On the Path to a New Normal: Gain Insights and Reassurance Using Data and Artificial Intelligence

A discussion on how AI is the new pandemic response team member for helping businesses reduce risk of failure and innovate with confidence.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: Hewlett Packard Enterprise.

Dana Gardner: Hello, and welcome to the next edition of the BriefingsDirect Voice of AI Innovation podcast series.

Gardner

I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this ongoing discussion on the latest insights into artificial intelligence (AI) strategies.

As businesses and IT strategists plan their path to a new normal throughout the COVID-19 pandemic and recovery, AI and data science are proving impactful and indispensable.

By leveraging the latest tools and gaining data-driven inferences, architects and analysts are both effectively managing the pandemic response -- and giving more people better ways to improve their path to the new normal.

Stay with us here and now as we examine how AI forms the indispensable pandemic response team member for helping businesses reduce risk of failure and innovate with confidence. To learn more about the analytics, solutions, and methods that support advantageous reactivity -- amid unprecedented change -- we are joined by two experts.

Please join me in welcoming Arti Garg, Head of Advanced AI Solutions and Technologies, at Hewlett Packard Enterprise (HPE). Welcome, Arti.

Arti Garg: Good morning.

Gardner: We’re also here with Glyn Bowden, Chief Technologist for AI and Data, HPE Pointnext Services. Welcome, Glyn.

Glyn Bowden: Thanks, Dana.

Gardner: Arti, why should we look to data science and AI to help at a time when there’s not much of a historical record to rely on? It seems we’re in a time when people are in uncharted waters in dealing with the complexities of the novel coronavirus pandemic.

AI in the fight against COVID-19

Garg: Because we don’t have a historical record, I think data science and AI are proving to be particularly useful right now in understanding this new disease and how we might potentially better treat it, manage it, and find a vaccine for it. And that’s because at this moment in time, raw data that are being collected from medical offices and through research labs are the foundation of what we know about the pandemic.

Garg

This is an interesting time because, when you know a disease, medical studies and medical research are often conducted in a very controlled way. You try to control the environment in which you gather data, but unfortunately, right now, we can’t do that. We don’t have the time to wait.

And so instead, AI -- particularly some of the more advanced AI techniques -- can be helpful in dealing with unstructured data or data of multiple different formats. It’s therefore becoming very important in the medical research community to use AI to better understand the disease. It’s enabling some unexpected and very fruitful collaborations, from what I’ve seen.

Gardner: Glyn, do you also see AI delivering more, even though we’re in uncharted waters?

Bowden: The benefits of something like machine learning (ML), for example, which is a subset of AI, is very good at handling many, many features. So with a human being approaching these projects, there are only so many things you can keep in your head at once in terms of the variables you need to consider when building a model to understand something.

But when you apply ML, you are able to cope with millions or billions of features simultaneously -- and then simulate models using that information. So it really does add the power of a million scientists to the same problem we were trying to face alone before.

Gardner: And is this AI benefit something that we can apply in many different avenues? Are we also modeling better planning around operations, or is this more research and development? Is it both?

Data scientists are collaborating directly with medical science researchers and learning how to incorporate subject matter expertise into data science models.

Garg: There are two ways to answer the question of what’s happening with the use of AI in response to the pandemic. One is actually to the practice of data science itself.

One is, right now data scientists are collaborating directly with medical science research and learning how to incorporate subject matter expertise into data science models. This has been one of the challenges preventing businesses from adopting AI in more complex applications. But now we’re developing some of the best-practices that will help us use AI in a lot of domains.

In addition, businesses are considering the use of AI to help them manage their businesses and operations going forward. That includes things such as using computer vision (CV) to ensure that social distancing happens with their workforce, or other types of compliance we might be asked to do in the future.

Gardner: Are the pressures of the current environment allowing AI and data science benefits to impact more people? We’ve been talking about the democratization of AI for some time. Is this happening more now?

More data, opinions, options

Bowden

Bowden: Absolutely, and that’s both a positive and a negative. The data around the pandemic has been made available to the general public. Anyone looking at news sites or newspapers and consuming information from public channels -- accessing the disease incidence reports from Johns Hopkins University, for example -- we have a steady stream of it. But those data sources are all over the place and are being thrown to a public that is only just now becoming data-savvy and data-literate.

As they consume this information, add their context, and get a personal point of view, that is then pushed back into the community again -- because as you get data-centric you want to share it.

So we have a wide public feed -- not only from universities and scholars, but from the general public, who are now acting as public data scientists. I think that’s creating a huge movement.

Garg: I agree. Making such data available exposes pretty much anyone to these amazing data portals, like Johns Hopkins University has made available. This is great because it allows a lot of people to participate.

It can also be a challenge because, as I mentioned, when you’re dealing with complex problems you need to be able to incorporate subject matter expertise into the models you’re building and in how you interpret the data you are analyzing.

