Making Routine Healthcare Safer and More Efficient
This week I am talking to Robbie Hughes (@rbbhghs), Founder & CEO of Lumeon (@Lumeon_) who started out his career as an aerospace engineer but found his path in healthcare as he worked to bring some of the process and data analysis rigors to healthcare systems. Their approach to care coordination or orchestration takes data from a wide variety of different places consolidated into a decisioning engine that allows for working out what to do next. They then automate the steps that are routine, reliable, predictable, and things that we know need to happen on a patient-by-patient basis
We make comparisons with the Space Industry and the incredible progress that SpaceX has made in terms of building a reusable launch platform as their product. What SpaceX does really well is they measure and they have consistency and reliability of the process, and they understand where you get variation. Healthcare is not good at that and we need to be better.
Listen in to hear our discussion on how data and the elimination of variability in processes can be applied to any facility incrementally. We discuss why the actual outcomes from the process are not the primary target until the processes and data have been aligned and how you go about achieving that using small incremental steps
Listen live at 4:00 AM, 12:00 Noon or 8:00 PM ET, Monday through Friday for the next week at HealthcareNOW Radio. After that, you can listen on demand (See podcast information below.) Join the conversation on Twitter at #TheIncrementalist.
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Raw Transcript
Nick van Terheyden
And today, I’m delighted to be joined by Robbie Hughes. He’s the founder and CEO of Lumeon. Robbie, thanks for joining me today.
Robbie Hughes
Nick, it’s a pleasure to be here. Thank you for having me.
Nick van Terheyden
So, before we dive in, just for clarity, for those that listened to my show, I know it’s gonna be confusing to British accents, it will just make life very difficult. It was normally very easy. Robbie obviously hails from the same shores as I do. For the benefit of the listeners. Could you tell us a little bit about your background and how you arrived at this point?
Robbie Hughes
Sure. So I, I was born in 1980. And I grew up basically, as a computer geek, I’ve always been interested in programming computers and using them to do things better. And what I found as I hit my 20s, was that there are a lot of industries that are computerized without really digitizing. And what I mean by that is, they take an old processes and just said, Write the salaries to do it on paper. Let’s put that into a computer and hope for the best. And obviously, computers are really expensive and unreliable pieces of paper when it comes down to things like documenting and process. And that just didn’t seem like a good thing to me. So I got an opportunity to work with some clinicians in UK in the UK working on a problem of how do you deliver consistent high quality care across a network and a reference based pricing model, which is fairly niche. I was an aerospace engineer at this point, I was interested in systems engineering. And this seemed like a really interesting thing to be going after. And what I quickly discovered was that there’s technology that we built through this computerization process that allowed us to document and build for what we’ve done. And there was other bits of technology that allowed us to kind of advise physicians in terms of how they might make better decisions, and technology that could perhaps engage patients and remind them to do things. But there wasn’t anything that bound this together along a process. And so I conceived of as this sort of this kind of pathway engine or this this decisioning technology that would allow us to work out for a given patient what the optimal next thing to happen could be. And then to use some technology we put together to really try to work out how to automate that. And what we created, I think, today, we think it’s really as a next generation care coordination solution, we call it care orchestration. But the idea is that we can take data from a wide variety of different places consolidated into a decisioning engine that allows us to work out what to do next. And then automate the steps that are routine, reliable, predictable, and things that we know need to happen on a patient by patient basis. And then what’s left is really used to elevate the care team to their highest possible licensure. So they’re focusing on the things that matter where they can make a difference, where they can really apply their medical training and their passion. And that’s what we do. And we started in the UK, come over to the US, we count people like optim Kaisers, like the largest public health system in the in the country in New York Health and Hospitals, some of our customers, and we’re very, very fortunate that it works.
