Friday, June 19, 2020

How the right data and AI deliver insights and reassurance on the path to a new normal


The next BriefingsDirectVoice of AI Innovation podcast explores how businesses and ITstrategists are planning their path to a new normal throughout the COVID-19 pandemicand recovery.

By leveraging the latest toolsand gaining data-driven inferences, architects and analysts are effectively managing the pandemicresponse -- and giving more people better ways to improve their path to thenew normal. Artificial intelligence (AI) and data science are proving increasingly impactful and indispensable.

Stay with us aswe examine how AI forms the indispensable pandemic response team member forhelping businesses reduce risk of failure and innovate with confidence. To learnmore about the analytics, solutions, and methods that support advantageousreactivity -- amid unprecedented change -- we are joined by two experts.

Listento the podcast. Find it on iTunes. Read a full transcript or download a copy.

Please welcome Arti Garg, Head ofAdvanced AI Solutions and Technologies, at Hewlett Packard Enterprise (HPE), and Glyn Bowden,Chief Technologist for AI and Data, at HPE Pointnext Services. The discussion is moderated by Dana Gardner, PrincipalAnalyst at Interarbor Solutions.

Here are some excerpts:

Gardner: We’re in uncharted waters in dealing with the complexities of the novelcoronavirus pandemic. Arti,why should we look to data science and AI to help when there’s notmuch of a historical record to rely on?  

Garg: Becausewe don’t have a historical record, I think data science and AI are proving tobe particularly useful right now in understanding this new disease and how wemight potentially better treat it, manage it, and find a vaccine for it. Andthat’s because at this moment in time, raw data that are being collected frommedical offices and through research labs are the foundation of what we knowabout the pandemic.

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

And so instead, AI -- particularlysome of the more advanced AI techniques -- can be helpful in dealing withunstructured data or data of multiple different formats. It’s therefore becomingvery important in the medical research community to use AI to better understandthe disease. It’s enabling some unexpected and very fruitful collaborations, fromwhat I’ve seen.

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

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

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

Gardner: Andis this AI benefit something that we can apply in manydifferent 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: Thereare two ways to answer the question of what’s happening with the use of AI inresponse to the pandemic. One is actually to the practice of data scienceitself.

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

In addition, businesses areconsidering the use of AI to help them manage their businesses and operationsgoing forward. That includes things such as using computervision (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: Arethe pressures of the current environment allowing AI and data science benefitsto impact more people? We’ve been talking about the democratization of AIfor some time. Is this happening more now?

More data, opinions, options

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

As they consume thisinformation, add their context, and get a personal point of view, that is thenpushed back into the community again -- because as you get data-centric youwant to share it.

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

Garg: Iagree. Making such data available exposes pretty much anyone to these amazingdata portals, like Johns Hopkins University has made available. This is greatbecause it allows a lot of people to participate.

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

And so, unfortunately, we’vealready seen some cases -- blog posts or other types of analysis -- that get alot of attention in social media but are later found to be not taking intoaccount 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 misinformationpandemic. Yet businesses and governments need good, hard inference informationand data to operate responsibly, to make the best decisions, and to reducerisk.

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

Garg: Oneof the things that’s underappreciated is that a foundation, a data platform,makes data managed and accessible so you can contextualize and make strongerdecisions based on it. That’s going to be critical. It’s always critical inleveraging data to make better decisions. And it can mean a larger investmentthan people might expect, but it really pays off if you want to be a data-drivenorganization.

Know where data comes from 

Bowden: Thereare a plethora of obstacles. The kind that Arti is referring to, and that isbeing made more obvious in the pandemic, is the way we don’t focus on theprovenance of the data. So, where does the data come from? That doesn’t alwaysget examined, and as we were talking about a second ago, the context might notbe there.

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

Organizations need to look at thesedata sources because that’s ultimately how you determine the trustworthinessand 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 aretaking data from external sources to aggregate with internal sources. And sothe data platform piece that Arti was referring to applies to properly bringingthose data pieces together. It shouldn’t just be you running data silos and treatingthem 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 bringthem together in a meaningful way.

So understanding theprovenance of the data, understanding where it came from or where it wasproduced -- that’s key to knowing how to bring it together in that dataplatform.

Gardner: Alongthe lines of necessity being the mother of invention, it seems to me that acrisis is also an opportunity to change culture in ways that are difficultotherwise. Are we seeing accelerants given the current environment to the useof AI and data?