And so, unfortunately, we’ve already seen some cases -- blog posts or other types of analysis -- that get a lot of attention in social media but are later found to be not taking into account things that people who had spent their careers studying epidemiology, for example, might know and understand.

Gardner: Recently, I’ve seen articles where people now are calling this a misinformation pandemic. Yet businesses and governments need good, hard inference information and data to operate responsibly, to make the best decisions, and to reduce risk.

What obstacles should people overcome to make data science and AI useful and integral in a crisis situation?

Garg: One of the things that’s underappreciated is that a foundation, a data platform, makes data managed and accessible so you can contextualize and make stronger decisions based on it. That’s going to be critical. It’s always critical in leveraging data to make better decisions. And it can mean a larger investment than people might expect, but it really pays off if you want to be a data-driven organization.

Know where data comes from 

Bowden: There are a plethora of obstacles. The kind that Arti is referring to, and that is being made more obvious in the pandemic, is the way we don’t focus on the provenance of the data. So, where does the data come from? That doesn’t always get examined, and as we were talking about a second ago, the context might not be there.

All of that can be gleaned from knowing the source of the data. The source of the data tends to come from the metadata that surrounds it. So the metadata is the data that describes the data. It could be about when the data was generated, who generated it, what it was generated for, and who the intended consumer is. All of that could be part of the metadata.

Organizations need to look at these data sources because that’s ultimately how you determine the trustworthiness and value of that data.

We don't focus on the provenance of the data. Where does the data come from? That doesn't always get examined and he context might not be there.

Now it could be that you are taking data from external sources to aggregate with internal sources. And so the data platform piece that Arti was referring to applies to properly bringing those data pieces together. It shouldn’t just be you running data silos and treating them as you always treated them. It’s about aggregation of those data pieces. But you need to be able to trust those sources in order to be able to bring them together in a meaningful way.

So understanding the provenance of the data, understanding where it came from or where it was produced -- that’s key to knowing how to bring it together in that data platform.

Gardner: Along the lines of necessity being the mother of invention, it seems to me that a crisis is also an opportunity to change culture in ways that are difficult otherwise. Are we seeing accelerants given the current environment to the use of AI and data?

AI adoption on the rise 

Garg: I will answer that question from two different perspectives. One is certainly the research community. Many medical researchers, for example, are doing a lot of work that is becoming more prominent in people’s eyes right now.

I can tell you from working with researchers in this community and knowing many of them, that the medical research community has been interested and excited to adopt advanced AI techniques, big data techniques, into their research.

It’s not that they are doing it for the first time, but definitely I see an acceleration of the desire and necessity to make use of non-traditional techniques for analyzing their data. I think it’s unlikely that they are going to go back to not using those for other types of studies as well.

In addition, you are definitely going to see AI utilized and become part of our new normal in the future, if you will. We are already hearing from customers and vendors about wanting to use things such as CV to monitor social distancing in places like airports where thermal scanning might already be used. We’re also seeing more interest in using that in retail.

So some AI solutions will become a common part of our day-to-day lives.

Gardner: Glyn, a more receptive environment to AI now?

Bowden: I think so, yes. The general public are particularly becoming used to AI playing a huge role. The mystery around it is beginning to fade and it is becoming far more accepted that AI is something that can be trusted.

It does have its limitations. It’s not going to turn into Terminator and take over the world.

The fact that we are seeing AI more in our day-to-day lives means people are beginning to depend on the results of AI, at least from the understanding of the pandemic, but that drives that exception.

The general public are particularly becoming used to AI playing a huge role. The mystery around it is beginning to fade and it is becoming far more accepted that AI is something that can be trusted.

When you start looking at how it will enable people to get back to somewhat of a normal existence -- to go to the store more often, to be able to start traveling again, and to be able to return to the office -- there is that dependency that Arti mentioned around video analytics to ensure social distancing or temperatures of people using thermal detection. All of that will allow people to move on with their lives and so AI will become more accepted.

I think AI softens the blow of what some people might see as a civil liberty being eroded. It softens the blow of that in ways and says, “This is the benefit already and this is as far as it goes.” So it at least forms discussions whenever it was formed before.

Garg: One of the really valuable things happening right now are how major news publications have been publishing amazing infographics, very informative, both in terms of the analysis that they provide of data and very specific things like how restaurants are recovering in areas that have stay-in-place orders.

In addition to providing nice visualizations of the data, some of the major news publications have been very responsible by providing captions and context. It’s very heartening in some cases to look at the comments sections associated with some of these infographics as the general public really starts to grapple with the benefits and limitations of AI, how to contextualize it and use it to make informed decisions while also recognizing that you can go too far and over-interpret the information.

Gardner: Speaking of informed decisions, to what degree you are seeing the C-suite -- the top executives in many businesses -- look to their dashboards and query datasets in new ways? Are we seeing data-driven innovation at the top of decision-making as well?

Data inspires C-suite innovation 

Bowden: The C-suite is definitely taking a lot of notice of what’s happening in the sense that they are seeing how valuable the aggregation of data is and how it’s forwarding responses to things like this.

