1119: How PopHIVE Is Rewiring Public Health Intelligence

JOHN SHEEHAN:
This is Public Health Review Morning Edition for Tuesday, May 5, 2026. I'm John Sheehan with
news from the Association of State and Territorial Health Officials.

Much of the data public health
leaders need already exists, but it just isn't as accessible as it could be.
Today, we'll hear about a new platform aiming to unlock the full potential of population health
data. Dr. Anne Zink, a senior fellow at the Yale School of Public Health and former ASTHO
president, tells us about PopHIVE, or the Population Health Information Visualization Exchange.
Born out of frontline frustration during COVID-19, PopHIVE brings together de-identified data from
across healthcare, public health, and even non-traditional sources like Google search trends and
home healthcare monitoring devices into one open interactive tool.

Later, Dr. Jen Layden, senior vice president of population health and innovation at ASTHO, will
talk about other data sharing, public-private partnerships, and tools like PopHIVE that are
improving early detection of threats and empowering public health decision-makers before the
next crisis begins.

ANNE ZINK:
PopHIVE stands for Population Health Information Visualization Exchange, but the simple way to
think about it is it's just de-identified data that you all, as state health officials, as state leaders,
can use it today for free. I like to think about it as the gift to my former self. I was incredibly
frustrated by not being able to see particularly de-identified data, spending a lot of time having
really meaningful but challenging conversations with EHRs, TEFCA, and HIEs, and all sorts of
other things, and those all play a really important role in this ecosystem of data and data
exchange that we need.

But the number of times, like particularly, like during COVID, that I went to Our World in Data, or I
went to the New York Times, or I went to the Johns Hopkins website, to just have a better sense
of what was happening around me and to see how we compared to other states, how we were
doing. It felt like there needed to be better ways to have this information. This was really
accelerated, then when I had a grant to decrease diabetes, and my team could only see the
BRFRS data.

And that data is great, but in Alaska, that is 527 people that they interviewed on the phone, and
that's limited. And as a clinician, I knew that there was data sets such as hemoglobin A1c that are
required for me to be able to test, to be able to monitor someone's diabetes. That gives me a lot
of detail to the data outside of just, 'has someone been diagnosed with diabetes?'
It tells me how sick they are with diabetes, how poorly controlled their diabetes is. That's a really

important metric if I'm going to use millions of dollars to address chronic diseases or other
challenges. So, when I stepped down from the state, I was trying to think about what I wanted to
do to try to make population health better.

And part of that was thinking about how to make state health officials, epidemiologists' world a
little bit better, and was lucky enough to spend some time at Yale School of Public Health. And
together, we built this project called PopHIVE. And we've been able to sit on top of all sorts of
really interesting and great data sets and sources, making them publicly available.

The biggest kind of change and game-changer for us was being able to sit on top of the Epic
Cosmos data, which really has not been available in the past, publicly available to be able to see
that sort of data. But then we can also sit on everything else, from home thermometer testing data
to crisis line, AI-generated synthesis of what people are stressed about, and what they're texting
about. So, instead of worrying about specific data standards, which are really important if I'm
looking at line-level  data, and I'm really trying to understand this TB case, it's able to really ingest
data from different ways, different avenues, and be able to understand context of population
health in a different way.

SHEEHAN:
Wow. And tell us a little bit more about how Yale fits into the picture and why academia is so
important to building these kinds of tools.

ZINK:
Yeah. I mean, I live in Palmer, Alaska. I don't live in New Haven, Connecticut.
I've never, I mean, I went to med school, I went to residency, but that was kind of my last
experience, honestly, with academia. But what I've really found and loved working in this
partnership between the private sector, the public sector, and academia is being able to align
different skills and resources to be the best version of ourselves. So, sometimes I feel like Yale is
like a Mary Poppins bag.

I'm like, I need an epidemiologist to help to understand this, or who is a modeler, or who's an AI
expert to help with this? And suddenly, like, I don't know where they come from, but then finds
some great person who's there, and they're all connected to this other entire network of
academics who are then incredibly helpful. So, for example, BRFRS data, the CDC has done a
great job of trying to model that data to understand, well, how representative is this or not?
But that question really hasn't been asked in the same sort of way of electronic health records
data. 'How much representation do I need? What is good representation?
How much can I use this for community? Can I make real decisions based on real dollars based
on this data?' But there's people at NYU doing that work.

There's people in Philadelphia doing that work. There's people at Yale doing that work. There's
people at Emory doing that work, and they all know each other.
So, then one of the really fun things that's happening with our partnership with ASTHO is we can
come back and we can ask them, 'How representative is this electronic health record data? How
much can I trust it? And then you get these modelers who really understand the representation,
looking at different data sources, and can put that to action.

