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Transcript

So, thank you again, everyone, for joining this morning. My name is Jamie Hernandez. I’m the Vice President of Member Provider Experience at Simplify Healthcare.

We’re excited to have all of you with us this morning as we share how Highmark Health successfully reduced average call times by 50% while also cutting operational costs.

With that, I am pleased to introduce our speakers this morning, Chris Atkinson and Ashish Desai. Ashish, over to you

(0:36) Ashish Desai: Thank you very much, Jamie, and thanks everyone for joining us for today’s discussion. My name is Ashish Desai. I lead the Xperience1™ platform at Simplify Healthcare. Xperience1™ is an AI-powered solution designed for delivering accurate and consistent benefits information across the enterprise, across different channels for members, providers, and other stakeholders.

These days, any tech conversation sounds incomplete without the mention of AI, and for good reasons, right? Particularly in health care. There is a significant opportunity to make a positive impact on the lives of members through member servicing using AI. There’s a lot of interest and potential to utilize AI. But in reality, if you look at it, there are very few or rare real-world success stories that we hear. A few little bits of news here. A lot of initiatives, but not real success stories.

I think the reason behind that is taking the hype and turning it into more reality is certainly a pretty complex journey. It’s very easy to spin up a GenAI demo, or do a quick POC, and get excited about the possibilities. But putting that into production, making it available, and helping members make important life decisions is a totally different ball game.

Today, we are going to dive into one such, you know, real-world success story where enGen and Simplify Healthcare partner to modernize the benefit quoting process for Highmark Health.

I’m really excited to be joined by Chris Atkinson. He is the pioneer who led this initiative for Highmark Health. Chris, thank you very much for joining us. We are all very eager to hear from you, to get an insight into what this journey has been—this bold initiative—what the outcomes are, what lessons we learned on the way, and the vision for the future. And to give you an idea, we have twenty-five-plus health plan leaders registered for this event. So, there is certainly a lot of interest and excitement in this particular topic.

So, to kick this off, can you give us a quick idea about your role at enGen and how enGen fits into the world of Highmark?

(03:33) Chris Atkinson:

Sure. And thanks for having me, Ashish. Again, my name is Chris Atkinson. I’m Senior Vice President of Payor Solutions at enGen. enGen is a Highmark Health subsidiary. We have just over eleven thousand team members and serve over eleven million members with our products and solutions.

enGen is a company that supplies a wide array of software products and services that support health plans, payers, and the delivery arm of health plans, whether in hospitals or provider systems. We have solutions that do print fulfillment. Print is still very much a big thing. We have a full offshore capability in India—in our offices in Chennai and Hyderabad—that supplies operations and engineering services. And enGen also does a full, complete suite of operations services for the health plan, whether it’s customer service, benefit coding, claims, or clinical fulfillment. So, a big company with lots of services and lots of value that we drive every day. And our mission is to drive a remarkable health experience for members through innovative health tech.

(04:48) Ashish Desai: Thanks, Chris. And the bigger the payer, the bigger the challenges, right? That's how I look at it, right? When you talk about member servicing or member experience, there are so many areas for improvement for almost all the payers. Why benefit coding or benefit inquiry became the area of focus, and why you chose to put your attention there and say, ‘Hey, this is the area we can really make a significant impact by automation?’

Chris Atkinson:

Well, we started a customer service evolution journey a few years ago. And when we peeled apart the strategy of that, we started to look at what the biggest areas of opportunity or pain are when it comes to a customer service agent servicing a member or a member trying to obtain service. One of the clearer areas of focus for us was the particular engagements from members regarding their benefits. And when you look at the distribution of calls to a call center, I would be surprised if the top call type is not for benefits. And it could be as simple as, you know, ‘What’s my co-pay? What does it mean?’ Or it could be as complex as ‘I’m going to go get a knee replacement, and I need some information about what’s covered, what’s not, and how it works.’

So, the calls range from or have a wide array of topics. They’re very frequent and they take a large amount of time. I think when we looked at it originally, the amount of time it was taking on average was close to around 10 minutes per call. And in those calls, you typically have a large amount of silent time. And that’s usually when a customer service agent is researching the particular benefit in question or the particular service that the member is inquiring about. So, for us, it was one of the key targets to find a better way to do things to help improve the experience, reduce the time, and ultimately reduce the cost of the call.

(07:06) Ashish Desai: Right. Right. Yeah. And certainly that's, I think, typically what we have seen talking to other payers, the call volume for benefit inquiries could be in the range of 40 to 50% of overall calls. And certainly, that's an area where we can see a significant impact. Now, getting a little deeper, I think you talked about the challenges in terms of call handle time and your experience for members as well as customers. What were the underlying issues or challenges in terms of your idea or system setup?

