In this episode of ShiftShapers, host David A. Saltzman talks with Ramesh Kumar, CEO and co-founder of zakipoint health. Ramesh shares how a personal mission to help his father navigate healthcare led to a professional pursuit: transforming overwhelming claims data into actionable insights for employers, advisors, and members.
The conversation explores the evolution of predictive modeling, the growing importance of fiduciary responsibility in plan design, and how AI-powered platforms are helping advisors simplify benefit decisions and improve outcomes. Ramesh offers real-world examples of how data can shape plan strategy, change behavior, and improve care—before costs spiral out of control.
🤖 Sponsored by BenePower
BenePower is an AI-powered platform helping advisors build high-impact, self-insured health plans quickly and seamlessly. By integrating best-in-class point solutions and eliminating inefficiencies, BenePower reduces costs, improves member outcomes, and positions advisors as industry leaders.🔗 Learn more at BenePower.com
🔑 Key Takeaways from This Episode
📌 Claims Data Is the Starting Line—Not the Finish
TPAs can’t block it. Advisors must request it. With the right tools, data can reveal inefficiencies and drive smarter decisions.
📌 Predictive Modeling Meets Personalization
Ramesh explains how modern AI doesn’t just stratify risk—it tailors messages and care nudges to drive better action at the member level.
📌 Fiduciary Awareness Is on the Rise
High-profile lawsuits and rebate opacity are forcing plan sponsors to demand better data—and use it.
📌 AI Is Rewriting the Navigation Experience
Agentic AI will soon act as your members’ personal guide—making healthcare access faster, smarter, and more human (even if it’s not human).
📌 Member Engagement Must Be Proactive
You can’t educate after the ER visit. The future belongs to platforms that reach members before the bill comes.
⏱️ In This Episode
00:00 Introduction to Ramesh and zakipoint health
02:00 From personal mission to industry disruption
04:00 The problem with too much (bad) data
06:00 Turning claims into strategy: risks, costs, gaps
08:30 How fiduciary pressure is reshaping advisor roles
10:00 Making healthcare navigation member-centric
14:00 Predictive modeling and behavioral segmentation
16:30 Agentic AI and rethinking healthcare workflows
19:00 AI in mental health, coaching, and chronic care
20:45 What's next for advisors and tools
22:00 Ramesh’s 5-year vision: frictionless healthcare access
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
To advise clients on renewals, plan strategy and plan
design adjustments, you needdetailed and deep insight into
all plan parameters.
How do you do that?
We'll find out on this episodeof Shift Shapers.
Speaker 2 (00:15):
Change either energizes or paralyzes.
The choice is yours, is yours.
This is the Shift Shaperspodcast, bringing the employee
benefits industry interviewswith individuals and companies
who are shaping the industryshifts.
And now here's your host, DavidSaltzman.
Speaker 1 (00:37):
And to help us answer that question, we've invited
Ramesh Kumar.
Ramesh is CEO and co-founder atZaki Point Health.
Welcome, ramesh, thanks forbeing here.
Well, thank you.
Speaker 3 (00:47):
Thank you, David.
I look forward to ourconversation.
Speaker 1 (00:50):
So question for you we always like to start out with
how did you end up doing whatyou're doing today?
Because most people, mostpeople's careers aren't a
straight line and it's oftenfascinating how they got to be
doing what they're doing.
Speaker 3 (01:02):
Yeah Well, so I've always seen myself as an
entrepreneur, growing up as anNES.
When I was 40 years old and gotinvolved in stopping multiple
tech businesses and that's onethat did well in the mobile
couponing and ticketing spaceBuilt that company in the UK
selling to telecom operators,really trying to bring
(01:23):
innovation in how we use mobilephones in our day-to-day life
and using it for promotion,marketing, couponing.
Sold that business to aUK-listed company and I wanted
to do something meaningful.
I wanted to do something moreimpactful using my skills, my
energy technology capabilities,and then I was actually during
(01:44):
that time.
And then I was actually duringthat time as I was searching for
new ideas, helping my dad to behealthier, who's been an
inspiration to me as anentrepreneur himself, and he has
high BMI and had bypass surgeryin his mid-40s, and so it was
always on his case to behealthier and I tried to help
him be healthier.
But that was very hard.
We all find it very challengingto change our behavior.
(02:08):
But through that journey I wasable to see that he could have
saved money on his mail-ordermedication program.
But he just didn't know and Icouldn't even find it easily
enough and that kind of reallyopened up this whole challenge
that we face in healthcare.
It is really opaque, reallydifficult to know and navigate
(02:33):
what is really beneficial to you, what is high quality, what is
good care that is low cost.
So that really got me thinkingand working on this problem and
combining some of my backgroundin data science, data analytics
and mobile technology.
And here we are.
Speaker 1 (02:50):
We certainly picked a place that needs help.
We're glad you're here, solet's talk a little bit about
that.
Most of the audience arebenefit advisors.
