As health departments modernize their data systems, an unexpected challenge has emerged: traditional public health job classifications no longer match the reality of today’s data landscape. In this episode, Ari Whiteman, ASTHO’s Senior Advisor for Public Health Data and Informatics Workforce, talks about why the field urgently needs new informatics-focused roles, and what it will take to build them. Whiteman explains how interoperability, electronic health records, and complex data pipelines have outpaced legacy classifications like epidemiologist or public health analyst. Leveraging the Public Health Infrastructure Grant (PHIG), state, local, and territorial health agencies can build classification systems that enhance recruitment and retention of an informatics-savvy workforce. Updating job classifications can help clarify new roles, alleviate pressure on existing roles, and enable health agencies to sustain workforce infrastructure that is flexible and forward-looking. He discusses the hesitancy and bureaucracy that make change difficult, the opportunity cost of doing nothing, and why modernizing job classifications is essential for faster outbreak response, stronger surveillance, and smarter public health decision-making.Data Modernization Primer and Tactical Guides | ASTHOHow to Modernize Data Infrastructure: A Toolkit for Public Health Leaders | ASTHOASTHO Announces Sixth Developing Executive Leaders in Public Health Cohort | ASTHO
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This is Public Health Review
Morning Edition for Wednesday,
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December 17th, 2025.
I'm John Sheehan with news from
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the Association of State and
Territorial Health Officials.
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Today we discuss an unexpected
challenge in public health.
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Traditional job classifications
no longer match up to the
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reality of the modern data
landscape.
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Leveraging the Public Health
Infrastructure Grant, or FIG,
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State, local and territorial
health agencies can build
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classification systems that
enhance recruitment and
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retention of an informatics
savvy workforce.
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Updating job classifications can
help clarify new roles,
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alleviate pressure on existing
roles, and enable health
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agencies to sustain workforce
infrastructure that's flexible
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and forward-looking.
Here to explain what that means
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and why it's crucial for public
health departments and agencies
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to understand it is Ari
Whiteman, ASTO Senior Advisor
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for Public health data and
Informatics Workforce.
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Ari Whiteman, welcome to the
show.
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Thanks for having me.
Ari, your brief.
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Says that.
Recent public health.
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Data modernization efforts have
actually exposed kind of a
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weakness, and that weakness is
traditional job classifications.
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What's going on?
So as state and territorial
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health offices continue to
modernize and move forward into
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the next, you know, few years of
public health, our understanding
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of what's needed from a data
management standpoint has
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changed as well.
As we really get a better sense
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of the interoperability of
electronic health records that
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come from all different places
across our communities.
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We now require staff that are
able to handle and understand
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those nuances from a really sort
of back end perspective.
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You know, we for many years and
decades even have had
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epidemiologists and public
health analysts that have been
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trained to understand, you know,
how to run analysis and come to
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conclusions about various health
trends.
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But this new landscape of health
data is much more complicated.
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And the attempts to breakdown
silos behind, you know, where
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data comes from and how it's
interconnected across this
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health landscape requires new
sets of skills and expertise.
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And that's really what this sort
of forward thinking, you know,
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new approach towards job
classification series is all
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about.
Sure.
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And and that makes complete
sense that as technology
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progresses, that landscape
changes.
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And I think it's interesting
that sort of that where the
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rubber meets the road in that
case is, is just the job
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description of like what a job
is that someone's expected to
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perform.
Can you explain some of the some
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of the hesitancy or resistance
to change that might be that
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might that might come up as a
result?
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Yeah.
So, you know, any process we
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know in government, whether it's
state or federal, takes a long
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time.
There's a lot of people
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involved.
It's, you know, it takes a lot
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of power to move a big ship.
And I think that some of the
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hesitance might be, you know,
about, you know, you know, the
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world.
What's been working so far has
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been working for us or maybe
there's funding challenges, but
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I think that one of the, you
know, major arguments for doing
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this kind of thing, for creating
a new informatics based
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classification series for jobs
is really in the opportunity
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cost of not doing it.
