Breaking Down Data Silos with Interoperability w/ Dr. Donald Rucker

Interoperability has long been a buzzword in healthcare 

In an ideal world, our healthcare system would exchange timely, high-quality data to empower patients, improve clinical quality, and keep costs under control. 

How close are we to making this a reality?

In this episode of the HealthBiz Podcast, host David E Williams talks with Dr. Donald Rucker, Chief Strategy Officer at 1upHealth, about how their health data management platform is transforming the way healthcare organizations access, share, and leverage patient data to improve outcomes, enhance efficiency, and drive more personalized care.

TOPICS
(0:25) Introduction
(0:52) How Dr. Donald Rucker Got Into Healthcare
(2:54) Rucker's Educational Path and Career
(11:14) What Does 1upHealth Do?
(21:13) Why Are CMS APIs So Important?
(25:15) How Do APIs Affect Prior Authorization?
(31:37) Book Recommendations from Dr. Donald Rucker

🎙️⚕️ABOUT HEALTHBIZ PODCAST 
Healthbiz is a CareTalk podcast that delivers in-depth interviews on healthcare business, technology, and policy with entrepreneurs and CEOs. Host David E. Williams — president of the healthcare strategy consulting boutique Health Business Group — is also a board member, investor in private healthcare companies, and author of the Health Business Blog. Known for his strategic insights and sharp humor, David offers a refreshing break from the usual healthcare industry BS.

🎙️⚕️ ABOUT DR. DONALD RUCKER
Dr. Donald Rucker is the Chief Strategy Officer at 1upHealth and a nationally recognized leader in health IT. As National Coordinator of Health IT at HHS from 2017 to 2021, he led the development of the ONC’s 21st Century Cures Act Interoperability Rule, which standardized FHIR APIs and advanced patient access to electronic health data by enforcing the ban on information blocking.

A board-certified physician with experience at Kaiser Permanente, Beth Israel Deaconess, and the University of Pennsylvania, Dr. Rucker has made significant contributions to clinical informatics. He co-developed the first Microsoft Windows-based electronic medical record and designed a computerized physician order entry module that won the 2003 HIMSS Nicholas Davies Award. He holds degrees from Harvard, the University of Pennsylvania, and Stanford.

🎙️⚕️ ABOUT 1UPHEALTH
1upHealth is revolutionizing healthcare data interoperability by enabling seamless access, exchange, and analytics through modern FHIR-based technology. Founded with a mission to bridge the gap between payers, providers, and app developers, 1upHealth empowers organizations to unlock the full potential of their health data, driving better outcomes and efficiency.

With a focus on simplicity, scalability, and compliance, 1upHealth delivers cutting-edge solutions that transform healthcare operations. By leveraging standardized FHIR APIs, the company ensures secure and actionable data sharing, making healthcare more connected, patient-centric, and innovative.

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Transcript

David: 0:00

Interoperability has long been a buzzword in healthcare, but it's finally coming to life. In an ideal world, our healthcare system would exchange timely, high quality data to empower patients, improve clinical quality, and keep costs under control. So how close are we to retaining that reality? Hi everyone. I'm David Williams, President of Strategy Consulting Firm Health Business Group and host of the Health Biz Podcast, where I interview top healthcare leaders about their lives and careers. My guest today is Dr. Don Rucker, Chief Strategy Officer at OneUp Health, whose mission is saving lives with data. Do you like this show? If so, please subscribe and leave a review. Dr. Don Rucker, welcome to the Health Biz Podcast. Thank you, Dave. We're going to be talking about OneUp. But before that, let's start and wind the clock back a little bit, all the way back to childhood, if you don't mind, and I'd love to know a little bit about any, you know, childhood influences that have stuck with you, any notable stuff about your, about your childhood. I'd love to hear about that. You know, when I

Dr. Rucker: 1:07

went off to college, my dad, who was a physicist, who designed blasting caps. So. Very obsessive compulsive. Yeah. He told me there were two things he didn't want me doing. And, you know, I'm thinking sex, drugs, and rock and roll are three things. Yeah. But what two out of those three would he not want me to do? And the surprise was he didn't want me to taking any courses in programming or in statistics. again, my dad's a physicist. Yeah. You know, I was puzzled and his comment, which has, you know, large elements of truth, but maybe not fully truth. was well programming you, you know, you'll learn it when you need it. which I don't these days believe and statistics, if you need statistics to show something, it's probably not true. Yeah. And, anyway, so I dunno, maybe it's rebellion, but you know, my career is based on computing and statistic. With a dose of healthcare thrown in.

