Artificial Intelligence, RBTs, BCBAs and Treatment Plans--Special Guest Michael Gao from Alpaca Health

Episode Transcript
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SPEAKER_00 (00:00):
Welcome to ABA on Tap, where our goal is to find
the best recipe to brew thesmoothest, coldest, and best
tasting ABA around.
I'm Dan Lowry with Mike Rubio,and join us on our journey as we

(00:23):
look back into the ingredientsto form the best concoction of
ABA on tap.
In this podcast, we will talkabout the history of the ABA
brew, how much to consume toachieve the optimum buzz while
not getting too drunk, and therecommended pairings to bring to
the table.

(00:43):
So without further ado, sitback, relax, and always analyze
responsibly.
All

SPEAKER_03 (00:54):
right, all right, and welcome yet again to another
installment of ABA on Tap.
I am your co-host, Mike Rubio.
along with Mr.
Daniel Lowery, as usual.
Mr.
Dan, how you doing, sir?

SPEAKER_00 (01:05):
Good to see you, Mike.
I'm doing great.
Excited to hear you navigatethis AI discussion today, buddy.

SPEAKER_03 (01:11):
Well, you jumped right in, yeah.
You're alluding to the fact thatI haven't been much of a fan.
I'm also not a fan of medicalnotes.

(01:47):
said, I am not a fan of medicalnotes.
In fact, I love to talk aboutthe fact that in my 20 plus
years of practice, this idea oftime conversion and completing
notes has been an ongoing issueand not at one place that I've
worked at ever can I say that wefigured it out.
A lot of this now deals with thecomplexities, right?

(02:08):
This idea you've got a fundingsource, they want to know
certain things, why they want toknow those things.
Maybe we'll get into thosemotives later.

SPEAKER_00 (02:15):
And they're not going to pay you to do it.
Wow.
Yeah, that's correct.
Or it comes out of the clienttime.
Either way, right?

SPEAKER_03 (02:21):
But today, we've got a very special guest, Mr.
Michael Gao from Alpaca Health,and he's got a solution for us.
I think he's got this figuredout, and we're going to help him
figure more of this out as we gothrough this conversation
because he's going to make ourlives easier.
Michael, welcome to ABA on Tap.
Thank you for your time, sir.

SPEAKER_01 (02:39):
Thank you, guys, for having me.
I'm excited.

SPEAKER_03 (02:42):
All right.
Just pardon the...
Pardon the Zoom interruptionthere.
We forgot to turn that on.
So we're officially startingnow.
All good, yeah.
So you're working on integratinglanguage models, AI, as we're
commonly calling it, into makingthe life of professionals in ABA
or maybe any other medicalprofession a little bit easier

(03:05):
to make sure that those extra15, 20, 25 minutes, depending on
which funding source you'rewriting a note for, after you've
spent a better part of two hoursdoing dealing with challenging
behavior and really putting allof yourself into the session.
And now you've got this finaltask, which often gets put off.
It's complicated.

(03:25):
It's important because you wantto capture what you did and you
want to capture aspects of thedata.
And then again, it's coming atthe end of a rigorous situation.
And a lot of us put that off andthen you put that off.
And before you know it, you'vegot a week's worth of medical
notes to complete.
So tell us a little bit aboutyourself, a little bit about
your background and then whatgot you to this particular

(03:47):
project please

SPEAKER_01 (03:49):
Yeah, absolutely.
So before Alpaca Health, I wasbuilding an online education
company called Dewey Smart,where I was able to meet and
work with a number of familieswho had kids on the spectrum in
special needs programs.
And we worked with all kinds ofparents, and parents of all
kinds are always stressed out,but parents of special needs

(04:10):
kids seem the most stressed out.
And I just remember walkingthrough how they should handle
getting a learning accommodationfor a standard test or

SPEAKER_02 (04:20):
trying

SPEAKER_01 (04:20):
to help them deal with paperwork from school and
just thought that there was alot more that we could do.
And so earlier this year,started to kind of dig more into
the clinical side of helpingspecial needs kids, the autism
spectrum, found out about ABAand started talking to
clinicians.
And when I realized how muchpaperwork y'all are doing on top

(04:42):
of spending time in sessions,watching sessions, seeing how
chaotic they often were, I waslike, wow, there's a lot we
could do better here.

SPEAKER_00 (04:52):
Well, the good thing about our paperwork is that it's
unpaid.
So that makes it a little biteasier to deal with.

SPEAKER_01 (04:59):
That's awesome.

SPEAKER_00 (05:01):
We love a lot of paperwork, right?
Michael, can you just tell usmaybe a little bit about
yourself?
I know you mentioned yourprevious company that you were
with, but just a little bitabout your background and
anything that you think might berelevant for our listeners to
know about you, man?

UNKNOWN (05:15):
Yeah.

SPEAKER_01 (05:16):
Yeah, yeah.
Well, I was born and raised inDallas and went to a Title I
school.
And so I was always reallyinterested in all of these
things that rich kids got, butthe Title I low-income students
didn't.
And that's honestly why Istarted the education business
originally.
There's ways to make this advicethat is so hidden and

(05:38):
specialized more common to morepeople.
Went to New York for school,went to Columbia, studied
computer science.
Nice.
I think I'm a decent programmer,although I think that as I've
kind of gone through my career,other things have started to
become more interesting.
You graduated from

SPEAKER_03 (05:56):
Columbia with a computer science degree.
I'm going to say you're a goodprogrammer.
We're just going to leave it atthat.
Come on.
Come on, man.
Columbia is a nice, small,somewhat prestigious school.

SPEAKER_01 (06:08):
Although I'll say ChatGPT is probably better at
programming than every singleengineer out

SPEAKER_00 (06:14):
there.
Oh, shots fired.
I thought you said you weren'tgoing to say any controversial
stuff.
About

SPEAKER_03 (06:21):
ABA.
He wasn't going to say anythingcontroversial about ABA.
Okay.
Excellent.
So, um, yeah, continue.
So you, uh, CS at Columbia, didyou do, uh, work in computer
science for a little while aswell?

SPEAKER_01 (06:35):
Yeah.
Yeah.
Spent some time as an engineer,did some product manager work
in, in, um, financial technologyand really just found that kind
of unfulfilling.
Okay.
And so all the while, whiledoing that, I was doing this
education thing and really ableto see, um, how we could change
kiddos lives.
Like, like honestly, like one ofthe first kids I worked with

(06:56):
wanted to become a chemicalengineer.
engineer.
She had no interest in chemicalengineering.
She hated STEM.
She was like, my mom wants me todo this, so that's why I'm
majoring in it.
But after working with her for afew years, we got her and also
the family OK with her doing ajoint major between creative
writing, which she actuallyreally

SPEAKER_02 (07:13):
liked,

SPEAKER_01 (07:14):
and chemistry.
And I think she's way happier incollege for it.
And so really lent myself todoing things that related more
to people and seeing the impacton people rather than just pure
Interesting.