SHEEHAN:
Yeah. And you described this a little bit already, but tell us some more about the kinds of sources
that are going into this tool and what kinds of gaps it's illuminating.

ZINK:
Yeah. I mean, it's one of my favorite parts of this whole project, to tell you the truth. Sometimes it
feels like a little Easter egg hunt.
I just feel like there's always some other data set or source out there, and we're very nimble in
how we build it in. So, some things that we did initially was to take big public data sets that were
already available. So, for example, CDC has a lot of data on wastewater, on syndromic
surveillance, on different rates that are happening from infectious diseases to chronic diseases,
etc.
So, ingesting that. There's more and more data available from Medicare that you can be able to
ingest, looking at Medicaid data. So, looking at claims and billing data.
We've been able to sit on top of previous work. So, the Carnegie Mellon group, the Delphi group
out of Carnegie Mellon had previously looked at and been working with CDC in many states to
look at near-real-time claims data. So, when I'm in the emergency department working, I'm
charting, but that is actually on the back-end using technology to claim, to bill, and claim in real-
time.
And that is another signal that can be used on top of things like ADT feeds to understand very
quickly what's happening in a community or in everything from infectious disease to like what I'm
putting in my chart. So, they use claims data. So, we sit on top of their claims data.
We've been having conversations about looking at larger claims data to understand the costs and
the implications as another way to help build the health care-public health joint infrastructure, so
that we're using our dollars as wisely as possible to improve public health. But then the other
really funny thing about it, there's always this joke that like Google knows if you're pregnant
before your family does because of what you're putting in there. There's a lot that we're constantly
putting into social media and into asking questions.

So, we have a partnership with Google. We can look at Google Trends data. We are working with,
as I mentioned, home thermometer and blood pressure cuff monitoring.
I think particularly as we get more of this individualized personalized data, patients bring into the
ER to me all the time on a piece of paper what their blood pressure looks like all written out. But
what if we had that as their home blood pressure monitoring, and they're pregnant, and we can
see signals in that much earlier to see when is this: a lack of access to care? 'What are the
challenges in getting someone's blood pressure well-controlled at different stages of their life?'
So, I think all of that information. And then, honestly, with AI, our ability to sit on top of
conversation. There's data, but then there's information.
And I think AI was really allowing us to sit on top of information in a different sort of way. So, for
example, if people are using a text chat line to discuss their frustration or even what they're

searching in different searches, AI can kind of pull out of trends and themes. PopHIVE is only de-
identified population-level data.

And so, we only sit on, there's no way for me to even, someone could hack all of our data
tomorrow, and there's still no way to cross it because everything is de-identified before it even
comes in. And we're not trying to patient match it. But what we're trying to do is to help to show
where data comes together and where it's different.
So, for example, someone says, I'm not sure I trust CDC's data right now. Well, when you look at
syndromic surveillance data from CDC, it's lining up really closely and very well with everything
from the Delphi claims data to other datasets that we have from Epic Cosmos. So you're like,
'Okay, well that looks like it's pretty consistent.'
And our goal is to be totally transparent about the data datasets. You can go play with it. You can
click on and off.
You can compare them. We're working on an AI model so you can ask it lots of questions
because we really believe that community, local health officials, state health officials have the
best context to put this context into use and to augment it with what they understand about
community, augment it with their own data to be able to make data for action.

SHEEHAN:
Wow. You just went through a lot of different aspects to this program, partly how people can trust
the information going into it by cross-referencing it between various sources, to how you can use
AI to add new levels of complexity and context to these disparate datasets that you have. That's a
lot of power in one place.

ZINK:

It's been fun. I mean, I think that there has been this model for good reason, where we had an
individual patient that got sent up to the health care or the local health authority, then set up to the
state, then got it sent to the federal government. They understood it or analyzed it and sent
information down, but maybe I'm biased because of my experience in a state like Alaska.
When you're close to the ground, you have a different understanding of what's really happening.
Utqiagvik, Alaska is really different than Atlanta, Georgia. But in the meantime, there's been a lot
of consolidation of what's been happening within the healthcare and within tech.
I think if we can be able to take that consolidation, for example, like Epic data, Google Trends
data, and we can push it down back to state, local, community, individual level, then people can
put that into context in a different way to be able to take action. This is never going to replace your
TB case control. Like I'm being able to look at this, it's never going to be the end-all, be-all for all
data that's needed in public health and health care.
But I think it's an additional super useful tool in understanding population-level data that can
compare states, that can compare datasets and sources, and can be both hypothesis-generating
as well as just ease of use, so that you don't have to. I'll never forget when a very large hospital
system was begging CMS to regulate them better because it was so hard to work with all of our
different jurisdictions. That was an eye-opening moment to me about how hard it can be to work
with all the different states.
If we can take some of that burden off by, at least for some of the data, trying to get that back out
to states and local communities as easily as possible, rather than having to have 50 different
agreements, then we can just accelerate this data for transformation.

