Month: January 2020

We give hospitals the ability to quickly and securely send patient records to outside clinicians

Episode #35

Podcast with Dr. Peter Tippett, Founder and CEO, careMESH

"We give hospitals the ability to quickly and securely send patient records to outside clinicians"

paddy Hosted by Paddy Padmanabhan

In this episode, Dr. Peter Tippett, one of the first person to develop a commercial antivirus software, discusses how careMESH is providing easy and secure communication and collaboration between clinicians locally to share digital patient records. Peter also discusses the issues related to information security in healthcare.

Healthcare, like in other industries, requires digital communication in everything, be it care coordination, patient safety, reducing readmissions, unnecessary ER visits, or analytics. To address this marketplace requirement, careMESH makes a set of national secure service that helps health systems to easily communicate about patients and share patient records from their own EHR to any outside physicians/ clinicians, reducing the time consumed by traditional communication ways within health systems.

According to Peter, health systems have started investing in and adopting digital transformation to provide ‘virtual health’ through their own EHRs, EMRs to provide care coordination, social determinants, and enabling home health workers for patients. These health systems are the powerhouse, incubating the innovative startups and providing them the focus they require to make the change happen in healthcare.

Welcome to The Big Unlock podcast where we discuss data analytics and emerging technologies in healthcare. Here are some of the most innovative thinkers in healthcare information technology talk about the digital transformation of healthcare and how they are driving change in their organizations.

Paddy Padmanabhan: Hello again everyone. Welcome back to my podcast. This is Paddy and it is my great privilege and honor to introduce my special guest today, Dr. Peter Tippett, Founder and CEO of careMESH. Peter, thank you for joining us and welcome to the show.

Peter Tippett: Thank you so much. It’s a great privilege.

Paddy Padmanabhan: Thank you. So, Peter, you have a very interesting background. And among other things, you are also the first person to have developed a commercial antivirus software. So, tell us how all that came about.

Peter Tippett: Well, I was one of those tech engineering nuts even when I was a teenager. I was a ham radio operator and a commercial radio engineer and a pilot. I was one of those couple of kids that were allowed in high school to touch the 55-board teletype locked in the closet. And in college, I stumbled into more things. I used a very similar computer in a lab doing really early cholesterol and Hyperlipidemia work. And I used it to automate their analysis and results. And then for my college seasons, I wound up as an apprentice and an assistant for two different Nobel Prize winners. The first guy sequenced the first protein and the second guy, Bruce Merrifield, synthesized the first protein. And I was there, you know, and used computers in his lab to automate that whole process. And along the way was the first guy to synthesize an active immunoglobulin. And of course, all of that got me a scholarship for an M.D. Phd at Case Western Reserve. And then when I was at Case, I was President of the Cleveland Computer Club. I started a software company in my attic trying to do other sorts of things. And when the virus problem came along, I created the first commercial antivirus. It was called Vaccine, but eventually changed its name. We wound up in a booth a few booths down from Steve Jobs at the West Coast Computer Fair. We grew that company, which was called Certus, for a couple of years before McAfee and the other guys came along and we sold it to Symantec and renamed it Norton Antivirus and then grew it in two more years past 300 million bucks. It was the big heydays that everybody likes to hear about.

Paddy Padmanabhan: Yeah. Well, Norton Antivirus. Now, that’s a household name almost.

Peter Tippett: Well, that’s a lot to do with those guys now of course.

Paddy Padmanabhan: Well fascinating story. I do want to spend a few minutes, given your background with software security and antivirus software and so on. I do want to spend a few minutes on this podcast talking about the current state of cybersecurity. You know, healthcare has been the target of cybercriminals for several years now. And my understanding is that it is the favorite industry for cyber-attacks. I read somewhere that the annual cost of healthcare data breaches in the region of four billion dollars and there’s no sign that is abating anytime soon. And four out of five data breaches are attributed to healthcare data breaches, and providers, in particular, are being singled out for these attacks. So, can you kind of break it down for us and tell us what the big issues are today as it relates to information security in healthcare?

Peter Tippett: Yeah, absolutely. Obviously, security is a huge subject. Maybe I can talk you into doing a whole podcast on it down the line. But, you know, security is hard, but it’s really not as hard as we all give it credit for. I’m kind of a scientist in this world and I spent a lot of energy over the last 20-30 years trying to get a sense of how the risk economics really work. And my biggest take home over the years is that we’ve really typically get talked into putting what my mom says is putting the emphasis on the wrong syllable. We have spent a huge amount of money and user equity on things that have very low marginal value and we ignore and allow the simple, inexpensive things that are relatively easy. For example, you mentioned ransomware. The basic solution to that is backup. Nothing fancy. Right. And oddly, using some of these newer information sharing services like my company’s new careMESH offering that gets some of your data accessible in other ways, all by itself is a mitigation for things like ransomware. If you look at the breach science and look at how that works out in risk dollars, there’s really just two things that reduce the overall costs and risk and likelihood of a breach by vast of the majority than all other things combined. The first one is a strong identity, despite what everybody says, making passwords stronger or more complex doesn’t do a squat. But adding a second factor like the code that comes to your phone or a token or whatever, that reduces risk by many, many orders of magnitude. So, turn those things on. That’s really simple. And it is really, hugely strong. The other thing is around network management. Running your own data server and data centers and firewalls and all that stuff is hard and expensive and we’re all error prone. But any one of the cloud providers has a hundredfold more security and ops people than any IT health organizations does. And you know, they have the experience, use the cloud and embrace it. Those are the key issues.

Paddy Padmanabhan: Yes. You know, just coincidentally this morning, I was on a Twitter chat with a group of cybersecurity professionals and a couple of things came out of that discussion. And these are very commonsensical type of things. The two big issues that the participants in the chat pointed out were, one, it’s a cultural issue, less of a technology issue, more of a culture issue. And really educating everyone in the organization at every level to be watchful of phishing attacks or to your point, turn on two-factor authentication. It’s a cultural thing. And so, you’ve got to have the right kind of culture to protect yourself against cyber-attacks. The second thing they talked about was in the context of healthcare the business associates are a big point of vulnerability. So, care to comment on those two observations?

Peter Tippett: Yeah. I mean, you know, the power is clearly in the hands of the attacker if you’ve got a million people and you can succeed at one percent opening a phishing email. That one percent is in trouble. So, a big attack surface is how we talk about that. But two-factor authentication works even against phishing attacks. The bad guy gets your password. So, what? Still doesn’t work. So really, really, you have to do both. Don’t spend all your energy worrying about one thing. You need seatbelts and airbags and then speed signs and all the others, and they all work together to really reduce things.

Paddy Padmanabhan: Yeah, OK. I kind of agree with you. We should do a separate podcast down the road just talking about cybersecurity issues. But for today, let’s switch gears here and talk about your company. Tell us about careMESH briefly, the company and the solution you’ve developed and what does a marketplace need you’re trying to address?

Peter Tippett: Yeah. Thanks so much. I’m a doctor. I spent most of my time doing emergency medicine and paying for all these startups by working at night in the ER, but I’ve long been frustrated that doctors can’t simply send a patient record to some other doctor or to some other clinic. It’s like we’re in the years before the internet ever came along. A huge hospital system client of ours reinforced that for me again lately. They worked at this giant referral academic center. They take care of 20-30 percent of their patients come from more than 25 miles away. You know, when they send those patients back home, half of those doctors get a two-page fax. And the other half get two pages sent to them in the US mail. I’m not kidding. The likelihood of getting a digital record outside of that 25 or 30-mile range is nearly zero. And this is not right This isn’t how the world should work. It drives the doctors nuts on both ends. It drives the patients crazy. Even the average hospital is not at that pinnacle of referrals to the world where two-thirds of their community doctors are using a different EHR than the hospital. Less than 20 percent of those outside doctors routinely get digital-only useful patient data. And almost none of them can communicate back and forth with a big hospital or the doctors or whatever. So careMESH came along to change all of that. We decided to make a set of global national, you know, secure services that don’t require complex IT infrastructure. So, hospitals can easily discharge patients or send referrals right from their own EHR to any physician or practice in the country and not make that other end, have to do anything or buy anything or even know who the heck careMESH is. Like when you send a FedEx, they came along with the idea, which was, give it to us and we’ll get it wherever it needs to go, even if it’s on some weird island somewhere that’s our problem. So, hospitals should be able to simply look up a patient in their own EHR enhanced by our national careMESH provider directory and push the send button or the complete button. So, hospitals can also automate the setting of detailed admission and discharge summaries, not just ADTs and PIDs without requiring the recipient to submit patient panels or log into portals or pull lists of patients or other things like that. So, careMESH is a solution like none other available in the healthcare industry, giving hospitals the ability to quickly and securely send patient records to any outside clinician. Of course, we want to completely embrace the new cloud, compute models and strong identity and modern high-end security and privacy and all that and make those problems go away as well as further participating hospitals. And any big platform can do a lot more than just sending records from hospitals or getting two-way communication going or keeping things digital. Because hospitals need to be able to efficiently share data outside their walls. Care coordination, patient safety, reducing readmissions, unnecessary ER visits, analytics, you know almost everything requires digital communication. So, we want to be complementary to the stuff that already works like HIEs and EMRs. But they just don’t work well enough.

Paddy Padmanabhan: So, it seems like there’s two aspects to what you’re trying to do. One is having a robust provider data management system, process platform where you can go to it as a single source of truth. And it really is the truth as it relates to providing data and then using the same platform or related functionality as a draw on the platform, you’re using it for care coordination, doctor-patient communication and so on and so forth. Am I right? Are these two broad components of your platform?

Peter Tippett: Yeah. We think of it as finding the doctor in the first place or the clinic. I want to send a message to Dr. Smith in Salt Lake City; the patient just knows Dr. Smith right. And figuring out which Dr. Smith and making that part easy from within your own EHR for whoever the clerical or clinical person is. That’s the directory problem. And then once you find the person making it so that just doing whatever you normally do. You know, a doctor’s order to discharge and a clerical person following up with the pieces need to happen to get the record out there or the doctor going into the messenger or the basket or whatever it is in their EHR and finding somebody that’s outside their building and saying, you know, asking them a quick question or something. That’s the directory problem. Then once you find the person, you want all the natural things to happen so that when you hit the complete button or the send button, they actually receive the message and it works. And it’s digital and it helps them at the other end as well. So that’s the delivery problem. Of course, it’s not as easy as all that you’ve got to HIPAA get going and compliance and interoperability and make it easy on the other end and make the reimbursements all happen. Big compliance and incentive payments from PI and all that stuff work. But yeah those are the main two components.

Paddy Padmanabhan: Yeah. Let’s talk about the competitive landscape that you operate in. Provider data management has long been an issue in healthcare. If I recall it right, it’s like a three billion-dollar problem or something like that. There are lots of companies trying to address it and using different technology. You know, there’s one aligned group of companies using blockchain, for instance, to create a single version of truth among other things. And everyone every doctor that I’ve talked to would love to have this single source of truth where they don’t have to keep on credentialing them again and again. They go to this one place where, you know, everybody has it all in one place, and it’s all a single source of truth. But it is a competitive landscape and lots of people are trying to solve this problem as well. At the same time, it comes to the other aspects of your platform, the care coordination, the messaging for the EHR vendors, Epic, Cerner, big tech firms. How do you see yourselves in this competitive landscape and what do you think makes you a little bit different?

Peter Tippett: Yeah. The technology like blockchain versus not seems to me to be pretty relevant. The most important thing is, as you said, figuring out how to solve, I call this “the surround problems.” I wrote one of the chapters in Ed Marx’s book on innovation – ‘Voices of innovation.’ And I know you’re working a little bit with him. What a great project you guys are working on. And it seems to me that things that actually get the job done when there’s a huge legacy installed base of things is not trying to fight the installed base, but trying to complement it to work within the system that’s already there and figure out how to extend it relatively easily. The trend of making programs to decide you’re going to blow up whatever is there and start over again is kind of crazy. So, if you can make a directory, you know, ours is FHIR enabled and it’ll work through a browser or a phone or any of that. But that doesn’t help the hospital. You need to make it so that it just becomes the natural directory that’s used by all the services that already use the directory in the hospital like your Epic in basket or the discharge floors or whatever. It doesn’t make that disappear so that no workflow changes happen. And then you’ve got the other issue. But when you get to the competitive things, I think of this as healthcare is wildly local and always has been. And the technology that follows it has been local as well. So, it’s been really easy to hire a big contractor and spend a million bucks hooking your hospital at the other hospital. After you spend a year planning and you’re doing in a year fixing, it works. But now you’ve got two points connected. Well, you know, if you do the math, there’s five thousand plus hospitals and two or three hundred thousand clinics. That would be two hundred three hundred thousand factorial connections and BAAs and all that. That’s by the way, more than our grains of sand on earth. So, this is stupid. This isn’t something that could possibly scale. So, what we need is analogous to what we got when we built the internet. We need a way that everybody can use the same network for all of the basics to not to find the other guy, but also to get something to them without file size limits or anything like that. We need something that works with the EMR vendors and the HIEs and extends their functionality naturally. And we need something that enables all the care coordination platform. I don’t want to build a care coordination platform. I just want to make the ones that are out there actually work for somebody who isn’t involved or some other end that didn’t buy the other end. Making everybody buy both ends of a fax machine or a telephone is nuts. That’s not how those industries evolved and ours can’t get there either.