SPEAKER_03 (07:30):
Interesting.
So what, tell us a little bitmore about that.
So what, you know, in anutshell, what is the positive
aspects of technology andeducation?
Where are we using it well?
Where are we not using it well,in your opinion?

SPEAKER_01 (07:45):
Yeah.
Well, I think this is prettysimilar to how I think about it
in ABA as well, but there's justso much paperwork everywhere.
Like in the school system,especially for IEPs and the
conversations we have withparents who are advocating for
their kids, there's just so muchpaperwork.
It's so much like letterwriting, email writing to the

(08:05):
right people at the right time,following up.
And that's sort of the case withABA too, right?
It's like writing these notes,following up on these notes.
There's a denial of frominsurance, so writing something
or readjusting your note to bemore insurance compliant.
And so these are the placeswhere I think that new
technology and AI has the,hopefully will have the biggest

(08:28):
impact, right?
Like humans are really smartcreatures.
We're social creatures.
We do a lot of really cool,creative, great things.
And that comes with interactingwith other people.
It comes from sort of human tohuman connections, working with
others, creating ideas,teaching, learning, Not
paperwork, right?
And so the less we can do of theboring stuff that nobody wants

(08:51):
to do, the more we can do thecool stuff that really does make
a difference.

SPEAKER_00 (08:56):
That's such a good point.
And you had a sentence that Ithought was really interesting
because it's so true.
You said readjusting the notesso it's more in compliance with
what insurance is looking for.
It's funny how that happens allthe time.
And the session didn't changebecause the session has already
happened.
But it's like, how are we goingto communicate to the insurance?
It's just weird how that Itcomes down to the note.

(09:17):
It just shows the differencebetween the documentation and
the session because I guess thedocumentation is supposed to be
a representation of the session,but so often then the session...
either becomes a representationof the documentation or they
become two totally differentthings.
You're like, well, this is whathappened in the session, but
just write this in the note.

SPEAKER_03 (09:35):
Well, and it's this fear.
You mentioned about somethinggetting kicked back or whatever
the case may be when you submitit to the insurance.
But it's this fear now to makethem align, which then runs the
risk of your documentation notbeing accurate, not actually
capturing what you did.
And then now maybe it'scompliant with the insurance,
but for the clinician, itdoesn't really capture the

(09:57):
history or the important stuffthat you wanted.
To put this out there, there areplenty of colleagues who might
say, yeah, I've got my notestemplated already, so I just cut
and paste at the end.
And it makes it easy.
And it's like, oh, great.
But does that really then meetthe purpose of the
documentation?

(10:17):
So we're always just kind oftrying to appease the insurance,
I guess.
To be fair and not to bedisparaging, we are, and that
can be a big problem.
This is where you come in.
Tell us a little bit aboutlearning about being compliant
with insurance, how that'saffected your approach in
creating this product towardquicker, easier, accurate
medical notes.

SPEAKER_01 (10:39):
Yeah, absolutely.
So Alpaca Health's first productis our AI note taker for BCBAs.
We listen to parent interviews,parent trainings, and also BT
supervision sessions, andautomatically take notes, write
summaries that hopefully you cancopy and paste as the session
summary for documentationpurposes, and also call out

(11:02):
important highlights that youmight need to know to work with
the client in the future.
When I think aboutdocumentation, I think the big
reason why people do it isbecause they need to get paid by
insurance but the other piece isalso that taking notes helps you
remember what happens so thatwhen you need to go back and
think about how was Joey doingtwo months ago you actually have

(11:22):
a note summarizing it and Ithink we kind of forget about
that second reason why we dodocumentation that is equally if
not more important and so what Ithink about this tool is if you
have a listener in two sessionsthen you can create the best
summary possible because it'sliterally right there listening
to the entire session that youjust conducted.

(11:43):
And it can create a summary thatcaptures all the important
details that you would need forclinical interactions going
forward, but also write that andspin that in a way that is
insurance compliant, whichunderstanding what that means
has been a whole journey initself, right?
Templates, requirements, everyinsurer has a different medical

(12:04):
necessity brochure that seems tochange randomly, and it's not
clear which document is out ofdate on their website until you
call them, or just because youknow the payer guy.
This is how we've come todiscover what insurance
requires, is just having...
really great run-ins atconferences sometimes.

SPEAKER_02 (12:23):
And

SPEAKER_01 (12:25):
so I can't imagine being like a solo BCBA who's
running a practice, managingBTs, intaking new clients, at
the same time trying tounderstand what

SPEAKER_03 (12:35):
insurance wants.
We appreciate your empathytremendously, man.
Everything you just mentioned ispreach it.
Sing it, brother.
Sing it.
Amen.
Hallelujah.
It's quite a terrain.
Go ahead, Mr.

SPEAKER_00 (12:48):
Dan.
No, I was going to say, Mike,you've always had an interesting
idea and it's actually createdsome friction with colleagues in
the field the idea of we've hadan interesting relationship with
data and I'll make data anddocumentation synonymous
although they are a little bitdifferent I think documentation
the way that we think about itnow is more of what are we
submitting to the insurance thedata is more of what are we

(13:10):
referencing as BCBAs but we'vehad an interesting relationship
with data and we understand itsimportance it absolutely is we
cannot run effective ABA withoutdata that because And you
brought up an interesting pointthat, you know, Michael's
software really does help withis this idea of we can be doing

(13:32):
one thing at a time, taking dataor interacting with the client.
And whenever, like Michael said,with the parent interview piece,
if we're writing down what theparents talking about, we're not
paying attention to what theparents talking about.
So we could have conversationsor we could take that or we
could do these things and thendo it on the back end, which is
really intriguing.
That means our interactions canbe that much more vibrant.

SPEAKER_03 (13:53):
I like that a lot.
The idea that I could...
you know, break down athree-part contingency or do ABC
data as I'm talking to a parentand then reference and back ops,
you know, so often in thatconversation, I'm coming up with
three, four differentantecedents or consequence
strategies that, you know, inthat dynamic conversation.
So to sit there, I have to stopmyself and write them all down

(14:14):
or remember them.
I'm not capturing it fully.
What you're saying now is thatwould be captured fully and then
I just call it up however I wantto and then I print it up, you
know, or edit the text however Ineed to and then hand it to the
parent.
And I like the way you pitchedthat, Dan.
I had a very rocky relationshipwith data as a developmentalist.