David: 2:15

So, well, that, that, that makes good sense. And so my dad had, you know, like these backgrounds, you know, can I think be influential. So my, my dad is a social psychologist and his faculty advisor at Harvard was Timothy Leary. Oh my goodness. So in high school, I could at least say, Hey, I don't need to try anything, you know, like, you know, don't, I don't need to rebel against my parents, you know, with, with drugs. and then, when I was in college, I was studying economics and my dad, he really didn't want me to be an economist. or, or a lawyer. So I'm either one of those really, but in that case, nice. All right. Well, you have had plenty of education. It visited most of the Ivy leagues along, along the way. And I also noticed in your career, you seem to pair usually a, a business job with a, with a, with an academic appointment. So what's been your whole sort of, if I've got that right, sort of your, you know, educational path and what you've done kind of career wise early on.

Dr. Rucker: 3:08

Yeah, I would say actually the thing I've always tried to do. and we can talk about what got me started on this, but, is to pair a clinical job. Yeah. you know, often sort of academic, you know, light academic, depending with a corporate job. Got it. and so, Other than the, you know, first bid at ONC, I've been lucky enough to be able to manage to do that, my, entire career. So one I saw was, Datamedic and EI, what was that about? so when I graduated from, Stanford's Medical Computer Science program, so this was after residency. Right. Most people who do a medicine residency, actually, you know, want to become a cardiologist, maybe primary care, or, you know, some, some proceduralists, I wanted to, change American health care. Yeah. My goal, you know, modesty, I suppose, was not one of the top considerations there. and that actually, honestly, all came out of, when I was starting med school, I thought about should I go to business school, should I go to med school, you know, standard career, lack of imagination type of choices. and then when I got to med school, I realized almost immediately the American healthcare system was utterly inefficient. this was on the wards of Penn's main teaching hospital, Hospital University, Pennsylvania. And as a first year student, you, you look around, of course, you know, this is the one time when, you know, you have fresh observational powers. That's right. You haven't been acclimatized. You're a brand new student. You got the fresh white student coat. You're a scared sh less. what am I going to say to the patient? Oh, I'm going to touch the patient, you know, I mean, you're just sort of, you know, all kinds of commotion, you know, at that very beginning, but looking around, I mean, you can just sense it even as a, you know, whatever I was at time, 23 year old, wow, this is inefficient. And this was now 1978. Yeah. So healthcare wasn't anywhere near the mess it is today. The mess was already moving pretty rapidly at that point. And so at that point, I always had in the back of my mind, I'm going to see if I can fix this, somehow. And, you know, the first fix was, first attempt at a fix was, being a student of a junior faculty member there, John Eisenberg, who, eventually became head of ARC, but he was sort of a Pied Piper, totally charismatic. He was literally a first year faculty member. Yeah. you know, there were a couple of us and he was talking about the appropriateness of lab tech, right? And it was all like diddle squat stuff, you know, nothing like prior auth today And you know what you could spend in health care back then was like de minimis, right? CT had just started there was no MRI I don't believe there was, I mean, I'm sure I'm wrong, but there wasn't really fiber optic much of anything. you know, no monoclonal antibodies, a handful of, medications compared to today. I thought, wow, we'll just build decision trees. Yeah. That was sort of John's early learning. Again, he was only a couple years older than I was. and so we'll build decision trees and we'll help those. You know, everybody, you know, do the right thing. Okay, now that sounded like a viable plan. Then I got to residency at UCSD, and then I realized, sadly, That was a really poor plan since, the world was not miss, the health care world was not missing decision trees. It had no data. Yeah. there was nothing to feed the decision tree. So then that was scary. So here I am, you know, late twenties, middle of a residency. So now, you know, the realization, I mean, I didn't know him, but Bill Gates was a college classmate. The personal computer had just come out. And it's sort of various forms. And I said, I'm going to have to learn computer science. And that's pretty daunting when you're in your late twenties. Yeah. Am I going to spend a couple more years learning computer science? you know, in my thirties, as opposed to, you know, getting a job and practicing medicine and being a real human and well, so I decided to do that. Yeah. and so now I'm a resident at UCSD. And now I'm trying to figure out how the heck do I learn computer science as a 30 year old? I looked at everything. I looked at classes on matchbook, you know, like when people smoked back in the day, they would advertise like national university, which I think is not where they fly by. And I think, I think it's a real universe. But they had classes. I tried taking class at UCSD and night school, but it's very hard to take night school classes when you're a resident on call, you, you don't get to go to too many of the classes. and I ended up through, you know, luck and fortune and maybe good grades or whatever, being able to roll a joint degree at Stanford and a brand new medical computer science program. And also get into Stanford business school. I might've done those things totally separately from each other. but, I was able to roll that into a program and again, finding out about the medical computer, I mean, everybody knew about Sanford business school, but. finding out about the medical computer science program was writing letters on a typewriter to various people to describe what I wanted to do. And they said, Oh, do this. So anyway, I did that early AI with Ted Shortliffe, who invented rule based expert systems and, had, just a great education. Did the entire undergraduate Stanford computer science curriculum. except for compilers, which I've since learned. and then Datamatic was my first job and that was a early EMR company. It was the first company building EMRs with, in Microsoft Windows. That, was interesting. So Windows. At the time, 2. 1 crashed all the time. Yeah. It was an absolute nightmare to program, or to demo, or to do anything in. but we, so I was employee number six. guy named Paul Gertman founded that. Great guy, unfortunately passed away. We were the first people to use Dragon in healthcare. Yeah. So, you know, now that's nuance, but, and it went through a whole bunch of Yings and yangs and learn how to speak and, you know, a bunch of other stuff. But, so we and actually built a purpose built language to marry speech recognition with templates. Kurzweil had done a version of that. but I think we were the first to do it formally as a programming language. at times didn't work out for a couple. Very simple business mistakes that were absolutely unforced errors, but, you know, I wasn't in control of the company. So there

