and integrating

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/25-4
00:04:26.006 --> 00:04:27.498
those two data streams today?

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/27-0
00:04:28.378 --> 00:04:32.934
Yeah, so to be very clear,
the data streams that we're integrating

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/27-1
00:04:32.934 --> 00:04:35.926
today,
because you've got to have the right

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/27-2
00:04:35.926 --> 00:04:39.938
prior wells, right,
we can only collectively be looking at

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/26-0
00:04:38.698 --> 00:04:39.178
Mhm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/27-3
00:04:39.938 --> 00:04:44.766
the data of that 50%. But we actually,
we have put that data together.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/27-4
00:04:44.766 --> 00:04:47.418
We both used to have sets of that data.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/28-0
00:04:47.818 --> 00:04:52.712
But when we're speaking and talking with
each other about quality,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/28-1
00:04:52.712 --> 00:04:57.825
about our HCCs or our stars,
or looking at the cost and affordability

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/28-2
00:04:57.825 --> 00:05:02.135
of maybe it's cancer care,
maybe it's MSK for a commercial

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/28-3
00:05:02.135 --> 00:05:06.079
population,
maybe we're looking at the chronic CHF or

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/28-4
00:05:06.079 --> 00:05:07.978
COPD patients in Medicare,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/29-0
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that we're actually looking at the same
data together so that we can drill down

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/29-1
00:05:12.873 --> 00:05:16.558
and have a very detailed conversation.
Because more importantly,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/29-2
00:05:16.558 --> 00:05:20.583
for us to get to affordability,
we have to have executable strategies.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/29-3
00:05:20.583 --> 00:05:23.927
And when you talk about something like
Medicare Advantage,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/29-4
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you're really looking and focusing on
readmissions, right? And

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/30-0
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How can we provide information back into
the delivery system so that we can

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/30-1
00:05:32.613 --> 00:05:37.398
effectuate that? Or better yet,
what kind of signals can we send into our

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/30-2
00:05:37.398 --> 00:05:42.312
care managers to keep our chronic
patients who we know are at high risk out

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/30-3
00:05:42.312 --> 00:05:46.127
of the hospital?
So we really try to develop those signals

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/30-4
00:05:46.127 --> 00:05:50.589
together. And at the same time,
we want signals coming back from the

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/30-5
00:05:50.589 --> 00:05:52.658
delivery system. So for example,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/31-0
00:05:53.098 --> 00:05:58.196
any patient that leaves Henry Ford and is
discharged gets a text on their phone.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/31-1
00:05:58.196 --> 00:06:01.091
And then they can respond to a few
questions.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/31-2
00:06:01.091 --> 00:06:04.364
And that helps us gauge where they are in
our risk,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/31-3
00:06:04.364 --> 00:06:07.574
whether they're moving up in risk,
in rising risk,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/31-4
00:06:07.574 --> 00:06:12.420
or they're coming down and whether or not
they need a phone call. And again,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/31-5
00:06:12.420 --> 00:06:15.818
we might have done everything right on
the discharge.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/33-0
00:06:16.098 --> 00:06:18.857
but somehow the patient's feeling like
they're at risk.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/33-1
00:06:18.857 --> 00:06:22.108
And so sometimes it can be a very easy
just reassuring them, yes,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/33-2
00:06:22.108 --> 00:06:24.866
you have your doctor's appointment,
you're ready to go.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/33-3
00:06:24.866 --> 00:06:28.511
Other times they're like, well,
I didn't get those meds when I left and I

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/33-4
00:06:28.511 --> 00:06:32.451
didn't get this and I didn't get that.
We can fill in the gaps so we don't wind

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/33-5
00:06:32.451 --> 00:06:35.258
up with a readmission before that seven
day appointment.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/34-0
00:06:35.578 --> 00:06:38.288
So again,
the execution becomes a very important

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/32-0
00:06:35.898 --> 00:06:36.298
Oh.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/34-1
00:06:38.288 --> 00:06:41.218
part of what we're trying to get done
with the data.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/35-0
00:06:41.498 --> 00:06:46.954
So I love that signal that you have with
the text message post discharge.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/35-1
00:06:46.954 --> 00:06:51.967
I imagine that's one of your highest
profile executable strategies.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/35-2
00:06:51.967 --> 00:06:57.718
Can you walk us through how you were able
to execute that particular project?