(14:36):
You're not quite divorced

SPEAKER_00 (14:38):
from

SPEAKER_03 (14:38):
data yet, but you're in counseling.
I love data.
I just don't like when it'sclingy or always on me.
I need some space once in awhile.
But as a developmentalist, morelooking at developmental theory
or the idea of narratives andhow you can extract data from,
that's always been much more myapproach to that interaction,
this idea that a very clear orvery truncated trial, ABC data

(15:03):
point, ABC data point, I'vealways found that a very
disjointed way to interact withclients.
So what you're talking aboutsounds fantastic.
Even looking into the future,the notion that this could be
capturing certain trial sets,and then you just call up your
verbal consequence to count howmany times there was a correct

(15:24):
response versus an incorrectresponse.
So I mean, this Hopefully I'mnot stretching this too far, but
this opens up a lot ofpossibilities where I see
clinicians being able to free uptheir hands and their attention
toward the interaction and theimplementation of protocols.
And now the data is still beingcaptured.
Just a matter of calling it backup after the fact.

SPEAKER_01 (15:46):
Yeah.
Research shows that humans don'tmultitask.
We don't.
We become good at it by trainingit over time, kind of flipping
back from one thing to another.
but we don't actually multitask.
We don't actually have twothings going on in our mind at
the same time.
We're just thinking back betweenthose things really quickly.
And so, you know, like moreexperienced BCBAs tell me like,

(16:08):
oh, I got note taking down.
This is actually super easy forme.
And that's great for you becauseyou've had to do it this way,
right, for so long.
And you've trained yourself tomultitask really quickly to
switch between the clinicalinteraction writing notes.
But for everybody else, it'sstill hard.
And even for a lot ofexperienced folks, it is hard as
well.
And so that's sort of of ourthesis with AI and just better

(16:32):
technology is how can we just dothe note-taking, the
documentation, the datacollection on behalf of
clinicians so they can trulyhave 100% focus on the clinical
interaction.
One of the places that we'retaking the product, and today
everything's audio-based.
You record parent interviews,you record caregiver training

(16:53):
sessions, you record BTsupervision sessions, but we
know that audio is only just oneof the things that happens in a
session.
A lot of ABA is physical.
A lot of autistic kids that wework with are nonverbal.
And so what we are working on isthe video-based approach to data
collection.
Stick a video camera in thecorner of a session, record
what's happening, and we'll takeall that behavioral data for

(17:16):
your BT or for you in a directsession so that you can get the
most granular pieces of data.
You can go back in the videorecord and see what happened.
You can automatically pull outout ABC data that maybe your BT
can't pull out because they'renot experienced trained
professionals like you are andall have that done while the BT

(17:38):
focuses 100% of the clinicalinteraction because there's a
bit of camera and AI watchingthe video.

SPEAKER_00 (17:44):
That's always been the challenge with ABA is that
we are lab focused.
We're evidence based.
So we tell this laboratorybasis, for lack of a better
term.
But when things are actuallygoing on in the lab, you have
somebody interacting with theperson and then an independent
observer taking data.

(18:04):
The person interacting isn't theperson taking data.
So when you have that personbecome the same person, you run
into a lot of issues.
So I like what Michael's saying.
I didn't know that you weregoing to bring that up, Michael.
We still will talk about theaudio piece, but that allows us
to kind of run both sides.
It allows us to have theinternal validity of the lab

(18:27):
with the external validity ofnot having to take the data
while we're interacting with theperson.

SPEAKER_03 (18:31):
It takes training to a whole new level.
Being able to sit with your BTand re-watch a session.
The idea of reliability betweendata sets.
Now that doesn't have to happenlive.
It can happen more comfortablyafter the fact during a
supervision session.
Hey, let's just, let's see whatAI punched out.
Let's see what you and I punchout in terms of rewatching these

(18:53):
three minutes of a session, youknow, whatever that, yeah, that
really changes the whole game.
That's exciting.
I like to, my little analogyhere, people see the law says if
you're texting who's driving,That's the way I feel about data
and the clinical interaction,right?
I mean, if you're reallydividing your attention between
even just a push button on atablet, I understand that's very

(19:16):
minimal, but it's like talkingto somebody and then them going
to do text.
There's a shift in thatconversation and that
interaction, and it's probablynot modeling the best that we
can model, especially for ourclients, in terms of something
socially significant.
So, yeah, what you're presentinghere changes the whole game.
That's exciting.

SPEAKER_01 (19:37):
Absolutely.
I mean, I know that there aresome BCBAs who've started typing
during parent interviews becausethey're like, I want to write
everything down.
I know I need this in mycomputer to write this initial
treatment plan later.
And I understand why they'redoing it.
But I also think if this is yourfirst time meeting with the
parent of an autistic kid whojust got diagnosed, who's

(20:00):
extremely freaked out abouteverything, who just wants,
honestly, a chance to ventbecause you're the first person
they're talking to who actuallyunderstands what's going on
beyond just like, oh, sorry,autism diagnosis, hoo-hoo for
you, can actually have a realconversation with you about your
kid, how you're parenting thekid, then it's like, that's a

(20:21):
very human conversation.
And to have a computer screentyping in between is just really
jarring as an experience.
It's like we all know the doctorwho spends the doctor's
appointments with us looking atthe computer screen and not us.
And it's frustrating, but it'salso not the worst thing in the
world because I just came inwith a strep throat and I just

(20:42):
need antibiotics.
Parent interviews, parenttrainings are just a whole
different ballgame.
So to have a computer screenkind of blocking that
interaction feels not reallygreat.
And that was one of the piecesthat really stood out to me of
like, wow, we need to find abetter way to do this.

SPEAKER_00 (20:58):
Wow, that's a good point.
Yeah, that becomes very like...
What's the word?
Sterile, I guess would be a goodway to put it.
Like you have this persontyping, and yeah, I think that
sometimes they do just want thatconversation.

SPEAKER_03 (21:10):
And you're right.
I mean, the typing is almost anecessary evil.
You want to capture thatinformation.
It's important information, andthen you're absolutely right.
Again, now you're texting.
You're no longer driving.
So it's going to change the gamein terms of how actively we will
be listening with this tool, butthen how we will emote and

(21:31):
present that active listenershipto the parent.
which I think is amazing.
It's going to go a long way.
I mean, we think about it interms of ease and convenience,
but in terms of focus and beingable to pay attention now to
those details that a veryconcerned or frustrated parent
or somebody that's going throughtheir steps of grief, they just
got some really tough news, justhow much more empathic and

(21:55):
focused we may be able to be byknowing that everything we want
to hear and all the questionswe're asking are being
documented, recorded without ushaving to lift one finger.
That's amazing.