Nick van Terheyden
So you you raise an interesting area that I often hear in this show. And for me, it’s part of the incremental steps. And I’m wondering if this is true, and it’s what I termed the adjacent possible you talk about your history as an aerospace engineer. And, you know, I love that concept. I don’t think I’ve heard that before computerized but not digitized. But I think I’m absolutely that’s the case. I think that the electronic medical record is perfect case in point. But you talk about workflow and the process and the sequencing. And what I’d like to understand is how you arrived at that and was that contributory from the aerospace industry where I imagined that it’s a really well oiled refined process and machine. And did you bring that across? Or am I misunderstanding that?
Robbie Hughes
No, well, I’d love to say that all the stuff we’re doing is new and original and never been done before. But the reality is nothing that we do or frankly should be doing in either this company or healthcare more broadly. I don’t think it’s all been done elsewhere. So the challenge becomes why is it not being applied or why is it not being used? And I think the healthcare industry is interesting because although we’ve got a wide body of evidence, we have a huge amount of science. We have a huge amount of the extremely smart people that are, you know, that are well trained and how to be inquisitive and how to ask the right questions. I think, culturally, we still have an industry that’s that is very it’s, it’s foundationally, based on on opinion. And as a result, the idea that we can systematize either processes, decisions, whatever else is traditionally met with some resistance, and not universally. But you know, whether it’s in the US whether it’s in Europe, frankly, elsewhere, in a in a industry where people have been trained to use opinion, the idea that you can come along and say, well, actually, this can be done programmatically is somewhat of a challenging idea. And we should also be clear, the incentives for doing this differently haven’t really existed. I mean, the healthcare, the healthcare industry is a cost plus industry at its core, you, you look at pricing, you look at how healthcare is delivered. And effectively, what we’re doing is we’re saying, Here’s a unit of stuff, and we’re going to put some sort of margin on top of it, and then we’ll just increase that every year. So the idea that you might do a systems review and optimize it end to end is, you know, that’s quite a challenging thing to do, when that’s going to compromise both the revenue and the margins of the industry. But you know, let’s be clear, the only other industry that that I’m aware of, at least that that has this construct is NASA. And you’ll look at what SpaceX has done to NASA in terms of having a product where the product is a launch, which is reusable. And you think if you can take that mindset of a product of care delivery via an episode, be it a diagnosis, be it however you want to characterize it, but setting this kind of beginning and end to it, and optimizing for the delivery of that. It’s a very, very powerful financial construct that can align incentives very well. And there’s reasons why it’s difficult, you know, whole looking after the whole patient problem part of a patient after population, cradle to grave, you know, all of these things are, are challenging, but we have to start looking at these things, to coin your term incrementally, to start driving some of this change.
Nick van Terheyden
Yeah, it’s interesting, you bring up space, because I had the fortune of being on the Space Coast last week, and, you know, it reinvigorated what is a long standing interest and passion around it, I’m certainly a space baby grew up with that. And, you know, it inspired me to end up where I am, you know, huge amount of respect for what they achieved, you know, NASA achieved this process and went through it, Space X as you bring them up, approach this slightly differently, I think, which was, we’re not going to continue to refine things on paper, we’re going to test and keep testing. And I know, that doesn’t quite translate into healthcare, because, you know, they actually, I don’t want to say applaud failure, but they really like failure, because they learn from it. We can’t quite do that. But there has to be some sort of, you know, target. And I think that’s what you define, which was, this is what we’re ending for, and we should have the same is that maybe the way to approach this in healthcare is to say, it’s, it’s patient wellness, not patient health, I think is that the ultimate goal that we drive towards?
