Exploring innovations in clinical technology and care delivery

Transcript

Sarah Binder:  …exploring innovations in clinical technology and care delivery. I’m Sarah Binder, with PerfectServe. I’ll be your moderator today. Before we get started, let’s review the webinar platform.

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Now, it’s my pleasure to introduce Andrea Martin, as today’s speaker. Andrea is a consultant with the Advisory Board and contributes to the Health Care Advisory Board’s Community Hospital Initiative. She’s one of the leading researchers for the community hospital quarterly newsletter, which provides strategic research to smaller organizations, independent hospitals, and rural facilities.

Prior to joining Advisory Board, Andrea was a practicing physical therapist in Northern Virginia. She received her doctorate of physical therapy from Duke University and a Master of Public Health from the Bloomberg School of Public Health at Johns Hopkins University.

Andrea, thank you so much for joining us. We look forward to hearing what you have to say.

Andrea Martin:  Thank you, Sarah. Good afternoon. Thank you everyone for joining me today to discuss the new Innovation agenda. There’s been a lot happening in the healthcare industry across the past two years. We’ve seen policy changes, megamurders, new competitors entering our market.

But what I think hasn’t gotten quite as much press is the new developments in clinical technologies that are starting to enter our field, that have the potential to completely change healthcare delivery.

Today, I want to provide insight on some of those technologies. Here’s how we’re going to spend our time today, we’ll start by looking at how healthcare innovation has evolved over the past decades. Next, we’ll look at a curated set of technologies that we believe have great potential to transform healthcare delivery.

We’ll talk about our initial thoughts in business applications for providers. Finally, we’ll wrap up today by placing innovation strategy into the larger context of organizational priorities.

Flashback to 10 years ago, some of you who have worked with the advisory board, may remember that we had an innovation center back then, two providers at that time, innovation largely meant technology assessment and service line planning.

The number one question that we were asked at the advisory board was about CT scares, so, if 64th [inaudible 4:12] was better than what’s the difference between being ahead of the curve or behind the curve, and that’s what the innovation center meant back then.

Innovation means something totally different at hospitals and health systems now. About two‑thirds of providers bandwidth is going into innovation today. It’s actually for delivery system innovation and process innovation.

The prevalence of acute care hospitals and health systems having some kind of formal innovation center is striking. By the end of 2018, it’s estimated that about three‑quarters of hospitals will have some kind of innovation center that’s up and running, either at the hospital itself or I would guess somewhere in their health system. That’s remarkable.

Today, I want to keep our focus to clinical technology, we have a lot of information at advisory board on process information and delivery system innovation. You can see some of those focus areas on the left side of your screen. I’m happy to follow up with some of those resources. Today, I want to focus on the future and a little bit of a different viewpoint on innovation.

What’s coming down the pipe and what shall we be tracking in healthcare? The answer to that question is there’s a lot of incredible clinical technologies that are either already here or in development, or realistically both. What you see in these slides here are vectors of innovation.

They have many applications. Some which already may be practiced in healthcare today. Yet, each of these many vectors ‑ think genetic screening, robotics, artificial intelligence, precision medicine, bioelectronics, and so on ‑‑ is generating its own pipeline of additional technology. Each of those has its own distinct business case.

Those business cases are a lot more complex than the making of a pro‑forma for a new type of CT scanner that we were working with 10 years ago.

Starting at the top left and going clockwise, let’s walk through some of those business challenges of the pipeline innovation. Many of these innovations are IT‑related. They’re developed by non‑healthcare entities.

IT giants evolved a crop of venture‑capital‑backed start‑ups. The ones most appealing to consumers can be difficult to shoehorn and to provide our economic and our healthcare regulatory framework.

Many new clinical products in the pipeline apply to narrow markets, which is forcing the prices up and raising a pretty big question of coverage and consumer affordability. Then there’s traditional service line plan that’s probably not capturing the full scope of today’s innovation vectors, which tend to cut across multiple service lines.

Finally, there’s demand distraction. This is more of a long standing problem. We’ve seen pharmaceuticals in general that have taken out chunks of our procedural businesses. Some of the innovations we’re going to be looking at today have that effect times a thousand. It can be hard to detect the opportunity as well as the threat early enough to adapt to some of these.

