0:03

The power of data is undeniable and

0:06

unharness, it's nothing but chaos. The

0:08

amount of data was crazy. Can

0:10

I trust it? You will waste

0:12

money held together with duct tape.

0:14

Doom to failure. This season, we're

0:17

solving problems in real time

0:19

to reveal the art of

0:21

the possible. Making data your

0:23

ally, using it to lead

0:25

with confidence and clarity, helping

0:27

communities and people thrive. This

0:29

is data-driven leadership, a show

0:31

by resultant. Public

0:34

health isn't about hospitals, vaccines,

0:36

or disease prevention. It's woven

0:38

into the fabric of our economy,

0:40

our national security, and our daily

0:42

lives. In this episode of data-driven

0:44

leadership, we sit down with Dr.

0:47

Heidi Steineker, who's the senior director

0:49

of our Health and Human Services

0:51

team at resultant. We explore how

0:53

economic trends, social policies, and public

0:55

health outcomes are deeply interconnected. Housing

0:58

remains unaffordable for many and economic

1:00

uncertainties persist. Public health is often

1:02

at the center of these challenges.

1:04

Economic stability influences health outcomes in

1:07

profound ways. When families can't access

1:09

nutritious food or health care or

1:11

live in a safe environment, the

1:13

consequences ripple through communities. These stressors

1:16

don't just affect individual well-being.

1:18

They impact workforce productivity, strained

1:20

health care systems, and even

1:22

pose risks to national security.

1:24

Heidi sheds light on these connections

1:26

through the lens of one health,

1:28

a forward-thinking approach that recognizes the

1:31

link between human, animal, and environmental

1:33

health. The perspective underscores how

1:35

issues like climate change, food

1:37

supply chains, and disease outbreaks

1:40

require cross-sector collaboration and innovative

1:42

policies. We also discussed the evolving

1:44

role of data and shaping policies

1:46

within HHS. From analyzing social determinants

1:48

of health, like housing, transportation,

1:51

education, to designing proactive public

1:53

health interventions, data-driven insights are

1:55

becoming essential tools for decision-makers.

1:57

By leveraging advanced analytical... policy

2:00

makers can move beyond reactive solutions

2:02

to creating long-term sustainable strategies and

2:04

improve public well-being. Join us for

2:06

a thought-provoking discussion on the future

2:09

of health and human services, the

2:11

policy shaping our communities, and how

2:13

data can drive smarter, more effective

2:15

solutions. Dr. Steineker's expertise offers a

2:17

unique look at how leaders can

2:19

navigate these challenges to build a

2:21

healthier, more resilient society. Let's get

2:24

into it. Welcome

2:27

back to Data Driven Leadership. I'm your

2:29

host, Jess Carter. Today we have Dr.

2:31

Heidi Steineker, senior director of our Health

2:33

and Human Services team here at resultant.

2:35

Let's get into it. Heidi, welcome. Thank

2:38

you, Jess. Glad to be here today.

2:40

Yeah, we're really glad to have you.

2:42

I am super excited about our conversation

2:44

because I think there's, I don't know,

2:47

at least 16,000 directions it could take.

2:49

Correct me if I'm wrong in any

2:51

of this, but you've spent your career

2:53

helping state agencies make data-driven decisions. One

2:56

of the questions I might be curious

2:58

about is what are some of the

3:00

biggest challenges that you see in getting

3:02

leaders to trust and use data effectively?

3:04

Can you think of a time when

3:07

you were like, oh, there's this huge

3:09

concept around something that is just not

3:11

based on data and how do I

3:13

help leaders really lean on the data,

3:16

not their assumptions or anecdotal evidence? Yeah,

3:18

no, happy to. So I do want

3:20

to say, like, my crew really hasn't

3:22

been all about states, but I think

3:25

that the common theme, whether I was

3:27

using data for community outreach in working

3:29

with policy and operations with cancer, or

3:31

whether it was working with a large

3:33

health care system and seeing how they

3:36

could be able to do better utilization

3:38

of their even their practice for access.

