Balancing Big Data and Claim Professionals to Optimize Outcomes
Chapter #1 | Chapter #2 | Chapter #3 | Chapter #4 | Chapter #5 | Chapter #6 | Chapter #7 | Full Webinar Video
Now more than ever, big data, AI and predictive models are transforming how third-party administrators (TPAs) manage claims for large firms – but technology is just the start. Constitution State Services professionals explore what it takes to operationalize the balance between big data and Claim professionals to achieve optimal results.
Chapter #1
What to look for in a team of claim professionals
Attracting the right talent is key to developing a team of Claim and medical professionals, according to Todd Mattiello, Vice President of Claim Account Executives. As the environment changes, continuous training and development opportunities can help prepare the team to manage more complex claims.
Learn more about training and development for Claim professionals.
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Text, Constitution Sate Services. Bringing more to the claim experience.
(SPEECH)
SPEAKER 1: Anybody who's seen me out in marketplace, talking to customers, prospects, or brokers, would pick up on the fact that I start and I finish with talking about the talent that we have. To me, without competent, effective experience, claim, and medical professionals, it doesn't matter what else that you bring to the table. It's a non-starter, right?
And for us, it's really, really important that we not only attract the right talent, but that we continue to invest in their development throughout their career, right? It's not enough to just do it as newbies coming on board. But as the environment changes and those changes are thrown at our claim and medical professionals, developing and training them for that, developing and training them as complexity in the types of claims that they manage over the years becomes more difficult. So investing in their careers and their development over time is hugely important.
It's one of the reasons, I believe, the investment in their development, that we boast a really strong retention of our talent. In fact, it's in the single digits, which I'm not sure many can compete with. And because of that longevity of our claim and medical professional, it lends itself to really getting to understand our customers and what their desires are and our ability to build our relationships with those customers, which to me is critically important. So in summary, absent the foundation of competent claim and medical professionals, you're really not starting in the right place. So it's really, really important to have.
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Chapter #2
How are some TPAs using predictive modeling today?
TPAs are leveraging their own first-party data, as well as third-party data in predictive models, according to Kevin Mahonney, Vice President of Analytics and Research. For example, advanced social listening can help confirm or contradict elements of a claim to detect fraud.
Learn more about predictive analytic models.
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Bringing More to the Claim Experience. How are some TPAs using predictive modeling today?
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SPEAKER: What I wanted to do real quick was actually start with the data before we get into the predictive modeling. So I'll just give you an example from CSS. We collect about somewhere around 1,500 elements on each of our more than 30 million claimants that we've had over the past 10 plus years. So when you think about that, that's about 45 billion structured data points that we have on our claimants, right?
When you think about the TPA space, I think those TPAs who are going to be most successful are the ones who have the market share and the data to be able to really appropriately either use analytics to triage and handle claims. You know, we have over a billion and a half claim notes. So not just the structured data that I talked about before but unstructured data we can also tap into for building our predictive models.
We have more than 60 million medical bills. So this is really- like these are the ingredients that you need in order to build effective predictive models. That's just our internal data. So now if we turn to third party data sources, we also need to leverage that information to get a complete view of the claim.
So one of the areas that I think is really key in the TPA space is looking at prior claim history for whether it's claimants or injured workers, just understanding if there are any patterns of behavior that we should be aware of or be concerned about. Something new in the space that has come up over the past year or so is the idea of automated social listening. So just going out and being able to capture data that's out there- publicly available on the web to understand what facts exist that might either confirm the elements of the claim or contradict the elements of the claim.
So I think you're starting to see more TPA work with social listening vendors to be able to pull all that information in, to be able to quickly assess is this a claim that's valid or is this a claim that we have some concerns about? And then certainly you're seeing with all the social inflation going on these days, more and more attorney activity. I think folks should be looking for TPAs who can understand connections between attorneys, providers, medical providers, claimants just to understand-- get a full picture of claims.