While this is all going on, at the bottom of the slide, our own economics are up in the air. We need to figure out when, where, and to what degree we should invest in changes if we’re to keep our business in the block during this transitional period. Our research teams spent the better half of 2017 sorting through the innovation pipeline.

What we found was not just about consumerism, convenience, digital help, or access. The most important themes we found were personalization, customized care, precision medicine, and cutting‑edge clinical innovation ‑‑ getting the right care to the right person all the way down to the molecular level, in some cases.

With that in mind, what we’re going to do today is pull out the innovation vectors that follow these themes and walk you through the pipeline. Before we start, I want to face three critical disclaimers.

First, these are not best practices. They’re important themes and trends with example to show what, or…even thinking about whether the applications will fan out. We’re not endorsing any of these particular applications or companies.

Second, this is not an in‑depth scientific explanation. If you have detailed questions about how these technologies work, I’m happy to discuss that offline. Today is more of a high‑level overview.

Finally, there are going to be a lot of next‑level business questions to tackle ‑‑ reimbursement issues, impact on cost per case, the ways in which we might secure consumer or pair of [inaudible 9:32] . That’s going to be our next goal. Today, just touring these themes is going to take our 60 minutes.

[pause]

Andrea:  Let’s go through the clinical technology innovation landscape, roughly following a patient’s journey through the healthcare system. Let’s start with new sources of data for diagnosing risks.

Then we’ll look at how we can process the ways in more information, from diagnostics and from everywhere else in healthcare, to make the best treatment decision. Finally, we’ll go through treatments that improve the precision and customization of care delivery.

There is a ton of innovation happening in clinical treatments today. These new treatments are not only really fascinating but very transformative to your business. Each step of the way, we’re going to pause and share very early thoughts on implications for business.

[pause]

Andrea:  We’re going to start with detecting new indicators of disease. There’s a lot of inventions allowing us to prove our diagnostic capabilities for new data inputs into our system. Let’s start with where we stand today.

Advances in research and technology have enabled great strive in our diagnostic capabilities over the past century. We’ve gone from taking a two‑dimensional picture to now creating a three‑dimensional model of a beating heart.

We now have over 10,000 different conditions that a physician can diagnose. Better diagnostics means fewer complications and better outcomes. That said, we can never be satisfied with the state of the art and diagnostics. Many diseases are not still detected early enough to treat effectively.

There are still 80 percent of cancer cases that aren’t detected unless final third of the disease that are fatal. We also have a large category of diagnostics that are toxic for patients. I’ve seen a study that estimated that CT scans across a single year could generate 29,000 future cancer cases. Meaning that the cancer in those patients were caused by having a CT scan earlier in life.

In the never ending quest for better diagnostics, there’s two main vectors of real groundbreaking transformation here. If we can get our hands on more molecular and polygenic data, we can now accurately detect a disease risk earlier. That means either before it’s developed or once it’s developed, before it’s symptomatic.

When we’re armed with earlier warning, we can move to real‑time management. Collecting data from patients with developed diseases and integrating it in the clinical workflow to detect escalations and intervene right away.

Our ability to understand and sequence the human genome set a foundation for precision medicine. We can now use an individual’s genomic data to identify a patient’s risk for developing certain diseases and to also guide treatment decisions by clinicians.

The human genome product was completed over a decade ago. At that time, it cost over a million dollars to sequence one gene. Since then, two factors have allowed gene sequencing to transition closer to mainstream.

The first is the cost. If you look at the graph on the bottom right‑hand side of the slide, over the past decade and a half, technology has enabled the cost of sequencing in an individual genome to plummet. The second is research. We can now identify more actionable treatments to match with gene mutation which continues to progress as we speak.

Together, the lower cost plus those actionable insights are going to pave the way for a broad uptake of genetic screening. That means that someday soon, health systems could begin to gather baseline information on more of their primary care patients. Linking the detection of risk systematically into ongoing management and treatment.

