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What exactly is digital health? How can big data, artificial intelligence (AI) and digital health improve healthcare? Our expert panel gives their final thoughts on the empowerment of these technologies in the health sector:

Leveraging Big Data and AI in the Hospital of Tomorrow

 (David and I) both trained at Duke University and there's a very famous professor there who used to say, “What this patient needs is a doctor, and he needs him right now.”

Well, of course, that was the doctor saying that.

It was a doctor saying that to the junior doctor.

Watch the last PX: What to Consider When Taking on a Big Data Initiative

Because it was basically a doctor insulting junior doctors that this patient, who you just presented to me, needs a real doctor.

We need to get better, we need to improve.

Dr. Eugene A. Stead, a very famous chairman of medicine at Duke University. I think we all know that we need more innovation — this area of digital health. I’ve been asked many times: what is digital health to you? Digital health is nothing more than focusing on the patient, whether it’s patient-sourced data or patient in the home, it is focusing on the patient. Pharmaceutical companies now market to patients, but they’ve traditionally marketed to doctors. Medical device companies still market exclusively to doctors, to hospitals, and the patient, unfortunately, should be the center of those activities. To me, the essence of digital health is that we’re finally bringing the patient into that circle of the stakeholders and putting them in the middle of it, which is where they should be.

 The biggest problem to that concept — so I think we are all completely aligned that the patient needs to be at the center &mdash;

I’m not aligned with that even remotely.

 Transparency, right? Number one, it’s very hard to be a consumer in medicine; you don’t know what you’re buying half the time.

 Two, you don’t know how much it costs.

 And then you don’t know how to interpret it. Even if you have all your data, lab work and x-rays, you have no clue what you’re looking at. That’s one of the challenges for the consumer in today’s model: the idea that there needs to be interpretation and transparency.

 But I also think, and John might agree with me on this, that we have ways to get that data interpreted through reliable searches, websites and other reliable sources, so that I can understand. I can’t tell you how many of my friends say, “Hey, my doctor just did this lab work. I have no idea what it means,” and they’ll text me their lab work and I interpret it for them. But we could find another way to do that, potentially.

I don’t think that digital health is a social science. I think that digital health is the empowerment of technology, whether it be nanoparticles, CRISPR [clustered regularly interspaced short palindromic repeats], or immuno-oncology. That’s the magical convergence of science and technology, which will ultimately serve patients. But if you ladder the endeavor up to the ethereal feature, benefit, value — everything becomes consumer-centric &mdash; cars, restaurants. And it just becomes laddered up into an undifferentiated pablum of: “I’ve got my life back; whether it’s an arthritis advertisement, I’ve got my life back; CHF [congestive heart failure], I’ve got my life back; schizophrenia, I’ve got my life back.” All of them say the same thing. And the pharmaceutical companies all have the tagline: “Innovate for life.” It’s nonsense. I think we should return to the roots of science and the wonder of discovery and recognize that we are here to serve the patient, but we don’t have to ascend to this notion of patient-centricity. I think it’s a big, big mistake. And, finally, for anyone who’s climbed that ladder &mdash;

Why are we doctors? We’re doctors to serve patients. They are at the center of the doctor universe. Why the heck do we even have medicine if all we want to do is play with toys?

Watch PX: Is Regulation the Pitfall of Technology and Innovation?

The practice of medicine may be a little bit closer to that construct, but I believe the initial premise was digital health, and I expanded that into pharmaceutical.

You just said that digital health has nothing to do with the patient.

And you just said that the patient is not at the center. Nothing in medicine, whether it’s digital, whether it’s a stethoscope, whether it’s my hands, my breath, and my voice, is not patient-centric.

The hotel business is the same way; it all matters about the customer. The restaurant business is the same thing. It’s undifferentiated and it becomes laddered up to this top step. If you’ve ever climbed a ladder, it says something up here; it says, “danger,” “warning, do not step here,” and I think that’s where we’re moving into this undifferentiated patient-centricity. The magic of digital health, I believe, lives in the application of technology to patients.

Digital health is a phrase that’s used for a lot of things. Principally, it’s focused in today’s world on things related to that powerful smartphone in your pocket.

Whether it’s using Zocdoc to schedule an appointment, interfacing MyChart into the Epic of your healthcare provider, or using AliveCor’s ECG [electrocardiogram] device — it is around the ability to acquire, analyze and communicate information about you that hopefully will improve your health or the treatment of your disease. To me, again, I come back that that wasn’t medicine. Medicine was focused &mdash; when I trained a long time ago &mdash; medicine was focused on the hospital, first of all, and it was focused on treating diseases. And our thought about the patient was the patient is an example of a disease. And, oh, by the way, when the nurse wheeled that patient out the door, it was over.

Only when the government said, “If that patient comes back within 30 days, it’s your nickel now,” did they become concerned about what happened when that door closed on that car. And, by the way, that’s sad that that was it, but that was true. I think the smartphone is empowering people; it puts Dr. Google in their pocket anywhere, anytime.

I am wholeheartedly for it, even though sometimes Dr. Google is totally wrong. But we’re going to make mistakes, we’re going to stumble, but I think our trajectory is correct. I still see the notion that the patient is the critical factor and that we lift the veil off the cost conundrum, the master price list, and all these things that we do to hide that 19 percent of GDP [gross domestic product] that we’re spending. We want to hide it; it’s not over here, it’s over there, it’s third party. Any other business where you ever heard the term reimbursement? There is no other business where the term is reimbursement. It’s usually price, cost, pay, bill.

Why does a hip replacement cost 10 times in New York what it costs in Oklahoma City? Why? Same procedure, same tools.

I will hope that AI and big data — predictive analytics &mdash; will in some measure allow us to address those real issues, which come down to money.