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/35-3
00:06:57.718 --> 00:07:02.141
Did you have to, I mean,
from ideation to implementation to

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/35-4
00:07:02.141 --> 00:07:05.458
integrate the text that pushes from the
EHR?

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/36-0
00:07:05.498 --> 00:07:09.380
and then incorporating that data back so
that you have a care manager follow up

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/36-1
00:07:09.380 --> 00:07:13.358
with the call and then tying all of that,
those actions into the quality measures

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/36-2
00:07:13.358 --> 00:07:15.978
to make sure you don't have that 30 days
readmission.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/41-0
00:07:16.698 --> 00:07:19.649
Yeah, well, okay,
you get a lot of detail you want to ask

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/41-1
00:07:19.649 --> 00:07:22.040
there.
So I'm going to give you the high level

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/37-0
00:07:20.058 --> 00:07:20.218
I...

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/41-2
00:07:22.040 --> 00:07:25.143
version because I am sitting at the top
of the organization.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/41-3
00:07:25.143 --> 00:07:28.856
They don't actually let me go and play
with the mechanics. But you know,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/38-0
00:07:27.098 --> 00:07:27.178
Ann.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/41-4
00:07:28.856 --> 00:07:32.976
sometimes you have a really good idea.
We were using a tool called Cipher and we

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/39-0
00:07:31.818 --> 00:07:32.298
Mhm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/41-5
00:07:32.976 --> 00:07:34.858
were using it for patient experience.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/40-0
00:07:35.098 --> 00:07:35.578
Mhm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/44-0
00:07:35.258 --> 00:07:39.334
And because we were using it for patient
experience and asking,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/44-1
00:07:39.334 --> 00:07:43.855
we ask patients while they're in the
hospital questions, and you know,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/42-0
00:07:41.978 --> 00:07:42.458
Mhm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/44-2
00:07:43.855 --> 00:07:47.930
you have to do your CMS surveys,
and we use that tool for that.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/44-3
00:07:47.930 --> 00:07:51.560
It's something that patients were already
familiar with.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/44-4
00:07:51.560 --> 00:07:54.935
We didn't want to add another tool or
another layer.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/44-5
00:07:54.935 --> 00:07:57.418
We wanted it to feel consumer friendly.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/43-0
00:07:57.778 --> 00:07:58.258
Mhm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/46-0
00:07:58.218 --> 00:08:03.492
So we chose to extend that tool.
So as they were completing their survey

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/46-1
00:08:03.492 --> 00:08:07.032
and getting other patient education
information,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/46-2
00:08:07.032 --> 00:08:12.378
it's that tool that's actually sending
out the text message, not the EMR.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/46-3
00:08:12.378 --> 00:08:17.941
Then it comes back into that tool.
And that data then is we use Compass Rose

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/46-4
00:08:17.941 --> 00:08:19.458
for our care manager.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/47-0
00:08:19.818 --> 00:08:24.857
then our care managers are alerted in
Compass Rose that there's a call that

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/45-0
00:08:20.498 --> 00:08:20.618
Yeah.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/47-1
00:08:24.857 --> 00:08:29.498
they need to make and it's within their
queue and in their work list.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/49-0
00:08:29.898 --> 00:08:33.059
So on this topic,
but also on the related Gen AI topic,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/49-1
00:08:33.059 --> 00:08:35.712
you mentioned that with Gen AI and
automation,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/49-2
00:08:35.712 --> 00:08:38.986
they're most valuable when they're built
atop clean data.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/49-3
00:08:38.986 --> 00:08:43.333
And obviously that would also be true
with this text message and integration

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/48-0
00:08:40.698 --> 00:08:41.098
Good.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/49-4
00:08:43.333 --> 00:08:47.397
with Compass, Rose and Cypher.
Where would you honestly rate Henry Ford

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/49-5
00:08:47.397 --> 00:08:51.178
Health System on data readiness?
And what's the biggest bottleneck

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/50-0
00:08:51.218 --> 00:08:53.178
between where you are today and where you
need to be.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/53-0
00:08:53.978 --> 00:08:57.515
You know,
I think you have to think about the pools