David: 10:31

we go. That sounds good. Well, I want to make sure we leave plenty of time to talk about 1UP. So I'll just say you've had an illustrious career, from that point of data medic forward and, including a stint, as a head of the office of national coordinator, coordinator of health information technology. for HHS. and then at one up, I'd love to hear a little bit about just what does the company do and why, why were you attracted to it? Obviously the ride variety of things that you've done up until this point.

Dr. Rucker: 10:59

Yeah. the attraction is sort of simple and elegant and frankly, it just absolutely gets back to what got me into this in med school, and in residency, which is how do you compute and start automating healthcare? Right. I mean, healthcare is a very, very funny field when you look at the broad swath of computing in the U S or the world, right? We're the only field who uses computers. But has no automation. You know, I know there's some sample, you know, some sort of narrow versions of it, but it's stunning how little automation we have in health care, right? You go to your local, if such exists, auto plant or packaging or warehouse, and these things are rife with deep fundamental automation, right? We expect our packages in many cases, right? To come within hours, maybe a day, you know, we track pretty much everything if you want, right? We have none of that in health care, right? We have that, you know, we have our enterprise systems of record, our EMRs, which are totally built as document generating machines to get a higher revenue, truth be told, and, have. you know, the, the automation is teeny at the edges. and so then the question becomes, how can you uncouple this world, get this data out, and put it to a better, more productive, and more fundamentally useful thing that lowers costs, make patient lives better. it's just a win win, right, which I believe eventually will take the healthcare spend back down from the, you know, 19 percent or whatever it is of the GDP today, to roughly around the 6 percent or 7 percent it was when I started med school.

David: 13:02

That's pretty fundamental and does get back to what you were saying about, you know, revolutionizing the healthcare system. If you're going to, You know, cut it in half, through automation. That's a fairly lofty goal, to be able to do that.