This is what I’d love to do. We’re running short on time here. I would love to give every panelist one minute for final thoughts on AI, big data and medicine. John, let’s start with you.

Let’s keep it short and sweet: we have to ask ourselves: “Is AI man’s last great invention?” We are going to shift creativity from humanity to a thinking machine. Now, I’m not saying it’s going to happen, and I know you’re going to recoil when you hear that, but I think that is an emerging reality that has to be put in some kind of human context.

 I disagree. I think AI is a tool like any other tool, any number of tools that have come and gone in medicine, whether we talk about the da Vinci robot or invasive procedures that have now become noninvasive. I think AI is an extraordinary tool. It will be a powerful tool in medicine. It will be one of the many tools we use as doctors and continue to use in our practice to make things evolve and that much better for the patient.

I echo what Geeta says. I consider [that] AI, at least today and the promise of the future, will enable us to improve healthcare, access, lower costs and prevent disease. But it’s not going to happen tomorrow: this is a process like driverless cars that will take probably decades, but I’m very optimistic that when my children are my age, that they will have AI-enabled healthcare.

Well, I can’t thank you guys enough for being here today. This has been a fantastic discussion. John, Geeta, David, it’s just been great to have you on the panel. I love the fact that we’ve been able to discuss some really difficult issues and we’re actually trying to be fortune tellers. We’re trying to predict the future. So, on behalf of our panel, we thank all of you for joining us today. We hope you found this 

Peer Exchange® to be helpful, useful and informative. We look forward to seeing you again next time.

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PX: What Does the Hospital of Tomorrow Look Like?

Videos

What is digital health to you? A social science? Patient-centric? Our 4 KOL's have some differing views. 

PX: What Does Digital Health Mean to You?

What should health systems consider before taking on a big data initiative? How can they overcome initial failures to become successful? Our expert panel weighs in:

 Let’s take this another way. Every good project can fall apart if it’s not planned out and done properly. If you make early mistakes, it can doom a project down the road. What do you think healthcare systems must consider if they want to take on a big data initiative?

  I think that’s a great question. How do you fail and how do you fail early so that you can become successful? I don’t think we ever ask ourselves that enough. I’m a big believer in innovation, but the first thing that you have to do when you’re innovating anything, whether it’s big data—related or not, is what is the problem you’re trying to solve. So, I think number one as a health system, what are your top three strategic priorities? How do you then innovate back into that problem to find the solution? You have to create a culture where you are failing early or it is okay to fail, you budget that is kind of an X factor, since we don’t know how this is going to play out. We don’t know what we don’t know, and that’s OK to budget — this could be a revenue maker, it could not. The culture is so key, especially at health systems doing whatever you do with the medical staff, with the patients &mdash; not against them and not to them.

All stakeholders at the table, which is what our government failed to do with the ACA [Patient Protection and Affordable Care Act].

Watch the last episode: PX: Is Regulation the Pitfall of Technology and Innovation?

 You’re right. I’m concerned that fail fast is being confused with fail smart; I see this on the life science side, not in clinical medicine, but in pharmaceutical development that that’s sort of big idea — fail fast, fail fast. And I think being agile is part of that construct, recognizing where the errors are and making smart decisions about failing. The other thing is I want to echo the notion of culture, and I wrote about this several times: culture crushes innovation without a doubt.

 This is the single biggest inhibitor of transformational change in Corporate America today. I would argue it’s probably like that in the hospital — regimented rounds, right? It’s a pecking order; it’s military, and I think that unless people recognize the value of certain changes, unless a nursing staff recognizes the value of data and technology to varying degrees, you’re not going to drive those changes. We often look for the inspired leader, but it’s really not the inspired leader, it’s inculcating the nature of innovation into the rank and file that helps drive innovation; if you don’t do that, you’re doomed.

Dave, you and I have talked about fail fast and tech companies many times; I’d love to get your take. I like being agile. I like embracing risk. I have a bracelet that says, “Embrace risk,” but I say, “Embrace calculated risk.” Talk to me about fail fast.

 The issue is, the Silicon Valley culture of fail fast has with medicine is that a failure in medicine has a human cost. It’s not just this screen — people didn’t like this screen; they didn’t like this GUI [graphical user interface]; they didn’t like this form factor &mdash; it’s that patient didn’t do well. We have the added burden of, as I said before, human beings involved, handle with care. And so, the notion that we can change a process and fail fast and innovate, in medicine we have another thing to consider: that there is a human being who is hurting or is sick that’s involved in that process; we need to be sensitive to that. I’m not saying that should inhibit completely the notion of failing quickly, but it should help you put into your calculus &mdash; the fact that we have human beings and their lives at stake. And that’s a unique challenge and charge that we are given.

What do you think health systems must consider if they want to take on a big data initiative?

PX: What to Consider When Taking on a Big Data Initiative

How do regulations hinder the innovation and potential use of technologies such as big data, artificial intelligence (AI), virtual reality (VR) and telemedicine? How are we building hospitals around big data and AI? Our expert panel weighs in:

A Healthcare Analytics News™ Peer Exchange®

 When we talk about the hospitals of tomorrow, are you guys aware of specific examples of where technology is at the forefront of a hospital? I know [John] has some clients overseas that are looking. If you could, share with us some specific examples of how we are building hospitals around big data and AI.

