1ed87f62-c196-4bdd-af6f-d48896a69462/5-0
00:00:03.293 --> 00:00:06.061
Matthew Massaro,
the Program Director of Oncology at

1ed87f62-c196-4bdd-af6f-d48896a69462/5-1
00:00:06.061 --> 00:00:10.029
Northwestern Medicine and the Robert H
Lurie Comprehensive Cancer Center of

1ed87f62-c196-4bdd-af6f-d48896a69462/5-2
00:00:10.029 --> 00:00:13.893
Northwestern University. Matthew,
thank you so much for joining us today.

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00:00:13.053 --> 00:00:16.213
Thank you very much for having me.
Happy to be here.

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00:00:16.573 --> 00:00:19.836
As background,
Northwestern Medicine is a health system

1ed87f62-c196-4bdd-af6f-d48896a69462/9-1
00:00:19.836 --> 00:00:23.449
headquartered in Chicago,
IL with 2700 inpatient beds and 11,

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00:00:23.449 --> 00:00:27.819
000 providers across 12 hospitals and
other ancillary facilities. So Matt,

1ed87f62-c196-4bdd-af6f-d48896a69462/9-3
00:00:27.819 --> 00:00:32.481
Matthew, thank you for joining us today.
I I understand we'll be speaking about

1ed87f62-c196-4bdd-af6f-d48896a69462/9-4
00:00:32.481 --> 00:00:36.093
throughput and capacity with the
outpatient oncology setting.

1ed87f62-c196-4bdd-af6f-d48896a69462/10-0
00:00:36.413 --> 00:00:41.110
I'd love you to kick us off and provide
some background into what you're up to

1ed87f62-c196-4bdd-af6f-d48896a69462/10-1
00:00:41.110 --> 00:00:42.893
and what you're looking to do.

1ed87f62-c196-4bdd-af6f-d48896a69462/11-0
00:00:42.733 --> 00:00:46.358
Yeah. Well,
thank you for the opportunity again.

1ed87f62-c196-4bdd-af6f-d48896a69462/11-1
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So my role and scope within Northwestern
Medicine is oversight of all of our

1ed87f62-c196-4bdd-af6f-d48896a69462/11-2
00:00:52.053 --> 00:00:57.748
outpatient operations and oncology,
specifically as it relates to our breast

1ed87f62-c196-4bdd-af6f-d48896a69462/11-3
00:00:57.748 --> 00:00:58.413
programs.

1ed87f62-c196-4bdd-af6f-d48896a69462/14-0
00:00:58.413 --> 00:01:02.981
Gynon thoracic medical oncology and
benign hematology.

1ed87f62-c196-4bdd-af6f-d48896a69462/14-1
00:01:02.981 --> 00:01:09.708
And as far as how data intersects my life,
it's pretty much on a daily basis and

1ed87f62-c196-4bdd-af6f-d48896a69462/13-0
00:01:08.613 --> 00:01:09.093
Mhm.

1ed87f62-c196-4bdd-af6f-d48896a69462/14-2
00:01:09.708 --> 00:01:16.518
primarily focused on strategies that we
have where we implement growth or changes

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to our operations.

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Whether it's aligning to new regulatory
governances and abiding by those,

1ed87f62-c196-4bdd-af6f-d48896a69462/15-1
00:01:23.134 --> 00:01:27.983
or whether it's preparing for a future
where we're giving care in a different

1ed87f62-c196-4bdd-af6f-d48896a69462/15-2
00:01:27.983 --> 00:01:32.769
physical setting or expanding upon our
physical footprint of setting that we

1ed87f62-c196-4bdd-af6f-d48896a69462/15-3
00:01:32.769 --> 00:01:35.505
deliver care in.
In all of those instances,

1ed87f62-c196-4bdd-af6f-d48896a69462/15-4
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data is very vital to the decisions that.

1ed87f62-c196-4bdd-af6f-d48896a69462/16-0
00:01:38.173 --> 00:01:44.795
We make both in forecasting future state
but also reacting to current state and

1ed87f62-c196-4bdd-af6f-d48896a69462/16-1
00:01:44.795 --> 00:01:46.533
adjusting as need be.

1ed87f62-c196-4bdd-af6f-d48896a69462/17-0
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Yeah,
I understand that on on a few different

1ed87f62-c196-4bdd-af6f-d48896a69462/17-1
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metrics you work through financials,
you mentioned staff productivity and

1ed87f62-c196-4bdd-af6f-d48896a69462/17-2
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patient volumes.
Within that you kind of break it out into

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labs and infusions and visits.
Can you talk about how you are parsing

1ed87f62-c196-4bdd-af6f-d48896a69462/17-4
00:02:02.640 --> 00:02:03.853
out your different?

1ed87f62-c196-4bdd-af6f-d48896a69462/18-0
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Data sources and how you're able to look
at them,

1ed87f62-c196-4bdd-af6f-d48896a69462/18-1
00:02:07.853 --> 00:02:11.933
present them and analyze them in a
meaningful way.

1ed87f62-c196-4bdd-af6f-d48896a69462/20-0
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Yeah,
there's a variety of different sources

1ed87f62-c196-4bdd-af6f-d48896a69462/20-1
00:02:15.617 --> 00:02:21.308
that we can pull data from in our system.
We have data warehouses that exist,

1ed87f62-c196-4bdd-af6f-d48896a69462/20-2
00:02:21.308 --> 00:02:25.322
some that are directly embedded within
our EMR system,

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00:02:25.322 --> 00:02:31.013
and then some that exist outside of the
EMR system but are able to tap in and

1ed87f62-c196-4bdd-af6f-d48896a69462/20-4
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pull information.

1ed87f62-c196-4bdd-af6f-d48896a69462/21-0
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Out and really the gamut of anything that
we can think of as far as data can be

1ed87f62-c196-4bdd-af6f-d48896a69462/21-1
00:02:37.617 --> 00:02:42.042
helpful to us. Some of the data,
depending on how granular we get,

1ed87f62-c196-4bdd-af6f-d48896a69462/21-2
00:02:42.042 --> 00:02:46.995
can be a little trickier to pull,
a little more exhaustive because we want

1ed87f62-c196-4bdd-af6f-d48896a69462/21-3
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to make sure that what we're getting is
valid, is true.

1ed87f62-c196-4bdd-af6f-d48896a69462/22-0
00:02:51.453 --> 00:02:54.401
We'll use data in terms of our patient
volumes,

1ed87f62-c196-4bdd-af6f-d48896a69462/22-1
00:02:54.401 --> 00:02:58.883
in terms of different diagnoses that we
treat, medications that we give.

1ed87f62-c196-4bdd-af6f-d48896a69462/22-2
00:02:58.883 --> 00:03:03.980
Infusion is a big area for us in oncology.
So understanding the volume of patients

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00:03:03.980 --> 00:03:08.892
who necessitate the variety of different
medications that we that we give in an

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00:03:08.892 --> 00:03:09.813
infusion space.

