transcript by rev · intermountain, academic medicine at northwestern, and then population health...

Transcript by Rev.com

Dale Sanders: Hi everybody, thanks for joining us today. Looking forward to this.

A little more on my background.

The reason I'm sharing this background, friends, is that as I look back on this stage of my career I'm becoming more comfortably confident in my ability to see patterns and predict events and things like that.

So this is my reminder to me, but also maybe a little bit of a reminder to everyone out there that I think I'm pretty good at spotting trends and predicting things, and I'm just glad to be here.

I spent about fifteen years in the military industrial complex where, ironically, conflict leads to profits. I spent the last 21 years of my career in the healthcare industrial complex where illness leads to profits. I joke that, in my next life, I'm going to engage in economic models and careers that are a little more common sense.

That was a little ... that was humor, there.

My life started out back in 1983, my professional life, I have a chemistry and biology degree. The air force sent me back to their version of a master's program in information systems engineering. My official title there was air force command, control, communications and intelligence officer. I specialized in nuclear warfare and nuclear decision-making and planning. I also spent a fair amount of time, as a consequence of that, in space operations.

After the air force and the meltdown of the Soviet Union I went to work for TRW and was contracted out to the national security agency, supporting, among many other things, the START treaty and working on nuclear non-proliferation. Very data-intensive environments, very time-critical environments.

I did a little stint as a consultant contractor designing Intel's enterprise data warehouse. Actually starting out at Rio Ranch of New Mexico, but it expanded from there to include all of Intel.

I made this complex decision-making transition and data-driven career change from that world of healthcare and landed at Intermountain because I was intrigued by the possibility of computerizing better decisions in healthcare, based on my background prior to that.

Intermountain was gracious enough to let an uniformed, non-healthcare person get involved. They treated me very well, gave me all sorts of great opportunities. I'm enormously grateful for that experience.

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I went on to work at Northwestern as the CIO for the faculty group. Also, kind of had a campus-wide responsibility for analytics and data warehousing, working very closely there with Tim Zoph and many others. David Liebovitz.

Then had a stint at the national health system at Cayman Islands and, even though it seems like an odd pattern, what I was trying to do is understand integrated delivery systems at Intermountain, informatics leadership at Intermountain, academic medicine at Northwestern, and then population health in a national health system in the Cayman Islands. So there was a little bit of planning and forethought that went into those career moves.

Then a few years ago I formerly joined Health Catalyst, even though I'd been involved in the roots of it since Intermountain, to lead technology and product development. That's where I reside now.

My reference in the opening slide to raising the digital quotient of healthcare is inspired, and thanks goes to McKinsey. They have a nice framework. I would encourage everyone to borrow and learn from it, maybe even engage McKinsey in an assessment of your organization's digital quotient. They have a simple way of describing the DQ of an organization. That's the data assets, times the skilled labor, times the usage of those assets. If any of those goes to zero, of course, your overall DQ goes to zero.

I would argue that those are also listed in order of priority and implementation. In other words, it makes no sense to have skilled labor if you don't have assets to use, and you certainly can't achieve data utilization if you don't have skilled labor and assets first.

So, here's what I would suggest, that this is your data assets roadmap for healthcare. This is a cartoon that I've been toting around since about 2010 when I was giving lectures at Northwestern. It's stayed pretty much the same.

In those early days epigenetics and microbiome were just starting to emerge as common topics in healthcare. I would argue that most organizations, in their data government strategy, if they would peel these bubbles off of this chart, lay them out on a timeline, that that should represent your data acquisition strategy and data government strategy if you really want to understand the patient at the center of healthcare.

We are still stuck, for the most part, down in the lower left quadrant of this diagram. We as an industry, I would say. But. I'm seeing some interesting movement in the availability of data in these other areas. So as an industry, and as professionals, we have to propel forward the progression of data in these other bubbles. But then we also have to figure out a way for the patient and their care providers to utilize that data effectively in the work-flow decision making.



So for today's story I want to focus on improving the human and soft side of our digital healthcare strategy. I think for the most part we've ignored that, and as a consequence it's having very negative effects, especially on physicians. So the soft side of this technology is as important as the hard science and engineering.

I'll talk a little bit about the attributes of the modern digital platform, what that looks like going forward. Then I'll wrap things up with some thoughts on AI and precision medicine.

I would assert that our national data strategy has so far been a train wreck, and that we're losing, if not already we've lost, our physicians. My deepest empathy goes out to physicians. At this time in history, I'm so grateful that you manage to stay motivated.

There was a good article that came out in a journal a couple months ago about some problems with our current national strategy for measuring clinical performance. In the article they described 271 measures in the quality payment program from CMS. They identified 86 of those that were related to general internal medicine, and they found that 65% of those 86 were either completely invalid or questionable in their validity. 65%.

Imagine trying to be a physician working in an environment like that? It's really unforgivable, and we have to put a stop to it.

Related to that, an article came out in the annuals of internal medicine documenting physician burnout and attributing a lot that to electronic health record era. It's very sad to note that physicians now have the highest suicide rate of any profession. That's according to a study in an abstract in the American psychiatric association this year. We have greater than 50% burnout in all specialties.

I would say that we can tie a lot of this problem back to the poor way that we've implemented a data-driven strategy in the US healthcare system, and it's time to put a stop to it.

The MGMA conducted a survey, that's the Medical Group Management Association, in August of 2018, just a couple of weeks ago, identifying 84% of physicians are now participating in MIPS. 82% percent of those consider the MACRA Quality Payment Program as very or extremely burdensome. 73% said MIPS is a government program that does not support their practice's clinical quality priorities.

These are all significant data points indicating how far we've alienated physicians in our data driven strategy of healthcare. If you at a clinicians life, this box represents the total available time for analytics associated with local process improvement, creativity, and patient care. It's being completely



squeezed out by the time required by compulsory measures from CMS, private payers, and professional societies.

There's something wrong with this story in this box.

This is a half-joking, full-truth cartoon of how clinicians feel right now.

"Think positive, think positive, think positive," all the time being squeezed by all these compulsory measures that have little to do with the quality of the outcomes they're achieving, the relationships they have with patients, and more to do with how they conduct themselves. Measuring their processes instead of their outcomes. We have to change this.

Health and Human Services has some interesting priorities right now. Azar has identified the following four areas to emphasize during his leadership: end the opioid crises, reform health insurance to provide greater availability and affordability, do something about lowering the cost of prescription drugs in the US, and accelerate the progression towards value-based care.

These feel like pretty good priorities to me.

There's an interesting debate, of course, to regulate or not. On the left, Democrats would say, "More regulation is better." On the right, Conservatives/Republicans would say otherwise. Always this interesting struggle between the boundaries of federal regulation versus free market creativity in an economic model.

Generally speaking, if you look back at the history of the federal government, we regulate safety and adherence to standards that benefit the customer. Historically the federal government has not regulated quality of products or consumer experience, only to the extent that it benefits safety and supports what amounts to interoperability standards. Either interoperable technically or logistically.

The utilities industry is a little different in that we've allowed for fairly tight regulation of cost-quality service and standards.

