64e47f89-b05c-43ba-b508-b44818cd5acf/5-0
00:00:03.313 --> 00:00:05.589
with Colin Gibson at Northwestern
Medicine.

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00:00:05.589 --> 00:00:08.537
Colin is a senior consultant of
Value-Based Care. Colin,

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00:00:08.537 --> 00:00:10.553
thank you so much for joining us today.

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00:00:10.873 --> 00:00:12.393
Good to be here, Jordan.
Thanks for having me.

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00:00:12.873 --> 00:00:16.878
Sure. So for our listeners,
Northwestern Medicine is a health system

64e47f89-b05c-43ba-b508-b44818cd5acf/8-1
00:00:16.878 --> 00:00:20.766
headquartered in Chicago, Illinois,
with 2,700 inpatient beds, 11,

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00:00:20.766 --> 00:00:25.176
000 providers across 12 hospitals and
other ancillary facilities. So Colin,

64e47f89-b05c-43ba-b508-b44818cd5acf/8-3
00:00:25.176 --> 00:00:29.586
today we're going to be discussing a
variety of data challenges that you're

64e47f89-b05c-43ba-b508-b44818cd5acf/8-4
00:00:29.586 --> 00:00:32.313
working on at Northwestern Medicine,
including

64e47f89-b05c-43ba-b508-b44818cd5acf/9-0
00:00:32.593 --> 00:00:36.645
the challenges that are associated with
disparate payer sources,

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00:00:36.645 --> 00:00:40.260
internal data builds,
supplemental data feeds, et cetera,

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00:00:40.260 --> 00:00:42.940
et cetera.
We're touching upon value-based

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00:00:42.940 --> 00:00:47.676
contracting, medical loss ratios,
et cetera. So please, the floor is yours.

64e47f89-b05c-43ba-b508-b44818cd5acf/9-4
00:00:47.676 --> 00:00:51.353
Walk us through some of your data
challenges and all these

64e47f89-b05c-43ba-b508-b44818cd5acf/10-0
00:00:51.433 --> 00:00:52.313
Married areas.

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00:00:52.953 --> 00:00:57.574
Yeah, sounds good.
I think it'd be helpful to start kind of

64e47f89-b05c-43ba-b508-b44818cd5acf/12-1
00:00:57.574 --> 00:01:01.347
like a level set baseline of my team,
what I do,

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00:01:01.347 --> 00:01:06.044
and maybe some background on our
value-based care footprint,

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00:01:06.044 --> 00:01:12.127
just so we are all talking about data
with a level of understanding from where

64e47f89-b05c-43ba-b508-b44818cd5acf/12-4
00:01:12.127 --> 00:01:13.513
I'm speaking from.

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00:01:12.393 --> 00:01:12.873
Mhm.

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So NM as a system has close to 400,
000 patient lives that sit in some sort

64e47f89-b05c-43ba-b508-b44818cd5acf/14-1
00:01:21.887 --> 00:01:27.860
of risk-based agreement.
The way we divide those on our team is

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00:01:27.860 --> 00:01:35.047
basically government contracts versus
private payer or what we call Medicare

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00:01:35.047 --> 00:01:37.193
Advantage contracts. So

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00:01:38.433 --> 00:01:43.217
Probably half of that is our Medicare
shared savings ACO,

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00:01:43.217 --> 00:01:49.816
which are broken up into our two medical
groups. So almost 200,000 lives there.

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00:01:49.816 --> 00:01:56.084
And then the other half sits in various
private payer contracts in Medicare

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00:01:56.084 --> 00:01:58.393
Advantage agreements. And so

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00:01:59.113 --> 00:02:05.184
Going forward in this conversation,
my portfolio sits in that second bucket,

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00:02:02.713 --> 00:02:02.793
The.

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00:02:05.184 --> 00:02:10.781
that Medicare Advantage bucket.
So we contract with all the big names,

64e47f89-b05c-43ba-b508-b44818cd5acf/17-2
00:02:10.781 --> 00:02:15.353
United, Aetna, Cigna,
Blue Cross Blue Shield, as a way of

64e47f89-b05c-43ba-b508-b44818cd5acf/19-0
00:02:17.033 --> 00:02:20.502
getting to those alternate payment
agreements.

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00:02:20.502 --> 00:02:26.185
So that's a little bit where I sit and
where we're going to be talking about

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00:02:26.185 --> 00:02:32.015
data. So I think the natural segue,
saying all that is on the government side,

64e47f89-b05c-43ba-b508-b44818cd5acf/19-3
00:02:32.015 --> 00:02:36.444
when you talk about the Medicare Share
and Savings Program,

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00:02:36.444 --> 00:02:37.993
that's a very uniform

64e47f89-b05c-43ba-b508-b44818cd5acf/20-0
00:02:38.873 --> 00:02:44.606
government subsidized reporting process.
So a lot of the ACOs receive the same

64e47f89-b05c-43ba-b508-b44818cd5acf/18-0
00:02:39.993 --> 00:02:40.033
Oh.

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00:02:44.606 --> 00:02:49.976
type of patient level reporting.
They receive their claims data in a very

64e47f89-b05c-43ba-b508-b44818cd5acf/20-2
00:02:49.976 --> 00:02:52.153
uniform manner. And there's...

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00:02:53.593 --> 00:02:59.596
Various vendors that help NM and other
health systems manage that claims data,

64e47f89-b05c-43ba-b508-b44818cd5acf/21-0
00:02:54.953 --> 00:02:55.073
Yeah.

64e47f89-b05c-43ba-b508-b44818cd5acf/22-1
00:02:59.596 --> 00:03:04.383
that utilization data,
in order to get a clear picture of that

64e47f89-b05c-43ba-b508-b44818cd5acf/22-2
00:03:04.383 --> 00:03:09.398
patient population. In contrast,
when you have Medicare Advantage

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00:03:09.398 --> 00:03:13.273
agreements that sit with so many
different payers,

64e47f89-b05c-43ba-b508-b44818cd5acf/25-0
00:03:15.113 --> 00:03:19.745
As many of us know,
not a lot of the payers have agreed upon

64e47f89-b05c-43ba-b508-b44818cd5acf/25-1
00:03:19.745 --> 00:03:23.313
best practices for reporting data,
data usage,

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00:03:23.313 --> 00:03:29.007
what those transfer processes look like,
what data to give to the provider

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00:03:29.007 --> 00:03:33.107
organization.
So there's a lot of variables that come

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00:03:29.353 --> 00:03:29.513
No.