Dr. Rucker: 13:14

Yeah. So then the question is, okay, well, yeah, how in the hell do you do that? Right? Like, what are you smoking? Yeah. right. Well, the way you do it is something we know from the rest of our lives, right? We are at the trailing edge of innovation healthcare, at least healthcare services delivery. I'm not talking about, you know, the molecular science, I'm talking about services delivery. Now we're miles behind Amazon, McDonald's, even the worst airlines, anybody, right? I mean, you'd be hard pressed to come up with an industry that has less automation at the scale and expense that healthcare exists. And that answer, in many ways, is free flow of data with. APIs, Application Programming Interfaces, and the data that's most valuable, I would throw out is clinical data, which is for the audience, data in a data standard called FHIR. and what brought me to 1UP, that's what we do, that's frankly exactly what the Cures Act required of ONC, and that's what we did at ONC. and so, just so everybody's on the same page, FHIR is Fast Healthcare Interoperability Resources. And, you know, for folks who may not be familiar with that, it's like, oh, hell, some tech jargon. Well, let me simplify it. It is built. It is the healthcare version of another tech acronym called JavaScript Object Notation. JavaScript Object Notation, in its simplest form, is just putting data into an outline. And then you can send it along, that putting in the outline. So humanized could potentially read it, not saying they always do. They don't, but could read it. along with something called restful APIs, which stands for representational state transfer, is what powers the entire internet economy. Every one of those little icons on your smartphone pretty much is communicating back. With a server that is where the business value is created using JSON, right? That's the free flow of information. Now, in healthcare, we haven't really had that for, sorry, a variety of reasons. that really have to do with, well, they have to do with the huge, fundamentally, it's not computer science that's the issue. It's our payment system and sends all of the big delivery systems to not share data. So, our payment system is optimized, optimizes, the doing of procedures, and capturing those referrals. And so, any town, any large place, you see the sort of the landscape of these massive delivery systems, heavily consolidated, and the economic imperative of their consolidation is simply to capture all referrals, you know, primary care docs, anything they can get their hands on. Because their margin is generated by not letting the high margin procedures get, you know, go elsewhere. Yeah. if a high margin procedure goes elsewhere, that is called leakage. It's just a very different concept of the same thing in the consumer world. In the consumer world, that's called choice. Yeah. in healthcare, it's called leakage. And maybe that, Orwellian, Distinction is all you need to know. So all of these systems naturally gravitate to software, you know, in many cases from a certain vendor, that is designed to be inward looking and not to share data because, in fact, these people have built their entire business on not sharing data. Now, they can't totally get away with not sharing data. So we have sort of faux interoperability, you know, the biggest current thing there is TAFCA, which is another acronym, the quote unquote trusted exchange framework common agreement. it is a privacy destroying thing that violates every modern notion of security. I don't know if we can get to that, but that's a whole other issue. But the, the fundamental transformative point is with FHIR data, you can represent clinical thinking, you can represent problem lists, you're in a better position to represent medical care. And finally, we can start putting computers and automation and now, of course, AI at. The problem, which we've never been able to do. So how do you, so the trick is how do you take these API and, empower them, right? How do you get the data out from the sources? and there've been a couple of federal rules that have really gotten at that. the first one where the cures act, so 21st century cures act. That was past December, 2016. So, you know, the, I guess we can call it interregnum. Yeah. Between Obama and Trump, the first interregnum. and, it was overwhelming bipartisan support. you know, 390 votes in the house. 92, I believe in the Senate. a lot of it had to do with FDA real world evidence. So was really fundamentally an IT rule, but they said APIs without special effort, was the, the premise of that. And, you know, the vision was that we're going to have modern computing. The data is going to get out to the public, you know, people will have control over their health. and then CMS did a series of parallel rules, including one of January of this year. that are payer to patient, that was around before, but added a payer to provider and payer to payer. So, you know, you can exchange, for example, your prior authorization history and allow you to switch, for example, insurance plans like MA plans. so those APIs are extraordinarily powerful conceptually. and then how do you go about making them real?

David: 19:57

And so that's where I see, that sounds, that sounds good. So I see that, you know, one up advertises that has the most CMS APIs in the market. Yeah. why is it important to have a lot of them? and what does it really mean? Who, who's the customer of a CMS API who, who, who wants to buy them? So, you know,

Dr. Rucker: 20:16

gee, I don't know. It's not per se, I mean, there are some network effects, but most of these are point to point. So network effects aren't the dominant thing. It's not necessarily important to have the most, it's important to do them well. Yeah. And, you know, you know, from your, BCG days about the experience curve. Yes. And, you know, you probably did some consulting business here. I I'm guessing maybe back in the day.