They're client facing andthey're being called on to do
more and more analytical workand to try to help plans
understand what's going on andmake those intelligent decisions
that we talked about in ouropening.
A lot of their problems orchallenges are being caused by a
lack of data.
How do you get past that?
Speaker 3 (03:12):
Meaning?
How do you access data?
How do you analyze data?
How do you draw insights?
Yes, all of the above.
Speaker 1 (03:18):
If you're an advisor, how do you do that?
I mean, it's not like it'sbeing laid on your plate easily.
Sometimes it's inaccessible orthe data is not clear or
intelligible or whatever.
How does an advisor deal withthat problem?
Yeah, it's a great point andquestion.
Speaker 3 (03:35):
So self-funding, which is really where the plan
sponsor is the employer and, onthe behalf of the employer, the
broker benefit consultant canactually request and access, you
know, claim level data, whichis basically an invoice, so each
claim is an invoice that theemployer is already paying for,
and so it is actually accessible.
(03:57):
It is available and there'snothing that should stop you
from the TPA third partyadministrator or the health plan
that is kind of managing theadministration of it to stop you
from getting it.
But the bigger challenge oftenhas been with all of that data,
how do you make sense of it?
How do you draw insights?
How do you make it moremeaningful to draw and take an
(04:19):
action?
That's probably where a bulk ofthe energy effort is being put
in place.
Over the last I would say, 15,20 years, Technologies have
changed what's possible now andhow many different variety of
data sets could be.
I've done it with these medicalclaims or pharmacy claims or
social demographic data, but nowall of these other data sets
(04:41):
around price transparency data,what people are paying for at
each provider for each procedure, what is contracted rates and
all of that stuff.
So now you have access to a lotmore data, but that also means
how do you actually turn thatinto anything meaningful?
That's where a lot of work hasto be going in.
Speaker 1 (05:03):
So what kinds of things are you working on?
I mean you've got a pile ofwork has to be going in, so what
kinds of things are you workingon?
I mean you've got a pile ofdata.
Let's assume that it's clean,legitimate data and it's useful.
How are you guys working onsifting that and sorting that so
that it's actionable andintelligible?
Speaker 3 (05:17):
Yeah.
So once you bring all of thatdata up together and match it at
a member level, you can use itto build predictive models to
understand where the futurecosts and risks will be.
Understand where the riskdrivers are.
Understanding gaps in care.
Understanding, you know, arepeople going to places of care
that are expensive.
(05:38):
They could be going to analternative place that is low
cost, high quality.
Understand that they are goingto inappropriate places.
Understand that they're goingto inappropriate places of care.
They're going to ER instead ofurgent care, they are not using
main-order medication program,just like in the case of my dad,
or they're not usingtelemedicine when it's available
.
So, all of these kinds ofanalytics, to understand where
(05:59):
the inefficiency is, where therisk is not being mitigated,
where the costs are not beingmanaged, and understand all of
that behavior.
To map it against what actionscan you take?
And most of the time thoseactions fall into three or four
simple things, you know.
Can you turn that into acommunication campaign to get
(06:19):
the members to do thealternatives?
Can you put a solution in placethat can focus on that risk
driver, that cost driver toprovide a musculoskeletal
program if there is a lot ofcost related to surgeries and
things like that.
Or if you are seeing diabeticsdriving a lot of the costs and
they have gaps in care, are wepleasing those gaps in care?
(06:39):
Maybe could put solution inplace.
So then driving those actionsbecomes key and communicating
that becomes key.
Maybe a plan designed toincentivize people becomes key.
So these are some of theactions that we certainly are
embedding into our platform, andthis is kind of the way we
should be thinking about usingdata.
Speaker 1 (07:00):
Are you finding, let's say, a heightened interest
in plans being able to getactionable data, with all of the
talk that's happened over thelast year or two about this
renewed interest in being properfiduciaries?
Speaker 3 (07:17):
Is that driving some of the conversation?
Yes, plan sponsors have beensued by members that they're not
doing a good job, all the wayto even health plans who are not
showing that the rebates forthese pharmacy benefits are not
being passed down to the plansponsor, and there have been
cases against that, whichbasically is creating this
(07:39):
environment where flip-flopaccessing and requesting for
data should be a norm.
You should just assume that youcan.
If somebody's blocking it, youcould just go and complain as a
self-underduty and in terms ofall of these kind of nuances
where, if these rebates are notclear from the data, you can
request okay, because of thetraditional responsibility for
(08:01):
the plan sponsor, but also itactually makes economic sense
because you will save as a plansponsor as well you can request
for a campaign to understandthese plan debates or other kind
of nuances that are not easilyaccessible from the data.
Speaker 1 (08:18):
So how does this trickle down to employees?
I mean, that's really where therubber meets the road.
It's employee satisfaction,it's employee retention, it's
all the things that employers goto the trouble of building
plans for An example or two of.