 What I find is that the value of technology in many developing countries and places around the world is not so much to find that nano-mediated early detection— nanoparticle mediated detection of cancer&mdash; but to use existing technology to leapfrog problems. For example, in Mexico, there’s a problem with physicians, so let’s implement telemedicine as a solution. Technology allows you to leapfrog and solve solutions, and I’m seeing that around the world. I’m seeing that in the Soviet Union. One of the biggest questions is: how can we build a hospital integrated into an existing infrastructure? In Bahrain, they’re looking at taking a hospital and building it around cardiovascular disease. In Bolivia, they’re looking at how we can leverage technology to not be a bastion of innovation, but leapfrog to a point where we can take care of our citizens. So I think it’s a hot button now around the world; people want to build new hospitals because they see it as an extraordinarily high capital expense. What’s it cost to build a hospital today?

Watch the last episode: PX: What Does the Hospital of Tomorrow Look Like?

 It’s probably one of the most expensive things that you can build in a community. It’s essential, it’s necessary, but it’s really expensive. So the solution here is: how can we leverage VR and train my house staff by using guys over at Johns Hopkins or Duke University, and have them walk us through it. Those are really interesting options.

 And at the same time, rural hospitals are closing at an incredible rate.

 Those are easy access points for poorer people.

 And so we have had a migration to urban areas in our country, but we still have tens of millions of people living in rural areas that are basically unserved— you don’t have to go to the South Pacific to find people to serve.

 Absolutely, there’s plenty here at home.

 As I said, we could go to the South Pacific or South Jersey, both have a shore, right? I just think that technology will be critical. The technology we’ve discussed today — whether it’s big data, AI, telemedicine, virtual reality, these are all going to be critically important because it comes down to money and access. We’re not going to train more doctors, and doctors can’t come out of medical school with $350,000 of debt and then think they can go into rheumatology. There won’t be any rheumatologists because you can’t go into rheumatology and pay that back; my wife was there.

WATCH: PX: Will AI Affect Medicine's Evolution?

 That brings up an important point, because I don’t think the question is: is the technology there? The technology is there; the innovation is there. What always hinders innovation is regulation; it’s the reimbursement and regulatory bodies that are saying what do we do with global medicine.

 It’s the money, right? So my doctors — we have 500 doctors &mdash; we can offer telemedicine. My doctors are not going out of business.

 They’re not going out of business. Their brick and mortar practice is not going out of business. Why would they do telemedicine? Why would they make $50 less or $100 less doing a virtual visit when the same advice can be given in person for more? That, you have to ask the regulators.

 We have to change that in Congress. I can’t tell you how many times I’ve been to Capitol Hill and I’ll talk to leading legislators in both the House and the Senate and say “Remote monitoring. For every 1,000 patients we remotely monitor that have a pacemaker defibrillator, we can save 53 lives, and we can save this many hundreds of thousands of dollars to CMS [Centers for Medicare & Medicaid Services] and Medicare.” They look at me and say, “Well, why aren’t we doing that?” And I respond, “Because you guys don’t reimburse remote monitoring or you penalize remote monitoring indirectly.” Their lights go off and then the next step is you’re in a room with some intern who has no power, no idea, and nothing ever happens. That’s what’s wrong.

WATCH: PX: Can AI Be Smarter Than Humans?

 I know that health spends are capitated, the days of the DRG [diagnosis-related group]. And people think that it’s a zero sum game— that there is $100 on the table and if I start using telemedicine, they’ll get $20 and I’m going to lose that $5 that I got. That’s a real concern. It’s interesting, the days when radial keratotomy and LASIK surgery came en vogue. There was a real problem because eye doctors, even optometrists at that level, were afraid they were going to lose their business &mdash; people will not need glasses anymore and there was a real fear. What happened is the market expanded. It became more robust, and they ended up selling more reading glasses and sunglasses, and the market went in a different place than they thought. So I recognize that medicine is capitated. I’m not saying that we grow, but I think the market will change and physicians will find different revenue streams to accommodate some of those financial needs.

 I think they’re going to have to, and we’re also going to have to decide if it is okay for medical schools to charge $100,000 a year. Is it okay for pharmaceutical companies to charge what they charge? Is it okay that we allow these types of huge punitive damages in a malpractice suit, so that physicians have to carry incredibly high-deductible policies?

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Articles

How are regulations hindering innovation in healthcare?

PX: Is Regulation the Pitfall of Technology and Innovation?

How do we build the hospital of tomorrow and what does it look like? How do technology and data play in a role in the hospital of the future? Our expert panel weighs in:

In our next segment, we’re going to talk all about building the hospital of tomorrow. It’s essential that we in healthcare leverage the power of big data and AI to improve care to our patients. One of the ways in which we can best implement these technologies will be through using them in direct patient care. One of the costliest aspects of healthcare today is inpatient care in hospitals — we see these rising costs in both academic and private-practice settings. To that end, let’s now explore how we can utilize these novel technologies to build the hospital of the future. Dr. Albert, I want to start with you: you’ve got all of these technologies, all these innovations; how do we build the hospital of tomorrow? Where is the hospital of tomorrow?

Kevin, my view has always been that instead of building more and more tertiary care temples of medicine, that the hospital of tomorrow will be, in many ways, the home — that we will care for patients utilizing technology and AI &mdash; building on knowledge from big data. We will enable those patients to be diagnosed and treated outside of those ivory tower institutions. I think we have to do it if for no other reason than cost.

Watch the last episode: PX: Will AI Affect Medicine's Evolution?

I think that’s a great point. How do we preserve the doctor-patient relationship in a setting like that, Geeta?

I think Dave is spot on. The hospital of tomorrow is in the home, and so is the doctor, nurse and patient, whether it’s remote patient monitoring, virtual visits, or the doc actually coming to the household, assessing the home environment and socioeconomic status along with diagnosis and treatment. There’s no doubt that to make the shift to value-based care that more prevention will be needed. Keeping people happy and healthier in the home will be key, and meeting the patients where they’re at, right? When we see the retail-ization of medicine — CVS, Walmart, Costco and Amazon &mdash; patients are going where they are going and living in their neighborhood.