So they are beginning to look internally at what data sources are available within their own organizations. I am thinking now about how do we bring this together so we can get a better view of not only the tactical decisions that we have to make, but using the macro environmental data, and how do we now start making strategic decisions, and I think the value is being demonstrated for them in plain sight.

So rather than having to experiment, to see if there is going to be value, there is a full expectation that value will be delivered, and now the experiment is how much they can draw from this data now.

Garg: It’s a little early to see how much this is going change their decision-making, especially because frankly we are in a moment when a lot of the C-suite was already exploring AI and opening up to its possibilities in a way they hadn’t even a year ago.

And so there is an issue of timing here. It’s hard to know which is the cause and which is just a coincidence. But, for sure, to Glyn’s point, they are dealing with more change.

Gardner: For IT organizations, many of them are going to be facing some decisions about where to put their resources. They are going to be facing budget pressures. For IT to rise and provide the foundation needed to enable what we have been talking about in terms of AI in different sectors and in different ways, what should they be thinking about?

How can IT make sure they are accelerating the benefits of data science at a time when they need to be even more choosy about how they spend their dollars?

IT wields the sword to deliver DX 

Bowden: With IT particularly, they have never had so much focus as right now, and probably budgets are responding in a similar way. This is because everyone has to now look at their digital strategy and their digital presence -- and move as much as they can online to be able to be resistant to pandemics and at-risk situations that are like this.

So IT has to have the sword, if you like, in that battle. They have to fix the digital strategy. They have to deliver on that digital promise. And there is an immediate expectation of customers that things just will be available online.

With the pandemic, there is now an AI movement that will get driven purely from the fact that so much more commerce and business are going to be digitized. We need to enable that digital strategy.

If you look at students in universities, for example, they assume that it will be a very quick fix to start joining Zoom calls and to be able to meet that issue right away. Well, actually there is a much bigger infrastructure that has to sit behind those things in order to be able to enable that digital strategy.

So, there is now an AI movement that will get driven purely from the fact that so much more commerce and business is going to be digitized.

Gardner: Let’s look to some more examples and associated metrics. Where do you see AI and data science really shining? Are there some poster children, if you will, of how organizations -- either named or unnamed -- are putting AI and data science to use in the pandemic to mitigate the crisis or foster a new normal?

Garg: It’s hard to say how the different types of video analytics and CV techniques are going to facilitate reopening in a safe manner. But that’s what I have heard about the most at this time in terms of customers adopting AI.

In general, we are at very early stages of how an organization is going to decide to adopt AI. And so, for sure, the research community is scrambling to take advantage of this, but for organizations it’s going to take time to further adopt AI into any organization. If you do it right, it can be transformational. Yet transformational usually means that a lot of things need to change -- not just the solution that you have deployed.

Bowden: There’s a plethora of examples from the medical side, such as how we have been able to do gene analysis, and those sorts of things, to understand the virus very quickly. That’s well-known and well-covered.

The bit that’s less well covered is AI supporting decision-making by governments, councils, and civil bodies. They are taking not only the data from how many people are getting sick and how many people are in hospital, which is very important to understand where the disease is but augmenting that with data from a socioeconomic situation. That means you can understand, for example, where an aging population might live or where a poor population might live because there’s less employment in that area.

The impact of what will happen to their jobs, what will happen if they lose transport links, and the impact if they lose access to healthcare -- all of that is being better understood by the AI models.

As we focus on not just the health data but also the economic data and social data, we have a much better understanding of how society will react, which has been guiding the principles that the governments have been using to respond.

So when people look at the government and say, “Well, they have come out with one thing and now they are changing their minds,” that’s normally a data-driven decision and people aren’t necessarily seeing it that way.

So AI is playing a massive role in getting society to understand the impact of the virus -- not just from a medical perspective, but from everything else and to help the people.

Gardner: Glyn, this might be more apparent to the Pointnext organization, but how is AI benefiting the operational services side? Service and support providers have been put under tremendous additional strain and demand, and enterprises are looking for efficiency and adaptability.

Are they pointing the AI focus at their IT systems? How does the data they use for running their own operations come to their aid? Is there an AIOps part to this story?

AI needs people, processes 

Bowden: Absolutely, and there has definitely become a drive toward AIOps.

When you look at an operational organization within an IT group today, it’s surprising how much of it is still human-based. It’s a personal eyeball looking at a graph and then determining a trend from that graph. Or it’s the gut feeling that a storage administrator has when they know their system is getting full and they have an idea in the back of their head that last year something happened seasonally from within the organization making decisions that way.

We are therefore seeing systems such as HPE’s InfoSight start to be more prominent in the way people make those decisions. So that allows plugging into an ecosystem whereby you can see the trend of your systems over a long time, where you can use AI modeling as well as advanced analytics to understand the behavior of a system over time, and how the impact of things -- like everybody is suddenly starting to work remotely – does to the systems from a data perspective.