SPEAKER_00 (22:07):
100%.
Let me take a step back and getinto your current product.
I'm assuming you're with a teamor maybe it was just you.
What was your impetus of like,all right, this is Where I see,
did you conduct outreach toBCBAs or is it just kind of what

(22:29):
you've seen?
Why did you kind of get to whereyou are now with the product
that you're offering, if thatmakes sense?

SPEAKER_01 (22:36):
At this point, we've talked to over 100 BCBAs and
even more VTs and ABA operationsfolks.
I went to ABA International overMemorial Day in Philly.
I was able to talk to a lot ofBCBAs there, listen in on
sessions.
And I think just like the commontheme among all of them was this

(22:58):
complaint around documentation,initial treatment plans, and
also the accuracy of datacollections that VTs were doing.
doing.
And so we really started there.
There's a lot of other thingsthat we've heard about, like
scheduling.
phone all the time, things thatwe're definitely working on.
But we've started here withdocumentation and paperwork

(23:19):
because we've heard it's sort ofthe biggest challenge from
clinicians.
And I guess taking a step backand thinking about the field and
where it is, it makes a lot ofsense, right?
Like ABA only became insurancecovered a decade ago, 2014.
So we're still, as an industry,payers as a funding source,
we're still trying to understandhow to properly track ABA

(23:41):
efficacy, what is value, How doI make sure things are actually
happening?
And so, of course, things aren'tthat standardized.
And of course, it's still alittle bit of the wild west of
documentation because the fieldis still tough.
But we don't have to letclinicians and clients be the
victim of that.
We can help them deal with allthis while still being able to
have fidelity to the core humanelements of clinical ABA.

SPEAKER_00 (24:06):
So with your initial product, some of the cool
features that you showed us whenwe met prior, maybe you could
talk a little bit about that.
So you mentioned that it recordsand can transcribe parent
trainings, parent interviews,things like that.
I feel like a lot of softwarecan do that.
Yours does some really coolstuff in addition to just the

(24:28):
transcription.
Can you talk about some of thecool features that make your
product unique?
Because I think those are prettyawesome.

UNKNOWN (24:35):
Yeah.

SPEAKER_01 (24:36):
Yeah.
So we are building everythingfor our ABA specifically.
And so when we're summarizingthese sessions, we're doing it
in a way where the AI knows whatis important out of an ABA
sessions.
It knows the language of ABCs.
It knows the language ofreinforcers.
It knows the questions thatyou're intending to ask in a

(24:58):
parent interview.
It knows what's important andalso knows what's not.
So when the parent goes on along tangent, they'll sort of
ignore it.
keep it in the transcript, butkeep the summary focused on what
is important to you as aclinician.
for parent training sessions andBT supervision sessions.
It creates a summary that doesthe same thing, focuses on the

(25:20):
clinical pieces of theinteraction.
It also summarizes the actionitems that you have for the
parent, the BT, and also foryourself so that after, say, a
parent training session, you caneasily just put that in an email
or a text message to the parentand say, today, here's what we
talked about, summary, here areyour action items for the
upcoming week.
Oh, I love that.

(25:42):
so that no parent can tell you,oh, you didn't tell me to do
that because there is a writtenrecord of what you were supposed
to do in your inbox at any time.
And I think a similar thingwould be really helpful for PTs.
Then the last piece that'spublicly available that anybody
can sign up and try is we'vegone deep into how treatment

(26:04):
planning should work.
It just feels like a lot oftreatment planning is document
summarization, document review,rewriting it in a Word document.
When really what matters iswriting your goals in a smart
way and creating a behaviorsupport plan that now your BTs
can reference and use whenworking in sessions.

(26:27):
And so I'd rather you use thefour or eight hours you were
authorized doing greatdefinitions and great behavior
support plans instead of tryingto comb through an 80-page IEP
and summarize it for one sectionof your treatment plan.
And so what we've done is afteryou have recorded and summarized
your parent interview, you cango back to our platform, upload

(26:47):
your medical Sure.

(27:09):
Essentially, you can write the75% of the treatment plan that
is boring document summarizationso that you can focus the other
25% on the really cool stuff,the interesting stuff, the
complicated stuff, the humanstuff that I think every BCBA
would rather be spending theirtime on.

SPEAKER_03 (27:27):
Hell yeah, man.
That's amazing in the sense thatas you were talking, one of the
things that I talk to youngerclinicians about in terms of
intake appointments is saying,hey, we get this whole lot of
collateral.
And then we've got our ownin-house forms that start up our
FBA, whatever it is, that aregoing to ask the very same
questions that these parentshave already answered a thousand

(27:50):
times.
And the fact that you're askingthe same questions again to fill
out this stupid form that weneed for us in-house means that
you didn't read the collateral.
And now you're just some otherclinician asking the same
questions and means you don'tknow that child.
What you're talking about opensup a whole lot of opportunity to

(28:11):
go in fully well-versed, to goin from that collateral
documentation having alreadybeen uploaded and used to fill
out whatever in-home form youneed.
And then now your conversationis heartfelt, very human, very
clinically sound, very concernedwith the right amount of focus.
So that's super exciting.

(28:32):
I can't wait to learn more aboutthat as you start using it.
That reminds me of

SPEAKER_00 (28:38):
when you You call the phone and they ask you,
like, what's your accountnumber?
Enter your social and thenwhatever.
And then as soon as therepresentative comes on the
line, they're like, pleaseconfirm your account number,
your social, all the stuff youjust entered in like 10 seconds
ago.

SPEAKER_03 (28:51):
And by the way, Dan, make sure you listen to all the
options because they may havechanged recently.
And wait times are longer thannormal as well.

SPEAKER_00 (28:58):
Right, wait times are longer than usual.
But yeah, no, that's what you'retalking about, Michael.
It's just allowing us to becomemore efficient with our time.
Now, let me ask you this.
Well, in

SPEAKER_03 (29:10):
time for documentation, just to be clear,
it's more efficient and havingmore time, clinically speaking,
to give to the client.
So I think that's what youmeant.

SPEAKER_00 (29:19):
Yeah.
So on the other end, kind of theskeptical end of it, how can we
be confident that the softwarewill pull out all of the
information that we want to theextent that we won't need to go
back and see what was missed andthen read the whole 80 pages
over again, if that makes sense.