You can have drones delivering your beta blocker from Amazon before this is over.

It’s not just the home, but the workplace as well: you have Apple building clinics and staffing them, as do many large companies, and people love that because it improves their productivity, it decreases their time. It’s just like having daycare centers.

Dave, remember Karl Malden and American Express? What did he say? “Don’t leave home without it.” Remember, you always carry your American Express card. Well, for me, here it is; this is my card, I carry it with me everywhere because it becomes part and parcel of care. It goes with the patient. But I want to pose another interesting perspective: let’s destroy the hospital. Instead of trying to say, “Well, the hospital is going to move from a brick and mortar to your house,” let’s look at things like stage-zero disease, restorative and regenerative medicine, stem cells. Think about the opportunity of stopping disease from happening at earlier and earlier and earlier stages.

So, for me, we look at a hospital now; a hospital is — wait for it &mdash; it’s where you go to die. I mean ultimately that’s sad, but in many instances that end-of-life care is defined by a hospital. Walk through the wards and you see a lot of sick, old patients there. So that I think is intrinsically a problem in healthcare; for me, I think that we can change that dynamic. We can address disease at again that proverbial stage-zero: how can we find cancer at the earliest stage? How can we find an MI [myocardial infarction] at the point where maybe we’re looking at thromboxane, free-circulating thromboxane prior to the actual platelet plug? Who knows what the mechanism is, but I think that’s going to be interesting because technology will change the need for the hospital in the first place.

I certainly agree with those points because I think we have to shift the focus on healthcare in the United States from treatment to prevention. If we can use predictive analytics and tools like this with AI in our pocket and AI leading the charge, I think that we will be able to prevent a lot of disease. We certainly can prevent the complication of diabetes; we can prevent multiple strokes; we can prevent multiple MIs; we can prevent a lot of this just by knowing early, and that’s, again, the dichotomy of big data. We use all these big data sets and the analytics and then we personalize that care based on your DNA.

I will say one thing. We’re all optimists here. And you know when you’re a kid and you say, “Mommy, can I go out to play,” and your mommy goes, “No honey, it’s may I go out to play.” The problem is we’re back to the patient. I don’t have to be compliant. In fact, many times I don’t want to eat those vegetables and I can be an adult and say that I’m not just a kid. I happen to like vegetables, but the point is, we are going to have to have something that today we don’t have much of — personalized coaching and motivation. We sit there and I write a prescription &mdash; now an e-prescription &mdash; for you for atenolol, or for Entresto, but if you don’t take it, it’s not doing any good.

And if you can’t afford it, and if you can’t get it, you can’t have access to it.

Exactly. And so best intentions do not always equal best results.

Our healthcare system does not have any individual accountability placed on the patient; the patient is part of the team. There’s doctors, patients, nurses and healthcare systems, but the patient needs to be held accountable in some way, otherwise we don’t get anywhere.

Sure, no doubt about it, but with the rise of high deductible plans, patients are now accountable because it’s affecting their pocket, right? I completely agree with you, Dave. It’s about lifestyle choices; coaching and lifestyle choices. It’s also about embracing technology, particularly in the youth of today when we look at childhood obesity. For the first time, we have a generation that may not outlive their parents — that is a travesty, right? People are not going outside to play, whereas we did. Now you’re doing something on the computer. Do we need to start looking at gamification and AI, and ways to keep a different generation engaged and physical and prevent?

Think about the suicide rates in our youth today because they’re sitting in a room engaged —

On a game: they’re lonely, they have no human interaction, they’re overweight, and it just spirals.

Watch: PX: Can AI Be Smarter Than Humans?

There’s got to be a way that we can intervene there.

I think two points: one is that I’m not convinced that the solution is in nudge psychology and making people eat their green leafy vegetables; I don’t think that the pivotal change in care will come that way. I think it will be technology mediated through extraordinarily sophisticated diagnostic modalities that see things happening earlier and earlier and earlier.

You may see it happening, but then you have to do something about it.

The patient still has to do something about it.

 Let me get to my second point: the interesting thing — and when we talk about just traditional technology today &mdash; we live in the world of the activity tracker, of the proverbial 10,000 steps. Now, the only person who really is interested in that is the guy at the gym who’s looking to woo a mate, right? That’s an athletic option. We need to move it from an athletic option to a clinical imperative, mediated by data, clinical trials and validation, exactly what you’re doing with AliveCor. But the point is, if I can tell a 38-year-old woman whose status is post-mastectomy for cancer, that if she wears this tracker and it’s going to tell me if the cancer comes back really, really soon, will she wear it, or will it become a dust collector like so many of these other trackers? We’re changing it from an option to an imperative, and I think that’s a data dynamic, and we’re going to see that sooner than later.

PX: Is AI a Physician's Tool or Future Physician?

Machine Learning: The Future of Medicine?

How can we use technology to build the hospital of the future?

Are physicians putting too much trust in artificial intelligence (AI)? How much has medicine evolved with AI, and what will medicine look like in the future? Our expert panel weighs in:

I’ve got a question for you, John, and this may be a setup but: is it possible to put too much trust in AI? And I know that’s a bit of a loaded question yet again.

To me, AI is not a fixed reality but a journey. I think that to try to establish it in the context of the here and now is a false premise. What interests me is that we spend a lot of time worrying about the AI error. Does anybody spend time thinking about the physician error?

We have M&M [morbidity and mortality] rounds —

Watch the last episode: PX: Can AI Be Smarter Than Humans?

 But that’s the best we’ve got. The best we’ve got is the human construct. And I’m just saying that I think we could do better. Simple as that.