AI adoption on the rise 

Garg: Iwill answer that question from two different perspectives. One is certainly theresearch community. Many medical researchers, for example, are doing a lot ofwork that is becoming more prominent in people’s eyes right now.

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


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

In addition, you aredefinitely going to see AI utilized and become part of our new normal in thefuture, if you will. We are already hearing from customers and vendors aboutwanting to use things such as CV to monitor social distancing in places likeairports where thermal scanning might already be used. We’re also seeing moreinterest in using that in retail.

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

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

Bowden: Ithink so, yes. The general public are particularly becoming used to AI playinga huge role. The mystery around it is beginning to fade and it is becoming farmore accepted that AI is something that can be trusted.

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

The fact that we are seeing AImore in our day-to-day lives means people are beginning to depend on theresults of AI, at least from the understanding of the pandemic, but that drivesthat 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 howit will enable people to get back to somewhat of a normal existence -- to go tothe store more often, to be able to start traveling again, and to be able toreturn to the office -- there is that dependency that Arti mentioned aroundvideo analytics to ensure social distancing or temperatures of people usingthermal detection. All of that will allow people to move on with their livesand so AI will become more accepted.

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

Garg: Oneof the really valuable things happening right now are how major newspublications have been publishing amazinginfographics, very informative, both in terms of the analysis that theyprovide of data and very specific things like how restaurants are recovering inareas that have stay-in-place orders.

In addition to providing nicevisualizations of the data, some of the major news publications have been veryresponsible by providing captions and context. It’s very heartening in somecases to look at the comments sections associated with some of theseinfographics as the general public really starts to grapple with the benefitsand limitations of AI, how to contextualize it and use it to make informeddecisions while also recognizing that you can go too far and over-interpret theinformation.

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

Data inspires C-suiteinnovation 

Bowden: TheC-suite is definitely taking a lot of notice of what’s happening in the sensethat they are seeing how valuable the aggregation of data is and how it’s forwardingresponses to things like this.

So they are beginning to lookinternally at what data sources are available within their own organizations. Iam thinking now about how do we bring this together so we can get a better viewof not only the tactical decisions that we have to make, but using the macroenvironmental data, and how do we now start making strategic decisions, and Ithink the value is being demonstrated for them in plain sight.


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

Garg: It’sa 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 wasalready exploring AI and opening up to its possibilities in a way they hadn’t evena year ago.

And so there is an issue oftiming 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: ForIT organizations, many of them are going to be facing some decisions aboutwhere to put their resources. They are going to be facing budget pressures. ForIT to rise and provide the foundation needed to enable what we have beentalking about in terms of AI in different sectors and in different ways, whatshould they be thinking about?

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

IT wields the sword to deliverDX 

Bowden: WithIT particularly, they have never had so much focus as right now, and probably budgetsare responding in a similar way. This is because everyone has to now look attheir digital strategy and their digital presence -- and move as much as they canonline to be able to be resistant to pandemics and at-risk situations that are likethis.

So IT has to have the sword,if you like, in that battle. They have to fix the digital strategy. They haveto deliver on that digital promise. And there is an immediate expectation ofcustomers 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 inuniversities, for example, they assume that it will be a very quick fix to startjoining Zoom calls and to be able to meet that issue right away. Well, actuallythere is a much bigger infrastructure that has to sit behind those things inorder to be able to enable that digital strategy.

So, there is now an AImovement that will get driven purely from the fact that so much more commerceand business is going to be digitized.

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

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

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

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

The bit that’s less wellcovered is AI supporting decision-making by governments, councils, and civilbodies. They are taking not only the data from how many people are getting sickand how many people are in hospital, which is very important to understandwhere the disease is but augmenting that with data from a socioeconomicsituation. That means you can understand, for example, where an agingpopulation might live or where a poor population might live because there’sless employment in that area.

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

As we focus on not just thehealth data but also the economic data and social data, we have a much betterunderstanding of how society will react, which has been guiding the principlesthat the governments have been using to respond.

So when people look at thegovernment and say, “Well, they have come out with one thing and now they are changingtheir minds,” that’s normally a data-driven decision and people aren’tnecessarily seeing it that way.

So AI is playing a massiverole in getting society to understand the impact of the virus -- not just froma 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 AIbenefiting the operational services side? Service and support providers havebeen put under tremendous additional strain and demand, and enterprises arelooking for efficiency and adaptability.