So the models-to-be need to catch up in that sense as well. But absolutely, AIOps is desirable. If it’s not there today, it’s certainly something that people are pursuing a lot more aggressively than they were before the pandemic.

Gardner: As we look to the future, for those organizations that want to be more data-driven and do it quickly, any words of wisdom with 20/20 hindsight? How do you encourage enterprises -- and small businesses as well -- to better prepare themselves to use AI and data science?

Garg: Whenever I think about an organization adopting AI, it’s not just the AI solution itself but all of the organizational processes -- and most importantly the people in an organization and preparing them for the adoption of AI.

I advise organizations that want to use AI and corporate data-driven decision-making to, first of all, make sure you are solving a really important problem for your organization. Sometimes the goal of adopting AI becomes more important than the goal of solving some kind of problem. So I always encourage any AI initiative to be focused on really high-value efforts.

Use your AI initiative to do something really valuable to your organization and spend a lot of time thinking about how to make it fit into the way your organization currently works. Make it enhance the day-to-day experience of your employees because, at the end of the day, your people are your most valuable assets.

Those are important non-technical things that are non-specific to the AI solution itself that organizations should think about if they want the shift to being AI-driven and data-driven to be successful.

For the AI itself, I suggest using the simplest-possible model, solution, and method of analyzing your data that you can. I cannot tell you the number of times where I have heard an organization come in saying that they want to use a very complex AI technique to solve a problem that if you look at it sideways you realize could be solved with a checklist or a simple spreadsheet. So the other rule of thumb with AI is to keep it as simple as possible. That will prevent you from incurring a lot of overhead.

Gardner: Glyn, how should organizations prepare to integrate data science and AI into more parts of their overall planning, management, and operations?

Bowden: You have to have a use case with an outcome in mind. It’s very important that you have a metric to determine whether it’s successful or not, and for the amount of value you add by bringing in AI. Because, as Arti said, a lot of these problems can be solved in multiple ways; AI isn’t the only way and often isn’t the best way. Just because it exists in that domain doesn’t necessarily mean it should be used.

AI isn't an on/off switch; it's an iteration. You can start with something small and then build into bigger and bigger components that bring more data to bear on the problem, and then add new features that lead to new functions and outcomes.

The second part is AI isn’t an on/off switch; it’s an iteration. You can start with something small and then build into bigger and bigger components that bring more and more data to bear on the problem, as well as then adding new features that lead to new functions and outcomes.

The other part of it is: AI is part of an ecosystem; it never exists in isolation. You don’t just drop in an AI system on its own and it solves a problem. You have to plug it into other existing systems around the business. It has data sources that feed it so that it can come to some decision.

Unless you think about what happens beyond that -- whether it’s visualizing something to a human being who will make a decision or automating a decision – it could really just be hiring the smartest person you can find and locking them in a room.

Pandemic’s positive impact

Gardner: I would like to close out our discussion with a riff on the adage of, “You can bring a horse to water but you can’t make them drink.” And that means trust in the data outcomes and people who are thirsty for more analytics and who want to use it.

How can we look with reassurance at the pandemic as having a positive impact on AI in that people want more data-driven analytics and will trust it? How do we encourage the perception to use AI? How is this current environment impacting that?

Garg: The fact that so many people are checking the trackers of how the pandemic is spreading and learning through a lot of major news publications as they are doing a great job of explaining this. They are learning through the tracking to see how stay-in-place orders affect the spread of the disease in their community. You are seeing that already.

We are seeing growth and trust in how analyzing data can help make better decisions. As I mentioned earlier, this leads to a better understanding of the limitations of data and a willingness to engage with that data output as not just black or white types of things.

As Glyn mentioned, it’s an iterative process, understanding how to make sense of data and how to build models to interpret the information that’s locked in the data. And I think we are seeing that.

We are seeing a growing desire to not only view this as some kind of black box that sits in some data center -- and I don’t even know where it is -- that someone is going to program, and it’s going to give me a result that will affect me. For some people that might be a positive thing, but for other people it might be a scary thing.

People are now much more willing to engage with the complexities of data science. I think that’s generally a positive thing for people wanting to incorporate it in their lives more because it becomes familiar and less other, if you will.

Gardner: Glyn, perceptions of trust as an accelerant to the use of yet more analytics and more AI?

Bowden: The trust comes from the fact that so many different data sources are out there. So many different organizations have made the data available that there is a consistent view of where the data works and where it doesn’t. And that’s built up the capability of people to accept that not all models work the first time, that experimentation does happen, and it is an iterative approach that gets to the end goal.

I have worked with customers who, when they saw a first experiment fall flat because it didn’t quite hit the accuracy or targets they were looking for, they ended the experiment. Whereas now I think we are seeing in real time on a massive scale that it’s all about iteration. It doesn’t necessarily work the first time. You need to recalibrate, move on, and do refinement. You bring in new data sources to get the extra value.