 And I think we will. I mean we all are hopefully optimistic. I tell my children — I have four kids &mdash; and I tell them I want them to go do something and be happy. But I tell them there are two jobs that I really don’t want them to go into: truck driving and diagnostic radiology because AI will take those jobs away. It may not be this year, it may be in 25 years, but truck driving is going to go away because you go from point A to point B, and we know that it’ll be able to go from point A to point B. That robot truck will be able to do it.

The same thing with diagnostic radiology. Diagnostic radiology — looking at films. They’re going to not miss things, they’re going to do it better. And so you know that’s a little tongue in cheek but not really. And so, I think that it will come in stages. You’re right, it’s a journey, John, it’s an absolute journey, like the Gartner Hype Cycle is a cycle.

And is that journey, if you draw the dots of artificial intelligence, linear or asymptotic?  Does it become, at a point, where the threshold becomes a sense of humanity that it can get closer and closer and closer as it rises up, but it never passes the threshold? I think that is probably less clinical medicine and more philosophy, but I think that is where we are now, we are on that exponential journey. I think it would serve us well to be less arrogant as humans and think about the very nature of change in the world today.

Well, we don’t have Skynet — machines creating machines &mdash; yet, but I think humans are in charge today. And I think where this goes is in our control, and I am very optimistic. I may not be quite with my friend, Vinod [Khosla], but I’m very optimistic that AI’s going to play an important role in addressing some of the problems we have in medicine: errors, costs, things of that nature. You know, access.

Prediction, prediction and intervention prior to disease.

 As Geeta said, “Prediction is prevention.” If we can predict you’re going to have a fall, we can potentially prevent that fall. If we can predict you’re going to become septic in the hospital, we can prevent that sepsis and prevent that potential death, so I’m very optimistic. And I also believe we’re just at the beginning of this journey. Thirty years from now we’ll look back and this will be the infancy of AI in medicine and I just hope I’m around to see it.

If we’re bold enough to look back and talk about the profound changes that have come in the past 30 years, keeping in mind that the cellphone is seven or eight-years-old, where does that put us 30 years from now? I think it’s extraordinary and it will tear at the fabric of humanity. It’s going to be a very, very interesting ride.

 But remember, as has been stated before, the future’s already here, John, it’s just not evenly distributed. And that will remain the same, although, you can go to Africa, South America, you can go anywhere in the developing world and people have a super computer, globally connected in their pocket that cost them $50. And that revolutionary tool, which by the way is only 11-years-old, will be your primary care access point. That will be your banking access point. That will be your education access point — speaking as one who has two kids in private college.

Not the physician? So the access point to care will be technology?

 Because there aren’t enough physicians, John. There aren’t enough, we aren’t going to make more physicians. We built a bunch of new medical schools, but demand still outstrips supply. You know if you’re in a subspecialty like Geeta and my wife — unbelievably underserved.

 It takes months to get in to see my wife because, by the way, it’s not a fancy, procedure-driven specialty; it’s a cognitive specialty. And the number two thing is, everybody hurts, everybody’s joints hurt when they get older. I can verify that.

 And that’s why also, telemedicine, virtual medicine, we talked earlier about Nicklaus Children’s [Hospital] — you know in South Florida they’re doing an enormous amount of work in telemedicine, and it’s really so important. I can tell you as a rheumatologist, pediatric rheumatology, you can’t find one.

 No, it goes to my wife. My wife went and did six months in Cincinnati at their children’s hospital so that she could see pediatric rheumatology patients, as an internist, and she was scared every time she’d go in with a baby who was having some kind of autoimmune problem. But this is the problem we’re not going to solve. The way we’re going to augment that is with technology, John, you’re right.

I would totally agree. I just got back from a mission trip during which I sailed all over the South Pacific, went to remote islands in Fiji, some places they had never seen a white person or doctor. Every person on that island had access to someone who had a cellphone. They’re all on Facebook. I saw a patient who had neurofibromatosis, and that’s not my area of expertise.

I took a picture. I uploaded it to a private discussion group on Facebook, and there were some folks there who had expertise in that area and they said, “Yes, you’re exactly right. This is what you need to do about this problem.” That is in a remote island where they have no running water, no electricity, they barely subsist on fishing and farming, yet they have this power and we’re able to bring care to them, and that’s what this is all about.

Thank you all for this very spirited discussion. This has been a great segment and I look forward to continuing on.

With the rise of artificial intelligence (AI) in healthcare, how will it affect the evolution of medicine?

PX: Will AI Affect Medicine's Evolution?

Is human emotion more important than IQ? Is it possible for humans to train artificial intelligence (AI) to be smarter than people? Our expert panel weighs in:

John, one of the issues is, today, when we’re talking about the great strides in AI, we’re talking about the great strides in multilevel, deep neural networks. That’s the technology that has made Google Photos able to say, “find my wife on the beach,” and boom picks up the pictures. Or translate my Alexa, you know: “Alexa, what time is the football game this afternoon?” That is a pattern-recognition technology.

Yes, a lot of artificial intelligence is: I remember you, you’re John Nosta; I remember you, you’re Kevin Campbell: that’s pattern recognition. But that’s not everything involved in intelligence — we’re going to see whether or not it can become creative. I’ve heard about art that’s been created by deep neural networks. I’ve heard about music that’s been created. I’ve heard about writing. And we have companies [as] sophisticated as Google who say they’re AI first, or Microsoft AI first, and they’re going to push the boundaries. I’m waiting to see if they can do more than just win at “Jeopardy” or at ”Go.” And we’re going to find out. I don’t know.

Watch the last episode: PX: Is AI a Physician's Tool or Future Physician?

To add on to that, right, not everything is a pattern or an algorithm. I’m a rheumatologist. There’s not enough data on certain rheumatic diseases.