3:40

or working with the Ministry of Health

3:42

in Germany, on how they were going

3:45

to be able to try to infuse

3:47

a huge population of Syrian refugees into

3:49

their health care system, and then working

3:51

with California State, and then of course

3:54

working with multiple states and governments in

3:56

consulting. How are you using data for

3:58

visibility? so that you can understand where

4:00

you need to be able to put

4:02

your emphasis on what are the root

4:05

causes. And then if you tackle those

4:07

root causes, right, then hopefully you're going

4:09

to be able to leverage data to

4:11

be able to get to where better

4:14

outcomes are going to be. So that's

4:16

the common thread. It's funny. I think

4:18

a lot of people think that careers

4:20

are supposed to be like this straight

4:23

line. It's certainly been more like that.

4:25

Whatever I did ask to speak with,

4:27

like, different women's forums or leadership forums

4:29

or mentoring emerging leaders, I always tell

4:31

them, stop thinking that your career is

4:34

this way. You should be collecting experiences

4:36

along the way. I'm still surprised in

4:38

the rooms I get to be in

4:40

sometimes. And it's all because of some

4:43

random experience that I collected years before

4:45

they got me into where I was

4:47

in that moment. That is so well

4:49

said. That's amazing. To your point, it

4:52

makes sense. Like, we're set up to

4:54

think, I'm going to major in this

4:56

thing in college. And this thing that'll

4:58

take me to this thing that'll take

5:00

me to this, it looks like it's

5:03

going to be really linear and it's

5:05

just super not. I mean, you know,

5:07

I'm looking at my educational career, so

5:09

technically, yeah, I'm an infectious disease doctor.

5:12

Okay. And so technically, epidemiologist, I can

5:14

implement global health policy anywhere. However, I

5:16

love to tell students. My bachelor's degree

5:18

was in English and history. Totally not

5:21

even science related. No, granted, I was

5:23

a cell bio major for three years,

5:25

and then of course, my senior year

5:27

decided, I don't want to be a

5:30

doctor, so my husband's doing something fun

5:32

my senior year, right? You know, but

5:34

again, like that background or that collected

5:37

experience, okay, fine, I had the cell

5:39

bio background for three years and interned

5:41

at the hospital and all of those

5:43

things at the university I was at.

5:46

Yeah. But that senior year of doing

5:48

just history and English and English. policy.

5:50

Like that helped me later on to

5:52

be able to then do a master's

5:55

in public policy and really understand like

5:57

where does policy practice in public health

5:59

setting. Okay, well that's so cool. So

6:02

you also just created. the most effective

6:04

and efficient summary of your career that

6:06

I could have never asked you to

6:08

do. So people probably know understand why.

6:11

I'm like, which direction do we take

6:13

this? And so you and I haven't

6:15

talked about this, I don't think, but

6:17

I've been to like 26-ish countries. I've

6:20

been to like 26-ish countries. I've been

6:22

lots of places not Western Europe. So

6:24

like Jordan and worked with Iraqi refugees

6:27

there and Ukraine, I work with special

6:29

needs. So there's all this stuff that

6:31

is like in your travels I'm so

6:33

in your travels. Is this correct that

6:36

you just spent some time in DC

6:38

with like EU ambassadors, Department of State

6:40

leaders, legislators from both sides? Like, is

6:42

that real? Oh yeah. Oh yeah. Okay.

6:45

Just a casual week for you? Just,

6:47

you know, hanging out, having dinner with

6:49

the, you know, Swiss ambassador in his

6:52

residence. Was insane to me. So like

6:54

in those conversations, are you seeing data

6:56

and analytics? transforming public policy decisions? Is

6:58

that part of what those conversations are?

7:01

100% yes, because you can't make good

7:03

policy decisions or even thinking about strategy.

7:05

I like to look at it as

7:08

strategy. You can't even think of public

7:10

policy or diplomacy, international diplomacy is a

7:12

big chess game, right? And you can't

7:14

know those different moves if you can't

7:17

understand what data you have or visibility.

7:19

I always like to equate data with

7:21

visibility because when I'm trying to ask

7:23

myself even a question of how do

7:26

I solve for how many beds do

7:28

I need to evacuate during a wildfire?

7:30

If I don't have line of sight...

7:33

as to actual what's the acuting normal,

7:35

how many people I have to transfer,

7:37

what I mean, I might know they're

7:39

licensed bed, right? I don't have like

7:42

line of sight as to what is

7:44

the actual ground zero issue going on,

7:46

right? So when I think about it

7:48

from a research perspective, it's mixed methods.

