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Technology Officer of Lead North. Chi,
thank you so much for joining us today.

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Glad to be here.

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So for those who don't know,
Lead North is a woman-owned IT consulting

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firm based in Brush Prairie, Washington,
specializing in healthcare

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interoperability.
So we have an interesting topic that

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we're going to be discussing today, Chi.
The topic is payers are from Mars,

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providers are from Venus. Specifically,
we'll be talking about

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health data interoperability in the
United States. As background,

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health plans and providers serve a common
patient population. As everyone knows,

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they operate with overlapping set of
health data.

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But when the two ecosystems are brought
together, there's still a divide.

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They speak the same,
they're speaking the same data exchange

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languages. They have HL7, CCDA, and FHIR.

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But today we're going to be examining the
foundation of the payer provider divide

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so that we can more effectively dr
communications among all stakeholders and

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have value-based solutions across the
health data interoperability landscape.

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That was a mouthful.
So I'm going to pause.

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I'll let you take over.
What does that mean to you?

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What are you seeing? Let's go from there,
Chi.

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Yeah, thanks for that.
And the first thing I want to just kind

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of call out,
and I realize that this may be in my

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original description,
but now that I heard it said out loud,

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that word divide is something I do want
to address first because I'd like to

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reformat or readdress that word because I
don't believe

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there's exactly a payer provider divide.
And right now there's a lot of push for

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payer provider interoperability,
integration, and data exchange.

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And I believe there is,
the title says it all, right?

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It feels like we should be able to be
speaking the same language.

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but what is the disconnect or the
misalignment?

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And what I find in my role as an
interoperability consultant,

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a lot of times almost feels like being a
relationship counselor, right?

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We're speaking what we think is the same
language,

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Mm-hmm.

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but what's actually important to

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Mhm.

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both parties or both entities,
and how do we bridge that gap?

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I think the easiest way to,
the easiest way to understand what I see

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as this, you know,
pairs are from Mars and providers are

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from Venus sort of misalignment or gap is
that

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The data is the same,
but different things or different

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processes are important to either side.
So for instance,

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on the clinician or the provider side,
it's about care delivery. And so the data,

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the patient data is there to be able to
provide better care.

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for the patient.
And so there's information to support

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that. Now that same or very,
it looks like that same set of

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information is available or used by the
payer or the health plan side,

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but they are looking at claims and risk
adjudication and billing.

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And so the types of,
at the very low level,

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the types of identifiers that they need,
the types of

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data elements that are meaningful,
maybe slightly different,

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even though they seem like they're the
same on the surface. And also,

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even when they use the same type of data,
like diagnoses or visit information,

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encounter information,
it really means different things or

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different parts of it have different
levels of importance.

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to the payer side versus the provider
side.

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And that's sort of what we're trying to
figure out how they can talk to each

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other.

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So, Chi,
there's a few kind of buckets that we

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were going to kind of try to delve into
today. We may not get to all of them,

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but we'll do our best.
I think we're going to cover risk

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adjustment, quality measurement,
and a CMS regulatory environment.

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Would you help us approach these buckets
by grounding us in a few anecdotes with

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customers you've worked with

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and the different data issues that the
payers and providers are dealing with and

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how they may observe the same data
elements,

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but extract different meaning or have
different incentives that shape their

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behavior.

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Yeah,
I think a great one to look at is risk

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adjustment. And I'm going to dig down,
I'm A technologist at heart,

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so I'm going to dig down and kind of show
this interesting example,

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because I've hit this more than once.
When you go in and the ask,

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the ask from either the provider or the
payer is to integrate the systems.

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And here's a bunch of data.

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get the data to flow in.
So on the surface, it's like, okay,

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Mm-hmm.

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that seems like a very algorithmic nuts
and bolts problem, right? I will parse it,

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Mm-hmm.

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I'll understand the data,
I'll put it into reasonable buckets that

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you can all understand.
And so I had a scenario where we were

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doing exactly that.
And then in the background,

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someone on this was an insurer or a payer,
they were like,

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right now we're using PDFs,
but we want to start consuming this data

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so we can do better analysis on it.
So we can sort of do a business

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intelligence and do these,
create these models. And so yes,

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this is exactly what we're doing.
We're just aggregating the data.

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We're putting it into reasonable buckets.
We're

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normalizing it for you.
And then there was always this,

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but right now our people, we look at PDFs,
like we pull up documents and we have a

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human looking at PDFs and that's really
what we want to move away from.

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And so I hear this, I hear this,
and it was almost at the 11th hour.

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And we said, right, we will preserve PDFs.

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absolutely, or we will convert to PDFs.
And then at the 11th hour, they're like,

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Hmm.

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okay, we're about to go live,
but where are the PDFs? And we're like,

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well, we're archiving them, like you said.
And we're like, no, no, no. Phase one,

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our people need to look at these PDFs.
Like this is how they do risk adjustment.

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This is, you know,
they're not ready to retrain on this

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whole new system.

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And it just,
this is probably one of the first

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incidents that made me realize that,
you know,

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we might have been on slightly different
planets here.

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I understand that the long-term goal was
to disaggregate and, you know,

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Mhm.

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normalize this clinical data so you can
do more powerful, you know,

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use cases with it.

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But in the short term,
there is a current process.

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And for risk adjustment,
they're looking at this overall document.

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And more importantly,
this is the document that they're used to,

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they're standardized on looking at.
And that's what you have to deliver is

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you have to remember the human process
that's involved on both sides,

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on the payer side and the provider side.

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as you dig deep into the technology and,
you know,

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put all your whizbang tools to effect.

