CO APCD Research Showcase - September 2025

All right,

I'm seeing folks starting to filter in.

Hello everyone. Welcome to the co-apd

research showcase. I'm going to give it

just a couple more seconds here and then

uh we can get going.

Okay.

Uh we're on a kind of a tight schedule

today, so uh I want to be sure to uh go

ahead and get started. Um

so hello. Uh, welcome to today's

Colorado all peer claims database co-apd

research showcase. Uh, my name is Sarah.

I am a digital communication specialist

here at Civic. Uh, and I'm going to just

go ahead and get us going. Um, thank you

all for being here today to talk about

uh, the cool work being done with the

COAPCD.

So,

whoops.

Jumping ahead. Here we go. All right,

quick look at our agenda uh real quick,

but it's a pretty tight agenda today

like I mentioned. Uh so I want to get

going quickly. Um we will give you first

a quick overview of Civic and the COAPCD

and then we're going to turn it over to

Lauren and Emory to take us through the

new uh dental health dashboard from

Civic. Uh then Jack is going to talk to

us about his research into healthcare in

rural Colorado and opportunities that

they've identified for expansion. Uh

then Greg is going to take us through uh

claims identified opportunities for

healthcare cost savings. And finally,

Andrew is going to wrap us up talking

about his research into the Colorado

option and impact on behavioral health

utilization and costs. So, uh just a

real quick rundown of some housekeeping

materials here. Um all lines are muted.

If you have a question that comes up

during someone's presentation, we will

have time for a very brief Q&A after

each presentation. Please go ahead and

just write your question into the chat

box and then we will take a look at

those after each presentation. Um, if we

are not able to get to your question, I

will be posting uh the presenters emails

into the chat uh at the end of their

presentations. Please feel free to go

ahead and take that email and shoot them

uh your question and they will be happy

to follow up with you. Uh this webinar

is being recorded and uh a link to that

recording should you like to revisit it

will be posted on our website on the

event resources page

at CI at civic.org. Uh real quick uh who

we are here at Civic um we are an

independent nonprofit. Our mission is to

equip partners and communities in

Colorado and across the nation with the

resources, services, and unbiased data

needed to improve health and health

care. The way we do that is through

working with our change agents. Our

change agents are people who are working

to identify ways to lower costs, improve

care and health here in Colorado. Uh we

work with a wide variety of

uh people throughout the healthcare

landscape. everyone from government and

legisl legislators to health plans,

hospitals, employers, and as we're going

to focus on especially today, uh

researchers and the way that we serve

them and work towards that mission is

primarily through our work as the

administrator of the Colorado allpayer

claims database. Uh we through the color

all pay claims database release both

public COAPC data that is available on

our website through interactive

dashboards and downloadable data sets

that explore healthcare utilization qu

cost quality equity and more uh in

Colorado. We also do licensed non-public

releases of data for those who have

questions or working on a project that

is aiming to improve health or health

care in uh their communities or in our

state. We also offer analytic services,

research and evaluation services and do

a lot of community engagement work. Uh

real quick look at what's in the COAPCD.

We now have over 1.3 billion claims uh

going back to 2013 all the way up

through uh it says 2024, it's actually

2025. We do 2025 claims data. That

accounts for over that accounts for 33

commercial payers and includes uh

Medicaid and Medicare, both FFS and

Advantage.

So uh all told that represents over 70%

of insured lives here in Colorado and

accounts for over 5.7 million lives in

the state. What we don't have in the

COPCD, we don't collect federal program

data. Uh so VA, Triricare or Indian

Health Services. Obviously we are not

able to collect uh claims from uninsured

individuals. And finally, um, because

the, uh, it's optional for Orisa based

self-insured employers to submit data to

the COAPCD. We do collect some, uh, data

from some Orisa based, uh, self-insured

employers, but not all. And with that,

uh, let's get into the presentation and

get it over to who you're really here to

see. Uh, so I'm going to go ahead and

turn it on over, uh, to Emry and Lauren

who are going to talk to us about the

dental health dashboard.

Thank you so much, Sarah. Uh, my name is

Lauren Harvey. I am the director of

government relations for the Colorado

Dental Association. Um, and I will be

happy to do this presentation with Emry,

who will talk through a lot of the

actual data. Um, but we're very, uh,

excited to talk through this um, a

commercial dental health analysis that

we, um, partnered with Civic on this

year, and we are thrilled with um, the

information that we now have publicly

available. Um, you can go to the next

slide.

Um so the purpose of this uh project

that we undertook so obviously it goes

without saying that oral health is a

vital v vital part of overall health and

well-being. It's something that we talk

about all the time. Um if you have poor

oral health, chances are you also will

have um other issues with your overall

health that are going to impact you on

your day-to-day life. Um so it's really

important to understand how we can

increase access to dental care and

especially preventative dental care. Um

so we partnered with Civic to introduce

an interactive dashboard um which will

provide uh a clear picture of dental

care in Colorado. So we um asked them to

look at commercial claims across

Colorado for these three years and have

them now publicly available in this

dashboard. Um, we're really thrilled

with this because we do believe this

promotes a greater public understanding

of utilization trends. Um, and it's

also, you know, whether you're a

consumer, uh, a dental provider, other

provider, a policy maker, or an

advocate, uh, this data can really help

drive conversations, best practices, and

also policy changes that may be needed

to increase access to care. Um, and this

kind of data really hasn't been

available in the past. we've sometimes

relied on self-reported data from

providers or trying to get um

information from insurers. So, this is

really a great tool that we now have

publicly available.