Health and Human Services and CMS is kind of an interesting hybrid. Economically it's the world's largest customer of healthcare, plus it's the world's largest government of healthcare. So finding a balance between acting like a free market purchaser of healthcare versus the largest governor of healthcare is what we face today.

So. Let's ask a poll question about this.

If you had to choose only one, where should HHS and CMS apply their influence most? Should it be on quality of care and ensuring good outcomes for the



dollars that they spend as the largest consumer of healthcare services in the world? Would it be focusing on cost of care and reducing it? Or would it be around safety of care and at least "Do no harm?"

And note that I didn't give everybody the easy option of choosing all of the above. I want to force a little prioritization of thought, here.

Ticktock, ticktock.

Sarah Stokes: The votes are still pouring in.

Dale Sanders: The numbers are still climbing, so let's see what we see here.

I think this will inform whether we look at CMS as a free market consumer or as a government agency.

So. Interesting.

So, Sarah, are they seeing the results of this?

Sarah Stokes: They are, they are.

68% came back reporting quality of care outcomes, 13% reported cost of care, and 19% reported safety of care, "Do no harm."

Dale Sanders: That's interesting. I think what that suggests is that we would generally look at CMS as being a driver of the free market which is mostly about quality, less about governance. Fascinating. Huh. Interesting.

OK.

So I am seeing signs of common sense at HHS and CMS. As crazy as the Trump administration can be sometimes, I am very encouraged by what I am seeing with the leadership of HHS and CMS. I might throw ONC in that as well.

So in some of the final rules that were recently published we see a great emphasis on price and transparency. Publicly available on website is now required, and there are future rules forthcoming that will force transparency around out-of-network costs as well. So this is big.

Removing 18 measures from the inpatient quality reporting program where the cost of collecting that data outweighs its value or it's no longer relevant. So we're starting to trim down the measures, that's great.

Removing 25 measures from IQRP that are redundant in other programs.



Easing the document requirements for certification. For example, why in the world did we ever require folks to tell us specifically about where the data is located in EHR in order to qualify for incentive payments? Makes no sense at all.

So, good for them for making progress on this. I really like the common sense I'm starting to see out of HHS and CMS.

Oh, and then finally, of course, promoting interoperability and moving meaningful use from what I think a lot of us pejoratively call meaningless use to focusing on standards. Right?

And the interchange of data, again, a role that the federal government can play a part in historically, standards are an important part of federal influence.

So we're getting there.

There are several proposed rules open for comment now until Sept. 10. I would encourage everyone to contribute. Under the MACRA QPP and physician fee schedule rules that are open for comment now, we're seeing some progress towards remote patient monitoring reimbursements. This is significant. A significant change in the ability to be reimbursed for monitoring patients at home or at work, wherever they might be.

Removing 34 quality measures.

Adding 10 measures, 4 of which are outcomes-based, which is great. We're starting to move towards outcomes.

Consolidates some of the reporting from PQRS, meaningful use, and value-based payment program.

Simplifying E&M coding. I would eve say, "How can we just get rid of E&M coding altogether?" I think that if I were a physician I would sure advocate for that.

And advancing virtual care. So, lots of good things happening.

Medicare part B drug payments that will actually reflect costs.

So it appears that HHS and CMS are listening and they're being proactive, smart from a business perspective as well as informing and putting patients at the center of this. Also taking caring of physicians. Hopefully that will all continue.

Again, I would assert that we've missed the human, softer side of becoming a data-driven industry, so let me just talk a little bit about my philosophy that I've tried to apply as a data professional, as a CIO and chief analytics guide in healthcare. I've always tried to appreciate the softer side of these relationships.



I love the book written by Daniel Pink, one of my favorite all-time books about leadership and business, the surprising truth about what motivates us, and it boils down to mastery, autonomy, and purpose.

What I would suggest is, as you engage with physicians and we engage with patients, we have to ask ourselves: is our data driven strategies, our digital strategy, adding to physicians' sense of mastery, autonomy, and purpose in their role?

In other words, are we giving them the data to be better with their passion, mastering their skill? Are we making them feel more autonomous, rather than being constantly watched over their shoulder, taking way from their autonomy? And is all of this contributing to a greater sense of purpose and a sense of mission for them?

And I mean this very literally, friends. Please, when you engage with your clinicians and your local organizations, ask yourself, "What can we do with our analytics and our digital strategy to contribute to the mastery, autonomy, and purpose that our clinicians feel?

And eventually we have to apply the same principle to patients, making them feel like they're mastering their own health, they're autonomous, and they're contributing to a mission or purpose that's bigger than themselves.

I would also assert that quantitative predictability is the metric of scientific precision. OK? Think about that for a second. I'll say it otherwise.

The progression of any body of science is measured by its predictability.

This is where we sometimes overstate our ability to predict what's happening in medicine.

The reality is, as a body of science, medicine, human biology, etc., is still very qualitative. We still don't understand it. In part because we haven't digitized it to understand it, but it's just very complicated. As long as we keep pretending that we know it and understand it better than we do, and we force that poorly informed perspective on physicians, we're going to continue to alienate them because they know that medicine isn't a precise science at this time. We're getting better at it, but it's still a relatively imprecise science. It's still largely qualitative.

So let's engage in the discussion with clinicians from the point of view of reality, and just know that it's not nearly as predictable as we think it is.

So, to that point, if I had a tattoo, this is what it would be: find the truth, tell the truth, and face the truth. That's been a guiding philosophy of mine in my whole life, but it especially applies to data and healthcare.



So the truth in healthcare data is rarely the truth, but we talk to physicians as if it were. When you communicate the truth, whatever version of it you manage to define, you have to realize, in healthcare right now, that it's only an approximation.

And be sensitive to the human who's receiving the message. So be sensitive in the way you deliver and tell the truth to clinicians and patients.

And then help people face the truth without feeling threatened or over-measured. This even ties back to the trend in the US around fake news, and the way we keep engaging in these debates from a data-driven perspective, but the way we engage in these debates entrenches people in their belief. It doesn't move them, it entrenches them.

So when you engage and tell people what you believe the truth is, you have to help them face the truth in a way that doesn't entrench them in an ill-informed opinion. This is where we're missing the human side of the data-driven strategy in healthcare, these soft things like this.

So, this is ...

This hangs in my office as a goal. We want to enable the digital healthcare conversation between a physician and their patient. I'm going to pain you by reading through this.

"I can make a health optimization recommendation for you, informed not only by the latest clinical trials but also by local and regional data about patients like you, the real world health outcomes over time of patients like you, and the level of your interest and ability to engage in your own care. In turn, I can tell you within a specified range of confidence which treatment or management plan is best suited for patients specifically like you, and how much that will cost."

That's our aspirational statement.

If you step back and you start parsing that from a data perspective you see lots of interesting things start to show up. You see outcomes in cost data, predictive analytics, machine learning, social determinants of health, and recommendation engines.

For example, there's a recommendation engine: "Optimization recommendation for you."

Clinical trials suggests that we're going to bring clinical trials data to bear more effectively in decision making at the point of care.

We have to integrate local and regional data.



"Patients like you" is a pattern recognition suggestion.