SHEEHAN:
And now I'd like to bring in Dr. Jen Layden, ASTHO's senior vice president of population health
and innovation.

Dr. Layden, I think that was a pretty compelling argument for public health and
private partnerships. What an incredible tool that sounds like.
And the mind reels: it's the opportunities for clinicians, for public health officials to use these tools
to inform their decisions.

JEN LAYDEN:
Yeah, no, absolutely. And it's hard to follow Anne, but happy to be here and really excited to have
ASTHO be partnering with Yale, and the Strategic Plan highlighted and resized the importance of
public-private partnerships, not just to support the work that ASTHO does, but really importantly
to support the work of our members, so our state and territorial health agencies. And I think
PopHIVE is one great example of how we're doing that, how we're making data available, pulling
all the data together in one place so that not just health agencies, but others that rely on the data

can access it as well.
I think there's some other great examples. Dr. Zink mentioned the U.S. Data Tracker that John
Hopkins is working with. We're working with that effort.
And I think it just, it shows the way in which public health can be working with academia, can be
working with the private sector to tackle and solve some really hard challenges that better serve
our communities.

SHEEHAN:
And it can almost be overwhelming, sort of the amount of data that sounds like are at members'
fingertips. How can ASTHO members sort of dig into some of these tools, some of these
partnerships?

LAYDEN:
Yeah, in a couple of ways. One, it's just to familiarize yourself with what's there. I think the tools
become better the more folks use it, the more people provide guidance and thoughts of what
would make it better.
So, I think one of the things that we've done recently is launch a group of sessions to engage
members across state and territorial health agencies, wanting their input to help shape and
inform continuing evolution of what the data and the website looks like and what it serves.

SHEEHAN:
And Dr. Zink touched on this a little bit, but can you talk about how expanding or increasing use
of these tools can actually reduce that administrative burden?

LAYDEN:
Yeah. So, in the data technology space, it's a constantly and rapidly evolving field. In public
health, we often don't have the resources, the person power, financial resources, the technical
backbones to do what's needed with the data, right?
There's, as you mentioned, an influx of data and it's so critical to the work of public health, whether
it's identifying an outbreak, a novel infection, understanding the impact of climate, for example, on
health disease and health outcomes. But it sometimes takes time if we're just relying on
ourselves to make that data meaningful, understandable, and then to disseminate it out. So, such
a tool like this, it really puts it at the fingertips.
So, it's bringing in different areas of expertise, bringing in additional person power to help us make
data more understandable. So, to me, I see this, as Anne mentioned, as a tool that can be
used not just by state health agencies, state health commissioners, but by the public as well to

understand what's happening in their communities and surrounding communities.

SHEEHAN:
And Dr. Zink mentioned in the case of Yale, being able to have access to these different experts
in different fields, and this sort of spidery network of experts that know everything. I'd like to talk a
little bit more about sort of the role of institutions, especially academic institutions, in developing
these tools.

LAYDEN:
Yeah, no, absolutely. Having been in the data space for several years now, not just here at
ASTHO. But at the federal level, at the state level, city level, I think one of the biggest takeaways I
have is that public health can't do it alone in the data tech space. We won't have enough financial
resources, but we don't have the technical capacity or expertise.
It's hard to hire, hard to retain the right expertise. And yet we are surrounded by folks that have
expertise that can be so valuable. The private industry, and health care, and the industry in
academia, people that want to help solve really challenging problems, and that can be part of the
solution.

And so, as I mentioned, the strategic priority, a strategic plan of ASTHO, that concept of public-
private partnerships is something that's really important to us and a priority.

SHEEHAN:
And certainly, the benefit to health agencies seems pretty obvious to me. What are the companies
getting out of this relationship?

LAYDEN:
Yeah, it's a great question. I think that depends on what the public-private partnership is. I can't
speak for the PopHIVE per se, but I'm sure that there's huge value in what they see as participation
in this.
From my experience working with private industry and others, they, one, see the value and
importance of public health. Many of them were helping with different solutions during the start
of COVID and recognized the challenges we have in the data space and the data ecosystem.
And they don't want us as a nation, as our communities, to continue to struggle.
So, I think there is some genuine desire to help solve these problems in their recognition that they
can. I think at the same time, as we're working on, for example, the Public Health Data
Consortium, our model there is that we need to have a common, united mission that we're all
aiming for. And that's improving data access and quality.