And on the next slide,

um so just wanted to share some reasons

why we are hopeful this will be really

impactful for Coloradoatans and some use

case examples. Um you know, as you can

see here, there may be an opportunity to

identify if there are disparities. So if

we see, you know, lower utilization in

um rural parts of the state for certain

codes, that may tell us that we should

be doing some investigation and research

into what may be happening and um if

there's a need to identify um barrier

access and barriers to care. We also are

hopeful that this can help us inform um

our public programs. We know that having

some um benchmark data for our Medicaid

program is especially helpful. um

Medicaid often benchmarks to Medicare

which is not available for dental. So

this is even more important in this um

uh area of medicine of dental to have an

opportunity to identify what actual

costs and actual payments are so that we

can um incentivize the public program to

be sufficient and adequate and allow

more Medicaid patients um to receive

dental care as well through having more

providers involved. And we just really

are interested in having that greater

transparency um into uh dental payer

rates and um utilization so that we can

have greater transparency around the

value of dental plans and really um

bring transparency to any health equity

considerations reflected in current

reimbursement models um and looking at

you know subsequent reforms that can

help improve health outcomes for

sometimes overlooked populations.

Hey everyone. Um, my name is Emry and I

am a healthcare data analyst here at

Civic. Um, and I am excited to share our

new dashboard with all of you. But

before we hop on to that, um, just want

to briefly touch on our methodology

here. Um, this analysis includes

commercial dental claims data from the

Colorado allpayers claims database for

calendar years 2022, 23, and 24. Um, it

explores trends in utilization and cost

using our current dental terminology

codes. Um, and it also provides

breakdowns for various cost measures

listed here. I went ahead and I dropped

the link in the chat for those of you

who would like to follow along. Um, I

encourage you to navigate there as I

also try to gracefully share my screen.

All righty, we should be able to see my

screen now. Um,

before we uh drop in, I just want to

share that when navigating to dashboards

through the civic website. You can do so

by going to this get data button. We can

then navigate to public data and

featured focus areas uh in order to

explore all of the dashboards that Civic

has available including the dental

health analysis which we will be

exploring today. Within the website um

there's a ton of other really useful

information about our dashboards um

including our specific methodology and

summary of findings which I encourage

you to dig into um after this

presentation.

Scrolling down, we will then find our

awesome new dashboard um which hosts

three different pages. Um first, we're

going to dive into our cost tab.

We have two specific views on this tab

which we're able to filter um using our

filter bar above here. We have our years

which include 2022, 23, and 24.

Geography options such as statewide,

rural, and urban. And then our specific

cost measures including the allowed

amount, charged amount, health plan

only, and patient only. If you are

curious about the definitions for those,

you can hover over our information

button and it'll provide you more more

details on that.

Orienting ourselves with the dashboard

itself, there are two primary views on

this page. Um the first view includes

CDT codes um with the average payments

and then percentile payments where the

25th percentile payment represents um

payments that 25% are below this value.

Again, if you want more information on

these on these definitions, you can

hover over the information button and

it'll provide you with information for

that. One of my favorite parts about

this table is that it's interactive. And

so, if we select a CDT code on this

table, it'll then populate the visual

below. And so, now we're able to see the

variation in allowed amount by

commercial payer. So we can see the

minimum, median, and maximum payment by

commercial payer.

But my actually my favorite part is that

if we have a CDT code that we know or a

group of CDT codes, we can type it into

the bar here. So, for example, say we

wanted to look into imaging services. We

can type that in here and it'll filter

to all of the imaging um CDT codes

available and we're able to easily

compare the costs making it even better.

We can sort by the highest to lowest

easily making it um easily making it so

that we can compare the values.

Moving along,

the demographics tab of course is public

professional public health professionals

we know is a great place to go to better

understand disparities that exist.

Before looking into this dashboard, I

just want to highlight there are two

different sets of filters we want to be

aware of for this page. Um the first set

um has the year and geography like we

already navigated through on the first

tab. It also has the demographics

options which include age group, race,

ethnicity and sex as well as our measure

groups.

Co for cost measures we include the

total cost as well as cost per person

per year as well. And then for

utilization we include total utilization

which represents the total number of

services as well as utilization per

10,00 people.

Now, not forgetting that second filter,

um you can either compare cost measures

to the utilization or it probably makes

sense to have it more concise and select

another utilization measure to to

compare. In order to easily compare um

our our demographic groups above to the

trend by hovering over, it'll bring a

specific demographic group of interest

into view.

Finally, we have our top CDT code chart.