There are certain populations, we don’t have enough South Asian women with lupus to even study, what is the best treatment? So, it’s not that simple. We have to remember that these are tools. These are absolutely tools, just like we have always seen in medicine, whether it’s robotic surgery, whether it’s going from open-heart surgery to less invasive interventional radiology tactics and procedures. The evolution of medicine is this story. Whether today it’s AI and tomorrow it’s something else, I think it’s phenomenal, the opportunity is phenomenal. But it’s not the silver bullet that everyone seems to think it is. It’s not the doctor that’s the tool. And the same for the patient. The patient’s not the tool either.

Well the patient’s the center of the universe for what we do.

 The patient is the center and the key. And oftentimes you have a patient that tells you, “I just don’t feel well, something doesn’t feel right.” And you as a doctor start doing your investigation, especially in rheumatology.

It’s also a balance, and this is very trite, of IQ versus EQ [emotional quotient]. AI has a high IQ and zero EQ. I think that doctors are being built now to have higher EQs, acceptable IQs, but [EQs] may be a more desirable position.

That’s certainly a noted venture investor, Vinod Khosla’s, view, who famously said, “80% of doctors will be replaced by AI.” But what he did say is that we’re training, but maybe we should be, instead, training and cultivating more empathy into our doctors. At the same time, we should be training doctors who are also quantitative.

Watch the fourth episode: Machine Learning: The Future of Medicine?

 Or maybe we change the game and have IQ/EQ and TQ — maybe there should be a technology quotient. Why do we live in the bounds of humanity? I find it very, very sad that we hold humanity as the standard upon which every innovation is based. Why can’t we be smarter, better than humans? Why not?

Because we are human beings. And as human beings, we are emotional creatures, and that’s part of what makes life worth living: hurting, crying, laughing, enjoying company, going through a health journey with a patient—that’s what makes medicine worthwhile for me.

 But our sensory perspective is meager, Kevin. You know our ability to hear an opera is limited; a dog hears an opera better than we do; we process it differently. Our hearing, our eyes, our smell are meager compared to the broad electromagnetic spectrum that we bathe in every day. I’m just saying that there’s a broader reality. And somehow, just saying, “Well it’s as good as a human,” it’s just not; it’s not the end game I want to play to.

You’re right, but remember what I brought up about big data, well-labeled data — so who labels that data? Today, it’s experts labeling the data. So, do they make mistakes? I can tell you: I just had three board-certified cardiac electrophysiologists review 2,000 EKGs [electrocardiograms]. Two of them are young; young meaning less than 40. And they disagreed on 350 of them. And then I had a guy my age, who’s very well published, adjudicate. So, you have three experts; they’re all well trained, academic and incredibly well respected, and they disagreed 15 to 20 percent of the time. And so, you know I worry that no matter how good our data [are], it’s still going to have errors in it, and that means it will train.

 The Promises and Pitfalls of Big Data in Healthcare

You know, yeah, you’re right, you’re spot on, but does that —

But you can do pictures and say there’s a cat in this picture, or there’s not a cat in this picture. And that data [have] shown to be very effective at training deep neural networks. When you have rheumatology — my wife’s a rheumatologist and understands your problems &mdash; that’s a very complex notion. You have all these autoantibodies and you have all these symptoms.

It’s unbelievable. At the same time, you have a patient coming in saying, “I hurt every day. I can’t do this can you help me?” And so, I think, I believe that AI is going to be a critical tool. But I also think, you know, you’re right, maybe it’ll be smarter than us, but we’re the ones training it. So, I have a little bit of a problem of thinking, if an expert is training it, maybe it’ll be smarter than most of us.

AI Struggles Across Health Systems, Needs Wide Range Testing&#8203;&#8203;&#8203;&#8203;&#8203;&#8203;&#8203;

Based on the limited IQ of humans, can artificial intelligence (AI) be trained to be smarter than people?

PX: Can AI Be Smarter Than Humans?

Should artificial intelligence (AI) in medicine be used as tool for a physician, or could AI theoretically replace the physician entirely? Our expert panel weighs in:

A Healthcare Analytics News® Peer Exchange®

John, you work with a lot of customers, a lot of companies, and I’m sure that you counsel them on what’s the role of AI in their business. What’s the role of these types of technologies? What would you tell the average healthcare company [about] how they should use AI? What are the downfalls of AI? I know that’s a loaded question.

It is a loaded question, but first of all I think that the hype is appropriate. You know we talk about big data, and to me, big data [are] data. It’s the AI— it’s the analytics component that is the spot where we take all of this information. Earlier Dave mentioned the Gartner Hype Cycle; I think that’s a standard tool that looks at about 3,000 points of innovation, and they put AI on top of the hype. So now it’s what everybody is talking about. It’s all hyped up. I guess to a certain degree there may be an overreach in utility of AI in clinical medicine today or experimental medicine.

But the interesting thing is what Gartner said. And I’m using Gartner as an arbiter of mainstream thinking, not fringe, kookie, over-the-top stuff. Gartner said, in the headline to the story, is that today we see the blurring of the distinction between man and machine, and I think that is the sort of buried little secret here that we are seeing. Advanced computation that will actually mimic human reason and thinking. Dave, as you said, you make a mistake and you learn, you make a mistake and you learn, you make a mistake and you learn—that [sounds] kind of like an internship to me, doesn’t it?

>> WATCH: Machine Learning: The Future of Medicine?

Right, so I think that AI is really becoming part of the human reality. So, I really talk to my clients about understanding the nature of hype—but don’t take your eye off the ball&mdash;because this is the fundamental game changer in society today.