"Health outcomes" suggests that we're collecting true outcomes of patient's data.

There's another "Like you" pattern recognition.

"Level of your interest and ability to engage in your own care" are the social determinants of healthcare, the patient activation measures kind of data.

"Range of confidence" suggests that we're statistically informed about how we present this data.

"Treatment" suggests tailored, precision, patient-specific treatments.

"Like you" is another pattern recognition.

And then finally, and critically important, we understand our costs at a detailed level.

So that's what I think, for the most part, we should aspire to enable that conversation. I would even suggest that, in the future, it's not necessarily a physician that's the "I" in this statement. It's the algorithm. I think, in the future, the "I" in this statement becomes the algorithm, and the algorithm is informing both sides of the conversation, the physician as well as the patient.

And I want to thank the learning health community for inspiring our version of this here at Health Catalyst.

So, what's required to become digitized? If we're going to progress this strategy to digitize healthcare, what do we have to do to create these digital twins of patients?

In any industry you have to digitize the assets you're trying to manage and optimize, and then you have to digitize your production system for managing those assets that you're trying to understand and optimize.

I'll use the aircraft/airline industry as an example.

Airlines, airplanes rather, are the digital assets. The processes that we've digitized, air traffic control, baggage handling, ticketing, maintenance, etc., is a reflection of our ability to manage both the people as well as the aircraft.

By the way, just as a side note, I think the airline industry is the single most complicated system that humans have ever created. When you think about technology and the uncertainty of it from the ground-up, it's pretty amazing.



So, drawing that analogy to healthcare, patients are the assets that we're trying to understand. They have to be more digital. So far we've digitized registration, scheduling, encounters, diagnosis, orders, billings, and claims.

That's OK, but that's not enough. The reality is, what we've digitized is a clinical encounter to drop a bill. What we have to digitize going forward is the optimization of health and the minimization of cost.

Optimization of health, minimization of cost.

At best, EHRs hold only 8% of the data that we need. You come to those numbers pretty quickly when you realize that only 20% of the factors affecting health outcomes fall inside a traditional healthcare delivery system. Most of what affects health outcomes fall outside of the four walls of healthcare delivery.

So we're not even collecting data within the 20% very effectively, and we're collecting almost no data in the other areas, social factors, genetics, lifestyle, that would inform the complete picture of health outcomes.

If you think about it, on average, patients in the US have three healthcare encounters per year. What happens to the data in the other 362 days of the year? It's blind. We have no clue what's happening in the other 362 days of the year. That's not adequate sampling.

And if healthy patients represent our ideal artificial intelligence training set, we have no data on those healthy patients. Right? They have no clinical encounters. We have no data to train and understand our AI algorithms about how to achieve more healthy patients, not treat sick patients.

Speaking of all this, my observation about patient engagement as we continue down the road of care management and population health, is about two-thirds of patients don't want or cannot be engaged.

So they don't want to be engaged. They want to be bothered to a minimum extent necessary from their healthcare system. Or, in some cases, patients literally can't be engaged in their own care. They don't have the cognitive skills, the language skills, the geographic proximity to care. About two-thirds of patients, I think, fall into these two categories.

What I would argue is that most patients really want, most of us really want, is, when we're sick, we want to be diagnosed and then treated safely, affordably, personally, efficiently, and precisely.

Again, getting back to mastery, autonomy, and purpose, right? The more that we try to engage patients in what we believe they should be engaged in, the less



autonomous they feel. We're not adding to their sense of autonomy, in fact we're disempowering it.

What I would suggest going forward is that we get our act together and we treat patients exactly as they should be treated and diagnosed when they need us. I think we should keep that in mind as we lay out our strategy and priorities for digital health. I think we should temper our expectations about what we can do with wellness programs and patient engagement strategies.

There is evidence, of course, and I think we all feel this, that patients are starting to own their own care.

Interesting article that came out this year, a study in the Netherlands about the rate of patient requests for a specific therapeutic or diagnostic intervention from 1985 to 2014. So basically, how often are patients asking for a specific test or therapeutic intervention from their general practitioners? What this study saw was a significant increase in requests by patients during that time frame, and an interesting increase in compliance by the GPs.

I thought this was fascinating. Requests for blood tests went up by a factor of two, requests for urine tests up by 26 times, radiology and imaging went up two and a half times.

And, this was interesting, this surprised me, I thought it would be higher. Requests for medication didn't go up that much. Barely, actually, in comparison.

What this tells me is that patients want data about their health. Look at those other three requests. Patients want data about their health. They want to be informed and make their own decisions.

Dale Sanders: They want to be informed and make their own decisions. They're not necessarily seeking to treat themselves. They want to inform themselves, and I think this is a trend that we'll see going forward. So let's have a quick review of the relationship between digital accuracy and it's proportional impact on digital sampling for just a minute.

So this is a picture. If anybody knows who this is, I'd be surprised. Harry Nyquist is his name. He's kind of the father of digital sampling, and, in essence, what he suggested is that when trying to estimate an analog signal, the rate at which you sample that analog signal is critically important to the recreation of that analog signal in a digital environment, okay? He's the father of digital sampling.

And I would argue that this problem we have in healthcare is it boils down to a digital sampling problem. We can't possibly provide personal health or precision medicine with only three patient data samples per year, so we have to raise our sampling rate.



I use this picture to illustrate what I'm talking about. We're analog human beings. We don't exist, at this level, as anything but analog human beings moving slowly and fluidly from one phase of life to another. Those are my little kiddos by the way, first day of preschool for my daughter.

That's my analog life, but this is healthcare's digital view of my life. It's completely inaccurate and smeared. That's what we have to change. And what's happened is the physicians have become healthcare's analog to digital samplers. And that's just not a good role for human beings.

No more clicks is my mantra for physicians. If I were still a practicing CIO, I would say, "We're not going to add another click to a physician's or a nurse's life. We're going to do everything we can to fight against that, and, in fact, where we add one, we're going to take at least one away." We should reduce what they already have, to be honest.

For Artificial Intelligence to be successful, it needs breadth and depth of data within the domain. So you'll hear a lot of organizations, including some of the startup vendors, talk about how many millions of patient records they have, for example. But it's not the number of records that matters exclusively. It's the number of facts you have about those patients that matters.

So it's rows times columns that matters. It's records times facts that matters. It's not just a long list of patients. You have to round out that digital ecosystem, and you have to collect more facts about each of those patients.

So these bubbles in this cartoon represent the facts about the patient. And you need to multiple this diagram by 10s and hundreds of millions to build the training sets that we need to fully leverage AI in healthcare.

Again, for the most part, we're operating in the lower-left quadrant of this diagram. That's not a truthful place to operate and inform Artificial Intelligence. We have to round out the features and the facts that we feed in to these algorithms, or otherwise, the algorithms in the AI will be no better than the precision of the data that we have, which we know is imprecise, today.

So we have to accelerate. And from a National Data Strategy perspective, I would hope that HHS and CMS would start thinking about their data strategy in this context. How can they progress? What can they do now as the world's largest consumer and purchaser of healthcare data or healthcare services? What can they do to accelerate the progression of data in all of these areas and demand more precision about the patient at the center?