We get a lot of questions about this

chart. So, again, I really want to

encourage you to use this information

button for additional information. Um,

it has great definitions of what exists

here as well as friendly examples of how

to interpret the data because we all

need that sometimes. Um so for example

here we see for 0 to 17 year olds um

periodic oral evaluations make up 20% of

build services.

Finally last but not least we have our

trends tab. This tab is going to

automatically populate with all years of

our our data and all all different

geography options. So with that, I just

want to highlight that the um text bands

at the top here are all going to

represent statewide values with all

years included.

However, if we want to look at a

specific data year, we can select one of

our analysis years, so 2022 through 24,

or we can select a specific geography of

interest, and it will filter to those

specific values. Um, finally we have all

of our different utilization and cost

measures here um as options for filters.

The last thing I want to touch about on

about this uh dashboard is that we have

the op the option to download any of

these images so you can throw it into a

presentation or send it quickly to a

legislator. Love to see it. Um, again, I

know this is a really quick run through

of the dashboard. So, if you have any

questions, you're always welcome to send

them my way or if you have questions

now, you can drop them in the chat.

Thanks guys.

>> Awesome. Thank you so much, Emory. Uh,

I'm going to go ahead and take back

the screen here. We'll open it up. Um,

looks like you've got a question from

Lucia in the chat while I go ahead and

deal with this.

>> Yeah, thank you Lucia. It says, um, how

can the information available in the

dental health dashboard impact access to

care? Um, you know, I would just know I

think having the data in general will

help us identify first and foremost

where there may be um, you know, lower

utilization levels and that might tell

us is there a reason that there's less

utilization of a certain code in rural

areas versus urban. And I think that

opens the door to us um doing some

further digging and guides us in

investigations of where we may need to

um look are there you know certain

providers who aren't available in that

area and how can we increase access to

those provider types um in dental. So I

think that's you know one way where it

really can just having the information

available lets us identify where there

may be a need and begin to explore why

there is a need and what the barriers

may be in a certain um population or

part of you know the community of

Colorado.

>> Awesome.

Just give it a couple seconds more here

if there's any further questions. I'm

putting Lauren and Emry's emails in the

chat. So, please feel please feel free

to follow up with them. Uh, and ask any

questions that may come to mind uh may

come to mind later. Um,

not seeing any further, I think we can

go ahead and move along here. Uh, so

we'll turn it over to uh to Jack.

>> Hi, this is Jack Westfall. Um uh really

happy to be here, honored to be here and

presenting these data. Um you can go to

the next slide

or do I have control of this?

>> It's me. Uh

>> okay. So this is a presentation that I'm

doing for another organization of the

North American Primary Care Research

Group. This data was selected as

distinguished paper. I put this together

then I traveled for three weeks. Just

got back yesterday and didn't have time

to adjust it. So you get to see the

whole thing, but I'm not going to go

through the whole thing. It's going to

be a little quicker. What we're trying

to look at is using an exemplar zip code

to look at the health care services

obtained by people within a geographic

location compared to the care that they

receive in other parts of the state to

see if we can maximize local care

delivery. We used 80759 because that's

my hometown. It's the place where I work

clinically at the Yuma Hospital and

clinic in Yuma, Colorado. Very excited

to be able to to share these data with

you. Next,

uh next you can go past the land

acknowledgement and you know the map of

rural Colorado and critical access

hospitals. You can go to the next one.

So this is what we're trying to look at.

>> So all the health care services

delivered in 80759 is that middle

circle. So these are services that are

specifically located within this zip

code, but then people who live in that

zip code may access services in other

parts of the state. That's the big

circle. So that tells you all the

services necessary in a given year for

people living in a geographic area. And

then the little circle is health care

services delivered within 80759

to people who aren't residents of 80759.

They come in from a neighboring

community or a neighboring county where

they're visiting. This really will help

us identify the care that a community

needs and help a hospital or a health

care system identify what of those

services they can most likely deliver

locally versus what needs to be done

elsewhere. Next,

this is the context. You can go to the

next one. I can. So, we wanted to

describe the breadth and volume of

health care services and we sought to

answer the three following questions.

What services were accessed by patients

living in one community? Where are those

services obtained? And who provides

clinical services to those patients?

Number three, we're not quite there yet.

We're still cleaning the data to

identify the level of uh provider who's

delivering the services, but we did get

sort of the what and the where next.

So this is Yuma. This is the zip code

80759 out on Highway 34 and Highway 59

for those of you who don't know where it

is. Next,

that's a little picture of Yuma and the

grain elevators. Next,

we used the allpayer claims data for

five years

using ICD10, CPT, ENM codes and the same

zip code by the by patient and uh

delivery site. Residents of Colorado zip

code, there's about 4,600 people that

live in that zip code. And then again,

the service is delivered there and the

service is obtained elsewhere. Next,

Oh, couple more pictures of Yuma next.

Okay, so there were about 5,294

residents of 80759 received care over a

5-year period. There is some movement in

and out with a population, a stable

population of about 4,600. There are

about 600 more people that come in and

out during the time period. 3,300 of

those people received some care, at

least a little bit of care in 80759.