And that's something that is of benefit to them because they want access to data for the types of
customers that they serve. For example, working with Epic, they serve hospitals, and they need
better types of data to understand the patients and the populations that they serve. So, they see
value, and I think that's a key with public-private academic partnerships, is having a shared value,
shared mission that you can unite on.
Perhaps how you approach it might be different, but I think that really ties groups together on
these complex issues.

SHEEHAN:
And I'd like to close with sort of a thought that it seems to me like some of these tools and these
partnerships were developed at least in the aftermath of COVID, and certainly with lessons
learned from the pandemic. And I'm wondering, now that these tools sort of exist in the world, is
there the potential to at least, you know, mitigate the next emergency?

LAYDEN:
Yeah, it's hard to, you know, mitigate some of these public health threats that pop up. But I think
what we can do is help us be prepared to identify threats faster, to get information out there
faster. I think one of the challenges we saw during COVID was, and continue to see, is diminished
trust in public health.
And part of that is getting data information in a way that people see the value of it. So, I think
every time there's a lesson learned in how we respond in public health, similar to really any
profession or thing that we do, there's that responsibility to say, how can we do it differently? How
can we do it better?
And some of these challenges that are really complex, how do we bring in different areas of
expertise that can help us with these efforts?

SHEEHAN:
Dr. Zink, any final thoughts?

ZINK:
Yeah, I guess I wanted to add one thing about what is the value to others. I think as a state health
official, I thought a lot about this being a gift to my former self. What has surprised me is the gift
that this has been to so many others.
And as Jen had mentioned, how many people are motivated and excited to be able to solve
health challenges in partnership with us. So, for example, the testing data that we have, the NIH is
using to better model what's happening with RSV. What's happening, where cases are happening

across the country, is helping clinical trials better understand what new therapeutics are needed in
the future.
I also think getting this information back to clinicians. I remember taping up in our bathroom wall
health advisories from our state. And while it's one way to get information out, but it's much easier
if RSV is on the rise or we're seeing something new, it comes in open evidence, or up-to-date, or
some tool that I'm using at the bedside, or Epic, or Cerner, where I'm actually documenting on my
patient chart so that I can understand as a clinician.
And to your question earlier, is this going to help reduce the next public health emergency? Every
emergency starts with one case or one place. And the more that we're able to address issues and
challenges early, the earlier we are from keeping this to becoming a large problem.
So, I, as an individual frontline clinician, when I have tools in front of me that allow me to detect
measles quicker, to educate patients faster, to be able to report it to the state quicker, to know
what's happening faster, I can help be a partner to my public health officials to keeping measles
under control or being able to prevent the next pandemic. So, I think we need to really be thinking
about not just how do we respond, but how do we every single day address health challenges
that are arising? And this data and tool can make a big difference for that.

SHEEHAN:
Dr. Jen Layden and Dr. Anne Zink, thanks both of you so much.

ZINK:
Thank you. Great to see you.

LAYDEN:

Awesome.
Thank you so much.

SHEEHAN:
Dr. Anne Zink, a senior fellow at the Yale Law School of Public Health and former ASTHO
president and chief medical officer for the State of Alaska. Dr. Jen Layden is ASTHO senior
vice president of population health and innovation.

State and territorial health agency leaders
are invited to join ASTHO and the PopHIVE team for virtual engagement sessions to explore the
PopHIVE platform, share feedback on the utility and applicability of the data, and help shape how
it can generate value for public health programs and decision makers.
Learn more and register through the link in the show notes.

The flux within HHS has ongoing
impacts on the process for issuing public health guidance, approving and altering vaccine
recommendations, and more. Congress remains interested in understanding the impact of
reforms occurring within HHS, including in hearings centered around agencies and nominees.

Find ASTHO's Federal Health Policy Update on the changes within HHS, as well as responses from
legislators, at the link in the show notes.

Join ASTHO for the webinar, 'Thriving Under Pressure:
Building Resilient Dialysis Systems and Teams.' Understanding the structures, processes, and
practices that influence patient safety during times of stress is essential to informing priorities
and targeting interventions that strengthen resilience. This one-hour session convenes frontline
clinicians, workforce leaders, and public health partners to examine practical, evidence-informed
strategies that enhance both system and worker resilience in the outpatient dialysis setting.
The link is in the show notes.

This has been Public Health Review Morning Edition. I'm John
Sheehan for the Association of State and Territorial Health Officials.