You know, once an employer orplan has this data, some of the
things that they can do with itto improve employee outcomes and
(08:39):
experiences do with it toimprove employee outcomes and
experiences.
Speaker 3 (08:44):
You know, so I get.
What you're trying tounderstand is how all of this
change this environment, thisdata can be useful at a member
level.
Yeah, exactly so.
This is where the realinnovation is happening and
should be kind of impact.
You will see, first of all,when you really bring it down to
simple things.
You and I have no idea whatthings cost in the healthcare
system before we use it.
(09:04):
You know, you and I don't evenhave much understanding of how
to navigate our benefits, likewhat is really available to me,
if there are benefits, if thereare incentives, if there's a
particular program.
That's not really clear andeasy for the member, and
certainly the understanding ofwhat is high quality care, what
(09:25):
is good quality care that isactually low cost.
That is, that equation, whichshould be very personalized, is
not quite clear to the members.
So I believe that with all ofthis data that's now available,
we can address these problemsfor the member and make it more
proactive.
What's fundamentally missing isyou cannot go back and tell a
(09:46):
member if they've gone for theirsurgery and say, hey, you could
have done X, like oh, thank you.
You really have to be veryproactive.
You have to be very kind ofunderstand where the risks are
going, where the costs are going, where the gaps might be, where
the member might have a needwhen they're actually calling in
at the call center to say, hey,I need to know where my
(10:07):
deductible situation is.
Oh, can I make an appointmentwith a specialist for this?
That is a signal that we shouldall be kind of leveraging and
using to help the member better.
So, using all that proactivenature of the data, predictive
nature of the data, to be ableto provide that transparency
regarding what things might costyou, what it might mean for you
as an individual how tonavigate with all of the
(10:29):
benefits that are available.
So what that really means insimple terms is when you know
what's available on the benefitplan, with all the documents,
with all the technology, nowthat's available, now that's
artificial intelligence youshould be able to pinpoint okay
for you if you have thiscomplicated pregnancy situation.
Here are the exceptions in yourplan documents.
(10:50):
And, by the way, we do havethis benefit program available
to our members to provide thatsupport.
So being able to bring all ofthat in those two statements, it
should be easy and we should bedoing that for the member.
We should not make it like fivephone calls and people are
searching for differentdatabases.
So that's kind of where it'sgoing.
(11:10):
It is happening.
We are also working on theforefront of this.
How do we use all of this dataand add a layer of that
artificial intelligence to beable to serve the member,
support the member, navigate andthen be able to help them
understand what's high quality,low cost?
Speaker 1 (11:28):
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And now back to ourconversation.
You touched on it a little bitand I'd like to kind of dial
back a little bit.
The industry has spent so muchtime when it has had data in the
past kind of looking in therearview mirror, and I know
there have been some companiesthat have done some work on
(13:21):
predictive modeling.
Is that something thatZakiPoint Health is involved in
and, if so, where do you seethat going?
How fast is it improving andwhat kind of R-squares are you
getting?
Speaker 3 (13:32):
That's great.
Obviously, now we're geekingout on some statistics here.
So the predictive modeling forrisks and costs has been out
there for the last 15 odd years.
You know companies likeMilliman, a bunch of other
companies, have been working onthis and using machine learning
technologies and techniques whenI see this predictive modeling
(13:55):
getting more interesting.
So we leverage all of that.
So, rather than kind ofreinvent the wheel here, we
actually partner with Millimanand we use those adjusted risk
models to embed into a platformto be able to provide those
insights and identify cohorts ofpeople.
Where is this most exciting?
That you predict is for thisindividual, what is the right
(14:16):
next step?
Ie, if there's a gap in care,they should be taking an action.
What kind of message isactually working well for this
type of individual?
And segmenting the populationsand trying to understand okay,
this person works with the gameframe, ie when you tell them
something like, hey, you couldgain X by doing this or you
(14:37):
could save that Y from doingthis action.
So being able to use thatpredictive analytics and some of
that personalization throughartificial intelligence, being
able to improve that memberexperience, to improve that
nudges, to improve how themember responds, and taking that
response data to be able tothen give something that's a lot
(14:59):
more relevant and contextual.
That's where all thispredictive modeling is getting
very, very powerful as well.
So combining, you know,stratification, risk, strat, you
know stratification to costpredictions, to all of this, how
do you get the member tounderstand, use and take an
action?
Speaker 1 (15:19):
Is that where AI comes into the picture and, more
to the point, maybe someagentic AI?
Taking that data and making itaccessible and useful to them at
the member level.
Speaker 3 (15:31):
Yeah, absolutely.
We actually had a big sessiontoday with one of the thought
leaders in the industry aboutagentic AI, speaking to an
entire company today.
It's a huge topic.
It's very early innings in this, what's going on right now, but
broadly to summarize, when itcomes to you know, ai, it could
(15:53):
become a co-pilot for all ofthose care navigators who are
providing care navigationsupport on the phone or
otherwise through chat.