Chris Atkinson:

Well, I think there are a few. One is, you know, the topic itself is complex, and then the underlying data that you would use to answer those questions is often, you know, not very well organized.

The structure isn’t typically clean or standard as we might like. And there’s just a large volume of it, and there’s a vast amount of combinations that you have to be able to address related to a particular member’s coverage, the particular procedure code, the direct benefit, and the related benefits. So, it’s just a very complex ecosystem in general. And when you throw on top of that a challenge with your data, it typically drives, you know, that makes for a hard thing to solve.

And then I would say the other part is that a customer service organization, typically, isn’t staffed with tenured experts in the healthcare industry. So, anything you give to them needs to be done in a way that a person who isn’t familiar with health plan coverage benefits in general can understand the question, and they can get a response that is simple and consistent.

And then I think the last part is that many health plans that we serve are looking to try to distribute the engagement across multiple channels and try to reduce the need—as much as possible—for a member to pick up the phone and call. So can they get an answer quickly, in an easy-to-understand way, so that they don’t have to place the phone call. So, for us, that meant any solution we develop has to have a consistent response regardless of the channels provided.

(09:41) Ashish Desai: Right. Right. No, absolutely. Makes sense. And when you embarked on this journey, do you remember how many steps typically a customer service agent had to take or how many systems they had to navigate to answer a benefit inquiry?

Chris Atkinson:

Yeah, for us, it depended on the nature of the question, but, you know, it could have been 11 to 16 different places they might have to navigate through. In some cases, they’re actually looking at a benefit code. Now, that might not be true for everybody, but I’ve yet to find somebody that’s got a simple response that pops up for someone to look at. So, it was a large amount of systems on it.

(10:41) Ashish Desai: And then what was your expectation getting into this? What would success look like by doing this kind of automation?

Chris Atkinson:

So, I would say a couple of areas.

One, that we could provide a consistently simple response that a customer service agent could provide to the member on the phone. That response was also available on a chatbot or a web form.

And then we also realized that we have to service both members and providers. So, the nature of the solution has to be able to solve both of those types of inquiries, which are typically a little bit different in the way they come in.

And then lastly, it would be, are we reducing the amount of operational effort and cost to answer these questions?

(11:32) Ashish Desai: Right, right. And I understand this solution has been live in production for more than a year now. So, what kind of results have you seen, and how satisfied are you with that?

Chris Atkinson:

So, I think what we’ve seen is the average or median time to answer the question—now that’s not the handle time or the total handle time of the call, but the average time it takes to answer the question—has gone down to somewhere in the neighborhood of two to four minutes, I would say. And again, that time difference would be based on the type of question it is. For a plan that’s just on the solution, I would say maybe it goes from 10 minutes down to seven. And then in a few months, you see it go down below five.

So, it takes a little bit of time for people to get used to it. But our objective has always been that with customer service agents, it’s a much better strategy to train them how to use the tool and train the tool how to answer the question. Trying to train new customer service agents, wherever they are located, on medical benefits and how to answer those questions is a long-term endeavor.

(12:53) Ashish Desai: Right. Really, I appreciate it. I think that's very smart thinking, training the system rather than training the agents. So that would have significantly cut down even the onboarding time and time to proficiency.

Chris Atkinson:

Yeah. I mean, you have a real opportunity there. The total operating cost can be significantly impacted—in a positive way—by not having to spend as much effort on training and then wait for the agents to be as proficient as you need them to be.

(13:21) Ashish Desai: So 10 minutes to three minutes is kind of a significant improvement in the experience, as well as cost takeout, right?

Chris Atkinson:

Yeah. And we’re looking to make that even better. I think for us in particular, we’re driving towards what we would call our fully scripted response, which is every question gets a paragraph response that the agent can read to the member and explain, and it’s constructed in a level of English that’s very understandable, regardless of the member. And then that same response gets distributed on multiple channels.

(13:54) Ashish Desai: So, I think this is a phenomenal outcome we're talking about. This kind of automation transformation is never easy, right? There are always challenges on the way. What are the lessons learned—if you look back at the last couple of years—in taking up this kind of initiative?

Chris Atkinson:

Well, I think the first one is probably pretty obvious, but you need to have a good team. And I think the team at enGen that’s taken this on is phenomenal. I think they understand not just the industry, but they understand the operation. So, I’m lucky—in that way—to have such a great team that’s doing this work.

I think the other thing is to engage the operations, the customer service, and the digital teams as early as possible. Lots of communication. I mean, it’s a very complex topic that has a lot of, you know, off ramps on the road to success, right? You have to be able to communicate effectively amongst the different constituents in order to keep it on track. Adoption of this tool and readiness to use the tool are all really, really hard. And communication is the only way to get through that.