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00:03:33.107 --> 00:03:34.473
into play when we.

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00:03:34.673 --> 00:03:38.735
think about the Medicare Advantage
value-based care space.

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00:03:36.273 --> 00:03:36.393
Yeah.

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00:03:38.735 --> 00:03:43.967
So I guess I'll pause there, Jordan,
and let you ask any questions that may

64e47f89-b05c-43ba-b508-b44818cd5acf/26-2
00:03:43.967 --> 00:03:44.793
have arisen.

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00:03:45.433 --> 00:03:49.907
So it sounds like there are a ton of
different data sources that you're,

64e47f89-b05c-43ba-b508-b44818cd5acf/27-1
00:03:49.907 --> 00:03:53.216
mostly it's claims data,
it's quality reporting data.

64e47f89-b05c-43ba-b508-b44818cd5acf/27-2
00:03:53.216 --> 00:03:56.034
Some of it comes from public payers like
CMS,

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00:03:56.034 --> 00:04:00.569
some of it are requirements of submitting
data to public payers like CMS,

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00:04:00.569 --> 00:04:05.165
and then you're getting private claims
data from many different commercial

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00:04:05.165 --> 00:04:05.593
payers.

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00:04:05.833 --> 00:04:10.488
and then you're providing care and you
have access to clinical data.

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00:04:10.488 --> 00:04:15.884
And you're supposed to reconcile those
clinical and claims data and somehow use

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00:04:10.673 --> 00:04:11.113
Correct.

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00:04:15.884 --> 00:04:21.415
that to address, well, various use cases.
So would you please walk us through the

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00:04:21.415 --> 00:04:26.541
various use cases that you use that you
are intending to address with these

64e47f89-b05c-43ba-b508-b44818cd5acf/29-4
00:04:26.541 --> 00:04:29.913
different data sources?
And then I'd like to hear

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00:04:30.153 --> 00:04:33.593
How do you kind of integrate all those
different data sources?

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00:04:34.193 --> 00:04:37.811
Sure.
I think the best way to start is talking

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00:04:37.811 --> 00:04:43.737
about vendors and the space that exists
currently, just from what, you know,

64e47f89-b05c-43ba-b508-b44818cd5acf/31-2
00:04:43.737 --> 00:04:48.508
my very limited view.
I think the space to innovate in is the

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00:04:48.508 --> 00:04:53.433
combination of the clinical data and the
claims data. If you...

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00:04:53.753 --> 00:04:58.789
If A vendor was able to come in,
we've had various trial runs with

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00:04:58.789 --> 00:05:04.650
different vendors that are able to
integrate, you know, the epic side or the,

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00:05:04.650 --> 00:05:08.333
you know,
the clinical data side with the claims

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00:05:08.333 --> 00:05:12.842
data from the payer.
I think the challenge there that we've

64e47f89-b05c-43ba-b508-b44818cd5acf/32-4
00:05:12.842 --> 00:05:16.073
run into is that normalization of data.
So

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00:05:16.473 --> 00:05:17.033
Um...

64e47f89-b05c-43ba-b508-b44818cd5acf/37-0
00:05:18.233 --> 00:05:23.258
You know, I'm not a data guy myself,
but I work on the back end using this

64e47f89-b05c-43ba-b508-b44818cd5acf/37-1
00:05:23.258 --> 00:05:25.871
data.
So I don't know exactly what the

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00:05:25.871 --> 00:05:29.555
challenges are,
but we work closely enough with our IT

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00:05:29.555 --> 00:05:33.441
and analytics team.
And I think the real challenge is the

64e47f89-b05c-43ba-b508-b44818cd5acf/34-0
00:05:29.593 --> 00:05:29.753
Yeah.

64e47f89-b05c-43ba-b508-b44818cd5acf/37-4
00:05:33.441 --> 00:05:38.599
different formatting that we receive
these claims data in and using those or

64e47f89-b05c-43ba-b508-b44818cd5acf/35-0
00:05:37.753 --> 00:05:37.833
The.

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00:05:38.599 --> 00:05:42.953
integrating those in a uniform fashion
across different sources.

64e47f89-b05c-43ba-b508-b44818cd5acf/38-0
00:05:43.113 --> 00:05:48.443
So I guess that's the challenge. I think,
you know, where I sit is, you know,

64e47f89-b05c-43ba-b508-b44818cd5acf/36-0
00:05:44.873 --> 00:05:45.153
Okay.

64e47f89-b05c-43ba-b508-b44818cd5acf/38-1
00:05:48.443 --> 00:05:53.977
trying to reconcile what we receive from
the payer and what we see clinically on

64e47f89-b05c-43ba-b508-b44818cd5acf/38-2
00:05:53.977 --> 00:05:57.666
our Epic system or in various other
clinical systems.

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00:05:57.666 --> 00:06:01.833
And so I think a great example of that is
quality reporting.

64e47f89-b05c-43ba-b508-b44818cd5acf/39-0
00:06:03.193 --> 00:06:09.120
I'm going to use Humana as an example.
They gave us really great live patient

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00:06:09.120 --> 00:06:12.540
data.
They have a live dashboard that we can

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00:06:12.540 --> 00:06:16.415
look at, go in,
and drill down to specific measure

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00:06:16.415 --> 00:06:20.670
denominators.
So who are the patients that are eligible

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00:06:20.670 --> 00:06:24.393
for breast cancer screening?
Where do they live?

64e47f89-b05c-43ba-b508-b44818cd5acf/40-0
00:06:24.713 --> 00:06:28.503
Who's their primary care provider?
Were they compliant or not compliant in

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00:06:28.503 --> 00:06:31.433
breast cancer screening in the last,
you know, 12 months?

64e47f89-b05c-43ba-b508-b44818cd5acf/41-0
00:06:33.953 --> 00:06:38.407
And one of the unique things about our
team is we have, you know,

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00:06:38.407 --> 00:06:44.076
value-based care quality leaders that are
working with providers on these gap lists

64e47f89-b05c-43ba-b508-b44818cd5acf/41-2
00:06:44.076 --> 00:06:49.002
that we receive from the payer.
And oftentimes we do a sprint at the end

64e47f89-b05c-43ba-b508-b44818cd5acf/41-3
00:06:49.002 --> 00:06:52.713
of the year and say, okay,
here's our 400 patients who

64e47f89-b05c-43ba-b508-b44818cd5acf/43-0
00:06:52.913 --> 00:06:56.895
don't have a breast cancer screening.
That's what Humana's saying.