A handful of systems are already leading the day on integrating broad genomic testing into primary care. The two keys making it affordable to the patient and actionable, testing for things that we know how to handle and appropriate clinical next steps.

Take a look at what’s happening here at North Shore. North Shore Medical Group is on the frontline for the genomic program at the system. PCPs email patients an optionable questionnaire on family health and history. They have an algorithm that then identifies candidates who would benefit from further genetic screening.

At that time, the PCP sends the patient’s screening referrals to make sure that they receive any recommended follow‑up. The consumer testing price of the screening varies based on the number of gene sequence and the patient’s insurance coverage.

Some plans today do reimburse if the patient has a detectable risk for genetic disease. At which point, it’s just a matter of the patient’s co‑insurance or co‑pay.

Regardless of coverage member, I said that the price of genomic screening has really gone down. The maximum cost for a patient to pay out of pocket is cap at $500 by the lab that’s offering this test. We’ll go over that a little bit more in a moment.

North Shore’s program is only six months old. It has had impressive momentum. Over 9,000 patients have completed the online questionnaire and of that subset, 15 percent turned out to have actual risk that called for an additional clinical action. This works out to be a couple hundred patients for whom actionable risks were detected.

The most interesting thing here, from a growth perspective for health systems, is how receptive patients are to the service. According to a survey that North Shore conducted, over a third of current primary care patients reported that they were willing to change their primary care physician to have access to this genetic screening program.

[pause]

Andrea:  In North Shore’s case, the systems partnered with Invitae, which is commercial laboratory in San Francisco. Remember, the advisory board is not endorsing this company. I just want to share with you the example of a agreement that we think is interesting.

After a patient receives a genetic screen at North Shore, Invitae sequences the DNA for up to 139 health conditions that meet the bar of having actionable follow‑up interventions. The lab does not report mutations for genes that we can’t treat at this time because there isn’t necessarily actionable follow‑up. That’s not reported to the patient.

The team at Invitae interprets the results and reports actionable findings including evidence‑based guidelines for the next steps and refers the patients to a genetic counselor. The question of next steps really tease up a critical health question for health systems. As these genetic screens find risks, how can we respond with both preventative management and increased surveillance?

Let’s say you’ve completed genetic screen for the patient and find out that they are very high‑risk for cancer. The high risk to genetic screen does not mean that the patient has cancer. You need ways to continually check if or when cancer actually develops, with as little as invasiveness as possible, few liquid biopsies.

These detect disease DNA floating in a person’s blood, one of the most promising diagnostic indicators in the pipeline today.

Here’s an example on the slide ‑‑ GRAIL. GRAIL is a Silicon Valley biotech start‑up. This test can detect cancer DNA in a patient’s bloodstream. They use gene sequencing to determine the specific genes that are causing the cancer mutation and guide treatment.

GRAIL has over 1.1 billion dollars in funding across just 2017 alone. It has the most ventured capital out of any clinical technology in the market last year. You can see why here. Three quarters of patients that were tested, received actionable treatment intervention.

GRAIL is currently in clinical trials across the country. If this is improved for the market, it’s going to be transformative for oncology program.

[pause]

Andrea:  When we step back and look at implications for hospital’s in health system, there’s still immature business cases related to some of these molecular testing. That being said, there’s a lot in the pipeline that could happen that would change our business models, and how we refer patients throughout our systems today.

[pause]

Andrea:  Welcome to the Internet of Things, our second innovation vector that we’re going to go over. If you’re not familiar with this term, it refers to how we are now surrounded by everyday objects that constantly send and receive data.

It seems that there are at least hypothetical consumer demands for health and wellness devices of every kind, with more developments to come across the next decade. Talk about digital overload. Look at the screen. On the bottom of the list on the right side, it does say implantable sensors.

I know there’s probably some people that are reading that and thinking, “Like what?” Here we go. On the left‑hand side of the screen, there’s a glucose‑monitoring sensor. Can you imagine instant monitoring of a patient’s blood glucose from a sensor placed under the skin?

All you have to do is wave a wand over the sensor and the glucose level will appear. How about a smart tattoo that changes colors when your sodium levels are too high? I’m distracted by the design question. What should this body chemistry‑sensing tattoo look like?