So now as we get into predictive modeling, I think the three areas that you see people focused on the most are fraud detection, that's using information from our structured, from our unstructured data, and third party data sources like the social listening that I talked about. To understand claimants as well as medical providers on a claim because we do see quite a bit of medical fraud or fraud as well. Litigation propensity is another hot area, where you're trying to understand which claimants are most likely to hire attorneys.
We know attorneys are likely to drive up the medical costs on a claim. We've seen that over time. We want to make sure that we pay what we owe on a claim. But not more than we owe, not less than we owe. And so understanding when attorneys might get involved. I think helps us get a leg up on how we can handle the claim more effectively and potentially communicate with the claimant in a way that I think leads to the best outcome for them and for their employers.
Lastly, claim triage is really, really important as well. So how do we handle claims in the most effective way? How do we get the right resources on claims? So for example, getting nurses on certain claims, where we think that will be most effective. So you're starting to see a lot of predictive modeling in really those three areas.
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Chapter #3
What is the balance between the technology and the claim professionals?
Too much reliance on the human element can be a missed opportunity to augment the understanding of the Claim professional with data, according to Rich Ives, Vice President, Workers Compensation Claim. It's also important not to over-rely on technology. That balance between the nuance of human skill and predictive analytics can help optimize claim outcomes.
Hear more about finding the balance.
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Text, Bringing More to the Claim Experience. What is the Balance Between the Technology and the Claim Professionals?
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SPEAKER: If you have too much reliance on just the talent aspect, just the human aspect of the process of claim handling and medical management, then what you miss is the ability to augment the understanding of that individual, that claim professional, to actually expand upon their knowledge and their expertise. So if you lean too much on the side of just the human interaction and the human process, then it's not as efficient. And it mitigates some of the effectiveness on your ability to create better outcomes.
Now on the opposite side, if you have too much leaning the way of digital technology and predictive modeling capabilities, and you miss the very important nuance of the human skill, then you lack the ability to change the outcome or the ability to tailor that process to actually make it very meaningful to both the overall outcome, as well as to the injured employee, or the injured customer, or for that matter, a third party claimant.
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Chapter #4
What sort of models combine technology and human skill?
The Early Severity Predictor® model helps determine when injured employees could decompensate into chronic pain, which could lead to opioid abuse and dependency. Our medical staff intervenes to develop a personalize level of care that can help injured workers avoid opioid addiction.
See more about the Early Severity Predictor.
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Text, Constitution State Services, Bringing More to the Claim Experience
Rich Ives
(SPEECH)
SPEAKER: Carriers, TPAs-- it'll be real successful in this space are those who can have that excellent balance of both technology and human skill and capability. We see that play out really well in our area of early severity predictor model.
But if you think about what that does, that basically says, you know, instances of chronic pain where an injured employee might decompensate into chronic pain many months after an injury, and by the way, at the moment they are diagnosed with that, it's really hard to change the outcome at that moment in time. And they become suspect to things like opioid abuse and dependency, for example.
We've developed a capability that gives us the opportunity within the first few weeks after an injury to identify for a claim professional and a medical professional the likelihood of those injured employees that would decompensate into chronic pain. Now, to be able to predict the future provides tremendous value, but to be able to intervene and course correct or change that future is extremely valuable.
So in this instance, our folks would get a flag. We would then dedicate more resources. We would prioritize those claims a little more. We would work with medical providers to develop a personalized care path for that individual.
We would engage with those injured employees a little differently, along with involving their employers to produce some what we think are just terrific industry leading outcomes. And so it provides for us just a practical good use case of how the two have to come together in order to get to your ultimate objective.
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Chapter #5
Pitfalls of a one-size-fits-all approach
Listening, understanding and offering up solutions can help TPAs optimize results throughout the course of a relationship with a customer rather than taking a more one-size-fits-all approach.
Learn more about working with a third-party administrator.