64e47f89-b05c-43ba-b508-b44818cd5acf/43-1
00:06:56.895 --> 00:07:01.472
Let's punch into their Epic record and
see if we can't find their, you know,

64e47f89-b05c-43ba-b508-b44818cd5acf/43-2
00:07:01.472 --> 00:07:05.335
evidence of their screening,
whether they got it elsewhere. And,

64e47f89-b05c-43ba-b508-b44818cd5acf/42-0
00:07:01.593 --> 00:07:01.793
Okay.

64e47f89-b05c-43ba-b508-b44818cd5acf/43-3
00:07:05.335 --> 00:07:08.366
you know,
it exists in that little care everywhere

64e47f89-b05c-43ba-b508-b44818cd5acf/43-4
00:07:08.366 --> 00:07:11.634
portal on Epic. And, you know,
based on Humana claims,

64e47f89-b05c-43ba-b508-b44818cd5acf/43-5
00:07:11.634 --> 00:07:13.833
they're just not allowed to see that.

64e47f89-b05c-43ba-b508-b44818cd5acf/46-0
00:07:14.393 --> 00:07:17.953
It just doesn't come through on the
Humana claim side.

64e47f89-b05c-43ba-b508-b44818cd5acf/44-0
00:07:17.113 --> 00:07:17.153
Oh

64e47f89-b05c-43ba-b508-b44818cd5acf/46-1
00:07:17.953 --> 00:07:23.196
So what supplemental data can we provide
back to Humana in order to fill some of

64e47f89-b05c-43ba-b508-b44818cd5acf/45-0
00:07:21.473 --> 00:07:21.953
Mmh.

64e47f89-b05c-43ba-b508-b44818cd5acf/46-2
00:07:23.196 --> 00:07:26.561
those gaps in data availability or data
visibility?

64e47f89-b05c-43ba-b508-b44818cd5acf/46-3
00:07:26.561 --> 00:07:31.998
And we've been very successful with that.
Oftentimes we move a percentage or two or

64e47f89-b05c-43ba-b508-b44818cd5acf/46-4
00:07:31.998 --> 00:07:37.046
a star level or two at the end of the
year based on these abstraction reports

64e47f89-b05c-43ba-b508-b44818cd5acf/46-5
00:07:37.046 --> 00:07:38.793
that we're able to provide.

64e47f89-b05c-43ba-b508-b44818cd5acf/47-0
00:07:39.113 --> 00:07:40.473
Ah, he man on the back end.

64e47f89-b05c-43ba-b508-b44818cd5acf/48-0
00:07:40.873 --> 00:07:45.351
So you have, let's say, 400 patients,
you go through the clinical records and

64e47f89-b05c-43ba-b508-b44818cd5acf/48-1
00:07:45.351 --> 00:07:49.829
you try to identify maybe some of these
people already have been successfully

64e47f89-b05c-43ba-b508-b44818cd5acf/48-2
00:07:49.829 --> 00:07:53.790
screened for breast cancer.
What proportion would you say remains of

64e47f89-b05c-43ba-b508-b44818cd5acf/48-3
00:07:53.790 --> 00:07:58.153
the 400 once you've gone through that
clinical data reconciliation process?

64e47f89-b05c-43ba-b508-b44818cd5acf/50-0
00:07:58.113 --> 00:08:01.455
Yeah,
I'd say it's hard to put a number too.

64e47f89-b05c-43ba-b508-b44818cd5acf/50-1
00:08:01.455 --> 00:08:05.688
I'd say we find less than 10% of,
you know, gap closure,

64e47f89-b05c-43ba-b508-b44818cd5acf/50-2
00:08:05.688 --> 00:08:09.030
but I think depending on what the,
you know,

64e47f89-b05c-43ba-b508-b44818cd5acf/50-3
00:08:09.030 --> 00:08:14.749
overall denominator of the population is,
sometimes that can move, you know,

64e47f89-b05c-43ba-b508-b44818cd5acf/49-0
00:08:13.993 --> 00:08:14.233
So.

64e47f89-b05c-43ba-b508-b44818cd5acf/50-4
00:08:14.749 --> 00:08:17.273
if we're sitting on the edge of...

64e47f89-b05c-43ba-b508-b44818cd5acf/52-0
00:08:17.713 --> 00:08:21.385
you know,
the 86% threshold to hit that five star

64e47f89-b05c-43ba-b508-b44818cd5acf/52-1
00:08:21.385 --> 00:08:24.690
or that four-star,
that next star threshold.

64e47f89-b05c-43ba-b508-b44818cd5acf/52-2
00:08:24.690 --> 00:08:30.492
Sometimes that makes all the difference
in receiving incentive or boosting our

64e47f89-b05c-43ba-b508-b44818cd5acf/52-3
00:08:30.492 --> 00:08:34.531
overall star level.
And so I think we've seen a lot of

64e47f89-b05c-43ba-b508-b44818cd5acf/52-4
00:08:34.531 --> 00:08:40.553
success and continue to apply effort and
manpower behind our abstraction efforts.

64e47f89-b05c-43ba-b508-b44818cd5acf/51-0
00:08:38.473 --> 00:08:38.713
Okay.

64e47f89-b05c-43ba-b508-b44818cd5acf/53-0
00:08:40.873 --> 00:08:45.376
Just to make sure I'm understanding and
our listeners are understanding,

64e47f89-b05c-43ba-b508-b44818cd5acf/53-1
00:08:45.376 --> 00:08:50.002
you're saying maybe about 90% of the
people in the gap list from the payer

64e47f89-b05c-43ba-b508-b44818cd5acf/53-2
00:08:50.002 --> 00:08:54.073
actually have gotten screening,
but the payer's unaware. No, 10%.

64e47f89-b05c-43ba-b508-b44818cd5acf/57-0
00:08:53.353 --> 00:08:56.327
No, I'm sorry.
I must have said that confusing.

64e47f89-b05c-43ba-b508-b44818cd5acf/57-1
00:08:56.327 --> 00:08:59.981
I got that opposite.
And so that's probably 90% correct in

64e47f89-b05c-43ba-b508-b44818cd5acf/54-0
00:08:56.553 --> 00:08:57.113
Okay.

64e47f89-b05c-43ba-b508-b44818cd5acf/57-2
00:08:59.981 --> 00:09:04.317
that these patients weren't seen.
They are truly failing the measure.