It can be a great co-pilot toolwhere it's accessing all of the
data.
It could also be a verypowerful chatbot technology, q&a
service or 24 by 7 self-servicetool to help the members
(16:16):
diagnose things, understand andunderstand their own benefits
better.
But AI can also rewrite the waywe actually access our
healthcare data or evenhealthcare services.
But in the world of healthcareadministration, the whole
workflow could be rewritten withthese AI agents where, for
(16:38):
example, in the past you'rehaving to call to do x and then
their message comes back and youknow, a week later that you'd
be a prior authorization hasgone through, all our workflow
could change and these ai agentscan actually go off and you
initiate something as a member.
It actually runs through all ofthose different steps and comes
back and does the work for you.
(16:59):
All those rules and parametersare set in place anyway, so
that's where AI can actuallyspeed things up to gain those
insights for the member and thecare navigator much more quickly
, so that better decisions arebeing made.
Speaker 1 (17:16):
Well, that's, I think , one aspect of it and another.
You know we're working in myfirm.
We're working with a companythat's doing a lot of work with
creating an agentic front endfor people with chronic
conditions or multiple chronicconditions, who are maybe
reticent to pick up the phoneand speak to a human being but
who, for some reason, will speakto a disembodied voice at the
end of a phone.
(17:36):
It's kind of a whole newfrontier, especially given the
amount of money that chronicconditions and people who are
multi-chronic and multi-pharmacare driving.
Is that something you guys aretalking about as well?
Speaker 3 (17:49):
Yeah, so we are not so much talking or working on
that.
I certainly, being in theindustry, pay close attention to
all of this.
I host a podcast called Voicesof Self-Funding where I get to
interact with some people whoare moving and shaping things in
the industry.
So, on the chronic conditionmanagement yes, it's very
powerful there, powerful there.
(18:16):
It's also quite relevant andpowerful when we talk about not
just the chronic conditionmanagement but the overall, the
workflow, how that could be kindof rewritten, shifted, how
these AI tools can be quitepowerful there.
So there's a whole host of areaswhere mental health I mean
another example just to dwell onthis where, for example, you
know we may not like talking topeople at least certain
generations AI tools canactually act as a consciousness
(18:40):
for you to some extent, or theycan be a very helpful coach to
you to some extent and 24 by 7,where it can understand you how
you make decisions, what yourblockers are, how you kind of
look at things or what's kind ofholding you back, or even just
overall simple mental healthkind of counseling when there's
a real shortage of this.
These kind of places data,personalization, understanding
(19:04):
of that individual can be verypowerful whether it's used
directly to serve the member orit's augmenting some sort of
support that you're given.
Speaker 1 (19:14):
Is having a toolkit like this accessible to the
average benefit advisor?
Is it something that they canbring to the table easily these
days, or is that still kind ofcoming?
Speaker 3 (19:25):
So some of the stuff is already there.
So when you say benefitadvisors, are these tools where
they can analyze the storybehind a particular plan
sponsor's data and be able tomake decisions and be able to
activate some of these campaignsand communicate with the
members, promote some sort ofzero dollar deductible plans or
(19:46):
tiered plans.
All of that is available and weserve really some
forward-looking benefit advisorsdoing this.
So really some forward-lookingbenefit advisors doing this,
Some of the things around how AIcan rewrite the workflow
instead of having to searchthrough the tools and get some
of these answers.
And I believe, in the future,not too distant from now whether
(20:06):
it's a year from now you couldactually be asking using prompts
Help me figure out X for thisplan sponsor.
So half an hour before themeeting you could actually have
this prompt-driven communicationwith the tool and be able to
have a much better meetingfocused on the benefits and
benefit plans and be able tomake decisions.
(20:28):
So that sort of stuff is coming, with all the data being
available.
Speaker 1 (20:32):
I would say about a year from now, so that sort of
stuff is coming, with all thedata being available, I would
say, about a year from now.
Speaker 3 (20:41):
So as we wrap up our conversation today, take it out
five years and tell me whatyou're seeing, what you envision
.
So if I think I had five, it'sa great question.
I didn't fully think about this.
In healthcare, it's reallyimportant to bring the patient
and the provider together andmake it easier for that
interaction and any otherstakeholder who's supporting
(21:03):
that patient and the member.
I believe, with all the dataand technology, how you serve
those two to three stakeholdersin that interaction in a
fundamentally different manner.
That's where, over the nextfive years, I'll see a lot of
innovation.
You know examples of this couldbe you are able to take a
(21:26):
picture of X, you're able to doa certain diagnostic and you're
able to send that over to yourdoctor.
Being able to then pass thatdata to a specialist and then be
able to send that over to yourdoctor.
Being able to then pass thatdata to a specialist and then be
able to find a high qualityprovider very quickly without
having to kind of call and doall of that stuff.
These agents behind the scenescould actually help you access
(21:47):
all of this.
