The Informonster Podcast
Episode 1: an Origin Story, Charlie Harp Talks About Healthcare Information Technologists, our Aspirations, Challenges and Opportunities
August 20, 2019
For this first episode, Charlie Harp, CEO of Clinical Architecture, talks about the purpose of the Informonster Podcast. This includes a short journey through the history of Clinical Architecture, as well as a perspective on the objectives of healthcare IT, the barriers to achieving them and the mindset needed to overcome them.
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I’m Charlie Harp and this is the Informonster podcast brought to you by Clinical Architecture.
So for this first episode of the Informonster podcast, what I thought I would do is kind of explain what the Informonster podcast is all about. Let’s start by talking about Clinical Architecture only briefly. Clinical Architecture is a company that I started back in 2007. I started Clinical Architecture after working in clinical labs, clinical trials, drug information, evidence based medicine. I spent a lot of time in the trenches. And what I found was that the whole plumbing of healthcare terminology, ontologies, rules, structured knowledge and how that integrates with patient data both at the point of care and at the point of discovery, analytics was kind of underserved. And I think part of that was because, traditionally, it wasn’t the sexiest part of healthcare. And so I thought that standing up an organization whose focus was really around improving the plumbing, improving the quality of the data, the integrity of that data, the the way we move encapsulated knowledge and those things like terminologies and ontologies and structured data that are foundational to everything else we do in healthcare was important. And it’s been a journey, the last 12 years. We’ve learned a lot at Clinical Architecture. I’ve learned a lot personally, and we’ve had some fantastic partners and clients that we’ve worked with. And so the whole idea of the Informonster podcast is to be a venue where we, as informatics people, engineering people, clinical people can talk about some of the challenges that we face in health care every single day, Talk about what our objectives are, what our barriers are, and ideally find ways that together we can make the journey a little more enjoyable, maybe a little easier, maybe a little bit quicker. So if the goal of the infer monster podcast is to kind of shed light on these things and share information about these things, it’s only fair to talk about healthcare IT itself in order to talk about how we’re going to improve things in healthcare IT, we gonna kinda talk about what the objective is.
So let me share my perspective on this and see if it resonates with what you’re thinking. In healthcare IT, software is designed to take some of the burden away from a human being who’s trying to achieve an objective. The objective of healthcare itself ideally is improving outcomes, taking good care of patients, learning about new ways to treat conditions and issues and generally improve the human condition either at the level of a single human or at the level of a population; and to do so in a way that makes the people that are involved in that mission, the providers, the care teams, the patients, their families, their support systems, make their lives a little bit easier, make the outcomes better, and do it in a way that’s cost effective. So let’s say that’s the dream of healthcare generally, but let’s talk about healthcare IT. What are the objectives of healthcare IT?
Well, I kind of talked about one of them. The first objective is to take some of the burden away from a human being involved in the process. Some of the ways we do this are, you know, processing reimbursements, scheduling, doing those administrative healthcare things. And that’s something that I think we have a lot of experience in healthcare because for a long time healthcare was not a computerized space. It was a very much human oriented space with handwritten notes and paper charts and things like that. And so when we started automating healthcare, we took those charts and those things that people did like to do the billing type things and we automated it; the scheduling things, we automated it. What we haven’t really been able to do successfully is improve the other side of it, the care side of it, and the question you would ask is, well, what do we need to do to improve the care side?
Why do we need to improve the care side of it? The challenge we face is a couple of things. One is the rate of information change in healthcare is astronomical. I’ve heard quotes from anywhere from 30,000 to 40,000 medical journal articles coming out every single month in healthcare the year after you graduate from medical school, you’re 10 years behind. When we learn things, how do we make that available at a time when it’s relevant to a provider or treating a patient? I call that artificial experience where you’re taking information that somebody needs at the time they need it and delivering it to them in a way that is effective. So that’s part of it. Another part of it is being able to make sure that you have a complete picture of everything that the people caring for patient need to know, or the people that are trying to understand a disease process or a population need to know.
And that has to do with the kind of coalescing and harmonizing information from across multiple venues into a single continuum that allows somebody to look at that and say, “Aha, I see everything. I see the things that are right and I see the things that are wrong. And based upon that, that holistic understanding of what’s going on, I can make an optimal decision”. That is not where we are in healthcare today, unfortunately. I think when it comes to streamlined processing of invoices and bills and schedules, I think we’ve got a pretty decent handle on doing that kind of thing. I think when it comes to actually helping remove some of the burden from the providers of care or bringing information to the people that are doing research, I think we still struggle with that a lot. That’s actually an area that is near and dear to my heart because what we focus on and what I focused on for the last 10 years of my career is what I call high resolution healthcare.
High resolution healthcare depends upon a great deal of understanding and awareness and very high quality data. So let’s say the objective of healthcare IT is to be of assistance, remove the burden, streamline healthcare, and help people make good decisions; augment their ability to make good decisions. We’re not there today and we’ve been at this for awhile. What are the barriers to getting there? In my opinion, the barriers to getting to high resolution health care, to achieving this objective of augmenting healthcare in a positive way, I think there’s a handful of of issues and some of them are just bound up in the nature of healthcare itself. So let’s talk about a few of those. The first thing is healthcare information is very sensitive, so we have to treat it delicately whenever we change it or we display it. We’re in a situation today where we have to treat it with kid gloves and that’s perfectly fine.