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Actually,
I think a lot of our listeners will feel

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that the scenario you just described
resonates with them with regards to human

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pushback due to cultural changes and
change management processes and the fact

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that many organizations don't so much
have, they may have a technology problem,

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but they also have a political and
cultural

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problem that accompanies any kind of
technological changes and processes.

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Is that, are you able,
so I'd like to dive deeper into that.

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So you said that this was a payer who was
relying on PDFs and looking to change

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extract data,
discrete data elements from the PDFs for

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long-term analytics,
but still had a process that was

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dependent now on PDFs and failed to
communicate that.

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How could payers better kind of
articulate their needs in the future?

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And also,
was there a way to avoid that or what was

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a lesson learned from that experience?

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I do want to frame this.
It was a good lesson learned for all

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Mm-hmm.

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parties.
I don't think the payer or the process is

32ceee19-497a-41aa-ad51-38f50fe8f6c4/40-0
00:08:03.426 --> 00:08:04.026
Mm-hmm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/44-2
00:08:05.114 --> 00:08:08.379
wrong.
We have to start somewhere and we want to

32ceee19-497a-41aa-ad51-38f50fe8f6c4/41-0
00:08:06.386 --> 00:08:06.866
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/44-3
00:08:08.379 --> 00:08:12.176
bridge the gap.
We want technology to make lives easier,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/42-0
00:08:09.906 --> 00:08:10.226
Thank you.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/43-0
00:08:11.826 --> 00:08:12.306
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/44-4
00:08:12.176 --> 00:08:14.841
right?
And before we decide to reinvent

32ceee19-497a-41aa-ad51-38f50fe8f6c4/44-5
00:08:14.841 --> 00:08:17.106
everything or reformat everything,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/48-0
00:08:17.746 --> 00:08:22.526
But I think that what lesson learned,
what could have avoided that is I think

32ceee19-497a-41aa-ad51-38f50fe8f6c4/45-0
00:08:22.066 --> 00:08:22.546
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/48-1
00:08:22.526 --> 00:08:25.591
asking at a base level before making
assumptions,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/48-2
00:08:25.591 --> 00:08:29.206
which I admit I did in this situation,
making assumptions,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/46-0
00:08:26.986 --> 00:08:27.586
Mm-hmm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/48-3
00:08:29.206 --> 00:08:33.803
I'm going into a technical project.
These are the sorts of things we do in

32ceee19-497a-41aa-ad51-38f50fe8f6c4/48-4
00:08:33.803 --> 00:08:37.235
technical projects. You know,
strip away that language.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/47-0
00:08:35.946 --> 00:08:36.546
Mm-hmm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/48-5
00:08:37.235 --> 00:08:41.586
Like we assumed we had a common language
because they were exchanging.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/49-0
00:08:41.706 --> 00:08:47.066
these data formats that were common to
everyone, strip away that first and ask,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/49-1
00:08:47.066 --> 00:08:51.152
what are your needs?
What is your number one priority on day

32ceee19-497a-41aa-ad51-38f50fe8f6c4/49-2
00:08:51.152 --> 00:08:56.311
one, right? On day one of go live,
what do you want your staff to be able to

32ceee19-497a-41aa-ad51-38f50fe8f6c4/49-3
00:08:56.311 --> 00:09:01.000
do? And then, well, it's, you know,
phase two, what's important next?

32ceee19-497a-41aa-ad51-38f50fe8f6c4/49-4
00:09:01.000 --> 00:09:06.226
And I think that was a very fundamental,
a very fundamental conversation that

32ceee19-497a-41aa-ad51-38f50fe8f6c4/52-0
00:09:06.306 --> 00:09:09.524
that needs to happen,
especially when you're sort of on the

32ceee19-497a-41aa-ad51-38f50fe8f6c4/52-1
00:09:09.524 --> 00:09:12.474
cutting edge.
You're doing these projects where people

32ceee19-497a-41aa-ad51-38f50fe8f6c4/50-0
00:09:11.626 --> 00:09:12.146
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/52-2
00:09:12.474 --> 00:09:15.156
will say,
this is the first time we've done this.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/52-3
00:09:15.156 --> 00:09:18.536
And that's almost a little warning bell
to be like, all right,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/52-4
00:09:18.536 --> 00:09:22.505
take it down and ask for what is really
important. At the end of the day,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/52-5
00:09:22.505 --> 00:09:25.026
what are the things that are important to
you?

32ceee19-497a-41aa-ad51-38f50fe8f6c4/54-0
00:09:25.986 --> 00:09:29.792
I mean, on the same level,
payers member eligibility and member

32ceee19-497a-41aa-ad51-38f50fe8f6c4/51-0
00:09:26.146 --> 00:09:26.466
Not.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/53-0
00:09:29.786 --> 00:09:30.306
Mmh.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/54-1
00:09:29.792 --> 00:09:34.430
eligibility checking and verification is
probably the most important feature.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/54-2
00:09:34.430 --> 00:09:39.187
And dealing with those identifiers for
member eligibility is the most important

32ceee19-497a-41aa-ad51-38f50fe8f6c4/54-3
00:09:39.187 --> 00:09:41.981
thing.
Those same identifiers on the clinician

32ceee19-497a-41aa-ad51-38f50fe8f6c4/54-4
00:09:41.981 --> 00:09:45.430
side are not needed.
They're not needed for patient care.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/54-5
00:09:45.430 --> 00:09:48.225
They're not needed for those provider
systems.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/54-6
00:09:48.225 --> 00:09:50.306
So whether or not they track in the