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Text, Constitution State Services. Bringing More to the Claim Experience
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TODD MATTIELLO: So in working with a new prospect, an existing customer, it's really, really important because we all bring a full suite of products and capabilities to bear. But unless you're really truly sitting down and listening to that customer in terms of what's important to them, how they manage their risks today, what their goals and objectives are for today and in the future, it's really difficult to just throw everything at them.
It's really understand what those needs are and then provide solutions. How can we complement their existing risk management program with some of the resources and capabilities that we bring to bear. Because you don't need the full suite perhaps. Sometimes you need components thereof or sometimes you want to bring what you have together with what we can complement that with.
So listen, understand, and then offer up solutions. And that's not just a one time event. Having a customer over the longer term allows us to learn together as to what additional things we can bring to bear that may not have made sense at the beginning, but certainly makes sense as we grow together.
So it's part customization. To me it's more tailoring the resources, the capabilities that we have to the goals and objectives of that customer.
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Chapter #6
What are some tools and models on the horizon?
Artificial Intelligence is starting to proliferate across claim and provider fraud detection, helping TPAs understand connections between providers, attorneys and injured employees. It's also helping identify claims that are likely to become outliers, which can help determine the need for extra attention from Claim professionals.
Learn more about promising new tools.
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Logo, Constitution State Services.
Kevin at top right.
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KEVIN MAHONEY: It certainly varies by line of business. I know we talk a lot about workers compensation. But certainly across all lines, I think you're starting to see more what I would call artificial intelligence, like actual artificial intelligence in the field. So better use of computer imagery is key, better use of text data and turning text data into predictions.
So think of all the data that we might have in our unstructured claim notes. I think you're seeing that proliferate across the broad space, whether it's claimant fraud or whether it's provider fraud. Understanding networks-- that's a big part of I think where you'll see the industry going. Understanding connections between providers, attorneys, claimants or injured workers.
On the triage side, being able to predict which claims are going to be outliers, which is a difficult thing, but you don't have to be perfect at it, right? So if I can tell you with 40% or 50% accuracy, hey, this claim looks a little bit funny- one out of every two claims, if I can get that right, that's a huge win, to be able to pull those claims out, give them a little extra attention, and hopefully be able to stop a claim that might get off the path. So I think those are some of the key areas you'll be seeing in the future. And honestly, we're always working on ways to improve our claim handling process with analytics. We've probably got five or six different predictive models going on right now to do just that.
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Kevin nods.
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RICH IVES: The only thing that I probably could add to Kevin's piece is that at a high level, when you think about the use of AI, automation, predictive modeling, there's the ability to automate a process. And we think about that as in areas where our customers are looking more for digital and technology automation tools. There's obviously a play for that that happens across the high-frequency, low-severity side of the business.
But then it's the ability to take some of those same capabilities that you would need to develop on the low-severity side to then help bring insight earlier into the process for those claims that are a little bit larger in nature. And this is one area where we think that-we'd by no means roll out something and we say, OK, we're done. With true machine learning, through our ability to go back and look at, where did we use our judgment rather than just act on the insight from the model?
Where were we right? Where were we wrong? How do we see further strengthen the accuracy along with additional variabilities? We think it's an area where it's one that just helps us keep going, keep going, find additional use cases that will keep us at the front leading edge.
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Chapter #7
How does advanced social listening help TPAs?
Advanced social listening offers relevant information on claimants that helps Claim professionals evaluate the validity of a claim. For example, an injured employee could post a photo on social media about a top road race result when they are out of work, which may help contradict a workers compensation claim.
Learn more about ways that TPAs are using social listening.
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Constitution State Services, Bringing More to the Claim Experience, How does advanced social listening help TPAs?
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SPEAKER 1: Let me give you some examples first of what kind of information that we have back.
So in the case of, let's say confirming an injury to a worker, it's amazing how many people will post a photo or post something on their Facebook page to say, hey, you know what, I had an accident at work, or I was in a car accident and here's a picture of my injured leg or broken arm, that sort of thing. That's the kind of information that confirms the injuries that we might see.