Robbie Hughes
I think that’s certainly a goal. I think a slightly nearer term goal is one of measurement. So the point around SpaceX is not that not just that they celebrate failure, but they measure what they’re doing. They measure what happened, they measure the output. And I think one of the challenges in in our industry is that, are we, you know, are we measuring the right things? We’re measuring activity, are we measuring? symptom? Are we measuring presentation? Are we structuring that in a coded way so that we can look at it retrospectively? Are we are we measuring the concept of not just what was done in the consulting room, but the intervening activities? are we comparing and contrasting not just the decisions that were made, but maybe also the timeliness of their execution? And I think there’s a real challenge when you look at and again, I’m a Systems I’m a process guy, is a decision that is well made in a consulting run but poorly executed better or worse than the a, a medium or a mediocre decision that is reliably and perfectly executed. Well, you know, from my point of view, neither is ideal, but at least if I can eliminate the variability of execution, I can measure and I can understand that the decision itself is partially fluid, I can do something about it. And if I can’t count on the decision itself being reliably executed then I have no way of understanding and whether the decision was right or not. So the idea that we have a complex system with multiple moving parts with variability, both in the decisioning, but also in the execution. And just to be clear, what I mean by execution is, we may be may decide that a patient needs to come back following a discharge event after seven days. But how many times does that happen are we can we absolutely guarantee the fact that that patient will come back and know that they are going to come back in the right way at the right times in the right place? That’s what I talked about when I’m talking about execution, the intent to haven’t come back fantastic. But the execution that’s not something we’re great at. And it’s universal. You know, you look at it screening, recall, medical and wellness. I mean, there’s a million different flavors of this. But what SpaceX does really well is they measure they have consistency and reliability of process, and they understand where you get variation. We’re not good at that in our industry, and we need to be better.
Nick van Terheyden
For those of you just joining, I’m Dr. Nick the incrementalist today. I’m talking to Robbie Hughes. He’s the founder and CEO of LUMION, we just launched into space x as an analogy, I think, you know, importantly, you highlight the fact that, not just about failure, and you know, ultimate goal, but you know, you bring up this issue of data. And that’s, I don’t want to say completely lacking, but we certainly haven’t had the measurement going through the process. And I’ll pick an example that’s just still jaw dropping to me, we have no idea if patients are taking the medications that we prescribe, I mean, literally no idea. best guesses, it drops off at levels that are astoundingly high 50% or more that don’t even take the medication. And then we’re saying, did this work or not? So you’re absolutely right. So as you start to think about solving this, so one of these problems is getting the data. So that’s a, and that’s been a challenge. So have you solved that? And how you solving that? And if it’s, if it was the data now, if you have the data Have you got a better handle on the process is, is that sort of part of the critical process that you bring to the table that allows us to start to measure and show where we are having success? Is that how it works? That’s That’s
Robbie Hughes
it. I mean, we think of this as a flywheel. So the first, the first job, and the most critical job is to eliminate the variability in the process. And it doesn’t really matter candidly, whether the process is fantastic or terrible. The point is to make sure that it happens. Once you’ve done that, then you can start looking at whether providers care teams, patients are deviating from that process. And you can start to then understand whether that’s a positive or a negative influence. And once you’ve done that, then you can start saying, Okay, well, maybe the process can be better in this way or that way. So an example for us that is a firm favorite at the moment is around surgical optimization of getting patients into elective surgery as a problem making sure that it’s it’s done reliably and effectively so that the O R ‘s are well used as a real issue. And, you know, it sounds complicated, there’s a lot to do, but actually, it’s very predictable, we should know precisely for a given patient what needs to happen. And it’s not the same for every patient needs to be different for every patient by because every patient is different. But that doesn’t mean it’s not open to massive optimization, what we find is that we can increase the productivity of that care team by roughly two thirds, simply by ensuring that the right things are happening on a per patient basis. Now, what’s really interesting about this is not so much that we’re making that kind of best