32ceee19-497a-41aa-ad51-38f50fe8f6c4/55-0
00:09:50.346 --> 00:09:54.225
the same way.
It's a big gap that needs to be addressed

32ceee19-497a-41aa-ad51-38f50fe8f6c4/55-1
00:09:54.225 --> 00:09:58.381
early on when trying to get these parties
to talk together.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/55-2
00:09:58.381 --> 00:10:03.506
And so back to the basics, I guess,
is the short answer to your question.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/56-0
00:10:03.746 --> 00:10:07.553
Yeah, so again,
the topic today is payers are from Mars

32ceee19-497a-41aa-ad51-38f50fe8f6c4/56-1
00:10:07.553 --> 00:10:12.448
and providers are from Venus.
And we're talking about payers right now.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/56-2
00:10:12.448 --> 00:10:17.751
And you just mentioned that this PDF was
important to payers for some kind of

32ceee19-497a-41aa-ad51-38f50fe8f6c4/56-3
00:10:17.751 --> 00:10:22.306
process to drive analytics.
Was this PDF used at all by providers?

32ceee19-497a-41aa-ad51-38f50fe8f6c4/58-0
00:10:22.466 --> 00:10:26.191
And, and, and, and how,
what were their kind of needs for this

32ceee19-497a-41aa-ad51-38f50fe8f6c4/58-1
00:10:26.191 --> 00:10:29.029
data?
Were they involved in the conversation at

32ceee19-497a-41aa-ad51-38f50fe8f6c4/58-2
00:10:29.029 --> 00:10:29.266
all?

32ceee19-497a-41aa-ad51-38f50fe8f6c4/62-0
00:10:30.146 --> 00:10:37.450
I think that you've actually hit upon a
very key point is usually the payers and

32ceee19-497a-41aa-ad51-38f50fe8f6c4/59-0
00:10:36.586 --> 00:10:37.066
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/62-1
00:10:37.450 --> 00:10:42.230
providers are not talking directly
together anymore.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/62-2
00:10:42.230 --> 00:10:49.083
It's because their needs are quite
different and the process almost feels a

32ceee19-497a-41aa-ad51-38f50fe8f6c4/60-0
00:10:47.106 --> 00:10:47.706
Mm-hmm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/62-3
00:10:49.083 --> 00:10:50.706
little bit siloed.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/66-0
00:10:50.866 --> 00:10:54.059
we did our business here,
you do your business there.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/61-0
00:10:51.466 --> 00:10:51.946
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/66-1
00:10:54.059 --> 00:10:57.311
And though there is some,
there are some touch points,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/66-2
00:10:57.311 --> 00:11:01.687
there's not the driving need to put them
into the same room and have that

32ceee19-497a-41aa-ad51-38f50fe8f6c4/66-3
00:11:01.687 --> 00:11:04.762
conversation.
And you made me think of some things,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/64-0
00:11:02.946 --> 00:11:03.426
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/66-4
00:11:04.762 --> 00:11:08.014
right? It's a,
I'm not saying that that's what we need

32ceee19-497a-41aa-ad51-38f50fe8f6c4/66-5
00:11:08.014 --> 00:11:10.734
to do,
but I think that that's one of the key

32ceee19-497a-41aa-ad51-38f50fe8f6c4/65-0
00:11:08.866 --> 00:11:09.346
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/66-6
00:11:10.734 --> 00:11:11.266
things is

32ceee19-497a-41aa-ad51-38f50fe8f6c4/69-0
00:11:11.346 --> 00:11:17.173
the provider's job is done. They have,
they are provided this and that PDF or

32ceee19-497a-41aa-ad51-38f50fe8f6c4/67-0
00:11:13.706 --> 00:11:14.186
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/69-1
00:11:17.173 --> 00:11:21.057
that CCDA, right,
that comprehensive care document,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/69-2
00:11:21.057 --> 00:11:25.614
it's the same document on both sides.
It really is. However,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/68-0
00:11:23.186 --> 00:11:23.346
Mm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/69-3
00:11:25.614 --> 00:11:30.246
it's used very differently.
And so that conversation of like,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/69-4
00:11:30.246 --> 00:11:33.906
why can't you send us this data?
It does happen.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/71-0
00:11:34.026 --> 00:11:37.226
But whenever that happens,
the answer is always like,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/71-1
00:11:37.226 --> 00:11:39.951
we just don't have it,
or why do you need it?

32ceee19-497a-41aa-ad51-38f50fe8f6c4/71-2
00:11:39.951 --> 00:11:44.514
There's a lot of kind of pushback,
and I don't think that will ever go away.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/70-0
00:11:44.186 --> 00:11:44.786
Mm-hmm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/71-3
00:11:44.514 --> 00:11:48.010
It's the knowledge that on one side,
this is so important,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/71-4
00:11:48.010 --> 00:11:51.802
why can't you just send it over?
It's so basic to our business.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/71-5
00:11:51.802 --> 00:11:53.106
And on the other side.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/73-0
00:11:53.306 --> 00:11:57.988
Well, it's not needed for our business.
Why would we clutter our systems with all

32ceee19-497a-41aa-ad51-38f50fe8f6c4/73-1
00:11:57.988 --> 00:12:00.786
this extra information?
It's just not available.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/72-0
00:11:59.506 --> 00:12:00.066
So...