What we are seeing throughout this pandemic is the more expertise and data science you throw in an instance, the much better the outcome at the end. It’s not about that first result. It’s about the direction of the results, and the upward trend of success.

Gardner: I’m afraid we’ll have to leave it there. We have been exploring how AI and data science are proving impactful and indispensable as business architects chart their path to a new normal.

And we have learned how AI is the new pandemic team member for helping businesses reduce risk of failure and innovate with confidence. So please join me in thanking our guests, Arti Garg, Head of Advanced AI Technologies and Solutions at HPE. Thank you.

Garg: Thank you.

Gardner: And we have been with Glyn Bowden, Chief Technologist for AI and Data at HPE Pointnext Services. Thank you.

Bowden: Thanks, Dana.

Gardner: And a big thank you as well to our audience for joining us for this sponsored BriefingsDirect Voice of AI Innovation discussion. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host for this ongoing series of Hewlett Packard Enterprise-supported discussions.

Thanks again for listening. Please pass this along to your IT community, and do come back next time.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: Hewlett Packard Enterprise.

A discussion on how AI is the new pandemic response team member for helping businesses reduce risk of failure and innovate with confidence. Copyright Interarbor Solutions, LLC, 2005-2020. All rights reserved.

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Data Science Helps Hospitals Improve Patient Payments and Experiences While Boosting Revenue

Transcript of a discussion on new approaches to healthcare revenue cycle management and outcomes that give patients more options and providers more revenue clarity.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: HealthPay24.

Dana Gardner: Hi, this is Dana Gardner, Principal Analyst at Interarbor Solutions, and you’re listening to BriefingsDirect.

Gardner

Our next healthcare finance insights discussion explores new ways of analyzing healthcare revenue trends to improve both patient billing and services. Stay with us now as we learn about new approaches to healthcare revenue cycle management and outcomes that give patients more options and providers more revenue clarity.

To learn more about the next generation of data-driven patient payments process improvements, we’re now joined by Jake Intrator, Managing Consultant for Data and Services at Mastercard. Welcome, Jake.

Jake Intrator: Great to be here and excited to talk about our joint-solution with HealthPay24.

Gardner: We’re also here Julie Gerdeman, CEO of HealthPay24. Welcome back, Julie.

Julie Gerdeman: Thanks so much, Dana.

Gardner: Julie, what trends or market pressures are driving healthcare providers to seek new and better ways of analyzing data to better manage patient billing? What’s wrong with the status quo?

Healthcare’s sea change 

Gerdeman: Dana, we are in such an interesting time, particularly in the US, with this being an election time. There is such a high level of visibility -- really a spotlight on healthcare. There is a lot of change happening, such as in regulations, that highlights interoperability of data and price transparency for patients.

Gerdeman

And there’s ongoing change on the insurance reimbursement side, with payer plans that seem to change and evolve every year. There are also trends changing provider compensation, including value-based care and pay-for-performance.

On the consumer-patient side, there is significant pressure in the market. Statistics show that 62 percent of patients say knowing their out-of-pocket costs in advance will impact their likelihood of pursuing care. So the visibility and transparency of costs -- that price expectation -- is very, very important and is driving consumerism into healthcare like we have never seen before due to rising costs to patients.

Finally, there is more competition. Where I live in Pennsylvania, I can drive a five-mile radius and access a multitude of different health providers in different systems. That level of competition is unlike anything we have seen before.

Gardner: Jake, why is healthcare revenue management difficult? Is it different from other industries? Do they lag in their use of technology? Why is the healthcare industry in the spotlight, as Julie pointed out?

Intrator: The word that Julie used that was really meaningful to me was consumerism. There is a shift across healthcare where patients are responsible for a much larger proportion of their bills than they ever used to be.

Intrator

And so, as things shift away from hospitals working with payers to receive dollars in an efficient, easy process -- now the revenue is coming from patients. That means there needs to be new processes and new solutions to make it a more pleasant experience for patients to be able to pay. We need to enable people to pay when they want to pay, in the ways that they want to pay.

That’s something we have keyed on to, as a payments organization. That’s also what led us to work with HealthPay24.

Gardner: It’s fascinating. If we are going to a consumer-type model for healthcare, why not take advantage of what consumers have been doing with their other financing, such as getting reports every month on their bills? It seems like there is a great lesson to be learned from what we all do with our credit cards. Julie, is that what’s going to happen?

Consumer in driver’s seat 

Gerdeman: Yes, definitely. It’s interesting that healthcare has been sitting in a time warp. Historically, there remain many manual processes and functions in the health revenue cycle. That’s attributed to a piecemeal approach -- different segments of the revenue cycle were tackled either at different times or acquisitions impacted that. I read recently that there are still eight billion faxes happening in healthcare.

So that consumer-level experience, as Jake indicated, is where it’s going -- and where we need to go even faster.

Technology provides the transparency and interoperability of data. Investment in IT is happening, but it needs to happen even more.

