Overcoming Barriers to Interoperability and Patient Data Ownership

In a recent podcast, Matthew Michela, CEO and president of Life Image, a global network for sharing clinical and imaging data powered by industry leading interoperability standards, discussed innovations in interoperability and the value of giving patients control of their own health data. The following is an edited excerpt from that podcast. 

Over the last three decades I’ve worked and experienced the payer world, focusing on insurance and risk management, reimbursement, and financing. In the provider world, I have supported the delivery of health care services at the physician practice level. 

In technology, I focused on automating solutions and accelerating the application of health care decisions by physicians and patients in chronic care and disease management, organizing health care services for very ill people and populations. As for broader population health, my efforts focused on trying to provide systematic public policy and population based solutions in health care that really affect health care quality. 

Now, I am leading Life Image, a company that has spent eleven years focused on improving interoperability. We began with the field of medical imaging, which historically been the most difficult to share. Today, we orchestrate the flow of all types of clinical information, including imaging, to help care teams across the broad health care ecosystem to make better clinical decisions, in hopes of providing better care for the patients. 

Today, it includes connection points between 10,000 United States hospitals and medical facilities within a health system, and another roughly 60,000 hospitals, clinics, and facilities around the globe, serving on average to between 10 and 12 million unique patients a month. Life Image provides data for a wide variety of clinical uses and clinical use cases including telehealth services, physicians in and out of hospitals, and transferring imaging from one location to another. 

We have a consumer application we’re quite proud of, called Mammosphere, which in essence really breaks the data silos down completely by putting the patient in the center of the world by providing a platform to acquire their medical records and their imaging, irrespective of where it was received. This helps to avoid the conversation of running around and trying to collect partial records. 

Life Image also partners with medical device companies, collaborates with payers and providers, but above all utilizes the broad network connections and ecosystem of constituents for whatever type of medical information needed, so that decisions can be made for a patient. 

You can make a decision to help improve a drug, to create a device, to decide whether payment is appropriate, etc. We help facilitate access to that information inside the health care system itself, because health care data is very challenging to acquire. We help solve those really complex technical problems at Life Image. 

When you can’t access medical information, what happens to patients? The way I explain this is by offering a comparison. Think of health care data as the blood that flows through our own systems today. 

If you don’t have blood to deliver oxygen, then you’re certainly not going to live very long. In health care, you need data in order to make medical decisions that impact where a patient goes. It’s about having the right information and the correct information at the right time. That determines, ultimately, the quality of care that a patient ultimately receives. 

Think about a mammogram. Our patient may have this experience, where they go in for a mammogram and perhaps that particular mammogram may not be entirely optimal. This could be because of machine error, improper setup, unavailable technicians, software issues, or a patient’s breast tissue being slightly denser than normal causing the imaging itself to be cloudy and less precise, all amounting in less than great data. The consequence could include unnecessary follow-up testing or undetected abnormalities. 

Having quality data at the right time matches up to determine what a patient outcome ultimately would be. 

Before innovations in technology, we had paper records sitting in paper files influenced by the human data collection process, which means they’re likely error ridden, and frankly, inaccessible. 

However, the application of health care technology also creates other barriers to accessing data. While it eliminates the physical transportation time burdens, most health care data is accessed maintained, and resides in different kinds of technical health care standards. Outside of health care, most data is ultimately standardized—you can share it, talk with systems, and computers can talk with each other. 

Then we have the science itself that changes in health care. We have an implicit bias in health care that says we want to improve health care quality all the time. 

Thinking about an imaging example, my focus, if I’m creating an MRI machine to take pictures, is to create better science so that imaging is more precise and accurate, including better digitization, improving the ability to understand tumors and other things in the imaging. 

The imaging we have today is light years ahead of imaging that existed even 10 years ago, let alone 20 years ago. So, the science of creating almost magical, better science for diagnosis and treatment is fantastic. But if you’re in the business of creating better science and improvement in quality, that has to be clinically tested. That means sometimes you’re creating new health care standards and formats for the data, because it never existed before. 

In health care, we’re creating all of these new standards because the science is driving it. It’s a secondary use to say, “I’m not going to put this wonderful health care technology in the market for five more years, because we’re going to focus on the technical issue of standardization.” We have an underlying arms race in health care improvement that creates technical barriers. While we say technology solves all problems, we need to standardize it. 

It takes a long time to create health care technology and validate it. Sometimes software has to be approved by the FDA. Sometimes trials have to be conducted. That extensive process adds a lot of cost and complexity, and then creates an inflexible system that’s even more costly to modify and validate. Then you have a really high cost of implementation of health care technology, and to train health care technology for people who actually use it themselves in the practice area. 