practice happen by default. But what’s fascinating is that we’ve put in place a process that the care teams may sometimes choose to deviate from. So we may be recommending an auditor, for example, for a particular patient, because we can detect that maybe half of the labs are already on file, we don’t need to reorder them. And we know that for this procedure, and this type of patient, and this combination of clinical factors, these are the things we need to be doing. But it may well be that an anesthesiologist says, we know what for this patient, I want to do something different. And I want to add this test or takes us away. What that does, from a data point of view for us, it says for this patient, in these circumstances, we’re going to do something different. And then in aggregate across a population, we can then say that patients like this, but these characteristics are being overwritten with these outcome. And the guidelines are not saying we should do that, but that’s what the judgment of the care team says we should do. And so let’s talk about that. Is it that the anesthesiologist is wrong? Are they right? Do they know something? Have they is there something that as a learning system we can do better? And if that’s having a positive outcome, and more often than not, it does tend to be a positive outcome. Can we then update Our best practice so that that then becomes the new best practice. And as a result, the the general level of quality of, of compliance and all of these things grows. Because the system and the capital S in terms of the people, the patient’s everyone gets smarter. And quality improves, that’s the me is what we need to do to drive up standards, drive up quality drive up the coordination of care, because we’re then having a proper conversation about whether things work or not, when we’re in a world where we’re either doing the same thing to every patient, irrespective of whether or not it’s it’s warranted, or we don’t know what we’re doing, because we’re inconsistent or unreliable in our execution, then you can’t have that conversation and you end up with incomplete data, you’re making incomplete assumptions, you’re trying to change based on things, you don’t understand that that’s not how you move either a health system, a group or an industry forward. So that’s what I believe. And that’s why I think the very precise coordination of these processes is so critical, because when it’s done well, it’s absolutely transformative. And that stuff, that example I just gave was actually straight from a customer where it was a very particular lab test that they found they were overusing. And they were able to get rid of that and reduce the cost of the process without compromising policy.
Nick van Terheyden
Yeah, so I don’t know what the cycle is like in aerospace and space in terms of innovation being applied in real world, I suspect that SpaceX shorten that timeline. Medicine, I know is notoriously slow at learning from anything, I mean, even the application of new treat, we’re not even new treatments, treatments that we prove in the literature in science can take 15 to 20 or more years to reach the clinical coal phase. Maybe that was a data problem, or the lack of you know that understanding? What’s your timeline for that correction? Because that, to me, sounds really exciting, because now, instead of this, just if we are improving a very slow improvement, this sounds like a much faster improvement process for everything is that how it’s how fast you’re doing this.
Robbie Hughes
So in that example, I just gave from the implementation of the, let’s call them industry standard guidelines to the identification of a warranted variation, and subsequent kind of interrogation of that, and then implementation of new best practice, six to eight weeks. Wow, roughly, system wide. But I mean, that, that’s not because what we’re doing is hard or easy. It’s because we have implemented a technology which allows us to measure, it’s a technology that allows us to analyze, and then it’s a technology that allows us to actually execute and implement these changes. And if you think about it kind of more generally, in a normal system, without this technology, you don’t have the data, because you’ve got massive variations over what’s going on on the ground. So you can’t trust what you see. That means your ability to analyze is based on incomplete data. So the assumptions you’re drawing are going to be somewhat more limited. And then even in the best case scenario, and those two are not true. And you do have the data and the analysis. How would you then change a care team across 100 hospitals, comprising 1000s of people to change the way that they practice? I mean, nevermind the fact you’ve got to convince them to do in the first place. How do you actually make sure that it happens? That’s hard. I mean, hard slash impossible, I would say. But it’s it comes down to can you somehow find a way to use technology to institutionalize best practice, such that the stuff that you know, you need to do happens automatically. And what you’re doing then is freeing up your care team to focus on the areas where they add value, apply their judgment, apply their skill?