Are they pointing the AI focusat their IT systems? How does the data they use for running their ownoperations come to their aid? Is there an AIOpspart to this story?

AI needs people, processes 

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

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

We are therefore seeingsystems such as HPE’sInfoSight start to be more prominent in the way people make thosedecisions. So that allows plugging into an ecosystem whereby you can see thetrend of your systems over a long time, where you can use AI modeling as wellas advanced analytics to understand the behavior of a system over time, and howthe 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 tocatch up in that sense as well. But absolutely, AIOps is desirable. If it’s notthere today, it’s certainly something that people are pursuing a lot moreaggressively than they were before the pandemic.

Gardner: As welook to the future, for those organizations that want to be more data-drivenand do it quickly, any words of wisdom with 20/20 hindsight? How do youencourage enterprises -- and small businesses as well -- to better preparethemselves to use AI and data science?

Garg: WheneverI think about an organization adopting AI, it’s not just the AI solution itselfbut all of the organizational processes -- and most importantly the people inan organization and preparing them for the adoption of AI.

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


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

Those are important non-technicalthings that are non-specific to the AI solution itself that organizationsshould think about if they want the shift to being AI-driven and data-driven tobe successful.

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

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

Bowden: Youhave to have a use case with an outcome in mind. It’s very important that youhave a metric to determine whether it’s successful or not, and for the amountof value you add by bringing in AI. Because, as Arti said, a lot of theseproblems can be solved in multiple ways; AI isn’t the only way and often isn’tthe best way. Just because it exists in that domain doesn’t necessarily mean itshould 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 anon/off switch; it’s an iteration. You can start with something small and thenbuild into bigger and bigger components that bring more and more data to bearon the problem, as well as then adding new features that lead to new functionsand outcomes.

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

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

Pandemic’s positive impact

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

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

Garg: Thefact that so many people are checking the trackers of how the pandemic isspreading and learning through a lot of major news publications as they are doinga great job of explaining this. They are learning through the tracking to seehow stay-in-place orders affect the spread of the disease in their community. Youare seeing that already.

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

As Glyn mentioned, it’s aniterative process, understanding how to make sense of data and how to buildmodels to interpret the information that’s locked in the data. And I think weare seeing that.

We are seeing a growing desireto 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’sgoing to give me a result that will affect me. For some people that might be apositive thing, but for other people it might be a scary thing.

People are now much morewilling to engage with the complexities of data science. I think that’sgenerally a positive thing for people wanting to incorporate it in their lives morebecause 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 moreAI?

Bowden: Thetrust comes from the fact that so many different data sources are out there. Somany different organizations have made the data available that there is aconsistent view of where the data works and where it doesn’t. And that’s builtup the capability of people to accept that not all models work the first time, thatexperimentation does happen, and it is an iterative approach that gets to theend goal.

I have worked with customers who,when they saw a first experiment fall flat because it didn’t quite hit theaccuracy or targets they were looking for, they ended the experiment. Whereas nowI think we are seeing in real time on a massive scale that it’s all aboutiteration. 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 extravalue.

What we are seeing throughoutthis 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’sabout the direction of the results, and the upward trend of success.

Listento the podcast. Find it on iTunes. Read a full transcript or download a copy. Sponsor: Hewlett Packard Enterprise.

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Tuesday, June 9, 2020

Data science helps hospitals improve patient payments and experiences while boosting revenue


The next BriefingsDirect healthcare financeinsights discussion explores new ways of analyzing healthcare revenue trends toimprove both patient billing and services.

Stay with us as we explore new approaches to healthcare revenue cycle management and outcomes that give patientsmore options and providers more revenue clarity.

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy.

To learn more about the nextgeneration of data-driven patient payments process improvements, we’re joined by Jake Intrator,Managing Consultant for Data and Services at Mastercard, and Julie Gerdeman,CEO of HealthPay24. The discussion is moderated by Dana Gardner,Principal Analyst at InterarborSolutions.

Here are some excerpts:

Gardner:Julie, what's driving healthcare providers to seeknew and better ways of analyzing data to better manage patient billing? What’swrong with the status quo?

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

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

On the consumer-patient side,there is significant pressure in the market. Statistics show that 62 percent ofpatients say knowing their out-of-pocket costs in advance will impact theirlikelihood of pursuing care. So the visibility and transparency of costs -- thatprice expectation -- is very, very important and is driving consumerism intohealthcare 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 amultitude of different health providers in different systems. That level of competitionis unlike anything we have seen before.