1ed87f62-c196-4bdd-af6f-d48896a69462/24-0
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And also correlating that to how long do
these infusions take?

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00:03:14.414 --> 00:03:19.713
Is this an infusion that's 30 to 45
minutes or is this an infusion that's two

1ed87f62-c196-4bdd-af6f-d48896a69462/23-0
00:03:19.213 --> 00:03:20.133
Mm-hmm.

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to five hours?
And then understanding the quanti

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quantifying those those volumes gives us
a sense of what capacity we have in our

1ed87f62-c196-4bdd-af6f-d48896a69462/24-4
00:03:28.546 --> 00:03:29.293
operations.

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Also aligning with our staffing models
and how many individuals we need on

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00:03:34.290 --> 00:03:38.945
certain teams or what types of roles.
Nursing is a big one, of course,

1ed87f62-c196-4bdd-af6f-d48896a69462/25-2
00:03:38.945 --> 00:03:44.255
and looking at patient hours per nurse.
So it's a it's a pretty wide spectrum of

1ed87f62-c196-4bdd-af6f-d48896a69462/25-3
00:03:44.255 --> 00:03:48.253
data that we look at and it's a little
bit more finite, but.

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Spectrum of sources for the data,
where we're pulling those sources and and

1ed87f62-c196-4bdd-af6f-d48896a69462/26-1
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putting that all together, yeah.

1ed87f62-c196-4bdd-af6f-d48896a69462/27-0
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So Matthew,
once you can quantify the volumes,

1ed87f62-c196-4bdd-af6f-d48896a69462/27-1
00:04:00.813 --> 00:04:04.472
for example,
figuring out how long an infusion is

1ed87f62-c196-4bdd-af6f-d48896a69462/27-2
00:04:04.472 --> 00:04:09.669
taking, how do you take action?
What's kind of intervention can follow

1ed87f62-c196-4bdd-af6f-d48896a69462/27-3
00:04:09.669 --> 00:04:15.523
understanding how long an infusion takes
if if it if it actually takes about 40

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00:04:15.523 --> 00:04:17.133
minutes and you would.

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It would take 50 minutes.
Are you able to add an additional patient

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00:04:22.704 --> 00:04:26.312
each day?
Are you able to add more staffing?

1ed87f62-c196-4bdd-af6f-d48896a69462/28-2
00:04:26.312 --> 00:04:33.045
Just walk us through how exactly once you
see what your KPIs are and you define how

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00:04:33.045 --> 00:04:36.973
long an infusion might take,
then how does that?

1ed87f62-c196-4bdd-af6f-d48896a69462/29-0
00:04:37.013 --> 00:04:40.613
Affect operations and allow you to
increase throughput and capacity.

1ed87f62-c196-4bdd-af6f-d48896a69462/31-0
00:04:40.493 --> 00:04:43.524
Sure.
It certainly depends on the lens of which

1ed87f62-c196-4bdd-af6f-d48896a69462/31-1
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we're looking through and why we're
looking at the data.

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What is the problem that we're looking to
solve to?

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00:04:47.613 --> 00:04:48.333
Mhm.

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What are we trying to understand about
ourselves? So for example, with infusion,

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as we continue to to grow,
we have certain areas where.

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00:04:59.653 --> 00:05:04.714
Recent renovations and expansion projects
have happened where we build out the

1ed87f62-c196-4bdd-af6f-d48896a69462/33-1
00:05:04.714 --> 00:05:08.044
capacity so that we have it and then we
look at OK,

1ed87f62-c196-4bdd-af6f-d48896a69462/32-0
00:05:06.613 --> 00:05:06.653
I.

1ed87f62-c196-4bdd-af6f-d48896a69462/33-2
00:05:08.044 --> 00:05:13.233
how much if we have a chair or two that
we don't utilize throughout certain days

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00:05:13.233 --> 00:05:16.051
of the week,
if we were to open that chair,

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00:05:16.051 --> 00:05:18.613
how do we make sure that as we bring in?

1ed87f62-c196-4bdd-af6f-d48896a69462/34-0
00:05:18.653 --> 00:05:22.512
More appointments.
If we add more appointments and patients

1ed87f62-c196-4bdd-af6f-d48896a69462/34-1
00:05:22.512 --> 00:05:25.921
to be seen,
how do we ensure that we're not going to

1ed87f62-c196-4bdd-af6f-d48896a69462/34-2
00:05:25.921 --> 00:05:30.873
overload what we have for for capacity?
So we'll look at the average of in a

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00:05:30.873 --> 00:05:34.089
given area,
what is the percentage of the patient

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00:05:34.089 --> 00:05:38.333
population that receives XY and Z
treatments and then looking at.

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00:05:38.413 --> 00:05:41.576
The length of time that those treatments
make.

1ed87f62-c196-4bdd-af6f-d48896a69462/36-1
00:05:41.576 --> 00:05:46.758
So it's all about forecasting for us to
understand what is that future state

1ed87f62-c196-4bdd-af6f-d48896a69462/36-2
00:05:46.758 --> 00:05:51.604
going to be. Let's go ahead and expand.
Let's add a couple more chairs.

1ed87f62-c196-4bdd-af6f-d48896a69462/36-3
00:05:51.604 --> 00:05:55.574
Now when we do that,
what's that going to look like in the

1ed87f62-c196-4bdd-af6f-d48896a69462/36-4
00:05:55.574 --> 00:05:58.333
clinic for the physicians? How many more?

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00:05:56.613 --> 00:05:57.133
Mhm.

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00:05:58.413 --> 00:06:02.220
Questions would be open?
How many more slots on a given day would

1ed87f62-c196-4bdd-af6f-d48896a69462/37-1
00:06:02.220 --> 00:06:05.449
we would we open?
And that's kind of what we're piecing

1ed87f62-c196-4bdd-af6f-d48896a69462/37-2
00:06:05.449 --> 00:06:10.293
those two things together to ensure that
we have a, you know, a stable environment.

1ed87f62-c196-4bdd-af6f-d48896a69462/38-0
00:06:09.453 --> 00:06:12.119
So in order to forecast those future
estates,

1ed87f62-c196-4bdd-af6f-d48896a69462/38-1
00:06:12.119 --> 00:06:16.348
I understand you've built methods and
models of calculating staffing and

1ed87f62-c196-4bdd-af6f-d48896a69462/38-2
00:06:16.348 --> 00:06:19.708
capacity needs.
Can you speak about how those models have

1ed87f62-c196-4bdd-af6f-d48896a69462/38-3
00:06:19.708 --> 00:06:20.693
been constructed?

1ed87f62-c196-4bdd-af6f-d48896a69462/39-0
00:06:21.373 --> 00:06:24.980
Yeah, yeah,
it's so in in some of the areas where we

1ed87f62-c196-4bdd-af6f-d48896a69462/39-1
00:06:24.980 --> 00:06:30.151
more recently constructed some models,
it's it's really on the clinic side.