Now on the other side of it, it's amazing to me, and I'm sure to some of the folks out there- it's amazing how many people will post, even though they're out of work, they'll post information about a gym workout that they just did, or a 5K race that they just ran, or we'll see information on a running site where somebody placed highly in a race, even though they're out of work. We see people who are traveling halfway around the world and yet they're supposedly out on a back injury.
So finding all this information is a bit of a challenge, right. I think if you went back a year or two, you'd say most people- it was really up to the clean professional to be doing some kind of Google search to see if they could find anything.
Now there are vendors out there that are starting to do this on a more automated basis. And I think you see that a lot in the carrier space. I would say it's pretty commonplace in the carrier space. I think it is very uncommon in the TPA space right now.
But by using these vendors, what you can do is you can send a list of injured workers or claimants, let's say on a nightly basis, they have a process that scans for all related information to that particular injury. And then they'll send reports back saying, hey, I'm on this subset of what you sent us, here is relevant information we found.
So the nice part about using a vendor is you don't get everything. You just get the information that's relevant to an injury. So they summarize that in a quick report with some links. It's pretty cheap, efficient process. So it's not like you're paying hundreds of dollars to get this information.
And what happens is then that information is fed directly to the claim professional, in our case through our electronic file cabinet, so that they are aware when that report lands, that that information is available to them and then they can go in and they can look at it and take action.
In the case of where it confirms an injury, maybe they're able to settle the claim a little bit faster.
In the case of where there's a dispute, maybe the next step is to refer that claim to an SIU organization for more investigation.
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Balancing big data and claim professionals to optimize outcomes
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SHERRY HERSEY: Welcome to our panel discussion on balancing big data and claim professionals to optimize outcomes. Now more than ever, artificial intelligence, predictive modeling, and big data are transforming how TPA's manage claims for larger firms.
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Innovation
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But technology is just the start. Operationalizing these advantages requires people with expertise and empathy armed with the right tools and processes. I'm your moderator, Sherry Hersey.
We're going to take a look at the disclaimer here. We will answer questions at the end of our discussion this afternoon. There is on Zoom, if you're not familiar, there's a Q&A function. Feel free to click that and ask your questions throughout our presentation, and we will address all the questions together at the end.
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Text, Our Panel.
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These slides and a recording of the webinar will be available from your CSS representative and via email after our session. So please join me now in welcoming our industry experts with over 30 years of experience in claims management. Let's start with Kevin Mahoney, would you introduce yourself?
KEVIN MAHONEY: Sure, thanks for having me Sherry. I'm Kevin Mahoney, I've been working in the industry for about 15 years now. I spent half of that on the pricing side building predictive models to price business insurance. The last seven or eight years I've been in claim, also running a research and development area focused on transforming the way we handle claims.
SHERRY HERSEY: OK, next panelist is Todd Mattiello.
TODD MATTIELLO: Thanks Sherry and thank you everyone for joining us today. Todd Mattiello, I have been with Constitution State Services for over 30 years now. The majority of the time has been in the claim department. My current responsibilities are for an organization, we refer to as claim account executives. Many of you, at least on the broker side, have probably worked with our CAE's, as we refer to them, as they are the liaison between our larger customers and our brokers. And ensuring that they take advantage of all the capabilities that claim has to offer. Looking forward to spending time with you this afternoon.
SHERRY HERSEY: Thank you, and next is Rich Ives.
RICH IVES: Hello everyone, welcome. I have over 20 years experience in the industry. My day to day role is responsible for workers compensation claim for the whole of our organization, and then I have some business practice responsibilities across business insurance claim.
SHERRY HERSEY: Thank you to our illustrious panelists. So as our title suggests, balancing big data and claim professionals to optimize outcomes, the people involved are most critical to the claim outcome. So this is a question for Todd, when it comes to choosing and evaluating a TPA, what elements should a company look for in a team of claim professionals?