You know, as as States and
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territories move forward and
modernizing their data systems,
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which at this point is, you
know, a train that's left the
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the station already it's, it's
continuing, you know, in, in
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hopefully in perpetuity.
You know there is going to be a
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cost of bring things the more
traditional way.
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You know, there's a cost to to
not having people that
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understand the
interconnectedness of health
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data from across different
sources and you know, locations
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of health day it comes from
whether it's hospitals, clinics,
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other states, other territories,
all the various places that
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these records can come from.
Having someone who doesn't
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understand how to collect these
pieces of information from
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various sources, combine them
and make them into a usable
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format for the epidemiologist.
So then analyze it limits what
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you're able to do from a
surveillance standpoint, from a
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response standpoints.
So this really is thinking about
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how can we modernize and
breakdown the silos that we've
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experienced in public health
data really up until the last,
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you know, 10 years or so.
This is about thinking forward.
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And you touched on this a little
bit, but can you can you expand
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a little more on what it means
to create an informatics based
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job classification?
Yeah.
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So we know it's, it's not
necessarily a simple process,
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but basically there's a few
steps in the process.
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So we think about the idea of
identifying the need.
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You know, what makes this
different job classification
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series more valuable than what
we already have?
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You know, that's something that
we have to think through for
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certainly at first,
understanding the approval
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processes and requirements that
are required for your particular
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health department.
Every health department has
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different ways of doing things.
Understanding what's needed from
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an approval and sort of
bureaucratic or administrative
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process for your particular
health department is an
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important next step.
After that, you need to gather
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job descriptions, competencies
and key skills that are required
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for this, for this particular
type of job series that are
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unique across the other public
health job job classification
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series.
And then lastly, there's salary
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benchmarking and other processes
and considerations that take
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place as you go through this,
this, this sort of journey, if
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you will.
We are at ASTO putting together
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documentation to help guide you
through this process.
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We have links and external
documents and resources from
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across the, you know, network of
collaborators that we work with
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that have already gone through
processes like this that that
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hopefully will assist you in
taking similar steps.
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And can you give us an example
of some of the the roadblocks or
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the log jams that can happen
when maybe a job role is
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different than the way it was
traditionally classified?
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Yeah.
You know, I think one of the,
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the issues that we're
experiencing now is that, you
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know, there are new skills that,
you know, as we've talked about,
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there's, there's new skills and
new challenges that are coming
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about because of this data
modernization wave that's taking
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place across the country.
And one of the issues we're
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seeing is that traditional
public health analysts, data
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analysts or, you know,
epidemiologists don't
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necessarily have the backgrounds
or training to be able to
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understand how to, you know,
navigate these complicated
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waters.
In many ways, these new
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processes are not health
related.
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They're more sort of general
data related issues.
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Theoretically, data
modernization can take place
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across any industry.
And I think that it'd be the
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same sort of challenges.
So here what we're asking people
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to do in many cases is people
with complex and well trained
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backgrounds in Health Sciences
or, you know, understanding
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health disparities are being
asked to work on projects or
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problems that are really more
about data infrastructure
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structure or data storage,
interoperability standards
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across electronic health
records.
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Things that honestly they don't
really teach you about in grad
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school very much.
That's a whole separate
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conversation.
But really that's I think where
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we see, you know, in particular
log jams where people just don't
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have the skills and training to
meet this new need.
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And so that's really where this
is coming about as identifying
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the responsibilities and the
potential to bring on new
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people, new staff that do have
these skills and expertise.
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And many of them might not even
have those backgrounds in
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particularly Health Sciences.
They might just be data
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management gurus that are just
really good at this kind of
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thing, understand how to break
down barriers and join data sets
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from across the, you know, the
landscape.
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And again, that doesn't
necessarily require a a health
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background in particular.
So, you know, I think that's
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that's where this is
particularly exciting is the
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idea and potential to bring in
people who may maybe are in
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other disciplines who could
provide a lot of value in public
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health because of their
knowledge and experience in
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working with data.
Yeah.
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And continue with that train of
thought, what what's sort of the
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upside of having people that
that know a current or a modern
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data infrastructure?
Yeah.