Uh, but 1,900 received no care in 80759,

but got some care outside of 80759. So,

there is a large population who never

get medical care within their own zip

code.

Now, one caveat to this is the radiology

that we do at the Yuma Hospital is um

read by a black uh Nighthawk service

elsewhere. And so while the radiology is

done in that zip code, it is read in

another zip code. So that's where the

most of the billing comes from. And so

it uh the radiology

data are a little bit sketch. So next,

so here's what we have. Here's a little

heat plot of what kind of services they

get. You can see office visits are a big

chunk. Radiologic exams, psychological

and psychiatric services,

hospitalization, and then you get down

to the smaller services, nursing homes,

cataract surgery, um some of these you

can't even read, echo cardiography,

dialysis. Um so this is sort of the

breadth of services obtained by people

living in 80759.

Next.

Next.

So each year there are about 18,000

services um for patients living in zip

code 80759. 6,500 office visits, 1100

hospitalizations, 1,500 radiologic

exams, 700 ER visits, etc., etc. You can

see as we get smaller and smaller about

40 deliveries um done for people who are

living in 80759. Next,

however, you'll see in that first um

well, you'll see in the next heat map,

uh let's just go to that one.

So here we have these broken out by

where they got the services. So office

visits about half of the office visits

were obtained in 80759.

But as I said radiologic exams while we

do a lot of radiologic exams

in 80759

they're read elsewhere and so the

billing data comes from a different zip

code. hospitalizations.

Um you can see a number of people are

hospitalized within 80759, but it's a

small portion of people hospitalized.

Next,

ER visits. The majority of people who go

to the ER who live in 80759 get that ER

care locally. That's what we would

expect. They live there. They're in an

emergency. Where are they going to get

care? They get it locally. Uh next

physical or uh physical therapy. Um I we

do most of the physical therapy locally.

Uh we have very good PTO department. So

most of the people getting physical

therapy get it there locally. Next

dialysis. Guess what? Yuma 80759 does

not have a dialysis unit. So we don't do

any dialysis. None of the dialysis done

for people living in 80759 is done

locally, nor are the deliveries. The

next smaller circle down there in the

lower right, we don't do any deliveries

locally. So, you can see there's a broad

array of variation in the services that

are delivered locally versus services

that are done elsewhere. Next,

oh, we talked about this. Next. So, this

got us thinking, well, what are the

services that we're not ever going to

do? And what are the services that we

already do a little of that we might be

able to capture more of the market

share? So, we're never going to do open

heart surgery, right? Human's not going

to do that. I'm not going to do that.

So, that's probably not a place to

expand services. But in the medicine

specialties, that purple um the purple

piece of pie with the dark blue in the

middle, medicine specialties, the vast

majority of medicine specialty visits

are done outside the zip code, but some

are done locally. Why not try to make

that dark blue piece of pie a little bit

bigger? Same with orthopedics, the

purple piece of pie, the dark purple. We

do some orthopedics. We do hips and

knees and and shoulder. We do a lot of

surgery, but we could potentially

capture more of that. We already have

the facilities set up for that. Same

with general surgery. We have facilities

to do general surgery, but we're not

capturing a big piece of that market.

So, this is going to help our hospital

administrator and our hospital team

think about what are the services that

none of these are done.

at at the Yuma Hospital. And what do we

already have? What would we have to

build from scratch? What do we just need

to grow next?

This just says what I just said. You can

go on.

And then I have a series of slides and I

think I'm about out of time so I won't

go through these too much. This is again

the all all the care provided within

80759. This is all the stuff we do. You

won't find obstetrics and deliveries on

here. This is care delivered in 80759.

And then we were wondering about our

market in the surrounding communities.

So how much of the care do we that's

provided in 80759 is for the surrounding

communities. So next slide. This is uh

all other zip codes. So the dark is

people within our zip code, but we

provide a substantial amount of care to

people who live in other zip codes. Our

catchment area is much bigger than

80759. Next, here's the next town over,

Otis, where I was born, 80743.

You can see we provide a substantial

amount of care there. What this table

does, what this figure does not show is

what proportion of care to people who

live in Otis

is delivered in 80759. And that will be

our next piece to say for the people who

live in 80743,

how much of their care is delivered in

80759?

Could we capture more of that? It's 13

miles away. Could we capture more of

that? Next, here's Akran, another small

town in eastern Colorado. These are the

top five catchment areas for zip codes.

Next, Ray is a small town 27 miles to

the east, but they have a very large

health care system. They have a surgeon

and an orthopedist and a a a very stable

primary care service. So, we don't get a

lot of care from 80758. A little bit,

but not too much. Next.

So, here's the sort of uh crux of that.

Are there ways to expand health care

services within 80759

in areas that were already delivering

those services? And how might we capture

more of the people who live in those

surrounding zip codes into our facility

so they don't have to travel to the

Denver, the Front Range area.

specifically thinking about primary

care, specialty care, orthopedic

surgery, general surgery. This has led

us, these analyses have led us to to uh

hire a new opthalmologist to do

cataracts because we weren't capturing

any cataracts. Um, but we're now able to

uh uh do cataract surgery in Yuma and

that's helping us deliver those services

locally.