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Todd Mattiello
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TODD MATTIELLO: Thanks Sherry, I actually love this question. Anybody who's seen me out marketplace talking to customers, prospects, or brokers would pick up on the fact that I start and I finish with talking about the talent that we have. To me, without competent, effective, experienced claim and medical professionals, it doesn't matter what else that you bring to the table, it's a non-starter.
And for us, it's really, really important that we not only attract the right talent but that we continue to invest in their development throughout their career. It's not enough to just do it as newbies coming on board, but as the environment changes and those changes are thrown at our claim and medical professionals developing and training them for that. Developing and training them as complexity in the types of claims that they manage over the years becomes more difficult. So investing in their careers and their development over time is hugely important.
It's one of the reasons I believe the investment in their development, that we boast a really strong retention of our talent, in fact it's in the single digits, which I'm not sure many can compete with. And because of that longevity of our client the medical professional, it lends itself to really getting to understand our customers, and what their desires are, and our ability to build our relationships with those customers. Which to me is critically important, on top of the other investmentsthat we will spend time and energy talking to you about today. So in summary, absent the foundation of competent, claim medical professionals, you're really not starting in the right place. So it's really, really important to have.
SHERRY HERSEY: So the foundation or starting point is talented claim professionals and experienced claim professionals. The next leg of our three legged stool in achieving this balance between big data and claim professionals is our technology portion. So data and analytics, we all know have transformed the way we manage insurance claims. So this is a question for Kevin, how are some TPA's using predictive modeling today?
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Kevin Mahoney
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KEVIN MAHONEY: Yeah, thanks Sherry. I definitely agree with Todd, that the claim professionals are really a critical part of making sure claims are handled appropriately, they're using our analytics appropriately in order to triage claims and handle claims more effectively. So I couldn't agree more with that.
So what I wanted to do real quick was actually start with the data before we get into the predictive modeling. So I'll just give you an example from CSS. We collect about somewhere around 1,500 elements on each of our more than 30 million claimants that we've had over the past 10 plus years. So when you think about that, that's about 45 billion structured data points that we have on our claimants. When you think about the TPA space, I think those two TPAs who're going to be most successful are the ones who have the market share and the data to be able to really appropriately use analytics to triage and handle claims.
We have over a billion and a half claim notes. So not just the structured data that I talked about before, but unstructured data we can also tap into for building our predictive models. We have more than 60 million medical bills. So these are the ingredients that you need in order to build effective predictive models. That's just our internal data.
So now if we turn to third party data sources, we also need to leverage that information to get a complete view of the claim. So one of the areas that I think is really key in the TPA space is looking at prior claim history. For whether it's claimants or injured workers, just understanding if there are any patterns of behavior that we should be aware of or be concerned about.
Something new in this space that has come up over the past year or so is the idea of automated social listening. So just going out and being able to capture data that's out there publicly available on the web to understand what facts exist that might either confirm the elements of the claim or contradict the elements of the claim. So I think you're starting to see more TPAs work with social listening vendors to be able to pull all that information in, to be able to quickly assess is this a claim that's valid or is this a claim that we have some concerns about.
And then certainly you're seeing with all the social inflation going on these days, more and more attorney activity. I think folks should be looking for TPAs who can understand connections between attorneys, providers, medical providers, claimants. Just to understand, get to get a full picture of claims.
So now as we get into predictive modeling, I think the three areas that you see people focused on the most are fraud detection. That's using information from our structured, from our unstructured data, and third party data sources like the socialist thing that I talked about, to understand claimants as well as medical providers on a claim. Because we do see quite a bit of medical fraud as well.
Litigation propensity is another hot area, where you're trying to understand which claimants are most likely to hire attorneys. We know attorneys are likely to drive up the medical costs on a claim, we've seen that over time. We want to make sure that we pay what we owe on a claim, but not more than we owe, not less than we owe. And so understanding when attorneys might get involved I think helps us get a leg up on how we can handle the claim more effectively and potentially communicate with the claimant in a way that I think leads to the best outcome for them and for their employers.