And then lastly, if you don’t have a way to get your data right, the solution is going to be hard to deliver. So, really focusing on the standardization of your data, governance of the data once you get it standardized and clean, and the integration of the data to the various channels is really important. And I think that’s where we’ve done the work with Simplify Healthcare, not just with Xperience1™, but with Benefits1™, and how we set up the products, how we create the standard data model for each of the products, services, provisions, and benefit data. And then we’ve put governance on top of that.

So, to me, it’s a critical piece, getting the data right.

(15:48) Ashish Desai: So, building the foundation first and then thinking about AI. I mean, we talked with several payers. There are times we see that one approach taken, is sometimes saying, ‘hey, we have all the data, you know, all the documents, we have the knowledge repository, we pull everything together and put GenAI on top of it, will speed up an answer.’ But many times, that's not adequate in terms of servicing a member, for example, in a critical situation. What's your take on that?

Chris Atkinson:

I think there are plenty of solutions out there that will come and tell you that all you have to do is scan in your benefit book, your benefit grids, and they can answer the questions. And I would say that’s true for maybe 10 to 20 percent of the questions you might get. If it’s, if all they’re asking is about co-pay, about, you know, deductibles, about out-of-pocket, then yeah, or the number of visits you can have for a chiropractor. But the minute you just scrape the surface below that, get into related benefits, or you get into complex procedures, services, diagnosis codes, procedure codes, those solutions tend not to be able to provide the level of response you would need, which is why we’re doing what we’re doing.

(16:56) Ashish Desai: Yep. Absolutely. Makes sense. And of course, you'll see a significant impact on member services. Are there other areas where you're looking to, you know, take up this kind of transformation initiative where you can expect positive results beyond benefit inquiries?

Chris Atkinson:

Well, I think in general we’re looking to drive the same kind of transformation across the customer service landscape, whether it’s benefits, whether it’s claims, or whether it’s the ID card that needs to be replaced. So, applying that same level of thought around AI and automation and bringing the answer to the person so they don’t have to go find it. I think those are the strategies we’re starting to go after to try to make it as easy as possible. I mean, customer service is the hardest job, I think, in the company. You know, they’re never easy conversations. And these people aren’t, as I said before, industry experts. So, it’s a really hard job. And our goal is to make it as easy as we can for them to be able to service the members.

(18:06) Ashish Desai: And it has been a great partnership between enGen and Simplify Healthcare across different areas. For other payers on the call, if anybody's looking to take up this kind of initiative, trying to make a decision whether to do this in-house versus engaging a partner, like what are the best practices, or what should you look for when choosing a partner to set this up for success?

[18:31-19:31] Chris Atkinson:

Well, I think the biggest thing for us was that this isn’t an out-of-the-box solution. And I don’t think it would be for anybody. Everybody’s got a different flavor of data, a different flavor of integration, and the systems are a bit different. So, I think having partners and teams that have the ability to dig into the various challenges and have flexible solutions to get to the answer you want is important. From my perspective, this has been a journey. And I think what I’ve noticed between our organizations is that we’ve both been very open to how we solve the problems. And we’ve worked very well together. Like I said, I love my team. I think I’ve some of the best experts in the industry on this. And I think you guys have matched that. And we’re on the right track. That’s all I can say. We’re on the right path.

(19:32) Ashish Desai: No, you have a phenomenal team. It's always a pleasure working with your team. It's always very exciting. Now, AI is certainly a hot topic these days, right? Generally, beyond the topic for our discussion today, how is this AI evolution that you're seeing for the future? And, you know, is there any area that scares you?

Chris Atkinson:

I don’t know if I would use the word scare. I think I would say that the pace of change is where we’re trying to find the right mix of leadership, structure, and organization to be prepared.

Because it’s going to continue—the acceleration of change is going to continue to go faster and faster. And you’re going to start a project, and a month later, two months later, there’s going to be a brand-new solution that might do exactly what you were trying to do. And you have to have the right organizational structure, the right mindset, and the right procedures, policies, and leadership to navigate through that.

And I think we’re all still trying to figure some of that out. I’m very confident in our organization and how we’ve structured ourselves around AI.

And I think the other part of it is just within our industry; there’s a huge amount of regulation that we’re going to have to navigate. That is going to require a full approach from your legal, privacy, compliance, and technology teams as well. So, I’m very comfortable with where enGen’s is related to that. We have a great AI team that’s focused on it. We have a great organization. So, very, very, very excited for the future.

(21:08) Ashish Desai: All right. Sounds good. And before we go to the questions, are there any closing comments for the payers in terms of advising and saying, ‘hey, if you're looking at something like this, pay attention to one or two things.’

Chris Atkinson:

Well, for me, I can’t emphasize enough on data, right? And, you know, the other part that I think is really important is just the organizational change management of this, right? This is going to change the way you operate and communicate within, and that’s very important. And having a plan for how you’re going to drive the change through the organization.