32ceee19-497a-41aa-ad51-38f50fe8f6c4/75-0
00:12:01.266 --> 00:12:04.677
Let's segue into quality measurement
enterprise.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/75-1
00:12:04.677 --> 00:12:10.177
A lot of electronic clinical quality
measures are used to inform reimbursement

32ceee19-497a-41aa-ad51-38f50fe8f6c4/75-2
00:12:10.177 --> 00:12:13.448
models.
So it seems like this is an area where

32ceee19-497a-41aa-ad51-38f50fe8f6c4/75-3
00:12:13.448 --> 00:12:18.739
both payers and providers might find
financial alignment in terms of taking

32ceee19-497a-41aa-ad51-38f50fe8f6c4/74-0
00:12:17.506 --> 00:12:18.306
Absolutely.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/75-4
00:12:18.739 --> 00:12:21.106
care of their patient populations.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/76-0
00:12:21.426 --> 00:12:27.037
and hitting certain metrics which affect
reimbursement from the payer.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/76-1
00:12:27.037 --> 00:12:33.517
Could you talk about a specific example
where even despite the apparent financial

32ceee19-497a-41aa-ad51-38f50fe8f6c4/76-2
00:12:33.517 --> 00:12:39.603
alignment between two organizations
around a very specific set of numerators

32ceee19-497a-41aa-ad51-38f50fe8f6c4/76-3
00:12:39.603 --> 00:12:40.946
and denominators,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/77-0
00:12:41.146 --> 00:12:43.803
You still find that they're kind of on
different pages,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/77-1
00:12:43.803 --> 00:12:47.266
and what have you been seeing in terms of
attempts to bridge those gaps?

32ceee19-497a-41aa-ad51-38f50fe8f6c4/78-0
00:12:47.746 --> 00:12:51.106
Yeah,
I think that this is a good point is this

32ceee19-497a-41aa-ad51-38f50fe8f6c4/78-1
00:12:51.106 --> 00:12:51.666
is where

32ceee19-497a-41aa-ad51-38f50fe8f6c4/80-0
00:12:52.826 --> 00:12:55.861
Both sides,
we need to improve data quality,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/80-1
00:12:55.861 --> 00:12:59.907
and if we do not see,
we have a good line of sight into it,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/80-2
00:12:59.907 --> 00:13:04.223
you know, how can we do that? And,
in this case, like you said,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/80-3
00:13:04.223 --> 00:13:07.865
there's monetary benefit,
there's monetary, you know,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/80-4
00:13:07.865 --> 00:13:12.586
kind of tags to be able to improve this
data quality. I think that...

32ceee19-497a-41aa-ad51-38f50fe8f6c4/79-0
00:13:09.266 --> 00:13:09.746
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/82-0
00:13:13.106 --> 00:13:17.376
in a specific situation,
I think this is actually not even a

32ceee19-497a-41aa-ad51-38f50fe8f6c4/82-1
00:13:17.376 --> 00:13:20.806
specific,
I've seen this across implementations,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/82-2
00:13:20.806 --> 00:13:24.026
both on the provider side and the payer
side,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/82-3
00:13:24.026 --> 00:13:28.506
is the data has been exchanged for a long
time now, by the way.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/81-0
00:13:25.866 --> 00:13:26.386
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/82-4
00:13:28.506 --> 00:13:32.216
So just already ensures by, you know,
by compliance,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/82-5
00:13:32.216 --> 00:13:35.506
the providers that are sending data or
sending

32ceee19-497a-41aa-ad51-38f50fe8f6c4/84-0
00:13:35.826 --> 00:13:39.938
sorry, I have members in their network,
are already sending the data.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/84-1
00:13:39.938 --> 00:13:42.582
So there's this mountain of historical
data.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/84-2
00:13:42.582 --> 00:13:47.341
There's this mountain of historical data,
but before they had the conversations,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/83-0
00:13:42.946 --> 00:13:43.426
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/84-3
00:13:47.341 --> 00:13:51.101
who knows what was in there? It was never,
it was never, again,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/84-4
00:13:51.101 --> 00:13:54.626
it was never parsed and inspected and
analyzed in that way.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/87-0
00:13:55.106 --> 00:13:59.986
And so you're looking to like a seven
year look back of data and you're like,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/87-1
00:13:59.986 --> 00:14:05.116
oh, but these were always missing, right?
Now we can maybe go back and try to fix

32ceee19-497a-41aa-ad51-38f50fe8f6c4/85-0
00:14:02.626 --> 00:14:03.106
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/87-2
00:14:05.116 --> 00:14:08.369
those ills,
but the data was always missing and how

32ceee19-497a-41aa-ad51-38f50fe8f6c4/87-3
00:14:08.369 --> 00:14:12.623
much of it can be recreated. Some of it,
you know, it's historical,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/87-4
00:14:12.623 --> 00:14:17.440
it may not be relevant because you're
looking at quality measures right now.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/87-5
00:14:17.440 --> 00:14:18.066
But it is,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/86-0
00:14:17.586 --> 00:14:18.146
Can you?

32ceee19-497a-41aa-ad51-38f50fe8f6c4/88-0
00:14:18.226 --> 00:14:22.123
going back to an existing process and
having a conversation to actually pull

32ceee19-497a-41aa-ad51-38f50fe8f6c4/88-1
00:14:22.123 --> 00:14:23.186
over what's relevant.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/89-0
00:14:23.626 --> 00:14:26.132
Chi,
can you ground us and work on a specific

32ceee19-497a-41aa-ad51-38f50fe8f6c4/89-1
00:14:26.132 --> 00:14:29.891
set of quality measures or particular
quality measures, for example,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/89-2
00:14:29.891 --> 00:14:34.250
30-day readmissions or something that
some healthcare delivery system and payer

32ceee19-497a-41aa-ad51-38f50fe8f6c4/89-3
00:14:34.250 --> 00:14:35.666
were looking at improving?