I’m going to say lay it aside and let’s tackle some of the more important question which is, “How do all these patient data sources evolve?” They will continue to multiply. What we need to do is figure out the biggest opportunities for the health system. In the landscape of futuristic patient data stuff, where can we pick out the most important vectors of innovation for us to focus on?

The most important principle for identifying data from outside of a hospital furlough is that it can be used to trigger a different type of clinical care. This often means incorporating patient generative data into our ongoing clinical workflows.

One of the examples that I wanted to share was a high‑QT high‑stake example from Children’s Mercy. Babies with hypoplastic left heart syndrome, that needed series of surgeries after birth, are extremely fragile between them. After discharge and between surgery, it’s currently typical for parents to record a lot of data on the babies and share with their providers maybe once a week.

At Children’s Mercy, their parents are given monitoring devices. The parents use the devices and input data including qualitative observation onto an app which then pushes the information to the care team at Children’s Mercy. The team can review the status at any time and send an alert if anything urgent happens.

Hypoplastic left heart syndrome is a serious condition in which outcomes are often bad. No baby with this disease under Children’s Mercy care has died since the CHAP program was adopted in 2014. Here’s a quote from one of their clinicians, “If we follow 30 kids a year, that’s a whole kindergarten class that we’ve saved, and that’s crazy.”

We didn’t really know what we didn’t know before. We didn’t realize that we’re missing trends though they’re only getting numbers once a week. Of course, it’s actually really complicated to integrate data from any digital health device into real‑time care plan within a health system.

Children’s Mercy did a lot of work behind the scenes with Microsoft to build the technology in cloud platform that allowed real‑time monitoring. With any source of potential data, we have to actually think about how to integrate this into real‑time clinical workflow.

That tease up the question of a platform that could do such a thing. Let’s look at an example of a platform on the next slide. This example, Zels, allows clinicians to integrate data into the eMAR for actual clinical outcome, selfless incubated at Providence Health & Services and is in use there and at UPMC.

It is compatible with a ton of digital health centers, and allows clinicians to even prescribe specific apps or monitoring devices based on the patient’s medical record. When a clinician prescribes a patient an app, patients receive an email. The majority of patients open the email from the Zels platform.

About half of those actually fall through on the recommendation of the digital prescription. The really neat aspect of the Zels is that clinicians are able to monitor patient’s data in real time, which is directly implanted into the eMAR. Providence is using Zels right now to monitor over 20,000 patients through their Zeepad devices.

They check on actually used and ongoing effectiveness of these devices to treat sleep apnea. Over 20,000 patients, starting to really talk about some scale.

[pause]

Andrea:  Zels implications for the real‑time data from the Internet of Things, beyond the fact that real‑time patient data is really cool. There’s a massive proliferation of digital health devices in huge venture capital going into this phase. We can’t get distracted.

There’s a lot of operational workflow in which we’re going to have to get the data, and clinicians acting on it. There’s a lot of work. We need to take out the noise from the sources of data that are actually going to prompt a meaningful change in clinical management, with measurable impact on patient outcome.

[pause]

Andrea:  Let’s take a step back to Orient. In the world of pop innovation, we’ve just taken a look at the most important theme in evolving diagnostics. The richness of data that we can someday get from a broad variety of sources. As a consequence of those new data input, in the future, we’re going to be more overloaded with data than we already are today.

The goodness on this one is that the next critical innovation vector for us to examine is all about improving the capability to path into that milieu of information, with actually less effort than expended today.

[pause]

Andrea:  Some data to probably reinforced what you already now. The amount of information washing into our system is more than we can handle. Depending on the study, clinicians purportedly spend as much time on “desktop medicine as they deal with actual patient care.”

Meanwhile, the amount of raw data that is produced from one small blood sample from GRAIL ‑‑ the cancer blood test that we talked about a moment ago ‑‑ is equal to about 500 hours of movies.

We cannot make sense of all this information at regular human “read and analyze pace.” Enter artificial intelligence. Notice we put some shorthand definition on this slide so we don’t get lost in the technical jargon. Artificial intelligence is an umbrella concept.