Now, there is hope ahead. This is really exciting, really exciting. John Rodgers is the founder and the executive director of the Center for Bio-Integrated Electronics at my old stomping grounds at Northwestern. I'm just sad I wasn't



there when he was there. I'd be knocking on his door to be one of his grad students.

It's Renaissance science that's occurring in John's area. I had the great fortune to meet with John and his team a couple of weeks ago. I spent some time with them in their labs, and it's truly Renaissance science. There's a picture of John and a couple of his grad students in front of one of their projects.

In these bio-integrated electronics, they're producing microns-thin, one-inch squared, skin-pliable sensors, and in these ... I wish I had a picture of one. We'll get a picture of one next time. In these tiny, little wafers, microns thin, they have a Bluetooth antenna, they have a CPU, they have physiologic monitors, and a wireless power system. It's amazing, and it's going to happen. It's already starting to happen. Some of the professional sports teams are already starting to wear these during competition, so this is on our doorstep.

This is a picture of John. The goal is to eventually print these on skin as a dissolvable tattoo. And by the way, they're also working on dissolvable, implantable devices so that you can place one of these directly on a heart, a kidney, a liver, monitor in real-time the telemetry of those organs, and then, over time, the telemetry system dissolves. So this is really, really cool and very exciting stuff.

Similar work is happening at the University of California in San Diego where they are producing 3-D, bio-integrated, stretchable sensors, and these sensors have applicability to EEGs, EMGs, ECGs, respiration, skin and temperature, eye movement, body motion, et cetera. So it's starting to happen, friends. It's happening, and it's really exciting to see this kind of progression. We're at an inflection point with these sensors.

What I would suggest is we're going to see an end of the data monopoly in healthcare. In 160 AD, Ptolemy said that the earth is the center of the universe. It took a long time for that view of the world to go away. Copernicus came along in the 1500s and said the sun is the center of the universe. That cost him some credibility with the Pope.

Today, the healthcare system is the center of the patient's data universe, and it creates a monopoly of knowledge. With the help of people like John Rodgers and the folks at UCSD, tomorrow, patients will generate and control more data about themselves than their healthcare provider.

This is how we like to portray the center of the data ecosystem for patients at this time. Patients embedded within what I would consider a largely disintegrated healthcare delivery system, all sorts of problems with handoffs between the bubbles surrounding this patient. Very little data actually being generated about the patient from these different bubbles. The data that we're



generating is mostly administrative. It's not telemetry about the patient's health. It's administrative data we're generating.

So I would say this view of the future of care management and pop health is wrong. And that what we need is this view, the patient at the center of their data ecosystem, sharing it back to the healthcare system, so putting them at the center of the data ecosystem, sharing it back to the healthcare system.

So here's my view of the future of diagnosis and treatment. Enabled by these bio-integrated sensors, patients will soon hold more data about themselves than the healthcare system. Their data will be constantly updated and uploaded to a cloud-based AI platform. Those algorithms will diagnose the patient's condition, calculate a composite and specific heath risk scores, and will recommend options for treatment or maintaining health.

The algorithm will then suggest options for a best-fit care provider, based around volumes, outcomes, in-network ... I hope someday we get rid of the term in-network ... and ability to socially interact with other patients like them.

So we'll help diagnose, we'll help suggest treatments, we'll align patients with the best-fit care provider, and we'll also allow patients to interact with other patients like them to become an extended member of their care team. And the patient will then engage with a care provider, enabled with the output of the AI algorithms. And both parties will have an informed conversation that's balanced from a knowledge perspective.

I can't imagine. There's probably not ... Well, it'll be interesting. I should've taken a poll about whether that we believe this scenario's likely or not. Most folks that I talk to believe that some version of this scenario is going to happen, and it's not very far away. The question is how do we do it, and how do we accelerate it?

While we're figuring out how to create the data assets to enable that conversation, if I were still a practicing CIO, CAO, I would create a role. Some would suggest it's an evolution of the medical informaticist, and I would call that role a digitician. And I would create patient data profiles.

I would suggest that different patient types have different data profiles required for the active management of their outcomes in health, okay? Different data profiles for different patient types, and I'm not talking about quality measures. I'm talking about telemetry, diagnostics, and functional status about the state of the patient, not the state of the healthcare process.

And I would suggest that the digitician's job, while we're waiting for the patient to become fully digitized with sensors, that we would define these patient data profiles. It would be the digitician's job, by hook and by crook, to collect that data, associate it with those profiles in any way possible, be it manually, on



paper, or if sensors exist, prescribe those sensors and proactively collect this data for patients in their panel. And then feed the results of that analysis back to the care team and patient.

I like the digitician name. I think it's kind of catchy. That's my personal pat on the back, but it's a logical extension for the medical informaticist role and evolution to sit in this space and enable this kind of thing.

All right. We've talk about data and everything, all the data we need. Well, we need a platform that's modern and can handle the digitization of health, and it has to be an integrated platform, in terms of data. It can't be disparate.

As computer scientists, we overlook the last and critically important layer in the technology stack. So typically, this is how we portray the tech stack in computer science. Hardware at the bottom, operating systems on top of that, application software, and then a user interface.

But it struck me not long ago that we'd made enormous progress in all of these layers. I mean, it's amazing what we can do with the public cloud at the lower-two levels. It's amazing what we can build with modern software development tools and user interfaces and devices. It's the Renaissance of software engineering.

But what's still very difficult to do is manage the data within a domain, especially, a domain like healthcare. So those application developers working at that layer still have to wrangle and deal with data on their own. No one has pre-processed and made the data layer easy for them to access and utilize, and that's why I've coined this term or this concept that we call the data operating system.

That's they last layer, I think, in the stack, to make it easier for application developers to take advantage of complex software, especially in an industry like healthcare. So that's where we're headed with this thing called the data operating system.

I also want to comment for just a minute about the evolution of data modeling in analytics. Along the continuum, at one extreme, we have a monolithic enterprise data model, IBM, Oracle, Teradata.

To some degree, modern EHR vendors have characterized their data models in this context in this way, still following what amounts to a monolithic representation of data in an ecosystem.

On the other extreme, and this is what made health catalysts famous for a while was our approach to late binding. This was a concept I introduced when I came into healthcare from the military. I would say invented this concept of late binding and data models back in when I was working for the National Security



Agency in TRW. Nowadays, it's called schema on read. Back then, I borrowed late binding from software engineering.

In the monolithic extreme, we tend to believe that we know all the use cases of the data, a priori. That's essentially what a monolithic data model suggests, that we know all the use cases, a priori, and we know that that doesn't work. We know that it's not an accurate concept.

At the other extreme, of late binding, we tend to believe that we know none of the use cases, a priori, and we will bind data at the very last second according to the use case. Well, the reality is operating at either of these two extremes is wrong and will lead to failure.

We've had all sorts of problems in health catalysts unwinding an overemphasis on late binding. Quite honestly and frankly, we're recovering from too much late binding in our tech stack. The reality is we do know some of the analytic use cases, a priori, hence the creation of these intermediate data models.