32ceee19-497a-41aa-ad51-38f50fe8f6c4/93-0
00:14:36.466 --> 00:14:43.299
I think something that we see a lot is
there's specific risk measures about

32ceee19-497a-41aa-ad51-38f50fe8f6c4/90-0
00:14:42.066 --> 00:14:42.826
Mm-hmm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/93-1
00:14:43.299 --> 00:14:49.323
diagnoses like COPD. Oh,
I'm going to get it wrong because I'm not

32ceee19-497a-41aa-ad51-38f50fe8f6c4/91-0
00:14:44.226 --> 00:14:44.866
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/93-2
00:14:49.323 --> 00:14:56.066
a clinician. Chronic pulmonary disease,
thank you, COPD, diabetes markers.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/92-0
00:14:50.066 --> 00:14:51.866
Obstructive pulmonary disorder.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/98-0
00:14:56.426 --> 00:14:59.440
And so at the technical level where I
come in,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/94-0
00:14:59.186 --> 00:15:00.226
Mhm, mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/98-1
00:14:59.440 --> 00:15:04.506
that is looking at specific diagnosis
codes. And so, and those are frequently,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/95-0
00:15:02.986 --> 00:15:03.466
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/98-2
00:15:04.506 --> 00:15:08.289
those are frequently kept.
Those are frequently stored and

32ceee19-497a-41aa-ad51-38f50fe8f6c4/98-3
00:15:08.289 --> 00:15:12.265
frequently exchanged.
But missing pieces of information might

32ceee19-497a-41aa-ad51-38f50fe8f6c4/96-0
00:15:09.866 --> 00:15:10.466
Mm-hmm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/98-4
00:15:12.265 --> 00:15:15.856
be the exact service date when certain
things happened.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/98-5
00:15:15.856 --> 00:15:20.858
Are you keeping this as a historical
record of the patient's history or is it

32ceee19-497a-41aa-ad51-38f50fe8f6c4/97-0
00:15:20.066 --> 00:15:20.626
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/98-6
00:15:20.858 --> 00:15:20.986
an

32ceee19-497a-41aa-ad51-38f50fe8f6c4/99-0
00:15:21.066 --> 00:15:24.153
encounter?
Is it an actual representing a visit?

32ceee19-497a-41aa-ad51-38f50fe8f6c4/99-1
00:15:24.153 --> 00:15:28.373
So some of that data within the EHR,
the electronic health system,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/99-2
00:15:28.373 --> 00:15:31.460
electronic health record system of the
provider,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/99-3
00:15:31.460 --> 00:15:34.736
they recorded enough for them to provide
that care,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/99-4
00:15:34.736 --> 00:15:38.830
but what they transmit on doesn't
necessarily have that level of

32ceee19-497a-41aa-ad51-38f50fe8f6c4/99-5
00:15:38.830 --> 00:15:39.586
granularity.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/101-0
00:15:39.906 --> 00:15:43.349
to the payer system,
the health plans that are trying to do

32ceee19-497a-41aa-ad51-38f50fe8f6c4/101-1
00:15:43.349 --> 00:15:46.563
the reporting. And so I think, again,
the conversation,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/101-2
00:15:46.563 --> 00:15:50.294
but to have the conversation,
it really needed to be very exact.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/101-3
00:15:50.294 --> 00:15:54.082
I like what you said to me at the
beginning of this conversation.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/100-0
00:15:53.146 --> 00:15:53.426
And.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/101-4
00:15:54.082 --> 00:15:58.329
People don't want this high level.
They don't want to like have this very

32ceee19-497a-41aa-ad51-38f50fe8f6c4/101-5
00:15:58.329 --> 00:15:59.706
long conversation. It is

32ceee19-497a-41aa-ad51-38f50fe8f6c4/103-0
00:16:00.386 --> 00:16:04.717
Just the facts, like, fine,
we need to change. Every change is costly.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/103-1
00:16:04.717 --> 00:16:09.840
Everything costs money. Tell me exactly.
Tell me exactly what you need. And in the,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/102-0
00:16:09.266 --> 00:16:09.906
Right.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/103-2
00:16:09.840 --> 00:16:12.768
you know,
scenario that I had worked on in this

32ceee19-497a-41aa-ad51-38f50fe8f6c4/103-3
00:16:12.768 --> 00:16:16.489
particular situation,
what was key was being able to profile

32ceee19-497a-41aa-ad51-38f50fe8f6c4/103-4
00:16:16.489 --> 00:16:18.746
that data quickly, right? Before you.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/105-0
00:16:18.866 --> 00:16:23.844
build a huge implementation,
get visibility into that data on a large

32ceee19-497a-41aa-ad51-38f50fe8f6c4/105-1
00:16:23.844 --> 00:16:27.115
scale,
and to be able to analyze and identify

32ceee19-497a-41aa-ad51-38f50fe8f6c4/105-2
00:16:27.115 --> 00:16:32.519
what exactly are the exact things you
need to ask going forward in order to

32ceee19-497a-41aa-ad51-38f50fe8f6c4/104-0
00:16:31.986 --> 00:16:32.466
So.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/105-3
00:16:32.519 --> 00:16:33.586
make that work.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/106-0
00:16:33.826 --> 00:16:39.834
So it sounds like that a provider
organization was collecting risk measures