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So, so again, this sort of gets
at this opportunity cost.
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And if you think about, for
example, in, in the event of a,
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of an outbreak or even COVID or,
you know, we experienced this
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very much during, during COVID a
few years ago, where States and
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federal authorities and
territories were attempting to
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gather testing data or vaccine
data that was coming from all
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different types of sources.
Hospitals, clinics, university
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systems, nonprofits that were
doing all kinds of different
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community outreach and, you
know, all kinds of information
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that was being collected from
all different types of sources.
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There were people that had to
gather and put that information
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into a single usable format.
And it took a lot of time to
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understand, you know, how to
make data from different sources
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speak to each other, how to
allow standards for across the
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data landscape so that we can
glean actionable information in
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an operational standpoint from
this data that's coming from all
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different types of places.
That is a costly in, in terms of
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a time and resources process.
And therefore, I think a lot of
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the value that's that's in a
position like this or in in
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creating positions like this is
having people on staff that
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understand those processes.
Yeah, absolutely.
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Data is only as good as the
people you've got interpreting
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it.
Right.
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And this is also, you know, I
think there's a difference
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between data interpretation and
then the what's required to get
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the data into the shape that can
be interpreted in the 1st place.
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One of the criticisms that
people had, you know, during the
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the COVID response was that it
would take, you know, the the
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latest data der ONS on some of
these national dashboards was
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only from three weeks or a month
ago.
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But the reality was it takes a
lot of time for data from 50
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states, plus all the territories
and all the health centers and
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hospitals that are within the
those systems to take those in
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for all those disparate pieces
of information and put them
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together and make them usable.
So ideally, you know, part of
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what we are looking to do out of
advising States and territories
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to bring on staff that have
these skill sets is to allow for
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us to have again, shorter
processes for accumulating,
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storing and understanding data
from different systems.
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And again, that that all leads
to more actionable and faster
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interpretation, which therefore
leads to faster and more
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actionable public health, you
know, interventions in the
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community.
Along those lines, do you have
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any suggestions or like a first
step for say an office or an
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even an individual who wants to
get get started down this road?
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Yeah.
So ASTO is working on a number
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of different documents and
resources for States and
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territories that are interested
in developing a public health
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and dramatics job classification
series.
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We have a how to guide that will
be appended to this brief and
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we'd love for you to check out
these documents on our website
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where you can follow the steps
that we've laid out to create
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this this, you know, job
classification series for
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yourself.
I would also like to note that
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we at ASTO have directly
supported States and territories
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in their process for doing this
work.
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So if you're interested in
working with us or collaborating
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with us or receiving, you know,
support or advice from us on
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going through this and
hopefully, you know, we can
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tailor our advice to your
specific situation, we would
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certainly look forward to doing
so.
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All right.
Wightman is Asto's senior
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advisor for public health data
and informatics workforce and
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holds a PhD in health, geography
and epidemiology from UNC
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Charlotte.
Pasto announced the 6th cohort
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of his developing Executive
Leaders in Public Health
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program, which aims to
strengthen leadership capacity
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among mid to senior level public
health professionals.
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This year's scholars will
participate in a cohort based
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experience that includes
executive coaching, leadership
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development and skill building
sessions.
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Find more details in the show
notes.
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This has been public health
review morning edition.
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00:12:40,960 --> 00:12:43,560
I'm John Sheehan for the
Association of State and
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Territorial Health Officials.
00:00:01,600 --> 00:00:04,240
This is Public Health Review
Morning Edition for Wednesday,
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00:00:04,240 --> 00:00:08,080
December 17th, 2025.
I'm John Sheehan with news from
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00:00:08,080 --> 00:00:11,000
the Association of State and
Territorial Health Officials.
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00:00:11,720 --> 00:00:14,920
Today we discuss an unexpected
challenge in public health.
5
00:00:15,320 --> 00:00:18,040
Traditional job classifications
no longer match up to the
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00:00:18,040 --> 00:00:20,160
reality of the modern data
landscape.