Health care data is generated from so many different kinds of health care sources. The type of data coming out of a blood pressure machine measuring your blood pressure is different than the kind that comes from your pharmacy for which pharmaceutical you had be prescribed. A different kind comes out of your imaging for head CTs, imaging for breasts, and an altogether different kind exists for claims, health care services, and what was paid for. 

Since those systems, solutions, companies, and technologies are created for very specific uses, and in some cases in non standard ways, or in some cases with technology that takes years of investment to fix, creates a very slow technical environment to create change. 

Again, that comes back to this inherent theme that makes it more challenging to get correct data in the correct time frame to the correct people, so that patient outcomes and costs can be managed and optimized across the board. 

I can take a picture, an image of my knee, and with that, determine if I have physical abnormalities, tendon or ligament damage, or if there is bone deterioration. I can look at it, and I understand what’s going on. But to compare to mammography, breast tissue sits outside of a woman’s body, and is frankly unique to her. You can’t make a comparison between patients without the technology and individual’s history. 

Protocols for mammography determine that at a certain age you get a baseline exam. From then on, you make comparisons. It’s that delta that a radiologist, and a physician examine. The problem, of course, is getting that information from where it might be held, in a timely way at the right quality, into the hands of the physician when you come in and order as a woman to get a subsequent exam and just determine, “Do I have a problem or not? Do I ultimately need to be treated?” 

Life Image helps do that, by making sure that data, that mammogram and maybe five years of prior mammograms are available at the time you do your next screening. It could be the difference between good and bad outcomes, and whether you have additional testing. 

Every day in America, there are women that undergo costly and unnecessary treatment because they didn’t have the prior data available at the time that’s needed for diagnosis, causing stress and terrible experiences. Closing that gap is really important here for patients. 

Why is medical information so hard here? Why does the health care industry continue to struggle making data like mammograms here interoperable and available to patients all the time? 

I mentioned one of the challenges was the science itself. We’re changing the science constantly to adapt to new technologies. That affects the ability to standardize. 

A second cause here is behavior of people who participate in the health care system. It’s very hard to change practice patterns, approaches, and physician workflow. A physician’s time is incredibly valuable. They can’t see enough patients in a day. We create, in our health care system, teams that wrap around physicians. 

Whether it’s a physician office or hospital or a surgeon, there are teams of support people that are there to prepare the patient, get the paperwork, do the consent, get the test data, ready the equipment, set up the surgical suite so that the physician’s time is maximized. When you create that kind of workflow that affects three people or ten people, it takes a lot of energy to create it. 

This is time that then has to be adapted for the workflow. People have to be retrained. For physicians and health care providers whose daily life it is to treat as many patients as possible, putting all that burden for change occurs, but it’s costly and it’s slow. 

Whether a company is large or small, like any given hospital, it might have hundreds of technology providers. To connect them and integrate them all, move data between them and try to create a seamlessness, is just this massive technological overhaul challenge and barrier, again, to interoperability. 

People talk about medicine being a science, but it’s an art here too. What we have in the health care system, if I focus just on physicians, as one consistency, they have a lot of varied experience. They have a lot of varied skills. They have a lot of varied capabilities. 

There’s a wide population of physicians that have different skills and experiences and capabilities, based on their exposure to the treatments they’re talking about, on their training, just on their individual philosophies and approaches. What that ultimately means, with how our system is set up is that allows an individual physician and an individual patient to ultimately make a decision of treatment, a diagnosis and treatment together. 

With hundreds of thousands and millions of those occurring every day, it’s very hard to say, “How do I standardize health care delivery?” Even if the science is built, even if the FDA has approved large clinical trials, it is difficult. 

And every day we’re learning new things about what today we thought was good and tomorrow isn’t so good to do anymore. Stronger, newer research shows it’s good. Imagine that in an environment. We have millions of individual decisions in order to make that happen. 

With that, I want summarize the big picture here among this complexity and the one thing that would ultimately drive it, is having data in the hands of patients and the patients in the center of the health care system. 

It’s this increased drive of consumerism, which requires consumers to be knowledgeable, that requires the government to continue to influence providers to cooperate and change their practice patterns to deliver to patients. 

That is the major factor that solves all these problems overall in the long run, is patient demand in health care when they have the data to evaluate the service they received, the cost they received, and the ability to own their own data so it’s transportable. That’s what’s going to create the greatest change in the system over time.