Machine learning and natural‑language processing are under the umbrella of artificial intelligence. Machine learning is more a set of algorithms that can become smarter by making connections on their own.

Natural‑language processing, which I’m going to come back to in a minute in more details, is machines interpreting, synthesizing and acting on qualitative information ‑‑ more word and not data. Take a look at the market size for AI applications on healthcare on the right. Sure, 6.6 billion in 2021 is a big market. What’s really stunning here is the growth.

The amount of data we have flooding into healthcare is what’s spurring that giant market opportunity. Let’s start with how we could boost stuff to top of license care by streamlining care for low acuity‑conditions. Break MD Interact is an interactive software program that integrates into a virtual visit platform.

It uses AI to help it learn and refine the quality of its inters as it goes along. A lot of health systems are using Break MD. Advantage Health, Green Bell Health Systems, Rush University Medical System ‑‑ they’re all using this technology.

What happens is when a patient logs in for a virtual visit, instead of a clinician having a live visit, a white label interface powered Break MD interviews the patient. It asks it a lot of diagnostic questions. The interview takes about 15 minutes of patient AI time to do this.

At this time, there is no clinician in the picture. Then, it synthesizes that information and provides it to human clinician with the recommended diagnosis and treatment. The clinician is able to review the information and propose treatment plan, and either can accept it or reject it.

That clinician review time typically takes about two minutes, instead of the average 15 minutes for a live virtual visit. Break MD estimates about 10 percent of time that it can’t come up with a diagnosis.

If that happens, or if the clinician doesn’t to look at what Break MD came up with on its own, the clinician prompts the patient to schedule an actual office visit. If that happens, the patient or insurance is not charged for the virtual visit. The low acuity application makes sense. As a goal, it’s good for boosting clinicians to top of license.

It works great for patients, too. Thinking about something like, “My pre‑schooler has pink eye,” or come to a case that leaves to mind of a parent. AI can also be leveraged as a higher acuity application, too. There’s several intriguing examples to choose from when it comes to AI potential to assist in the diagnosis of complex conditions.

My personal favorite is Stanford’s platform to analyze clinical images of the skin. It can recognize over 2,000 types of skin diseases. Stanford’s platform is a conventional neural network. It has the accuracy of recognizing melanoma as a controlled group of 21 dermatologists.

Now, there’s some obvious challenges using AI in medicine. Let’s hold that thought for a minute because I want to show you one more potentially transformative use of AI before we step back and critically assess all of this. I’m talking about natural language processing. NLP is an extremely important application of AI for processing written language.

In a world where the volume of clinical information, not just the data but the words, is exploding, NLP holds out hope that almost instantaneously can analyze massive amounts of written language and form conclusions. It’s estimated that IBM Watson can process millions of documents in a matter of seconds.

I want to show you a couple instances, one, for cutting through this noise in a medical record, to make individuals more efficient and also keeping evidence‑based standards up to date. It’s not unusual for clinicians to be faced with wading through long patient histories on a short time frame.

For example, when a patient shows up at the ED, NLP could provide a critical assist. MedStar has used NLP to develop a solution called Dictation Lens. It takes key words related to the patient’s preventing issue and reads through the unstructured pretext in the patient history.

It flags the pieces that are most likely to be relevant in the case. It’s a huge efficiency boost for clinicians and you can imagine the clinical gains from getting faster insight in an emergency visit.

This is where I want to stop and take a moment to say that, I’m sure a lot of people would feel this way and we agree with you. Probably many of your physicians at this case, that we might need to tap the breaks on AI.

Here’s a story at Mount Sinai on the screen, where Mount Sinai created an AI platform called VPatient that was able to make connections across patient histories and make diagnosis recommendations. The good news was that the program was completely accurate and it predicted the onset of complex conditions like schizophrenia.

Bad news, the program couldn’t explain how it was making those predictions. We need to unlock the potential of AI, but we also need to learn a little bit more about its potential. There is some clear win‑win gains in efficiency, but we need to understand and be able to assess everything the AI is telling us.