Paddy Padmanabhan: So how do you build a business case? I understood what you said that you’re working with the existing technology stack solutions that are out there and making them better. So how do you actually build a business case? What do people look for when trying to justify investment in your platform?

Peter Tippett: Yeah. I was on the PITAC, the President’s Information Technology Advisory Committee. I know it’s going on 20 years ago with Baylor and that whole gang. And we said if health, you know, this is a triple aim, in my words, slightly. If health care could only use information technology in rough parity with, the banking or other industries would get three things right. We’d get wildly healthier people and better long lives and all that. We’d get wildly lower costs to our study in the PITAC showed about 70 or 80 billion dollars a year. But the Institute of Medicine came along and did the big study and came to 700 billion dollars a year of savings for the country. And we get an entirely new kind of science. But other than that, it’s, you know, it’s probably not worth doing. So, we’re all married to this, right. And we now have computers everywhere. But there is pain. Everybody hates them. That’s largely because we haven’t had this sharing in the internet part that makes that work. So meaningful use came along we checked our 25, 15 or 20 or 10 or 16 boxes and got our checks. And now it’s switched to PI, promoting interoperability. And the PI penalties are real. Two of the six criteria are called referral loops or HIE measures or, you know, getting your care coordination going. They explicitly require getting of facts for a large proportion of referrals and discharge and transitions of care out of your own organization, 40 of the 50 points you need for PI and that’s 2 percent or 3 percent of your hospital payments from Medicare. So that, you know, for a medium or a bigger hospital, that’s 5, 10, 15 million bucks a penalty. So, there’s real meat now behind some of those and those the screws are tightening a little bit on that arena. And so, there’s some value there. We see the biggest value for getting this working, you know, the two thirds or three quarters or whatever it is of doctors and clinics that don’t work for you in a hospital. We really need to coordinate with these guys. In the past, we’ve ignored the people on the other side. But now that we’ve fixed the inside and it’s possible to do all the basics in the hospital, now it’s time to sort of extend. I hear this all the time from the CIOs. We’ve spent the last five years making this work at all. Now if we can only get the outside provider’s data and get them engaged and make it so that their job is easier and maybe make it so that they get some PI benefit or efficiency benefit, we’re still spending a huge amount of our time on the telephone and waiting around for the other doctor to talk to the other doctor or hiring a massive care coordinators to call and to show up at eight o’clock every morning and dial for dollars. And this is all nuts. This is 20 years ago. The internet fixed that for other industries. And it’s easy enough to find the efficiency value of tightening up your referral network and getting above 50, 60 percent referral leakage. And, you know 2, 3, 5 percent improvements in referral leakage add up to many millions of dollars of new revenues for a hospital.

Paddy Padmanabhan: Yeah, it’s very interesting. You mentioned banking and you mentioned how other industries are much further ahead. And John Glaser, who is the former CIO of Partners Healthcare, who is on my board of advisors, he wrote an article about this in the Harvard Business Review, where he pointed to this exact same contrast between banking and healthcare. And he makes the argument that you don’t have to do the whole hog, do everything the banking has done. But even if you do it selectively and move the needle, their significant gains to be had. And one of my other guests on the podcast, Daniel Barchi, who is the CIO of NewYork-Presbyterian, he made a very telling comment, he said we have really low thresholds today for digital engagement in healthcare. If somebody uses an online platform just to schedule an appointment that counts as digital engagement and that counts towards digital-enablement patients, and it can qualify you PO points for all kinds of incentives or conversely, penalties as the case may be. Healthcare I think is very unique in that regard because it is a system of incentives and penalties that are driving in many ways digital adoption. Is that a fair statement?

Peter Tippett: Yeah, I think so. You know, I think that the regulators have the right end game in mind. And I think that the knobs are roughly aligned and reasonably aligned. But nobody no business aligns themselves around regulatory incentives unless it’s also valuable to the business. I’ve had I can’t tell you how many CIO discussions I’ve had where they said, why aren’t you worried about this three-million-dollar penalty? And the answer is, if I spend so much of my energy worrying about that I wouldn’t do my business. We have to solve our real problems inside the business. And if we can make it align with getting two or three or million dollars or 10 or whatever it is, the feds fine, right? But it can’t be the principal driver. And so, the argument in banking is they’ve got a simpler data set than we do in healthcare, and that’s true. But tearing things down to the simple issue, you know, meds, problems, allergies, and demographics get that actually working, make it actually digital and get it sharing in both directions and make it work easily, whether at the other end is using a browser or there’s hundreds of EMR as it might be when your brother in law invented and there are twelve other users in the country that you still have to make it work with whatever the other guy is using and getting down to the basics and making the communication work at a really basic level is the key. And you know, once the basics are working, it’s easy enough to extend those a little bit.

Paddy Padmanabhan: Yeah. So, we are at the close here Peter. I would just love to hear your thoughts on what you’re seeing, your customers and health systems, in general, investing in as it relates to digital transformation. What are the top two or three things that you think that you see them focused on?

Peter Tippett: Yeah, I think that as a community, the health systems and IT activities in hospitals and bigger health systems has gotten the inside job pretty well under control. They are feeling like they’ve got, you know, actual functional EMRs, EHRs that actually do the basics and people are being productive with them on the inside. And so, I think there is a view towards the outside. We call it different names of call care coordination, we call social determinants, we call it enabling, you know, the home health workers, all those. We get lots of different names for all this stuff. In the end, it’s very virtual health. It’s getting, you know, getting the communication working. In the case of B2B, getting it working among providers means that you don’t have to force the patient to carry the record or come get it or be the middleman. And everybody wants the patient to have the data and be able to deal with it. But none of us make it. It doesn’t make a lot of sense to force the patient to be the connectivity link. So, I think that we’re getting towards this place in our world where we are enabling the communications. These platforms like carequality and the national sharing platform they’re getting some traction. The vendor platforms by Epic and others, they’re getting good traction. They enable good pieces of what needs to happen. But they don’t enable two-way communication. They don’t enable messaging. They don’t often enable giant things like x rays, sharing or other pieces. They often don’t enable a little guy very well on the Oddball platform. And so, you know, providing the glue that sort of fills in the gaps between the stuff that does work seems to me to be the place to be. And I think the venture community and the venture incubators and hospitals and health systems and those kinds of groups, they’re really a powerhouse. They’re the ones that can get the little startup guys and the new innovation guys. They can keep them on track. They can give them the focus they need because all kinds of people have good ideas. But all of us inside, you know, we largely are scientists in this world and businesspeople and the venture world in an incubator, they’re supposed to be experienced. And the good ones do help focus on actually making the change happen.

Paddy Padmanabhan: That’s said. In fact, in my recent podcast, I had a couple of senior executives from Epic and kind pretty much said the same thing that you just said, at least in terms of their product, focus on their platform, focus in terms of facilitating the seamless exchange of information, if you will. Well, Peter it has been such a pleasure speaking. There’s a lot that we can talk about and hope to carry on with the conversation and have you back on our podcast sometime soon. In the meantime, I wish your company, careMESH, and your team all the very best and look forward to staying in touch.

Peter Tippett: Great. Thanks so much.

We hope you enjoyed this podcast subscribe to our podcast series at and write to us at

About our guest

Dr. Peter Tippett is Founder and CEO of careMESH, former Chief Medical Officer of Verizon, and a leader in Health IT transformation, information security and regulatory compliance. Among other start-ups, Tippett created the first commercial anti-virus product, which became Norton, and founded TruSecure and CyberTrust. He was a member of the President’s Information Technology Advisory Committee (PITAC) under G.W. Bush and served with both the Clinton Health Matters and NIH Precision Medicine initiatives.

Tippett is a physician, board-certified in internal medicine, and was Research Assistant to R.B. Merrifield (Nobel Prize, 1984) and Stanford Moore (Nobel Prize, 1972) at Rockefeller University. He received a PhD in Biochemistry and an M.D., from Case Western Reserve University, and a B.S in Biology from Kalamazoo College.

Throughout his career, Tippett has been recognized with numerous awards and recognitions — including E&Y Entrepreneur of the Year, the U.S. Chamber of Commerce “Leadership in Health Care Award,” and was named one of the 25 most influential CTOs by InfoWorld.

About the host

Paddy is the co-author of Healthcare Digital Transformation – How Consumerism, Technology and Pandemic are Accelerating the Future (Taylor & Francis, Aug 2020), along with Edward W. Marx. Paddy is also the author of the best-selling book The Big Unlock – Harnessing Data and Growing Digital Health Businesses in a Value-based Care Era (Archway Publishing, 2017). He is the host of the highly subscribed The Big Unlock podcast on digital transformation in healthcare featuring C-level executives from the healthcare and technology sectors. He is widely published and has a by-lined column in CIO Magazine and other respected industry publications.


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We essentially see ourselves as stewards helping clients manage their data

Episode #34

Podcast with Seth Hain, VP of R&D and Sean Bina, VP of Access and Patient Engagement, Epic

“We essentially see ourselves as stewards helping clients manage their data”

paddy Hosted by Paddy Padmanabhan

In this episode, Seth Hain, Vice President of R&D and Sean Bina, Vice President of Access and Patient Engagement at Epic discuss the next wave of opportunities for Epic and how the company has evolved by focusing on patient experience and advanced analytics.

At Epic, the focus has always been on providing patients with access and tools to view and have control over their data. Epic works with over 300 health systems today to help them manage their data. Over 160 million consumers have or are using its MyChart patient portal which has been around for nearly two decades. The company uses advanced analytics such as AI/ machine learning and monitors how they are performing on different populations before embedding it into workflows, be it clinical-facing or patient-facing.

Epic has lately started focusing on providing transparency around healthcare costs and has been working on creating accurate estimates for patients so that they have price transparency at the point of care.

Welcome to The Big Unlock podcast where we discuss data analytics and emerging technologies in healthcare. Here are some of the most innovative thinkers in healthcare information technology talk about the digital transformation of healthcare and how they are driving change in their organizations.

Paddy Padmanabhan: Hello again, everyone and welcome back to my podcast. This is Paddy and it is my great privilege and honor today to introduce my special guests. We have two of them today, Sean Bina, who is a Vice President for Patient Experience with Epic and Seth Hain, who is VP of R&D for Epic. Seth and Sean, thank you so much for joining us and welcome to the show.

Sean Bina: Thanks for having us.

Seth Hain: Thank you.

Paddy Padmanabhan: You are most welcome. So why don’t we get started? Maybe you can give us a little bit about your background and your current roles at Epic for the benefit of our audience.

Sean Bina: Yeah, so I’ll start. So, this is Sean and I started at Epic 23 years ago doing implementations of our systems. And then over the years, I’ve worked with a variety of our different products and now focus my time on the patient experience. And that’s really my goal, to help patients get connected into their health and wellness in a way that they’ve never been able to before.

Seth Hain: My name is Seth and I started a little after Sean. I’ve been here for about 15 years and when I came to Epic, my focus was on architecture and the kind of systems infrastructure behind the scenes. And then I combined that with my prior experiences around mathematics and focused on and continued to today the research and development around analytics and machine learning and in particular embedding that type of intelligence into workflows, be it clinical facing or patient-facing, for example. And the tools that are used throughout the system for analytics and machine learning.

Paddy Padmanabhan: And between the two of you, I would argue that you are looking after the top two focus areas for health systems today – patient experience and advanced analytics. So, I’m looking forward to this conversation. I want to start with this, now we have near-total penetration of electronic health record systems in the country today. Of course, it’s been a great run for Epic as leaders in the market. So, what are you seeing as the next wave of growth opportunities for Epic?