1ed87f62-c196-4bdd-af6f-d48896a69462/39-2
00:06:30.151 --> 00:06:35.662
So it's outside of the infusion space.
So we have a model for navigation for new

1ed87f62-c196-4bdd-af6f-d48896a69462/39-3
00:06:35.662 --> 00:06:39.404
patient navigation.
We use nurse navigators to to help

1ed87f62-c196-4bdd-af6f-d48896a69462/39-4
00:06:39.404 --> 00:06:41.173
connect with new patients.

1ed87f62-c196-4bdd-af6f-d48896a69462/40-0
00:06:41.213 --> 00:06:46.668
Who are newly diagnosed and they call our
our centers and within 24 hours they're

1ed87f62-c196-4bdd-af6f-d48896a69462/40-1
00:06:46.668 --> 00:06:51.724
on the phone with a nurse navigator,
an embedded ally within our within our

1ed87f62-c196-4bdd-af6f-d48896a69462/40-2
00:06:51.724 --> 00:06:56.979
practice who provides upfront education
because sometimes they might get their

1ed87f62-c196-4bdd-af6f-d48896a69462/40-3
00:06:56.979 --> 00:06:59.373
diagnosis, they call our facilities.

1ed87f62-c196-4bdd-af6f-d48896a69462/43-0
00:06:59.533 --> 00:07:04.315
And if their appointment is a few days or
a week out leading up to that time,

1ed87f62-c196-4bdd-af6f-d48896a69462/43-1
00:07:04.315 --> 00:07:08.238
we want to be sure that we're able to
connect with the patient,

1ed87f62-c196-4bdd-af6f-d48896a69462/41-0
00:07:04.773 --> 00:07:05.253
Mhm.

1ed87f62-c196-4bdd-af6f-d48896a69462/43-2
00:07:08.238 --> 00:07:13.203
meet them where they're at and give them
some support and some education so that

1ed87f62-c196-4bdd-af6f-d48896a69462/43-3
00:07:13.203 --> 00:07:16.636
when they have that first appointment in
the exam room,

1ed87f62-c196-4bdd-af6f-d48896a69462/42-0
00:07:14.853 --> 00:07:15.333
Mhm.

1ed87f62-c196-4bdd-af6f-d48896a69462/43-4
00:07:16.636 --> 00:07:19.333
it is a very efficient and effective
visit.

1ed87f62-c196-4bdd-af6f-d48896a69462/45-0
00:07:19.533 --> 00:07:24.419
And so that that involves getting the
patient ready for it as well as our our

1ed87f62-c196-4bdd-af6f-d48896a69462/45-1
00:07:24.419 --> 00:07:28.553
clinical practice teams.
So how we modeled that to your question,

1ed87f62-c196-4bdd-af6f-d48896a69462/44-0
00:07:27.613 --> 00:07:27.893
OK.

1ed87f62-c196-4bdd-af6f-d48896a69462/45-2
00:07:28.553 --> 00:07:33.313
how we built quantified the capacity we
look at for new patient navigation,

1ed87f62-c196-4bdd-af6f-d48896a69462/45-3
00:07:33.313 --> 00:07:37.385
we look at how many new appointments are
we completing per week.

1ed87f62-c196-4bdd-af6f-d48896a69462/45-4
00:07:37.385 --> 00:07:39.013
So if the idea is we want.

1ed87f62-c196-4bdd-af6f-d48896a69462/46-0
00:07:39.133 --> 00:07:43.346
Want to get on the phone and have
interactions at least a 60 minute call

1ed87f62-c196-4bdd-af6f-d48896a69462/46-1
00:07:43.346 --> 00:07:47.155
with every new patient before they even
touch down in the clinic.

1ed87f62-c196-4bdd-af6f-d48896a69462/46-2
00:07:47.155 --> 00:07:51.484
We need to understand how many new
patients are coming in per week and how

1ed87f62-c196-4bdd-af6f-d48896a69462/46-3
00:07:51.484 --> 00:07:55.639
long do they exist in navigation,
meaning how long is it from the first

1ed87f62-c196-4bdd-af6f-d48896a69462/46-4
00:07:55.639 --> 00:07:58.813
point of contact to when they first see
the physician.

1ed87f62-c196-4bdd-af6f-d48896a69462/47-0
00:07:58.893 --> 00:08:04.829
That period of time is easy to quantify
because we understand all those metrics.

1ed87f62-c196-4bdd-af6f-d48896a69462/47-1
00:08:04.829 --> 00:08:08.713
Beyond that,
there's a little bit of work that needs

1ed87f62-c196-4bdd-af6f-d48896a69462/47-2
00:08:08.713 --> 00:08:11.937
to be done to get deeper into the
granular,

1ed87f62-c196-4bdd-af6f-d48896a69462/47-3
00:08:11.937 --> 00:08:17.213
like a time study where we'll have teams,
we'll sit with our teams and.

1ed87f62-c196-4bdd-af6f-d48896a69462/48-0
00:08:17.333 --> 00:08:21.212
Quantify how much work is being done on
each patient. They get off the phone.

1ed87f62-c196-4bdd-af6f-d48896a69462/48-1
00:08:21.212 --> 00:08:23.797
We understand that it's a telemedicine
appointment.

1ed87f62-c196-4bdd-af6f-d48896a69462/48-2
00:08:23.797 --> 00:08:27.377
It's easy to quantify that that's a 40
minute call or a 60 minute call.

1ed87f62-c196-4bdd-af6f-d48896a69462/48-3
00:08:27.377 --> 00:08:30.311
But beyond that,
does the patient reach back out over that

1ed87f62-c196-4bdd-af6f-d48896a69462/48-4
00:08:30.311 --> 00:08:32.548
next week?
What are they reaching out about?

1ed87f62-c196-4bdd-af6f-d48896a69462/48-5
00:08:32.548 --> 00:08:36.078
Are there records that need to be gotten
from the administrative team?

1ed87f62-c196-4bdd-af6f-d48896a69462/48-6
00:08:36.078 --> 00:08:36.973
Do they call back?

1ed87f62-c196-4bdd-af6f-d48896a69462/49-0
00:08:36.973 --> 00:08:41.276
With symptoms that have come up that are
necessitating further phone calls.

1ed87f62-c196-4bdd-af6f-d48896a69462/49-1
00:08:41.276 --> 00:08:45.693
So we do time studies where we'll sit in
with our teams and quantify how many

1ed87f62-c196-4bdd-af6f-d48896a69462/49-2
00:08:45.693 --> 00:08:50.392
extra minutes beyond the initial call is
every individual patient having the nurse

1ed87f62-c196-4bdd-af6f-d48896a69462/49-3
00:08:50.392 --> 00:08:55.091
be accountable toward so that we can take
that if every single patient before they

1ed87f62-c196-4bdd-af6f-d48896a69462/49-4
00:08:55.091 --> 00:08:56.733
have their first appointment.