I’m going to quickly recap these implications, but I want to move to be able to highlight our next section of innovation. To be clear, with AI, none of the technology I mentioned is replacing clinicians with computers. It’s more about offloading some of the tasks that have become unsustainable.

To be able to reduce the burden, or some times, improve the quality of going through some of this data with clinicians and patients [inaudible 34:07] . It’s completely reasonable for physicians and others to have concerns about safety and the accuracy of these platforms. I think that’s something we’ll need to address as we move forward with AI in healthcare.

Let’s review our progress. We’ve looked at innovation vectors in both generating and processing of riches, of clinical innovation and also about processing the information that we get from data. In this last section, I want to actually tackle intervention. Heads up, this section is going to get even more futuristic.

There are some incredible and at least potential developments out there that we want you to be aware off. No question, that history to date is full of incredible leaps forward in healthcare intervention. That said, just as we can never be effective enough in our early diagnostics, we will never exhaust our potential to treat intervention.

When you look at the next crop of clinical interventions, what you see is a strong trend of treatments that are more precise, meaning that they’re more effective or less toxic and/or ideally both. We’re going to walk through four vectors of innovation. Each with its own clinical and economic transportation potential.

Let’s start with molecular immune therapy. Our focus today on molecular and gene therapy is that they target specific molecules and genes at a much greater precision and customization than most conventional treatments today.

Their economics in a nutshell consist of being expensive to develop and they apply to very narrow niche markets, thus they are astronomically expensive. There’s a tremendous market for these types of treatment in the pipeline and it’s full of additional applications under testing today.

With the data at the top of this line, I’m trying to convey the size of spending, even today, on specialty pharmaceuticals. Many of which are the molecular therapies we will discuss. Specialty drugs accounted for 36 percent of all drug spending in 2016 and 70 percent of spending growth from five years previously.

If you look at the bottom of the slide, you’ll get a sense of how rapid the pace of development is. The two statistics on the right start to paint the picture for gene therapy. Over 500 are in trial phases today. Over 180 of which are CAR T, which we are about to talk about.

You probably may have seen the news over this second ever CAR T therapy, which was recently approved by the FDA. Just to make sure we have all the basic concepts, when we say precision in drugs, we’re talking about two basic mechanisms. One is molecular therapeutic.

That refers to drugs that target a very specific cell pathway in the body. The second is gene therapy that corrects gene mutations within cells. Focusing on molecular and gene therapies is the critical point that can drastically increase precision. Let’s look at the cutting edge in molecular therapy first.

What I have on the screen here is CAR T. At the end of last summer, the FDA issued a historic ruling and approved the first gene therapy in the US and then after we finished this research it immediately approved a second.

The first approved therapy is Kymriah which treats a certain pediatric patient with acute lymphoblastic leukemia. To administer Kymriah, a physician removes some of the patient’s white blood cells, which should have been recognizing and eliminating the cancer, but hadn’t.

Those cells are sent to a manufacturing facility in New Jersey and re‑engineered to destruct and destroy cancer. They’re then sent back and administered to the patient. It looks like the approval of this drug could be a turning point for CAR T therapies like this.

Remember there are a 180 more of these in the pipe line. I mentioned this earlier, but these drugs have an extremely narrow market with high R&D costs and they’ve added up to some very expensive precision drugs. Kymriah is $475,000 a dose.

The developer says that they are still working with payers to fully ensure they understand the value of this therapy to provide drug coverage to patients accordingly, but of course insurance coverage varies.

Medicaid has said that it will only cover Kymriah if the treatment is successful and the child’s cancer enters remission. Otherwise the patient does not have to pay and the developer has that risks of the costs.

This is a sign of our times of pharma companies begins to experiment with value based reimbursement arrangements for these expensive drugs. It’s possible that CRISPR, which is another form of gene therapy, will be able to help with the cost problem in gene editing.

CRISPR has gotten a lot of press lately with the promise to cure some of the single gene mutations such as cystic fibrosis and sickle cell anemia, as shown on the right hand side of the screen.

That would be obviously transformative for this industry, but what if we could cure these diseased for $30? That’s the estimated cost to edit one gene using CRISPR. This is still a couple years out from entering our industry, but a horde of researchers from biotech companies are still pushing it along.