Gardner: Wherever there is waste, inefficiency, and a lack of clarity is an opportunity to fix that for all involved. But what are the stakes? How much waste or mismanagement are we talking about?

Intrator: The one statistic that sticks out to me is that care providers aren’t collecting as much as 80 percent of balances from older bills. So that’s a pretty substantial amount -- and a large opportunity. Julie, do you have more?

Gerdeman: I actually have a statistic that’s staggering. There is waste of $265 billion spent on administrative complexity. And then another $230 to $240 billion attributed to what’s termed pricing failure, which means price increases that aren’t in line with the current market. The stakes are very high and the opportunity is very large.

We have data that shows more than 50 percent of chief financial officers (CFOs) want better access to data and better dashboards to understand the scope of the problem. As we were talking about consumerism, Mastercard is just phenomenal in understanding consumer behavior. Think about the personalized experiences that organizations like Mastercard provide -- or Google, Amazon, Disney, and Netflix. Everything is becoming so personalized in our consumer lives.

But healthcare? We are not there yet. It’s not a personalized experience where providers know in advance what a consumer or patient wants. HealthPay24 and Mastercard are coming together to get us much closer to that. But, truly, it’s a big opportunity.

Intrator: I agree. Payers and providers haven’t figured out how they enable personalized experiences. It’s something that patients are starting to expect from the way they interact with companies like Netflix, Disney, and Mastercard. It’s becoming table-stakes. It’s really exciting that we are partnering to figure out how to bring that to healthcare payers and providers alike.

Gardner: Julie, you mentioned that patients want upfront information about what their procedures are going to cost. They want to know their obligation before they go through a medical event. But oftentimes the providers don’t know in advance what those costs are going to be.

So we have ambiguity. And one of the things that’s always worked great for ambiguity in other industries is to look at the data, extrapolate, and get analytics involved. So, how are data-driven analytics coming to the rescue? How will that help?

Data to the rescue 

Gerdeman: Historical data allows for a forward-looking view. For HealthPay24, for example, we have been involved in patient payments for 20 years. It makes us a pioneer in the space. It gives us 20 years of data, information, and trends that we can look at. To me, data is absolutely critical.

Having come out of the spend management technology industry I know that in the categories of direct and indirect materials there have long been well-defined goods and services that are priced and purchased accordingly.

But, the ambiguity of patient healthcare payments and patient responsibility presents a new challenge. What artificial intelligence (AI) and algorithms provide are the capability to help anticipate and predict. That offers something much more applicable to a patient at a consumer level.

Gardner: Jake, when you have the data you can use it. Are we still at the point of putting the data together? Or are we now already able to deliver those AI- and machine learning (ML)-driven outcomes?

Intrator: Hospitals still don’t feel like they are making the best use of data. They tie that both to not having access to the data and not yet having the talent, resources, and tools to leverage it effectively. This is top of mind for many people in healthcare.

In seeking to help them, there are two places where I divide the use of analytics. The first is ahead of time. By using patient estimator tools, can you understand what somebody might owe? That’s a really tricky question. We are grappling with it at Mastercard.

By working with HealthPay24, we have developed a solution that is ready and working today. Answering the questions gets a lot smarter when you incorporate the data and analytics.

By working with HealthPay24, we have developed a solution that is ready and working today on the other half of the process. For example, somebody comes to the hospital. They know that they have some amount of patient payment responsibility. What’s the right way for a hospital to interact with that person? What are the payment options that should be available to them? Are they paying upfront? Are they paying over a period of time? What channels are you using to communicate? What options are you giving to them? Answering those questions gets a lot smarter when you incorporate data and analytics. And that’s exactly what we are doing today.

Gardner: Well, we have been dancing around and alluding to the joint-solution. Let’s learn more about what’s going on between HealthPay24 and Mastercard. Tell us about your approach. Are we in a proof of concept (POC) or is this generally available?

Win-win for patients and providers 

Gerdeman: We are currently in a POC phase, working with initial customers on the predictive analytic capability that marries the Mastercard Test and Learn platform with HealthPay24’s platform and executing what’s recommended through the analytics in our platform.

Jake, go ahead and give an overview of Test and Learn, and then we can talk about how we have come together to do some great work for our customers.

Intrator: Sure. Test and Learn is a platform that Mastercard uses with a large number of partner clients to measure the impact of business decisions. We approach that through in-market experiments. You can do it in a retail context where you are changing prices or you can do it in the healthcare context where you are trying different initiatives to focus on patient payments.

That’s how we brought it to bear within the HealthPay24 context. We are working together along with their provider partners to understand the tactics that they are using to drive payments. What’s working, what’s working for the right patient, and what’s working at the right time for the right patients?

Gerdeman: It’s important for the audience to understand that the end-goal is revenue collection and the big opportunity providers have to collect more. The marriage of Test and Learn with HealthPay24 provides the intelligence to allow providers to collect more, but it also offers more options to patients based on that intelligence and creates a better patient experience in the end.