Sean Bina: So, we see a few different areas where we’re continuing to do a lot of work. One is we just continue to work with our customers to expand the adoption of Epic and then to add new additional modules. One of the things that we’ve learned over time is that every specialist needs a system that’s really designed specifically for them, whether they are core things that all physicians and clinicians use around ordering and reviewing results and doing some basic documentation. But then there’s a lot of subspecialty support that’s needed for doing things like managing images, for registry support and for doing the kind of specific documentation within a given subject area. So, we continue to go deeper and deeper into those areas to create a great experience for physicians. The one thing I would add is when we look at the industry in general, we do see our customers are really focused on three core areas. One being patient experience, two being analytics, but then three is really bringing the joy back to practice for physicians.

Seth Hain: I would also add that the space around healthcare continues to change and evolve. And I think there’s a lot of opportunities as we start to look at and see more collaboration between, say, payers and providers in the space. There is an opportunity there to help facilitate faster exchange that benefit both the provider and their workflow, but also the patient and the care they’re receiving in a timely manner. And that expands as you start to think about a broader definition of health. Thinking about things like dental, long term care, even where people get care, be it a telehealth encounter through, you know, they pay from the app directly on their phone, or they’re in a retail clinic and they just need to swing by for a flu shot and making sure that’s a continuous experience from a health perspective.

Sean Bina: The other thing that we continue to work on is we continue to move internationally. So, you know, outside the U.S., we work with an increasing number of countries on our software and there’s kind of a wonderful cross-pollination that we get as a result of that. So, for example, we’ve been working a lot with Finland and they’re very focused on social determinants of care, really, really focused on reducing the need to get into the hospital or readmissions into the hospital. And so, a lot of the work that we’re doing with them around their social care system then ends up benefiting our U.S. customers.

Paddy Padmanabhan: Right. Right. And we’ll unpack some of these, especially around the emerging tech stuff in the context of digital transformation, which is what we mostly focus on as a part of this podcast. So, let me ask you a simple question. What is your understanding of the term digital used by everyone? And almost everyone has a definition for it. And how do you define digital? What is your understanding of the term and how is it impacting all your choices or investments in health systems, specifically from your point of view with your clients?

Sean Bina: Yeah. If you don’t mind, we take a quick trip back down memory lane. You know, when I started back around 20 plus years ago, the world was very different in terms of what was available from a digital perspective. You know, there weren’t integrated ambulatory inpatients solutions. You couldn’t do an end to end revenue cycle system that covered all your hospital and clinics. And so, people were using best of breed systems and trying to cobble systems together to manage all of that. And until around 2003, there wasn’t even such a thing as a patient portal. So, one of the things that when I think about what it means to be digital today, it’s obviously changed over the years. But I think it’s important to remember how far we’ve come that we now do have fully integrated systems that cover all the kind of food, warmth, and shelter that is needed by healthcare organizations. So that now we’re getting to the point where we can do a little bit more of the poetry.

Seth Hain: I think that depending on who you ask or the definition of digital transformation, you tend to hear a pretty different perspective. Some people immediately come at it from the patient experience perspective and the possibility of having access to health care through your phone at any point in time. Others and I tend to take a foundational view of systems perspective on some of these topics. You know, start to also bring in things like cloud computing behind the scenes and the running of machine learning algorithms on data that is flowing into the system from a combination of devices. Then being able to be back into wherever the provider might be so that they can be sure that in the ICU, be that walking down the hallway so that they have a better-informed picture of what patients they might want to spend some time with at that exact moment in time. So, I think that gets to more of the transformation point in building on what Sean was saying.

Sean Bina: Yes one other key element of digital is the interoperability piece. So, if you go back to the year 2005, 2006, 2007, interoperability was a fax machine. We’re now passing five million records a day around the country and it’s starting to be around the world for patients. And so we’re really starting to see where physicians have gone from thinking of records from the standpoint that I have my record at this site versus there are other records at other sites to wanting to have combined digital views of all of a patient’s information pulled seamlessly together.

Paddy Padmanabhan: Yeah, I think we certainly have seen a significant amount of progress on interoperability. But I just wanted to make one observation. You know, when you look at the past decade or so, well, the single biggest thing that happened in terms of digital transformation was really the digitization of medical records. I remember working with paper records with my physician 10 years ago, and it so happened that he was fighting tooth and nail about going digital. But if you really look at it, I don’t think any of the sectors has seen the kind of transformation that healthcare, in particular, has seen, just by virtue of digitization of medical records. So now it looks like we’re, you know, phase one of the mission has been accomplished. Now they have the strong foundation of digital records. And so, everyone seems to be talking about what do we do next with it? Advanced analytics, building better experiences, looking at data from multiple sources and so on. So, in that context, the whole competitive landscape is also changing. So I want to probe a little bit on how you see Epic evolving in the context of this emerging landscape of technology players and the evolving needs for health systems, as they compete with a whole different marketplace with a lot of nontraditional competitors in all kinds of other things going on. Do you want to comment on that? How are you evolving and what are the changes you’re seeing your clients go through and how are you evolving in step with that?

Seth Hain: You’re getting back to that kind of world of transforming from a paper chart to a kind of maybe a desktop PC where somebody would go to get information, I think is an interesting analogy to kind of transformation we’re seeing right now where in many cases it’s not about the chart, right? It’s about a continuous health experience that folks are receiving. Be that a patient or a provider. So, we see somebody like Rush down in Chicago who builds automated workflow to understand as patients come into the E.D., their likelihood to leave based on machine learning algorithm using inputs from a variety of different sources, not just the medical record, and then use that to help drive workflows where they can walk around, touch base with the patient, let them know where they’re at from in my perspective, to see a nurse or a clinician. And they saw a drop of about 50 percent in folks leaving without having been seen from the emergency department. So, you start to see a different type of transformational workflow emerge that isn’t based around a single machine but is more driven by a backend kind of ubiquitousness of data accessibility from a cloud perspective. And then differing devices be those iPads being used for rounding or watches used to alert physicians in the ICU of patients that might be at risk of deterioration from based on a machine learning model. So, it really starts to transform how clinical practice is being given some of the financial aspects as well, are also are being looked at.

Sean Bina: Yeah, I would just add from the patient perspective, I think they don’t think in the same way as they used to that I have a record at a particular healthcare organization. Increasingly, they’re thinking about their health and wellness and how that includes what’s going on their Fitbit, and on their Peloton and on the medications that they’re taking. And so, they’re looking at a much broader ecosystem of inputs. And I think the expectation is growing that what healthcare organizations are going to be able to do is take all of those inputs, pull them all together, and then provide recommendations based on a much broader set of data than that’s ever been data available in the past.

Paddy Padmanabhan: Yeah. And my firm did some research back in the summer of 2019. On the current state of digital transformation in healthcare, and what we found was that over half of the health systems that we polled in the study were looking at the electronic health record platforms systems as a starting point for the digital transformation. One of the reasons was that integration aspects in pulling data adding it all from within the workflow of an EHR system is easier to do. And when you talk about digital transformation, people are talking about integrating data from multiple sources. But it’s still a lot of integration work that is involved here. I want to switch at this point to talk about the data itself. You know, your obvious strength for Epic is in the data that you have access to, all the patient records that are being processed through your system, across all these health systems across the country. Now, that is a huge advantage to Epic as you try to build out your models and as you try to build out your experiences and just get a better understanding of your patient populations. Can you share a couple of examples of how you’re actually using the access to the data to improve experiences as well as outcomes for patients? Do you want to talk about one or two examples? Maybe you mention a couple of clients where you’re doing some work in this regard?

Seth Hain: Yes, sure. So, I can address a couple of those points. One of the things to be clear here about is that organizations work with us and we essentially see ourselves as kind of stewards helping them manage the data that they have on-site. And we work with them to kind of build out workflows that have the opportunity to be fully informed by the data in their system and the context around the patient and the provider. We often think of this internally as a concept we call relevance, where we want to make sure that full picture is brought to bear. I mean, some of the easiest examples to think about in this context are around the acute space where we rapidly see folks deploying machine learning models around things like sepsis, deterioration, and fall risk being three of the most common ones. We see folks start with often implementing them as a bundle. And at this point, we have over 300 organizations either running directly in their system or in the midst of implementing machine learning models in those types of contexts. And it’s exciting to see the impact that it has. And, you know, it ranges from something like a 17.7% decrease at the North Oak, which is a community hospital down in Louisiana in mortality reduction for sepsis patients to, you know, also a decrease in alerts that providers are saying you use machine learning models to better identify patients. It also helps save folks time. So, UC Health, who spoke to Amy about this, saw a 19 percent reduction in the number of alerts they were seeing in these types of contexts as well. So, it both benefits from a provider time saving perspective while improving care.

Sean Bina: We also do models on the operational side of the house. So, doing things like identifying the patients that are most likely to “no show.” So, in the past, you obviously could run kind of massive report and you could do a lot of analysis and trying to find this information. But now we can’t just have the system waiting. What are the most important variables and identify which patients are the most likely no shows and then do things automatically based on that information? So, whether it’s doing a reminder phone call or texting the patient or whether it’s overbooking the patient because they’re unlikely to show up at a particular day in time. We can automate some of those processes. For me, that’s part of the excitement as you mentioned, as we’ve got completed the underlying digital transformation; we can now do these things. One of my favorite examples is what we call a FastPass at Epic, what it does is it automatically monitors the waitlist, identify the patients that are at the highest risk and need to get in the soonest, and then will automatically text or email them when new appointments become available. John Hopkins, they saw about a twenty-seven-day improvement from when patients were scheduled with a specialist until when they got in based on using this FastPass. So at one time improves the convenience and access for patients, but then at the same time, it also helps the healthcare organization because you’re filling times that would have otherwise gone unfilled or where you would have had to have a lot of staff managing the situation.

Seth Hain: And to your point, Paddy, I think, about platform, that equally important to data in regards to machine learning and these types of scenarios is the workflow in understanding what data is present and ready to be used at the point in time that somebody can make an intervention that will really matter and how to get that information into the people’s hands. They can do something with it. And so, as we build out more machine learning models here in the data science team at Epic, that’s actually where we start. It’s not with the data that’s available or those sorts of things, but it’s about the impact that we want to have in the workflow and how we see that fitting in and then work back towards the true kind of machine learning training processes and the stuff that the data scientists really do day to today.

Paddy Padmanabhan: Yeah, there was this one question that I have on this. When I talk to CIOs and digital transformation leaders, one thing that I hear often is that it’s very important to identify the right kind of use cases if you will, or AI II and machine learning applications. And often I’ve heard that the bigger opportunities today, maybe in more in non-clinical use cases, administrative functions, and revenue cycle management as an example or even for the patient experience related applications. Is that the sense you get in based on all of the work that you’re doing that we’re further ahead? Or maybe there’s a bigger opportunity in the short term with non-clinical versus clinical use cases? What would you have to say on that?

Seth Hain: I would hesitate to say that that is exactly what we see. I think it’s different depending on the area. Certainly, in the operational areas, we see real opportunities for automation, and we see folks using machine learning embedded into the workflow to save folks time and energy in regards to moving through those operational workflows. On the acute side, we see a variety of impactful outcomes like the ones I just referenced, be it around deterioration, be it around sepsis, be it around palliative care. There is a lot of opportunity that folks see there and documented outcomes such as on the North Oaks, one that I shared a moment ago. There is also real opportunity in the population health space. I think it is harder there to truly measure the outcomes when you’re looking out two to three years in regard to the impact that folks have and directly tying it to the clinical management and care management that takes place. That’s not to say that it doesn’t help. You know, we have documented evidence and dug in as we build machine learning models around, say, sorting outreach for diabetic patients just based on an A1 C value compared to a predictive model of their two-year risk of Type 2 diabetes complications. We can really see a difference in the math when we dig into that. It’s harder and takes longer to produce those studies, though, about outcomes longer term.