David: 20:42

Yeah.

Dr. Rucker: 20:43

well, you know, that's true. And in every other human endeavor. I think the real, so, you know, one other sort of setting of, of background is there are two in this allocation of healthcare, which is really, you know, the big picture thing we're solving, whether it's prior auth or adequacy of networks or case management or quality measurement, right? They're all allocation of care things. they're not even honestly making the care more efficient. You know, hopefully we'll get to that, but right now we're in a much coarser world. there are two big flows of information there. One is out of the EMRs, any clinical data you can get. And then the other is the whole claims pipeline. So, you know, so that's where, I see you. In my case, in the er, the hospital generates a bill, sends it to a clearinghouse, clearinghouse sends it to a payer, payer sends it back to the clearinghouse. It's dispersed to, you know, a bank account. No, I'm a Red Cross volunteer at Fort Belfor. Yeah. You know, you know, they're, they're not generating a lot of bills, but that's the way the, the way of the world. So you have claims data, you have clinical data. The, the. IT abyss has historically been, clinical is in one language, or not at all, right before FHIR, not really at all, other than for, you know, imaging and, and, you know, blood test type of results. And claims are in a totally different, the X12 electronic data format. So all of the data forms, explanation of benefits. What FHIR allows is these things to finally all be in FHIR. So, if you have them separate, it's very hard to compute about the value of health care, right? I mean, value, you know, the most basic consumer thing is what you pay and what you get. And, what you pay is dollars, a. k. a. claims, and what you get is clinical care. Well, if you can't tie the claim to the care, the whole search for quality in healthcare, which we've been on for 25 years, is honestly a little bit fatuous. Payers are now, through these CMS APIs and through their access, not that they've used it much, of clinical data, which they have a right to and can also write network contracts to more freely get, through APIs or the bulk fire. API process, finally payers who have been delegated to allocate care, right? Whether it's commercial, whether it's MA, or actually fee for service, they need data to do that. And finally they have it. And that's the huge innate power and beauty of FHIR

David: 23:49

APIs. Got it. So how does, so how does the API help address. a friction that you hear a lot about, which is prior authorization, and this gets a lot of attention. a lot of probably the topics you're discussing about the lack of automation relate to that as well. Where, what's the relationship between the APS and not prior auth?

Dr. Rucker: 24:07

So, that's a, well, that's a superb example. And it's, I mean, politically the hotspot, I mean, I just saw something in, in a moderate healthcare this morning. I mean, you can barely go three or four days without some commotion about prior authorization. And sometimes, as we know, from the recent. You know, the United Healthcare shooting, you know, in a horrific kind of way. it's ultimately allocation of care, right? It is ultimately who's going to get what care, right? That's what this is about. I mean, you can give it a name on narrow network or prior off. Yeah. Utilization management or quality measurement, but ultimately all that. All, all comes down to who gets what, and how do we provide care and hopefully in a way that is efficient, and, you know, morally the right thing to do by patience and, you know, fits the, you know, thereby the grace of God go I type of thing that I think. You know, sort of, you know, John Rawls, you know, theory of justice back, yeah, back some time ago, yeah, back 50 years ago. Hopefully it fits that. And I'm actually more of a libertarian. So I do have many Robert knows it. Anarchy thing, utopia leanings, but, hopefully it fits that. That's what we've decided politically. And then how do you do that computationally? That's what one up does. That's what the power of this is. so once you have the data, then you can be smart about it. The way we do it today is very coarse, right? So we have an algorithm, for each of these, quote, inappropriate or expensive services where we, you know, say, well, you know, if you do this and if you have that, you know, we get in this primitive high friction environment. And most of the friction is gratuitous, right? Depending on how you balance it, half of these services are subject to prior auth. you know, you saw in the Kaiser Family Foundation stuff, last week that, you know, the rates of putting stuff into prior auth of all services is, you know, relatively broad when you look at the number, right? M. A. plans. have something, not quite, two services per year per member. Yeah. Right? So the people who are sick are probably getting a lot more. And then, ultimately, if you appeal it, they end up approving most of it. So this is just blisteringly inefficient, high friction. And as taxpayers, and as companies, we pay for that friction, right? you know, that's the heat, if you will, but it's not warming us. It's horrible. And so how do you get out of that? Well, There are many algorithms to take a set of inputs and come up with an efficient set of outputs, right? Essentially, every algorithm in computer science does that, right? That's what they're all about. Whether it's a sorting algorithm, whether it's an indexing algorithm, whether it's a storage algorithm, whether it's, you know, the Google traversing of the internet algorithm, right? So. Right now, today, the prior author algorithms are mostly pretty coarse. They're notebooks with quote unquote experts who have an opinion. for better or worse, and then it sort of leads to a set of often implicit or pseudo explicit, not computationally explicit, rules, and then they, you know, approve or deny, very little awareness of the patient, the cost, it, it's just dumb. Yeah. I mean, it's just dumb. What we are doing, At 1UP, what's exciting there is, if you have the right platform, we already have the data from our payer customers. that's part of the OO57, we can actually compute on what's far more efficient to even think about putting into PryorAuth. and so then the, the question is, if you're going to go from current PryorAuth, which is the world of PDFs. Yeah. look, you know, PDFs were a big deal, you know, Donald Knuth and the people and, you know, you know, the Adobe folks who invented PDFs. Right. You know, splines, which is, you know, what described the curves in the characters in PDF. That was sort of a big deal in the day. Right. Like, boy, you know, when they get into a static form, that's not much. No. And so now, can we get the data? And the answer, finally, to your original question is yes, can we get data and put it together in a way that we can really be smart about this, explain it and do the right thing. And that's the excitement of it is that now with modern big data platforms, you can do that. Now in healthcare, what 1UP is doing is sort of, you know, arguably an extraordinary novelty. If you were at any big, consumer company, Amazon, Netflix, any big retailer, this would all be sort of laughable. Yeah, we've been doing this for the last five years. You just discovered Lakehouse, right? Twitter, whatever. but you know, in healthcare. Better late than never.