The marriage of Test and Learn with HealthPay24 provides the intelligence to allow providers to collect more, but it also offers more options to patients based on that intelligence, and creates a better patient experience.

If a particular patient will always take a payment plan and make those payments consistently – that is versus when they are presented with a big amount and wouldn’t pay it off – the intelligence through the platform will say, “This patient should be offered a payment plan consistently,” and the provider ends up collecting all of the revenue.

That’s what we are super-excited about. The POC is showing greater revenue collection by offering flexibility in the options that patients truly want and need.

Gardner: Let’s unpack this a little bit. So we have HealthPay24 as chocolate and Mastercard’s Test and Learn platform as peanut butter, and we are putting them together to make a whole greater than the sum of the parts. What’s the chocolate? What’s the peanut butter? And what’s the greater whole?

Like peanut butter and chocolate 

Intrator: One of the things that’s made working with HealthPay24 so exciting for us is that they sit in the center of all of the data and the payment flows. They have the capability to directly guide the patient to the best possible experience.

They are hands-on with the patients. They can implement all of these great learnings through our analytics. We can’t do that on our own. We can do the analytics, but we are not the infrastructure that enables what’s happening in the real world.

That’s HealthPay24. They are in the real world. When you have the data flowing back and forth, we can help measure what’s working and come up with new ideas and hypotheses about how to try different payment programs.

It’s been a really important chocolate and peanut butter combination where you have HealthPay24 interacting with patients and us providing the analytics in the background to inform how that’s happening.

Gerdeman: Jake said it really well. It is a beautiful combination because years ago, the hot thing was propensity to pay. And, yes, providers still talk about that. It was best practice many years ago, of pulling a soft or even hard credit check on a patient to determine their propensity to pay and potentially offer financial assistance, even charity, given the needs of the patient.

But this takes it to a whole other level. That’s why the combination is magical. What makes it so different is there doesn’t need to be that old way of thinking. It’s truly proactive through the data we have in working with providers and the unique capabilities of Mastercard Test and Learn. We bring those together and offer proactively the right option for that specific patient-consumer.

It’s super exciting because payment plans are just one example. The platform is phenomenal and the capabilities are broad. The next financial application is discounts.

Through HealthPay24, providers could configure discounts based on their own policies and thresholds. But, if you know that a particular patient will pay the amount when offered the discount through the platform, that should be offered every time. The intelligence gives us the capability to know that, to offer it, and for the provider to collect that discounted amount, which might be more than that amount going to bad debt and never being collected.

Intrator: If you are able to drive behavior with those discounts, is it 10 percent or 20 percent? If you give away an additional 10 percent, how does that change the number of people reacting to it? If you give away more, you had better hope that you are getting more people to pay more quickly.

Those are exactly the sorts of analytical questions we can answer with Test and Learn and with HealthPay24 leading the charge on implementing those solutions. I am really excited to see how this continues to solve more problems going forward.

Gardner: It’s interesting because in the state of healthcare now, more and more people, at least in the United States, have larger bills regardless of their coverage. There are more co-pays, more often there are large deductibles, with different deductibles for each member of a family, for example, and varying deductibles depending on the type of procedures. So, it seems like many more people will be facing more out-of-pocket items when it comes to healthcare. This impacts literally tens of millions of people.

So we have created this new chocolate confection, which is wonderful, but the proof is in the eating. When are patient-consumers going to get more options, not only for discounts, but perhaps for financing? If you would like to spread the payments out, does it work in both ways, both discounts as well as in payment plans with interest over time?

Flexibility plus privacy

Gerdeman: In HealthPay24, we currently have all of the above -- depending on what the provider wants to offer, their patient base, and the needs and demographics. Yes, they can offer payment plans, discounts, and lines of credit. That’s already embedded in the platform. It creates an opportunity for all the different options and the flexibility we talked about.

Earlier I mentioned personalization, and this gets us much closer to personalization of the financial experience in healthcare. There is so much happening on the clinical side, with great advances around clinical care and how to personalize it. This combination gets us to the personalization of offers and options for patients and payments like we have never seen in the past.

Gardner: Jake, for those listening and reading, who maybe are starting to feel a little concerned that all this information -- about not just their healthcare, but now their finances -- being bandied about among payers, providers, and insurers, are we going to protect that financial information? How should people feel about this in terms of a privacy or a comfort level?

We aspire and really do put a lot of work and effort into being a leader in data privacy and allowing people to have ownership of their data and to feel comfortable.

Intrator: That is a question and a problem near and dear to Mastercard. We aspire and really do put a lot of work and effort into being a leader in data privacy and allowing people to have ownership of their data and to feel comfortable. I think that’s something that we deeply believe in. It’s been a focus throughout our conversations with HealthPay24 to make sure that we are doing it right on both sides.

Gardner: Now that you have this POC in progress, what have been some of the outcomes? It seems to me over time the more you deal with more data, the more benefits, and then the more people adopt it, and so on. Where are we now, and do we have some insight into how powerful is this?