Healthcare’s sea change

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

Intrator: Theword that Julie used that was really meaningful to me was consumerism. Thereis a shift across healthcare where patients are responsible for a much largerproportion of their bills than they ever used to be.

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

That’s something we have keyedon to, as a payments organization. That’s also what led us to work withHealthPay24.

Gardner: It’sfascinating. If we are going to a consumer-type model for healthcare, why nottake advantage of what consumers have been doing with their other financing, suchas getting reports every month on their bills? It seems like there is a great lessonto be learned from what we all do with our credit cards. Julie, is that what’sgoing 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’sattributed to a piecemeal approach -- different segments of the revenue cycle weretackled either at different times or acquisitions impacted that. I read recentlythat there are still eight billion faxes happening in healthcare.

So that consumer-levelexperience, as Jake indicated, is where it’s going -- and where we need to goeven faster.

Technology provides thetransparency and interoperability of data. Investment in IT is happening, butit needs to happen even more.

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

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

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

We have data that shows morethan 50 percent of chief financial officers (CFOs) want better access to data andbetter dashboards to understand the scope of the problem. As we were talking aboutconsumerism, 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 sopersonalized in our consumer lives.

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

Intrator: Iagree. Payers and providers haven’t figured out how they enable personalizedexperiences. It’s something that patients are starting to expect from the waythey interact with companies like Netflix, Disney, and Mastercard. It’sbecoming table-stakes. It’s really exciting that we are partnering to figureout how to bring that to healthcare payers and providers alike.

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

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

Data to the rescue 

Gerdeman: Historicaldata allows for a forward-looking view. For HealthPay24, for example, we havebeen involved in patient payments for 20 years. It makes us a pioneer in thespace. It gives us 20 years of data, information, and trends that we can lookat. To me, data is absolutely critical.

Having come out of the spend managementtechnology industry I know that in the categories of direct and indirectmaterials there have long been well-defined goods and services that are priced andpurchased accordingly.

But, the ambiguity of patient healthcarepayments 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 thedata together? Or are we now already able to deliver those AI- and machine learning(ML)-driven outcomes?

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

In seeking to help them, thereare 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’sa 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 halfof the process. For example, somebody comes to the hospital. They know thatthey have some amount of patient payment responsibility. What’s the right wayfor a hospital to interact with that person? What are the payment options thatshould be available to them? Are they paying upfront? Are they paying over aperiod of time? What channels are you using to communicate? What options areyou giving to them? Answering those questions gets a lot smarter when youincorporate 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 learnmore about what’s going on between HealthPay24 and Mastercard. Tell us aboutyour approach. Are we in a proof of concept (POC) or is this generally available?

Win-win for patients and providers 

Gerdeman: Weare currently in a POC phase, working with initial customers on the predictiveanalytic capability that marries the Mastercard Testand Learn platform with HealthPay24’splatform and executing what’s recommended through the analytics in ourplatform.

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

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

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

Gerdeman: It’simportant for the audience to understand that the end-goal is revenuecollection and the big opportunity providers have to collect more. The marriageof Test and Learn with HealthPay24 provides the intelligence to allow providersto collect more, but it also offers more options to patients based on thatintelligence 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 alwaystake a payment plan and make those payments consistently – that is versus whenthey are presented with a big amount and wouldn’t pay it off – the intelligencethrough the platform will say, “This patient should be offered a payment planconsistently,” and the provider ends up collecting all of the revenue.

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

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

Like peanut butterand chocolate 

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

They are hands-on with thepatients. 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 infrastructurethat enables what’s happening in the real world.


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

It’s been a really importantchocolate and peanut butter combination where you have HealthPay24 interactingwith patients and us providing the analytics in the background to inform how that’shappening.

Gerdeman: Jakesaid it really well. It is a beautiful combination because years ago, the hotthing was propensity to pay. And, yes, providers still talk about that. It wasbest practice many years ago, of pulling a soft or even hard credit check on apatient to determine their propensity to pay and potentially offer financialassistance, even charity, given the needs of the patient.

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

It’s super exciting becausepayment plans are just one example. The platform is phenomenal and thecapabilities are broad. The next financial application is discounts.