Paddy Padmanabhan: Yeah, yeah. But let’s switch to the patient experience. Patient access, patient experience, these are hot topics for health systems today, high focus areas. And there’s also a teaming ecosystem of a digital health startup funded by billions and billions in venture capital money that are addressing specifically the patient experience and patient access aspects of the healthcare value chain. Now, you know, when I look at health systems, I ask myself – what would we be looking beyond an electronic health record system for? As I mentioned earlier that half of the health systems that we polled in our study were already using electronic health records systems for most of their digital functionalities. But there is also a growing trend of using startups. So, I guess this is a question for Sean. Where do you see Epic fitting in this overall milieu of digital startups that are coming up with maybe new ways of defining experiences and new solutions? Where do you see Epic fitting in this overall context?

Sean Bina: Yes. So, first of all, a little bit of context. MyChart has been around for a long time now. It goes all the way back to 2003 when we first went live with it. And kind of the patient side has always been a focus for us at Epic in terms of providing patients with access and tools to be able to see and view their records. That’s always been a core thing that we want to make as seamless as possible. And we now have almost I think we’re over one hundred and sixty million MyChart accounts. So, we’re closing into the point where about one in two people in the country having and are using a MyChart account today. And we’re starting to see much greater adoption than in the past. So, I think in the past, you know, we would see our customers have around a quarter to a third of their patients be active MyChart users. But the trend is way up. And so, for example, one of the most interesting things I’ve heard recently is that at M.D. Anderson, if a patient is seen three times, then there’s a 90 percent chance that they’re using MyChart for M.D. Anderson. Now, of course, those are patients that are sick, and they have a whole set of issues. But what it shows is that patients really will adopt the technology when it comes to using it, when they do have health issues. And so, I think one of the questions for us is not will patients use MyChart if they’re sick and they have chronic diseases and they’re in for surgeries and all of that. We know that for patients that are connected to health systems, that they will become active adopters of MyChart. But for people that are generally well or have particular health concerns are just trying to manage their health issues, but are not constantly going in to see the doctor, how do we reach out and get connected to those patients? And so, our focus is really kind of turning to help patients do a lot more self-management and do a lot more wellness within the system than we’ve ever done in the past. Some of that is providing people with targeted education based on the information that’s flowing into Epic. So, when you talk about this whole ecosystem of startups, a lot of startups feed information in which we can then consume and take advantage of in Epic. So, whether it’s your blood pressure monitor, your heart rate monitor, whether it’s your Fitbit, whether it’s your Apple Watch, all of those things then become data feeders that then get, consumed through MyChart up into the EHR and then we can provide monitoring and management of that data based on configuration within Epic.

Paddy Padmanabhan: Yeah. And you know, one of my recent guests on the podcast mention that right now we have a fairly low threshold for option of digital tools by patients. If people start using the tool, that itself is a significant change. And it’s really heartening to hear that you’ve got 160 million patients who are now beginning to actively use MyChart in some way. I am one of them, by the way, and I can’t remember the last time I actually called into my physician’s office for scheduling an appointment or just for non-emergency type questions. I do that all through MyChart today.

Sean Bina: Yes. I’m the same way and I use MyChart all the time for managing my daughter’s care and then managing my care. And so, you know, I love to access digital tools and I would much prefer to always do something online than have to make a telephone call. And I think many, many patients are in that same boat. There is a cultural change that still needs to happen at many health care organizations to give patients more control. So, one of the things that I’m continually advocating is that we don’t need to wait to give patients their test results until after a physician has reviewed them. We should be providing open notes to patients as much as possible. And so, we have those capabilities within MyChart today. And so, it’s just a matter of transforming the healthcare system. And some of this will almost certainly be mandated by the government in the next year or two is that really to provide that full context for patients when they go into their shared medical record.

Paddy Padmanabhan: Yeah and that would be a huge leap, actually, especially the comment you made about the notes and all that. So, let’s talk about emerging tech stuff again. You know, we talk mostly about digital transformation and now we are on the cusp of some big breakthroughs with some of the emerging technologies that can potentially play a big role in the way health care is delivered in the future. So, we just touch on a few of them. And let’s start with this one, cloud computing. What are your thoughts on the role of cloud going forward in digital health?

Seth Hain: I think there’s a number of things that cloud computing provides, but at the end of the day, I think it is really about faster delivery of technology to folks to be able to put it into practice. So, a couple of years ago we released our cloud-based machine learning platform, which is essentially provided as a service and allows organizations to embed directly into their workflows, machine learning algorithms that run in real-time on the latest data in the chart. And when we built that out, we build it out in a manner that used. Forgive me, I’ll dive into a little bit of techno-jargon here, but used containers, which is a kind of new approach for deploying software out on the cloud and is agnostic, so that runs on Microsoft Azure today can run on other platforms as well in the future. And that enables organizations to also both getting access to new things we’re developing here in Verona, but also to embed their own software more efficiently. So, we’ve seen organizations like Ochsner who now have deployed nine different machine learning models directly onto that platform and embedded them back into their workflows. So, they see this as a tool to allow them to more rapidly evolve both their clinical and financial operational workflows. And they share those types of results and approaches our UGM conference, XGM, and in other forums so that folks can learn how to do that and move more quickly with it. So, I really see cloud as an approach for faster delivery and that then enables that type of faster execution on new clinical programs and the like.

Paddy Padmanabhan: Yeah, I had a quick follow up question on that. So you mentioned Microsoft Azure, so they are the big tech firms that have their own plans for the healthcare market, in some ways they may well be competing with you, and in other ways, you could be partnering with them. So how do you approach this today at Epic? Where are you partnering with? What do you think you’re going to be competing and what do you see as it relates specifically to the big tech? I’m talking Microsoft, Google, Amazon in particular.

Sean Bina: Its really customer driven. So, you know, its what customers are coming to us and asking us for and then us doing an evaluation on our side in terms of what models are going to work the best, who’s the best groups to partner with?

Paddy Padmanabhan: Right. OK, good. So, let’s move on to the next one on my list – voice recognition. Boy, I have to tell you, I’m pretty excited about what I see in terms of its potential. I just saw a news item that said that we now have the ability to identify biomarkers based on voice. And I thought that the future of health is here. But anyway, I don’t want to get too far ahead of myself. What do you guys think?

Sean Bina: Oh, we’re super excited about voice too. So, you know, people have been using voice obviously for years in terms of using systems like drag and then model to capture notes. And we have a lot of physicians that are highly efficient doing that. But we certainly want to kind of add a few additional layers onto them. And so, the first thing that we did was we started creating a voice assistant. We now have a voice assistant that runs on our mobile platform where I can say, hey Epic, and then have it answer certain sorts of questions for me. And then we are doing the work to move that into hyperspace. So on the workstation, you’ll be able to have a microphone where it will work in the ambient fashion and you’ll be able to use voice commands to drive workflow, to find out information about a patient, and to really work hands-free. I think a lot of our focus is, you know, where does this technology make sense? Whether it’s, you know, in the room for an inpatient where the patient is the driver, whether it’s in the OR, where people are scrubbed in or whether it’s in the clinic where the physician is focused on the patient instead of being focused on the workstation. And how can they quickly get the information that’s most relevant and then get things cued up in a simple and easy fashion? And we feel like the voice assistant is going to be a great way to do that. And then a little bit farther, but not that much farther down the path is the conversational capture with diarization and natural language understanding to basically be able to start to construct a note out of the natural conversation that is happening during a visit. We’re already seeing kind of experimental groups are doing this in areas like orthopedics where you have pretty structured common visits that are happening over and over again and then using machine learning to eventually get to the point where instead of having a human being as your virtual scribe, the system is really the virtual scribe creating that note.

Paddy Padmanabhan: Yeah, ambient clinical computing environment I think that’s kind of becoming a term in vogue today. I saw one of my earlier guests on his podcast mention that in 10 years’ time we’re going to completely keyboard-less and we’re going to have a voice-enabled or ambient computing environment where you don’t need a keyboard anymore. That’s where physicians are looking forward to that because that is going to significantly reduce their burden. But, how close or how far away are we? Is voice recognition mature enough today? What are the error rates within control, what’s your quick comment on that?

Sean Bina: Kind of we’re learning a lot right now is what I would say. So, we know that voice recognition works when I have a microphone in my hand and that they are at 95, 96, 97 percent accuracy using the new cloud computing platforms. So, for example, when Dragon moved to the cloud, the accuracy increased, and it can handle more accents and different styles of speaking better than ever before. So, we know that the accuracy is really good in kind of that clinical scenario. We’re kind of in the first layer of watching how the voice commands are being used and that hit rates and success for that. And we’ll learn a lot as more and more customers go-live. And then, you know, we’re kind of doing close monitoring on these first areas where people are piloting ambient voice assistance in specialty areas like orthopedics. So my sense is that in areas where you have a fairly structured dialog, you’ll have fairly fast adoption over the next year to two years in areas where a kind of a more classic internal medicine visit, where a patient might have nine different problems and you have a 45-minute visit where you’re covering all different types of things with the patient and doing a lot of education. But that’ll take a little bit longer.

Paddy Padmanabhan: Yeah. Yeah. Well, I’m still trying to get my car to listen to me and play the exact song that I want to hear. And so, I am, I guess, a little further away on this. So, the last one on my list for emerging tech and we’ve covered a fair bit of this with Seth as far as, you know, artificial intelligence. But I do want to touch on one thing as it relates to that topic. I hear a lot from people who are practitioners in the field and the customers as well that there are some concerns, they have about black-box algorithms, algorithmic bias, even some ethical considerations around the use of AI in certain contexts. So, Seth, do you have any thoughts on how we address these and where we are in really gaining or enabling customers and users to gain more confidence in these tools?

Seth Hain: Two things really, I think come to mind. The first one is really understanding how they’re being put into practice, where machine learning is really embedded into this system to kind of augment the information available to a user. I have a quote here from a user group meeting presentations from Denver Health, where they were talking about implementing a deterioration index model and a nurse shared with the folks, then put it in the plot practice that the deterioration index doesn’t change the way I nurse my patients, but it gets me into the right rooms faster. Right. So, understanding how those are embedded into the workflows I mentioned previously, I think can certainly go a long way to addressing it. The second piece that comes to mind is really about the process that an organization goes through as they implement I and put models into practice. First, understanding how it is performed on similar populations. What went into building the models? We published briefs on every model we create that organizations can review prior to putting it into their system. The second is the ability to run that model silently and understand how it performs at the organization in the context that is likely to be used in prior to putting it into practice. The third is obviously putting it into the workflow and making sure that users understand the context in which is being included and having it embedded directly there in an explainable fashion.

Sean Bina: And actually, this is true not only for AI/ machine learning, this is true for all decision support. You can’t turn on the decision support alert without first running it silently seeing when it’s going to be triggered. How often it is going to be triggered and whether it’s being triggered in the right circumstances? And then you have to measure over time how a decision support alert is being used for it to be effective.

Seth Hain: And I think that last point is key here. It is often not talked about in the context of machine learning, where after you have machine learning models live and in practice, it’s important to continue to monitor them and understand how they’re performing on different populations and then taking steps to adjust that where appropriate. It might mean adjusting the model. It might mean adjusting the workflow. But understanding its performance on a variety of different individuals, and in a variety of different circumstances over time is key and we provide that type of monitoring capability directly within the platform for augmentation.

Paddy Padmanabhan: Yeah, I think that makes a lot of sense. We’re coming up to the close for our podcast here. Is there anything that you’d like to share with our listeners about any new product features or new functionalities that you planning to launch this quarter or the next quarter?

Sean Bina: One thing that has been available for a while that I would just quickly highlight because we didn’t talk about it. We didn’t talk very much about the financial side of the house. But transparency around how much healthcare costs is absolutely essential. We have to make it so that as patients are coming in for their visits, they have a good sense of what this visit or procedure is going to cost them, what’s going to be out-of-pocket, what’s going to be covered by their insurance, and provide them with an understanding of the mechanics of that. In many cases, a patient might even be charged differently going to the same healthcare organization, depending on whether they’re going to the clinic or the hospital for the same disease. So, we have to make sure that patients know about that as they’re making these decisions about when and where to go in and who they’re going to be seen by. And so, we’ve been really focused on being able to create estimates for patients that are highly accurate based on historical data and can be provided at the point of care by the doctor. So, the doctor can say this is what an upper GI is going to cost. He or she can say what the medications are going to cost and whether there are less expensive alternatives. And then also providing that same information as a patient is going in to schedule their visits and procedures. So, to me, that’s a really big deal and it’s something that could really transform a patient’s experience by not being surprised by what the costs of things are in the end.