7:51

It's you need quantifiable data, but you

7:53

need to be able to mix in

7:55

some quality. of data of what's going

7:58

on on Ground Zero whenever you're operating

8:00

from a policy or immediate situation that

8:02

you have to be able to sell

8:04

for it. That is amazing. And you've

8:07

done that kind of work for a

8:09

while. Would you say that you spend

8:11

most of your time working on policies

8:13

to plan for events and prevent events

8:16

or to respond to events once they've

8:18

occurred? I would say it's a little

8:20

bit of both. I would say depends

8:23

on the year. let's say January 2020,

8:25

that was a lot of responding to

8:27

an uncertain event, right, that we're happening

8:29

and collecting a variety of different data

8:32

points to be able to determine what

8:34

should be done next, right? At the

8:36

time I was the deputy director and

8:38

the CMS agency oversight director for the

8:41

state of California, which oversees the largest

8:43

health care system in the US. And

8:45

I would have to say that of

8:48

course we were using some a variety

8:50

of different types of data points to

8:52

be able to determine. When most states

8:54

were looking at this in March, we

8:57

had already set up on January 28th,

8:59

a instant command structure. Wow. We're already

9:01

doing the math behind the back of

9:03

the math going, oh, times this potential

9:06

are not times 40 million people in

9:08

our state. Whoo, times this are not

9:10

times this, right? And these are the

9:13

amounts that we have, right? So we

9:15

were contingency planning really, really early on

9:17

and prepping for that. So that would

9:19

be an example of a response. Yeah.

9:22

Some of the works that I'm doing

9:24

right now, which a lot of my

9:26

meetings in DC and working with different

9:29

folks around the club is more now,

9:31

I would say. preparatory, right, of thinking

9:33

of, okay, here's the different scenarios that

9:35

can happen that can affect health and

9:38

population health. I think of health, I

9:40

also think of health as global security,

9:42

national security, because health is absolutely a

9:44

national security or global health security issue.

9:47

So it just depends. It's only here,

9:49

right? What are you, Brooklyn's job? So

9:51

you have talked a bit about sort

9:54

of both this high level strategy and

9:56

on the ground impact with something so

9:58

substantial. How do you get from, here's

10:00

the strategy we agree on in a

10:03

room in DC to, here's the change

10:05

that we're seeing on the ground in

10:07

California, like when you. When you go

10:09

to balance those things, what does that

10:12

look and feel like? I think there's

10:14

a lot of people where that's happening

10:16

behind the curtain, and they're like, what

10:19

is it actually like? I know that's

10:21

really open-ended, but I'm curious what you

10:23

think. So first of all, I would

10:25

say that decisions are iterative. They are

10:28

constantly changing. And it's a point in

10:30

time. of that information, right? And I

10:32

can forecast, you can do predictive analytics,

10:34

you can do different forecasting, you can

10:37

kind of read the tealings by what

10:39

you're seeing and other data points around

10:41

the world, but you have to make

10:44

a decision at the time that you

10:46

have to make a decision. And then

10:48

you iterate and you're constantly iterating. And

10:50

although that frustrates or sometimes confuses mass

10:53

public, right? Because we're like, what's going

10:55

on? How come it's constantly changing? Well,

10:57

because the situation's constantly changing. And I

10:59

think one thing that we can do

11:02

better in health and human services is

11:04

communicating the reason why we're constantly iterating

11:06

and the new information and how that

11:09

has to change in real time and

11:11

we have to adjust. Just like we

11:13

change what we wear every day based

11:15

off of what the weather is. Right?

11:18

We look at the forecast and then

11:20

we plan, right? So it's the same

11:22

concept when we're looking at overall strategies,

11:24

particularly when it comes to infectious disease

11:27

or other types of things in the

11:29

health and human services sphere. That makes

11:31

a lot of sense. Well, and I'm

11:34

going to throw a term out there

11:36

and ask you entities that are so

11:38

familiar with this and people who have

11:40

no idea what I'm talking about. So

11:43

social determinants of health. And I heard

11:45

that there's like, there's kind of a

11:47

new phrase, like it's evolving. So one

11:49

of the things I was going to

11:52

ask you is, hey, if I'm a

11:54

citizen, just walking down the street in

11:56

the United States or in a country,

11:59

if I'm the director of an HHS

12:01

entity in a state or the governor,

12:03

like, could you pitch to me, what

12:05

does that mean, and why should I

12:08

think it's important? Absolutely. So essentially, think

12:10

of it this way. All these factors

12:12

of where you live, what food you

12:15

have access to, what health care access

12:17

you have, what insurance you have, what

12:19

insurance you have. your type of employment,

12:21

all of these things factor in to

12:24

your likelihood of being healthy or unhealthy.