Lastly, claim triage is really, really important as well. So how do we handle claims in the most effective way, how do we get the right resources on claims? So for example, getting nurses on certain claims where we think that will be most effective. So you're starting to see a lot of predictive modeling in really those three areas.
SHERRY HERSEY: So there's a lot packed in there Kevin. Again, a reminder, if anyone has any questions, please use the Q&A function to submit your questions. You can send technical questions in for Kevin to answer.
So you mentioned 45 billion structured data points, and then fraud detection models, and litigation propensity models. So is there a danger of too much data or AI, or even on the other hand, too much human contact, over emphasis on the people side versus the technology side? So maybe let's go to Rich now. Rich, could you share some examples of how the two that balance between the technology and the claim professionals come together to strike the right balance?
RICH IVES: Yeah absolutely Sherry, I would love to because we believe that this is a very important question that needs to be considered in this light of transformational time. And so the reason why we say that is that if you have too much reliance on just the talent aspect, just the human aspect of the process of claim handling and medical management, then what you miss is the ability to augment the understanding of that individual, that claim professional, to actually expand upon their knowledge and their expertise. So if you lean too much on the side of just the human interaction and the human process, then it's not as efficient and it mitigates some of the effectiveness on your ability to create better outcomes.
Now on the opposite side, if you have too much leaning in the way of digital technology and predictive modeling capabilities, and you miss the very important nuance of the human skill, then you lack the ability to change the outcome. Or the ability to tailor that process to actually make it very meaningful to both the overall outcome as well as to the injured employee or the injured customer or for that matter a third party claimant, that we think has a big impact.
So let's make that practical just for a second, OK? You take what Kevin's talking about and the ability to use our historical scale of data and understanding over a long period of time. A claim professional is even somebody who's been in the business for 30 or 40 years, they're only going to be able to bring their thoughts, their insights, based on that moment in time and for that claim. And they can only be involved in one claim at a time.
So the ability to bring some of those insights of what we have seen historically to otherwise predict where a claim might be going or to direct a path or provide guidance to change an outcome, is where all of the magic happens. So we think that the real folks, carriers, TPAs, who will be real successful in this space are those who can have that excellent balance of both technology and human skill and capability. We see that play out really well in our area of early severity predictor model. And that's just one predictive model as Kevin was mentioning.
But if you think about what that does, that basically says, instances of chronic pain where an injured employee might decompensate into chronic pain many months after an injury, and by the way, at the moment they are diagnosed with that, it's really hard to change the outcome at that moment in time. And they may become suspect to things like opioid abuse and dependency for example. We've developed a capability that gives us the opportunity within the first few weeks after an injury to identify for a claim professional and a medical professional the likelihood of those injured employees that would decomensate into chronic pain.
Now to be able to predict the future provides tremendous value, but to be able to intervene and course correct or change that future is extremely valuable. So in this instance, our folks would get a flag. We would then dedicate more resources. We would prioritize those claims a little more.
We would work with medical providers to develop a personalized care path for that individual. We would engage with those injured employees a little differently, along with involving their employers to produce some, what we think are, just terrific industry leading outcomes. And so it provides for us just a practical good use case of how the two have to come together in order to get to your ultimate objective.
SHERRY HERSEY: Thank you, Rich, for that example of how the model, the early severity predictor model, comes together to enable our claim professionals and to achieve that balance. And so part of the value of a TPA is the ability to customize and adapt to the needs of the employer, the customer. Todd, could you talk a little bit about the pitfalls of a one size fits all approach in this space?
TODD MATTIELLO: Happy to, and I love this dialogue, I love the whole art and science that we're getting into. This last piece speaks to customer insights. So in working with a new prospect, an existing customer, it's really, really important because we all bring a full suite of products and capabilities to bear. But unless you're really truly sitting down and listening to that customer in terms of what's important to them, how they manage their risks today, what their goals and objectives are for today and in the future, it's really difficult to just throw everything at them and really understand whatthose needs are and then provide solutions.