Paddy Padmanabhan: Yeah. And I agree completely with you. It is a big deal. I think the price transparency, cost transparency is something that is unfinished business as far as healthcare is concerned. Well, Seth and Sean, it’s been such a pleasure speaking with both of you. I greatly appreciate your joining us on this podcast. And I look forward to catching up with you again soon. Thank you again.

Sean Bina: Thank you.

Seth Hain: Take care.

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About our guests

Seth Hain, Vice President of R&D at Epic, focuses on integrating analytics and machine learning into healthcare. This includes the development of business intelligence tools, data warehousing software, and a platform for embedding machine learning across Epic applications. During his 13 years at Epic, Seth has also led the Systems and Performance group, with an emphasis in database performance and architecture.

A native of Seward, Nebraska, he received a BS in Mathematics from the University of Nebraska and an MS in Mathematics from the University of Wisconsin. Seth currently resides in Madison, Wisconsin with his wife and two children.

Sean Bina is the Vice President of Access and Patient Engagement at Epic. His focus is on improving health and wellness by helping people to become more connected, knowledgeable, and in control of their care. He currently divides his time between strategic application planning and product management. During his 23 years at Epic, Sean has worked as an account manager, team leader, implementer, an RFP writer, and as a salesperson.

Sean graduated from Beloit College with a degree in Philosophy and Literary Studies. He lives with his wife, daughter, and dog in Madison, Wisconsin.

About the host

Paddy is the co-author of Healthcare Digital Transformation – How Consumerism, Technology and Pandemic are Accelerating the Future (Taylor & Francis, Aug 2020), along with Edward W. Marx. Paddy is also the author of the best-selling book The Big Unlock – Harnessing Data and Growing Digital Health Businesses in a Value-based Care Era (Archway Publishing, 2017). He is the host of the highly subscribed The Big Unlock podcast on digital transformation in healthcare featuring C-level executives from the healthcare and technology sectors. He is widely published and has a by-lined column in CIO Magazine and other respected industry publications.


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Digital medicine is just medicine

Episode #33

Podcast with Daniel Barchi, SVP and CEO, NewYork-Presbyterian

"Digital medicine is just medicine"

paddy Hosted by Paddy Padmanabhan

In this episode, Daniel Barchi discusses the current state of digital transformation in healthcare, their goal to bring cutting-edge technology, and focus on delivering outstanding patient care.

According to Daniel, a good technology is one that saves clinicians and caregivers time without getting in their way. He believes that healthcare technology is “80% people, 15% process, and 5% technology.” He further cautions that while using advanced technologies such as AI, health systems need to be thoughtful, careful, and respectful of the way technology interacts with patients.

The healthcare system as a whole has very low thresholds for measuring progress in adoption rates for digital health tools such as digital front doors. Digital health startups have a lot of brilliant ideas; however, they are years away from being integrated into core EHR systems. Daniel advices startups to get deeply embedded with their clinical partners to develop innovative solutions for healthcare.

Welcome to The Big Unlock podcast where we discuss data analytics and emerging technologies in healthcare. Here are some of the most innovative thinkers in healthcare information technology talk about the digital transformation of healthcare and how they are driving change in their organizations.

Paddy Padmanabhan: Hello again, everyone, and welcome back to my podcast. This is Paddy and it is my great privilege and honor to introduce my special guest today, Daniel Barchi, CIO of the NewYork-Presbyterian Hospital. Daniel, thank you for joining us and welcome to the show.

Daniel Barchi: Great. Thank you for having me Paddy.

Paddy Padmanabhan: You’re welcome. So, Daniel, I was at your presentation recently at the CHIME Falls Forum and your presentation was titled ‘Digital Medicine is just Medicine.’ We know that healthcare is in the early stages of a digital transformation. So maybe you could start by giving us an assessment of the current state of digital transformation in the healthcare sector.

Daniel Barchi: Sure. Well, thank you. First of all, I’d think I’d start by saying digital medicine is just medicine in the same way that really good technology is not about technology. It blends into the fabric of what we do in our everyday lives. So, at one point, I’m sure it was novel that somebody owned an automobile and today we don’t think about owning or using an automobile. I was reading the book ‘Thinking Machines’ recently about the birth and growth of artificial intelligence. And it pointed out the fact that the first supercomputers were huge, and they filled rooms and now they’re small and, on our wrists, and we just don’t think about technology. And in the same way, technology and healthcare is important. Quite frankly, if we’re eating up physician or nurse time dealing with technology, then technology is not doing what he or she needs. The technology that’s important for medicine called digital medicine is that which blends seamlessly into what we do daily in taking care of our patients. And so, it’s my goal and that of my team to certainly being on the cutting edge of what technology can offer. But it’s not an end to itself. It just blends into our larger focus on delivering outstanding patient care.

Paddy Padmanabhan: Right. And I recall you had mentioned something along the lines of technologies, 80 percent people’s, 15 percent process, and 5 percent technology. Did I get that right?

Daniel Barchi: That’s true. Although, you know, I’ve been using this quote for years and it was originally coined by my colleague, Marc Probst, CIO of Intermountain. And I use it all the time because it’s absolutely true. I’ll repeat it again. Healthcare technology is 80 percent people, 15 percent process, and only 5 percent technology. Day in and day out people who are leading technology transformation in healthcare are not focused on python programming, or XML, or interfaces, or FHIR. What we’re focused on is how does this work for the end-user? What do they need? Do they need the two of them? Or can we get down to one of them? Can we cut down the time that they spend digging around these systems by making it more ubiquitous? It’s all about the people on the process side, not the technology side.

Paddy Padmanabhan: Right. My firm’s research suggests that health systems are driving a lot of digital transformation initiatives. However, they seem to be a portfolio of standalone projects and for the most part, if I look at the health system landscape as a whole, most health systems are relying primarily on their electronic health record platforms for driving digital initiatives. Is this consistent with what you’re seeing in the market? And maybe you can talk a little bit about how you’re approaching it at NYP?

Daniel Barchi: You make a very good point. We always strive to adopt technology, which is going to be cutting edge and it’s going to help our physicians. At the same time, we want to make sure that it’s not getting in their way. And so, there’s a push-pull, the push being that we want to embrace small companies that are coming up with new ideas. And then pull being to make sure it’s part of the overall fabric of what we’re doing. And so, it’s a fine balance between being on the bleeding edge of what’s happening and being on the trailing edge of what’s happening. So, we like to think that we at Presbyterian were thinking about that balance from the physician and nurse’s point of view all of the time. We’re really focused on our core electronic medical record. And let’s be honest, that’s where our clinicians spend the bulk of their day. And we want everything to be accessible through the electronic medical record. We don’t want to say, sure, you do your core data and your core documentation and ordering in the electronic medical record. But when you want to use a cool decision support tool, log out and log into this other system, or when you want to use the latest PACS system, log out of the EMR and log into this other system. And so, you know, the great technology that comes in startups that are being innovative is generally years away from being well integrated into the core EMR. So, we need to think about where we can embrace the best of what’s cutting edge and coming from small companies, small startups, small standalone tools versus what we can incorporate in the larger EMR. And there’s probably a threshold, you know, something that is a 100 percent great idea, a standalone application versus 70 percent as good using the functionality of the EMR. Probably the 70 percent embedded in the EMR beats the 100 percent standalone because of the ease of working and for the idea that everything that’s done in the core system is interfaced with everybody else. So, it benefits not only the clinician who’s using that tool, be it the outside tool or the inside tool, but the inside tool is integrated into the seamless care of patients end to end.

Paddy Padmanabhan: Yeah, and this is very consistent with what I hear from other CIOs, as well as. This constant trade-off between what might be the absolute best in class on the one hand, but also what is more practical and optimal for the here and now. And you made a couple of very good points about the importance of not adding to the physician burden, which was kind of your underlying message about using the electronic medical record as the landing page or a landing point for physicians to use some of the advanced functionalities. Let’s talk about the front end a little bit. There is a lot of talk about digital front doors today and primarily relating to patient access. And a lot of health systems have launched some very intuitive apps, including NewYork-Presbyterian. And there are also nontraditional players like Walgreens getting into this space. What are your thoughts on how these digital front doors are reshaping the patient experience? And maybe you can share some thoughts from your own experience with the apps that you’ve launched, at NewYork-Presbyterian?

Daniel Barchi: Great. Thanks for bringing this up. Just to use an example, going back 20 years or more, we can think about a lot of this in the way that airlines did about booking and ticketing systems. In that 20 years ago, it was all about how the consumer, the traveler, gets in contact with the airline to start the process and make things happen. And today, it’s all about putting the perfect app in travelers’ hands and letting them make their reservations, do the special requests and drive the process. We can think about the healthcare industry being on the early phases of doing this, where certainly the clinical care is delivered by doctors and nurses in physician practices or in the hospital. But the coordination of it more and more is getting into the patient hands. And the only way you can allow them to do this if you give them access to the fundamental operating systems, primary through a portal. We’re going through the process of implementing a single common EMR across all 10 of our hospitals as well as Columbia doctors and Weill Cornell. Medicine is simply a huge endeavor, and as we think about this core EMR that we’re implementing, there will probably be about 45 to 50 thousand clinical and financial and operational users on a daily basis. But what we realized at one point is, you know, they’re probably going to be one hundred and fifty thousand patients that use this system every day through the portal. So, it’s great that we’re doing it for physician efficiency and for operations in the hospital. But it has to be a really good tool as a portal for the patients to use it and get the data themselves. And then I tie this back when I comment about standalone. Sure, it’s great if you’ve got a perfect fertility app or motherhood app or depression screening app, and it’s great that specialized standalone tool can go deep. But I think the best applications that face patients are the ones that go deep. But they’re also broad. They tie into the larger environment of care, including legacy records, including prescriptions and allergies and the ability to schedule follow up appointments.

Paddy Padmanabhan: Yeah, and can you talk to any metrics or how do you track the effectiveness of how these apps are truly reshaping the patient experience or impacting your own inflows if you will? What kind of metrics do you track for, telling whether it’s successful or not?

Daniel Barchi: Well, I will start by saying that I think that health care is generally still very new into this. Even core EMRs that have very good patient portals, it is the few and far between health systems that have really made great inroads in getting their patients to use them. And even when the functionality exists, getting the physicians and physician practices to use them and saying, you know, we probably don’t need Daniel at the front desk answering phones and making every single appointment for Dr. Jones, maybe we should open up Dr. Jones’s schedule. And I know that Dr. Jones is reluctant and that she really likes control over her schedule and understanding exactly what patients are getting scheduled when. But wouldn’t it be more efficient if we either had the front desk staff answering questions and doing follow up and not just making appointments and putting this capability in the hands of the end-user? So, I’d say that we as an industry are very, very new to this. And I think in many cases we’re testing the waters in terms of effectiveness. Most health systems, including us, are just measuring the percentage of our patients that are even signed up on the portal, never mind using it. It’s a very low threshold. So, what percentage of our active patients are using the portal today? The next step is going to get into, instead of process metrics like simply signing up but outcome metrics. So, we have more than nine million inbound phone calls to our health system annually. How do we reduce that over time by making a lot of what patients do online self-service? And we’re starting to adopt some artificial intelligence and putting it on the front end of our phone calls so that we can answer basic questions about scheduling or visiting hour time or directions, just very, very basic things to at least call off those basic things that can be best answered automatically for a patient. So that people who are answering calls are better suited to answer and more deeply the kind of question that our patients raised.

Paddy Padmanabhan: All right. I’ll come back to AI in a moment. But you mentioned health care outcomes in general. And of course, in the current era that we’re in. It’s all about data. It’s about harnessing data for insights. And it’s the number of data sources is increasing. The types of data is increasing. However, my understanding is that aggregating and analyzing the data in a healthcare context has been a challenge and remains a challenge despite some progress due to data quality, data silos, interoperability issues and so on. Can you share your experience at NewYork-Presbyterian on how you’ve approached this in your world?