So what I would suggest going forward from a data modeling perspective is that we ... The monolithic data model doesn't work. We know that, but intermediate data models in late binding are still very applicable. So the curation of data in between can add a lot of value to an application developer's life by harmonizing vocabulary and then binding data where we see comprehensive and relatively persistent agreement, for example, around CMS value sets that aren't that volatile.

So when you see that, and you can pass that test for comprehensive and persistent agreement, go ahead and bind in you data model the logic associated with those so application developers can take advantage of that.

Overtime, what I predict will happen is that we'll see binding of data in the pipeline, not in the data model. So as the data flows into a system, we will bind that in real-time as callouts to SQL R, Python, et cetera, other forms of logic. And you'll see a decrease in persistent data models in a relational sense, in the future. And then on the right, various modes of analytic expression. So overtime, you'll see fewer and fewer persistent data models and more real-time binding.

These are the seven attributes of a modern digital platform, as it describes a data operating system. It has to support reusable clinical and business logic with open APIs, things like registries and value sets. It has to be designed from the beginning to support third-party application development.

A single data stream has to feed analytics and workflow applications. We can no longer support workflow and one-space analytics and another. They have to come together, they have to be merged, including not just architecturally, but at



the user-experience level. We have to feed workflow with analytics in an embedded way, single-view.

It has to support the integrated structure and unstructured data content, so text images and discrete all have to exist within the same infrastructure, both at the technological as well as a UI layer.

It has to have closed-loop capability, especially bending analytics back to the workflow. As I mentioned earlier, we have to fuse analytics and workflow together in the same user interface. We have to close that loop.

It has to support microservices architecture, so regular, constant updates rather than these big, giant, gnarly annual updates, and upgrades of software just won't fly anymore. It has to be microservices-based.

It has to natively support AI machine learning. It can't be a separate thing anymore. If you noticed in that previous slide, I showed AI machine learning bindings right in the natural flow of the pipeline, so it can't be something that a data scientist works on. You have to enable AI and commoditize AI machine learning to anyone working in the data pipeline.

And then finally, it has to support the notion of an agnostic data lake. So we have to be able to lay the logic over the top of any data source, and different computational engines. SQL, Spark SQLs, SQL on Hadoop, et cetera.

So I would suggest hold this up against your own digital strategy and infrastructure. And ask yourself to what degree do you score your organization or your products, if you're a vendor, according to these criteria? How well do you support this?

This is our health catalyst data operating system architecture. I offer this not to sell it, friends, but rather to inform. I believe that this represents an architecture that's indicative of the future.

We're building this out right now, moving away from what I would consider a fairly old-school, traditional, but arguably effective data warehouse platform to this data operating system. By the way, these slides will be made available so that you can reference these.

Notice the curated data content in the upper-third of the diagram there, and that's those intermediate data models where we see comprehensive and persistent agreement about logic, making it easier for application developers in the upper-right, the DOS marketplace, to develop apps and take advantage of all the infrastructure underneath it.

Okay. We have another poll question. Speaking of all this, who will be the digital disruptor of healthcare? Is it going to be employers, like Amazon, Berkshire,



Chase, the ABC consortium? Is it going to Silicon Valley players like Apple, Google, Microsoft?

Is it going to role model provider systems like Geisinger? Will it be the current crop of EHR vendors or none of the above? I'd love to hear and see your thoughts on this. This will be very interesting.

Sarah Stokes: And the votes are pouring in.

Dale Sanders: The votes are coming in.

Sarah Stokes: I'd also like to use this time here to remind you to submit your questions. We've had a lot of great ones coming in, and we are slowly approaching the Q and A sessions. So be sure to get those questions in.

Dale Sanders: Still trickling in. Tick, tick, tick.

Sarah Stokes: Okay. We're going to go ahead and close that poll, and I'll share the results with your here. So as you can see, 27% of respondents said employers. 39% said Silicon Valley. 6% said current provider systems-

Dale Sanders: Wow.

Sarah Stokes: -7% said EHR vendors, and 21% said none of the above.

Dale Sanders: Wow.

Sarah Stokes: So what do you think of that, Dale?

Dale Sanders: That's interesting, isn't it? It's kind of sad, in a way, that the current provider systems ... I have so much respect for Geisinger, but I think it also plays to the fact that it's hard to disrupt yourselves. And I think people recognize that. Interesting that Silicon Valley would come in at 39%, isn't it?

Yeah, yeah, we should maybe write a little blog on that or something or publish those results. That's interesting. Okay. Thanks everyone. There we go.

Sarah Stokes: There you go.

Dale Sanders: Okay. Some thoughts on AI and precision medicine now. One of the things I learned, my team learned, working for the National Security Agency, NSA, we became incredibly capable of predicting very interesting events. We kind of turned the intelligence community and the defense community on its head in our ability to accurately and quantitatively predict events that had previously been difficult to predict.



So, we felt really good about ourselves for a while, and we were invited to all sorts of important meetings. And everybody thought we were the brainy guys from TRW. But here's what happened. All we did was issue predictions. We didn't do much to issue interventions.

And so we wore out the decision makers, the senior decision makers at NSA, DOD, State Department, et cetera. They started uninviting us to meetings because predictions of risk without a plan or the ability to intervene are a liability to the decision maker and not an asset. And it took us a little while to figure this out.

All of a sudden, we weren't as popular as we once were. We couldn't quite figure out why, and we managed to pull a senior official from the Pentagon aside and ask, "What in the world is happening? How come we're not held in the higher regard that we once were?" And he said, "Look." He said, "Nobody wants to talk to you guys because all you ever tell us is the risks that we have, and every time you tell us about a risk that we can't do anything about, it just increases our liability. So nobody wants to talk to you anymore."

So that was a big aha moment in my career. And wisely, we adjusted to that. But we need to face that in healthcare now as well and stop overwhelming our clinicians with all these predictions of risks, unless they're coupled with specifics about how to intervene and what to do about it, okay? Think about that, please.

This is just simple diagram to represent the analogy between a human brain and pattern recognition, AI pattern recognition. So in this context, a human is looking at a crowd of people. The retina is the data collection system for feature extraction.

So as this human is looking at people, it's starting to extract features about the people, height, weight, skin color, hair color, age. The cerebral cortex then is the database and the algorithms that are associated with classification and clustering. So immediately start thinking about number of women in the crowd, the number of men in the crowd, the number of red heads, the number of blonds, brunettes, et cetera. And the more times you pass through this loop with different data, the faster and better you become at feature extraction and classify people or whatever it is that you're looking for.

So discriminative neural networks mimic the human pattern recognition and classification process. It tells us those are people. That's a discriminative neural network.

The rise of these very interesting GANs or generative adversarial networks is interesting because it mimics the opposite human process, and it is describing what people look like. And that's very significant because these GANs can actually start producing images of people, images of data in general, that look exactly like real images and real data.



So if you think about this from an AI perspective, we might have the ability with these GANs to generate training sets like we've never been able to before. The jury's still out on that, but it's potentially very significant. And it's a fascinating, creative evolution of neural networks.

By the way, neural nets as the algorithms and such have been around for a long time. There's really nothing new to the algorithms. The algorithms we used in the 90s are still basically the same. They're being used today. But, boy, the creative application of these is the big difference, things that we never even thought about.