64e47f89-b05c-43ba-b508-b44818cd5acf/57-3
00:09:04.317 --> 00:09:09.148
And maybe we've done outreach to these
patients and, you know, things happen,

64e47f89-b05c-43ba-b508-b44818cd5acf/55-0
00:09:07.433 --> 00:09:07.553
Yeah.

64e47f89-b05c-43ba-b508-b44818cd5acf/57-4
00:09:09.148 --> 00:09:11.873
right?
We're not going to close every game.

64e47f89-b05c-43ba-b508-b44818cd5acf/59-0
00:09:12.553 --> 00:09:17.576
But I will say maybe 8 to 10 percent,
we do find a care everywhere, you know,

64e47f89-b05c-43ba-b508-b44818cd5acf/56-0
00:09:12.673 --> 00:09:12.953
And.

64e47f89-b05c-43ba-b508-b44818cd5acf/59-1
00:09:17.576 --> 00:09:22.534
faxed in document from another health
system or this patient's new to Humana

64e47f89-b05c-43ba-b508-b44818cd5acf/59-2
00:09:22.534 --> 00:09:27.299
and they're still in the measure
denominator and they had a breast cancer

64e47f89-b05c-43ba-b508-b44818cd5acf/59-3
00:09:27.299 --> 00:09:31.033
screening at their old facility with
their old care team.

64e47f89-b05c-43ba-b508-b44818cd5acf/58-0
00:09:31.513 --> 00:09:31.633
Yeah.

64e47f89-b05c-43ba-b508-b44818cd5acf/60-0
00:09:31.993 --> 00:09:35.633
And that appears in that chart.
And so we're able to send some of those

64e47f89-b05c-43ba-b508-b44818cd5acf/61-0
00:09:35.433 --> 00:09:38.966
Got it.
So the benefits here of going through

64e47f89-b05c-43ba-b508-b44818cd5acf/60-1
00:09:35.633 --> 00:09:36.593
examples to Humana.

64e47f89-b05c-43ba-b508-b44818cd5acf/61-1
00:09:38.966 --> 00:09:44.342
this process is maybe 8 to 10% can be
used to improve your numerator.

64e47f89-b05c-43ba-b508-b44818cd5acf/61-2
00:09:44.342 --> 00:09:49.871
You get an improved star rating,
potentially improved remuneration from

64e47f89-b05c-43ba-b508-b44818cd5acf/61-3
00:09:49.871 --> 00:09:55.016
CMS for your star rating,
and you also have a more refined list of

64e47f89-b05c-43ba-b508-b44818cd5acf/61-4
00:09:55.016 --> 00:09:55.553
the 90%

64e47f89-b05c-43ba-b508-b44818cd5acf/62-0
00:09:55.673 --> 00:09:59.481
who your care manager should reach out to
to try to see if you can actually get

64e47f89-b05c-43ba-b508-b44818cd5acf/62-1
00:09:59.481 --> 00:10:03.240
them a screening, which is recommended,
which would not only improve your star

64e47f89-b05c-43ba-b508-b44818cd5acf/62-2
00:10:03.240 --> 00:10:06.096
rating in the future,
but also would be good for quality of

64e47f89-b05c-43ba-b508-b44818cd5acf/62-3
00:10:06.096 --> 00:10:08.713
care and patient outcomes.
Is that what you're saying?

64e47f89-b05c-43ba-b508-b44818cd5acf/64-0
00:10:09.033 --> 00:10:11.962
Yeah,
and it helps us on the care management

64e47f89-b05c-43ba-b508-b44818cd5acf/64-1
00:10:11.962 --> 00:10:16.972
side because we do see a high level of
patient frustration when we get those

64e47f89-b05c-43ba-b508-b44818cd5acf/64-2
00:10:16.972 --> 00:10:21.136
things wrong. So for example,
if we are calling a patient, hey,

64e47f89-b05c-43ba-b508-b44818cd5acf/64-3
00:10:21.136 --> 00:10:23.999
you haven't had your annual wellness
visit,

64e47f89-b05c-43ba-b508-b44818cd5acf/63-0
00:10:22.153 --> 00:10:22.193
A

64e47f89-b05c-43ba-b508-b44818cd5acf/64-4
00:10:23.999 --> 00:10:28.033
we haven't seen you for a diabetes eye
exam, and those things

64e47f89-b05c-43ba-b508-b44818cd5acf/68-0
00:10:28.113 --> 00:10:32.420
do exist somewhere in the ether,
whether that be Care Everywhere or the

64e47f89-b05c-43ba-b508-b44818cd5acf/65-0
00:10:31.793 --> 00:10:31.913
Play.

64e47f89-b05c-43ba-b508-b44818cd5acf/68-1
00:10:32.420 --> 00:10:36.785
Humana patient list is off by denominator
or two. And they're like, hey,

64e47f89-b05c-43ba-b508-b44818cd5acf/68-2
00:10:36.785 --> 00:10:40.254
we have done this.
Why is Northwestern or Humana reaching

64e47f89-b05c-43ba-b508-b44818cd5acf/68-3
00:10:40.254 --> 00:10:44.740
out to me? What's going on over there?
That I'm getting another call about

64e47f89-b05c-43ba-b508-b44818cd5acf/67-0
00:10:43.193 --> 00:10:43.913
Mmh.

64e47f89-b05c-43ba-b508-b44818cd5acf/68-4
00:10:44.740 --> 00:10:46.713
something I've already completed.

64e47f89-b05c-43ba-b508-b44818cd5acf/69-0
00:10:46.913 --> 00:10:51.497
So we have a higher level of confidence
that we're reaching out to the right

64e47f89-b05c-43ba-b508-b44818cd5acf/69-1
00:10:51.497 --> 00:10:51.913
people.

64e47f89-b05c-43ba-b508-b44818cd5acf/70-0
00:10:52.153 --> 00:10:56.566
So I'm hearing two issues with trust.
One is a data trust issue where you're

64e47f89-b05c-43ba-b508-b44818cd5acf/70-1
00:10:56.566 --> 00:11:01.265
having to manually kind of take the data
from Humana and try to reconcile it with

64e47f89-b05c-43ba-b508-b44818cd5acf/70-2
00:11:01.265 --> 00:11:05.678
the clinical data at Northwestern in
order to ensure that the numerators and

64e47f89-b05c-43ba-b508-b44818cd5acf/70-3
00:11:05.678 --> 00:11:09.002
denominators are accurate.
So there's a data trust issue.