1ed87f62-c196-4bdd-af6f-d48896a69462/52-0
00:08:56.733 --> 00:09:01.697
Requires what we find is about 120
minutes, two hours of time for the nurse,

1ed87f62-c196-4bdd-af6f-d48896a69462/52-1
00:09:01.697 --> 00:09:05.888
for the nurse navigator.
What we do is then multiply that by the

1ed87f62-c196-4bdd-af6f-d48896a69462/52-2
00:09:05.888 --> 00:09:10.980
amount of new patients that we see every
week and that gives us a sense of how

1ed87f62-c196-4bdd-af6f-d48896a69462/50-0
00:09:07.053 --> 00:09:07.293
C.

1ed87f62-c196-4bdd-af6f-d48896a69462/52-3
00:09:10.980 --> 00:09:16.073
much work is to be done in every week for
a nurse navigator. How many minutes,

1ed87f62-c196-4bdd-af6f-d48896a69462/51-0
00:09:11.533 --> 00:09:12.573
Mm-hmm.

1ed87f62-c196-4bdd-af6f-d48896a69462/52-4
00:09:16.073 --> 00:09:16.653
how many?

1ed87f62-c196-4bdd-af6f-d48896a69462/55-0
00:09:16.933 --> 00:09:20.407
How many hours in a week are they
actively navigating,

1ed87f62-c196-4bdd-af6f-d48896a69462/55-1
00:09:20.407 --> 00:09:24.512
not going to a nurse meeting,
not going to an education session,

1ed87f62-c196-4bdd-af6f-d48896a69462/55-2
00:09:24.512 --> 00:09:28.679
not meeting with their managers,
not doing these other ancillary,

1ed87f62-c196-4bdd-af6f-d48896a69462/53-0
00:09:28.653 --> 00:09:28.853
3.

1ed87f62-c196-4bdd-af6f-d48896a69462/55-3
00:09:28.679 --> 00:09:33.479
but actively navigating our patients.
And when we look at our model to say,

1ed87f62-c196-4bdd-af6f-d48896a69462/55-4
00:09:33.479 --> 00:09:36.573
well, how much do we need,
we use these metrics.

1ed87f62-c196-4bdd-af6f-d48896a69462/54-0
00:09:34.093 --> 00:09:35.093
Mm-hmm.

1ed87f62-c196-4bdd-af6f-d48896a69462/56-0
00:09:36.573 --> 00:09:41.876
It's a few different sources of metrics
and how we put them together to

1ed87f62-c196-4bdd-af6f-d48896a69462/56-1
00:09:41.876 --> 00:09:45.853
understand what's the workload if we're
going to say.

1ed87f62-c196-4bdd-af6f-d48896a69462/57-0
00:09:42.893 --> 00:09:46.618
Sir, are you, Matthew,
are you and your team actively building

1ed87f62-c196-4bdd-af6f-d48896a69462/57-1
00:09:46.618 --> 00:09:51.289
your own new de Novo process measures?
Or are these things that you're lifting

1ed87f62-c196-4bdd-af6f-d48896a69462/57-2
00:09:51.289 --> 00:09:56.137
from the National Quality Forum or from
the American Medical Association or other

1ed87f62-c196-4bdd-af6f-d48896a69462/57-3
00:09:56.137 --> 00:09:59.626
kind of associations that standardize
metrics? Or are you,

1ed87f62-c196-4bdd-af6f-d48896a69462/57-4
00:09:59.626 --> 00:10:01.813
are you developing your own measures?

1ed87f62-c196-4bdd-af6f-d48896a69462/58-0
00:10:01.853 --> 00:10:02.013
2.

1ed87f62-c196-4bdd-af6f-d48896a69462/59-0
00:10:01.853 --> 00:10:06.146
You know, it's a little bit of both.
It's almost like sourcing different

1ed87f62-c196-4bdd-af6f-d48896a69462/59-1
00:10:06.146 --> 00:10:10.615
recipes and figuring out what are the
ingredients that kind of work for our

1ed87f62-c196-4bdd-af6f-d48896a69462/59-2
00:10:10.615 --> 00:10:15.260
area. There are certainly standards.
Nurse navigation isn't something new that

1ed87f62-c196-4bdd-af6f-d48896a69462/59-3
00:10:15.260 --> 00:10:19.847
we've that we've developed in the idea of
a nurse navigator connecting with a

1ed87f62-c196-4bdd-af6f-d48896a69462/59-4
00:10:19.847 --> 00:10:21.493
patient prior to their care.

1ed87f62-c196-4bdd-af6f-d48896a69462/61-0
00:10:21.853 --> 00:10:27.546
Isn't isn't this new novel concept that
exists only in our area,

1ed87f62-c196-4bdd-af6f-d48896a69462/61-1
00:10:27.546 --> 00:10:32.800
but the way we quantify and leverage it
is is unique to us.

1ed87f62-c196-4bdd-af6f-d48896a69462/61-2
00:10:32.800 --> 00:10:36.653
So how we come up with our calculations
is.

1ed87f62-c196-4bdd-af6f-d48896a69462/63-0
00:10:36.893 --> 00:10:41.629
Is the part that I would say is specific
and unique and homegrown where we are,

1ed87f62-c196-4bdd-af6f-d48896a69462/63-1
00:10:41.629 --> 00:10:46.129
but the concept of what we're trying to
deliver is more of a kind of a best

1ed87f62-c196-4bdd-af6f-d48896a69462/62-0
00:10:42.853 --> 00:10:43.013
OK.

1ed87f62-c196-4bdd-af6f-d48896a69462/63-2
00:10:46.129 --> 00:10:50.213
practice and I would say nationally
recognized standard of practice.

1ed87f62-c196-4bdd-af6f-d48896a69462/66-0
00:10:50.333 --> 00:10:55.110
I see you mentioned that you alluded to
the topic of data integrity.

1ed87f62-c196-4bdd-af6f-d48896a69462/66-1
00:10:55.110 --> 00:11:00.647
You mentioned that you want to ensure
that your data sources for these measures

1ed87f62-c196-4bdd-af6f-d48896a69462/66-2
00:11:00.647 --> 00:11:04.870
are valid and true.
You mentioned can you can you delve into

1ed87f62-c196-4bdd-af6f-d48896a69462/64-0
00:11:03.773 --> 00:11:04.253
Mhm.

1ed87f62-c196-4bdd-af6f-d48896a69462/66-3
00:11:04.870 --> 00:11:09.853
how you're solving for how to create
accessible data and reliable data?

1ed87f62-c196-4bdd-af6f-d48896a69462/65-0
00:11:05.133 --> 00:11:05.933
Yeah.

1ed87f62-c196-4bdd-af6f-d48896a69462/68-0
00:11:10.173 --> 00:11:13.447
Sure. Yeah,
that's that's a very good question.

1ed87f62-c196-4bdd-af6f-d48896a69462/67-0
00:11:10.293 --> 00:11:10.453
Yeah.