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/53-1
00:08:57.515 --> 00:09:01.800
of data that you have.
And I think that we would probably rank

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/51-0
00:08:58.538 --> 00:08:59.018
Mhm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/53-2
00:09:01.800 --> 00:09:05.541
different parts of our system higher or
lower in that.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/52-0
00:09:03.298 --> 00:09:03.778
Mhm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/53-3
00:09:05.541 --> 00:09:09.554
Like an example I'll give you are
community care services.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/53-4
00:09:09.554 --> 00:09:12.138
We have in some parts of our pharmacy,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/56-0
00:09:12.458 --> 00:09:15.320
you know,
a system that is sort of separated from

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/56-1
00:09:15.320 --> 00:09:18.181
the EMR,
which does the inpatient and outpatient.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/56-2
00:09:18.181 --> 00:09:22.702
We have retail pharmacy on a different
system. We have plans to bring that in.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/54-0
00:09:20.298 --> 00:09:20.858
Mhm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/56-3
00:09:22.702 --> 00:09:25.392
You know,
when you build those bridges between

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/56-4
00:09:25.392 --> 00:09:28.883
those different systems,
that's where you may have MRNs that

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/55-0
00:09:26.098 --> 00:09:26.698
Mm-hmm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/56-5
00:09:28.883 --> 00:09:32.260
aren't linked yet,
or they have to go through a process to

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/56-6
00:09:32.260 --> 00:09:35.178
be linked, right?
So any time that you can line up

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/57-0
00:09:35.498 --> 00:09:39.855
your process in a similar system,
ensuring you're using the same MRNs,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/57-1
00:09:39.855 --> 00:09:42.985
you know,
doing the basic level things to validate

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/57-2
00:09:42.985 --> 00:09:47.464
that you've got the same person,
because that matters on the value-based

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/57-3
00:09:47.464 --> 00:09:50.533
care side,
just like it does on the care delivery

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/57-4
00:09:50.533 --> 00:09:53.478
side.
You got to know you got the right person.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/57-5
00:09:53.478 --> 00:09:57.098
So I think it's those data connections
where we would rate

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/59-0
00:09:57.898 --> 00:10:00.179
You know,
we've got some noise in that data,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/58-0
00:10:00.178 --> 00:10:00.778
Mm-hmm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/59-1
00:10:00.179 --> 00:10:02.966
but there are like this com spec,
we call it com spec,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/59-2
00:10:02.966 --> 00:10:07.172
and where we have all of our claims data.
We probably rate that at a higher degree

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/59-3
00:10:07.172 --> 00:10:10.618
of satisfaction than where we were
probably four or five years ago.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/60-0
00:10:11.258 --> 00:10:15.209
So with those MRNs,
how is Henry Ford Health System doing

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/60-1
00:10:15.209 --> 00:10:20.113
enterprise master person identity
management in order to reconcile data

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/60-2
00:10:20.113 --> 00:10:23.178
sources outside of the epic source of
truth?

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/63-0
00:10:23.898 --> 00:10:27.260
Yeah,
so you're asking someone who doesn't have

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/63-1
00:10:27.260 --> 00:10:32.443
that level of knowledge to be fair.
And we are blessed here at Henry Ford

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/61-0
00:10:27.738 --> 00:10:28.138
Kat.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/63-2
00:10:32.443 --> 00:10:37.695
that we have one instance of Epic.
And with the exception of we're 45 days

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/62-0
00:10:34.378 --> 00:10:34.858
Yeah.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/63-3
00:10:37.695 --> 00:10:43.158
away from taking 13 separate EMRs and
putting them on Epic from our Ascension

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/63-4
00:10:43.158 --> 00:10:44.138
joint venture.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/66-0
00:10:44.858 --> 00:10:49.068
we will all be on one instance of Epic.
So that's a very,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/64-0
00:10:47.818 --> 00:10:49.738
So, yeah, let's...

