Speaker 1: 0:00

To advise clients on renewals, plan strategy and plan design adjustments, you need detailed and deep insight into all plan parameters. How do you do that? We'll find out on this episode of Shift Shapers.

Speaker 2: 0:15

Change either energizes or paralyzes. The choice is yours, is yours. This is the Shift Shapers podcast, bringing the employee benefits industry interviews with individuals and companies who are shaping the industry shifts. And now here's your host, David Saltzman.

Speaker 1: 0:37

And to help us answer that question, we've invited Ramesh Kumar. Ramesh is CEO and co-founder at Zaki Point Health. Welcome, ramesh, thanks for being here. Well, thank you.

Speaker 3: 0:47

Thank you, David. I look forward to our conversation.

Speaker 1: 0:50

So question for you we always like to start out with how did you end up doing what you're doing today? Because most people, most people's careers aren't a straight line and it's often fascinating how they got to be doing what they're doing.

Speaker 3: 1:02

Yeah Well, so I've always seen myself as an entrepreneur, growing up as an NES. When I was 40 years old and got involved in stopping multiple tech businesses and that's one that did well in the mobile couponing and ticketing space Built that company in the UK selling to telecom operators, really trying to bring innovation in how we use mobile phones in our day-to-day life and using it for promotion, marketing, couponing. Sold that business to a UK-listed company and I wanted to do something meaningful. I wanted to do something more impactful using my skills, my energy technology capabilities, and then I was actually during that time. And then I was actually during that time as I was searching for new ideas, helping my dad to be healthier, who's been an inspiration to me as an entrepreneur himself, and he has high BMI and had bypass surgery in his mid-40s, and so it was always on his case to be healthier and I tried to help him be healthier.

Speaker 3: 2:03

But that was very hard. We all find it very challenging to change our behavior. But through that journey I was able to see that he could have saved money on his mail-order medication program. But he just didn't know and I couldn't even find it easily enough and that kind of really opened up this whole challenge that we face in healthcare. It is really opaque, really difficult to know and navigate what is really beneficial to you, what is high quality, what is good care that is low cost. So that really got me thinking and working on this problem and combining some of my background in data science, data analytics and mobile technology. And here we are.

Speaker 1: 2:50

We certainly picked a place that needs help. We're glad you're here, so let's talk a little bit about that. Most of the audience are benefit advisors. They're client facing and they're being called on to do more and more analytical work and to try to help plans understand what's going on and make those intelligent decisions that we talked about in our opening. A lot of their problems or challenges are being caused by a lack of data. How do you get past that?

Speaker 3: 3:12

Meaning? How do you access data? How do you analyze data? How do you draw insights? Yes, all of the above.

Speaker 1: 3:18

If you're an advisor, how do you do that? I mean, it's not like it's being laid on your plate easily. Sometimes it's inaccessible or the data is not clear or intelligible or whatever. How does an advisor deal with that problem? Yeah, it's a great point and question.

Speaker 3: 3:35

So self-funding, which is really where the plan sponsor is the employer and, on the behalf of the employer, the broker benefit consultant can actually request and access, you know, claim level data, which is basically an invoice, so each claim is an invoice that the employer is already paying for, and so it is actually accessible. It is available and there's nothing that should stop you from the TPA third party administrator or the health plan that is kind of managing the administration of it to stop you from getting it. But the bigger challenge often has been with all of that data, how do you make sense of it? How do you draw insights? How do you make it more meaningful to draw and take an action? That's probably where a bulk of the energy effort is being put in place.

Speaker 3: 4:25

Over the last I would say, 15, 20 years, Technologies have changed what's possible now and how many different variety of data sets could be. I've done it with these medical claims or pharmacy claims or social demographic data, but now all of these other data sets 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 lot more data, but that also means how do you actually turn that into anything meaningful? That's where a lot of work has to be going in.

Speaker 1: 5:03

So what kinds of things are you working on? I mean you've got a pile of work has to be going in, so what kinds of things are you working on? I mean you've got a pile of data. Let's assume that it's clean, legitimate data and it's useful. How are you guys working on sifting that and sorting that so that it's actionable and intelligible?

Speaker 3: 5:17

Yeah. So once you bring all of that data up together and match it at a member level, you can use it to build predictive models to understand where the future costs and risks will be. Understand where the risk drivers are. Understanding gaps in care. Understanding, you know, are people going to places of care that are expensive. They could be going to an alternative place that is low cost, high quality. Understand that they are going to inappropriate places. Understand that they're going to inappropriate places of care. They're going to ER instead of urgent care, they are not using main-order medication program, just like in the case of my dad, or they're not using telemedicine when it's available.