So it'll improve the insightsthat are needed for the patient
and the provider to have a muchbetter interaction so that good
decisions are made, good care isdelivered and the members are,
throughout the journey or afterleaving that interaction with
the provider, are actuallysticking to things taking
(22:08):
medication, doing the exercise,eating the right things or
whatever else has been asked todo.
How you keep that patient ontarget?
Speaker 1 (22:17):
on point, that experience will continue to
change and will be phenomenal,and that's a great place to end
our conversation for today.
Ramesh Kumar, ceo andco-founder at Zaki Point Health.
Ramesh, thanks for afascinating conversation.
We hope you'll come back.
Speaker 3 (22:33):
Well, thank you so much, David, for the opportunity
for sharing this.
Speaker 1 (22:42):
I want to give a quick shout out to our sponsor
and our producer, hatcher Media.
Hey, if you need podcastproduction or professional
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Visit him at HatcherMedianet.
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Speaker 2 (23:00):
This Shift Shapers podcast is copyrighted content
and may not be reproduced inwhole or in part without the
express written permission ofShift Shapers Solutions LLC.
Copyright 2024.
To advise clients on renewals, plan strategy and plan
design adjustments, you needdetailed and deep insight into
all plan parameters.
How do you do that?
We'll find out on this episodeof Shift Shapers.
Speaker 2 (00:15):
Change either energizes or paralyzes.
The choice is yours, is yours.
This is the Shift Shaperspodcast, bringing the employee
benefits industry interviewswith individuals and companies
who are shaping the industryshifts.
And now here's your host, DavidSaltzman.
Speaker 1 (00:37):
And to help us answer that question, we've invited
Ramesh Kumar.
Ramesh is CEO and co-founder atZaki Point Health.
Welcome, ramesh, thanks forbeing here.
Well, thank you.
Speaker 3 (00:47):
Thank you, David.
I look forward to ourconversation.
Speaker 1 (00:50):
So question for you we always like to start out with
how did you end up doing whatyou're doing today?
Because most people, mostpeople's careers aren't a
straight line and it's oftenfascinating how they got to be
doing what they're doing.
Speaker 3 (01:02):
Yeah Well, so I've always seen myself as an
entrepreneur, growing up as anNES.
When I was 40 years old and gotinvolved in stopping multiple
tech businesses and that's onethat did well in the mobile
couponing and ticketing spaceBuilt that company in the UK
selling to telecom operators,really trying to bring
(01:23):
innovation in how we use mobilephones in our day-to-day life
and using it for promotion,marketing, couponing.
Sold that business to aUK-listed company and I wanted
to do something meaningful.
I wanted to do something moreimpactful using my skills, my
energy technology capabilities,and then I was actually during
(01:44):
that time.
And then I was actually duringthat time as I was searching for
new ideas, helping my dad to behealthier, who's been an
inspiration to me as anentrepreneur himself, and he has
high BMI and had bypass surgeryin his mid-40s, and so it was
always on his case to behealthier and I tried to help
him be healthier.
But that was very hard.
We all find it very challengingto change our behavior.
(02:08):
But through that journey I wasable to see that he could have
saved money on his mail-ordermedication program.
But he just didn't know and Icouldn't even find it easily
enough and that kind of reallyopened up this whole challenge
that we face in healthcare.
It is really opaque, reallydifficult to know and navigate
(02:33):
what is really beneficial to you, what is high quality, what is
good care that is low cost.
So that really got me thinkingand working on this problem and
combining some of my backgroundin data science, data analytics
and mobile technology.
And here we are.
Speaker 1 (02:50):
We certainly picked a place that needs help.
We're glad you're here, solet's talk a little bit about
that.
Most of the audience arebenefit advisors.
They're client facing andthey're being called on to do
more and more analytical workand to try to help plans
understand what's going on andmake those intelligent decisions
that we talked about in ouropening.
A lot of their problems orchallenges are being caused by a
lack of data.
How do you get past that?
Speaker 3 (03:12):
Meaning?
How do you access data?
How do you analyze data?
How do you draw insights?
Yes, all of the above.
Speaker 1 (03:18):
If you're an advisor, how do you do that?
I mean, it's not like it'sbeing laid on your plate easily.
Sometimes it's inaccessible orthe data is not clear or
intelligible or whatever.
How does an advisor deal withthat problem?
Yeah, it's a great point andquestion.
Speaker 3 (03:35):
So self-funding, which is really where the plan
sponsor is the employer and, onthe behalf of the employer, the
broker benefit consultant canactually request and access, you
know, claim level data, whichis basically an invoice, so each
claim is an invoice that theemployer is already paying for,
and so it is actually accessible.
(03:57):
It is available and there'snothing that should stop you
from the TPA third partyadministrator or the health plan
that is kind of managing theadministration of it to stop you
from getting it.
But the bigger challenge oftenhas been with all of that data,
how do you make sense of it?
How do you draw insights?