Next, I think that's it.

Next.

Med next. Thank you very much.

And I'm happy to take questions or I'm

happy to have my email in the chat and

would love to talk with anybody. Our

goal is to do this as a uh as a pilot in

80759, a place where because I live

there. I I have lived there. I've I work

there. We can test the fidelity and the

veracity of these numbers. Is this

really what's going on? Um because I

work there. Um and then we're hoping to

be able to do this in dozens of other

zip codes to help healthc care

facilities identify places for growth

and places for expansion. Thank you.

>> Fantastic. Thank you so much, Jack, uh

for that awesome presentation. Um, I

will go ahead and just give it a couple

of seconds here, uh, if anybody has any

questions or anything that's springing

to mind. I will also put his, uh, email

here in the chat in just a second, so

you can follow up with him if uh, you

wake up in the middle of the night with

any thoughts or questions or anything

like that. Um, it's really interesting.

So, thank you so much for sharing that.

>> First off, if you wake up in the middle

of the night, I'm so sorry.

I don't want to be that guy.

Sometimes I wake up in the middle of the

night just thinking about questions

about healthcare.

>> The all payer I have to say thank you to

the APCD for making these data

available. This was this has been a lot

of work a lot of fun but is meaningful

impactful for rural communities. Um and

I do wake up in the middle of the night

thinking about these analyses. So

thanks. Yeah. So, very interesting. Um,

and I can see everyone in the chat

agreeing. Seeing no questions at this

time, um, I think we will go ahead and

press on. But if you have a question,

uh, that does come up, please drop it in

the chat, um, and Jack can answer or

where are we going? Uh, or we can, uh,

follow up. How did we go back here? I

don't know.

Okay,

>> I'm so sorry. I had some extra slides in

there.

>> I apologize. They were my

>> No, that's that's a that's all user

error. My apologies. Sometime I don't

know. It's like you get on a

presentation, you forget how computer

works. Um all right, we'll go ahead and

bring it over to Greg now with no

further ado. Uh so go ahead, Greg, and

take it away.

>> Hey, thank thank you Sarah. My name is

Greg Smith. I'm with Health Price

Partners, which is a Colorado nonprofit.

We were formed with the idea of is there

a way to improve the functions of

markets in healthcare,

with the idea or the hope that that

would reduce cost and the further hope

that if cost is reduced that improves

access. So, we're trying to benefit the

state in that way. My background is

consumer products. Um, other members of

our team are former healthcare

executives in both the for-profit and

nonprofit provider area. So, um, that's

what that's what we are and what we're

trying to do. Next or first slide, next

slide please.

Okay, before we really get started,

let's let's look at the landscape of

markets in health care and do a little

bit of orientation. The Affordable Care

Act of 2010 had a provision in it that

required health care prices there. The

government could require health care

prices to be disclosed and in 2019

uh regulations came out requiring those

disclosures and there have been several

sets of regulations since continuing up

through this year. So this is an active

area where the government is

increasingly requiring disclosure. The

idea here behind these disclosures is

that more data will facilitate markets

and that will help consumers. But as the

young woman on the left of this picture

shows uh there are some obstacles. The

data is uh somewhat incomplete

and the prices are not binding. And a a

major issue which we'll touch on later

in the presentation is right now health

insurance plans which is how most people

probably get health insurance don't

offer a benefit if you price shop. And

so the question on the table is can we

take this landscape

and try to improve things so that

consumers can compare prices and save

money. Next slide.

What we're going to do in our

presentation here is we're going to talk

a little bit about free markets in

healthcare in general terms, try to

reference some data in doing that, and

then we'll talk about ways to fix the

system. But we think it's important to

talk for a minute about markets and

health care because many people in the

in the in the United States feel that we

do have a market-based system. And we

think the answer to that is in some ways

we sort of do, but in significant other

ways not really. And why why is that?

Let's let's dig there a little bit.

The major issues in

getting a free market to operate in

health care is there are a couple

factors that are just they're baked into

the health care system that pro that uh

insulate it somewhat not completely but

somewhat from market dynamics. Big one

is demand. You know when something

breaks it has to be fixed. you don't

have an option, you know, oh gee, I need

this very expensive procedure. Um, you

can't delay it.

Uh, that's also that there you you know

that you don't control demand works both

ways on the supply and demand equation

because if demand is static, it's what

the economists call inflexible. It's or

inelastic rather. It's inelastic to

price. You don't see hospitals running,

you know, a special on heart surgery or

hip replacement because that is just not

the nature of the demand. Um it so that

means there's no price reduction to

encourage demand, but there's also not

really a penalty for price increases

because the demand is fixed. And that's

a dynamic that has to be addressed if

you if you want to get a market running.

Um major one is the presence of

intermediaries. Again this is endemic to

our system. It is a feature of the

system. We have diagnosing

intermediaries physicians who determine

what you need. And then we have

financial in intermediaries

intermediaries also called insurance

companies which in tend to insulate

patients from some costs hopefully the

major costs by charging them a premium

for that insurance but that takes the

patient a step away from the transaction

in terms of

ability to control cost and also

motivation to control cost. Insurance

companies do want to get costs down, but

they're not as motivated as an

individual patient might be. Next slide,

please.