Speaker 3: 5:55

So, all of these kinds of analytics, to understand where the inefficiency is, where the risk is not being mitigated, where the costs are not being managed, and understand all of that behavior. To map it against what actions can you take? And most of the time those actions fall into three or four simple things, you know. Can you turn that into a communication campaign to get the members to do the alternatives? Can you put a solution in place that can focus on that risk driver, that cost driver to provide a musculoskeletal program if there is a lot of cost related to surgeries and things like that. Or if you are seeing diabetics driving a lot of the costs and they have gaps in care, are we pleasing those gaps in care? Maybe could put solution in place. So then driving those actions becomes key and communicating that becomes key. Maybe a plan designed to incentivize people becomes key. So these are some of the actions that we certainly are embedding into our platform, and this is kind of the way we should be thinking about using data.

Speaker 1: 7:00

Are you finding, let's say, a heightened interest in plans being able to get actionable data, with all of the talk that's happened over the last year or two about this renewed interest in being proper fiduciaries?

Speaker 3: 7:17

Is that driving some of the conversation? Yes, plan sponsors have been sued by members that they're not doing a good job, all the way to even health plans who are not showing that the rebates for these pharmacy benefits are not being passed down to the plan sponsor, and there have been cases against that, which basically is creating this environment where flip-flop accessing and requesting for data should be a norm. You should just assume that you can. If somebody's blocking it, you could just go and complain as a self-underduty and in terms of all of these kind of nuances where, if these rebates are not clear from the data, you can request okay, because of the traditional responsibility for the plan sponsor, but also it actually makes economic sense because you will save as a plan sponsor as well you can request for a campaign to understand these plan debates or other kind of nuances that are not easily accessible from the data.

Speaker 1: 8:18

So how does this trickle down to employees? I mean, that's really where the rubber meets the road. It's employee satisfaction, it's employee retention, it's all the things that employers go to the trouble of building plans for An example or two of. You know, once an employer or plan has this data, some of the things that they can do with it to improve employee outcomes and experiences do with it to improve employee outcomes and experiences.

Speaker 3: 8:44

You know, so I get. What you're trying to understand is how all of this change this environment, this data can be useful at a member level. Yeah, exactly so. This is where the real innovation 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 what things cost in the healthcare system before we use it. You know, you and I don't even have much understanding of how to navigate our benefits, like what is really available to me, if there are benefits, if there are incentives, if there's a particular program. That's not really clear and easy for the member, and certainly the understanding of what is high quality care, what is good quality care that is actually low cost. That is, that equation, which should be very personalized, is not quite clear to the members.

Speaker 3: 9:33

So I believe that with all of this data that's now available, we can address these problems for the member and make it more proactive. What's fundamentally missing is you cannot go back and tell a member if they've gone for their surgery and say, hey, you could have done X, like oh, thank you. You really have to be very proactive. You have to be very kind of understand where the risks are going, where the costs are going, where the gaps might be, where the member might have a need when they're actually calling in at the call center to say, hey, I need to know where my deductible situation is. Oh, can I make an appointment with a specialist for this? That is a signal that we should all be kind of leveraging and using to help the member better.

Speaker 3: 10:16

So, using all that proactive nature of the data, predictive nature of the data, to be able to provide that transparency regarding what things might cost you, what it might mean for you as an individual how to navigate with all of the benefits that are available.

Speaker 3: 10:31

So what that really means in simple terms is when you know what's available on the benefit plan, with all the documents, with all the technology, now that's available, now that's artificial intelligence you should be able to pinpoint okay for you if you have this complicated pregnancy situation. Here are the exceptions in your plan documents. And, by the way, we do have this benefit program available to our members to provide that support. So being able to bring all of that in those two statements, it should be easy and we should be doing that for the member. We should not make it like five phone calls and people are searching for different databases. So that's kind of where it's going. It is happening. We are also working on the forefront of this. How do we use all of this data and add a layer of that artificial intelligence to be able to serve the member, support the member, navigate and then be able to help them understand what's high quality, low cost?

Speaker 1: 11:28

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Speaker 3: 13:32

That's great. Obviously, now we're geeking out on some statistics here. So the predictive modeling for risks and costs has been out there for the last 15 odd years. You know companies like Milliman, a bunch of other companies, have been working on this and using machine learning technologies and techniques when I see this predictive modeling getting more interesting. So we leverage all of that. So, rather than kind of reinvent the wheel here, we actually partner with Milliman and we use those adjusted risk models to embed into a platform to be able to provide those insights and identify cohorts of people.

Speaker 3: 14:10

Where is this most exciting? That you predict is for this individual, what is the right next step? Ie, if there's a gap in care, they should be taking an action. What kind of message is actually working well for this type of individual? And segmenting the populations and trying to understand okay, this person works with the game frame, ie when you tell them something like, hey, you could gain X by doing this or you could save that Y from doing this action. So being able to use that predictive analytics and some of that personalization through artificial intelligence, being able to improve that member experience, to improve that nudges, to improve how the member responds, and taking that response data to be able to then give something that's a lot more relevant and contextual. That's where all this predictive modeling is getting very, very powerful as well. So combining, you know, stratification, risk, strat, you know stratification to cost predictions, to all of this, how do you get the member to understand, use and take an action?