12:26

Your access to water, fresh clean water,

12:28

right? Like air, all of these things

12:30

have an effect on how much you're

12:33

able to stay healthy or access treatment

12:35

to become healthy as well as be

12:37

able to have safety and quality as

12:40

care. People think, oh, well, you have

12:42

access to care. Well, access to care

12:44

doesn't always mean that you're going to

12:46

have safe quality care, right? Okay, part

12:49

of me is like, if I'm the

12:51

governor, I can't change the air. Why

12:53

do I care if I'm a governor?

12:55

Well, because it's affecting your economy, it's

12:58

affecting your population. And so what I

13:00

like to tell people is, yes, it

13:02

would be wonderful if people would think

13:05

of health as far as, yes, it's

13:07

great for us to be healthy, right?

13:09

We all want livelihoods. We all want

13:11

to be able to have families that

13:14

live on for generations. But the reality

13:16

of what a governor or a department

13:18

of state or anything else are looking

13:20

at, people run our economies. our systems

13:23

without healthy people to show up to

13:25

work, you no longer have an economy.

13:27

And so it's really an economical driver

13:30

when you get at these large systems

13:32

or state or country issues. And we

13:34

saw a little by that during the

13:36

pandemic, right? We had things shut down.

13:39

or be really expensive because it hits

13:41

your economy. I know the running joke

13:43

now is the cost of eggs, right?

13:45

Well, if you look at, we've had

13:48

a massive food disruption because we had

13:50

a disease hit our food chain, then

13:52

obviously drives our costs up, but also

13:55

think of all of the different farmers

13:57

and livestock owners who have lost their

13:59

entire life savings because of this. And

14:01

so it's economy. even at the state

14:04

level and even national level. We've lost

14:06

billions of dollars in the last year

14:08

off of disease. It kind of reminds

14:10

me of COVID, like I heard about

14:13

it for several months before I felt

14:15

it, but I was at a grocery

14:17

store last night and you know there's

14:20

a sign up. You can only buy

14:22

one carton a day per customer and

14:24

it's like most of them were gone

14:26

and it was like, wow, this is

14:29

like not a story on the news

14:31

anymore. It's happening in my neighborhood. One

14:33

of my curiosity is, you know, you've

14:36

done a lot of work in this

14:38

intersection of public health economics, national security,

14:40

and you've talked a little bit about

14:42

this already, but what are some of

14:45

the surprising connections or for other people?

14:47

They might think they're surprising between industries

14:49

and population health or economy and the

14:51

cost of food and public health outcomes.

14:54

I mean, this is something that you

14:56

could, I'm sure you could speak on

14:58

as a TED Talk or three. And

15:01

so I'm curious, I'm curious, you know,

15:03

what your real thoughts are about some

15:05

of some of this. Yeah, so I

15:07

mean, to liken it back to like

15:10

2020, right, because people can understand this

15:12

point. They've lived it. So in 2020,

15:14

we had all of our health care

15:16

data that sat here and our public

15:19

health data that sat here, and our

15:21

public health data that sat here, too

15:23

lived in silos and didn't talk, right?

15:26

And so the silver lining of the

15:28

pandemic was that we started to realize,

15:30

oh no, we need to do data

15:32

exchange between our health care system or

15:35

public health system so that we have

15:37

better visibility to be able to be

15:39

able to be able to be able

15:41

to be able to act and mitigate

15:44

and mitigate and mitigate. So that's kind

15:46

of joining all of our data points

15:48

for human health. Right. But at that

15:51

point, we didn't also add in what

15:53

about in. environmental health when it comes

15:55

to different kinds of waste or issues

15:57

that are affecting the animals. We didn't

16:00

also add in animal health or animal

16:02

inspection data because that sits in Department

16:04

of Agriculture or this data sits over

16:06

here in Department of Fish and Wildlife.