SPEAKER_01 (29:38):
Yeah, definitely.
AI technology is still new, soit's not going to be perfect,
and that's why...
They'll never replace humans andnever replace BCBAs.
But what we've done thatChachiBT and other AI tools
don't is we have trained our AIon what is important to a BCBA
in an ABA three-tier model.

(29:59):
And organizations can go in andcustomize our AI and tell it,
here are the things I reallycare about in a treatment plan.
Here are the things I reallycare about in a parent training.
Here are the things I care aboutin a BT supervision session so
it knows what things to look forand what things to pull out.
And so generally speaking, thesummary can be as customized as
you want it to be if you're anorganization partnered with

(30:21):
Alpaca Health.
From there, if it missessomething, if you're like, hey,
the client said something aboutcats and I forget what about
cats they were talking about.
You can go into the transcriptand instead of like command F,
type in cats, try to figure outwhat part of the transcript it's
in, you can chat with our AI andit'll tell you the place in the

(30:45):
transcript to watch and pull outan answer for you.
And so I would say it's likeinstead of having having to
refer to a transcript or yourreally messy handwritten notes
to figure out what happened, youcan just chat with an AI who
will help direct you to theright part of the transcript and
help give you a little bit ofinformation to start out with.

SPEAKER_00 (31:05):
Wow.
That's awesome.
AI is so abstract to me rightnow because I don't know exactly
what it is.
In terms of all of the uses,that just seems amazing.

SPEAKER_03 (31:18):
Go ahead, Mike.
I'm going to go back tosomething you said.
There's a lot of people that arefearful of AI in terms of it
replacing certain jobs, andthat's certainly a possibility.
You made an interestingstatement, particularly to ABA,
I think, particularly tomedicine.
In my opinion, it would bepretty hard to replace the human

(31:39):
element.
Just give us a little bit moreabout your insight on that.
Why, in particular at ABA, whywould it not be possible for a
language model or AI to replace,say, a BCBA?

SPEAKER_01 (31:52):
Yeah.
Well, I think about this a lot,which is like what if everything
goes right in ABA therapy, howis a family's life transformed?
How is a client's lifetransformed?
And almost always it hassomething to do with the way the
client communicates with theirfamily and the rest of the
world.
It has to do with the socialskills of the client.

(32:15):
It has to do with how the familycan now do regular family
outings with their child withautism.
All of those things arefundamentally human and
fundamentally social tasks,right?
And so it's just sort of like,almost impossible to think of a
world where you could use ABA tohelp a kid communicate better

(32:35):
without having another human inthe room to teach them how to
communicate better.
How is that possible?
Imagine the most future worldwhere you have some robot that's
able to interact with a human,with a kid.
That's not even what we want.
We want to teach our kids to beable to communicate with other
kids, other adults, other humanbeings, not how to communicate

(32:57):
with AI.
There's no way There's noconceivable way that you can
replace humans out of thatinteraction.
Now, if you're a doctor and allyou do is prescribe antibiotics
for strep throats, then maybeyou're in a little bit more
trouble.
But I think the world ofclient-facing interactions in
ADA will always be safe becausethey're the most human and the

(33:17):
most important parts of what wedo.

SPEAKER_00 (33:20):
I'm

SPEAKER_03 (33:21):
okay with that.
Yeah,

SPEAKER_00 (33:22):
that's actually interesting.
I didn't even think about ituntil you just brought it up.
Have there been applications,because a lot of the individuals
we work with are nonverbal,which really creates a huge
obstacle for them to communicatewith others.
Have there been applications ofAI explored in terms of speech
devices for individuals on thespectrum to allow them, I don't

(33:42):
even know if it's possible,because again, AI is very
abstract to me.
I'm very ignorant to the AIfield.
But almost as anothercommunication model to allow
individuals that are nonverbalalternative ways to communicate.
Sorry, I'm on a tangent.

SPEAKER_01 (33:55):
No, that could be really cool.
I mean, I could imagine sort oflike your typical, like, you
know, instead of saying words,they like have cards that they
use to communicate.
Maybe there's some digitalversion of that where instead of
it just being like, oh, he gaveme the milk card, so I'm going
to give him milk, that maybelike it speaks something to the

(34:16):
parent or something like that.
Like I can imagine interestingways that that could be applied.
I don't know of anybody now,but...

SPEAKER_00 (34:25):
They have Proloquo, which is like Pex was the
pictures.
That was kind of the initialone.
And then Proloquo was like theiPad electronic version of that.
Like you press car and it sayscar.
You say press I want car and itsays that.
I bet there's some interestingapplications of, like you were
saying, not communicating withrobots.
I totally agree with that.

(34:45):
However, I wonder if there's away that AI could allow us to
get on the same wavelength.
You're

SPEAKER_03 (34:52):
making me think here, and Michael, you'd be the
person to actually make thishappen, but yeah, this
technology actually changes theface of voice output or
augmentative alternativecommunication devices, right?
So the idea that now it could bea much more dynamic screen
that's listening and then givesthe learner an array of options
that are specific to thatresponse as opposed to the whole

(35:13):
screen that they have to sortthrough.
Now, that would then take awaysome of the learning, the
semantic part it but it wouldget to that voice output a lot
more accurately and faster andstill I mean you could still
narrow it down to a you knowthree or more array to make sure
that the learners having tochoose between options but yeah
I never thought of that whatyou're talking about Dan I'm not

(35:34):
sure if anybody's doing that butthis really changes the whole
face of voice output devices interms of now that device
listening and narrowing downoptions for the for the learner
to respond with I don't know ifthat makes any sense

SPEAKER_01 (35:47):
Yeah, no, it definitely does.
This is totally outside theworld of autism and ABA, but
there's really interesting andjust mind-boggling early
research and case studies ofhuman brain interaction, where
if you were a quadriplegic,hadn't been able to speak very

(36:08):
easily before, now we can sortof hook up to the electrical
signals in your brain andactually what you're thinking
your brain gets communicated outin Wow.
It's super early on, right?
And it's AI plus biology plusmedicine plus a whole bunch of
other disciplines comingtogether to make that happen.

(36:30):
But yeah, I think a lot of coolthings are happening that are
going to be really game-changingfor a lot of people who need
help.

SPEAKER_00 (36:38):
Yeah, just maybe another thing to explore
eventually.
Yeah, sorry, we go off ontangents.
This can be one of the oneslike, you know, how the AI can
skip over the cat disguise Ify'all are using AI to listen to
this, you can skip over ourtangents.
I thought that was a good one.
I thought that was veryrelevant.
No, I did too.