I’m going to skip forward so we have time to review some of our last vectors of innovation. Vector six, engineered organ replacement. Right now, we are falling short of meeting our transplant demands by over 80,000 transplants per year.

We have not found a way to produce a supply of healthy organs ready for patients that need transplants. There’s two emerging solutions to that question. The first is xeno transplants. Using organs transplanted from animals, stay with me there.

The second is using stem cell therapy to regrow human cells into new, healthy tissue. That’s the idea of course, but can we get past the barriers? We’ll look in just a minute. Hear me out here, I’m just going to spend a minute on xeno transplantation.

We’ve already started using pig valves in cardiac surgery. There’s a lot of other pig organs that are the right size and very similar to human’s and one company is already working to genetically alter pig organs for human use.

They’re using CRISPR in fact. There’s a lot of barriers we’d have to get over and this could be more than two years away as the company estimates here on the slide, but I think it’s something to follow.

Clearly, a better solution to addressing the transplant demand would be to be able to repair an individual’s own organs their patient’s own cells. This is an example by Arcadia, at the University of Wisconsin, that’s developed a therapy called CardiAMP.

It does exactly that. It treats damaged cardiac cells from heart diseases. Clinicians removed stem cells from a patient’s pelvic bone, they processed the cells and they inject it back into the patient’s heart and that triggers more growth of the cardiac tissue.

Clearly the implications here. This is still a couple years out from entering our market, but I think that compared to some of the other vectors we’ve got over here that these organ transplants are relatively straightforward opportunities for hospitals and health systems.

There’s huge clinical benefit, there’s a larger market and it’s really within provider’s wheelhouse. Our seventh vector of innovation, 3D printer‑enabled surgery. The main rate limiter on the custom implant market today is cost.

Custom orthotics, prosthetics and implants are extremely expensive and very few patients have access to them, but it’s reasonable to imagine that 3D printing is going to transform our procedural care business.

Looking at some of the uses on the right hand side of the slide, at 3D printing, the 3D printing process is expected to boom in the market in the next couple of years. To the point that one projection stated that 1 out of every 10 people will have a 3D printed object in their body in the next two years.

Look around the room where you are right now, are there 10 people?

A fair number of health systems are already using 3D printers today. 3D printers can be used to replicate a person’s exact anatomical makeup and use the model before completing a surgery on a real patient.

That’s the story of what’s happening at Mayo, in which surgeons were able to remove a tumor by practicing this way. Whereas without the model, they probably would have had to amputate a child’s lower extremities.

Our 3D printing capabilities are advancing every year. We can now print more materials than ever on 3D printing. Printers and hospitals and health systems are starting to invest more into these printers. I want to get to our last innovation today, our vector of innovation.

This is bio‑electronic device implants. Bio‑electronics are the most futuristic innovation profiled today. In the past, potentially the largest benefit for hospitals and health systems.

As the clinical treatments I showed you this afternoon are aimed towards treating some components and cells in your body, bio‑electronics treat the peripheral nervous system, rather than treating the cell itself.

The concept behind bio‑electronics, involve implanting a small computer chip‑sized device near a nerve that can then control cells and organs that the nerve shows downstream in the body.

Look at this example, with asthma, a patient is struggling to breath, because of a stimulus in their environment. The bio‑electronic chip can turn on an electric signal in the nerve that controls the lung and causes the airwaves to dilate. At this point the patient is able to breathe easily.

This is probably far superior than some of our pharmaceutical treatments today, because it can proactively treat and control chronic diseases and they don’t necessarily come with all the side effects that our pharmaceuticals have. You might ask how real are these technologies.

They’re still in the conceptual stage, but again researchers are pushing it along. Here’s an example, a real promising example is Galvani Bioelectronics. Galvani is a joint venture between Alphabet and GSK, both of which are pumping money for R&D for bio‑electronic solutions that cure chronic diseases.

Those chronic diseases make up a good portion of our book of business today. Keep an eye on this ball. It’s one that we’ll want to follow for the future.