7
00:00:20,720 --> 00:00:23,680
Leveraging the Public Health
Infrastructure Grant, or FIG,
8
00:00:23,920 --> 00:00:26,640
State, local and territorial
health agencies can build
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00:00:26,640 --> 00:00:29,720
classification systems that
enhance recruitment and
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00:00:29,720 --> 00:00:32,240
retention of an informatics
savvy workforce.
11
00:00:32,800 --> 00:00:36,200
Updating job classifications can
help clarify new roles,
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00:00:36,280 --> 00:00:39,120
alleviate pressure on existing
roles, and enable health
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00:00:39,120 --> 00:00:43,000
agencies to sustain workforce
infrastructure that's flexible
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00:00:43,000 --> 00:00:46,240
and forward-looking.
Here to explain what that means
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00:00:46,240 --> 00:00:49,200
and why it's crucial for public
health departments and agencies
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00:00:49,200 --> 00:00:52,760
to understand it is Ari
Whiteman, ASTO Senior Advisor
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00:00:52,760 --> 00:00:55,240
for Public health data and
Informatics Workforce.
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00:00:56,160 --> 00:00:57,360
Ari Whiteman, welcome to the
show.
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00:00:58,160 --> 00:01:00,560
Thanks for having me.
Ari, your brief.
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00:01:00,800 --> 00:01:03,440
Says that.
Recent public health.
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Data modernization efforts have
actually exposed kind of a
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weakness, and that weakness is
traditional job classifications.
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00:01:12,800 --> 00:01:16,920
What's going on?
So as state and territorial
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00:01:16,920 --> 00:01:20,920
health offices continue to
modernize and move forward into
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00:01:20,920 --> 00:01:24,280
the next, you know, few years of
public health, our understanding
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00:01:24,280 --> 00:01:26,840
of what's needed from a data
management standpoint has
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00:01:26,840 --> 00:01:30,120
changed as well.
As we really get a better sense
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00:01:30,120 --> 00:01:34,280
of the interoperability of
electronic health records that
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00:01:34,280 --> 00:01:37,360
come from all different places
across our communities.
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00:01:37,680 --> 00:01:42,560
We now require staff that are
able to handle and understand
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00:01:42,800 --> 00:01:46,840
those nuances from a really sort
of back end perspective.
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00:01:47,120 --> 00:01:51,000
You know, we for many years and
decades even have had
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00:01:51,000 --> 00:01:53,840
epidemiologists and public
health analysts that have been
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00:01:53,840 --> 00:01:58,120
trained to understand, you know,
how to run analysis and come to
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00:01:58,120 --> 00:02:00,440
conclusions about various health
trends.
36
00:02:00,840 --> 00:02:04,520
But this new landscape of health
data is much more complicated.
37
00:02:04,800 --> 00:02:09,759
And the attempts to breakdown
silos behind, you know, where
38
00:02:09,759 --> 00:02:12,520
data comes from and how it's
interconnected across this
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00:02:12,520 --> 00:02:16,960
health landscape requires new
sets of skills and expertise.
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00:02:16,960 --> 00:02:21,320
And that's really what this sort
of forward thinking, you know,
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00:02:21,320 --> 00:02:24,640
new approach towards job
classification series is all
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00:02:24,640 --> 00:02:26,480
about.
Sure.
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00:02:26,640 --> 00:02:29,400
And and that makes complete
sense that as technology
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00:02:29,400 --> 00:02:31,520
progresses, that landscape
changes.
45
00:02:32,440 --> 00:02:35,040
And I think it's interesting
that sort of that where the
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00:02:35,040 --> 00:02:38,440
rubber meets the road in that
case is, is just the job
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00:02:38,440 --> 00:02:41,880
description of like what a job
is that someone's expected to
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00:02:41,880 --> 00:02:46,040
perform.
Can you explain some of the some
49
00:02:46,040 --> 00:02:51,240
of the hesitancy or resistance
to change that might be that
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00:02:51,240 --> 00:02:52,840
might that might come up as a
result?
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00:02:53,240 --> 00:02:55,440
Yeah.
So, you know, any process we
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00:02:55,440 --> 00:02:58,920
know in government, whether it's
state or federal, takes a long
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00:02:58,920 --> 00:02:59,960
time.