(36:58):
We'll talk about that movingforward.
Ways of giving people voices.
The new Android, I think it'sAndroid 14 or whatever, you can
actively real-time translatewith somebody as opposed to, I'm
saying it, we can have aconversation and it translates
it in real-time.
Kind of like some way of I don'tknow whether it translates it
into a visual, some way to getpeople on the same wavelength.

(37:23):
Going back to what currently youhave out in that intake
assessment, parent trainingpiece, one thing that's useful
too is can it automatically takesome of the things that people
say and goal plan or developgoals from that kind of
automatically?
Is that correct?

UNKNOWN (37:42):
Yeah.

SPEAKER_01 (37:43):
We've started to work on that.
We've started to work on thatwhere it'll actually suggest
baseline goals and definitionsof goals.
We haven't released that yetbecause we're still testing it.
That feels like a piece that wewant to get really right into
the world.
But I think whatever AI doesthere, it'll never be enough.
I think this is the piece whereyou want a BCBA to go through

(38:05):
and use that clinical judgmentto adjust goals and make sure it
makes sense for the kid, the BT,the interaction, all those
things.
But we're certainly working onthat piece as well.
But I do think that's one of theplaces where you absolutely
still want to be CBA in theroom, in the loop, to make sure
that it's accurate.

SPEAKER_00 (38:25):
The cool thing about that, though, if it's generated
from the AI transcript from theparent discussion, is that it
would still be individualized.
One thing that I think concernsus is that as things become more
templated, they become lessindividualized.
So there's a lot of times, oh,you've got a three-year-old.
These are the 10 goals you'regoing to work on with a
three-year-old.
It doesn't matter what theparent said in the intake

(38:46):
interview.
These are your 10 three-year-oldgoals.
And then when they master these,these are your next progressions
because it fits within whateverthe template of whatever
electronic processor, or itcould even just be a template.
It sounds exciting that if itwas derived from the AI
discussion, it would then beindividualized.
It wouldn't be these are your 10three-year-old goals.
It would be these are your 10goals that the discussion of you

(39:07):
and Susie's mom talked about.

SPEAKER_01 (39:11):
Yeah.
And I would make the argumentthat AI is peak AI.
individualism, individualizedcontent and treatment planning,
as opposed to what is happeningtoday with templates.
Like I put template here, Iguess listeners, I gesticulate a
lot.
I would put templates to theleft and I would put AI to the

(39:33):
right.
And I think templates areactually pretty
anti-individualism.
I see a lot of, especiallylarger organizations, having
these cookie cutter templateswhere clinicians are rewriting
three words in an entireparagraph.
And I, whenever I see that, Idon't want to say it in the

(39:54):
moment.
I'm like, how could this be asindividualized as you think ABA
should be?
And AI be the scary thing thatlike prints out the same generic
thing for every kid.
Like that is what's happened ona large organization.

SPEAKER_02 (40:08):
have

SPEAKER_01 (40:09):
very robust templates for treatment plans.
What AI lets you do is it letsyou take like the core things
you need to include in atreatment plan, a rubric of
items, and then dynamicallygenerates that the words, the
sentences are going to bedifferent for every single
output for every single kidbased off of the documents that

(40:29):
you've uploaded, theconversations that you've had,
and the information you'veshared with the platform about
the kid.
Once that generates, you can goin there and make any changes
you want.
And so maybe this is acontroversial take, but I think
AI is actually much better thantemplates at providing
individualized care experiencesfor clients.

SPEAKER_00 (40:48):
That makes sense.
I think we were kind ofanti-template at our previous
company.
Templates do give you theopportunity to have access to a
gold bank, but what people runinto so often is you just
control H client and control Hthe kid's name in there, and now
you've got Your 15 goals justfrom the template with no
individualization.
I really like what you said,Michael.
Go ahead, Mike.

SPEAKER_03 (41:08):
No, no.
Again, this is super exciting.
Even thinking about activities,I think the same thing happens
with learning activities.
So traditionally in ABA, one ofthe challenges, depending on
which model you're using, is youdefine your goal and now you've
got this one particular set ofstimuli that you're using to
teach toward that goal.
AI sounds like it's going to beable to generate a whole bunch

(41:29):
of different ideas now, notbased on what you're generically
making it.
your office across clients, butnow more specified to the
client's environment.
The idea of a preferenceassessment becomes a lot easier
here.
Talk to us a little bit aboutthat and kind of your thought
process behind that or howyou've seen people use this
already successfully.

SPEAKER_01 (41:49):
This is something that we're really actively
thinking about is how can wemake protocol generation and
modification more tailored toclients?
Because it seems like what'shappening today is that you have
this like massive program book.
You sort of like, like, okay,we're going to work with the
patient.
I know that the BT Susie hasreally done this program before.

(42:11):
I'm going to take this page outand like put it in the treatment
plan and give it to Susie andput it in the behavior support
plan and call it a day.
What if we take that baseprotocol and use AI as an
individualization layer to makethat protocol custom for Susie,
for the BT, for the family, thechanges could be as simple as

(42:32):
use examples because this is achild who really likes cars, so
talk about different coloredcars rather than different
colored blocks, for instance.
It could go deep as this kid issomeone who struggles with
object identification.

(42:52):
So we're going to work on someother type of communication
first that I can customize.
But the kind of like basic pointis we can take base kind of
starting points that used tocall templates and really
individualize that using AIbased off of all the other
information the AI knows aboutthat kid.

(43:13):
And so another reason why Ithink AI is truly peak
individualization for ourclient.

SPEAKER_00 (43:21):
That's awesome.
That is exciting.
And I want to preface this too.
I think all the listeners knowwe're not going to show for any
random company.
Michael got in touch with us andwe spoke with him for about an
hour prior to this meetingbecause we were very skeptical
of AI.
A lot of technology we've seenbe very helpful.

(43:44):
Some of the billing programs andstuff and the stuff that we have
in there.
We use central reach a lot, lovecentral reach.
But what we found is that a lotof times, What happens is these
programs are designed to enhancethe efficiency, which they do,
but then all of a sudden thetreatment revolves around, can I
fit it into the way that thisprogram wants to dissect the

(44:06):
data, right?
So I'm going to program in a waythat I can graph it onto this
Central Reach platform.
Again, I'm shouting out toCentral Reach, and no way am I
disparaging them.
I think they have a greatproduct.
But they went from, okay, let mehelp you, to now I feel like,
For max efficiency, people areprogramming around the way to
get it into their platform sothey can distribute it to the
insurance.

(44:27):
What's really cool, and maybeyou could speak to it too,
Michael, is it seems like yourgoal is not necessarily just let
me make a way for treatment tobe built around us, but you're,
again, focusing on theindividualization piece of AI.
So we're not going to need toorganize our treatment around
you.
The AI will organize itsresponses around us.

