July 23, 2019
By Julie Gould
Matthew Michela, CEO and president of Life Image, a global network for sharing clinical and imaging data powered by industry leading interoperability standards, discusses a new partnership with Bialogics Analytics and highlights how this partnership will address interoperability challenges and improve patient outcomes.
First, can you briefly highlight the newly announced partnership among Life Image and Bialogics Analytics?
Matthew Michela: Absolutely. Here at Life Image, we're thrilled to be able to work with a company like Bialogics that has spent so much time and energy creating the ability to identify and extract data from hard‑to‑find places within radiology departments and machines in hospital settings in order to help drive value and help create insights within the workflow that improve productivity, save money and time, and help improve patient care itself.
We, as two companies here, share pretty similar missions about focusing on the improvement of patient care and making the health care system more efficient; and doing that by focusing on interoperability, breaking down data silos and democratizing data across the health care ecosystem so that there's a lot less friction in figuring out what to do and how to do it better here.
Bringing these two organizations together, Life Image which is a very large health care network company focused on interoperability and having the technical pipes to connect the data sources, and a company such as Bialogics that has the wherewithal and the smarts of understanding what some of that data is. Freeing it up and then applying analytics to it is a wonderful partnership of our core competences here to focus ultimately on the improvement of patient care and helping manage costs and resources more effectively for our provider customers.
Can you briefly highlight what interoperability challenges health care providers and services currently face? How does this new partnership address those challenges?
Mr Michela: Sure. When you talk about interoperability, it's a term that's been used probably now, and maybe we'll even say misused, for the better part of two decades in health care. It really describes what, in reality, is folks building health care solutions.
Those solutions could be a very big MRI, or hundreds of different types of databases, or monitors to show results and imaging to analytic solutions and software solutions where ultimately, the purpose for creating those technology solutions is to get to a specific goal which should be improving patient care in the form of manufacturers for imaging machines.
As an example, they would argue that they're spending massive amounts of time, and money, and resources to create a better way to diagnose the disease on behalf of the patient. That primary focus of: what is my use case? What's my end goal? What am I trying to do? I'm trying to focus on the science. I'm trying to make sure that it works. I'm trying to create something innovative. In many regards, making the data that results from that software solution or that hardware solution transparent and available is in some regards a secondary concern.
What we have is a history of many decades of health care companies building these technology solutions where standardization of the data, standardization of the code and software, as an example, is a secondary concern.
The result of that over time is that health care data gets held captive, we would argue, in silos, in databases and in so many different kind of technologies that don't talk to each other and don't allow that sharing of data. Interoperability is both a philosophy and a tactical and strategic approach of understanding what's not standard about where that data is hiding or what is not standard in the hardware or software that stores it.
Then, creating the ability to locate, find and extract that data in a way that allows you to combine it with other data and make it useful. Interoperability itself is perceived to be a technical challenge. It is. It is a technical challenge.
Fundamentally, it really is a business philosophy and a question of priorities of, “Do I recognize that when I'm creating a health care solution, I will do it in a way to make sure that that data then is available to other people or not?” It becomes an active choice that organizations make.
A company like Life Image, a company like Bialogics, we build everything we do into our basic value structure as a company that we will be interoperable. That allows us to connect. Part of this partnership between Bialogics and Life Image, and one of the reasons it makes sense, is because we actually can talk to each other.
Bialogics can use Life Image and our platform, and our footprint across this very broad hospital ecosystem that we have, to help access that data without having to build a very, very different, independent software solution for every single hospital, for every single software version, for every single manufacturing device.
We've already done that here at Life Image. We can plug and play with them. In many regards, that accelerates that ability to understand that to drive value. The challenges here, as we talked about, are perceived to be technical challenges. They are.
If the technology exists and it's already been created then, yeah, I’ve got a technical problem. It is a question of priority and focus among health care organizations themselves and all the vendors to be demanding that that data becomes transparent, to be demanding that they have access to this information in ways that standardize, and frankly not working with software companies or hardware companies and vendors who deliberately create these technical stats and these solutions with proprietary standards and ways that locks the provider into that system for an inordinate amount of time.
I would argue the challenges here while, yes, historically are technical, are not really that here across the board. Our partnership, as we suggested, addresses those challenges.
We're matching up what we have spent more than a decade to build, which is this highly interoperable software platform that allows the identification and the extraction, and movement, and exchange, and mobilization of data among thousands and thousands of different vendors and solutions out there.
What Bialogics is doing is figuring out, “How do I make that information even more discrete, getting more use out of it so that then we can help improve patient care and drive productivity improvements inside of radiology?” which is such an important part of every health care and hospital delivery system capability set.
Diving into the patient side of things. How will patient outcomes be impacted following the launch of this partnership?
Mr Michela: I'll give you an example of this from a patient perspective. When you think about a radiology experience for a patient, their doctor identifies they need to have an image taken. They get an appointment to go and do that. Sometimes they'll walk down the hallway and sit in a waiting room and wait. In other cases, they make an appointment and they have to come back.
Once they show up for that appointment a series of things have to happen in order for that diagnostic procedure to occur. The patient has to show up. The equipment they're going to use has to be available. They have to have the right mechanism and film. In the old days, if it was just an X‑ray, the basic supplies to do it.
They have to have a tech that's available who understands how to do that. They have to be able to enter the order in their EHR here and then keep track of what's going on. All of those things have to happen before that procedure happens. In real life, that means the patient sits and waits. Sometimes it means they get rescheduled.