Nick van Terheyden
Yeah. So interestingly, I think a lot of resistance to some of the thoughts that you have or the process, but you know, this is medicine isn’t widgets, where it’s variability, all of those things, but I think the undercurrent that I hear in your description of this, that I think would really persuade clinicians that this is a better way is that rapid return to say, Yeah, this was the protocol but we found an error, we can demonstrate that that error is actually causing poor care, you know, less quality, and it gets fixed so that now I’m no longer wasting my time. To change it. And I think that now sort of reverses the position that says, We’re not widgets, you know, everyone is different. I’m reminded of, you know, Life of Brian, unfortunately,
Robbie Hughes
I think you’re right. But I’ll give you another example of this. So I am one of the big objections you get to, using a technology like this is, oh, we’ve got to sort out our processes. First, you’ve got to have everyone both in the same way, so that we’re ready for this technology. And I understand how that mindset can occur. But I would challenge that and say, well, actually, you know what, you’ve got 12 different hospitals, and they’re all working different ways. Fine. Let’s put that let’s institutionalizes 12 different ways. And then let’s measure and see what’s working. And then once the technology is in place, maybe there’s one that is, you know, just a stand up performer, and then let’s tweak, pull a few levers, change a few things, and then rapidly roll that out across the entire system. And then you can avoid a year of conversations and runs, we’ve got 10 People with 11 different opinions, trying to work out, is this the right way to do things? Is there a better way, because they just don’t know, and want to know, but they don’t know? Objectively, they don’t know. And so I think this is the opportunity, we just need to have a different way of thinking about it.
Nick van Terheyden
Yeah, I like that a lot. I think it’s very appealing it, you know, puts you on a trajectory that not only it’s not you LUMION external resource, it’s my own peers who I work with, and respect, that are essentially contributing to that trajectory that, you know, we can all agree on, and we can disagree. But we have to disagree based on the data, and the actual results. And, you know, ultimately, we all drive in the same direction, you know, to my original point, what’s the ultimate goal is, you know, the best possible care at the lowest cost for that patient. So this is, you know, exciting times, tell us a little bit about where you see this going into the future, what are the opportunities? And, you know, does this just apply? Where does it get applied.
Robbie Hughes
So we have use cases sort of technology today and care transitions to home and surgery about a bunch of really cool stuff happening, their primary care, I mean, I hesitate to use the word limitless, but it is we are finding application for this approach and the technology across the entire spectrum. I think that the the important thing to bear in mind is you don’t have to do complete reengineering. On day one, you can, you can implement it in phases, you can start with very simple things, do your integration into it, and then add an add on, you know, a customer would Lumia will typically start with one thing, a particular acute problem they want to solve. And then we’ve got customers who’ve implemented it 2530 additional use cases on top of that spanning their entire health system. So there are there are lots of ways of thinking about this. But starting small, starting with a discrete pain point, understanding how you measure it. And then using the innovation within your own team to say, you know, we’ve got a check, we’ve got a challenge here, how can we approach this using this methodology? It it’s a constant feedback loop for us that allows us to see things that we couldn’t have imagined a year ago. And it’s just very, very exciting. It’s a pleasure to be part of it.
Nick van Terheyden
So you think about this, in terms of the resources, this is beyond sort of, you know, traditional resources, this is extend further out, I think.
Robbie Hughes
Yeah, so we obviously have a we have a transformation team that we use to support our customers. And one of the things that they will always think transformation is hard. No, there’s no point in trying to argue that. But it’s hard. If you don’t have an approach, it’s hard. If you don’t have mature support, it’s hard if you are doing this for the first time we do this. I think we did some like 65 projects last year. You know, we do this a lot. We have a we have a approach. It works and we teach our customers have fish. And so if you’re short on staff, if you’re if you’re struggling with burnout, all of these things are legitimate reasons why you need to think about a better way. And there’s no shame in asking for help, you know, there are that you don’t have to keep doing what you were doing before. Sometimes innovation can be found well outside of our normal approaches or normal industry and in times of crisis when, you know, frankly, the world has completely turned upside down in the last 24 months. And today, you know, there’s a CEO resigning every 72 hours in our industry. We need to embrace change and do things differently because the way we were doing it before just wasn’t ever going to be sustainable.
Nick van Terheyden
Well, exciting times I think, you know, I love the sort of approach obviously, it it’s incremental and in design. It takes the adjacent possible from other areas applies them, you know, and importantly applies data and insights to allow us to essentially approach this improvement process that I think will be highly attractive to healthcare systems and clinicians. Importantly, unfortunately, as we do each week, we’ve run out of time, so it just remains for me to thank you for joining me on the show. Robbie, thanks for being on the show.
Robbie Hughes
Thank you very much. It’s been a pleasure.