David: 29:54

Sounds good. Well, there's much more to talk about, but since we were running long time, I'm going to ask my last question, which is about, a book recommendation. I don't know if you're going to give me a John Rawls book, but I wonder if you've read any good books I was going to say lately, but at any point that anything you might recommend, for our audience,

Dr. Rucker: 30:10

so this is going to be a very weird choice. Okay. it's going to go back into the bowels of history, but it's probably, and this is I'm going to confess a bit of a dry read, but it is probably the book that best explains that dynamic of federal regulation, regulatory agencies, and the big government world that obviously, you know, arguably maybe is in some transition now. And the title of the book sounds bombastic, is called The Road to Serfdom. It's not a long book. It was written by Friedrich Hayek in the late 1930s. It's an absolute classic of economics. It's still in print. far as I know, I don't check every week, but, it's still in print. Hayek won the 1974 Nobel prize for this body of work. This is the simplest way to access. And what he basically describes are the destabilizing influences of. regulations, more regulations and more government. and he did it in the context of fascinating historical context of late 1930s. So if you put yourself in Europe in the late 1930s, you had, Russia and Germany, which had spiraled into socialism, and you had England and the United States, which were on the spiral, right? So, you know, circa 1940, Russia and Germany were essentially socialist, communist end states. and the U. S. and England were sort of trending that way. And so he examined those dynamics. And it's fascinating showing the history of socialism in Germany and Russia in the 1800s. So this is, he goes over the whole trajectory. and you know, you can argue history and, and you know, dynamic people and, and you know, bad people and good people and stuff. But if you understand the, you know, physics of it, if you will, the information theory of it. you'll be much smarter as in any of the jobs that probably everybody in this audience is actually participating in, you know, if this is your taste. so The Road to Serfdom, University of Chicago Press, I believe, Friedrich Hayek, and you will learn more, get more insights on the state of the world. Everything he said can be played into where we are in American healthcare straight on from the 1942 Stabilization Act, which was the start of the spiral in healthcare.

David: 33:04

Well, good. You know, I've done hundreds of episodes here in this. So we have a lot of repeats in terms of the book recommendations. That is a new one, but it's an old one, a classic, as you say. so thank you very much. so that's it for the latest episode of the health biz podcast. My guest today has been Dr. Don Rucker, chief strategy officer of OneUp Health and a whole lot more. thank you so much, Dr. Rucker for joining me today on the podcast.