1ed87f62-c196-4bdd-af6f-d48896a69462/68-1
00:11:13.447 --> 00:11:17.334
So a lot of it,
what I've found is how accurate the data

1ed87f62-c196-4bdd-af6f-d48896a69462/68-2
00:11:17.334 --> 00:11:22.584
is from the system that's pulling it is
often dependent on the users and how

1ed87f62-c196-4bdd-af6f-d48896a69462/68-3
00:11:22.584 --> 00:11:27.631
they're interacting with the platform
that the data is being pulled from.

1ed87f62-c196-4bdd-af6f-d48896a69462/68-4
00:11:27.631 --> 00:11:28.653
So for example.

1ed87f62-c196-4bdd-af6f-d48896a69462/71-0
00:11:29.933 --> 00:11:33.028
As practices exist for a while in the
same EMR system,

1ed87f62-c196-4bdd-af6f-d48896a69462/71-1
00:11:33.028 --> 00:11:36.067
there'll be different codes that mean the
same thing.

1ed87f62-c196-4bdd-af6f-d48896a69462/69-0
00:11:35.413 --> 00:11:35.973
Mhm.

1ed87f62-c196-4bdd-af6f-d48896a69462/71-2
00:11:36.067 --> 00:11:39.950
Let's take a new patient appointment over
time. As practices evolve,

1ed87f62-c196-4bdd-af6f-d48896a69462/71-3
00:11:39.950 --> 00:11:44.508
let's say they move physically from one
space to another, which is not uncommon.

1ed87f62-c196-4bdd-af6f-d48896a69462/71-4
00:11:44.508 --> 00:11:47.996
That happens a lot,
especially here in Chicago, where I'm at,

1ed87f62-c196-4bdd-af6f-d48896a69462/70-0
00:11:47.093 --> 00:11:47.333
Hmm.

1ed87f62-c196-4bdd-af6f-d48896a69462/71-5
00:11:47.996 --> 00:11:49.853
where we have a finite amount of.

1ed87f62-c196-4bdd-af6f-d48896a69462/72-0
00:11:49.933 --> 00:11:53.745
And we're always re tinkering existing
space and moving it.

1ed87f62-c196-4bdd-af6f-d48896a69462/72-1
00:11:53.745 --> 00:11:58.065
When that happens over time,
there might be 3 different new patient

1ed87f62-c196-4bdd-af6f-d48896a69462/72-2
00:11:58.065 --> 00:12:03.465
appointments that exist and they're coded
differently, but they mean the same thing.

1ed87f62-c196-4bdd-af6f-d48896a69462/72-3
00:12:03.465 --> 00:12:06.133
They might be worded slightly differently.

1ed87f62-c196-4bdd-af6f-d48896a69462/75-0
00:12:06.413 --> 00:12:11.293
Or they use a different code and so some
schedulers might come in and schedule a

1ed87f62-c196-4bdd-af6f-d48896a69462/75-1
00:12:11.293 --> 00:12:14.366
new patient appointment under one
particular code,

1ed87f62-c196-4bdd-af6f-d48896a69462/75-2
00:12:14.366 --> 00:12:17.137
same slot as any other new patient would
use,

1ed87f62-c196-4bdd-af6f-d48896a69462/73-0
00:12:14.973 --> 00:12:15.493
Mhm.

1ed87f62-c196-4bdd-af6f-d48896a69462/75-3
00:12:17.137 --> 00:12:22.137
whereas other schedulers happen to pick a
different code that means the same thing

1ed87f62-c196-4bdd-af6f-d48896a69462/75-4
00:12:22.137 --> 00:12:26.173
to the templates and getting scheduled in.
But when you go on the.

1ed87f62-c196-4bdd-af6f-d48896a69462/76-0
00:12:26.333 --> 00:12:30.153
Back end and you pull the data and you
ask the data and you filter down the

1ed87f62-c196-4bdd-af6f-d48896a69462/74-0
00:12:28.373 --> 00:12:28.853
Mhm.

1ed87f62-c196-4bdd-af6f-d48896a69462/76-1
00:12:30.153 --> 00:12:34.275
search criteria of what they're pulling.
You need to make sure that all different

1ed87f62-c196-4bdd-af6f-d48896a69462/76-2
00:12:34.275 --> 00:12:38.145
visit types are being accounted for.
And if there's three different types of

1ed87f62-c196-4bdd-af6f-d48896a69462/76-3
00:12:38.145 --> 00:12:40.859
new visits,
you need to make sure all three are being

1ed87f62-c196-4bdd-af6f-d48896a69462/76-4
00:12:40.859 --> 00:12:43.623
accounted for.
What we do on the back end then is work

1ed87f62-c196-4bdd-af6f-d48896a69462/76-5
00:12:43.623 --> 00:12:45.533
with our teams to standardize and say.

1ed87f62-c196-4bdd-af6f-d48896a69462/77-0
00:12:46.093 --> 00:12:49.382
We're going to go with one.
We know that some of you use this one,

1ed87f62-c196-4bdd-af6f-d48896a69462/77-1
00:12:49.382 --> 00:12:51.592
some use this one.
We're going to define it,

1ed87f62-c196-4bdd-af6f-d48896a69462/77-2
00:12:51.592 --> 00:12:55.224
winnow it down to one existing.
We're going to remove the other ones from

1ed87f62-c196-4bdd-af6f-d48896a69462/77-3
00:12:55.224 --> 00:12:59.152
the system and then we're just going to
have the one. So when we pull the data,

1ed87f62-c196-4bdd-af6f-d48896a69462/77-4
00:12:59.152 --> 00:13:01.852
we know it's true.
Let's say a new if I if I'm the one

1ed87f62-c196-4bdd-af6f-d48896a69462/77-5
00:13:01.852 --> 00:13:05.533
pulling the data and I and I'm no longer
in my position and somebody else.

1ed87f62-c196-4bdd-af6f-d48896a69462/79-0
00:13:05.613 --> 00:13:08.857
Into this position,
they're likely not going to have that

1ed87f62-c196-4bdd-af6f-d48896a69462/79-1
00:13:08.857 --> 00:13:13.052
deep rooted context that, Oh yeah,
let me pull up all of those visit types

1ed87f62-c196-4bdd-af6f-d48896a69462/78-0
00:13:11.093 --> 00:13:11.333
Hmm.

1ed87f62-c196-4bdd-af6f-d48896a69462/79-2
00:13:13.052 --> 00:13:17.583
that correlate with the new appointment.
Let me make sure I'm accounting for all

1ed87f62-c196-4bdd-af6f-d48896a69462/79-3
00:13:17.583 --> 00:13:21.442
of those. So we try and winnow that down,
but I scrutinize the data.

1ed87f62-c196-4bdd-af6f-d48896a69462/79-4
00:13:21.442 --> 00:13:25.413
We have a couple different databases here
and I like to pull the same.

1ed87f62-c196-4bdd-af6f-d48896a69462/80-0
00:13:25.573 --> 00:13:29.655
Same information from both of them to
make sure are they telling me the same

1ed87f62-c196-4bdd-af6f-d48896a69462/81-0
00:13:29.213 --> 00:13:32.240
So Matthew,
I understand from what you just said that

1ed87f62-c196-4bdd-af6f-d48896a69462/80-1
00:13:29.655 --> 00:13:29.973
thing?