64e47f89-b05c-43ba-b508-b44818cd5acf/70-4
00:11:09.002 --> 00:11:10.033
And then there's a

64e47f89-b05c-43ba-b508-b44818cd5acf/71-0
00:11:10.473 --> 00:11:13.033
Patient trust issue with the institution
itself.

64e47f89-b05c-43ba-b508-b44818cd5acf/72-0
00:11:13.193 --> 00:11:15.193
That's a great way to put it, yes,
exactly.

64e47f89-b05c-43ba-b508-b44818cd5acf/75-0
00:11:17.833 --> 00:11:22.453
And so given,
I'd like to dive back into the data that

64e47f89-b05c-43ba-b508-b44818cd5acf/75-1
00:11:22.453 --> 00:11:27.157
you're getting from Humana.
You mentioned that there's,

64e47f89-b05c-43ba-b508-b44818cd5acf/74-0
00:11:24.113 --> 00:11:24.593
Mhm.

64e47f89-b05c-43ba-b508-b44818cd5acf/75-2
00:11:27.157 --> 00:11:31.861
there is not clinical data so much as
demographic data.

64e47f89-b05c-43ba-b508-b44818cd5acf/75-3
00:11:31.861 --> 00:11:36.313
You said who's eligible for breast cancer
screening.

64e47f89-b05c-43ba-b508-b44818cd5acf/76-0
00:11:36.553 --> 00:11:39.713
How do they,
is that just based on demographic data?

64e47f89-b05c-43ba-b508-b44818cd5acf/76-1
00:11:39.713 --> 00:11:44.124
They're not using clinical data to
determine if the patient's at risk and

64e47f89-b05c-43ba-b508-b44818cd5acf/76-2
00:11:44.124 --> 00:11:45.673
needs screening, are they?

64e47f89-b05c-43ba-b508-b44818cd5acf/79-0
00:11:47.033 --> 00:11:50.719
It's twofold.
So it is that HEDIS definition of what,

64e47f89-b05c-43ba-b508-b44818cd5acf/79-1
00:11:50.719 --> 00:11:54.199
you know,
would qualify someone to be eligible for

64e47f89-b05c-43ba-b508-b44818cd5acf/79-2
00:11:54.199 --> 00:11:58.567
the breast cancer denominator.
I will say there's also clinical

64e47f89-b05c-43ba-b508-b44818cd5acf/77-0
00:11:57.113 --> 00:11:57.153
Oh.

64e47f89-b05c-43ba-b508-b44818cd5acf/79-3
00:11:58.567 --> 00:12:04.026
exclusions that exist within the measure.
So if we're using breast cancer as an

64e47f89-b05c-43ba-b508-b44818cd5acf/79-4
00:12:04.026 --> 00:12:06.073
example, bilateral mastectomy,

64e47f89-b05c-43ba-b508-b44818cd5acf/78-0
00:12:06.553 --> 00:12:06.753
Mm.

64e47f89-b05c-43ba-b508-b44818cd5acf/80-0
00:12:07.273 --> 00:12:10.873
things of that nature.
So there are clinical...

64e47f89-b05c-43ba-b508-b44818cd5acf/81-0
00:12:13.113 --> 00:12:18.873
Factors that play in that denominator.
I'll also caveat that by saying...

64e47f89-b05c-43ba-b508-b44818cd5acf/83-0
00:12:19.993 --> 00:12:24.093
We do find exclusions on our side. So,
hey, for example,

64e47f89-b05c-43ba-b508-b44818cd5acf/83-1
00:12:24.093 --> 00:12:28.048
we see this patient at the end of the
year, they have,

64e47f89-b05c-43ba-b508-b44818cd5acf/83-2
00:12:28.048 --> 00:12:33.442
they seem to be failing the measure per
se, and then we find an exclusion,

64e47f89-b05c-43ba-b508-b44818cd5acf/82-0
00:12:29.553 --> 00:12:30.033
Mhm.

64e47f89-b05c-43ba-b508-b44818cd5acf/83-3
00:12:33.442 --> 00:12:37.757
just like we would find maybe a disparate
passing evidence,

64e47f89-b05c-43ba-b508-b44818cd5acf/83-4
00:12:37.757 --> 00:12:40.633
and we report that to Humana as well. So

64e47f89-b05c-43ba-b508-b44818cd5acf/84-0
00:12:41.273 --> 00:12:44.626
People fall through the cracks,
clinical data on the payer side falls

64e47f89-b05c-43ba-b508-b44818cd5acf/84-1
00:12:44.626 --> 00:12:47.979
through the cracks sometimes,
but there are clinical aspects to those

64e47f89-b05c-43ba-b508-b44818cd5acf/84-2
00:12:47.979 --> 00:12:48.793
measures as well.

64e47f89-b05c-43ba-b508-b44818cd5acf/85-0
00:12:49.193 --> 00:12:52.495
Just to dive a little deeper on that
question,

64e47f89-b05c-43ba-b508-b44818cd5acf/85-1
00:12:52.495 --> 00:12:58.395
so I want to kind of ascertain the extent
to which Northwestern Medicine is sharing

64e47f89-b05c-43ba-b508-b44818cd5acf/85-2
00:12:58.395 --> 00:13:03.874
clinical data with payers like Humana,
and Humana is leveraging that clinical

64e47f89-b05c-43ba-b508-b44818cd5acf/85-3
00:13:03.874 --> 00:13:09.564
data extracted from Northwestern's Epic
instance and other clinical data sources

64e47f89-b05c-43ba-b508-b44818cd5acf/85-4
00:13:09.564 --> 00:13:12.233
in order to make claims determinations

64e47f89-b05c-43ba-b508-b44818cd5acf/86-0
00:13:12.513 --> 00:13:15.353
and also for use in measure reporting.

64e47f89-b05c-43ba-b508-b44818cd5acf/87-0
00:13:16.153 --> 00:13:20.597
Yeah,
I don't know the extent of the clinical

64e47f89-b05c-43ba-b508-b44818cd5acf/87-1
00:13:20.597 --> 00:13:27.745
sharing outside of supplemental data
builds that go out regularly to fill

64e47f89-b05c-43ba-b508-b44818cd5acf/87-2
00:13:27.745 --> 00:13:34.314
these quality measure gaps.
What I mean to say is Humana would only

64e47f89-b05c-43ba-b508-b44818cd5acf/87-3
00:13:34.314 --> 00:13:35.473
know about a

64e47f89-b05c-43ba-b508-b44818cd5acf/89-0
00:13:35.913 --> 00:13:39.833
clinical thing if it came through the
last 24 months of claims on their side.