How do you make it moremeaningful to draw and take an
(04:19):
action?
That's probably where a bulk ofthe energy effort is being put
in place.
Over the last I would say, 15,20 years, Technologies have
changed what's possible now andhow many different variety of
data sets could be.
I've done it with these medicalclaims or pharmacy claims or
social demographic data, but nowall of these other data sets
(04:41):
around price transparency data,what people are paying for at
each provider for each procedure, what is contracted rates and
all of that stuff.
So now you have access to a lotmore data, but that also means
how do you actually turn thatinto anything meaningful?
That's where a lot of work hasto be going in.
Speaker 1 (05:03):
So what kinds of things are you working on?
I mean you've got a pile ofwork has to be going in, so what
kinds of things are you workingon?
I mean you've got a pile ofdata.
Let's assume that it's clean,legitimate data and it's useful.
How are you guys working onsifting that and sorting that so
that it's actionable andintelligible?
Speaker 3 (05:17):
Yeah.
So once you bring all of thatdata up together and match it at
a member level, you can use itto build predictive models to
understand where the futurecosts and risks will be.
Understand where the riskdrivers are.
Understanding gaps in care.
Understanding, you know, arepeople going to places of care
that are expensive.
(05:38):
They could be going to analternative place that is low
cost, high quality.
Understand that they are goingto inappropriate places.
Understand that they're goingto inappropriate places of care.
They're going to ER instead ofurgent care, they are not using
main-order medication program,just like in the case of my dad,
or they're not usingtelemedicine when it's available
.
So, all of these kinds ofanalytics, to understand where
(05:59):
the inefficiency is, where therisk is not being mitigated,
where the costs are not beingmanaged, and understand all of
that behavior.
To map it against what actionscan you take?
And most of the time thoseactions fall into three or four
simple things, you know.
Can you turn that into acommunication campaign to get
(06:19):
the members to do thealternatives?
Can you put a solution in placethat can focus on that risk
driver, that cost driver toprovide a musculoskeletal
program if there is a lot ofcost related to surgeries and
things like that.
Or if you are seeing diabeticsdriving a lot of the costs and
they have gaps in care, are wepleasing those gaps in care?
(06:39):
Maybe could put solution inplace.
So then driving those actionsbecomes key and communicating
that becomes key.
Maybe a plan designed toincentivize people becomes key.
So these are some of theactions that we certainly are
embedding into our platform, andthis is kind of the way we
should be thinking about usingdata.
Speaker 1 (07:00):
Are you finding, let's say, a heightened interest
in plans being able to getactionable data, with all of the
talk that's happened over thelast year or two about this
renewed interest in being properfiduciaries?
Speaker 3 (07:17):
Is that driving some of the conversation?
Yes, plan sponsors have beensued by members that they're not
doing a good job, all the wayto even health plans who are not
showing that the rebates forthese pharmacy benefits are not
being passed down to the plansponsor, and there have been
cases against that, whichbasically is creating this
(07:39):
environment where flip-flopaccessing and requesting for
data should be a norm.
You should just assume that youcan.
If somebody's blocking it, youcould just go and complain as a
self-underduty and in terms ofall of these kind of nuances
where, if these rebates are notclear from the data, you can
request okay, because of thetraditional responsibility for
(08:01):
the plan sponsor, but also itactually makes economic sense
because you will save as a plansponsor as well you can request
for a campaign to understandthese plan debates or other kind
of nuances that are not easilyaccessible from the data.
Speaker 1 (08:18):
So how does this trickle down to employees?
I mean, that's really where therubber meets the road.
It's employee satisfaction,it's employee retention, it's
all the things that employers goto the trouble of building
plans for An example or two of.
You know, once an employer orplan has this data, some of the
things that they can do with itto improve employee outcomes and
(08:39):
experiences do with it toimprove employee outcomes and
experiences.
Speaker 3 (08:44):
You know, so I get.
What you're trying tounderstand is how all of this
change this environment, thisdata can be useful at a member
level.
Yeah, exactly so.
This is where the realinnovation is happening and
should be kind of impact.
You will see, first of all,when you really bring it down to
simple things.
You and I have no idea whatthings cost in the healthcare
system before we use it.
(09:04):
You know, you and I don't evenhave much understanding of how
to navigate our benefits, likewhat is really available to me,
if there are benefits, if thereare incentives, if there's a
particular program.
That's not really clear andeasy for the member, and
certainly the understanding ofwhat is high quality care, what
(09:25):
is good quality care that isactually low cost.
That is, that equation, whichshould be very personalized, is
not quite clear to the members.
So I believe that with all ofthis data that's now available,
we can address these problemsfor the member and make it more
proactive.
What's fundamentally missing isyou cannot go back and tell a
(09:46):
member if they've gone for theirsurgery and say, hey, you could
have done X, like oh, thank you.
You really have to be veryproactive.