1ed87f62-c196-4bdd-af6f-d48896a69462/81-1
00:13:32.240 --> 00:13:36.668
there there a lot of your solutions to
data integrity are governing standards,

1ed87f62-c196-4bdd-af6f-d48896a69462/81-2
00:13:36.668 --> 00:13:39.470
meaning that you're going to say at
Northwestern,

1ed87f62-c196-4bdd-af6f-d48896a69462/81-3
00:13:39.470 --> 00:13:43.898
though there may be 3 codes for doing X,
we're going to eliminate two of those

1ed87f62-c196-4bdd-af6f-d48896a69462/81-4
00:13:43.898 --> 00:13:47.933
codes that there's only one to simplify
data analytics on the back end.

1ed87f62-c196-4bdd-af6f-d48896a69462/82-0
00:13:49.213 --> 00:13:53.588
I'm wondering if there are any
technological challenges to normalizing,

1ed87f62-c196-4bdd-af6f-d48896a69462/82-1
00:13:53.588 --> 00:13:58.267
aggregating and deduplicating data in
order to ensure that you have reliable

1ed87f62-c196-4bdd-af6f-d48896a69462/82-2
00:13:58.267 --> 00:14:01.973
data when you're trying to develop your
own process metrics.

1ed87f62-c196-4bdd-af6f-d48896a69462/84-0
00:14:02.013 --> 00:14:06.892
Yeah, there's a few.
When you're in a system as large as we

1ed87f62-c196-4bdd-af6f-d48896a69462/84-1
00:14:06.892 --> 00:14:10.632
are,
you might locally determine that how the

1ed87f62-c196-4bdd-af6f-d48896a69462/84-2
00:14:10.632 --> 00:14:14.209
system is set up,
the data analytic system,

1ed87f62-c196-4bdd-af6f-d48896a69462/84-3
00:14:14.209 --> 00:14:19.413
the data poll system,
how it's set up might need to be modified

1ed87f62-c196-4bdd-af6f-d48896a69462/84-4
00:14:19.413 --> 00:14:21.933
to suit what we locally within.

1ed87f62-c196-4bdd-af6f-d48896a69462/88-0
00:14:22.013 --> 00:14:26.054
Our disease group need. However,
if we want to make a change,

1ed87f62-c196-4bdd-af6f-d48896a69462/83-0
00:14:23.693 --> 00:14:24.413
Mm-hmm.

1ed87f62-c196-4bdd-af6f-d48896a69462/88-1
00:14:26.054 --> 00:14:30.876
one of the tricky things is if this
change is embedded within the system,

1ed87f62-c196-4bdd-af6f-d48896a69462/88-2
00:14:30.876 --> 00:14:35.568
within the data warehouse system,
is that going to then create a ripple

1ed87f62-c196-4bdd-af6f-d48896a69462/88-3
00:14:35.568 --> 00:14:39.608
effect and impact other areas in the way
that they're set up?

1ed87f62-c196-4bdd-af6f-d48896a69462/85-0
00:14:36.653 --> 00:14:36.933
Yes.

1ed87f62-c196-4bdd-af6f-d48896a69462/88-4
00:14:39.608 --> 00:14:41.693
Is the change that we're making?

1ed87f62-c196-4bdd-af6f-d48896a69462/90-0
00:14:41.813 --> 00:14:46.043
Not possible to be made just for our team
and is it if that gets retooled,

1ed87f62-c196-4bdd-af6f-d48896a69462/86-0
00:14:42.093 --> 00:14:42.253
2.

1ed87f62-c196-4bdd-af6f-d48896a69462/87-0
00:14:44.333 --> 00:14:44.853
Mhm.

1ed87f62-c196-4bdd-af6f-d48896a69462/90-1
00:14:46.043 --> 00:14:48.580
is that going to change it for everyone?
Um,

1ed87f62-c196-4bdd-af6f-d48896a69462/90-2
00:14:48.580 --> 00:14:51.682
so that can be something that we always,
you know, uh,

1ed87f62-c196-4bdd-af6f-d48896a69462/89-0
00:14:49.293 --> 00:14:49.813
Mhm.

1ed87f62-c196-4bdd-af6f-d48896a69462/90-3
00:14:51.682 --> 00:14:56.080
that could be a roadblock and something
we always want to be mindful for. Uh,

1ed87f62-c196-4bdd-af6f-d48896a69462/90-4
00:14:56.080 --> 00:14:56.813
the other is.

1ed87f62-c196-4bdd-af6f-d48896a69462/91-0
00:14:57.373 --> 00:15:03.643
There's always there's there's constantly
new systems that are being developed and

1ed87f62-c196-4bdd-af6f-d48896a69462/91-1
00:15:03.643 --> 00:15:09.459
as a new platform comes to fruition,
if it looks promising and we believe in

1ed87f62-c196-4bdd-af6f-d48896a69462/91-2
00:15:09.459 --> 00:15:13.689
it, say locally,
but it doesn't exist yet in our system

1ed87f62-c196-4bdd-af6f-d48896a69462/91-3
00:15:13.689 --> 00:15:14.293
because.

1ed87f62-c196-4bdd-af6f-d48896a69462/92-0
00:15:14.573 --> 00:15:19.409
The information and the health medical
records are so secured in order to get a

1ed87f62-c196-4bdd-af6f-d48896a69462/92-1
00:15:19.409 --> 00:15:22.794
new system to have access to our medical
record system,

1ed87f62-c196-4bdd-af6f-d48896a69462/92-2
00:15:22.794 --> 00:15:25.877
say like a data system to be able to plug
into it.

1ed87f62-c196-4bdd-af6f-d48896a69462/92-3
00:15:25.877 --> 00:15:30.350
We got to make sure that everything's
vetted if we're going to have a new

1ed87f62-c196-4bdd-af6f-d48896a69462/92-4
00:15:30.350 --> 00:15:30.773
vendor.

1ed87f62-c196-4bdd-af6f-d48896a69462/93-0
00:15:31.253 --> 00:15:33.973
So to speak, um,
it's a it's a little more difficult to

1ed87f62-c196-4bdd-af6f-d48896a69462/93-1
00:15:33.973 --> 00:15:37.373
just kind of get that set up because
we're such a large organization.

1ed87f62-c196-4bdd-af6f-d48896a69462/94-0
00:15:37.333 --> 00:15:40.256
So Matthew, a lot of our listeners are,
you know,

1ed87f62-c196-4bdd-af6f-d48896a69462/94-1
00:15:40.256 --> 00:15:43.822
I think a lot of what you're saying will
resonate with them.

1ed87f62-c196-4bdd-af6f-d48896a69462/94-2
00:15:43.822 --> 00:15:46.744
Many of them are actively migrating to
the cloud.

1ed87f62-c196-4bdd-af6f-d48896a69462/94-3
00:15:46.744 --> 00:15:50.719
I believe this is something in
Northwestern Medicine has also begun

1ed87f62-c196-4bdd-af6f-d48896a69462/94-4
00:15:50.719 --> 00:15:55.395
doing and many of them are involved in
active merger and acquisitions activity.