32ceee19-497a-41aa-ad51-38f50fe8f6c4/106-1
00:16:39.834 --> 00:16:46.159
about COPD and was missing metadata about
the service date and did not transmit

32ceee19-497a-41aa-ad51-38f50fe8f6c4/106-2
00:16:46.159 --> 00:16:50.349
that to payers,
which made the data less valuable to

32ceee19-497a-41aa-ad51-38f50fe8f6c4/106-3
00:16:50.349 --> 00:16:53.986
payers. Meanwhile,
had they kept the metadata

32ceee19-497a-41aa-ad51-38f50fe8f6c4/107-0
00:16:54.186 --> 00:16:59.650
the providers would have been incurring
the cost of storing that data for their

32ceee19-497a-41aa-ad51-38f50fe8f6c4/107-1
00:16:59.650 --> 00:17:03.952
entire patient population.
So how do you make an argument to a

32ceee19-497a-41aa-ad51-38f50fe8f6c4/107-2
00:17:03.952 --> 00:17:09.279
provider organization that has to bear
the financial cost of maintaining data

32ceee19-497a-41aa-ad51-38f50fe8f6c4/107-3
00:17:09.279 --> 00:17:12.626
that is useful to payers,
but not to themselves?

32ceee19-497a-41aa-ad51-38f50fe8f6c4/110-0
00:17:13.586 --> 00:17:18.415
Yeah, that's an interesting one.
And I think this data, by the way,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/110-1
00:17:18.415 --> 00:17:22.533
it's for those years past,
it's sort of been fire, right?

32ceee19-497a-41aa-ad51-38f50fe8f6c4/108-0
00:17:21.386 --> 00:17:21.946
Mmh.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/110-2
00:17:22.533 --> 00:17:28.072
We fired it over. You already have it.
We sent you what we meant to send you.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/110-3
00:17:28.072 --> 00:17:31.906
So it's yours. We no longer own it.
I think the value

32ceee19-497a-41aa-ad51-38f50fe8f6c4/113-0
00:17:31.946 --> 00:17:35.729
right? The value is,
and now this is bridging a little bit

32ceee19-497a-41aa-ad51-38f50fe8f6c4/109-0
00:17:32.386 --> 00:17:33.026
Mm-hmm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/113-1
00:17:35.729 --> 00:17:39.128
into some of the current CMS compliance
regulations.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/113-2
00:17:39.128 --> 00:17:44.194
The value is really once you get these
different organizations speaking to one

32ceee19-497a-41aa-ad51-38f50fe8f6c4/113-3
00:17:44.194 --> 00:17:47.400
another,
the end result or the end goal really is

32ceee19-497a-41aa-ad51-38f50fe8f6c4/113-4
00:17:47.400 --> 00:17:52.466
improved care on both sides. For this one,
I'd like to tell kind of a personal

32ceee19-497a-41aa-ad51-38f50fe8f6c4/111-0
00:17:48.546 --> 00:17:49.146
Mm-hmm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/115-0
00:17:52.626 --> 00:17:58.164
anecdote. And it really maybe makes,
maybe explains why I'm particularly

32ceee19-497a-41aa-ad51-38f50fe8f6c4/112-0
00:17:53.626 --> 00:17:53.826
Aha.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/115-1
00:17:58.164 --> 00:18:01.578
passionate about this.
Payers are from Mars,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/115-2
00:18:01.578 --> 00:18:05.295
providers are from Venus.
I'm a mother of twins.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/115-3
00:18:05.295 --> 00:18:11.060
And when they were born premature,
so they were in the NICU for a while and

32ceee19-497a-41aa-ad51-38f50fe8f6c4/114-0
00:18:05.546 --> 00:18:06.026
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/115-4
00:18:11.060 --> 00:18:14.626
I was in the hospital as well.
And we incurred

32ceee19-497a-41aa-ad51-38f50fe8f6c4/119-0
00:18:15.026 --> 00:18:19.208
a pretty, you know,
a pretty hefty hospital bill.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/116-0
00:18:17.946 --> 00:18:18.426
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/119-1
00:18:19.208 --> 00:18:22.971
But then we hit, and I had,
I had insurance,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/117-0
00:18:22.186 --> 00:18:22.666
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/119-2
00:18:22.971 --> 00:18:28.241
but we hit these snacks because they said,
well, first of all,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/119-3
00:18:28.241 --> 00:18:33.426
your insurance is not valid. I was like,
impossible. And then

32ceee19-497a-41aa-ad51-38f50fe8f6c4/118-0
00:18:31.626 --> 00:18:32.106
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/121-0
00:18:33.666 --> 00:18:36.990
They're like, no, we can't tie it up.
It's not on your policy.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/121-1
00:18:36.990 --> 00:18:40.948
And we're like pulling up our policies.
We're trying to fight this, right?

32ceee19-497a-41aa-ad51-38f50fe8f6c4/121-2
00:18:40.948 --> 00:18:44.800
Hospital is getting involved,
the advocates, and they were really great.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/121-3
00:18:44.800 --> 00:18:47.966
And long story short, three years later,
three years later,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/121-4
00:18:47.966 --> 00:18:51.870
we finally get it all resolved.
I said that I didn't pay off the children