A quick recap of

how markets actually work at a consumer

level. This slide shows the actual ways

that markets try to influence price. On

the left side of this little chart is a

list of major expenses for a household.

We're looking throughout this

presentation, we're looking at health

care from the perspective of a consumer.

And these are the major costs, housing,

transportation, and health care is kind

of in the middle at 14%. Across the top

of the chart are the major strategies

that are used by consumers to control

cost. And you can look at those and you

know, you can see them in your daily

life. You can compare prices, you can

negotiate prices, you can sometimes

substitute things uh and so on and so

forth.

Most of those categories of spending

that a consumer encounters are

susceptible to one or more of those

strategies,

not health care. The big strategy and

arguably the only strategy in many cases

for a consumer to control health care

costs is by decreasing quantity. In

other words, you forego care. And the

data on this is extensive and very solid

that yes, in fact, many consumers of

health care do forego care and they're

subject for another presentation. But we

are lacking in health care really good

tools for consumers to control their

prices. So that's what we're trying to

address here. Next slide.

Um

we've looked at a lot of data as part of

our analysis and because of our time

limitations we didn't want to make this

a data heavy presentation. But here is

some data that I think is interesting.

What you're looking at here is one

procedure that we think is

representative. Uh cescareian section

birth. It has some features. You know in

some cases the timing of that can be

controlled. The facility of that can be

controlled by the consumer. Not in all

cases. There are many other procedures

that are expensive. Knee replacement is

another. Many many others.

What the dots each represent what Sigma

which we just picked them as an insurer.

what they would pay for this procedure

at various hospitals in Colorado. Each

dot represents basically a hospital. At

the bottom of this chart, there's

dollars. On the left side, the leftmost

dot is about $3,500.

There is a hospital in Colorado that

will do this procedure and they quote it

officially, we will do this or for Sigma

at $3,500. on the far right side of the

chart. The lowest dot out there is

27,000.

That's and everything else falls in

between. The point of this chart is to

show same insurance, same procedure, but

look at the variation.

This variation means among other things,

you know, there's something driving

variation in these prices. And we think

to a large extent that it's a lack of

market dynamics. There are other there

are other forces that are driving these

prices. And as I say, you know, this is

a a separate subject and I'm not trying

to beat up on Sigma or anybody here or

hospitals for paying different rates.

The point is this is the landscape we're

facing. There's great price variability.

And one of the reasons it's variable,

we're pretty sure, is that markets are

constrained in operating in healthcare.

Next slide.

Flipping through my notes here.

Okay, now we get to the part of the

presentation where we talk about, okay,

what do we do about this? We've talked

about markets, how they work, you know,

in some areas, but in a lot of

significant ways they don't work well.

We really dug in with Civic's help in

terms of Colorado spending. And we found

a couple of things that to us were very

interesting. The average out-of- pocket

spend for a person who's incurring

health care costs. Some people have no

spending in a year. They're young,

healthy, usually both, or often both. Uh

but when they do spend, it's 1,400 per

person on average. And we looked at that

and said, "Aha, this is worth digging

more because for most families, this is

significant expense." So there's

something to be worked with here in

terms of trying to save money, looking

at very broad data sets. And if you look

at that data and you say, "Well, what if

you could somehow shop these prices for

Colorado? We sampled to a 25 to 40%

savings if you could possibly shop."

This is consistent with many other

studies academically that show savings

up to 40%. So we sort of we feel at this

point we verified this is worth we

looking at

and before we get into sort of where we

think the savings might come from

uh a little bit of data uh to talk about

because we we try to let data guide our

choices and where we do our our

investigation. There is good solid

consumer research out there that where

consumers say, "Yeah, if I could shop, I

would if it got my cost down. I would do

that." And it's 89%.

And in, you know, retail dynamics,

that's significant. That gets your

attention. The consumers would like to

shop for care, but the obstacles that we

talked of earlier in the presentation,

you know, the the data is incomplete.

It's somewhat complex. Um, it can be

confusing. is preventing consumers. Um,

and then just kind of an aside, we think

Colorado is a really good place to maybe

try something for all the reasons

listed. You know, we have civic, we have

uh healthc care policy and financing.

They have some excellent price

comparison data on their website. Um,

and the state is very healthy. Next

slide.

Okay. So, here we come to what do we do

our best and we're working on this. We

do not have this equation solved,

but we think there's probably an app

that needs to be built.

Recent advances in artificial

intelligence may make it more less

expensive to build a powerful app than

it was as recently as a few years ago.