1ed87f62-c196-4bdd-af6f-d48896a69462/94-5
00:15:55.395 --> 00:15:56.213
I'm wondering.

1ed87f62-c196-4bdd-af6f-d48896a69462/95-0
00:15:56.693 --> 00:15:57.053
As we.

1ed87f62-c196-4bdd-af6f-d48896a69462/96-0
00:15:57.493 --> 00:16:01.034
We kind of approach the end of this
podcast episode.

1ed87f62-c196-4bdd-af6f-d48896a69462/96-1
00:16:01.034 --> 00:16:05.510
Just a few more questions,
but can you delve into the implications

1ed87f62-c196-4bdd-af6f-d48896a69462/96-2
00:16:05.510 --> 00:16:10.588
of migrating to the cloud or the
implications of acquiring other practices,

1ed87f62-c196-4bdd-af6f-d48896a69462/96-3
00:16:10.588 --> 00:16:14.128
other hospitals,
other entities on data reliability?

1ed87f62-c196-4bdd-af6f-d48896a69462/96-4
00:16:14.128 --> 00:16:16.533
How do you handle those data issues?

1ed87f62-c196-4bdd-af6f-d48896a69462/97-0
00:16:17.333 --> 00:16:21.946
That arise from cloud migrations and M
and A activity in order to ensure that

1ed87f62-c196-4bdd-af6f-d48896a69462/97-1
00:16:21.946 --> 00:16:26.380
your calculations are based on accurate
data when you're calculating those

1ed87f62-c196-4bdd-af6f-d48896a69462/97-2
00:16:26.380 --> 00:16:26.853
metrics.

1ed87f62-c196-4bdd-af6f-d48896a69462/98-0
00:16:28.093 --> 00:16:31.118
Yeah,
I mean the integrity of the accuracy,

1ed87f62-c196-4bdd-af6f-d48896a69462/98-1
00:16:31.118 --> 00:16:36.479
if you're going to be pulling in data and
hosting it to say this is what that

1ed87f62-c196-4bdd-af6f-d48896a69462/98-2
00:16:36.479 --> 00:16:41.840
number is, whatever, whatever it is,
you're going to be migrating that to the

1ed87f62-c196-4bdd-af6f-d48896a69462/98-3
00:16:41.840 --> 00:16:44.933
cloud,
I think before it moves to the cloud.

1ed87f62-c196-4bdd-af6f-d48896a69462/100-0
00:16:46.373 --> 00:16:50.392
There needs to be kind of a team centered
around scrutinizing that data to make

1ed87f62-c196-4bdd-af6f-d48896a69462/100-1
00:16:50.392 --> 00:16:53.155
sure it's accurate because once it moves
to the cloud,

1ed87f62-c196-4bdd-af6f-d48896a69462/100-2
00:16:53.155 --> 00:16:57.325
then the assumption is it's going to be
there. It's going to be there for a while.

1ed87f62-c196-4bdd-af6f-d48896a69462/100-3
00:16:57.325 --> 00:17:01.444
It's going to be accessible for to people
from different avenues and they'll want

1ed87f62-c196-4bdd-af6f-d48896a69462/100-4
00:17:01.444 --> 00:17:04.860
to believe that it's true.
So I think it's really important to just

1ed87f62-c196-4bdd-af6f-d48896a69462/100-5
00:17:04.860 --> 00:17:05.613
make sure that.

1ed87f62-c196-4bdd-af6f-d48896a69462/102-0
00:17:06.333 --> 00:17:11.081
Everything is scrutinized before it
migrates over to the cloud. You know,

1ed87f62-c196-4bdd-af6f-d48896a69462/102-1
00:17:11.081 --> 00:17:14.995
as far as the cloud,
I think there's a lot of cyber security

1ed87f62-c196-4bdd-af6f-d48896a69462/102-2
00:17:14.995 --> 00:17:20.384
is obviously very important so that when
that information goes into the cloud, one,

1ed87f62-c196-4bdd-af6f-d48896a69462/102-3
00:17:20.384 --> 00:17:24.234
it can't just be viewed by anyone,
but just as importantly,

1ed87f62-c196-4bdd-af6f-d48896a69462/102-4
00:17:24.234 --> 00:17:25.773
it can't be manipulated.

1ed87f62-c196-4bdd-af6f-d48896a69462/103-0
00:17:26.013 --> 00:17:31.739
Once it's there, it should stay there.
That's a, you know, in my experiences,

1ed87f62-c196-4bdd-af6f-d48896a69462/101-0
00:17:26.653 --> 00:17:27.133
Mhm.

1ed87f62-c196-4bdd-af6f-d48896a69462/103-1
00:17:31.739 --> 00:17:37.832
I think healthcare has been as far as the
technology of data and sourcing data and

1ed87f62-c196-4bdd-af6f-d48896a69462/103-2
00:17:37.832 --> 00:17:42.090
quantifying it.
Healthcare has been a difficult sector to

1ed87f62-c196-4bdd-af6f-d48896a69462/103-3
00:17:42.090 --> 00:17:45.613
stay up to date and stay current with
what the.

1ed87f62-c196-4bdd-af6f-d48896a69462/104-0
00:17:45.613 --> 00:17:52.034
At most advanced height of data analytics
is, and I think that has to do with,

1ed87f62-c196-4bdd-af6f-d48896a69462/104-1
00:17:52.034 --> 00:17:55.935
you know,
largely has to do with how secure the

1ed87f62-c196-4bdd-af6f-d48896a69462/104-2
00:17:55.935 --> 00:17:58.373
information needs to be, yeah.

1ed87f62-c196-4bdd-af6f-d48896a69462/105-0
00:17:57.613 --> 00:18:00.718
Yeah.
So as we approach the end of this episode,

1ed87f62-c196-4bdd-af6f-d48896a69462/105-1
00:18:00.718 --> 00:18:04.202
I'd like to pose this final question to
you. You know,

1ed87f62-c196-4bdd-af6f-d48896a69462/105-2
00:18:04.202 --> 00:18:07.434
you've been working for quite some time.
You know,

1ed87f62-c196-4bdd-af6f-d48896a69462/105-3
00:18:07.434 --> 00:18:12.629
you're the program director of oncology
and you've been working on throughput and

1ed87f62-c196-4bdd-af6f-d48896a69462/105-4
00:18:12.629 --> 00:18:15.733
capacity within the outpatient oncology
setting.

1ed87f62-c196-4bdd-af6f-d48896a69462/106-0
00:18:16.013 --> 00:18:20.400
You've been working on developing these
different measures, building models,

1ed87f62-c196-4bdd-af6f-d48896a69462/106-1
00:18:20.400 --> 00:18:24.616
calculating staffing capacity needs.
What advice would you give either to

1ed87f62-c196-4bdd-af6f-d48896a69462/106-2
00:18:24.616 --> 00:18:29.059
yourself a few years ago as you were
earlier in the process of developing and

1ed87f62-c196-4bdd-af6f-d48896a69462/106-3
00:18:29.059 --> 00:18:31.053
managing these models and measures?