16:09

It sits in all these different disparate

16:11

silos in a state government and at

16:13

the federal level, but they're not combined

16:16

to where you can actually run an

16:18

algorithm and a risk heat map. to

16:20

know where you have particular issues and

16:22

then be able to drive mitigation quicker

16:25

and faster. And so times of the

16:27

essence, whenever you're trying to solve for

16:29

infectious disease, because the quicker that you

16:31

can mitigate an outbreak, whether it is

16:34

in grassens that is affecting the types

16:36

of clover that your animals eat, or

16:38

whether it's in a human, or whether

16:41

it's in an animal. The time to

16:43

task is key, because if you can

16:45

mitigate that in intervening, you can cohort,

16:47

you can test, you can treat, so

16:50

that you're not having to kill off

16:52

entire livestock or cooling multiple different, you

16:54

know, poultry. And data gives you that

16:57

access to be able to mitigate quicker.

16:59

There is this persona I imagine very

17:01

much exists in the world who bops

17:03

around and doesn't think deeply about this

17:06

stuff and things like, huh, like there's

17:08

not a lot of eggs today at

17:10

the grocery store and there's like a

17:12

little bit of this entitled assumptiveness of

17:15

clover, grasses, that doesn't impact me, but

17:17

it seems like there's a sensitivity of

17:19

the food chain that maybe society is

17:22

finally sort of coming to understand and

17:24

appreciate beyond just people who spend their

17:26

whole career in ag. Does that makes

17:28

sense? Absolutely. So I mean, first of

17:31

all, like, I am not an ag

17:33

scientist. Sure. But I grew up in

17:35

a small town where we had almond

17:37

orchards and agriculture all around me. But

17:40

I think it's just because we have

17:42

taken it for granted, right? So I

17:44

spent last summer in rural Uganda. And

17:47

rural Uganda, I was on the border

17:49

of the DRC. Rwanda and then penetrable

17:51

forest. So this area is one of

17:53

those, what we call a hot spot

17:56

in the world for infectious disease, which

17:58

is why I was there. I had

18:00

one of the members of the hospital

18:02

come visit me from Uganda this last

18:05

fall and he stayed with me and

18:07

I took him to a grocery store

18:09

and he was just shocked. Like, oh,

18:12

well, what is this supposed to be?

18:14

And I'm like, oh, that's bacon, it's,

18:16

you know, sausage, you know, whatever. Because

18:18

what they're used to is like, like

18:21

you have a market. You have a

18:23

market and you can physically see you

18:25

are grabbing a pig, maybe the head

18:27

pointing off or whatever, but you have

18:30

a big, right? You are not in

18:32

this like grocery store environment where you

18:34

have all these options in the world

18:37

and we're so used to having olives

18:39

and not really thinking about how it

18:41

affects us. Like when kids realize their

18:43

bacon is from a pig and then

18:46

you have like, I don't want to

18:48

get it anyway. It's like that for

18:50

in my feeling it doesn't last very

18:52

long. But it's there's a separation of.

18:55

There's a separation of. what happens when

18:57

my great grandma was out ringing the

18:59

next of her chickens when it was

19:02

time to eat chicken. And so like

19:04

to your point it just seems like

19:06

there's a whole generation that sort of

19:08

normalized that I like that phrase you

19:11

used of like almost the sanitized environment

19:13

where everything came from. I just think

19:15

it's really interesting. Yeah. Yeah. And and

19:17

to your point earlier that you had

19:20

said something about like well like what

19:22

happens behind the cover when you're talking

19:24

these conversations at these levels. And one

19:27

of my favorite quotes that I saw

19:29

on the back of a colleague's laptop

19:31

during the pandemic, his quote on the

19:33

on the laptop said, no one else

19:36

is coming were it. It was that

19:38

moment that, oh, when you peel behind

19:40

the curtain, it is just humans like

19:43

myself and others. trying to make the

19:45

best decision possible, like doing what we

19:47

can, right? There is no sort of

19:49

Oz that is like almighty behind any

19:52

of these curtains. There's a number of

19:54

conversations. conversations from people to people to

19:56

people trying to access data and use

19:58

data in a way that's meaningful. Right.

20:01

Well, okay. So I have a scenario

20:03

for you. So we're where we are

20:05

with this egg problem. You are now

20:08

the queen commissioner of the egg problem.

20:10

You're in charge. We have blessed you.

20:12

What are you thinking about if suddenly

20:14

you're... in the spring of 2025, you're

20:17

in charge of solving this. And it's

20:19

already more than kind of an outbreak.