The other problem we have in healthcare is that the people that are involved in the process, there’s not enough of them. They’re victims of time famine. They don’t have enough time to do everything they need to do, to feel like they’ve given things appropriate attention. That’s another reason why we need software because software is essentially a time machine. Computers are fast and they can process a lot of data and they allow us to do things in a much shorter amount of time. The primary benefit of software other than connecting information is doing things fast and so that is a big problem in healthcare because people’s time, famine impacts a lot of things. Because people are time-famined because they are always in a crunch, it also affects the rate at which we can create change in healthcare, or we can evolve healthcare because every time we try to evolve healthcare, it creates a cycle of downtime that impacts people who already don’t have time.
So time famine is another thing that limits our ability to be as effective as we would like. The next thing is healthcare is local. I started out with a mindset that why don’t we just standardize everything? Why don’t we make everybody do the same thing? And I didn’t realize at the time that that’s unrealistic. When you think about what’s happening with health care, the actual process of healthcare is you have people with very little time dealing with literally life and death situations and trying to do their best to help their patients come to a successful outcome. And there’s a huge sense of urgency in that process. So when it comes time to decide to describe something or do something, you don’t have time to wait for a standard body on high to come to a ruling and produce something that you can use.
Every single healthcare institution in the United States is like MacGyver. You do what you can with what you have and you have to do it fast. You don’t have a lot of time. Especially when you talk about terminology and things like interoperability and normalization, that is probably one of the largest challenges. You want everything to be standard, but you’re talking to all these people that are desperately trying to do the best they can to treat their patients and deal with life and death situations. So as much as we might want it for our own convenience to say that healthcare should have one standard terminology, unless somebody drops the perfect terminology that satisfies all of us tomorrow, or you know, allows us to request something and get it near instantaneously, that whole idea of standard healthcare, whether it’s format terminology, I think it’s kind of a pipe dream.
And I hate to say that because I know there’s a lot of people that want that utopian reality, but after 30 years working in this industry, alongside the heroes that occupy healthcare and healthcare IT, I’m just telling you, I don’t feel like that’s an achievable dream. I think that as long as we hold out for that dream, we won’t address these incremental problems; these pragmatic, practical problems that are right in front of us. And so that’s the other thing. When we talk about things that are barriers, we have to deal with problems in incremental quanta. You can’t just assume that we’re going to flip a switch and fix everything tomorrow in healthcare. That’s not realistic. Expectation is actually another barrier in healthcare. This idea that change is hard. Change takes time. We’re in a time famine environment, so we have to have the fortitude to realize that if we’re going to make effective change, it’s going to be hard.
It’s going to be challenging, but changing incrementally in the right direction gets us one step further to our destination, which is to augment healthcare in a positive, effective way, improve outcomes, improve our understanding, and do a better job generally of caring for the people that need our help in healthcare. There is an additional barrier, which is just complexity. So you’re dealing with privacy, you’re dealing with interoperability, you’re dealing with unstructured information, you’re dealing with complexity, disease processes and treatment modalities and all the moving parts of healthcare are not simple. It’s complicated. It’s kind of like a physics problem. In a physics problem when you talk about, you know, the orbit of a planet, the only way you can do that is by taking all the complexity out and simplifying the problem. In healthcare, we don’t have the luxury of taking the complexity out.
The complexity is relevant, it’s contextual, and it will impact our trajectory if we don’t take the complexity into account. So these challenges, the only way to overcome them is to take incremental steps in the right direction. And part of my goal at clinical architecture was to solve my niche of a problem. I’ll focus on terminology, ontology, reasoning, looking at data in a longitudinal way; while other people build interfaces for providers or aggregate data and do warehouses or, or build rules. But the trick is if we don’t all feel like we’ve got to solve the problem individually, if we can work together to identify optimal patterns and leverage each other’s efforts, then maybe we can make effective change happen in our lifetimes. And there are a handful of examples of this. Things like FHIR for instance. FHIR by the folks at HL7, and of course Graham grieve is an exciting development with people coming together and focusing less on the politics of standards and the pragmatism of creating something that people can practically use to achieve an objective which is sharing information.
That’s an example of the kind of thing that, you know, I hope that we can foster with the informonster podcast and hopefully, what we’d like to do is some of the podcasts will be educational. Some, will talk about events and things that are happening in the industry and some will be us talking to people both within Clinical Architecture and industry thought leaders about those things that, that we should be sharing information on; those things that we collectively can leverage to move healthcare down the road in our journey of improvement. I, decided to keep this first one short, so I’m going to go ahead and wrap it up. If you have any ideas about the end of the Informonster Podcast, things you want to learn about, things you want to talk about, or if you want to know, get involved and be interviewed and talk about your thoughts on the subject, Just email email@example.com. I really appreciate you taking the time to listen and look forward to working with you in the future to make healthcare IT better. Thanks.