And it’s not just technical. There’s a ton of operational change, a ton of operational opportunity, and you need good operational partners, which we have at enGen. You know, it’s part of our ecosystem, right? Operations is part of enGen. So, I have somebody that I face off with; my team has their own counterparts, and we’re very well connected on how we’re going to do this and how we’re going to solve problems. And this doesn’t go smoothly, right? There are always things that happen, and you have to have good communication and good relationships.

(23:58) Ashish Desai: It's scalable on demand, I would say.

Jami Hernandez:

Great. Thank you so much.

Earlier in the conversation, you mentioned lessons learned. What are some of the early wins that kept the momentum going?

[24:12-26:57] Chris Atkinson:

Well, I think I would say there are two.

One is from a training perspective, and I mentioned this before. Our objective was to train an agent on how to use the tool, not train the agent how to quote the benefit, you know, or train them on the benefits themselves. And I would say right out of the gate, when you look at the way training was done prior to this—when a new person would come on into the organization—some of the classes they would have to take or some of the instruction they would have to get on was what is a procedure code? When somebody asks, I’m going to go see the chiropractor. Well, it’s not a chiropractor that you’re searching for in the benefits. It’s usually spinal manipulation. So, you have to train them on how to ask for things the right way in order to get the right answer.

The solution actually takes the natural language and converts it to the right benefit language or the right procedure code. So, right out of the gate, you saw an impact on training. And then just the ability to get a consistent response right out. You’re not going to get a 50% reduction, but what you are going to get is that if a member calls and gets an agent on a chiropractor question, and they provide a response, and then they call the day later and get a different agent, they’re going to get the same response.

So, you’re going to get a very high level of consistency in the answers that you deliver. What Simplify Healthcare has built allows you to train the tool. So, you create learning models, and then the tool itself learns how to quote the benefits based on the training provided.

So, that drives consistency because now they’re getting an answer that’s been trained in the system. And I would do a little sidetrack there. That’s like a new function. So, when I talked about organizational change management, that’s something new that the health plan operations didn’t have to do before, which is look at the benefit calls coming in, train the system on how to answer some of those questions, keep an eye on the learning models the algorithms that are in there, that’s a new function. It’s not difficult, but it’s new. And so, you have to kind of account for that in the program approach, on how you’re going to implement that and get people owning that and getting them aligned to the process procedures.

So, yeah, just a sidetrack there.

(22:21) Ashish Desai: Absolutely. Yeah. Yeah. It's a fascinating success story with phenomenal outcomes. All right. So, you know, I'll turn it back to Jami. Jami, if there are any questions, we can take them up.

Jami Hernandez:

Yes. Thank you so much. So, there are a few questions we will try to get to as many as possible. And I’ve been trying to, you know, group them together. One very common question is: How scalable is this solution for other blues or other health plans?

(22:51) Ashish Desai: Yeah. So, Jamie, I think when we started this journey, of course, we took steps to make sure that there is gradual adoption, but this system is set up to support thousands of customer service agents and handling a large volume of calls on a daily basis. So, the whole infrastructure is set up on Azure cloud. So, it's absolutely scalable to handle large volumes. So, it's a multi-tenant cloud-based solution. So, scalability and security are the two aspects we have paid attention to from the beginning. It's capable of scaling to really large volumes of calls and users.

Chris Atkinson:

Yeah. I mean, we have a number of clients that use the solutions we’ve built with Simplify Healthcare. So, that’s Xperience1™ and Benefits1™. But then within our own ecosystem, we’ve got a health plan that’s got over 1,500 agents. We’ve got a health plan that’s got just under 50. So, we’ve kind of got a broad array of these. So, I would say it’s very scalable.

(26:58) Ashish Desai: Absolutely. And I think here we are talking about benefit inquiry calls coming from members as well as providers. They call with procedure or other kinds of code, and handling those kinds of inquiries is also completely simplified. So, significant value there.

Jami Hernandez:

Great. Well, thank you so much.

So, to be mindful of everyone’s time, we probably will conclude. We do have additional questions in the chat that we will reach out to those individuals directly with a response. So, at this moment, I’d like to thank Chris and Ashish for a great session.

It was also great to see over 20 different health plans and their leaders attend today. We hope that you did find this session valuable, and there were some improvements and driven measurable impacts and performance outputs that you guys could see.

With that, we would also like to extend a warm welcome to our Connect 25 conference. It is our annual customer conference happening this September. It’s a great opportunity to dive deeper into innovation across ecosystems, hear from industry health plans, and connect with your peers.

So, with that said, there is some information on the screen here. Feel free to reach out to me directly at jamiehernandez@simplifyhealthcare.com or our website connect2025.simplifyhealthcare.com.

Again, thank you for joining us this morning. We hope you have a great day. Thank you.

(27:12) Ashish Desai:

Thanks everyone. Thanks, Chris. I really appreciate this.