Daniel Barchi: You raise a very good point. Data is certainly an outstanding tool to be able to improve our operations financially, from an efficiency point of view and from a clinical point of view. I often think that when people say it’s hard to get data out of systems, be it financial systems or billing systems or clinical systems, whether in healthcare or anywhere else. It’s sort of a lazy second hand for acknowledging that this work is challenging, doesn’t mean it’s impossible, but nothing’s easy. If you were to say, you know, organizing all the photos that my family has taken from all of our vacations and celebrations over the past 10 years. Yeah, that’s difficult. That’s not impossible. You need to do the work. I feel in the same way, aggregating and analyzing data is difficult, but not impossible. And where members of our research teams at Columbia and NewYork-Presbyterian and Weill Cornell have wanted to, they’ve gotten access to the data and been able to drill down and make real conclusions about efficiency or about clinical outcomes. And I think that it’s never going to be easily done until we get to national standard for how we record data in more discrete fields. We are always going to have issues of unstructured data, physician notes and the quality of the data and the quality of the data that comes from clinical care is never going to meet the standard that researchers want. And it’s our job as technology people who work in healthcare to tie the two together. But I wrap up again by making the point that just because it is challenging work doesn’t mean that it is impossible to do. And we should spend more time actually drilling down into what conclusions do we want to draw, what data sets we need to get that information from, and how do we go about taking the eight steps that are necessary to do it than simply saying it’s hard to get the data out of the system.

Paddy Padmanabhan: So, can you share a little bit of detail on what your data and data integration, data aggregation, and data management infrastructure looks like at NewYork-Presbyterian?

Daniel Barchi: Sure, we are doing a lot of good work led by our analytics leaders and informatics departments at Columbia, Weill Cornell and NYP to do two things, not only look at the data that we have on hand, but we’re planning the future because we are three institutions to top 10 Ivy League medical schools and a top 10 health system all working in concert. We have many, many different sources of data and teams using that data. And yet we’ve done a really nice job of having the leaders of these data sets and our analytics teams create shared governance. And in that way, we’ve been able to tie this shared governance to our new integrated electronic medical record and we’re looking for outcomes together. So, the analytic leaders from Columbia, Weill Cornell and NewYork-Presbyterian meet now twice weekly to look at data requests, figure out how best they meet those needs, and then to share the data that they need. We’re also planning a longer-term how we integrate data into a data lake and do a shared database so that we aggregate not only clinical data from the EMR but all of the different research that’s going on into one pool. So, it’s not a going back to comment before about 80 percent people, 15 percent process and 5 percent technology. It’s not a technology challenge and aggregating data or deciding where to store it. It’s about who has access to it and how do we make that access necessary available to the researchers and the clinicians who need it at any moment.

Paddy Padmanabhan: Now, let’s come back to AI which you brought up a little while ago. Now we are seeing significant advances in AI and machine learning tools and it’s being applied in the healthcare context in a wide variety of ways, both on the clinical as well as on the administrative side of the business. However, the sense I get is that for a vast majority of health systems, analytics is mostly still about retrospective analytics and AI is still in its early stages. And those enterprises that are making progress with AI are challenged with, you know, what kind of use cases are the right ones? How do you ensure transparency in the machine learning models? Algorithmic buyers, you know, ethical issues and so on. What are your thoughts on the current state of AI and how are you deploying AI at NewYork-Presbyterian?

Daniel Barchi: Well, first of all, Paddy, I really appreciate you raising the issues of algorithmic bias and the quality of the data. The black box problem and ethical use of AI, because as we think about using advanced technology with patient data, we have to be very, very thoughtful, careful and respectful of the ways technology interacts with our patients. This is people’s health. These are people’s lives that are at stake. And so, we can’t be cavalier with it in any way. And so even at the most senior levels, led by our CEO and the two deans, we talk about those challenges and we are very careful about what we do. So that said, we do know that AI can help us do a better job of delivering care and being more thoughtful about how we’re using data. Although if you’ve seen me speak publicly, Paddy, you know, I tend to talk about the fact that we’re still in a gold rush phase of artificial intelligence in healthcare, where if you think back to the gold rush of 49. People who made the money were not the miners who were using the picks and shovels to dig gold out of the hillsides. It was the people selling them, the picks and shovels. People like Leland Stanford, who accumulated money and was able to underwrite Stanford University or Levi Strauss, who is selling clothing and blue jeans to those miners. And so, I feel like at this point with artificial intelligence, the gold is not the clinical side of it. Equate the physicians and nurses to the miners. The gold right now is on the back-office side of it. The people who are creating the environment, the finance people, the IT people, the HR people, people who are running these large systems. And so it’s much easier to apply artificial intelligence to a billing system to make predictions about which bills will or will not be approved by a payer, or to use AI to look at documentation by a physician and see if it’s going to pass muster or use artificial intelligence to do the basic robotic process. Automation work of reaching out to an insurance company and looking up information online and aggregating that data so that somebody else saves hours of time by doing all that finger keyboard work and can more thoughtfully think about it. So, at NewYork-Presbyterian we are using AI in clinical ways, which I’d be happy to describe in a minute. But a lot of our focus is the recognition that it’s much easier. We have much more constrained data sets, meaning discrete data in the field that you can use to feed AI systems on the finance and the IT sides of the house.

Paddy Padmanabhan: Yeah, I love the analogy of the gold miners and people selling picks and shovels because, you know, unglamorous as it might sound, the people selling the picks and shovels are actually making money more consistently than were the people who were going after the shiny objects. So, I just love that analogy. Daniel, thank you for sharing that. I have seen some of your presentations where you talk about the robotic machine that carries the food between floors and releases the people in the kitchens to focus more on the food preparation. Doesn’t sound like the sort of thing that you would expect a hospital to be focusing on from an artificial intelligence standpoint. But that, to my mind, illustrates where the gold actually lies in today’s content. Would you sort of agree with that?

Daniel Barchi: I would agree. If you think about the fact that healthcare is a very labor intense business because we rely on the clinical skills and compassion of our physicians and nurses. The question is how do we give them more time to do their work and how much of all of the other administrivia can we take off of their plates? So the example that you just gave of the autonomous robots that we run in one of our large academic medical centers from the kitchen in the basement, down the halls, they’re robots automatically call the elevator and take the food trays directly up to the right floors and deliver it to the right area. So, a person can deliver the last 20 feet of the patient’s room. That’s an example of technology doing the basic work so that the people who are actually delivering the compassionate care, in this case, our food service workers have more time to deliver each meal personally to our patients. Ask them how they’re feeling, get a sense of whether the meals are meeting their needs and focus on those individualized patient needs. So, I feel like more and more AI will blend into care. But for right now, the big opportunity is taking tasks off of physicians, nurses, finance people, IT people and other support services that otherwise get in the way of the way we talk about that care.

Paddy Padmanabhan: It’s a fascinating example to me. So, in the remaining few minutes that we have, I wanted to walk through a few other topics really quickly with you. We do something called a lightning round where I ask for the top of the mind thoughts on some emerging technologies. Let’s get right into it. Let’s start with this one – cloud computing.

Daniel Barchi: Cloud computing is important. 10-15 years ago, every health system was very proud to talk about its data center and the investments it was making. And now we think, you know, do we really even want to own data centers? How can we get out of the data center business? Our skill set and healthcare is delivering outstanding care and making people’s lives better, not in running large facilities with a track and other fire suppression systems. So, I would like to put more and more what we do into the hands of third-party companies that do it really well. When we have to store data in its own state, I would be happy to do that using a large cloud computing system. The challenge is most large academic medical centers, in fact, healthcare generally is a relatively thin margin business and not for profit side. So everything that we do has a cost component to it and it’s relatively cheap to own a data center and keep servers in there in every two to four years as is appropriate, replace a five thousand dollars ten thousand dollar server, which is a capital cost. It’s much more expensive to pay a third-party company, an AWS or a Microsoft to store and manage that data for me as an operating cost year over year. So one of the challenges that many of my colleagues and I across the nation are finding is that we make them move to more, more cloud but cloud tends to be expensive operationally, I think that there are advantages from the security and reliability and a backup point of view. But we do face the challenge of the cost.

Paddy Padmanabhan: Yeah, OK. Next one on the list – voice recognition and natural language processing.

Daniel Barchi: I think that 20 years from now we’re going to look back on the state of healthcare and quite frankly, the state of technology in the United States and think, can you believe that the interface between somebodies brain and the computer was their eyes and their fingers and that we made people type into things? I think it’s going to get replaced over time by certainly voice and then other ways for people to ubiquitously transfer their ideas and thoughts into our systems. And so, voice recognition is the easiest way to get quickly to the next step. We’re starting to make investments in small companies that are doing voice recognition. We’re exploring artificial intelligence and voice recognition, listening to the conversations between physicians and patients with the patient’s consent so that the doctor can focus on the patient and the computer listens and documents what the physician says in terms of the current situation of the patient and what orders he or she needs to be placing. And so, I think voice recognition is going to get very important very quickly.

Paddy Padmanabhan: One of my previous guests, David Quirke, CIO of Inova Health System, he said in another 15 years from now we’re going to be in keyboard less environments. Do you think that’s something that we’re heading towards?

Daniel Barchi: Absolutely. I think there’s going to be much more optical character-driven management of our technology and a lot more voice recognition. You start to see it, although the order in the industry for its interfaces tends to be about 5-7 years behind. You can see the auto industry trying to free people from being having to touch anything and do more voice recognition. I see that happening in healthcare as well.

Paddy Padmanabhan: OK. Automation and RPA. I think you made a reference to RPA. But what do you think of those two?

Daniel Barchi: RPA, especially on the back-office side, especially in our finance side, we employ hundreds of really talented people on our finance teams who do repetitive tasks. We would rather have those people drilling down deep into solving problems, but for our health system and for our patients on a billing point of view instead of doing repetitive tasks they do today. So, we employ many bots and we’re expanding our fleet of bots to make us more efficient on the back office robotic process automation side so we can get more customer focus. And I think that’ll increase not only here NewYork-Presbyterian, but it’s happening across healthcare and it’s happening in all industries as well.

Paddy Padmanabhan: That’s correct. That’s correct. Last one in the lightning round, 5G networks.

Daniel Barchi: I think right now 5G is really high on the hype cycle and really low on what it’s going to deliver. If you think about what we’re able to do, both on the consumer side and on the business, side using wireless today, it is quite incredible. And in many cases, we’re still operating in a 3G or early-stage 4G. I think that 5G is being touted as something that is remarkable, going to change what we do. But if you really drill into the examples that people give about being able to do robotic surgery from across the world. So that’s possible for 5G, but it’s possible in a wireless or even a wired environment today. In many cases, being able to do something wirelessly is good, but not a crucial must-have. And if you think about it, every one of our ORs is wired for everything that we need. And the ability just simply for something to be an air gap in the data transfer is no real advantage. And in many cases, we’re not limited by speed either. One of the consumer things touted by 5G companies is imagine being able to download a two-hour room movie right before you get onto a plane. It’s not how people watch movies anymore; they don’t download movies onto their devices. They stream them in many different ways, including, you know, 20000 feet on an airline. So, I think that 5G right now is a outstanding technology looking for the kind of problems it’s going to solve. And I’ll add one more thing. I don’t think we’re spending enough time talking about the downsides of 5G. So, at the higher frequencies of 5G, the blanketing of cities that we’re going to need with higher frequency, shorter range antennas are not something we’re spending enough time thinking about. So essential in every light pole in every city we’re going to have 5G antenna, 5G touted to solve some of our rural wireless problems when in fact 5G doesn’t have the range that some older systems have. And so, I think that’s misleading. And if you think about hospitals, they tend to be older buildings that might be 20, 60, even 100 years old with thick walls, with lead-lined areas for imaging. And 5G doesn’t have the penetration that some other systems do. And so, we’re going to have to have repeaters in just about every space or room. So, these are the challenges that I think we need to face and that we’re not talking enough about as everybody benefits of 5G.

Paddy Padmanabhan: Yeah early days yet. So, you mentioned something about startups and working with startups early on into the conversation. And I have to ask because we are now seeing a lot of venture capital money pouring into literally hundreds and hundreds of digital health startups, many of whom have very promising solutions and are making great progress. Others that are not, but from the point of view of the CIO of a large health system looking to harness innovation, how do you go about really managing the risks and how are you doing it at NewYork-Presbyterian? What’s the advice you have for digital health innovators who want to be a part of your journey?