One of the things that I’ve been very impressed with from a regulatory standpoint is that our FDA commissioner, Scott Gottlieb, has embraced AI and automation, and is working very hard with his committees there within the FDA to create a way to quickly approve things like AI in medical devices. How do you think, Geeta, that’s going to impact innovation if we say, now, maybe we can make it easier for you, maybe we can make it less costly, and there’s an actual real pathway to approval with the FDA?

The biggest thing behind AI and machine learning is this idea that all of these patterns, all of these algorithms, looking at a million EKGs [electrocardiograms], will get us to the answer quicker, and we can identify a pattern or a diagnosis earlier and better and even more impactful if we can predict. So from a cost standpoint — from an FDA standpoint &mdash; if we can predict that someone is going to have a chronic disease, or is going to have an acute event, and we can make an intervention, we can come up with the treatment regimen, we can come up with a diagnosis that then prevents a future generation from suffering or being a cost to the system or having future morbidity or mortality. The prediction is the prevention and that is the cost-containment aspect to everything we’re trying to do in healthcare&mdash;the whole cost of devalue-based care &mdash; if AI helps us get there, it helps every stakeholder in the industry, and most importantly, the patient.

When my wife comes home from the pediatrician, one of the first things I ask her is, “What did the doctor say?” That’s my point of interest. Tomorrow, I’m going to ask her, “What did the computer say?” And then my next question will be, “And what did the doctor do about that?” I think that the empowerment of technology to help augment clinical practice is going to be profound.

Daniel Kraft talks about artificial intelligence. He flips the letters around. He talks about intelligence augmented and I think that’s the dynamic we’re seeing here, that there is an intrinsic intelligence to a physician that is not infinite. And I think that that intelligence can be augmented through AI and technology to provide a member of the group practice, if I dare go that far.

That’s a good point, John. One of the things I want to fundamentally say, that I think we’re all saying in different ways, and we have two cardiologists here on the panel, right— heart failure is heart failure, an MI [myocardial infarction] is an MI. The tools to diagnose those pathology states have evolved with time. The treatment for MI and CHF [congestive heart failure], they’ve evolved. AI is another tool. The MI is the MI. CHF is CHF. The EKG is AFiB [atrial fibrillation] or not, right? This is about embracing the tools. So, to your point, John, the doctor is still the one doing the treatment, still coming up with the plan of action, but if you have better tools — just like we did open-heart surgeries and now we do a lot more noninvasive cardiac procedures &mdash; why not? It’s still the same patient.

Yeah, but Geeta, to relegate AI as another tool, subordinates the power of this initiative.

I would disagree with that. It’s a tool, and I want every tool. I want the fanciest defibrillator. I want the fanciest catheter. I want the best operative tools. I want the best diagnostic tools. If I can have a tool like AI make me smarter, make me more effective, make me able to predict disease rather than treat disease, we’re going to make a difference. But an AI, a machine, cannot sit on the end of a bed, hold a dying woman’s hand, talk to her about the procedure that she’s about to have, empathize with her. So, it has to be both. You have to have doctor and machine. Dave, what do you think about that? I mean we both trained at Duke.

Medicine is more than just a science because you’re dealing with human beings, and I love the notion that human beings should be handled with care, OK? We are complex organisms. So, I see AI — you know, one of the great potentials &mdash; we talk about oncology, we talk about cardiology, we talk about dermatology and radiology, but, quite frankly, I think one of the great opportunities is in primary care. My oldest son is a resident in his last year of internal medicine residency, and as he says, “Dad, primary care is algorithmically driven when it’s done well.” Guidelines driven. But you know what, I don’t walk around with the guidelines in my head, and I don’t walk around with the algorithms in my head. But if we have people in a CVS, in a Walgreens, in a Walmart, in a consumer-oriented environment who can be augmented in terms of their effectiveness, diagnostic skills and their triage capability, by augmented intelligence, then I think that’ll be a huge perk. And by the way, coaching; we talked about it, Kevin. We spend all our time treating. We need to be spending more time preventing.

Lifestyle, diet, exercise, not smoking: all of these things are critically important if we’re going to address the issues that we face in America. And, quite frankly, face around the world. I think AI will have a place in all of those because it can take all this data and personalize to you.

I just wonder if AI will one day consider the physician a tool to prescribe.

Oh, come on. If you want your resource into AI to be Hollywood, yes, you can go that way. But I wanted to bring it into a more scholarly perspective. I would argue that AI, yes, can use the physician as a tool to inject the appropriate amount of empathy.

I don’t think that machine can show empathy. Unless you’re talking about an android that has not been delivered to us yet, there is no machine that can show empathy. That is a human emotion. That’s what makes us, that’s what gives us a soul, gives us a purpose: empathy, caring, compassion. A machine is a machine. My car can’t feel bad because it’s raining. My car just drives through the rain.

Well, plot, you know? Draw the data and plot the curve and I think you’ll be very, very interested in some of the potential changes about the context of humanity. And ultimately you have to ask yourself, is AI man’s last great adventure?

Artificial intelligence (AI) has proven to be a useful tool for physicians. Can AI take their role in the future?

What are the relationships between artificial intelligence (AI), machine learning and deep learning? How can AI be used in personalized and precision medicine? Are healthcare systems implementing the proper tools to make sense of their data and learn from it? Our expert panel weighs in:

Our second segment is all about analytics, AI and the search for answers. By now we all have heard about AI and big data analytics. In order to put things in perspective, it’s important that we understand exactly what AI is and how it can help us in healthcare. For starters, we really need a better idea of the terms, AI, machine learning and deep learning.

The easiest way to think about the relationship of AI to machine learning and deep learning is to think of them as a group of Russian nesting dolls — we call them matryoshka dolls. At the center of the stack is deep learning, followed by machine learning, and then the final and largest outer doll is AI. In the simplest of terms, artificial intelligence is the simulation of human intelligence by computer systems. Machine learning uses algorithms to analyze data &mdash; learn from it and ultimately make decisions, determinations and predictions. 