By the way, the way a GAN works is it pits one neural net against another, and one of those is the discriminator. One is the generator. And the generator is basically trying to fool the discriminator with fake images and by constantly evaluating real versus fake images. Then the discriminator becomes better and better at understanding what's real. It's fascinating. Anyway, that's forthcoming.

All of AIs is some form of pattern recognition, and what's interesting and what's happening most, at an accelerating perspective, is the use of pattern recognition in imagery.

So imagery AI has been around for a long time. We were doing pattern recognition on images back in the early 90s. In the intelligence community we took that technology. We applied it to mammograms and ultrasounds and things like that, so that concept's been around for a while.

But now we have more data than ever. And the techniques are getting better. There was a great article that just came out a couple of days ago about the real-time use of AI for the identification of small polyps during a colonoscopy, so this is real-time. This is not after the fact. This is a clinician using a scope with AI enablement during the diagnostic process. This is not offline. This is real-time.

And the results were amazing. It came from a consortium of Japanese researches and hospitals and academics. They found 94% accuracy in the detection of small polyps. That's significant. Those small polyps, as I understand, are quite hard to identify, from the human eye perspective. So, to get that kind of accuracy from AI is amazing.

So image analysis will and is moving fast in healthcare. Continue to look for that. Pattern recognition in EHR space is moving slowly because it's a digital density problem. We don't have enough data.

So if you look at the scopes that are being used, they're very high-resolution Olympus scopes in this study. And it creates a very dense digital image. Those are more data points for the AI algorithm to see edges and boundaries an anomalies in the image itself. We don't have that kind of digital density in the



data we have from EHRs and things, so it's all back to digital to sampling, all back to digital density.

Rules-based registries have flaws. That's my assertion. There was a very interesting study that came out of The Lancet, sponsored and conducted by Lund University in Sweden, about the ability to use k-means clustering and hierarchical clustering in AI, to identify previously unidentified sub-groups of patients and diabetes.

So they discovered five distinct sub-types of adult-onset diabetes using pattern recognition rather than rules-based registries. So if you look at the way we define diabetics now, it's usually rules-based registry that someone else defines according to ICD, maybe a lab test. But what will happen going forward is these registries will be more and more defined by the patterns we see in the data, not in the rules that we apply to and impose upon the data.

It's going to change our taxonomy of diseases, so, now, we have these five distinct sub-types of adult-onset diabetes. It's not just Type 2 diabetes anymore. It's five sub-types. This is going to have a dramatic impact on our taxonomy of diseases as this kind of insight grows and takes off.

Similar kind of a study at Mt. Sinai using topographical data analysis to identify diabetes sub-groups, and in that use of TDAs, they used it to visualize and explore clusters of patients grouped together by the algorithms.

They found that 2,500 Type 2 diabetic patients clustered on 73 clinical variables identified three sub-types, and a rules-based approach would've never found that, never would've found it.

The moral of the story is let the data tell us its story. And of course, we have to continue to increase the density of the data that we have about patients in order for it to tell us the truth of the story. We're still nibbling around the edges of the truth.

Very interesting study that came out of Beijing using convolutional neural networks applied to electronic health records found that it could automatically extract semantic information and perform automatic diagnosis without the construction and the imposition of rules or knowledge bases, okay?

So not even harmonizing across vocabulary, we're able to pull information out of an EHR with these CNNs that would have otherwise not emerged had we tried to query the data for the same kind of knowledge. Never would have happened.

There's an incredibly interesting movement afoot, in what's known as polygenic risk scores and there's a great article in MIT Tech Review back in, oh it was February, there's the date. And it said that forecasts of genetic fate are just



about to become a lot more accurate and I'm amazed at how fast this is taking off. It's not even my dedicated time, just what I can manage to keep up with, I'm amazed at what's happening in this space. In essence, the polygenic risk score is using genes that interact additively to influence phenotypic expressions. So in the past, I was involved at Myriad Genetics and University of Utah and LDS Hospital, back in the old days when the BRCA 1 and 2 genes were discovered.

Lots of validity to those and those have proved their worth over time but those are kind of single gene specific, right? And what these polygenic risk scores are finding now, that it's hundreds if not thousands of genes that are contributing to phenotypic expression. And the more data that we have is leading to dramatic increases in score accuracy for these polygenic tests.

So, interesting, in the MIT article, they note that one test described last year can guess a person's height to within four centimeters on the basis of 20,000 distinct DNA letters in a genome. So this is about to take off. Now it opens interesting ethical questions about how much do we want to know about our future fate based upon these polygenic interactions. But I think it's inevitable that we're going to start exposing these. Of course, it has all sorts of interesting downside possibilities to discrimination, and insurance, and things like that. But it's coming and there's no doubt it's going to have an impact.

This article came out very recently and talks about the use of a hybrid recommender in AI to better attribute patients to primary care physicians. So many of you know, all of us know, how hard it is to find a primary care doc, just in general, just any primary care doc that's actually going to take new patients.

But I think we all would feel better about having a primary care doc that we felt good about, that we could trust and there is a sense of homophily in that relationship and that's exactly what these researchers did. And it kind of bums me out a little bit because I've been talking about this concept for a long time, it goes back to my Northwestern days and I was hoping we could implement it commercially at Health Catalyst, but these folks beat us to the punch a bit. We'll still implement it. It's basically eHarmony and Tinder concepts applied to the patient-primary care relationship. And what they found is they can model the sense of homophily and trust and we all know there's plenty of studies that suggest that less primary care churn leads to better health. So I would anticipate seeing more of these kinds of patient-physician attribution models coming in the future.

It's worth noting that there's always this debate about data volume versus AI model sophistication. And in 2009, the folks at Google published in IEEE, a seminal piece around this debate. And there's always this debate: Can a complex AI model overcome a lack of data volume and data features? There was a school of thought for a long time that sophisticated models can overcome a lack of data. But what this paper revealed is that, invariably, simple models and a lot of data trump more elaborate models based on less data.



So it's interesting, right? What it suggests is the AI models are more and more becoming a commodity and it's the data that will make the difference. So if we want to achieve personalized, precision health care we all have to invest in the accumulation of better data about patients. Because AI model sophistication on its own is not going to overcome the limitations of poor data and inadequate data size.

So, AI algorithms are the commodities. The digital platforms and the infrastructure are not. In another paper that was published in NIPS in 2015, also a bunch of the engineers from Google behind this, titled, "The Hidden Technical Debt in Machine Learning Systems," and the point of this paper was to identify and highlight that "... it is dangerous to think of these quick wins as coming for free. Using the software engineering framework of technical debt, we find it is common to incur massive ongoing maintenance costs and real-world ML systems," machine learning systems. And the diagram from that paper indicates the significance of the infrastructure required to pull off machine learning in AI.

The yellow circle in the middle represents the AI code, all the boxes around it represent the infrastructure that's required, data collection, feature extraction, data verification, resource management, the analytic tools, the process management tools, the server infrastructure, monitoring. There's a lot of infrastructure required, and the reason I bring this up, the reason I bring this up, it's not the land of small niche start-ups or home grown systems. It takes a lot of time and money to build up the data and the hardware infrastructure to support AI and machine learning.