There's a lot of people
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00:02:59,960 --> 00:03:01,480
involved.
It's, you know, it takes a lot
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00:03:01,480 --> 00:03:04,560
of power to move a big ship.
And I think that some of the
56
00:03:04,560 --> 00:03:08,440
hesitance might be, you know,
about, you know, you know, the
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00:03:08,440 --> 00:03:09,920
world.
What's been working so far has
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00:03:09,920 --> 00:03:14,080
been working for us or maybe
there's funding challenges, but
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00:03:14,080 --> 00:03:18,240
I think that one of the, you
know, major arguments for doing
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00:03:18,480 --> 00:03:22,600
this kind of thing, for creating
a new informatics based
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00:03:22,600 --> 00:03:26,120
classification series for jobs
is really in the opportunity
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00:03:26,120 --> 00:03:29,880
cost of not doing it.
You know, as as States and
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00:03:29,880 --> 00:03:33,120
territories move forward and
modernizing their data systems,
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00:03:33,120 --> 00:03:36,120
which at this point is, you
know, a train that's left the
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00:03:36,160 --> 00:03:39,800
the station already it's, it's
continuing, you know, in, in
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00:03:40,040 --> 00:03:42,960
hopefully in perpetuity.
You know there is going to be a
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00:03:42,960 --> 00:03:45,320
cost of bring things the more
traditional way.
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00:03:45,600 --> 00:03:48,480
You know, there's a cost to to
not having people that
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00:03:48,480 --> 00:03:51,920
understand the
interconnectedness of health
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00:03:51,920 --> 00:03:56,440
data from across different
sources and you know, locations
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00:03:56,440 --> 00:03:59,040
of health day it comes from
whether it's hospitals, clinics,
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00:03:59,320 --> 00:04:02,960
other states, other territories,
all the various places that
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00:04:02,960 --> 00:04:05,560
these records can come from.
Having someone who doesn't
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00:04:05,560 --> 00:04:09,120
understand how to collect these
pieces of information from
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00:04:09,120 --> 00:04:12,680
various sources, combine them
and make them into a usable
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00:04:12,680 --> 00:04:16,640
format for the epidemiologist.
So then analyze it limits what
77
00:04:16,640 --> 00:04:19,519
you're able to do from a
surveillance standpoint, from a
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00:04:19,519 --> 00:04:23,080
response standpoints.
So this really is thinking about
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00:04:23,080 --> 00:04:27,720
how can we modernize and
breakdown the silos that we've
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00:04:27,760 --> 00:04:30,680
experienced in public health
data really up until the last,
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00:04:30,680 --> 00:04:33,600
you know, 10 years or so.
This is about thinking forward.
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00:04:34,800 --> 00:04:37,080
And you touched on this a little
bit, but can you can you expand
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00:04:37,080 --> 00:04:40,680
a little more on what it means
to create an informatics based
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00:04:40,880 --> 00:04:43,480
job classification?
Yeah.
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00:04:43,480 --> 00:04:47,080
So we know it's, it's not
necessarily a simple process,
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00:04:47,400 --> 00:04:50,520
but basically there's a few
steps in the process.
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00:04:50,520 --> 00:04:53,320
So we think about the idea of
identifying the need.
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00:04:53,600 --> 00:04:57,320
You know, what makes this
different job classification
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00:04:57,320 --> 00:04:59,840
series more valuable than what
we already have?
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00:04:59,880 --> 00:05:01,760
You know, that's something that
we have to think through for
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00:05:02,080 --> 00:05:04,880
certainly at first,
understanding the approval
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00:05:04,880 --> 00:05:09,000
processes and requirements that
are required for your particular
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00:05:09,000 --> 00:05:11,000
health department.
Every health department has
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00:05:11,000 --> 00:05:13,520
different ways of doing things.
Understanding what's needed from
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00:05:13,520 --> 00:05:16,560
an approval and sort of
bureaucratic or administrative
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00:05:16,560 --> 00:05:19,600
process for your particular
health department is an
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00:05:19,600 --> 00:05:22,240
important next step.