64e47f89-b05c-43ba-b508-b44818cd5acf/88-0
00:13:37.353 --> 00:13:37.393
Oh.

64e47f89-b05c-43ba-b508-b44818cd5acf/90-0
00:13:40.473 --> 00:13:48.045
Got it. So if there hasn't been a claim,
then it's fair to say that the payer may

64e47f89-b05c-43ba-b508-b44818cd5acf/90-1
00:13:48.045 --> 00:13:54.324
not be aware of the clinical background
of that particular patient.

64e47f89-b05c-43ba-b508-b44818cd5acf/90-2
00:13:54.324 --> 00:13:59.033
And in terms of trying to maximize shared
savings,

64e47f89-b05c-43ba-b508-b44818cd5acf/91-0
00:14:00.153 --> 00:14:03.325
program,
there may be information that can be

64e47f89-b05c-43ba-b508-b44818cd5acf/91-1
00:14:03.325 --> 00:14:06.633
better integrated between payers and
providers.

64e47f89-b05c-43ba-b508-b44818cd5acf/92-0
00:14:06.713 --> 00:14:09.373
Yeah, I think, I think,
I think that's totally accurate to say

64e47f89-b05c-43ba-b508-b44818cd5acf/92-1
00:14:09.373 --> 00:14:09.753
for sure.

64e47f89-b05c-43ba-b508-b44818cd5acf/94-0
00:14:10.233 --> 00:14:15.273
So for our listeners to this episode,
they may be wondering, all right,

64e47f89-b05c-43ba-b508-b44818cd5acf/94-1
00:14:15.273 --> 00:14:19.473
so we have a shared savings program.
I know that, you know,

64e47f89-b05c-43ba-b508-b44818cd5acf/93-0
00:14:17.033 --> 00:14:17.513
Mhm.

64e47f89-b05c-43ba-b508-b44818cd5acf/94-2
00:14:19.473 --> 00:14:24.093
we receive data from payers and
commercial and payers and public,

64e47f89-b05c-43ba-b508-b44818cd5acf/94-3
00:14:24.093 --> 00:14:28.363
we report to public payers.
And we'd love to know, you know,

64e47f89-b05c-43ba-b508-b44818cd5acf/94-4
00:14:28.363 --> 00:14:30.113
what can we do to improve

64e47f89-b05c-43ba-b508-b44818cd5acf/95-0
00:14:31.593 --> 00:14:35.671
our capture, improve our star ratings,
improve our data capture.

64e47f89-b05c-43ba-b508-b44818cd5acf/95-1
00:14:35.671 --> 00:14:40.628
What recommendations do you have for your
peers at other institutions that are

64e47f89-b05c-43ba-b508-b44818cd5acf/95-2
00:14:40.628 --> 00:14:45.459
looking to accomplish some of this,
address challenges in an efficacious way

64e47f89-b05c-43ba-b508-b44818cd5acf/95-3
00:14:45.459 --> 00:14:46.713
as Northwestern has?

64e47f89-b05c-43ba-b508-b44818cd5acf/96-0
00:14:47.993 --> 00:14:51.197
Yeah, I think it comes down to the,
I mean,

64e47f89-b05c-43ba-b508-b44818cd5acf/96-1
00:14:51.197 --> 00:14:56.804
the foundation starts at knowing your
patient population as well as you can.

64e47f89-b05c-43ba-b508-b44818cd5acf/96-2
00:14:56.804 --> 00:15:02.265
What I mean to say is who are the
patients that are, one, in this product,

64e47f89-b05c-43ba-b508-b44818cd5acf/96-3
00:15:02.265 --> 00:15:05.324
two,
are they correctly attributed to our

64e47f89-b05c-43ba-b508-b44818cd5acf/96-4
00:15:05.324 --> 00:15:05.833
system?

64e47f89-b05c-43ba-b508-b44818cd5acf/98-0
00:15:06.233 --> 00:15:11.012
And that comes in a two-fold answer,
I think. And it's, one,

64e47f89-b05c-43ba-b508-b44818cd5acf/98-1
00:15:11.012 --> 00:15:16.730
when you're signing these shared savings
or alternate payment model risk

64e47f89-b05c-43ba-b508-b44818cd5acf/98-2
00:15:16.730 --> 00:15:20.882
agreements,
there's various ways of attribution that

64e47f89-b05c-43ba-b508-b44818cd5acf/97-0
00:15:17.673 --> 00:15:17.753
Ohh.

64e47f89-b05c-43ba-b508-b44818cd5acf/98-3
00:15:20.882 --> 00:15:25.033
payers, how payers define attribution,
I should say.

64e47f89-b05c-43ba-b508-b44818cd5acf/99-0
00:15:25.273 --> 00:15:30.957
So I think knowing that process,
like the back of your hand, is important.

64e47f89-b05c-43ba-b508-b44818cd5acf/99-1
00:15:30.957 --> 00:15:36.943
And deciding how that attribution is
going to benefit you and your performance

64e47f89-b05c-43ba-b508-b44818cd5acf/99-2
00:15:36.943 --> 00:15:43.308
as a system is important. For an example,
Northwestern has a heavy specialty focus,

64e47f89-b05c-43ba-b508-b44818cd5acf/99-3
00:15:43.308 --> 00:15:46.793
right?
People are drawn to our system for our

64e47f89-b05c-43ba-b508-b44818cd5acf/100-0
00:15:47.593 --> 00:15:52.873
Specialty care,
and oftentimes payers want to attribute.

64e47f89-b05c-43ba-b508-b44818cd5acf/103-0
00:15:55.193 --> 00:15:57.766
patients to Northwestern based on,
you know,

64e47f89-b05c-43ba-b508-b44818cd5acf/101-0
00:15:56.873 --> 00:15:56.913
Oh.

64e47f89-b05c-43ba-b508-b44818cd5acf/103-1
00:15:57.766 --> 00:16:01.026
I think there's a hierarchy that goes on
a lot of times.

64e47f89-b05c-43ba-b508-b44818cd5acf/102-0
00:15:57.993 --> 00:15:58.113
Yeah.

64e47f89-b05c-43ba-b508-b44818cd5acf/103-2
00:16:01.026 --> 00:16:05.543
It's who is your primary care provider
that you've seen in the last 24 months.