How can we complement their existing risk management program with some of the resources and capabilities that we bring to bear? Because you don't need the full suite perhaps, sometimes you need components thereof, or sometimes you want to bring what you have together with what we can complement that with, to make sure we come up with a robust program that includes our resources and the capabilities that both Kevin and Richard just hit.
So listen, understand, and then offer up solutions. And that's not just a one time event, having a customer over the longer term allows us to learn together as to what additional things we can bring to bear that may not have made sense at the beginning, but certainly makes sense as we grow together. It's really, really important that a TPA be viewed as an extension of that customer's risk management program. So it's part customization, to me it's more tailoring the resources the capabilities that we have to the goals and objectives of that customer.
SHERRY HERSEY: Thank you, Todd. So all three are needed to achieve this balance, it's not just about the models, and the data, and the technology. It's not just about the talent that the TPA offers. It's all of that combined with the insights that the employer or customer brings to the table. So in this environment of COVID-19 how has this environment changed the way TPAs manage claims? Where do you, and any of our panelists, believe the industry is going next?
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Rich Ives
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RICH IVES: So I'll take that one. We're proud of this question first and foremost. So practically speaking, in the COVID world, the one that comes to mind the most is our ability to scale up, the ability to do our work from any location. So the quarantine orders across the country and various different phases created a working environment that we were able to adapt to very, very quickly.
Second, the technical ability to stay on top of really a moving environment during this period of time. So when you think about COVID risks, and specifically COVID-19 claims directively, and I'll speak about it from the workers compensation standpoint. The ability to utilize your technical expertise to identify which of those injuries or illnesses actually did arise out of or in the course of scope of work has been very key.
So our ability to stay on top of the regulatory changes, case law changes, to scale up an operation to bring a level of oversight and insight into those claims, to make sure that we are handling them appropriately for our customers, and to provide our customers expertise and knowledge during this period of time is something that we've been able to do really well. The third piece though, it really involves around the ability to further produce better outcomes leveraging our digital capabilities. And you see an example of that within our digital portal.
So we've got close to 70,000 people who have used our mobile capabilities over this period of time, registering over almost two million sessions. The ability to launch e-pay, the ability to be compliant with the changes in telemedicine use. And one might say that's easy, but the ability to then make sure that we are appropriately repricing those treatments and making sure that we're finding in any instances of fraud or abuse that might happen during this period of time, are also things that we launched very, very quickly. The ability to be able to push out documents and forms, e-signature, we think has just been a terrific example of the way that we used the challenge to drive innovation and pull a lot of our capabilities together at one time, to really help to mitigate the impact of this for all involved and to manage in a very dynamic environment.
SHERRY HERSEY: So that's another way that the investment in technology, TPA's investment technology is very important. So thank you, this is the end of our formal questions in our panel discussion. We have some time to take questions from our audience. So please use the Q&A icon, the future of Zoom, to submit your questions, and our people will be glad to answer them.
KEVIN MAHONEY: Sherry I think you promised the machine learning question earlier, is that true?
SHERRY HERSEY: Machine learning, video analytics.
KEVIN MAHONEY: Artificial intelligence, whatever.
SHERRY HERSEY: Technology, we can talk about it.
TODD MATTIELLO: I'll take it, Kevin.
SHERRY HERSEY: All the techie geeks out there. OK, here's our first question. As more data and tools become available to claim professionals, how has the training changed for claim professionals to maximize the use of these innovations?
TODD MATTIELLO: Rich, maybe you start with that one and I can add? That'd be great.
RICH IVES: Yeah, absolutely. So part of it is, let me use the example of virtual visit, maybe that would be a good one. And that was the ability to leverage a virtual visit with our nurse case managers to interact with an injured employee, so it's a work comp example, but we've got examples across auto, across property of using those video tools to evaluate damage or to connect with an injured person.
