April 24, 2019
Thought Leaders in Healthcare IT: Life Image CEO Matthew Michela
Life Image CEO, Matthew A. Michela was interviewed by Sramana Mitra of One by One Million for the Thought Leaders in Healthcare IT blog series.
An in-depth conversation on the stage of data in the medical world.
Sramana Mitra: Let’s start by introducing our audience to yourself as well as Life Image.
Matthew Michela: I’m the President and CEO of Life Image. Life Image is a medical evidence-based technology platform that services a wide variety of healthcare entities and partners inside the healthcare ecosystem on a global scale. We’re a relatively mature company of about 11 years.
We’re best known as someone that helps with access to medical information. We have about 10,000 hospitals in the United States where we are their interoperability layer for access to medical information. Roughly, there’re another 60,000 or so hospitals and clinics globally that connect into our broad network.
With that ability to access, move, normalize, identify, and curate medical information, we’re putting in the hands of physicians information in order to make medical decisions, diagnosis, and treatment. Those can be use cases for oncology, stroke, neuro, or urgent care. We serve all of those at an enterprise level.
We also work with technology companies building AI. We work with clinical trial companies developing drugs and devices. We work with telehealth companies and care management companies. Think of our services here at Life Image as the network that connects medical organizations and people that need medical information for any purpose.
Sramana Mitra: My next question is going to be about what trends are you seeing. How are your customers using data? Obviously you have a very rich dataset in your ecosystem.
What are some unique and interesting visionary ways that data is being used and interesting applications of it being developed on top of that data?
Matthew Michela: Excellent question. At the base of our role in healthcare is interoperability and extending the ability to break down data silos and make data available. I would actually argue that the trend itself and the language we use around interoperability is really about interconnectivity.
It rises a level above that to not just figure out, on a technical level, how to get data from A to B, but also how to make that data relevant by combining it with other data that then can be useful in making decisions or doing other things.
While we are a technology company at our core, it’s really about connecting these disparate constituencies in the healthcare ecosystem that have very different goals, purposes, and timing. Ultimately, they really do need access in a usable way. There’s this continued evolution of interoperability and the continued requirements by folks like the Federal government through OIC.
Matthew Michela: The second thing we’re doing in this big trend in AI, which I think you’re going to see dramatically more of in 2019 to 2021, is adoption of AI. We spent this first generation of AI creating the new algorithms and the computational tools and outcome. If you look at that entire industry, there’s very little adoption.
The reason there’s little adoption is because it’s not a technology solution. I can be the smartest data scientist in Silicon Valley and create an algorithm that gets approval and works. But what I have to get that software into the workflow where they’re not going to change everything they’re doing for one piece of AI. They need to fit.
Our provider customers are big academic medical centers. They’re looking at Life Image and saying, “You’re the interoperable technology platform that touches all data. You’ve been improving our workflow and have been working with us for five years. Why don’t you be the transport mechanism for AI so you can bring those algorithms in on your pipes. We might want to use five different AI companies but we’re not going to do five different independent AI integrations. We don’t have the resource, cost, time, and money to do that.”
Matthew Michela: Because data is so hard to get even in this new world of statistical evaluation in front of us, the data that you use is still inadequate. What data is easily accessible? Claims data or data out of an EHR?
That’s real world because I can tell you what I paid for two weeks ago or yesterday. I can pull data out of an EHR that happened yesterday or five days ago. That’s a payment evaluation. EHR has been built, principally, from the foundation of how do I keep track of things so I can bill and get reimbursed. It doesn’t have a lot of clinical information.
I can see from an EHR that an MIR occurred, but I absolutely don’t have any idea what that MRI was ordered for. I know how much I paid for that, but they don’t know if I’m looking at a disc, a nerve, or a tissue. Those are very hard to get and don’t make their way into the EHR unless a human being can read them and review them.
In our work and our ecosystem partners, we have access to the data underneath. The clinical information about what actually happened to the patient is unstructured. While this is novel, you will see over the course of the next three to four years a complete transformation of the way trials are done by pulling all of these novel data sources in.
What is your product roadmap? Where are you in that thought process?
Matthew Michela: Great question. I would say that we don’t have 11 years of data that we would ultimately use for clinical research. For many years of the company, we were a transactional-based model. Since we built this hospital and provider network and moved data for a single clinical decision episode treatment plan, we have years of history of just purging that data on a very quick routine basis.
We don’t have 11 years of data but we certainly have substantial data relative to the industry and we have an incredibly unique dataset of breadth and depth around imaging itself.
To your bigger question, moving to PaaS in the data world is something that is maturing. If this was a 26-mile marathon, we’re probably 5 miles in. We have the ability to understand access and bringing data. We have the ability to store it, query on it, and apply AI of various types.
We have the ability, in the first generation, to combine our data with other data sources like claims. The trick to all of this is are we building a platform so that others can query and we can run on that. The answer is yes. It’ll be 2020 before we’re GA with that in the marketplace.
Sramana Mitra: How long do you think it’s going to take for those seven kinds of datasets to build up in an accessible fashion? Are we a decade away?
Matthew Michela: We shouldn’t be 10 years away at all. We’re probably in a three to five-year timeframe. You’re going to see, next year, significant advancements. Where does this thing start to elevate?
This initial focus is going to be around patient and site identification. Everybody has this paucity of patients. That’s a hot problem to solve right now. You’ll see that. This needs to be solved. Not just how do I find patients but how do I make sure that I have the right data on what they’re doing today and tomorrow. It works its way back into pure research. It works its way through the three phases of trials. It has to be applied in post-market.
In that regard, it will probably take 10 years to look at a complete drug and device development process to say, “How do I work through these various phases from pure research all the way to post-market?” The initial focus in the next three years is going to be major advancements in select therapeutic areas as well as patient identification and site selection.