Gerdeman: We do. In fact, one example is a 400-bed hospital in the Northeast US that, through the combination of Mastercard Test and Learn and HealthPay24, were able to look at and identify 25,000 unpaid accounts. Just by targeting 5,000 of the 25,000, they were able to identify an incremental $1 million in collections to the hospital.

That is very significant in that they are just targeting the top 5,000 in a conservative approach. They now know that they have the capability through this intelligence and by offering the right plans to the right people to be able to collect $1 million more to their bottom line.

Intrator: That certainly captures the big picture and the big story. I can also zoom in on a couple of specific numbers that we saw in the POC. As we tackled that, we wanted to understand a couple of different metrics, such as increases in payments. We saw substantial increases from payment plans. As a result, people are paying more than 60 percent more on their bills compared to similar patients that haven’t received the payment plans.

Then we zoomed in a step farther. We wanted to understand the specific types of patients who benefited more from receiving a payment plan and how that potentially could guide us going forward. We were able to dig in, to build a predictive model, and that’s exactly what Julie was talking about. Those top 25,000 accounts, how much we think they are going to pay and the relative prioritization. Hospitals have limited resources. So how do you make sure that you are focusing most appropriately?

Gardner: Now that we have gotten through this trial period, does this scale? Is this something you can apply to almost any provider organization? If I am a provider organization, how might I start to take advantage of this? How does this go to market?

Personalized patient experiences

Gerdeman: It absolutely does scale. It applies across all providers; actually, it applies across many industries as well. Any provider who wants to collect more wants additional intelligence around their patient behavior, patient payments and collection behavior -- it really is a terrific solution. And it scales as we integrate the technologies. I am a huge believer in best-of-breed ecosystems. This technology integrates into the HealthPay24 solution. The recommendations are intelligent and already in the platform for providers.

Gardner: And how about that grassroots demand? Should people start going into their clinics and emergency departments and say, “Hey, I want the plan that I heard about. I want to have financing. I want you to give me all my options.” Should people be advocating for that level of consumerism now when they go into a healthcare environment?

Gerdeman: You know, Dana, they already are. We are at a tipping point in the disruption of healthcare. This kind of grassroots demand of consumerism and a consumer personalized experience -- it’s only a matter of time. You mentioned data privacy earlier. There is a very interesting debate happening in healthcare around the balance between sharing data, which is so important for care, billing, and payment, with the protection of privacy. We take all of that very seriously.

Nonetheless, I feel the demand from providers as well as patients will only get greater.

Gardner: Before we close out let’s extrapolate on the data we have. How will things be different in two or three years from now when more organizations embrace these processes and platforms?

Intrator: The industry is going to be a lot smarter in a couple of years. The more we learn from these analytics, the more we incorporate it into the decisions that are happening every day, then it’s going to feel like it fits you as a patient better. It’s going to improve the patient experience substantially.

The industry is going to be a lot smarter in a couple of years. The more we learn from these analytics, the more we incorporate it into the decisions that are happening every day. It's going to improve the patient experience substantially.

Personally, I am really excited to see where it goes. There are going to be new solutions that we haven’t heard about yet. I am closely following everything that goes on.

Gerdeman: This is heading to an experience for patients where from the moment they seek care, they research care, they are known. They are presented with a curated, personalized experience from the clinical aspect of their encounter all the way through the billing and payment. They will be presented with recommendations based on who they are, what they need, and what their expectations are.

That’s the excitement around AI and ML and how it’s going to be leveraged in the future. I am with Jake. It’s going to look very different in healthcare experiences for consumers over the next few years.

Gardner: And for those interested in learning more about this pilot program, about the Mastercard Test and Learn platform and HealthPay24’s platform, where might they go? Are there any press releases, white papers? What sort of information is available?

Gerdeman: We have a great case study from the POC that we are currently running. We are happy to work with anyone who is interested, just contact us via our website at HealthPay24 or through Mastercard.

Gardner: I’m afraid we will have to leave it there. You have been listening to a sponsored BriefingsDirect healthcare finance insights discussion on new ways of analyzing healthcare revenue trends to both improve patient billing and services.

And we have learned about new approaches to healthcare revenue cycle management and the outcomes that give patients more options and providers more revenue clarity. So please join me in thanking our guests, Jake Intrator, Managing Consultant for Data and Services at Mastercard. Thank you so much, Jake.

Intrator: Thank you.

Gardner: And thank you as well to Julie Gerdeman, CEO of HealthPay24. Always a pleasure, Julie.

Gerdeman: Thanks so much, Dana.

Gardner: And a big thank you to our audience as well for joining this HealthPay24-sponsored healthcare thought leadership discussion. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator. Thanks again for listening and do come back next time.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: HealthPay24.

Transcript of a discussion on new approaches to healthcare revenue cycle management and outcomes that give patients more options and providers more revenue clarity. Copyright Interarbor Solutions, LLC, 2005-2020. All rights reserved.

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