Our best guess, mainly in outpatient, is

that if you could robustly price shop

procedures in Colorado, you might be

able to save consumers hund00 million

per year. But there are two really big

challenges to do this. The tool has got

to be easy. And this is where a little

bit of my background comes into play

with consumer behaviors. There's a limit

to how much time people have and how

much learning curve they're willing to

invest. And probably as big or bigger is

we have to do something that we call

within health price partners a way to

break the financial insurance

intermediary barrier. We call it the

intermediary barrier. What is that?

right now going to, you know, using my

earlier example, if I have signap my

insurance and I work really hard and

find a lower price for a Sigma procedure

at a hospital, there is no incentive for

me to do that. I don't get a break on my

insurance. I don't get a uh a a a price

benefit to myself. And so, we have to

really look hard at that. and we're

that's that's where our focus is right

now. So, I'm done. If anyone has any

questions, I'll be happy to try to

answer them.

>> That was so great. Thank you so much. We

do have one question in the chat from

Cat uh who asks, "How does price

transparency help consumers when we are

at the mercy of our provers

participation in certain network

hospitals?"

The answer at the moment is it does not

help you. And for exactly that reason.

And again, I we're not beating up on any

part of the industry here, but there's a

structure in healthcare that that makes

it there's no reason that you should uh

shop for price if if you have insurance

generally. and we have got to find a way

to pierce that system or we're not going

to be able to save money for people at

the at the moment. You're you're

absolutely right.

>> Yeah. Um and not seeing any other

questions immediately. We and I do want

to keep us moving along, but that was a

great presentation, Greg. Thank you so

much.

>> Thank you.

>> I will I will uh I will put his email in

the chat as well here in just a second.

and feel free to follow up um if you

have any other questions that come to

mind. So, I will go ahead and uh kick it

over to Andrew who's going to uh go

ahead and take us home here.

>> All right, thank you so much. Um uh you

can move to the next slide, please. Uh

my name is Andrew Shermire. I'm a PhD

candidate at the University of Minnesota

and I'm going to discuss the effect of

eliminating cost sharing on behavioral

health service utilization and

affordability in Colorado. Uh next slide

please.

>> Um all this research um I owe a lot to a

lot of people. So uh thanks to Civic um

these individuals and uh some funding.

Uh next please.

>> Great. So to set the context, uh one of

the challenges we face is providing

comprehensive but also affordable

behavioral health care. Um there's a lot

of barriers when it comes to um to this

including stigma surrounding behavioral

health um availability of services and

more financial ones like um insurance

coverage and the high cost of care. So

there have been numerous strategies

states have undertaken to address this.

Um of late there's been um uh one one

has been to eliminate cost sharing

altogether. We've seen this in New

Mexico and another example is in

Colorado actually in the Colorado

option. So, if anybody is unfamiliar

with the Colorado option, um in 2023,

Colorado passed House Bill 21, oh

implemented, I should say, House Bill

21232,

established uh this uh public option,

quasi public option if you will, and

required all insurers in the marketplace

to offer additional standardized plans

with shared benefit designs. part of

this benefit design was a $0 behavioral

health um office visits. So essentially

economic reasoning we would think that

oh no cost sharing this could encourage

an incentivized use and that was exactly

what the policy intended. However, it

remains unclear if consumers actually

responded to the $0 co-pays and this

leads to the next slide please of the

research objectives.

um first uh to determine if individuals

enrolled in one of these Colorado option

plans uh utilize more behavioral health

services than those enrolled in a

non-option plan. And also um another

objective is to actually quantify the

difference of out of pocket cost savings

um for for these two groups of people.

Um next slide please. Uh to accomplish

this um I conducted a retrospective

cohort study using the Colorado APCD um

using years 2021 to 23. Um so 2021 and

2022 uh purely to establish baseline

health status. Um I used all three years

to find people who were continuously

enrolled um or had 11 or more months of

coverage. um each of those years. This

uh removed people from uh the special

enrollment period uh which has been in

the literature shown to have uh some

potential adverse selection. So that was

important. Um and then the outcomes I'll

discuss later um are from 2023 only. But

so the sample included about 50,000

individuals in total uh between 18 and

64 who were continuously enrolled in a

marketplace plan or coverage I should

say between these years.

Um the treatment group was about 7,200

individuals enrolled in the Colorado

option plan. I considered the comparison

group the remaining 43,000 individuals

enrolled on a nonoption plan. Um, so if

you're getting alarms in your head, uh,

it's totally valid. There is treatment

selection bias by defining treatment

this way. I'm going to discuss how I

mitigated that in, um, the methods

slides. So, um, next slide, please. Um,

so my outcomes of interest, I cared

about the probability of using any

behavioral health services. This is a

binary yes or no outcome. Um I also

cared about the amount of behavioral

health services used for individuals

that used any to begin with. Um this is

kind of like intensity of usage is

another way of thinking of this. Then

lastly of course to quantify um the

out-ofpocket savings. We want to know

the average cost of uh behavioral health

visits. Um so next slide please. That'll

go into uh the methods. So I used a

two-part model for this. It's also

called a hurdle model in the literature.

Um so the first part of the model is an

equation um called a decision model. And

this identifies the probability of using

any behavioral health services based on

someone's treatment status or the plan

they were enrolled in in 2023.