A reality check here with bio‑electronics, they’re not yet ready for prime time and we’re going to need to answer up some major questions before we can bring these to bed side, but imagine what our hospitals and health systems and healthcare system will look like if we would be able to proactively treat chronic diseases.

It would be just awesome. The market is huge, very large, and we’re going to have to go back to the drawing board on chronic disease management that we use today. We’ve made it. Final thoughts before we go our separate ways. The new innovation agenda.

The goal today was to take you on a tour of innovation vectors that we think have the greatest transformative potential in healthcare today. We’ve looked at new indicators of disease, processing and leveraging all the new information that’s flooding into our health system and taking advantage of new breakthrough interventions.

Going up a level, remember that we can look through the lens of individual technologies and I think it’s important to do that to have fluency with what these things are.

There’s absolutely innovation and disruption looming in our industry, but armed with that knowledge we can begin to be better prepared for our times of uncertainty. But thank you all very much for your attention, I’d now like to open the lineup for questions.

Sarah:  Thank you, Andrea, for that really visionary presentation today. I want to remind you all that to submit a question you can use the Q&A box on your screen. I’ll read the question that you’ve submitted to Andrea and she can answer it for us.

As you were talking about innovative technology and specifically offloading some tasks that are unsustainable and improving the quality of going through data, I wanted to remind our audience about PerfectServe.

PerfectServe incorporates each group’s workflow roles, multiple [inaudible 48:57] roles, patients assignments and preferences to create algorithms that will connect care team members to the right individual for the given clinical situation.

We analyze those combination of real‑time situational variables to ensure that this communication is delivered to the right physician or nurse or other care team member.

This capability eliminates the need to search and struggle to find this information and only to call the wrong person after you’ve gone through all that time to look for that. PerfectServe does that automatically for our client.

Andrea, one of the questions I have for you is, “This is so visionary, how can these innovations be leveraged for profitable growth for providers?”

Andrea:  Yeah, that’s a great question. Some of the more futuristic ones, that’s hard to see at this point.

When we look at some of the diagnostic innovations like genomic screening, that has the potential to open up providing care to patients that truly need it, and getting some of those downstream treatments.

Really uncovering some of those risk of disease that patients may not have known about, really opens the opportunity to form that bond between the consumer and the health system to then capture all of the further treatment that’s going to happen downstream.

Some of the innovations that are going to be more destructive to our business model, we’re going to have to make some big decisions about what providers will look like in the future. If there’s no longer chronic disease, we might not be housing inpatient meds at a hospital.

That might not be what patients need from us. Looking really futuristic, we might need to change what we offer and what we do to be able to serve our patients.

Sarah:  Sure, that makes a lot of sense. Folks, I want to remind you, you can submit a question using the Q&A box. We do have a survey at the end of our session today that we’d like for you to complete as well. Andrea, you touched on this a little bit. What impact will clinical innovations have on provider margins?

Andrea:  Another good question. I think that for some of the other innovations certainly going to be with that growth opportunity, especially with the genomic screening.

You’re going to see more revenue opportunities to add to our margins. With some of the more expensive innovations, like gene therapies and some of those expensive pharmaceuticals, there’s going to be a lot of questions about, is this affordable for patients and who’s going to pay for those pharmaceuticals might not be necessarily be a bump in margin for all providers.

Again, there’s going to be some level of patient’s are going to want access to those treatments and figuring out what your role is in the market. Do you maybe offer some of those treatments despite the expense and maybe capture the downstream revenue? Do you may be partner with another facility that does that to try to capture some of that downstream revenue need to bump the margins?

It’s not necessarily clear with everything that is coming down the pipes today. Some of these are going to be a challenge for us because it’s not necessarily the same way that we do business today. It could have a different effect on our margins that we’re used to with providing care.

Sarah:  Thank you for that. I like to take this opportunity again to thank Andrea Martin for a great presentation that’s really helpful to see what research you all come up with and what you see on the horizon whether it’s near time or super feels, like it’s super way off in the future. I know some of those things are probably a little closer than we think.

I do want to thank our audience for joining us. That concludes our webinar for today. Please take a moment to complete our short survey at the end. I hope you all have a great afternoon.