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/66-1
00:10:49.068 --> 00:10:54.585
it's a much simpler process inside of
that window than it is today in those

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/66-2
00:10:54.585 --> 00:10:55.818
other 13 systems.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/67-0
00:10:56.218 --> 00:10:59.631
Let's dive into that.
So the future of health at Henry Ford is

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/67-1
00:10:59.631 --> 00:11:01.960
a $2.
2 billion investment centered on the

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/67-2
00:11:01.960 --> 00:11:06.023
Destination Grand project in Detroit.
It's redefining the academic medical

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/67-3
00:11:06.023 --> 00:11:10.139
campus and follows on the heels of a
joint venture with Ascension Michigan,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/67-4
00:11:10.139 --> 00:11:13.877
which was finalized October 2024,
where you transitioned 8 Ascension

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/67-5
00:11:13.877 --> 00:11:17.018
Michigan hospital campuses and addiction
treatment center

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/69-0
00:11:17.258 --> 00:11:20.377
to the health system.
So I'd like to ask about the greatest

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/69-1
00:11:20.377 --> 00:11:24.379
challenges in getting new data sources to
conform to your standards quickly.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/69-2
00:11:24.379 --> 00:11:26.874
And some,
what does the data exchange look like

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/69-3
00:11:26.874 --> 00:11:30.460
with these external partners?
How are you consolidating them down to

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/69-4
00:11:30.460 --> 00:11:33.578
one Epic instance?
And where does data exchange break down?

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/72-0
00:11:34.698 --> 00:11:38.341
Yeah, so there's a lot of data separation.
And again,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/72-1
00:11:38.341 --> 00:11:43.466
I can't speak to it all because it
belongs to my Ascension colleagues. But,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/70-0
00:11:41.418 --> 00:11:41.898
Mhm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/72-2
00:11:43.466 --> 00:11:48.862
you know, again, we have 4 main EMRs.
There's A variety of other small EMRs for

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/72-3
00:11:48.862 --> 00:11:53.516
the addiction hospital. You know,
you have some other service lines,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/72-4
00:11:53.516 --> 00:11:56.618
some other physician groups that have
smaller

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/71-0
00:11:57.018 --> 00:11:57.498
Mhm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/73-0
00:11:57.738 --> 00:12:01.452
smaller EMRs.
And so it does break down in that sense

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/73-1
00:12:01.452 --> 00:12:04.685
that you have all these subsystems.
And again,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/73-2
00:12:04.685 --> 00:12:10.187
the ecosystem within the former Ascension
had actually built a bridge to have a

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/73-3
00:12:10.187 --> 00:12:15.552
data warehouse to have that all come
together. And it's fairly sophisticated.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/73-4
00:12:15.552 --> 00:12:16.858
So when, as we look

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/76-0
00:12:16.898 --> 00:12:20.242
forward,
we've had to convert for whatever data

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/76-1
00:12:20.242 --> 00:12:23.586
that we're taking and link it to our
standards.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/74-0
00:12:22.938 --> 00:12:23.178
Yeah.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/76-2
00:12:23.586 --> 00:12:29.297
We've done several other things and tools,
and I'm going to get you to talk to my

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/76-3
00:12:29.297 --> 00:12:34.313
CIO if you ask me about the tools.
To use those tools for the data that

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/75-0
00:12:31.018 --> 00:12:31.378
Ha ha.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/76-4
00:12:34.313 --> 00:12:39.258
we're going to house and use as readable
so that we can look back in a

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/77-0
00:12:39.578 --> 00:12:43.852
in a period of time that we appropriately
can have access to to do that.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/77-1
00:12:43.852 --> 00:12:47.951
We have other things like we have a
relationship with LabCorp, right?

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/77-2
00:12:47.951 --> 00:12:50.761
We have to share data back and forth with
them.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/77-3
00:12:50.761 --> 00:12:53.571
And so you work hard to agree on the
standards.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/77-4
00:12:53.571 --> 00:12:57.670
You may not have the same systems,
but you've got to use some sort of

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/77-5
00:12:57.670 --> 00:13:00.538
technology to make the bridge go back and
forth.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/78-0
00:13:00.618 --> 00:13:03.978
Between Epic and and the other parties.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/80-0
00:13:04.818 --> 00:13:10.311
So you mentioned two things just now.
Well, you mentioned the data lake,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/80-1
00:13:10.311 --> 00:13:15.729
I think, and the data warehouse.
How have you been managing a data lake

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/79-0
00:13:12.818 --> 00:13:13.298
Mhm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/80-2
00:13:15.729 --> 00:13:22.050
and data warehouse within the context of
these acquisitions and EHR consolidations?