>> Watch the last episode: The Promises and Pitfalls of Big Data in Healthcare

In short, machine learning occurs when a computer actually evolves, and learns without specific programming via complex pattern recognition. With machine learning, a computer is able to modify itself when exposed to new data. Now deep learning is a subset of machine learning and thus the smallest doll in our stack of dolls. In deep learning algorithms, artificial neural networks are used to recognize patterns and cluster and classify data. Deep learning maps inputs to outputs and finds correlations. This is exactly what doctors do when we’re making treatment decisions for a particular disease.

So let’s dive right in. I want to start with you, Geeta. Are healthcare systems implementing the tools needed to, one, make sense of their data, and two, learn from it? Do you have any specific examples that you’re aware of?

I think every health system struggles with this, right? Number one, with the advent of the EHR [electronic health record] revolution, we have all of this data, that’s the first piece. And now the question is, what do we do with the data, how do we analyze it? And then lastly, how do we actually action it, right? So it’s one thing to know you have a thousand diabetics in your panel, and you know half of them are high risk, the other half are moderate risk, and then lastly, how do you actually action that? So you know every hospital system is doing something different. There have been a number of investments made by the NIH [National Institutes of Health], and [The University of Texas] MD Anderson [Cancer Center], Stanford [University] — so many leaders in this space that are using AI technologies, particularly in oncology and radiology when it comes to precision medicine and predictive analytics, and personalized medicine, which is very particularly important in the oncology space, right? The specific genetic DNA of the patient sitting in front of you, and what is the chemotherapy regimen that is most tailored and most beneficial for them.

So, we’ve seen pieces of it. I wouldn’t say that anyone’s cracked the nut on it, but certainly there are health systems that are making investments in that space and trying to figure this out.

Dr. Albert, tell us how you and your company with the KardiaMobile ECG machine use big data and AI. I mean that’s a specific application of a specific tool that can help patients and doctors.

>> Watch the second episode: How Can We Share Healthcare Data If We Can't Agree on Who Owns It?

David E. Albert, M.D.: &#8203;&#8203;&#8203;

&#8203;&#8203;&#8203;&#8203;Like in radiology or dermatology where an image is labeled— this is a melanoma, this is a mole &mdash; we have ECGs [electrocardiograms] that are labeled, this is atrial fibrillation, this is bigeminy. And we’ve collected over 30 million ECG recordings from the last 7 years. And actually today record into our cloud over 1.25 million every month. And so that data allows us now, not only have we seen tremendous volumes of pathology, but we’re able to label that data and then feedback &mdash; because as machine learning does, it gets better and better. It makes mistakes and you teach it, and then it teaches itself: what are the reasons I made a mistake? How do I next time get it right? And that goes on certainly with deep learning inside a black box. You just give it the answer and you give it the input and it decides how to correlate those 2 together. So, I would tell you that the great strides that have been made in voice recognition, image recognition &mdash; certainly in medicine with imaging &mdash;dermatology, radiology, oncology&mdash; those types of benefits we’re taking advantage of because we have a simpler signal in the electrocardiogram. And it’s been computerized for 50 years, but we’re going to make some new great strides. Instead of using 100 ECGs to learn, we’re going to use 100 million.

Machine learning and artificial intelligence could usher in the future of medicine, but are they being utilized to their full potential?

Veritas Capital and Evergreen Coast Capital have signed a deal to buy the electronic health record and health-tech company athenahealth for $5.7 billion in cash, according to an announcement.

The private equity firms plan to merge athenahealth with Virence Health, which consists of value-based care properties that Veritas bought earlier this year. The resultant health IT company, still named athenahealth, is poised to help providers across the country navigate healthcare’s digital transformation from several angles.

READ: EHRs Make Up $30B Market that No One Seems to Like

“After a thorough strategic review process, we have decided to enter this agreement with Veritas, which we believe maximizes value for our shareholders and accelerates our goal to transform healthcare,” athenahealth executive chairman Jeff Immelt said in a statement. “Combining with Virence will create new opportunities for collaboration and growth.”

Founded more than 20 years ago and based in Watertown, Massachusetts, athenahealth has risen to healthcare prominence, with a network of more than 120,000 hospital and ambulatory providers and roughly 117 million patients. The company develops and sells EHR, billing, patient engagement, population health and care management solutions and services.

The new athenahealth will go from being publicly traded to privately held. As such, investors will receive $135 per share, which is 12 percent above last Friday’s closing stock price. That figure is 27 percent higher than athenahealth’s closing stock price from mid-May, just before Elliott Management Corporation announced its purchase of a 9 percent stake in the company.

When the latest transaction closes, the combined company will comprise executives from each organization, headed by Bob Segert, Virence’s chair and CEO. Virence, meanwhile, will spin out its Workforce Management business to another Veritas company, which will run under API Healthcare.

“Virence and athenahealth have differentiated and complementary solutions, deep relationships with their respective customer bases and a shared culture of commitment to innovation,” Veritas Capital CEO and Managing Partner Ramzi Musallam said in a statement.

“We look forward to leveraging our expertise in the sector, as well as the capabilities and solutions across both companies to provide superior value to customers and create exciting growth opportunities for both sets of employees as Bob and the team build the future of healthcare IT.”

How this could affect providers who use athenahealth solutions is unclear. As 

 the other day, many of the company’s customers like athenahealth because of its dedicated support staff, which insiders have said could be threatened by private equity.

The deal is slated to close in the first quarter of 2019.

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The new owners plan to merge athenahealth and Virence Health to create a “leading” health IT company.