After that, you need to gather
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00:05:22,240 --> 00:05:27,160
job descriptions, competencies
and key skills that are required
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00:05:27,160 --> 00:05:30,240
for this, for this particular
type of job series that are
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00:05:30,240 --> 00:05:33,760
unique across the other public
health job job classification
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00:05:33,760 --> 00:05:36,720
series.
And then lastly, there's salary
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00:05:36,720 --> 00:05:40,240
benchmarking and other processes
and considerations that take
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00:05:40,240 --> 00:05:43,280
place as you go through this,
this, this sort of journey, if
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00:05:43,280 --> 00:05:45,800
you will.
We are at ASTO putting together
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00:05:45,800 --> 00:05:48,320
documentation to help guide you
through this process.
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00:05:48,520 --> 00:05:53,080
We have links and external
documents and resources from
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00:05:53,080 --> 00:05:56,400
across the, you know, network of
collaborators that we work with
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00:05:57,080 --> 00:05:59,600
that have already gone through
processes like this that that
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00:05:59,600 --> 00:06:02,440
hopefully will assist you in
taking similar steps.
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00:06:04,240 --> 00:06:08,800
And can you give us an example
of some of the the roadblocks or
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00:06:08,800 --> 00:06:13,560
the log jams that can happen
when maybe a job role is
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00:06:13,560 --> 00:06:16,720
different than the way it was
traditionally classified?
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00:06:18,000 --> 00:06:19,240
Yeah.
You know, I think one of the,
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00:06:19,240 --> 00:06:22,040
the issues that we're
experiencing now is that, you
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00:06:22,040 --> 00:06:26,520
know, there are new skills that,
you know, as we've talked about,
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00:06:26,520 --> 00:06:29,200
there's, there's new skills and
new challenges that are coming
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00:06:29,200 --> 00:06:33,960
about because of this data
modernization wave that's taking
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00:06:33,960 --> 00:06:37,080
place across the country.
And one of the issues we're
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00:06:37,080 --> 00:06:40,240
seeing is that traditional
public health analysts, data
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00:06:40,240 --> 00:06:43,400
analysts or, you know,
epidemiologists don't
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00:06:43,400 --> 00:06:46,040
necessarily have the backgrounds
or training to be able to
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00:06:46,040 --> 00:06:48,800
understand how to, you know,
navigate these complicated
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00:06:48,800 --> 00:06:51,840
waters.
In many ways, these new
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00:06:51,840 --> 00:06:54,200
processes are not health
related.
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00:06:54,200 --> 00:06:57,520
They're more sort of general
data related issues.
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00:06:57,840 --> 00:06:59,880
Theoretically, data
modernization can take place
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00:06:59,880 --> 00:07:02,440
across any industry.
And I think that it'd be the
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00:07:02,440 --> 00:07:06,000
same sort of challenges.
So here what we're asking people
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00:07:06,000 --> 00:07:09,720
to do in many cases is people
with complex and well trained
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00:07:09,720 --> 00:07:13,360
backgrounds in Health Sciences
or, you know, understanding
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00:07:13,360 --> 00:07:16,960
health disparities are being
asked to work on projects or
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00:07:16,960 --> 00:07:19,720
problems that are really more
about data infrastructure
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00:07:19,840 --> 00:07:23,680
structure or data storage,
interoperability standards
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00:07:23,680 --> 00:07:25,400
across electronic health
records.
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00:07:25,680 --> 00:07:28,000
Things that honestly they don't
really teach you about in grad
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00:07:28,000 --> 00:07:29,680
school very much.
That's a whole separate
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00:07:29,760 --> 00:07:32,440
conversation.
But really that's I think where
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00:07:32,440 --> 00:07:35,280
we see, you know, in particular
log jams where people just don't
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00:07:35,280 --> 00:07:37,440
have the skills and training to
meet this new need.
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00:07:37,800 --> 00:07:40,520
And so that's really where this
is coming about as identifying
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00:07:40,520 --> 00:07:44,560
the responsibilities and the
potential to bring on new
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00:07:44,560 --> 00:07:48,160
people, new staff that do have
these skills and expertise.