Um from this we go into our outcome

models, the second part. Um and so the

second part is concerned with only um is

is conditional on people passing the

hurdle or having used services to begin

with. Um and I ran separate equations to

model usage and cost to get those um

intensity of utilization and the cost

outcomes. But I want to specify um

because of the treatment selection bias,

I had to use inverse probability of

treatment waiting um IPW to mitigate uh

this effect. I'll save y'all from the

methods discussion about how that's

actually done. I'd love to talk about it

later. It's really cool. But um moving

uh forward, let's talk about some

results in the remaining time we have.

So, interestingly enough, um, in terms

of the decision model that, um, binary

outcome, um, I didn't find a difference

between the probability of using any

behavioral health services based on

whether an individual was enrolled in a

Colorado option plan or a nonoption

plan. Um below I'm showing the average

marginal effect of treatment uh compared

to people enrolled in that plan or in

one of those plans versus a normal

non-option plan. Um and the 95%

confidence interval is also listed

there. So you're seeing it cross zero.

So it's statistically insignificant. Uh

next slide please. Um the outcome model

for utilization. um I actually didn't

see any um association between um the

plan choice of an individual and the

amount of services utilized. So there

was no difference I found between um

enrolling in an option and a nonoption

plan between the counts you used. Um

again we have average marginal effects

the confidence interval crosses zero not

significant. Um, next one please. This

is the kind of interesting result. So,

as expected, we are seeing um we're

seeing some savings um from enrolling in

the Colorado option plan. And uh it

makes sense eliminating cost sharing

should, you know, reduce out of pocket

uh spending, but now we've quantified it

and it's about $23 less. Um so, what

does this mean? And uh next slide,

please.

Uh so I think

this just raises some questions, right?

And I have like why didn't utilization

increase? So I have a few reasons. Um

one is that cost couldn't be the only

barrier. Um in the 2023 Colorado Health

Access Survey, um it was the first time

that uh the survey said that cost wasn't

the main barrier and also the benefit

design uh doesn't really touch upon

stigma, availability or accessibility of

services. Second reason, I think we need

to consider the context of behavioral

health services.

um you know a lot of the time they can

be correlated with um uh crisis and you

don't really plan on a crisis by

definition. So therefore um I think uh

people might be less price sensitive to

these services than other services. Um

the third reason is uh competing

mechanisms.

um you know there could be pri

authorizations different networks um

even provider reluctance to see

individuals based on negotiated

reimbursement rates we've seen this in

Medicaid the literature points to that

um I've not investigated that yet I'll

talk about next steps but um the other

reason would be a lack of awareness of

health benefits the 2024 consumer

engagement and healthc care survey um

said that about 40% of individuals don't

know their behavioral health um benefits

in insurance plans. So this this could

just be um something we've seen you know

it's a new product when people selected

one of these plans.

Um so uh next slide please. I'll just

wrap it up quickly. um essentially

eliminating cost sharing reduced

out-ofpocket costs like we expected um

by an average of $23 to visit but we

didn't see that increased utilization of

behavioral health services and I think

this suggests that we really should

prioritize um you know that we need to

think about um the non-pric barriers to

behavioral health care such as the

availability and the accessibility of

these services.

Um, and I plan to do more work on this,

um, to investigate how provider networks

and reimbursement rates changed. And I'd

also like to see if there's variations

based on region or if there's

heterogeneous effects in case, you know,

the newly enrolled in 2023 had um, a

different effect than uh, those

continuously enrolled. So, um, yeah,

that wraps it up. So, thanks so much for

listening. Um, feel free to type in

questions or email me with any and I'm

happy to discuss this.

>> Awesome. Thank you so much, Andrew. Uh,

that was great. Um, looks like we've got

just a couple of very uh quick questions

here. Um, oh my goodness, there's a

whole there's a whole bunch and we're

about to go uh we're about to go over.

Um, so everybody, uh, I would ask, um, I

am so sorry, uh, if you can reach out to

Andrew. Um, his email is right here.

I'll put it in the chat as well. And,

uh, ask him your, uh, your questions.

Um, there I just saw like four come in.

So, I don't want to I don't want to hold

everybody over. I'm sure people are

anxious to get to lunch or on to their

next meetings or whatever, and I want to

be able to get us out of here. So, my

sincerest apologies to that for that.

Um, Andrew and and everybody whose

questions we weren't able to get to. Um,

but I am copying the chat as well. Um,

and I will uh I will follow up with that

um as well from my end. So, my

apologies. Um, thank you so much,

Andrew. That was really, really

interesting. Clearly, uh, everybody else

thinks so, too. Um, all right. So, thank

you all so much for coming to our

presentation today. Um, we will get a

follow-up email uh that will have a

recording as well as further information

in the slide deck. Uh, so feel free to

touch back with our presenters um as

other questions or thoughts come up. Uh,

feel free to reach out to us if you uh

sparked some ideas for how you may be

able to partner with Civic, use COAPCD

for any of the projects you're working

on. Uh, please connect with us. We are

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We are on Instagram though. Uh and sign

up for our newsletter. So with that, I

will uh let you all get on to your

afternoons. Thank you so much for your

time today. Um and have a great rest of

your Thursday. Thank you.