20:21

Like, what do you do? What do

20:23

we, what levers can be pulled about

20:26

some of these things? Does that make

20:28

sense? Yeah, absolutely. First of all, people

20:30

need to understand that although it would

20:33

be fabulous to be able to work

20:35

state by county by county, right to

20:37

be able to work with their systems,

20:39

create electronic systems, have immediate data pools,

20:42

right? See all that. The reality is

20:44

our food chain is like a massive

20:46

freeway system between all of our states.

20:48

So a sick cow in California might

20:51

have come originally from through Texas, but

20:53

from Iowa. And so if you're trying

20:55

to hunt down the chain of where

20:58

this cow has been and possibly impacted,

21:00

right now they're using smart sheets, Excel.

21:02

fax, email, phone call, you name it,

21:04

fillable PDFs. And there are tech tools

21:07

that we can use now, right, that

21:09

can help with this process. It won't

21:11

fix the egg system, you know, the

21:13

egg problem all away, right, and in

21:16

our food, but it's a start. Cons

21:18

start with like data collection. How are

21:20

we even collecting data? They might still

21:23

be using, you know, the old-fashioned, you

21:25

know, like the three. three pieces like

21:27

carbon copy and they write you know

21:29

their ag inspectors are using you know

21:32

cursive right is that real like that's

21:34

still happening somewhere right still happening today

21:36

wow but you can use technology we

21:38

found what can you find take that

21:41

carbon copy scan it in and you

21:43

can use AI to be able to

21:45

read it and put it into a

21:48

database. That's one way. Other states are

21:50

now looking at, okay, state mobile phones,

21:52

right? State mobile phones can have an

21:54

app where you're doing data collection, talk

21:57

to text, take pictures, the whole thing,

21:59

and it can hit your data platform

22:01

immediately for the collection point, right? From

22:04

there, you need to run an analytics

22:06

layer and then be able to actually

22:08

have. on their analytics, not just to

22:10

report out or up. Like I always

22:13

tell people, they have plenty of data

22:15

at every state. It's just that it's

22:17

either latent data, it's not current, or

22:19

it's data that they're using for reporting

22:22

for transparency, which is great. But that's

22:24

not an action item. So I mean,

22:26

for example, even when I was working

22:29

in California, so during COVID, like, skilled

22:31

nursing facilities are one of the most

22:33

deadliest locations for any infectious disease. a

22:35

combination of the acuity of the patient

22:38

or the residents as well as the

22:40

types of locations that they are. And

22:42

they're really, really confined, not really great

22:44

air exchanges. And so one of the

22:47

things that we decided was, okay, well,

22:49

we can't just wait for an outbreak

22:51

to happen because then it spreads like

22:54

a wildfire in those facilities. So instead.

22:56

Can we do something proactive and predictive?

22:58

Can we pull up the last three

23:00

years of any time they had an

23:03

outbreak from just flu or mursa or

23:05

any other disease? Can we pull up

23:07

compliance data from their infectious disease regulatory

23:09

surveys? Can we pull up different types

23:12

of community level types of spread, right?

23:14

And create algorithms, look at their staffing

23:16

levels. And we were able to predict

23:19

with 80 to 85% what was going

23:21

to be the next facility that had

23:23

an outbreak. Wow. And when you can

23:25

do that and you have 1,500 facilities

23:28

but only 400 surveyors in a very

23:30

large state, then you're able to then

23:32

say, okay, here's our top 50 hit

23:34

list. This is what you're working this

23:37

week. These are the highest risk priorities.

23:39

So if you start to look at

23:41

your work by where the highest... risk

23:44

locations to use your people and mitigate

23:46

quickly. Yeah. Little leader, we have the

23:48

lowest case fatality ratio because of this,

23:50

even despite having the largest amount of

23:53

facilities. The key was you can't 3D

23:55

print people, right? You can get creative

23:57

with space, you can get creative with

23:59

supplies, but you can't get creative with

24:02

staffing. And so it's basically data can

24:04

be used as a tool to run

24:06

analytics. It can be. targeted so that

24:09

you can actually use your people in

24:11

a more prescriptive way to have better

24:13

outcomes. One of the things I'm going

24:15

to ask you about was One Health.