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/80-3
00:13:22.050 --> 00:13:26.338
Have there been data challenges
associated with the two?

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/81-0
00:13:27.178 --> 00:13:30.112
Yeah,
I'm sure my team would tell you there's

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/81-1
00:13:30.112 --> 00:13:34.575
all sorts of data challenges.
I probably see it a little more simply.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/81-2
00:13:34.575 --> 00:13:39.804
I mean, we sort of started fresh and anew.
We had our own data warehouses, right,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/81-3
00:13:39.804 --> 00:13:43.949
and you had the Ascension ones.
Probably two to three years ago,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/81-4
00:13:43.949 --> 00:13:48.858
we started really looking at the data
lake and taking the time and preparing

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/83-0
00:13:50.218 --> 00:13:54.397
to cleanse data and put it in canisters.
So if you think in that lake,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/83-1
00:13:54.397 --> 00:13:58.753
there's a canister for the insurance
company and there's one for the care

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/83-2
00:13:58.753 --> 00:14:02.461
delivery system. I mean,
there will be one where we're storing

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/83-3
00:14:02.461 --> 00:14:05.934
historical data that we're allowed to
keep from Ascension.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/82-0
00:14:05.138 --> 00:14:05.658
Mhm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/83-4
00:14:05.934 --> 00:14:10.466
And in some of those things, again,
you have to have the master indices that

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/83-5
00:14:10.466 --> 00:14:13.938
you're going to link together to use that
historical data.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/84-0
00:14:14.018 --> 00:14:18.441
data, but a lot of,
because our Epic instance is unique to us

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/84-1
00:14:18.441 --> 00:14:24.076
and we have standards built in there,
a lot of our history on data is going to

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/84-2
00:14:24.076 --> 00:14:28.713
be on the go forward, right?
When we start being able to compare

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/84-3
00:14:28.713 --> 00:14:32.778
quality and the harmonization of that
data, a lot of it.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/85-0
00:14:32.858 --> 00:14:37.272
the vast majority of it comes from coming
onto the same platform using the same

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/85-1
00:14:37.272 --> 00:14:39.258
workflow, the same definitions, etc.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/86-0
00:14:39.578 --> 00:14:44.050
So when you reconcile that data in your
data lake or with the different data

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/86-1
00:14:44.050 --> 00:14:47.824
warehouses, you consolidate the data.
So then you have a robust,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/86-2
00:14:47.824 --> 00:14:51.831
rich set of clinical and claims data
available in the health system.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/86-3
00:14:51.831 --> 00:14:56.244
But a lot of health systems talk about
there being a gap between that data,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/86-4
00:14:56.244 --> 00:14:58.218
which exists in the health system,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/88-0
00:14:58.458 --> 00:15:02.722
and operationalizing it,
making it useful to the providers at the

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/88-1
00:15:02.722 --> 00:15:07.179
point of care, at the time of care,
the right time, the right place,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/87-0
00:15:05.338 --> 00:15:05.658
You.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/88-2
00:15:07.179 --> 00:15:10.925
the right person.
How has Henry Ford Health System worked

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/88-3
00:15:10.925 --> 00:15:16.158
to enable, to empower clinicians to have,
to be able to have actionable insights

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/88-4
00:15:16.158 --> 00:15:18.418
from that data at the time of care?

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/90-0
00:15:19.418 --> 00:15:21.955
Yeah,
and I'm just going to go across the

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/90-1
00:15:21.955 --> 00:15:26.786
health system because there's lots of
people who use the data for actions and a

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/90-2
00:15:26.786 --> 00:15:31.497
lot of the actions with inside the
clinical care workflows is driven by Epic,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/89-0
00:15:30.458 --> 00:15:30.618
Mm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/90-3
00:15:31.497 --> 00:15:34.396
right,
and your ability to use Slicer Dicer and

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/90-4
00:15:34.396 --> 00:15:37.898
a lot of the tools that they have there.
We've also built