Speaker 1: 15:19

Is that where AI comes into the picture and, more to the point, maybe some agentic AI? Taking that data and making it accessible and useful to them at the member level.

Speaker 3: 15:31

Yeah, absolutely. We actually had a big session today with one of the thought leaders in the industry about agentic 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 it comes to you know, ai, it could become a co-pilot for all of those care navigators who are providing care navigation support on the phone or otherwise through chat.

Speaker 3: 16:08

It can be a great co-pilot tool where it's accessing all of the data. It could also be a very powerful chatbot technology, q&a service or 24 by 7 self-service tool to help the members diagnose things, understand and understand their own benefits better. But AI can also rewrite the way we actually access our healthcare data or even healthcare services. But in the world of healthcare administration, the whole workflow could be rewritten with these AI agents where, for example, in the past you're having to call to do x and then their message comes back and you know, a week later that you'd be a prior authorization has gone through, all our workflow could change and these ai agents can actually go off and you initiate something as a member.

Speaker 3: 16:55

It actually runs through all of those different steps and comes back and does the work for you. All those rules and parameters are set in place anyway, so that's where AI can actually speed things up to gain those insights for the member and the care navigator much more quickly, so that better decisions are being made.

Speaker 1: 17:16

Well, that's, I think, one aspect of it and another. You know we're working in my firm. We're working with a company that's doing a lot of work with creating an agentic front end for people with chronic conditions or multiple chronic conditions, who are maybe reticent to pick up the phone and speak to a human being but who, for some reason, will speak to a disembodied voice at the end of a phone. It's kind of a whole new frontier, especially given the amount of money that chronic conditions and people who are multi-chronic and multi-pharmac are driving. Is that something you guys are talking about as well?

Speaker 3: 17:49

Yeah, so we are not so much talking or working on that. I certainly, being in the industry, pay close attention to all of this. I host a podcast called Voices of Self-Funding where I get to interact with some people who are moving and shaping things in the industry. So, on the chronic condition management yes, it's very powerful there, powerful there. It's also quite relevant and powerful when we talk about not just the chronic condition management but the overall, the workflow, how that could be kind of rewritten, shifted, how these AI tools can be quite powerful there.

Speaker 3: 18:25

So there's a whole host of areas where mental health I mean another example just to dwell on this where, for example, you know we may not like talking to people at least certain generations AI tools can actually act as a consciousness for you to some extent, or they can be a very helpful coach to you to some extent and 24 by 7, where it can understand you how you make decisions, what your blockers are, how you kind of look at things or what's kind of holding you back, or even just overall simple mental health kind of counseling when there's a real shortage of this. These kind of places data, personalization, understanding of that individual can be very powerful whether it's used directly to serve the member or it'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 can bring to the table easily these days, or is that still kind of coming?

Speaker 3: 19:25

So some of the stuff is already there. So when you say benefit advisors, are these tools where they can analyze the story behind a particular plan sponsor's data and be able to make decisions and be able to activate some of these campaigns and communicate with the members, promote some sort of zero dollar deductible plans or tiered plans. All of that is available and we serve really some forward-looking benefit advisors doing this. So really some forward-looking benefit advisors doing this, Some of the things around how AI can rewrite the workflow instead of having to search through the tools and get some of these answers. And I believe, in the future, not too distant from now whether it's a year from now you could actually be asking using prompts Help me figure out X for this plan sponsor. So half an hour before the meeting you could actually have this prompt-driven communication with the tool and be able to have a much better meeting focused on the benefits and benefit plans and be able to make decisions. 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 the data 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 what you're seeing, what you envision. So if I think I had five, it's a great question. I didn't fully think about this. In healthcare, it's really important to bring the patient and the provider together and make it easier for that interaction and any other stakeholder who's supporting that patient and the member. I believe, with all the data and technology, how you serve those two to three stakeholders in that interaction in a fundamentally different manner. That's where, over the next five years, I'll see a lot of innovation.

Speaker 3: 21:23

You know examples of this could be you are able to take a picture of X, you're able to do a certain diagnostic and you're able to send that over to your doctor. Being able to then pass that data to a specialist and then be able to send that over to your doctor. Being able to then pass that data to a specialist and then be able to find a high quality provider very quickly without having to kind of call and do all of that stuff. These agents behind the scenes could actually help you access all of this. So it'll improve the insights that are needed for the patient and the provider to have a much better interaction so that good decisions are made, good care is delivered and the members are, throughout the journey or after leaving that interaction with the provider, are actually sticking to things taking medication, doing the exercise, eating the right things or whatever else has been asked to do. How you keep that patient on target?

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 and co-founder at Zaki Point Health. Ramesh, thanks for a fascinating 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 podcast production or professional graphic design, josh Hatcher is the expert to contact For more information. Visit him at HatcherMedianet. That's H-A-T-C-H-E-R-Medianet.

Speaker 2: 23:00

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