Through HealthPay24, providerscould configure discounts based on their own policies and thresholds. But, ifyou know that a particular patient will pay the amount when offered thediscount through the platform, that should be offered every time. Theintelligence gives us the capability to know that, to offer it, and for theprovider to collect that discounted amount, which might be more than thatamount going to bad debt and never being collected.

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

Those are exactly the sorts ofanalytical questions we can answer with Test and Learn and with HealthPay24leading the charge on implementing those solutions. I am really excited to see howthis continues to solve more problems going forward.

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

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

Flexibility plus privacy

Gerdeman: InHealthPay24, we currently have all of the above -- depending on what theprovider wants to offer, their patient base, and the needs and demographics. Yes,they can offer payment plans, discounts, and lines of credit. That’s alreadyembedded in the platform. It creates an opportunity for all the differentoptions and the flexibility we talked about.

Earlier I mentionedpersonalization, and this gets us much closer to personalization of thefinancial experience in healthcare. There is so much happening on the clinicalside, with great advances around clinical care and how to personalize it. Thiscombination gets us to the personalization of offers and options for patients andpayments like we have never seen in the past.

Gardner: Jake,for those listening and reading, who maybe are starting to feel a little concernedthat all this information -- about not just their healthcare, but now theirfinances -- being bandied about among payers, providers, and insurers, are wegoing to protect that financial information? How should people feel about thisin 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: Thatis a question and a problem near and dear to Mastercard. We aspire and really doput a lot of work and effort into being a leader in data privacy and allowingpeople to have ownership of their data and to feel comfortable. I think that’ssomething that we deeply believe in. It’s been a focus throughout ourconversations with HealthPay24 to make sure that we are doing it right on bothsides.

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

Gerdeman: We do.In fact, one example is a 400-bed hospital in the Northeast US that, throughthe combination of Mastercard Test and Learn and HealthPay24, were able to lookat 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 thehospital.

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


Intrator: That certainlycaptures the big picture and the big story. I can also zoom in on a couple ofspecific numbers that we saw in the POC. As we tackled that, we wanted tounderstand a couple of different metrics, such as increases in payments. We sawsubstantial increases from payment plans. As a result, people are paying more than60 percent more on their bills compared to similar patients that haven’t receivedthe payment plans.

Then we zoomed in a stepfarther. We wanted to understand the specific types of patients who benefitedmore from receiving a payment plan and how that potentially could guide usgoing forward. We were able to dig in, to build a predictive model, and that’sexactly what Julie was talking about. Those top 25,000 accounts, how much wethink they are going to pay and the relative prioritization. Hospitals havelimited resources. So how do you make sure that you are focusing mostappropriately?

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

Personalized patientexperiences

Gerdeman: Itabsolutely does scale. It applies across all providers; actually, it appliesacross many industries as well. Any provider who wants to collect more wantsadditional intelligence around their patient behavior, patient payments andcollection behavior -- it really is a terrific solution. And it scales as weintegrate the technologies. I am a huge believer in best-of-breed ecosystems. Thistechnology integrates into the HealthPay24 solution. The recommendations areintelligent and already in the platform for providers.

Gardner: Andhow about that grassroots demand? Should people start going into their clinicsand emergency departments and say, “Hey, I want the plan that I heard about. Iwant to have financing. I want you to give me all my options.” Should people beadvocating for that level of consumerism now when they go into a healthcareenvironment?

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

Nonetheless, I feel the demandfrom 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 bedifferent in two or three years from now when more organizations embrace theseprocesses and platforms?

Intrator: Theindustry is going to be a lot smarter in a couple of years. The more we learnfrom these analytics, the more we incorporate it into the decisions that arehappening every day, then it’s going to feel like it fits you as a patientbetter. 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 reallyexcited to see where it goes. There are going to be new solutions that we haven’theard about yet. I am closely following everything that goes on.

Gerdeman: Thisis 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 waythrough the billing and payment. They will be presented with recommendationsbased on who they are, what they need, and what their expectations are.

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

Gardner: Andfor those interested in learning more about this pilot program, about theMastercard Test and Learn platform and HealthPay24’s platform, where might theygo? Are there any press releases, white papers? What sort of information isavailable?

Gerdeman: Wehave a great case study from the POC that we are currently running. We arehappy to work with anyone who is interested, just contact us via our website atHealthPay24 or through Mastercard.

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy. Sponsor: HealthPay24.

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