And I think there's a little bit of a misplaced love affair with the small niche start-ups and homegrowns, and the belief that you can build these systems on your own and scale this on your own. I just don't think it's possible. Which is why we have to encourage the development of infrastructure that all of us can leverage and take advantage of commercially.

So what I suggest is, avoid the siren's temptation of home grown digital platforms. It's very tempting right now. The public cloud makes the infrastructure an incredibly appealing and affordable commodity. Thank you Google, Azure, AWS. It's amazing what we can spin up in an afternoon now. The hard part is the collection, curation, management of data and the logic associated with that data.

The development of APIs and the applications are tough and I would suggest that we all think back to the old days when we were all going to build our own PCs, right? We could build our own PCs from bits and pieces, better PCs than anything we could buy off the shelf. We all thought we'd be building those. The reality is, that wasn't scalable either and we're all buying PCs off the shelf now. Same kind of phenomenon is existing right now around digital platforms.

So I would say plug your sailor's ears, Odysseus. This is a tempting path to go down but I, again, back to my many years of experience and observation as a



career professional. You cannot scale what we've just talked about locally. It's just not possible.

Alright, that concludes my monologue portion of the webinar. Sarah, do you want to talk about our health care analytics summit a little bit?

Sarah Stokes: Yeah that would be great. Thank you Dale, that was a great presentation.

We do want to ensure that you are aware of our upcoming health care analytics summit. This is an annual event that we host with more than a thousand provider and payer attendees. It's occurring this year from September 11-13 in Salt Lake City, Utah. And we're going to have some brilliant keynote speakers from the health care industry and beyond. You can see them highlighted here.

We are quickly reaching capacity so we encourage you to register soon if you do plan to attend. And then lastly, before we have Dale wrap up and jump into the Q&A, we host these educational webinars for your entertainment and knowledge and today's was focused on the importance of digitizing health care.

But we've had several individuals ask to learn more about Health Catalyst next generation EDWs, the data operating system that Dale referred to earlier. If you'd like to learn more please let us know in the poll question and we will leave this open for just one more minute. It's also a great opportunity, once you respond, to submit any last minute questions for Dale.

Dale Sanders: While the poll is underway, I want to mention that this health analytics summit that we sponsor is not in any way, shape or form, a sales event for Health Catalyst. We purposely avoid that. It's an industry event to bring people together and motivate the progression of data in the industry.

Our satisfaction rate is something like 99.4%. Right?

Sarah Stokes: Mm-hmm.

Dale Sanders: So far it's really been good, and I think it may be even better this year. Every year we get a little bit better with the logistics. The speakers are incredible this year. It's a great event.

Okay, in closing. Drive, don't forget the soft side of this. Our digital strategy must enhance mastery, autonomy and purpose. Don't be fooled into thinking that our data is any bigger than it really is. There's a Freudian complex going on there. We have to push more sensors, more collection of data about patients in order to understand the truth of those patients. We need modern digital platforms, it's overdue in health care by at least 10 years. The adoption of these modern platforms and now is the time to do that.



The data ecosystem is shifting to patients and we've got to be prepared for that. And in fact, not just prepared as if it were scary. We have to accelerate it. It's our obligation to do that as a country.

And debt. AI is going to disrupt health care, there's no doubt about it. But it requires massive data infrastructure and the industry just can't afford for everyone to recreate those infrastructures. We have to figure out a way to make these platforms more scalable and commercially available. That's it. Q&A.

Alright, so let's see here if I can find my mouse, there it is. We'll go to some questions. Thanks for hanging in there everyone. I know it was a long webinar, hence the 90 minutes. Let's see here, let me make, I'll expand these questions. Okay, they're now expanding. There we go, oh no they're not. Alright.

That question, I can't make sense of that one. I'll go to the next one, "One thought about patient engagement is to provide an incentive program, either at federal or state levels that provides monetary rewards. People are driven by money."

I think there's some truth to that. I've always said, half-joking, full truth that I'd like to pay people for their health care data. I'd like to reduce their health care premiums, I'd love at some point to give health care away for free in exchange for people's contribution of their health telemetry data. There have been a couple of attempts at rewarding patients for participating in data sharing initiatives and the results on outcomes have been questionable. But I think like you, I think ultimately, we're homoeconomicists, not homo sapiens and that money tends to drive behavior. So that's what I would say there.

There's another question here, "How ruggedized are the sensors?" Very ruggedized, they're built to take a beating. The interesting thing about them, they don't really have to be ruggedized in the traditional sense. They're so flexible, and they're so thin, they don't lend themselves to breakage through twisting or breakage through compression. So they're ruggedized almost by design.

Another question from Leonard is, "Are sensor data harvesting an invasion of privacy?" Well not to me, it's not Leonard. I would happily contribute my health telemetry data. But I think for those folks who believe it's an invasion of privacy we give them the opportunity to opt out or remove their data.

There's a question here, "The AI generates data informed information," I think totally likely. This is from Steve Witter. "But what we need to do to initiate actions, protocols, modern escalate activity, otherwise don't we have still huge amounts of interoperable data still waiting for patient or care team to initiate activity? Where's the process and work flow interoperability needed to coordinate the massive amount of data into..." Yeah. Where is that model? Well, that's a good question, Steve and basically I'm going to summarize by



saying if we collect all this data how do we turn it into something that we can take action on and that's not overwhelming people? And I think actually that's where AI can play a role, in informing people about, informing decision makers, informing work flow about the best use of their time and the best way to intervene.

A question here, "Is data normalized prior to entering DOS, the data operating system?" No, it's not, Leonard. We purposely retain the precise fidelity of the data before loading it in and then we decide after that, how best to normalize and follow those intermediate data models.

There's a question here from Dee Sams, "How do you think pharmacists play a role in this new digital era?" Well, that's a good question. In other countries, as you probably know, pharmacists essentially can function as primary care physicians.

So I'm going to ask my compadre here, Chris, not to delete those quite as fast as he's deleting them.

Pharmacists definitely need to be trusted more and they quite often know way more about the complications and the effectiveness of different medications, so I think that we underutilize pharmacists and I think they could be utilized to a higher degree than the current sort of patriarchal physician centric view that everything must go through the physician. No order, no medications, anything, everything has to go through the physician. I think that we have to delegate that to more members of the care team.

"Is your book coming out?". Thanks, Randy. I have no time to write a book. That would be fun though.

What's my thought about the digital disrupter question? I think that's a good question, Lisa, you've put me on the spot here. I think it’s none of the above. I think each of those constituents that are currently involved are contributing to disruption, but I think that we have to create what amounts to a parallel health care system that's digitally driven from the ground up. So unless a consortium like Amazon, Berkshire, Chase, unless they get together and build their own health care delivery system from the ground up, I don't think it's going to have a significant disruptive effect. Because we're still going to be dependent upon the health care delivery system that so many of you, as indicated in the poll, have little faith in changing.