1ed87f62-c196-4bdd-af6f-d48896a69462/107-0
00:18:31.533 --> 00:18:35.981
Or to a listener who hasn't yet begun to
really go down this path.

1ed87f62-c196-4bdd-af6f-d48896a69462/107-1
00:18:35.981 --> 00:18:37.773
What advice would you give?

1ed87f62-c196-4bdd-af6f-d48896a69462/108-0
00:18:38.453 --> 00:18:38.533
Um.

1ed87f62-c196-4bdd-af6f-d48896a69462/110-0
00:18:40.973 --> 00:18:47.844
I would say as best as you can work to
standardize how you're organizing your

1ed87f62-c196-4bdd-af6f-d48896a69462/109-0
00:18:46.173 --> 00:18:46.413
And.

1ed87f62-c196-4bdd-af6f-d48896a69462/110-1
00:18:47.844 --> 00:18:51.808
data.
So what I do is I'll pull information,

1ed87f62-c196-4bdd-af6f-d48896a69462/110-2
00:18:51.808 --> 00:18:57.973
but then I like to store a lot of it in
my secured files that I have.

1ed87f62-c196-4bdd-af6f-d48896a69462/111-0
00:18:58.173 --> 00:19:03.176
Say in in in my drive and it's important
that I'm consistent with how I present

1ed87f62-c196-4bdd-af6f-d48896a69462/111-1
00:19:03.176 --> 00:19:07.741
the data to myself and to do that
consistently over time and making sure

1ed87f62-c196-4bdd-af6f-d48896a69462/111-2
00:19:07.741 --> 00:19:12.243
that there's a system for it.
Sometimes I can get lost in it and I swim

1ed87f62-c196-4bdd-af6f-d48896a69462/111-3
00:19:12.243 --> 00:19:17.246
in it and then I reinvent how I present
this data to myself or I reinvent how I

1ed87f62-c196-4bdd-af6f-d48896a69462/111-4
00:19:17.246 --> 00:19:17.933
analyze it.

1ed87f62-c196-4bdd-af6f-d48896a69462/112-0
00:19:18.173 --> 00:19:22.639
And I think staying consistent with with
what your approach is and how you wrap

1ed87f62-c196-4bdd-af6f-d48896a69462/112-1
00:19:22.639 --> 00:19:26.713
your mind around it is very important.
Sometimes you don't look at data,

1ed87f62-c196-4bdd-af6f-d48896a69462/112-2
00:19:26.713 --> 00:19:31.011
certain data as frequently as others.
So let's say there's data that I might

1ed87f62-c196-4bdd-af6f-d48896a69462/112-3
00:19:31.011 --> 00:19:34.807
pull once or twice a year.
It's really important that I can go back

1ed87f62-c196-4bdd-af6f-d48896a69462/112-4
00:19:34.807 --> 00:19:37.653
to the last time I I pulled that data and
look at.

1ed87f62-c196-4bdd-af6f-d48896a69462/113-0
00:19:37.853 --> 00:19:41.453
OK, this is how I organized it.
Here's the pieces that when I pulled the

1ed87f62-c196-4bdd-af6f-d48896a69462/113-1
00:19:41.453 --> 00:19:44.807
report that I parsed out.
I took these columns out because we don't

1ed87f62-c196-4bdd-af6f-d48896a69462/113-2
00:19:44.807 --> 00:19:48.259
need them. They're not as relevant.
They don't move the needle in our

1ed87f62-c196-4bdd-af6f-d48896a69462/113-3
00:19:48.259 --> 00:19:51.711
decision making and then keeping
everything consistent and organized.

1ed87f62-c196-4bdd-af6f-d48896a69462/113-4
00:19:51.711 --> 00:19:54.966
I think an organized file system is
massively important for data.

1ed87f62-c196-4bdd-af6f-d48896a69462/113-5
00:19:54.966 --> 00:19:57.333
And how do you label your files and how
do you?

1ed87f62-c196-4bdd-af6f-d48896a69462/114-0
00:19:57.733 --> 00:20:01.588
You go back and know right away when
you're looking into a into a folder, OK,

1ed87f62-c196-4bdd-af6f-d48896a69462/114-1
00:20:01.588 --> 00:20:03.911
that's what that was.
That's that time period.

1ed87f62-c196-4bdd-af6f-d48896a69462/114-2
00:20:03.911 --> 00:20:08.012
That's what that was telling me. Early on,
I would just kind of label things a lot

1ed87f62-c196-4bdd-af6f-d48896a69462/114-3
00:20:08.012 --> 00:20:10.879
very similarly and then I'd open up a
file and over time,

1ed87f62-c196-4bdd-af6f-d48896a69462/114-4
00:20:10.879 --> 00:20:13.992
over a couple of years,
it would just be this massive list and

1ed87f62-c196-4bdd-af6f-d48896a69462/114-5
00:20:13.992 --> 00:20:15.573
I'm clicking in three different.

1ed87f62-c196-4bdd-af6f-d48896a69462/115-0
00:20:15.613 --> 00:20:18.331
Excel documents,
four different Excel documents before I

1ed87f62-c196-4bdd-af6f-d48896a69462/115-1
00:20:18.331 --> 00:20:21.955
actually find the one that I needed and
my head is swimming in all of these

1ed87f62-c196-4bdd-af6f-d48896a69462/115-2
00:20:21.955 --> 00:20:24.053
numbers and all of these cells and
columns.

1ed87f62-c196-4bdd-af6f-d48896a69462/115-3
00:20:24.053 --> 00:20:27.057
So I'd say like your own organization
system, figure that out,

1ed87f62-c196-4bdd-af6f-d48896a69462/115-4
00:20:27.057 --> 00:20:28.773
define it rigidly and stick with it.

1ed87f62-c196-4bdd-af6f-d48896a69462/117-0
00:20:28.973 --> 00:20:32.670
It may not be sexy,
but in the pursuit of profit,

1ed87f62-c196-4bdd-af6f-d48896a69462/117-1
00:20:32.670 --> 00:20:37.846
vis-a-vis improved throughput and
capacity in the outpatient oncology

1ed87f62-c196-4bdd-af6f-d48896a69462/117-2
00:20:37.846 --> 00:20:42.800
setting at Northwestern Medicine,
it sounds like a a efficient and

1ed87f62-c196-4bdd-af6f-d48896a69462/117-3
00:20:42.800 --> 00:20:48.493
standardized method of organizing data is
the best path forward. So Matthew.

1ed87f62-c196-4bdd-af6f-d48896a69462/118-0
00:20:48.893 --> 00:20:50.973
I'd like to thank you for joining us
today.

1ed87f62-c196-4bdd-af6f-d48896a69462/119-0
00:20:51.253 --> 00:20:53.093
Thank you, Jordan.
Thank you so much for having me.