You have to be very kind ofunderstand where the risks are
going, where the costs are going, where the gaps might be, where
the member might have a needwhen they're actually calling in
at the call center to say, hey,I need to know where my
(10:07):
deductible situation is.
Oh, can I make an appointmentwith a specialist for this?
That is a signal that we shouldall be kind of leveraging and
using to help the member better.
So, using all that proactivenature of the data, predictive
nature of the data, to be ableto provide that transparency
regarding what things might costyou, what it might mean for you
as an individual how tonavigate with all of the
(10:29):
benefits that are available.
So what that really means insimple terms is when you know
what's available on the benefitplan, with all the documents,
with all the technology, nowthat's available, now that's
artificial intelligence youshould be able to pinpoint okay
for you if you have thiscomplicated pregnancy situation.
Here are the exceptions in yourplan documents.
(10:50):
And, by the way, we do havethis benefit program available
to our members to provide thatsupport.
So being able to bring all ofthat in those two statements, it
should be easy and we should bedoing that for the member.
We should not make it like fivephone calls and people are
searching for differentdatabases.
So that's kind of where it'sgoing.
(11:10):
It is happening.
We are also working on theforefront of this.
How do we use all of this dataand add a layer of that
artificial intelligence to beable to serve the member,
support the member, navigate andthen be able to help them
understand what's high quality,low cost?
Speaker 1 (11:28):
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And now back to ourconversation.
You touched on it a little bitand I'd like to kind of dial
back a little bit.
The industry has spent so muchtime when it has had data in the
past kind of looking in therearview mirror, and I know
there have been some companiesthat have done some work on
(13:21):
predictive modeling.
Is that something thatZakiPoint Health is involved in
and, if so, where do you seethat going?
How fast is it improving andwhat kind of R-squares are you
getting?
Speaker 3 (13:32):
That's great.
Obviously, now we're geekingout on some statistics here.
So the predictive modeling forrisks and costs has been out
there for the last 15 odd years.
You know companies likeMilliman, a bunch of other
companies, have been working onthis and using machine learning
technologies and techniques whenI see this predictive modeling
(13:55):
getting more interesting.
So we leverage all of that.
So, rather than kind ofreinvent the wheel here, we
actually partner with Millimanand we use those adjusted risk
models to embed into a platformto be able to provide those
insights and identify cohorts ofpeople.
Where is this most exciting?
That you predict is for thisindividual, what is the right
(14:16):
next step?
Ie, if there's a gap in care,they should be taking an action.
What kind of message isactually working well for this
type of individual?
And segmenting the populationsand trying to understand okay,
this person works with the gameframe, ie when you tell them
something like, hey, you couldgain X by doing this or you
(14:37):
could save that Y from doingthis action.
So being able to use thatpredictive analytics and some of
that personalization throughartificial intelligence, being
able to improve that memberexperience, to improve that
nudges, to improve how themember responds, and taking that
response data to be able tothen give something that's a lot
(14:59):
more relevant and contextual.
That's where all thispredictive modeling is getting
very, very powerful as well.
So combining, you know,stratification, risk, strat, you
know stratification to costpredictions, to all of this, how
do you get the member tounderstand, use and take an
action?
Speaker 1 (15:19):
Is that where AI comes into the picture and, more
to the point, maybe someagentic AI?
Taking that data and making itaccessible and useful to them at
the member level.
Speaker 3 (15:31):
Yeah, absolutely.
We actually had a big sessiontoday with one of the thought
leaders in the industry aboutagentic AI, speaking to an
entire company today.
It's a huge topic.
It's very early innings in this, what's going on right now, but
broadly to summarize, when itcomes to you know, ai, it could
(15:53):
become a co-pilot for all ofthose care navigators who are
providing care navigationsupport on the phone or
otherwise through chat.
It can be a great co-pilot toolwhere it's accessing all of the
data.
It could also be a verypowerful chatbot technology, q&a
service or 24 by 7 self-servicetool to help the members
(16:16):
diagnose things, understand andunderstand their own benefits
better.
But AI can also rewrite the waywe actually access our
healthcare data or evenhealthcare services.
But in the world of healthcareadministration, the whole
workflow could be rewritten withthese AI agents where, for
(16:38):
example, in the past you'rehaving to call to do x and then
their message comes back and youknow, a week later that you'd
be a prior authorization hasgone through, all our workflow
could change and these ai agentscan actually go off and you
initiate something as a member.
It actually runs through all ofthose different steps and comes
back and does the work for you.
(16:59):
All those rules and parametersare set in place anyway, so
that's where AI can actuallyspeed things up to gain those
insights for the member and thecare navigator much more quickly
, so that better decisions arebeing made.
Speaker 1 (17:16):
Well, that's, I think , one aspect of it and another.
You know we're working in myfirm.
We're working with a companythat's doing a lot of work with
creating an agentic front endfor people with chronic
conditions or multiple chronicconditions, who are maybe
reticent to pick up the phoneand speak to a human being but
who, for some reason, will speakto a disembodied voice at the
end of a phone.