So I think that's what it's going to take, I think it's going to take a digital company, with a digital mindset about health care that essentially buys and starts building its own parallel health care delivery system that's targeted straight at the employers, straight at the consumer and disintermediates the traditional insurance model. There are my thoughts. I should have put that option on the question, right?



Sarah Stokes: Hindsight is 20/20.

Dale Sanders: Okay. "If HIM stage 7 suggested data warehousing … achieve that level, why move away from EDWs?"

Joe, I would say that traditional EDWs are too disconnected from work flow. I would argue, I was probably the leader of data warehousing in health care back in the mid-90s. Sort of a Lone Ranger at Intermountain. Over time, I came to realize the disintegration of work flow from the data warehouse propagates the separation of data from action. That's a bad thing. So in modern architectures, the Kappa and Lambda architectures that emerged out of Silicon Valley, there's no distinction between the work flow and analytics anymore and there's no distinction in the architecture, and I think that's why we have to get there. So the data operating system is really a modern data warehouse. If you go back and you look at the architecture of our data warehouse at Northwestern that my team and I built. It looks a lot like the concepts that we now call a data operating system. It has all the features and functions of a traditional data warehouse but you can also write applications on top of it that support work flow. So that's what I would say there Joe.

Okay. Paul Nelson, "Do you have/know of a thorough definition of health?" No, I don't Paul. I know my definition of health is a balance, combination of physical, emotional and spiritual health. Being able to do the things that I want to do, being able to interact socially and emotionally with people in a healthy way. That's how I define health. And it's evolving over time as I get older. I'm not quite as fit as I used to be so the emphasis on emotional and mental health is probably growing as opposed to earlier days of physical health.

Okay. Daryl Burton asks, great question here, "I'm surprised you didn't mention..." I should have, I didn't mention the potential for block chain technology to break the stranglehold that EHRs have. It's fascinating, isn't it Daryl? There's no doubt in my mind as a technology, that's a good point, I'm going to put a slide in this deck as I share at another audiences about block chain.

I have some opinions about block chain. It's fascinating, it's going to be more disruptive than I think any IT since TCP/IP and HTTP. But it has some significant drawbacks in the TRES networks that are required to implement it. Of course, at a very technical level, the throughput processing capability of block chain is way too low right now. You can measure transaction rates in a few transactions per second right now rather than tens and millions of transactions per second, tens of thousands and millions of transactions per second. But you're right, I think it's a little over hyped right now. It has potential and it will be disruptive but it’s almost too disruptive to be honest. It's almost too disruptive. I think it's going to have to start smaller rather than at the top trying to disrupt the system from the macro level. But I'll resolve to refine my thoughts on that and be better about expressing those. Thanks for asking.



"You mention that two-thirds of patients often don't want to be engaged. What are some of the strategies you found to be helpful to engage patients?" Well, Sally, that goes back to mastery, autonomy and purpose. If you're clearly engaging with me, in a way that supports my sense of mastery, autonomy and purpose, then I'm likely to be engaged. But I don't think we give it enough deep thought about how to do that. I'm a good example of that, right? I'm a patient of Intermountain Health Care, thankfully I'm pretty healthy. On the rare occasion that I do need treatment, and follow up, they tend to bombard me with phone calls, text messages, emails and things like that, to the point that I just check out. I don't get delivered to me anything that contributes to my sense of mastery and autonomy. Mastery of my own health, and giving me more autonomy so that I don't have to depend on those reminders and alerts and interactions with Intermountain.

I want to do less and less of that. So maybe if they would promise me an extra hour and a half a day in my schedule, somehow, that would give me time to spend more time with the kids or exercise, then that might feel like I've got mastery and autonomy. Maybe if they would deliver healthier lunches to me free of charge maybe that would help. But I think our current strategies for engaging patients are, for the most part, creating disincentives to participate rather than more incentives. I'll give that some more thought too, thanks for asking.

Let's see here. Let me skip around and try to sample a couple of other... here's one from Teresa Girard, "Describe what skills and experiences would contribute to making the best digitician." Great question and I think the best role model we have for that right now is probably the medical informaticist, which is a combination of clinical training and data training. Data skills and clinical skills.

I'm going to give an example of how we approached this in the Air Force when I was involved in space operations. When we were building out the plan for a spacecraft, whatever it was, a satellite or a reusable spacecraft, whatever. Or even an aircraft, one of the first things you would do in the design of that spacecraft is decide what kind of telemetry you wanted about the mission of that spacecraft. So if it was overhead surveillance, for example. You'd want, depending on the type of surveillance, you'd want to collect a telemetry data profile that was unique to that payload, unique to that spacecraft.

And it required an interesting combination of the electrical engineering, knowledge of the spacecraft, knowledge of the spacecraft's mission and then some understanding of what you're going to do with the data, after you get it, and by the way, it’s one thing to have telemetry about a spacecraft. It's another to have a strategy to adapt and send up link commands to that spacecraft and intervene based upon the telemetry you collect. Somewhere in that I think is a pattern to apply to health care. I think I could actually serve as a pretty good digitician even though I'm not a clinician. I've got background in chemistry and biology, I've got a background now pretty rich in data and information systems engineering. I think a person like me could cross those boundaries pretty well. I



think a nurse with a deep data background and analytics background could serve that role very well. It would be interesting, let's write a job description for a digitician. If I were still participating in health care operations, I would do that. Remind me, let's write a job description for digitician. That's a good idea.

Okay. Naresh asks, "Are PAIRS in favor of accelerating the adoption of sensors?" I'll be very blunt here. I don't think PAIRS are in favor of doing anything that reduce costs or I don't really think they have great interest in improving quality unless it improves their bottom line. I don't have much of any love affair at all with our current general insurance model in US health care. There are just no incentives for the insurance industry to do anything but improve their margins and their bottom lines and if they can't do that, they pass on premium expenses to the rest of us over and over again. So they never actually feel the economic pain of bad quality and higher costs. They just pass it on to us. They might show some veiled interest in it, Naresh, but I don't think it’s very deep. And I would love it if someone out there in the insurance industry would refute that and prove me otherwise.

There's an interesting article in Wired magazine. You can Google it, I think I tweeted it today, actually this morning, about Oscar, the insurance start up that's operating in a couple of states, primarily in New York right now, and their approach to data. They have a pretty good vision around the use of data to improve the health care industry, but their fundamental insurance model really isn't any different than the existing model so my heart would love to believe that Oscar is a different kind of insurance company, but my brain reads what they're doing and it feels kind of incremental.

Okay. We're out of time. I'll answer one more question, "Do you think that it will be a patient who owns his data sometime in the future?" Miriam, I believe it's fundamentally important that we own our data in the future and that we shift the economic model accordingly, so that we become empowered with data and we can shop that knowledge around care providers who will make the most of it and engage with us at that level. It'll be interesting to see how that evolves. We will certainly own the generation of that data as we use sensors and things like that. The question is, will that be fed into a health care provider's platform and then will they consider ownership? I don't know. I hope that maybe there's legal work ahead to ensure that patients own their own data.

Alrighty. That's it friends. Thank you so much. I enjoy it. Keep doing great things out there and sorry that I couldn't get to all the questions.


transcript by rev · intermountain, academic medicine at northwestern, and then population health...

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