Daniel Barchi: So, the way that we manage the risk is that we are clinically driven, not technology or financially driven. So, we go to our Chair of Medicine, Chair Surgery, other clinical leaders and ask them what do they need? And ultimately, they’re the ones who say, here’s a promising technology or this small startup fills a gap that we don’t otherwise have. So, we don’t find technology to search for problems to solve. We go to the clinician to ask him what problems they have and then try to find things that meet their needs. And then once we find them, we do a lot of due diligence before we enter something into our health system. We have a program management office that looks at things from a financial, from a risk, from a technology, from a patient privacy idea, even from an algorithmic biased point of view before we embrace technology. So that’s from our side of the house. If I was on the startup side, I would get deeply embedded with clinical partners who know today’s problems. Unfortunately, there’s a lot of money and a lot of brilliant ideas in small companies. They’re working in healthcare space, but if they don’t have clinical insights, then they can be creating the world’s greatest X, whatever X is, and not recognize that a physician would look at that and she would say, that’s not what I do. That’s not my problem. It’s great idea, but it’s not going to work in my practice. So, thinking about things purely from a clinical point of view, first, would physicians use it? Would it make clinical care better? And then everything else to follow is really important mindset for a small startup or venture capital or private equity company to have.

Paddy Padmanabhan: Yes, that’s very insightful. Thank you for that. Well, we are in 2020. And would you care to share what your top priorities are for the coming year?

Daniel Barchi: So just like over the past couple of years, my top priority is reliability and integration. So, it’s my responsibility and that of our team to create new technologies and solutions. But for our clinical end-users, the doctors and the nurses that deliver the care, they want to see things integrated in a holistic, easy to use package. And so, while we’re constantly advancing the care that we’re delivering with great technology, it has to be part of a seamless environment. So, undoing and this is not just unique to our health system, but all health systems across the country. Undoing 20 plus years of worst of the breed and tying it together in easy to use integrated packages is our challenge. And it’s incumbent upon IT leaders to think about end-users and how they seem to use technology in the environment that they’re in. And that’s all going be our focus for 2020.

Paddy Padmanabhan: Daniel, thank you so much for taking the time to share your thoughts with us. It’s been such a pleasure speaking with you and I wish you and your team all the very best for the coming year.

Daniel Barchi: Thanks for having me Paddy, I enjoyed it and I appreciate what you do with the podcast.
Paddy: Thank you very much.

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About our guest

Daniel Barchi is SVP and CIO of NewYork-Presbyterian, one of the largest healthcare providers in the U.S. and the university hospital of Columbia and Cornell. He leads 2,000 technology, pharmacy, informatics, artificial intelligence, and telemedicine specialists who deliver the tools, data, and medicine that physicians and nurses use to deliver acute care and manage population health.

Daniel previously led healthcare technology as CIO at Yale and earlier as CIO of the Carilion Health System. He was President of the Carilion Biomedical Institute and Director of Technology for MCI WorldCom. Daniel graduated from Annapolis, began his career as a U.S. Naval officer at sea, and was awarded the Navy Commendation Medal for leadership and the Southeast Asia Service Medal for Iraq operations in the Red Sea.

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About the host

Paddy is the co-author of Healthcare Digital Transformation – How Consumerism, Technology and Pandemic are Accelerating the Future (Taylor & Francis, Aug 2020), along with Edward W. Marx. Paddy is also the author of the best-selling book The Big Unlock – Harnessing Data and Growing Digital Health Businesses in a Value-based Care Era (Archway Publishing, 2017). He is the host of the highly subscribed The Big Unlock podcast on digital transformation in healthcare featuring C-level executives from the healthcare and technology sectors. He is widely published and has a by-lined column in CIO Magazine and other respected industry publications.


AI predictions need a thoughtfully designed closed-loop to drive action

Episode #32

Podcast with Mudit Garg, CEO and Co-Founder, Qventus

"AI predictions need a thoughtfully designed closed-loop to drive action"

paddy Hosted by Paddy Padmanabhan

In this episode, Mudit Garg discusses the evolution of Qventus and how they are applying AI to help hospitals and health systems in managing their operations with real-time predictions for improved care delivery.

With a focus on patient flow automation, Qventus helps hospitals and health systems in reducing the length of stay and other operational metrics. Based on early insights that machine learning models and prediction scores can be confusing to users in the absence of an accountability engine, the company has developed an interdisciplinary approach, augmenting AI with behavioral science principles to drive sustained improvements in healthcare operations.

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Welcome to The Big Unlock podcast where we discuss data analytics and emerging technologies in healthcare. Here are some of the most innovative thinkers in healthcare information technology talk about the digital transformation of healthcare and how they are driving change in their organizations.

Paddy Padmanabhan: Hello again, everyone. This is Paddy and welcome back to my podcast. It’s my honor and privilege to have my special guest today, Mudit Garg, CEO and Founder of Qventus. Mudit, thank you so much for joining us and welcome to the show.

Mudit Garg: Thank you so much, Paddy. It’s an honor to be here. I appreciate the chance to be here. I’m looking forward to our conversation.

Paddy Padmanabhan: Wonderful. So, let’s get started. Tell us briefly about the company and its evolution and what is the marketplace need that Qventus is trying to address?

Mudit Garg: Yeah, it’s a great question. So, we provide a solution for hospitals and health systems to manage their operations in real-time. And specifically, where we focus is patient flow automation. So, reducing the length of stay, excess days in the inpatient environment, improving the throughput in the ED environment, things like that. Our platform has both artificial intelligence and behavioral sciences built together. And what it does is it empowers frontline managers so that they can identify, predict bottlenecks before they happen. But not just stop there but orchestrate solutions, drive accountability long term. And we layer on top of that a set of operational experts to bring those new capabilities to life in the hospital environments with process design and management practices alongside. So, that’s the market need where it comes from, as you know, is this immense pressure on hospitals and health systems across the country right now to drive a lot more efficiency. And this has been true for some time. You asked about the evolution of the company. And for me, a lot of this came from probably 10 to 12 years ago, doing process and performance improvement in health systems. And I don’t know if you probably felt the same way, but I definitely thought working in the hospital the first time that it was remarkable. The quality of the people; world-class equipment, world-class therapies, world-class clinicians are available at most hospitals that we work with. But on the other hand, as a patient, we really struggled to provide them with world-class care. And it’s really despite immense diving catches and super-heroic efforts from these clinicians. So, it’s really an odd dichotomy that those two things exist simultaneously. So, the market needs really, as we dug into it and as I dug into it early on, was how do you create operational reliability? How do you make everything else around the clinical care, reliable, repeatable and mature, so that world-class operations can exist to truly unlock the potential of people? So that’s kind of where we started from. The evolution of that has been very interesting. I was, you know, maybe biased a little bit to look at data. That’s one of its key ingredients to create that operational reliability from the beginning. But it was very clear in the beginning when we started focusing on AI and ML that the prediction was very important. The prediction of the bottleneck was very important, but not enough to drive that. That was the first phase of the company. We went from looking at the data and seeing that people were excited about dashboards but didn’t log in when they got busy. People decide what machine learning, but just putting a machine learning score and board didn’t do anything, that didn’t drive any action and what really needed was action. So, we built a really robust platform then that can take different parts of a machine learning distribution of a prediction and activate different decisions and action, over mobile, over boards, over email, over text, many, many different mechanisms. And that was really valuable in fact getting the insight into action. But as is the case with everything we saw that in the immense scale that exists in an operational environment like a hospital, things slowly started reverting back to the mean. So, then we built an accountability engine. And what this does is remarkable. If you’re in the world of manufacturing, there is something monitoring every machine so you can do preventive maintenance in the machine before it breaks down. Similarly, if you’re in a health system environment, there’s tons and tons of processes going on all the time. What sequence and preventative maintenance can you do? And that’s really behavioral. Right. You could have if you’re seeing this unit do a phenomenal job of planning, how can you make sure as a leader you can praise them? If this unit the person has changed or somehow the quality of the process is dropping, how can you actually make sure that there is coaching involved at the right time? Our statistical monitors are monitoring all these process metrics continuously and then searching opportunities for praises and opportunities for coaching for leaders so they can scale themselves. So those are the different phases of a company going from just information and dashboards to machine learning and prediction to action, to then the accountability engine coming through. And that was a core of the platform that we built in our evolution. I’d say the last probably evolutionary phase is recent in the last year or so. Whereas we went deeper and deeper, the market, we saw that to truly bring these capabilities to life; we needed to create a set of predefined best practices of how to use these capabilities where there’s predefined artificial intelligence models of software, but also operational processes and management practices, and then put together a team of world-class experts on clinical operations, doctors, nurses, AI experts to help bring all this to life. So those have been the evolution in trying to address that market need through the course of the company.

Paddy Padmanabhan: Very interesting. There’s a lot to unpack from what you said. A couple of things that come to my mind right away that no matter how good a solution is unless you’re integrated into the clinical workflow of a hospital, it’s very hard to get users to adopt it and use it. And so, it seems like that was kind of one of the early insights you had, and you quickly went about addressing that. What I do like about the fact is this closing the loop, if you will, in terms of getting people to not just to use the platform, but holding themselves accountable in some ways through some kind of a feedback loop which tells them how they’re doing in order to avoid the reversal to the mean, which, as you know, is the bane of all management consulting. That’s right. Interesting. We’ll unpack some of that. Just tell us a little bit about from a growth perspective. I’m aware that you raised a significant amount of venture capital. Do you want to just quickly walk us through how much you’ve raised, who your major investors are?

Mudit Garg: Yeah, absolutely. I think from a growth standpoint, we’ve been fortunate in partnering with our health system partners to see growth in the work we do with them and concurrently, therefore, in the investment and that we can make in growing the company as well. We have raised to date about 45 million and we’ve been very, very blessed to have some very top tier investors from the valley and in the healthcare specifically as well. Some of the largest investors are Mayfield, Bessemer Ventures, Norwest Ventures. We also have YCombinator as a very early seed investor in the company and many other phenomenal seed investors in the company. And what’s been amazing is also some of the customers who work with us also felt compelled enough by the results they have seen to become strategic partners in investments as well.

Paddy Padmanabhan: But are you allowed to name any of your customers? You want to name one or two just for our listeners?

Mudit Garg: Yes. I mean, Dignity and New York-Presbyterian are two are the ones that have actually both been customers, but also investing in the company as well.

Paddy Padmanabhan: Dignity and New York-Presbyterian, is that what you said?

Mudit Garg: Yes.

Paddy Padmanabhan: OK. All right. Switching back to our topic at hand, which is your solution and your platform? How do you see yourself in the context of the ecosystem in which is the technology ecosystem in which you operate, namely electronic health record vendors, big tech firms that are building a lot of the capabilities that you’ve talked about in terms of advanced analytics, AI, machine learning-based decision making. And last but not least, other digital health startups who may be on to the same kind of ideas that you have. How do you place yourself in the context and in the milieu?

Mudit Garg: Yes. So, yes, you’re right. I mean, it’s a really exciting time to be in healthcare. That is just so much innovation and excitement across the ecosystem. I mean, to start with, EHRs are supercritical right there. We complement the investment health systems have made in the EHRs. Without that, we wouldn’t exist. If the data and the workflow itself wasn’t digitized, it’d be impossible to drive any kind of improvement. What our customers have found is that they’ve made substantial investment, in these areas of operational improvement over the years, both in terms of, you know, it could be in terms of process improvement, it could be a technology investment. If you look at the length of stay across the board, for example, that has really not budged much over the last 10-15 years, it’s been increasingly plateaued. So, when they look at looking to partner with us, what they’re looking for typically is the next step function improvement. And what really stands apart is, one, let’s take the inpatient, for example. In the inpatient environment we aren’t just helping you provide an understanding of [00:09:39] the workflow as isn, but our machine learning algorithms are, A) Identifying the problems, like this patient may need an MRI upfront several days in advance, then helping orchestrate the action and then helping manage throughout the ability. That’s super unique because one of the things that you do talk about the closed-loop part of it, just providing the AI/ machine learning is simply not good enough, in fact, and can sometimes be even more confusing to end users but closing that loop, providing the AI/ machine learning, helping create the action, and then helping create long term sustainability. That’s what is supercritical to our customers. And that’s where they see it most different. Of course, EHRs are pretty critical in view of the process improvement consulting teams that exist are pretty critical ingredient from a mindset standpoint, do all of that. But this infusion of AI and behavior science, not just AI, but the behavior science of, we think of like, how do I change behavior as a human? I need to have a cue, something prompting me to do something. I need to have the right thing to do. The easy thing to do. And I need to have some feedback on accountability. We have incorporated those vague principles in creating this organizational behavior change as well.

Paddy Padmanabhan: Yeah, fascinating. So, is it fair to say you mentioned length of stay a few times? So, is it fair to say that, that is one specific problem that you’re focused on and have been able to demonstrate results, and by extension, is that kind of the main use case for your platform?