(17:36):
It's kind of a whole newfrontier, especially given the
amount of money that chronicconditions and people who are
multi-chronic and multi-pharmacare driving.
Is that something you guys aretalking about as well?
Speaker 3 (17:49):
Yeah, so we are not so much talking or working on
that.
I certainly, being in theindustry, pay close attention to
all of this.
I host a podcast called Voicesof Self-Funding where I get to
interact with some people whoare moving and shaping things in
the industry.
So, on the chronic conditionmanagement yes, it's very
powerful there, powerful there.
(18:16):
It's also quite relevant andpowerful when we talk about not
just the chronic conditionmanagement but the overall, the
workflow, how that could be kindof rewritten, shifted, how
these AI tools can be quitepowerful there.
So there's a whole host of areaswhere mental health I mean
another example just to dwell onthis where, for example, you
know we may not like talking topeople at least certain
generations AI tools canactually act as a consciousness
(18:40):
for you to some extent, or theycan be a very helpful coach to
you to some extent and 24 by 7,where it can understand you how
you make decisions, what yourblockers are, how you kind of
look at things or what's kind ofholding you back, or even just
overall simple mental healthkind of counseling when there's
a real shortage of this.
These kind of places data,personalization, understanding
(19:04):
of that individual can be verypowerful whether it's used
directly to serve the member orit's augmenting some sort of
support that you're given.
Speaker 1 (19:14):
Is having a toolkit like this accessible to the
average benefit advisor?
Is it something that they canbring to the table easily these
days, or is that still kind ofcoming?
Speaker 3 (19:25):
So some of the stuff is already there.
So when you say benefitadvisors, are these tools where
they can analyze the storybehind a particular plan
sponsor's data and be able tomake decisions and be able to
activate some of these campaignsand communicate with the
members, promote some sort ofzero dollar deductible plans or
(19:46):
tiered plans.
All of that is available and weserve really some
forward-looking benefit advisorsdoing this.
So really some forward-lookingbenefit advisors doing this,
Some of the things around how AIcan rewrite the workflow
instead of having to searchthrough the tools and get some
of these answers.
And I believe, in the future,not too distant from now whether
(20:06):
it's a year from now you couldactually be asking using prompts
Help me figure out X for thisplan sponsor.
So half an hour before themeeting you could actually have
this prompt-driven communicationwith the tool and be able to
have a much better meetingfocused on the benefits and
benefit plans and be able tomake decisions.
(20:28):
So that sort of stuff is coming, with all the data being
available.
Speaker 1 (20:32):
I would say about a year from now, so that sort of
stuff is coming, with all thedata being available, I would
say, about a year from now.
Speaker 3 (20:41):
So as we wrap up our conversation today, take it out
five years and tell me whatyou're seeing, what you envision
.
So if I think I had five, it'sa great question.
I didn't fully think about this.
In healthcare, it's reallyimportant to bring the patient
and the provider together andmake it easier for that
interaction and any otherstakeholder who's supporting
(21:03):
that patient and the member.
I believe, with all the dataand technology, how you serve
those two to three stakeholdersin that interaction in a
fundamentally different manner.
That's where, over the nextfive years, I'll see a lot of
innovation.
You know examples of this couldbe you are able to take a
(21:26):
picture of X, you're able to doa certain diagnostic and you're
able to send that over to yourdoctor.
Being able to then pass thatdata to a specialist and then be
able to send that over to yourdoctor.
Being able to then pass thatdata to a specialist and then be
able to find a high qualityprovider very quickly without
having to kind of call and doall of that stuff.
These agents behind the scenescould actually help you access
(21:47):
all of this.
So it'll improve the insightsthat are needed for the patient
and the provider to have a muchbetter interaction so that good
decisions are made, good care isdelivered and the members are,
throughout the journey or afterleaving that interaction with
the provider, are actuallysticking to things taking
(22:08):
medication, doing the exercise,eating the right things or
whatever else has been asked todo.
How you keep that patient ontarget?
Speaker 1 (22:17):
on point, that experience will continue to
change and will be phenomenal,and that's a great place to end
our conversation for today.
Ramesh Kumar, ceo andco-founder at Zaki Point Health.
Ramesh, thanks for afascinating conversation.
We hope you'll come back.
Speaker 3 (22:33):
Well, thank you so much, David, for the opportunity
for sharing this.
Speaker 1 (22:42):
I want to give a quick shout out to our sponsor
and our producer, hatcher Media.
Hey, if you need podcastproduction or professional
graphic design, josh Hatcher isthe expert to contact For more
information.
Visit him at HatcherMedianet.
That's H-A-T-C-H-E-R-Medianet.
Speaker 2 (23:00):
This Shift Shapers podcast is copyrighted content
and may not be reproduced inwhole or in part without the
express written permission ofShift Shapers Solutions LLC.
Copyright 2024.
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