Sramana Mitra: How long has this company been around?
Matthew Michela: Life Image is 11 years old.
Sramana Mitra: You have 11 years’ worth of data that is built up in your system. That data is valuable. My next question is, how do you make that data available to people who are trying to do research or anything that can be built on that data. What is the mechanism?
Let me elaborate a bit further. I don’t know if you’ve seen any of my writings about PaaS and the derivative of that would be Data-as-a-Service. You seem to be in a position to be able to do a PaaS kind of play on what you have built up over the last 11 years.
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.
What we’ve been doing in this last phase of the business is helping folks with fit-for-purpose data where they have a specific trial that they are looking for or there’s a specific kind of patient-specific population. It’s about novel data.
I know you appreciate big data aggregators. You look at Deloitte and others who, for a decade or more, are using data to support research. Most of that data is claims data or data that you can extract from an EHR. It could be pharma or biometric. In the last three to four years, we had this massive explosion of genomic data that’s now becoming available.
What we bring to the party is this imaging piece that nobody else can. If you think about clinical use cases and trials as an example, a great deal of them have to have imaging to evaluate the clinical endpoint. You can’t run a cancer trial if you can’t see the size of the lesion.
Enhancing those datasets with that capability is incredibly novel. It’s incredibly new. We’re probably the only ones that are going to be in the market for the next three to four years. If you look at the data aggregators, even someone as sophisticated as Optum, they don’t have imaging data. While their platform operates as a data service for many folks, it doesn’t include the capacity for imaging.
We are building that. You will start to see that evolve from the point of access, rights, and de-identify and re-identify the data. Can we normalize the data? Can we assure that the data is of the right quality, which means getting human beings out of the curation process? Can we automate? We are doing all of those things very aggressively. We are now able to provision data back but we’re not quite ready yet at this moment to answer questions that most of the industry still can’t answer.
Remember the circumstance that I told you about of a company developing a drug and looking for 70 patients. In order to find those patients, they need to extract relevant data around the demographics of their patient, their family history, their history in the last five years on pharmaceutical use. They need imaging data. Then they need some genomics data.
Those seven different types of data exist nowhere in the world together yet, even though you’ve got this multi-billion dollar data aggregation and trial support world. We’re doing it today and we have customers. We are running a marathon but nobody is in front of us yet.