64e47f89-b05c-43ba-b508-b44818cd5acf/103-3
00:16:05.543 --> 00:16:09.202
And if no one's seen that,
oftentimes the hierarchy moves on to

64e47f89-b05c-43ba-b508-b44818cd5acf/103-4
00:16:09.202 --> 00:16:12.690
specialty attribution.
Meaning if you haven't seen a primary

64e47f89-b05c-43ba-b508-b44818cd5acf/103-5
00:16:12.690 --> 00:16:14.233
care in the last 24 months,

64e47f89-b05c-43ba-b508-b44818cd5acf/105-0
00:16:14.553 --> 00:16:18.917
who was your most recent specialist that
you were seeing for care.

64e47f89-b05c-43ba-b508-b44818cd5acf/105-1
00:16:18.917 --> 00:16:23.281
And if that becomes Northwestern,
then we're going to attribute to

64e47f89-b05c-43ba-b508-b44818cd5acf/105-2
00:16:23.281 --> 00:16:26.277
Northwestern.
And now our philosophy in these

64e47f89-b05c-43ba-b508-b44818cd5acf/105-3
00:16:26.277 --> 00:16:31.358
agreements are it's harder to control
outcomes for patients who may be seeing

64e47f89-b05c-43ba-b508-b44818cd5acf/104-0
00:16:27.353 --> 00:16:27.473
Okay.

64e47f89-b05c-43ba-b508-b44818cd5acf/105-4
00:16:31.358 --> 00:16:36.112
primary care at another system,
but coming to Northwestern for specialty

64e47f89-b05c-43ba-b508-b44818cd5acf/105-5
00:16:36.112 --> 00:16:36.633
care. So

64e47f89-b05c-43ba-b508-b44818cd5acf/106-0
00:16:36.713 --> 00:16:41.008
All that is to say,
define your patient population as well as

64e47f89-b05c-43ba-b508-b44818cd5acf/106-1
00:16:41.008 --> 00:16:46.272
you can and know them and know the ins
and outs of that patient population.

64e47f89-b05c-43ba-b508-b44818cd5acf/106-2
00:16:46.272 --> 00:16:49.320
I think that brings us to another
question,

64e47f89-b05c-43ba-b508-b44818cd5acf/106-3
00:16:49.320 --> 00:16:54.722
is some payers are better than others at
providing you with that information,

64e47f89-b05c-43ba-b508-b44818cd5acf/106-4
00:16:54.722 --> 00:16:57.770
right?
If we're using Humana as an example,

64e47f89-b05c-43ba-b508-b44818cd5acf/106-5
00:16:57.770 --> 00:16:58.393
they have

64e47f89-b05c-43ba-b508-b44818cd5acf/108-0
00:16:58.553 --> 00:17:01.974
a 24 7 updated live website that,
you know,

64e47f89-b05c-43ba-b508-b44818cd5acf/108-1
00:17:01.974 --> 00:17:08.036
has great dashboarding capabilities,
has the ability to download your patient

64e47f89-b05c-43ba-b508-b44818cd5acf/108-2
00:17:08.036 --> 00:17:12.933
population at any time of the day,
what gaps they are missing,

64e47f89-b05c-43ba-b508-b44818cd5acf/107-0
00:17:08.753 --> 00:17:08.873
Yeah.

64e47f89-b05c-43ba-b508-b44818cd5acf/108-3
00:17:12.933 --> 00:17:18.219
whether that be medication adherence
information, SDOH information,

64e47f89-b05c-43ba-b508-b44818cd5acf/108-4
00:17:18.219 --> 00:17:23.193
your HEDIS star level, you know,
your star measure information.

64e47f89-b05c-43ba-b508-b44818cd5acf/109-0
00:17:24.473 --> 00:17:28.153
And so that data is very readily
available. Now,

64e47f89-b05c-43ba-b508-b44818cd5acf/109-1
00:17:28.153 --> 00:17:33.559
another payer that we work with,
they send us only quarterly reports on

64e47f89-b05c-43ba-b508-b44818cd5acf/109-2
00:17:33.559 --> 00:17:38.890
how who our patient population is,
you know, med adherence info, yeah,

64e47f89-b05c-43ba-b508-b44818cd5acf/109-3
00:17:38.890 --> 00:17:45.197
utilization management, spend information.
So I think there has to be some ruthless

64e47f89-b05c-43ba-b508-b44818cd5acf/109-4
00:17:45.197 --> 00:17:46.473
prioritization of

64e47f89-b05c-43ba-b508-b44818cd5acf/110-0
00:17:46.593 --> 00:17:53.152
where can we affect the most change and
what population that is going to be our

64e47f89-b05c-43ba-b508-b44818cd5acf/110-1
00:17:53.152 --> 00:17:54.873
focus on performance.

64e47f89-b05c-43ba-b508-b44818cd5acf/114-0
00:17:55.193 --> 00:17:58.440
So as we approach the end of this podcast
episode,

64e47f89-b05c-43ba-b508-b44818cd5acf/111-0
00:17:57.033 --> 00:17:57.353
Yeah.

64e47f89-b05c-43ba-b508-b44818cd5acf/114-1
00:17:58.440 --> 00:18:02.449
I would like to focus on attribution
management. So, you know,

64e47f89-b05c-43ba-b508-b44818cd5acf/112-0
00:18:01.433 --> 00:18:01.913
Yes.

64e47f89-b05c-43ba-b508-b44818cd5acf/114-2
00:18:02.449 --> 00:18:07.096
that's a topic that you just brought up
and I'd like to dial in on that.

64e47f89-b05c-43ba-b508-b44818cd5acf/113-0
00:18:06.713 --> 00:18:07.193
Mhm.

64e47f89-b05c-43ba-b508-b44818cd5acf/114-3
00:18:07.096 --> 00:18:11.105
So as you mentioned,
Chicagoland area is very competitive with

64e47f89-b05c-43ba-b508-b44818cd5acf/114-4
00:18:11.105 --> 00:18:15.433
many different health systems and it's
quite possible that a lot of

64e47f89-b05c-43ba-b508-b44818cd5acf/115-0
00:18:15.913 --> 00:18:20.734
the patient populations are overlapping
across different provider organizations.

64e47f89-b05c-43ba-b508-b44818cd5acf/115-1
00:18:20.734 --> 00:18:23.828
Some go to Northwestern for specialty,
as you said,

64e47f89-b05c-43ba-b508-b44818cd5acf/115-2
00:18:23.828 --> 00:18:27.934
but go to primary care at a competitor,
and then the payer needs to,

64e47f89-b05c-43ba-b508-b44818cd5acf/115-3
00:18:27.934 --> 00:18:31.624
and that patient only has,
is covered by one health insurance

64e47f89-b05c-43ba-b508-b44818cd5acf/115-4
00:18:31.624 --> 00:18:35.373
company. And well, great,
there's some savings or you're being

64e47f89-b05c-43ba-b508-b44818cd5acf/115-5
00:18:35.373 --> 00:18:37.753
docked and there's some kind of penalty.