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/93-0
00:15:38.298 --> 00:15:41.711
you know,
a lot of dashboards or workflows or BPAs

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/93-1
00:15:41.711 --> 00:15:46.730
within Epic to help alert the positions.
Obviously, there's a lot of data,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/91-0
00:15:44.298 --> 00:15:44.898
Mm-hmm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/93-2
00:15:46.730 --> 00:15:51.214
there's a lot of, you know, add-on tools.
There's, we are an Epic,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/93-3
00:15:51.214 --> 00:15:55.898
we are an Epic first house here,
like most people know that about us.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/92-0
00:15:54.418 --> 00:15:54.938
Mhm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/94-0
00:15:57.178 --> 00:16:01.724
But we have built things that allow us to
assist the assist the physician.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/94-1
00:16:01.724 --> 00:16:04.694
Obviously,
we've incorporated ambient listening,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/94-2
00:16:04.694 --> 00:16:08.937
which helps take the burden off the
physician as they're meeting with

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/94-3
00:16:08.937 --> 00:16:13.544
patients and allows them to help them get
through their notes more quickly.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/94-4
00:16:13.544 --> 00:16:18.029
We're super excited about the new tools
that Epic has sent out, you know,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/94-5
00:16:18.029 --> 00:16:20.938
Emmy and Art and Penny,
which are going to help

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/96-0
00:16:21.298 --> 00:16:26.459
across the entirety of that process of
visits set up to when you're in the room

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/96-1
00:16:26.459 --> 00:16:29.427
and what does the patient want to talk
about.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/96-2
00:16:29.427 --> 00:16:32.458
Those tools are going to be meaningful
for us.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/95-0
00:16:30.138 --> 00:16:30.538
Sure.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/98-0
00:16:32.858 --> 00:16:35.667
So Robin,
you just mentioned ambient listening.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/98-1
00:16:35.667 --> 00:16:39.354
Back to the Gen AI category.
I know that you have a three-tier

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/98-2
00:16:39.354 --> 00:16:42.222
governance model for Gen AI use cases.
I wonder,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/97-0
00:16:40.818 --> 00:16:41.338
Mhm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/98-3
00:16:42.222 --> 00:16:45.440
using ambient listening as an example to
talk through,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/98-4
00:16:45.440 --> 00:16:49.303
what happens at the data layer when a new
use case gets approved,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/98-5
00:16:49.303 --> 00:16:53.048
like ambient listening?
How quickly can your team get access to

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/98-6
00:16:53.048 --> 00:16:54.218
and prepare the data

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/99-0
00:16:54.378 --> 00:16:55.178
That it means.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/102-0
00:16:56.538 --> 00:17:02.152
So when the team decides, you know,
we divide the AI information into sort of

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/100-0
00:17:02.138 --> 00:17:02.618
Mhm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/102-1
00:17:02.152 --> 00:17:06.110
three layers,
like when you're working with an Epic or

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/102-2
00:17:06.110 --> 00:17:10.788
a Microsoft versus maybe when you're
working with the next tier,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/102-3
00:17:10.788 --> 00:17:14.458
it's really kind of a robust application
platform.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/101-0
00:17:14.978 --> 00:17:15.458
Mhm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/103-0
00:17:15.498 --> 00:17:20.296
And sometimes the Microsoft and Epic buy
the things in the previous layer.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/103-1
00:17:20.296 --> 00:17:25.670
So we go through a process to kind of vet
the use case to understand whether or not

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/103-2
00:17:25.670 --> 00:17:30.596
it's actually going to be efficacious.
The nice thing about some of these AI

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/103-3
00:17:30.596 --> 00:17:33.538
tools,
it doesn't take as long as it used to.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/104-0
00:17:33.618 --> 00:17:37.283
And particularly if you're doing
something in Microsoft or Epic,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/104-1
00:17:37.283 --> 00:17:41.511
they have these playground production
areas that you can turn it on in the

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/104-2
00:17:41.511 --> 00:17:44.950
background and compare it to the process
that's going there.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/104-3
00:17:44.950 --> 00:17:49.461
And you can decide anywhere from 10 to 60
days whether you're happy with it and

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/104-4
00:17:49.461 --> 00:17:53.633
whether or not the clinicians who are
watching the usual process and that

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/104-5
00:17:53.633 --> 00:17:55.098
process, do they trust it?