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00:07:48,160 --> 00:07:51,320
And many of them might not even
have those backgrounds in
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00:07:51,320 --> 00:07:54,360
particularly Health Sciences.
They might just be data
145
00:07:54,440 --> 00:07:57,080
management gurus that are just
really good at this kind of
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00:07:57,080 --> 00:08:00,960
thing, understand how to break
down barriers and join data sets
147
00:08:00,960 --> 00:08:02,640
from across the, you know, the
landscape.
148
00:08:02,640 --> 00:08:05,480
And again, that doesn't
necessarily require a a health
149
00:08:05,480 --> 00:08:08,200
background in particular.
So, you know, I think that's
150
00:08:08,200 --> 00:08:10,080
that's where this is
particularly exciting is the
151
00:08:10,080 --> 00:08:13,560
idea and potential to bring in
people who may maybe are in
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00:08:13,560 --> 00:08:17,440
other disciplines who could
provide a lot of value in public
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00:08:17,440 --> 00:08:19,760
health because of their
knowledge and experience in
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00:08:19,760 --> 00:08:22,200
working with data.
Yeah.
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00:08:22,280 --> 00:08:25,880
And continue with that train of
thought, what what's sort of the
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00:08:25,880 --> 00:08:29,960
upside of having people that
that know a current or a modern
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00:08:29,960 --> 00:08:32,039
data infrastructure?
Yeah.
158
00:08:32,039 --> 00:08:34,400
So, so again, this sort of gets
at this opportunity cost.
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00:08:34,400 --> 00:08:38,480
And if you think about, for
example, in, in the event of a,
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00:08:38,480 --> 00:08:41,960
of an outbreak or even COVID or,
you know, we experienced this
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00:08:41,960 --> 00:08:47,640
very much during, during COVID a
few years ago, where States and
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00:08:47,720 --> 00:08:50,760
federal authorities and
territories were attempting to
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00:08:50,760 --> 00:08:54,760
gather testing data or vaccine
data that was coming from all
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00:08:54,760 --> 00:08:58,280
different types of sources.
Hospitals, clinics, university
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00:08:58,280 --> 00:09:01,960
systems, nonprofits that were
doing all kinds of different
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00:09:02,040 --> 00:09:04,320
community outreach and, you
know, all kinds of information
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00:09:04,320 --> 00:09:06,640
that was being collected from
all different types of sources.
168
00:09:07,160 --> 00:09:11,360
There were people that had to
gather and put that information
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00:09:11,360 --> 00:09:15,800
into a single usable format.
And it took a lot of time to
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00:09:15,800 --> 00:09:19,440
understand, you know, how to
make data from different sources
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00:09:19,440 --> 00:09:24,240
speak to each other, how to
allow standards for across the
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00:09:24,240 --> 00:09:28,600
data landscape so that we can
glean actionable information in
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00:09:28,600 --> 00:09:31,560
an operational standpoint from
this data that's coming from all
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00:09:31,560 --> 00:09:36,440
different types of places.
That is a costly in, in terms of
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00:09:36,440 --> 00:09:40,560
a time and resources process.
And therefore, I think a lot of
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00:09:40,560 --> 00:09:43,680
the value that's that's in a
position like this or in in
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00:09:43,680 --> 00:09:46,400
creating positions like this is
having people on staff that
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00:09:46,400 --> 00:09:49,520
understand those processes.
Yeah, absolutely.
179
00:09:49,920 --> 00:09:52,880
Data is only as good as the
people you've got interpreting
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00:09:52,880 --> 00:09:53,960
it.
Right.
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00:09:54,160 --> 00:09:56,320
And this is also, you know, I
think there's a difference
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00:09:56,320 --> 00:09:59,960
between data interpretation and
then the what's required to get
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00:09:59,960 --> 00:10:03,480
the data into the shape that can
be interpreted in the 1st place.