32ceee19-497a-41aa-ad51-38f50fe8f6c4/120-0
00:18:48.866 --> 00:18:49.466
Mm-hmm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/121-5
00:18:51.870 --> 00:18:53.506
till they were three years old.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/125-0
00:18:53.786 --> 00:18:57.971
Turned out there was an incorrect
identifier. I had a subscriber ID,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/125-1
00:18:57.971 --> 00:19:01.246
an ID card, an old one.
It's not the one I presented,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/122-0
00:19:01.186 --> 00:19:01.666
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/125-2
00:19:01.246 --> 00:19:04.279
but I've been in the system as I moved
employers.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/125-3
00:19:04.279 --> 00:19:09.070
It was an old ID with the same insurer
and they just matched it and they said,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/123-0
00:19:05.626 --> 00:19:06.226
Mm-hmm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/125-4
00:19:09.070 --> 00:19:12.164
nope, and no one could untangle it. And,
you know,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/125-5
00:19:12.164 --> 00:19:16.106
I actually hit that same scenario on the
other side as the tech.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/127-0
00:19:16.186 --> 00:19:19.855
you know, as the technical consultant,
when they said, oh, well,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/124-0
00:19:17.186 --> 00:19:17.426
Hmm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/127-1
00:19:19.855 --> 00:19:24.257
we don't know which subscriber number,
just make a guess, right? Like if you,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/127-2
00:19:24.257 --> 00:19:26.740
if you can't, if you can't reach,
you know,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/127-3
00:19:26.740 --> 00:19:31.030
a definitive answer on which is the
correct subscriber number for a person,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/127-4
00:19:31.030 --> 00:19:35.601
because a person can have a whole history
of enrollments and subscriber numbers,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/126-0
00:19:34.306 --> 00:19:34.706
Right.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/127-5
00:19:35.601 --> 00:19:37.746
they gave me like a, they suggested a.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/130-0
00:19:38.026 --> 00:19:40.791
rule of, you know,
a shortcut or rule of thumb,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/130-1
00:19:40.791 --> 00:19:44.765
just default to this. And I was like, no,
no, no, we cannot do that.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/128-0
00:19:42.626 --> 00:19:44.266
Ha ha ha.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/130-2
00:19:44.765 --> 00:19:47.299
We cannot do that.
And let me tell you why.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/130-3
00:19:47.299 --> 00:19:51.388
Because there's a human cost behind this.
There's time, there's money,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/130-4
00:19:51.388 --> 00:19:54.786
but there's also the patients who have to
be in the cycle.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/130-5
00:19:54.786 --> 00:19:57.666
And then there's also the insurers.
This cycling,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/129-0
00:19:58.306 --> 00:19:58.786
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/131-0
00:20:11.426 --> 00:20:11.906
Yeah.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/132-0
00:20:12.386 --> 00:20:18.306
so much work on the hospital side,
their advocates, ourselves, my family,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/132-1
00:20:18.306 --> 00:20:21.506
and the insurers to work out this issue.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/135-0
00:20:20.866 --> 00:20:26.671
all of the result of an error of a manual
patient identity matching process,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/135-1
00:20:26.671 --> 00:20:32.476
which begs the need of why wasn't there
an enterprise master person index to

32ceee19-497a-41aa-ad51-38f50fe8f6c4/133-0
00:20:27.986 --> 00:20:28.466
Yeah.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/135-2
00:20:32.476 --> 00:20:35.266
reconcile these different identities.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/140-0
00:20:34.306 --> 00:20:38.268
Great, great need for that, right?
And I think also when we, you know,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/140-1
00:20:38.268 --> 00:20:42.509
when we talk about AI and human in the
loop, the decision, the algorithmic,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/136-0
00:20:40.786 --> 00:20:41.186
Yeah.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/140-2
00:20:42.509 --> 00:20:46.024
right, the algorithmic,
the algorithmic decision is not always

32ceee19-497a-41aa-ad51-38f50fe8f6c4/140-3
00:20:46.024 --> 00:20:48.926
the best.
And that was where I suppose this was not

32ceee19-497a-41aa-ad51-38f50fe8f6c4/137-0
00:20:46.866 --> 00:20:47.026
Mm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/138-0
00:20:48.746 --> 00:20:49.226
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/140-4
00:20:48.926 --> 00:20:52.441
with an AI solution,
but that was where I was the human in the

32ceee19-497a-41aa-ad51-38f50fe8f6c4/140-5
00:20:52.441 --> 00:20:54.506
loop saying like, no, no, no, no, no.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/139-0
00:20:55.386 --> 00:20:55.866
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/141-0
00:20:56.466 --> 00:21:01.940
It's not just the 1% of identifiers that
you couldn't find and you just took a

32ceee19-497a-41aa-ad51-38f50fe8f6c4/141-1
00:21:01.940 --> 00:21:05.266
default.
That default has potential human cost.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/142-0
00:21:05.506 --> 00:21:08.316
So, Chi,
we're approaching the end of this podcast

32ceee19-497a-41aa-ad51-38f50fe8f6c4/142-1
00:21:08.316 --> 00:21:12.117
episode. We've covered a bit of ground.
Again, payers are from Mars,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/142-2
00:21:12.117 --> 00:21:15.312
providers are from Venus.
We've spoken about the need for

32ceee19-497a-41aa-ad51-38f50fe8f6c4/142-3
00:21:15.312 --> 00:21:17.901
interoperability between payers and
providers.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/142-4
00:21:17.901 --> 00:21:20.876
We've covered some risk adjustment,
quality measures,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/142-5
00:21:20.876 --> 00:21:23.906
and aligning incentives from a regulatory
perspective.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/144-0
00:21:25.026 --> 00:21:28.329
Any kind of parting words as
organizations,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/144-1
00:21:28.329 --> 00:21:33.885
as providers and payers begin to
collaborate more, they become payviders.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/144-2
00:21:33.885 --> 00:21:39.741
You see payers acquiring provider groups
and provider organizations acquiring

32ceee19-497a-41aa-ad51-38f50fe8f6c4/144-3
00:21:39.741 --> 00:21:43.570
payer groups.
You're seeing 0057 and 9115 from CMS

32ceee19-497a-41aa-ad51-38f50fe8f6c4/144-4
00:21:43.570 --> 00:21:48.826
where there's required interoperability
between payers and providers.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/145-0
00:21:49.986 --> 00:21:54.635
kind of what should our listening
audience, be they payers or providers,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/145-1
00:21:54.635 --> 00:21:59.922
be keeping in mind about their colleagues
in the other vertical as they proceed to

32ceee19-497a-41aa-ad51-38f50fe8f6c4/145-2
00:21:59.922 --> 00:22:03.106
answer data challenges facing their
organization?