Sometimes it means they wait a really long time. Sometimes it means the tech isn't the exactly the person that needs to do it and isn't the most skilled to do it. Then, they have to come back for more imaging later. All these things happen in real life.
From a perspective of this partnership between Life Image and Bialogics, what they hope an organization should do in conjunction with us is to understand what actually is happening inside the workflow of that hospital.
As an example, we can figure out when that imaging machine is used and not used, when it is available, when it's most likely going to have capacity in order to make sure the patients don't have to wait. They can figure out what scheduling times are among the techs and who is doing what where.
They make sure the end goal is that that tech is available at the time that machine is open so that the patient can make the appointment in the most effective way. They can track the supplies that have to be available in order for it to move. Everything about that works well.
The kinds of things that you would see in an Amazon factory where they make sure that the product is there, and the box is there, the people are there, the robots do what they need to do. In the old days, General Motors would do that with making cars and production lines and thinking through: what are all my supplies and material ahead of time.
It’s really, really hard to do that work when you don't have data, when it's all observational, when the data on scheduling sits in a computer system that sits in HR, that doesn't get updated, that nobody can ever see except HR.
The time that the machine is up and running and working is, "Well, I think it's most busy at 2:00 in the afternoon through 5:00." The reason it's busy is because that's the only time the tech's available. You can't match those up.
What this partnership does is allows us to take this really important, fundamental data about who's doing what, where, when and how resources are being used in order to take that data, and combine it with other useful data so you could answer a simple question for a patient such as if you show up at 2:10, then your procedure will happen at 2:15.
All those resources are lined up so you can be done at 2:20 so that then your results can happen at 2:30 and can be read back to you. You then can get your diagnostic done and move on for your medical care.
From a patient point of view, in this simple example, it is about improving the patient experience, making sure that resources aren't wasted. We don't have techs who are sitting around for an hour waiting for a patient. We don't have machines that cost millions of dollars to purchase sitting unused while patients have needs and demands.
We're accelerating the ability to apply the right resource at the right time for the right task to the right patient. In that regard, they get their clinical impact most effectively. It accelerates their ability for diagnostic and treatment, which ultimately improves not just experience and cost but patient care. It's freeing that data up in order to have that kind of outcome on patients.
If they cannot waste their time, if they get the right procedure in the right time and the right resources, they've got a chance to participate in the system in ways that are very different than the patient experience today.
Why do analytics play such an important role in health care?
Mr Michela: Analytics are fundamentally important, obviously in any industry, but especially in health care itself. The delivery of health care is an art as we talk about it. It's also a science. Principally, when you think about physicians, and diagnosis, and medical care, we understand it's experiential based. Providers go to medical school for a very long time, then they train, they see patients, they see hundreds of patients and then thousands of patients.
Over time, it's that experience of data points of, "I've seen this before so many times in all of its variety that now I can accurately diagnose and understand it." It doesn't matter whether you're a pediatrician treating a child with a runny nose or you're a neurologist identifying, God forbid, a cancer in someone's brain. You don't do it on your first day of medical school. You learn it over time.
Each of those experiences are data. I'm describing it as the data of a human experience. Each one is an experience in health care, a piece of data here. The more data you have, then the more accurate your work is and the more that you can make health care slightly less of an art and slightly more of a science and objectify it.
Analytics is health care itself, data is the blood of a health care system itself. Without data, without information, we don’t have the ability to apply analytics to understand that data then people are guessing at what to do. That's not how health care should be delivered or needs to be delivered. It has to be more objectified. If you don't have data, you don't have anything.
What analytics does is continue the evolution of the understanding of the human body and the human condition and how from a pure medical point of view it needs to be maintained, supported and corrected when things go wrong. You have to have analytics in order to do that.
At a slightly lower level, analytics are the things that help you determine what is the appropriate resource allocation in health care. In our country, we know we spend an unbelievable amount of resources on health care. That's whether it's raw dollars or it's time people spend on health care. Arguably, there's wonderful things about the outcome of that. Arguably, there sometimes are not.
You only have so much resources, and time, and money in the world. What analytics allows you to do is look for those opportunities to do it better. By better, it's better quality and better outcome. It's also less expensive and better allocation.
In the example I have spoken about earlier, analytics and health care are the things that allow patients to get what they need, when they need it, from the people they need it, with the resources they need it and reduce waste.
That waste can be their time or that waste can be a bad diagnostic opinion that they've received. It could be the wrong drug that they get. It could be taking the right drug in the right amount of time. Analytics help drive understanding of those gaps so we can improve our health care system here across the board.
What are you most excited about in this partnership?
Mr Michela: First of all, I'd like to say I'm really excited to be able to work with Bialogics. It’s a fantastic organization, incredibly skilled and competent about what they do. I'm very excited about our ability here at Life Image to work with them and accelerate the adoption of their solution inside the hospital systems.
This is data and information that they all need that will improve what they're trying to do that will produce hard‑dollar ROIs for them while improving exactly the things that they all want to do.
What this partnership does is allow us to deploy that against thousands of hospitals here over time in a way that Bialogics couldn't do independently, and frankly might take a decade to be able to do those kinds of things. We can look at this and say, "We can help you do that much, much, much faster." What I'm very excited about here is being able to deploy that.
The end result, and goal, and outcome of that should be better patient care, better patient experience, better resource allocation for hospitals. It wins every constituency by doing the right thing here. That's a very exciting proposition for us to be able to spend our time and energy on.