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00:10:03,760 --> 00:10:06,640
One of the criticisms that
people had, you know, during the
185
00:10:06,680 --> 00:10:09,760
the COVID response was that it
would take, you know, the the
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00:10:09,760 --> 00:10:13,800
latest data der ONS on some of
these national dashboards was
187
00:10:13,800 --> 00:10:15,880
only from three weeks or a month
ago.
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00:10:16,240 --> 00:10:19,720
But the reality was it takes a
lot of time for data from 50
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00:10:19,720 --> 00:10:22,560
states, plus all the territories
and all the health centers and
190
00:10:22,560 --> 00:10:25,440
hospitals that are within the
those systems to take those in
191
00:10:25,440 --> 00:10:28,280
for all those disparate pieces
of information and put them
192
00:10:28,280 --> 00:10:31,720
together and make them usable.
So ideally, you know, part of
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00:10:31,720 --> 00:10:35,360
what we are looking to do out of
advising States and territories
194
00:10:35,360 --> 00:10:40,160
to bring on staff that have
these skill sets is to allow for
195
00:10:40,160 --> 00:10:43,960
us to have again, shorter
processes for accumulating,
196
00:10:43,960 --> 00:10:47,080
storing and understanding data
from different systems.
197
00:10:47,920 --> 00:10:52,760
And again, that that all leads
to more actionable and faster
198
00:10:52,760 --> 00:10:56,120
interpretation, which therefore
leads to faster and more
199
00:10:56,120 --> 00:10:59,920
actionable public health, you
know, interventions in the
200
00:10:59,920 --> 00:11:03,600
community.
Along those lines, do you have
201
00:11:04,280 --> 00:11:09,280
any suggestions or like a first
step for say an office or an
202
00:11:09,280 --> 00:11:13,800
even an individual who wants to
get get started down this road?
203
00:11:14,640 --> 00:11:17,240
Yeah.
So ASTO is working on a number
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00:11:17,240 --> 00:11:19,680
of different documents and
resources for States and
205
00:11:19,680 --> 00:11:23,160
territories that are interested
in developing a public health
206
00:11:23,160 --> 00:11:25,160
and dramatics job classification
series.
207
00:11:25,920 --> 00:11:30,440
We have a how to guide that will
be appended to this brief and
208
00:11:30,680 --> 00:11:33,800
we'd love for you to check out
these documents on our website
209
00:11:34,280 --> 00:11:38,840
where you can follow the steps
that we've laid out to create
210
00:11:38,840 --> 00:11:41,360
this this, you know, job
classification series for
211
00:11:41,360 --> 00:11:44,080
yourself.
I would also like to note that
212
00:11:44,080 --> 00:11:48,520
we at ASTO have directly
supported States and territories
213
00:11:48,520 --> 00:11:50,480
in their process for doing this
work.
214
00:11:50,480 --> 00:11:53,200
So if you're interested in
working with us or collaborating
215
00:11:53,200 --> 00:11:56,320
with us or receiving, you know,
support or advice from us on
216
00:11:56,320 --> 00:11:59,200
going through this and
hopefully, you know, we can
217
00:11:59,600 --> 00:12:03,240
tailor our advice to your
specific situation, we would
218
00:12:03,240 --> 00:12:04,520
certainly look forward to doing
so.
219
00:12:05,880 --> 00:12:07,480
All right.
Wightman is Asto's senior
220
00:12:07,480 --> 00:12:10,520
advisor for public health data
and informatics workforce and
221
00:12:10,520 --> 00:12:14,200
holds a PhD in health, geography
and epidemiology from UNC
222
00:12:14,200 --> 00:12:18,960
Charlotte.
Pasto announced the 6th cohort
223
00:12:18,960 --> 00:12:21,400
of his developing Executive
Leaders in Public Health
224
00:12:21,400 --> 00:12:24,200
program, which aims to
strengthen leadership capacity
225
00:12:24,200 --> 00:12:27,040
among mid to senior level public
health professionals.
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This year's scholars will
participate in a cohort based
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experience that includes
executive coaching, leadership
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development and skill building
sessions.
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Find more details in the show
notes.
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This has been public health
review morning edition.
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I'm John Sheehan for the
Association of State and
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Territorial Health Officials.