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/106-0
00:17:55.498 --> 00:18:00.213
So even if you turn some of these things
on, you don't like trust it instantly.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/105-0
00:17:58.858 --> 00:17:59.338
Mhm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/106-1
00:18:00.213 --> 00:18:04.986
Many times you're running them side by
side to learn about how the process needs

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/106-2
00:18:04.986 --> 00:18:08.462
to change. Wow,
that didn't quite come out how we thought.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/106-3
00:18:08.462 --> 00:18:12.175
Why does that happen?
Maybe you find in the way you structured

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/106-4
00:18:12.175 --> 00:18:14.826
the data that you left something out or
wow,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/106-5
00:18:14.826 --> 00:18:19.246
we got to start collecting that field.
We were never collecting that field

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/106-6
00:18:19.246 --> 00:18:19.658
before.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/107-0
00:18:20.098 --> 00:18:23.124
So there's learning that comes with
implementing.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/107-1
00:18:23.124 --> 00:18:28.145
And I think what we have found as well,
it's not just the technology and the data,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/107-2
00:18:28.145 --> 00:18:32.925
it's also the people and the process.
We've also found that maybe the workflow

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/107-3
00:18:32.925 --> 00:18:36.797
process has to change.
Maybe you have to retrain your employees

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/107-4
00:18:36.797 --> 00:18:39.338
because, oh, I don't have to do this part.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/108-0
00:18:39.658 --> 00:18:43.018
I can trust them to do it.
Why can I trust them to do that?

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/108-1
00:18:43.018 --> 00:18:47.218
And then how is it that, you know,
they get to go up to that next step and

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/108-2
00:18:47.218 --> 00:18:50.298
then really execute on the piece that
they need to do?

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/111-0
00:18:50.538 --> 00:18:53.214
So Robin,
as we approach the end of this podcast

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/111-1
00:18:53.214 --> 00:18:57.856
episode, I have a final question for you.
And perhaps unlike the previous questions,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/111-2
00:18:57.856 --> 00:19:01.570
this one's going to be definitely within
the wheelhouse of the CFO.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/109-0
00:18:57.978 --> 00:18:58.138
Yeah.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/110-0
00:19:00.458 --> 00:19:01.178
Okay.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/111-3
00:19:01.570 --> 00:19:05.447
If you could solve one data problem in
the next year, year and a half,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/111-4
00:19:05.447 --> 00:19:09.652
that would have the biggest downstream
impact on both costs and outcomes and

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/111-5
00:19:09.652 --> 00:19:12.218
lead to the greatest ROI for the
organization,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/112-0
00:19:12.338 --> 00:19:13.738
What would that data problem be?

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/114-0
00:19:14.778 --> 00:19:19.719
I think solving the data gaps would be it,
because, you know,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/114-1
00:19:19.719 --> 00:19:25.776
on any given data analysis you get,
you know, if it's 90% of the way there,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/114-2
00:19:25.776 --> 00:19:30.717
95% of the way there,
like when you're in the clinical realm,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/114-3
00:19:30.717 --> 00:19:35.578
people are going to make decisions.
They want a really high.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/113-0
00:19:33.258 --> 00:19:33.738
Mhm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/116-0
00:19:36.218 --> 00:19:39.973
really, really high.
It's like thinking about epic downtime.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/116-1
00:19:39.973 --> 00:19:43.421
We would never accept epic downtime not
being 99.99999.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/115-0
00:19:41.658 --> 00:19:42.138
Mhm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/116-2
00:19:43.421 --> 00:19:48.161
And I think in the clinical realm,
we're going to go through a little bit of

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/116-3
00:19:48.161 --> 00:19:51.300
this process where it's 80 or it's 85.
Well, okay,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/116-4
00:19:51.300 --> 00:19:55.855
I'm going to run it side by side.
And then we're going to learn about how

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/116-5
00:19:55.855 --> 00:19:55.978
to

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/117-0
00:20:01.298 --> 00:20:01.778
Mhm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/118-0
00:20:18.818 --> 00:20:19.298
Mhm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/119-0
00:20:19.338 --> 00:20:23.930
lots of data in a decision support system
and we bring that out and we would never