(44:48):
Is that correct?

SPEAKER_01 (44:50):
Exactly.
Yeah.
And so in our platform, ifyou're an organization that
partners with us, you can go inthere and customize every single
thing, right?
Like, what do you want the AI tosummarize?
What do you want in the summary?
Right?
There's an action item section.
What are the action items thatmight be important for AI to
really look into and pull out?
And so, like, we have a basestarting point, right?

(45:13):
Sure.
Just to kind of bolt somewhereto use.
But as you start using it,you're like, hey, like, the AI
seems to be consistently missingthe, you know, like...
Preferences, you know, thecaregiver talking about why data
collection is hard during parenttraining.
Let me make sure that the AIknows that that's something that
I want pulled out explicitly,because I know that my family's

(45:34):
tend to have a really hard timewith data collection.
You can go in there, type it outin natural language, and then
the AI will get smart on whatyou want.
And so that's, I think,something that's really
interesting about using this newtype of AI is that you can talk
to it as if it was your humanexecutive assistant, right?
Like if you had a humanfollowing you around in all of

(45:55):
your parent trainings, typingout notes for you, you would be
able to kind of coach thatassistant of yours like, hey,
remember to keep in mind X, Y, Zthings.
Or if they say something likethis, that's really important.
Please write it out.
Having a human executiveassistant for every BCBA would
be pretty expensive and probablywould be slightly awkward during

(46:15):
parent interactions.
And now you can have all of thaton an AI on your phone in every
single session that you conduct.

SPEAKER_00 (46:24):
That's all.
So AI, instead of artificialintelligence, we could call it
active individualization.
I like that.
Oh, look at you.
Michael's going to take thatone.
Take it.
Take it.
Take it.
Yeah, no, that's awesome.
Because it can change such realtime, right?
Again.
I'm just bringing up CentralReach or whatever.
For them to change the way thattheir program, that's going to

(46:46):
take a lot of coding and a lotof, like, this could change real
time, which is so, so exciting.
Go ahead, Mike.

SPEAKER_03 (46:52):
Well, you've changed my mind.
I think I told you this thefirst time we spoke, and I'm
just not experienced enough yetwith these models, but that was
my fear, and I think that's thefear I get when people go a
little too far with the, oh,this is going to replace humans.
I agree with you for the mostpart that there's going to be a
human element that's needed foreverything, but you've certainly
changed changed my mind in termsof my fear of how things were

(47:16):
just going to be templated andduplicated.
Obviously, part of that was mefearing how people are going to
use this technology, which Ithink is still an ongoing
concern.
And I think we need to getbetter at using it.
But more than anything, it wasjust more that fear that the
model was just going to bespitting out these generic
things.
But what you're talking aboutcan become client-specific.

(47:37):
And then if you use the systemwisely enough, you could
probably collapse thatinformation or that system
learning across all your clientstoward a more clinical practice
or your individualized standardof practice.
So that sounds, is thatsomething that's possible?

SPEAKER_01 (47:53):
Yeah, yeah.
Customize and individualize theclient based on client
information, but also customizeand individualize your practice,
your templates, your way ofdoing documentation.

SPEAKER_02 (48:03):
Wow.

SPEAKER_01 (48:04):
But yeah, I guess for you guys, I'd be curious to
know, like, what concerns do youstill have?
What concerns do you think otherpeople have?
I

SPEAKER_00 (48:14):
think you answered one of them.
My concern would be...
We were running into some techissues yesterday on Zoom and our
whole setup.
It seems like when AI getsinvolved, they were like, have
you done these 10 steps?
And you were like, yes, we'vedone these 10 steps.
But we had to suffice all ofthose 10 steps which you'd

(48:36):
already done to get to step 11.
So my...
And that way was actuallyinefficient because it was like
a protocol of like, well, youstart here.
Okay, we've done that.
We're already way past that.
No, you got to go here next.
You got to go here.
But it sounds like in whatyou're saying that it could be
like individualized sospecifically that I wouldn't

(48:59):
need to go through the previous10 steps if I didn't want to go
through the 10 steps.
I could just say we're going tostart with step 11.
Does that make sense?
Yeah.

SPEAKER_01 (49:08):
Yeah, it does, right?
Based off of all the informationyou've given us, what you've
done in the platform so far, wecan jump you to kind of like the
thing that you think we thinkyou should be doing next.
And I think like that's justwhat AI native software is able
to do, right?
Like I do think like people likeCentral Reach, Rethink, they've

(49:28):
been around for a long time.
They have a platform that has alot of like switches and toggles
and different things that you,that they've built with a lot of
difficulty over time, codingevery single one of those
toggles.
But now what AI native softwarelets you do is if you want to
make a change, you just type itin.
And you do that, like not aprogrammer.
Those toggles become

SPEAKER_03 (49:49):
fixed in SNC.
You now have to deal– that's theconstraints that Dan was talking
about.
So great software, but now it'sfixed, and its automation, if it
has anything, is going to almostbe restrictive sometimes.
I don't want it to do this, butit did it anyway.
Okay.

SPEAKER_01 (50:04):
Exactly.
It's restricted to the togglesthat were in the developer-coded
platform, right?
Like what if you want adifferent toggle?
You want the toggle to beslightly different.
Well, now you can do that inAI-native software like us just
to not– So I think that's thebiggest difference between old
software and new software,software 2.0 and AI-enabled

(50:25):
software 3.0, if you want to usesome tech people, San Francisco
language.
Oh, snap.
Well, you're in San Franciscoright now.
It is.
I am.

SPEAKER_00 (50:37):
So that would be my first concern, but I think
you've satisfied that.
That was our initial AI concern.
My second one would...
again, this is going to soundkind of pessimistic for the
field, that what I've seen isthat when these companies, like,
again, I'll use Central Reach,they have some such good
strategies, like they canautomatically graph it.
We used to have to puteverything in Excel and it would

(50:57):
take forever to graph it.
And they can just graph thingsand make things such more
efficient with the premise that,hey, if we can take these
efficiencies from you, you canspend more time from the client.
What I've seen, unfortunately,in the field is when this
becomes more efficient, Now theyjust spread the BCBAs thinner.
So instead of having to graphall your stuff, and now you can

(51:20):
spend that time actuallyreviewing your graphs because
Central Reach does that.
The BCBAs aren't even reviewingtheir graphs because Central
Reach are auto-progress.
Probably 8 out of 10 of them,unfortunately, from what I've
heard.
If you ask them where any clientis at any given time, a lot of
them won't even know becausethey're just letting Central
Reach progress through thegraphs and things like that so
they can go see more clients.