24:18

How close are we to that conversation

24:20

right now? I mean, can you help

24:22

me understand what is One Health? I

24:25

mean, you've done, you've worked in global

24:27

health security initiatives. What is it and

24:29

how does it better predict or prevent

24:31

outbreaks? Yes, so one health, which is

24:34

essentially just you combine environmental health data,

24:36

animal health data, and human health data,

24:38

and that is one health. It's very

24:40

simple when people ask me, well, what

24:43

is this, right? It was really something

24:45

that came out from the global community,

24:47

and particularly you can read a lot

24:50

about it on the World Health Organization's

24:52

website, and they focus a lot on

24:54

this, because the idea is if you

24:56

don't have... clean water, like I don't

24:59

want anyone to think that, oh, we

25:01

have always clean water, look at Flint,

25:03

Michigan, right? A lot of old lead

25:05

pipes in our system. So don't be

25:08

thinking that this is only relegated to

25:10

low-income countries, right? Any of this can

25:12

happen here. So you need, obviously, your

25:15

environmental health data, you need your animal

25:17

data, which we've now learned, right? We

25:19

need our human health data and that

25:21

trifecta is you can create a great

25:24

comprehensive disease surveillance system at a state

25:26

level to be able to then act

25:28

on any one of those three things

25:30

that might hit or flag or some

25:33

of them combine each other right one

25:35

affects one and one affects the other

25:37

right and then it becomes this whole

25:40

spiral. So if you can have access

25:42

to that kind of of data at

25:44

a state level? You can do a

25:46

lot, whatever is your particular problem in

25:49

your particular state, because it will be

25:51

different for each state. Wow. So does

25:53

it have to be one giant like

25:55

enterprise data warehouse? Or is it like,

25:58

no, this is about the interoperability of

26:00

data between systems and agencies? Obviously it

26:02

starts with data collection, right? The speed

26:05

time to task for data collection. Yeah.

26:07

Interropability, data governance, right? Data sharing agreements

26:09

and having them be able to understand

26:11

the different rules that they want to

26:14

create administratively internally for who has access

26:16

to act on each data. Is this

26:18

like a dream in your head? Or

26:20

are there states that are like, no,

26:23

they're on the roadmap to having this

26:25

or they already they have one health?

26:27

Where is that at a maturity scale

26:30

right now? We do have some states

26:32

in the US that are 100% focused

26:34

on this and they might. call it

26:36

different things and not knowing that they're

26:39

doing one health. And then there are

26:41

other states that actually have an office

26:43

of one health in our HHS agency.