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/119-1
00:20:23.930 --> 00:20:27.746
say on first pass, it's done.
We sit down and we start looking at it

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/119-2
00:20:27.746 --> 00:20:31.010
and like, then we go back in the detail,
we find the gaps,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/119-3
00:20:31.010 --> 00:20:33.941
we manually fill in those gaps.
And the question is,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/119-4
00:20:33.941 --> 00:20:37.592
how will these tools help us identify the
gaps, fill in the gaps,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/119-5
00:20:37.592 --> 00:20:38.698
so that we can again

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/122-0
00:20:38.818 --> 00:20:42.991
get that efficacy far higher when you
start talking about clinical decisions.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/122-1
00:20:42.991 --> 00:20:47.270
For what something when you have data
gaps was much more acceptable when you're

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/122-2
00:20:47.270 --> 00:20:50.801
talking about, you know,
things that don't impact people's lives,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/122-3
00:20:50.801 --> 00:20:55.134
right? You're doing a financial analysis.
You can do that again in another time.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/122-4
00:20:55.134 --> 00:20:58.504
But we too on the finance side are
looking for efficiency too.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/121-0
00:20:55.658 --> 00:20:56.138
Mhm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/122-5
00:20:58.504 --> 00:21:01.178
I love it that my analysts don't have to
go back.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/123-0
00:21:02.018 --> 00:21:05.942
So which means it's process, right?
We're going to have people focused on,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/123-1
00:21:05.942 --> 00:21:09.394
wow, you found a data gap.
It's way back here at the beginning of

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/123-2
00:21:09.394 --> 00:21:13.631
the process when the patient walked in
the door that we got to fill in that gap.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/123-3
00:21:13.631 --> 00:21:17.763
So just imagine that curating that garden
is really about us getting much more

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/123-4
00:21:17.763 --> 00:21:21.058
efficacious and accurate in capturing the
data in the process.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/125-0
00:21:21.418 --> 00:21:25.900
And you know, that's what other amazing,
amazing tools like ambient listening,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/125-1
00:21:25.900 --> 00:21:29.643
things that are going to help us get down
the human error, right,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/124-0
00:21:26.138 --> 00:21:26.618
Mhm.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/125-2
00:21:29.643 --> 00:21:31.458
that comes with data collection.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/127-0
00:21:32.178 --> 00:21:34.907
Well, Robin,
we've covered a lot of ground today.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/127-1
00:21:34.907 --> 00:21:38.945
We've spoken about your data analytics
strategy and leveraging predictive

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/127-2
00:21:38.945 --> 00:21:42.765
analytics at their data lake.
We've spoken about interoperability and

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/127-3
00:21:42.765 --> 00:21:45.658
Destination Grand,
your joint venture with Accenture

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/127-4
00:21:45.658 --> 00:21:48.059
Michigan,
and how you've had to consolidate

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/127-5
00:21:48.059 --> 00:21:51.824
clinical and claims data.
We've spoken about longitudinal data sets,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/127-6
00:21:51.824 --> 00:21:54.498
both spanning both clinical and claims
data with

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/128-0
00:21:55.058 --> 00:22:00.237
health alliance plan and having that
firewall between the payer and the

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/128-1
00:22:00.237 --> 00:22:04.696
provider organization.
And we've spoken about Gen AI maturity

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/128-2
00:22:04.696 --> 00:22:09.659
and kind of scaling and introducing new
solutions into the workflow.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/128-3
00:22:09.659 --> 00:22:13.542
I appreciate you joining us today.
For our listeners,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/128-4
00:22:13.542 --> 00:22:17.138
this has been Robin Damschroeder,
the EVP and CFO

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/129-0
00:22:17.258 --> 00:22:20.900
and president of Value-Based Enterprise
at Henry Ford Health System. Robin,

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/129-1
00:22:20.900 --> 00:22:22.098
thank you for joining us.

7f4b40b7-7deb-4e7b-bcf1-60d89d4c3557/130-0
00:22:22.418 --> 00:22:24.498
It was my pleasure, Jordan.
Have a good day.