Mudit Garg: So, our platform is fairly extensible. Patient flow ends up often being the first-place customers start because as one of the things for health systems among many, many things they can do. Length of stay and patient flow is something that no matter where in the spectrum of fee-for-service to the value-for-service organization you might be, It’s one of those rare problems where the incentive of the customer, the patient, the incentive of the hospital, of CMS or the payer are all aligned. No one wants the patients to have to stay an excess amount of time in the hospital. The patient doesn’t want that, payer doesn’t want that, the hospital doesn’t want that. So that is an area where we’ve seen a tremendous amount of pool as a result from the market. And increasingly as hospitals and health systems look at Medicare break even. How do we break even on a Medicare patient long term? Length of stay is such a massive part of that problem that we’ve seen a strong amount of pull there. But our platform extends beyond that to throughput in the E.D. and the operating room. We have worked in the outpatient access space. We are working on system operations similarly as well. But for most health systems inpatient as a state has been a place that they have had a strong interest and often a place to start with us. We have seen, as you asked the question, pretty significant improvements there as well. Statistically significant reductions in length of stay between 0.2 to 0.7 days, which is phenomenal. If you’re in a capacity constraints institution and being able to serve more patients and if you’re not in a capacity constraint, information in terms of being able to reduce the cost to serve patients.

Paddy Padmanabhan: So, it is a business case, fairly straightforward. Because it is a single number that you can track, which is length of stay. And if you reduce the length of stay by a factor of 0.2 or to 0.7, as you mentioned, the results kind of automatically speak for themselves and they are visible. Is it a fairly straightforward business case?

Mudit Garg: The business case is straightforward. The problem is complex. Yes, I think that would be a fair assessment. The business case, I mean length of stay is a top initiative for many, many health systems in the impetus to drive down unnecessary cost. And so therefore, that already exists. Any excess their patient spends is at least a thousand dollars of excess costsin the hospital. Along with the propensity to have a hospital-acquired condition or infection or other pieces and the lack of satisfaction that comes from it. So not even counting for those other downstream effects just a core excess cost is significant by itself.

Paddy Padmanabhan: Yeah. Who is your target audience for something like this? Who do you normally start conversations with? Is it a CIO, Chief Digital Officer, Chief Medical Officer?

Mudit Garg: That’s a good question. I mean, the operator is the Chief Operating Officer, the CNO, CMO. Those are ones who are already strategically often focusing on this problem, like the problem of length of stay, throughput flow. We help remove the cognitive boredom from the front-line teams that help to ease some burnout. So those are the folks that are often probably most directly eating and seeing the problem and looking to solve it. The CIO is a very critical stakeholder in the discussions right because we are complementing the EMR. They may have other tech investments. We want to make sure we have a good understanding of the data transfer and data lakes and all of those things. So, they are a critical component. And then the last piece of it, which is given the compelling financial return, the CFO are often important stakeholders as well. It can be between 10 to hundred billion dollars of annual financial benefit for assistance to the CFO and to be critical to that conversation as well.

Paddy Padmanabhan: So, if you look back at the past several years that you’ve built this business and built the platform and gone through their evolution. Can you talk to what have been the most significant challenges that you’ve faced in really validating your solution against a known problem?

Mudit Garg: So, most significant challenges in validating or are those two questions validating the solution and the challenges we face?

Paddy Padmanabhan: The length of stay problem is a well-known problem; it’s been a problem for long. So, when you went about trying to sell the idea of it. What were the most significant challenges you had to overcome in the process?

Mudit Garg: That makes sense. So, I mean, look, it is a big, big problem, right? Like when a patient, for example, is being taken care of in an inpatient environment, there are just so many things involved. There are physicians taking care of them, diagnostics taking care of them, there are procedures happening. They are going for imaging, a pharmacist helping them with medication reconciliation, so many things beingdone. It is a fairly complex problem. The first thing that we had done, and we needed to do was just to make sure we understood the complexity and size of the problem we’re going after. It is a hard problem. The next challenge is healthcare data, as you know, is often hard and messy because unlike advertising, where all the data is machine-generated, much of the healthcare data is not machine-generated. It’s human-generated, so by nature it’s messy. So, for us, one of the very core needs early on was to build a pretty significant platform capability to do real-time and automated data quality checks so we can pick up when things are off and not looking right. So, they don’t affect all the downstream applications significantly. So that was one of the big challenges, just the quality of the data, the availability of the data. How do you make sure, for example, building a machine learning model in an academic environment would control data where you can take out the outliers, where you have historical data and it has been cleaned. That is way different than running it in real-time. So, first that was a challenge to solve. The second piece is also just recognizing how much change people are going through already in healthcare on the front lines, how little time they have. And to some extent, how much fatigue or change fatigue may set in situations like that. And therefore, not just taking, hey, this is a cool prediction and putting it up on the board and expecting something to come out of it, but really very thoughtfully designing the closed loop that we were talking about. And I think that was an important challenge to recognize and to work towards because otherwise, it is easier in some ways to solve the mathematical problem that is to be solved. But forget the true problem of trying to drive change in the environment. Those I would say are probably two of the challenges along the path that we had to face, that shaped as I said in the beginning of the conversation our evolution from just predicting and prescribing actions, to actually building the accountability and getting the platform and then to actually creating these prescriptive, proven methodology combinations of tech and process that we now deployed to a team on the ground.

Paddy Padmanabhan: That’s fascinating. We hear a lot about the struggles of digital health startups, death by pilot, long sales cycles. So, on and so forth. Healthcare organizations want the innovation, they need to innovate. There is alignment around the problem to be solved to your point around something like the length of stay. But in reality, executing on an innovation program is incredibly hard. As you pointed out, should healthcare organizations be doing based on your experience so far to accelerate the adoption of digital innovation with all these constraints? You don’t have time, we have fifty other things, but we also want innovation, right?

Mudit Garg: Yeah. I mean, it is hard, right? It is really hard. I understand where it comes from because the business and the care, they are providing already is complex. There’s a lot of change in the market, so that takes a good amount of the bandwidth away from the day to day already. And so then in a way, it becomes harder. But I think what I’ve seen very effective organizations do is, one really thinks about what the no brainer moves are. We may not know what the latest CMS guidelines are going to be almost certain things. We may not know how the regulatory environment might shape. What are the no brainer things that we need to do as health systems no matter what? So for a lot of them, like reliability and cost comes to the top. Okay, so that’s that then. I think oftentimes in saying of what are the one or two or three partners we can pick and go deep with them? And that doesn’t mean you have to start with the big bang right away. It just means that, , you engage with them deeply. And I think that is supercritical. From an innovation standpoint, that it’s very hard to have a spray and pray kind of an approach where you have your hands in a lot of things or you’re just assessing the market, but actually doing it because I think for the organization to see some wins and to see some action, that’s supercritical. Honestly, even if it doesn’t result in wins, see that something was talked about, done and learnt from is critical. And the last thing I’m saying from an innovation standpoint that’s critical is that finding the operational alignment is important. The innovation cannot be devoid of what the people are feeling day to day. That operational alignment must exist. And I think in doing these three things, what we’ve found is like, for example, for us, we worked with early customers, went deep on their specific problems, created these best practices, AI models and all that stuff. So, when customers, then innovating, appreciated it. There are parts of what we do where they don’t need to reinvent the wheel and they are parts of innovation where we are learning with them and just sort of appreciating. We’re not trying to reinvent the wheel entirely. But, taking what’s already there and then actually finding unique ways of improving the innovation as well is something that I find to be effective. It is a hard space. So, the sales cycles for enterprise, in general, are hard, not just healthcare, they are long. But I think if we can do these things, then when you align with someone, you find the right partners and you make sure that it actually drives through, impacts the business and starts fueling an appetite for more and more innovation over time.

Paddy Padmanabhan: One of my guests on one of my earlier podcasts mentioned that she benefited greatly from having these sponsorship and support of some early believers and risk-takers in their client environment. And let’s face it, there is a lot of risk involved in innovation. Healthcare is a margin constrained environment. There’s not a lot of like to have. You also had that experience, you know, the early believers who make a huge difference who you co-innovate with and, you know, somehow make it happen? Has that been your experience as well?

Mudit Garg: Yeah. I mean, if you look back all the way back in the beginning, now we have a ton of outcomes and we have customers and stuff. So, it’s a little bit of a different risk profile today. If I go back three-four years ago, those same questions existed. And I think Mercy was a great example of an institution where we had operational champions, the head of their ED for example, the presence of a disintegration across the board. Folks who just jumped in, worked hard with us, understood it. But you have to have those early champions who are willing to connect the business pain to the problem to be solved, to the solution that’s coming through and help sort of get everyone excited and fired up on that. And that’s critical, especially even more so in the earlier days. Of course, as you go deeper in the market people evaluation and sort of reasons to buy change and become more and more business focused. But early on, that is absolutely critical.

Paddy Padmanabhan: Fascinating. So, guess we have coming up to the end of our time here. I have just one last question for you. You’re a classic Silicon Valley digital health startup. So, tell us what are the upsides and downsides of being in the valley?

Mudit Garg: Yeah, that’s a good question. Not surprisingly, everything sort of has this upside and downside. I mean, what I love about being in the valley is you have access to world-class talent; technical talent, and business talent. People who been at the vanguard of the AI and behavior science and innovative technology and most importantly, who have scaled businesses and transformation of industries and other industries before. And that is amazing and very, very critical to have that ecosystem around you and to have that quality of talent, it seems to be brought to bear for the problem you’re trying to solve. I mean, the flip side of that is, of course, it’s the value become extremely both costly and sometimes in that they hard to scale and just as so much of innovation comes out of here and I think over time now, a lot of the talent is actually going to other places in the country, which is phenomenal. And that’s allowing us to create much, much more of a remote enabled culture as well, where folks can still have the core of the same ethos that came out of here but actually scattered across the country and beyond. So that’s sort of the upside and downside of being here. The upside of this, that is like so the quality of talent and the experience of folks before the downside is dropped, perhaps the cost is sort of a distraction value that exists by being in the valley.

Paddy Padmanabhan: Well, not to mention the traffic on one-on-one.

Mudit Garg: Ya we’re gonna one-on-one is not a very friendly yes. Yes, you’re absolutely right.

Paddy Padmanabhan: Mudit, it’s been such a pleasure talking to you. And thank you so much for sharing your deep insights from all the work that you’ve done. And congratulations on the progress so far. All the very best to you and your team. And we’ll be watching.

Mudit Garg: Yeah. Thank you so much, Paddy. It’s always a great conversation with you. I am excited to continue the conversation. Thank you for having me.

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About our guest

Mudit Garg is the CEO and Co-Founder of Qventus. Qventus is an AI and behavioral science-based system that integrates with EHRs and automates patient flow. In this role, Mudit works closely with leading health systems including Dignity, Emory, Fairview, Mercy, NewYork-Presbyterian, and Stanford. Together with Qventus, these organizations have been able to transform their operations, reducing length of stay by 0.3 to 0.8 days, eliminating thousands of excess days, decreasing ED LWBS by 50%, and more – ultimately resulting in higher margins, decreased staff burnout, and a better patient experience.

Prior to Qventus, Mudit co-founded multiple technology companies including Vdopia and Hive. He also spent time in McKinsey & Company’s healthcare practice helping large providers with organizational transformation and performance improvement. Mudit has been recognized for leadership as one of the Silicon Valley Business Journal 40 Under 40. He is a Stanford-StartX mentor. He earned his Master’s in Business Administration and Electrical Engineering from Stanford University and a Bachelors from the Indian Institute of Technology.

About the host

Paddy is the co-author of Healthcare Digital Transformation – How Consumerism, Technology and Pandemic are Accelerating the Future (Taylor & Francis, Aug 2020), along with Edward W. Marx. Paddy is also the author of the best-selling book The Big Unlock – Harnessing Data and Growing Digital Health Businesses in a Value-based Care Era (Archway Publishing, 2017). He is the host of the highly subscribed The Big Unlock podcast on digital transformation in healthcare featuring C-level executives from the healthcare and technology sectors. He is widely published and has a by-lined column in CIO Magazine and other respected industry publications.


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The Healthcare Digital Transformation Leader

Stay informed on the latest in digital health innovation and digital transformation.

The Healthcare Digital Transformation Leader

Stay informed on the latest in digital health innovation and digital transformation

The Healthcare Digital Transformation Leader

Stay informed on the latest in digital health innovation and digital transformation.