26:45

You know, some are calling it, you

26:48

know, data interoperability, right? Every state's different,

26:50

right, on how, or a comprehensive disease

26:52

surveillance system. But no, there are several

26:55

states that are trying to move to

26:57

this initiative because they're seeing, especially with

26:59

effects of extreme weather events happening, more

27:01

natural disasters, and every single time, by

27:04

the way that you. of a natural

27:06

disaster, your environmental, animal, and human health

27:08

are all interrupted. And so it just

27:11

causes for concern for another outbreak. Okay,

27:13

I could talk to you forever. This

27:15

is so fascinating in the fact that

27:17

this gets to be the stuff you

27:20

think about today. I mean, I just

27:22

am, I'm so grateful for your knowledge

27:24

and abilities and the way that you

27:26

apply them. For people who aren't familiar

27:29

with any of this, that one health

27:31

exercise, it's just really simple, but you

27:33

understand how complex it is to actually

27:36

operate. But it makes sense. Like if

27:38

you think about your life, your community,

27:40

your society, it all tracks. In my

27:42

head, it sounds so impressive that we

27:45

could really look at health in this

27:47

way, that I get a little bit

27:49

overwhelmed by how hard it would be

27:51

to implement. Like if I was a

27:54

state and I was like, you know,

27:56

we haven't started this initiative and we

27:58

really ought to start thinking about moving

28:01

toward one health, like what advice would

28:03

you give? Yeah, I would say it

28:05

starts with relationships, relationship building between the

28:07

departments and the agencies to talk about

28:10

where are there overlaps between their different

28:12

authority points of where there could be

28:14

some synergy. And the example I'll give

28:16

is when I talked about the skilled

28:19

nursing, right? We discovered that, hmm, a

28:21

lot of the skilled nursing worker staff

28:23

are in their child bearing ages. And

28:26

we started to see this trend of,

28:28

huh, we looked at the data from

28:30

the childcare outbreaks. And if there was

28:32

a child care outbreak within a mile

28:35

radius of a skilled nursing facility, chances

28:37

were there would be an outbreak at

28:39

that facility. And so then we started

28:41

to build those relationships with other departments

28:44

and say, hey, this is why we

28:46

want to be able to have access

28:48

to your data, to be able to

28:51

put it into this algorithm, and it

28:53

will also help you because then we

28:55

can be able to share the outcomes

28:57

and you can have certain levels of

29:00

access so that then you can also

29:02

prepare for staffing levels in your child

29:04

care centers. Like having that conversation and

29:06

us seeing that there was mutual benefit,

29:09

of course with security in mind, cyber

29:11

security in mind, and data governance and

29:13

all of those things. Sure. By us

29:16

sharing that data, not only were we

29:18

able to save more elderly lives in

29:20

the skilled nursing facilities, but we were

29:22

able to reduce the amount of outbreaks

29:25

in those care centers and make sure

29:27

that they stayed open by them preparing

29:29

for staffing levels for when there were

29:32

outbreaks within their same mile radius. Because

29:34

when you look at it, like we

29:36

live in communities, we don't live in

29:38

vacuues, we go to stores, we go

29:41

to to schools, we have kids or

29:43

parents that we care for, right? And

29:45

we all live in these communities. And

29:47

so when something happens in one part

29:50

of our community, there most likely will

29:52

be a ripple effect in the other

29:54

part of our community. And so our

29:57

data should also not sit in these

29:59

silos, but. He used in protective of

30:01

course cyber secure ways to be able

30:03

to have some sort of shared data

30:06

governance to be able to help in

30:08

all of these areas. Do you have

30:10

any quick thoughts on like pitfalls to

30:12

avoid as you pursue a one health

30:15

survey? Like what have you seen where

30:17

you're like, oh my gosh, the value

30:19

would have been so substantive, but you

30:22

did this. Yes, I see this all

30:24

the time, whether it's for approaching 1L.

30:26

I also see this when people or

30:28

different states are trying to approach their

30:31

homelessness situation. All of these things take

30:33

multiple different agencies to come together, and

30:35

what I have seen is death by

30:37

committee. So whether there is, they set

30:40

up an interagency agreement for like, I've

30:42

seen interagency homelessness councils that just go

30:44

nowhere, they have their heads come and

30:47

talk once a month and then they

30:49

don't have progress. Same thing with one

30:51

health, you have agencies that do this

30:53

kind of office but no authority kind

30:56

of thing. What I tell governments is

30:58

you need to have an incident command

31:00

structure, just like you have for your

31:02

anything that you're wanting to do a

31:05

change management or performance improvement or have

31:07

side initiatives. Because most of our state

31:09

governments and federal governments, we are operational

31:12

managers. We have day jobs. You don't

31:14

have the time and the day to

31:16

really dedicate toward a huge massive change

31:18

implementation. And so you need to be

31:21

able to set up. a what I

31:23

call like a little organizational change seal

31:25

team on the side that is an

31:27

offshoot of that director to be able

31:30

to run change initiatives at the same

31:32

time and have a whole structure for

31:34

how you do that. And there's ways

31:37

to do that, whether it's for like

31:39

I said, the homelessness use. case or

31:41

one house or any just any

31:43

just any internal

31:46

change implementing different types

31:48

of technology into

31:50

their systems. All of

31:53

that, you just you

31:55

just need to be

31:57

able to have

31:59

that structure structure first. That is

32:02

is so I I

32:04

love that it

32:06

comes back to to these

32:08

really really complex, big,

32:11

hard human problems.

32:13

Start with how do

32:15

you help people

32:18

change their behavior? I

32:20

think that we are penciling you

32:22

in another another conversation because I still need

32:24

to interrogate you about all of your leadership

32:26

skills, which are so impressive to me

32:28

and a key part of data driven leadership.

32:30

me and a key part of thank you for being

32:32

on the episode today. So, Heidi, thank you. Thank

32:34

you guys for listening. I'm your host,

32:36

Jess Carter. you. Don't forget to follow the on

32:38

leadership wherever you get your podcasts and guys

32:40

review letting us know how these data

32:43

topics are transforming your business. We can't wait

32:45

for you to join us on the

32:47

next episode. your podcasts.