64e47f89-b05c-43ba-b508-b44818cd5acf/117-0
00:18:38.713 --> 00:18:43.114
Does that hit Northwestern Medicine?
Does it hit another organization?

64e47f89-b05c-43ba-b508-b44818cd5acf/116-0
00:18:42.553 --> 00:18:43.033
Totally.

64e47f89-b05c-43ba-b508-b44818cd5acf/117-1
00:18:43.114 --> 00:18:47.762
Can you speak about what kind of data
challenges you're addressing and how

64e47f89-b05c-43ba-b508-b44818cd5acf/117-2
00:18:47.762 --> 00:18:49.993
you're addressing them in this vein?

64e47f89-b05c-43ba-b508-b44818cd5acf/119-0
00:18:50.793 --> 00:18:53.843
Yeah,
I think it calls back to a lot of what I

64e47f89-b05c-43ba-b508-b44818cd5acf/119-1
00:18:53.843 --> 00:18:58.579
just spoke about, and it's where,
who is the real population that we can

64e47f89-b05c-43ba-b508-b44818cd5acf/119-2
00:18:58.579 --> 00:19:03.380
affect the most change on, right?
One of the ways we define that is like,

64e47f89-b05c-43ba-b508-b44818cd5acf/119-3
00:19:03.380 --> 00:19:07.208
who's seen us in the last 12 to 24 months?
And oftentimes,

64e47f89-b05c-43ba-b508-b44818cd5acf/119-4
00:19:07.208 --> 00:19:09.673
payers will have a roll-off period. So

64e47f89-b05c-43ba-b508-b44818cd5acf/118-0
00:19:07.833 --> 00:19:07.873
Oh.

64e47f89-b05c-43ba-b508-b44818cd5acf/120-0
00:19:10.233 --> 00:19:14.210
Say John Doe was attributed to
Northwestern Medicine.

64e47f89-b05c-43ba-b508-b44818cd5acf/120-1
00:19:14.210 --> 00:19:17.818
He saw XYZ primary care physician 36
months ago.

64e47f89-b05c-43ba-b508-b44818cd5acf/120-2
00:19:17.818 --> 00:19:21.205
And so when we review our patient
population,

64e47f89-b05c-43ba-b508-b44818cd5acf/120-3
00:19:21.205 --> 00:19:27.243
we're looking at utilization data. We say,
John Doe hasn't seen us for 36 months.

64e47f89-b05c-43ba-b508-b44818cd5acf/120-4
00:19:27.243 --> 00:19:32.986
He's been unresponsive to outreach.
We don't really consider him connected to

64e47f89-b05c-43ba-b508-b44818cd5acf/120-5
00:19:32.986 --> 00:19:34.753
the Northwestern system.

64e47f89-b05c-43ba-b508-b44818cd5acf/122-0
00:19:34.873 --> 00:19:39.408
right now, right?
But on the Humana side or the payer side,

64e47f89-b05c-43ba-b508-b44818cd5acf/122-1
00:19:39.408 --> 00:19:44.093
they're like, well,
this is the last utilization data we have

64e47f89-b05c-43ba-b508-b44818cd5acf/122-2
00:19:44.093 --> 00:19:48.099
for this guy.
So he's still going to show up on your

64e47f89-b05c-43ba-b508-b44818cd5acf/121-0
00:19:44.553 --> 00:19:44.673
Yeah.

64e47f89-b05c-43ba-b508-b44818cd5acf/122-3
00:19:48.099 --> 00:19:53.389
bottom line on this product.
And so I think the landscape is evolving

64e47f89-b05c-43ba-b508-b44818cd5acf/122-4
00:19:53.389 --> 00:19:53.993
to have.

64e47f89-b05c-43ba-b508-b44818cd5acf/123-0
00:19:54.113 --> 00:19:54.553
But...

64e47f89-b05c-43ba-b508-b44818cd5acf/125-0
00:19:55.673 --> 00:20:02.124
A negotiation piece in contracts to where
you can either submit rosters on who your

64e47f89-b05c-43ba-b508-b44818cd5acf/125-1
00:20:02.124 --> 00:20:07.806
patient population is or define a
roll-off term for a patient population,

64e47f89-b05c-43ba-b508-b44818cd5acf/124-0
00:20:03.873 --> 00:20:03.993
Yeah.

64e47f89-b05c-43ba-b508-b44818cd5acf/125-2
00:20:07.806 --> 00:20:13.335
meaning if this person hasn't been seen
in the last X amount of months,

64e47f89-b05c-43ba-b508-b44818cd5acf/125-3
00:20:13.335 --> 00:20:14.793
they will fall off.

64e47f89-b05c-43ba-b508-b44818cd5acf/126-0
00:20:14.873 --> 00:20:18.569
your next month's roster.
And so I think that is going to become an

64e47f89-b05c-43ba-b508-b44818cd5acf/126-1
00:20:18.569 --> 00:20:22.753
increased conversation and contract
negotiation between providers and payers

64e47f89-b05c-43ba-b508-b44818cd5acf/126-2
00:20:22.753 --> 00:20:23.513
going forward.

64e47f89-b05c-43ba-b508-b44818cd5acf/128-0
00:20:24.073 --> 00:20:27.682
Well, Colin,
I certainly appreciate this conversation.

64e47f89-b05c-43ba-b508-b44818cd5acf/128-1
00:20:27.682 --> 00:20:32.471
We've covered a lot of ground,
especially on value-based contracting and

64e47f89-b05c-43ba-b508-b44818cd5acf/128-2
00:20:32.471 --> 00:20:37.129
alternate payer model contracting.
There's a lot of data challenges we

64e47f89-b05c-43ba-b508-b44818cd5acf/128-3
00:20:37.129 --> 00:20:42.574
addressed from most recently attribution
management to quality measure improvement

64e47f89-b05c-43ba-b508-b44818cd5acf/128-4
00:20:42.574 --> 00:20:44.673
and knowing who your patient is.

64e47f89-b05c-43ba-b508-b44818cd5acf/129-0
00:21:01.193 --> 00:21:02.633
Thanks for having me, Jordan.
Appreciate it.