32ceee19-497a-41aa-ad51-38f50fe8f6c4/146-0
00:22:04.146 --> 00:22:07.737
I think the thing is, well, first of all,
all those alphabets,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/146-1
00:22:07.737 --> 00:22:12.012
all those letters and numbers are so near
and dear to what I do every day.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/146-2
00:22:12.012 --> 00:22:16.687
I think in order to bring order, right,
to this chaos, it can at first seem very,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/146-3
00:22:16.687 --> 00:22:19.765
very daunting.
I know people have talked about AI and

32ceee19-497a-41aa-ad51-38f50fe8f6c4/146-4
00:22:19.765 --> 00:22:22.786
bringing that into the mix as well.
And I think that

32ceee19-497a-41aa-ad51-38f50fe8f6c4/149-0
00:22:23.426 --> 00:22:28.148
It's a complex solution.
If we keep our eyes, right, and again,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/147-0
00:22:28.106 --> 00:22:28.586
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/149-1
00:22:28.148 --> 00:22:34.199
the source of this talk is if we keep our
eyes on the fact that there's a meaning

32ceee19-497a-41aa-ad51-38f50fe8f6c4/149-2
00:22:34.199 --> 00:22:37.962
behind this data,
and the core expertise is really

32ceee19-497a-41aa-ad51-38f50fe8f6c4/148-0
00:22:34.666 --> 00:22:35.266
Mm-hmm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/149-3
00:22:37.962 --> 00:22:41.946
unpacking that meaning. Do that first.
Do that first.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/151-0
00:22:42.026 --> 00:22:46.697
first before you barrel in with a
solution. The solution's the easy part.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/151-1
00:22:46.697 --> 00:22:50.484
I say this as a technologist.
The solution's the easy part.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/150-0
00:22:50.386 --> 00:22:50.866
Mhm.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/151-2
00:22:50.484 --> 00:22:54.903
The sort of in-depth knowledge,
domain knowledge to make those calls.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/151-3
00:22:54.903 --> 00:22:59.447
It's a difficult place to be,
but it's the place where we need to start

32ceee19-497a-41aa-ad51-38f50fe8f6c4/151-4
00:22:59.447 --> 00:22:59.826
first.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/152-0
00:23:01.186 --> 00:23:04.119
before we embark upon these very,
very huge endeavors,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/152-1
00:23:04.119 --> 00:23:05.826
which are happening as we speak.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/153-0
00:23:06.626 --> 00:23:09.740
Sounds like there needs to be a good data
foundation,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/153-1
00:23:09.740 --> 00:23:11.586
clean data that you're using to.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/155-0
00:23:11.186 --> 00:23:14.724
And a good data architect,
and a good data architect, architects,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/155-1
00:23:14.724 --> 00:23:19.119
not one person, but yes, great foundation,
clean the data, but be visible, right?

32ceee19-497a-41aa-ad51-38f50fe8f6c4/154-0
00:23:15.746 --> 00:23:16.226
Yeah.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/155-2
00:23:19.119 --> 00:23:21.906
Be able to look at the data and know what
it means.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/156-0
00:23:22.226 --> 00:23:26.557
And understand what the problem is that
you're trying to solve, which brings us,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/156-1
00:23:26.557 --> 00:23:29.177
you know,
thinking about the PDF issue you spoke

32ceee19-497a-41aa-ad51-38f50fe8f6c4/156-2
00:23:29.177 --> 00:23:32.225
about earlier.
Where do you actually want to be and what

32ceee19-497a-41aa-ad51-38f50fe8f6c4/156-3
00:23:32.225 --> 00:23:36.128
would be a band-aid and what would be
addressing the root cause problem?

32ceee19-497a-41aa-ad51-38f50fe8f6c4/156-4
00:23:36.128 --> 00:23:40.245
And could your solution itself be causing
a problem by eliminating the PDFs,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/156-5
00:23:40.245 --> 00:23:43.346
which was not desirable for that
organization? All right.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/158-0
00:23:41.426 --> 00:23:45.844
Yes, all those things. We rush in,
we rush in with the new technology,

32ceee19-497a-41aa-ad51-38f50fe8f6c4/157-0
00:23:44.146 --> 00:23:44.466
Yeah.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/158-1
00:23:45.844 --> 00:23:48.706
but there's a human element.
There always is.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/159-0
00:23:49.426 --> 00:23:51.895
All right, well, Chi,
I'd like to thank you for joining us

32ceee19-497a-41aa-ad51-38f50fe8f6c4/159-1
00:23:51.895 --> 00:23:52.146
today.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/160-0
00:23:53.346 --> 00:23:53.986
Thank you.

32ceee19-497a-41aa-ad51-38f50fe8f6c4/161-0
00:24:01.026 --> 00:24:01.426
Yep.