(51:41):
So yes, it's more efficient.
but the client-facing serviceshaven't necessarily improved, if
that makes sense.
The efficiency has.
The amount of hours that I canbill has improved, but the
client-facing service hasn'timproved.
So that would be my secondconcern, personally, just from
what I've seen, is that how canwe make sure that it's not,

(52:01):
cool, we got this AI that'sgoing to spit out all the
programs, so go run a bunch moreprograms as opposed to monitor
the programs.
Does that make sense?

SPEAKER_01 (52:11):
No, it makes total sense, right?
If you think of your ABA clinicas a factory and now you've made
one part of the factory slightlymore efficient, now you're like,
oh, how much more can I squeezein and out of the factory?

SPEAKER_00 (52:26):
Exactly, exactly.
How many more clients can I givethis BCBA now that we have
versus your BCBA was alreadybehind.
They can maybe just catch up onthe clients that they have.
No, we got AI.
They can see 15 clients nowinstead of 10.

SPEAKER_01 (52:39):
Yeah, or can a BCBA finally not work after 7 or
whatever time they actually getoff work?

SPEAKER_00 (52:47):
Can they not work Sundays?

SPEAKER_01 (52:48):
Yeah, not get burnt out.
So many BCBAs I talk to arelike, I fall asleep to
documentation.
I was writing a treatment planon Friday and then I actually
spilled my tea over my computer.
All this crazy stuff.
Yeah.
I think the challenge for me isI don't run an ABA agency.

(53:13):
I don't run the ABA agencies ofpeople who choose to use our
software or not.
I think you can use technologyvery poorly and you can be one
of those places who treats theirclinic like an input-output
factory.
We will all eventually kind ofturn on those places.
Insurance companies will startto realize that the care there

(53:36):
is actually not that costeffective and start to give them
lower rates and start to maybestop pushing clients to them.
I think providers, clinicians,BCBAs are starting to understand
that they have a lot of power,right?
There's a massive shortage ofBCBAs out there.
And so you as a BCBA actually dohave a choice about who you work
for and what they do.

(53:58):
My hope is that technology likeAlpaca Health helps more.
more clinicians go independent,start their own practices,
continue running theirpractices, grow their small
practices, because now we takethe back office scale of one of
these private equity roll upsand give it to every single BCBA
and every single clinician ownedand operated business.

(54:21):
So that's really my hope forwhat this technology can do is
it sort of powers people who arereally clinically minded, care
about their clients, care abouttheir clinician to run their own
agencies the way they want torun them rather than whatever
blueprint playbook privateequity has printed out and given
to all of its little companies.

SPEAKER_00 (54:40):
That's so cool.
I love it, man.
We vetted, Mike.
I want to say this.
We have plenty of people reachout with similar stuff, and a
lot of times we're just like,ah, nah.
We really do believe you'regenuine in what you're saying,
and that's why we wanted to haveyou on, and we're very excited
to promote your product becausewe do feel like it comes from
that part of how can we makeservices better.

SPEAKER_03 (55:01):
Yeah, this is really awesome.
As I said in the beginning, thehour flies by.
We're at our 55-minute mark.
We've got a ton more to talkabout.
I'm sure this doesn't have to bethe first and last time you
appear.
In fact, I can't wait to startusing this product, and we're at
the perfect time to start usingthis and learning more and then
have you on again to give youfeedback and actually learn more

(55:24):
from you about how to betterimprove our use of this product.
Because, again, I think as longas we stay smart about it and
don't try to, you know, squeezein more time or more clients.
As long as we keep the statusquo in terms of ethical practice
and manageable caseloads, butthen use this to improve the
quality, not increase thequantity, it stands to have a

(55:49):
lot of power.
I really appreciate that you'vebrought this to our field.
We're excited.
I

SPEAKER_00 (55:54):
guess in conclusion, Michael, kind of wrapping up for
you, let's say a BCBA islistening, a company is
listening, anybody like that,what would you say is kind of
your...
Perfect.

SPEAKER_01 (56:28):
Yeah.
I think in short, turn the timeyou spend on paperwork to time
spent with people.
All that documentation time, letthat go away because an alpaca
is a system.
In terms of how to evaluatewhether it actually works, I
think with a lot of these AIthings, proof is in the pudding,
right?
Give it a shot.
You like it, awesome.

(56:49):
If not, tell us why, let us fixit, please.
But also no sweat there.
And so we actually have alpacaassistant totally free for
anybody to sign up and give it ashot.
You don't even need to use itwith a real client.
We have a sample client clientrecords.
We have sample transcripts thatyou could just read aloud so

(57:11):
that you can test what AlpacaAssistant will do.
on your own time without aclient and without having to
worry about any of those things.
And so that way, when you use ityourself and you like it and you
want to start using it inclinical interactions, you also
know how to leverage the productthe best.
It's totally HIPAA compliant.
Every user who signs up with ussigns a business associates

(57:33):
agreement with Alpaca Health,the company.
And so folks are covered when itcomes to a HIPAA and privacy
confidentiality compliance pointof view.
So my ask for everybody who'smade it us this far into the
hour is to give it a try.
It's free.
You can use it without clientsand it's HIPAA compliant.
And you can go toalpacahealth.io and just start

(57:56):
using it.
And if you like it or if youhave feedback, please reach out.
I'm very active on LinkedIn.
My email's on the website.
So we'd love to continue theconversation.

SPEAKER_00 (58:07):
We'll have the website.
If you don't mind, we'll maybeput your email or something for
people to contact you in thedescription below.
We're very excited.
We've always been...
I feel like on the forefront oftrying to innovate in the ABA
field, that's why we're kind ofwhere we're at right now.
And yeah, I'm really excited tosee what Michael and Alpaca

(58:29):
Health Assistant can provide.

SPEAKER_03 (58:31):
You've convinced me.
I will actually now use AI, andit's because of you, Mr.
Gao.
Thank you so much for reachingout.
Thank you so much for creatingthis product.
I'd like to end it with a littlesynopsis here, and I'm going to
have to quote you.
You said, take the time you'respending on paperwork and spend

(58:51):
it with people.
I love that.
And then like we like to sayhere on ABA on tap, always
analyze responsibly.
Cheers.
Cheers.
Thanks a lot, Michael.

SPEAKER_01 (59:01):
Thank you guys.

SPEAKER_03 (59:03):
ABA on tap is recorded live and unfiltered.
We're done for the day.
You don't have to go home, butyou can't stay here.
See you next time.

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