Q&A: Value-Based Care and the Radiology-EMR Divide


Value-based Care interview with McKesson general manager Tomer Levy.
Tomer Levy, general manager of workflow infrastructure at McKesson Imaging & Workflow Solution

Editor’s Note: This article recently appeared on the Diagnostic Imaging web site and is reprinted here with permission.

The transition to value-based care is expected to be a lofty one for the radiology field. When outlining the struggles, the focus tends to be on how radiology can define value. While defining value is an overwhelming feat, it’s not radiology’s only challenge in the new payment model. Diagnostic Imaging spoke with Tomer Levy, general manager of workflow infrastructure at McKesson Imaging & Workflow Solutions (IWS), about what other challenges radiology can expect to face in the future of health care delivery.

Q: What is driving the trend of value-based care and what does it mean for radiology?

A: Usually when people ask this question, the automatic reply would be government forces or policies, but really what has been behind it is the need to change the way health care is delivered in America. Everyone understands the way it is delivered now is not sustainable, and one way to change that is to demonstrate more effective care giving and health care delivery, value-based care rather than volume-based care. It’s all about making the health system more effective in the way physicians provide care and make sure that we move to a sustainable health system in the U.S.

There are a lot of changes for radiology, radiology used to be a money printing machine for hospitals, there was an infinite number of procedures coming in, it was all about efficiency and effectiveness, and how many studies can you read in a day.

This change is turning their world upside down. Health systems are required to prove the value of their treatment and they have capitated costs and have to prove they spend their money in the most effective way. Radiologists will need to be more involved in the care continuum process and workflow, so it’s not that radiologists can continue sitting in the dark room reading those procedures in and out and just crunching through the list of procedures, now they need to take part in the wider group of care giving teams and be more involved with the ordering physician, more involved with other practices around the hospital, and provide their expertise to the entire team. This is unavoidable.

Radiologists will also be required to base their decisions on wider datasets from multiple data sources. Up until today, radiologists got the procedures and images, they read through the images, if there was a mistake, we just order a new one, it’s not a big deal. When you start measuring value of care and value of decisions, all of a sudden it is expected that before you make your decision you go back and access the patient record to understand exactly the patient history and background to make your decision higher quality in general. The access to multiple data sources is something that radiologists don’t do very well today, obviously they have some information floating in but not to the level that will be required for them when they transition to value-based care.

Q: What are the biggest challenges radiologists will encounter in this transition?

A: Think about the radiologist sitting in the dark room reading procedures and used to getting limited access to some data, you’d read through the data only if you were looking for something specific. All of a sudden, you’re expected to understand, read, and access the whole EMR. EMRs are not built for radiologists today. Radiologists face three main challenges: the first one is access, just the sheer problem of accessing the data. The EMR is one of them, we’ve seen cases where the radiologists all of a sudden need a different log in to the EMR, they may have forgotten it, by the time you’ve logged in the password has expired, now you need to go through a of where to get a new password. In some cases we even see that the EMR is not even integrated into imaging equipment. If you want to access the EMR, because of integration issues and compatibility issues, you need to turn around and access the EMR through another workstation. So just the access itself is a challenge. And this is just an example of one EMR, what if you need to access multiple systems? What if the patient has been moving around and the information is scattered? So access is the first challenge that radiologists are facing.

The second one is filtering the information. Once you access this big pool of information in the EMR, how do you filter and get to the relevant information that you need? How do you go and understand where is the piece of information you are looking for? And maybe you don’t know what you are looking for, maybe the piece of information that you need to see is something you’re not aware of, maybe it’s something that would change your decision. Filtering through the data is obviously a very big challenge and, especially, because the EMR is not designed for a radiologist.

The third challenge is to make sense of all of the information. Even if they are able to filter, there is still a lot of data points scattered in different places. There is nothing that pulls it all into one place and tells you, ‘Hey, this piece of information is really linked to this piece of information and there may be a conclusion that you should think about here.’ So making sense of all of the data, especially when you talk about very large datasets that are scattered between different systems and presented and organized differently is not a small challenge for radiologists.

So that would be one category, the data, how to access, filter, and make sense of it.

Another challenge is to be able to prove the quality of the work. It’s not a trivial thing to all of a sudden tell radiologists their work needs to be of higher quality. What does it mean? How do we measure quality over a long period of time? When you talk about thousands of procedures, not just on a specific procedure, how do we create processes? Who is doing the quality check? How much do we want to invest in it? How do we create ongoing improvement in the way decisions are made? So all of this is about defining the metrics of quality and having the ability to track those metrics and prove that you are getting better in the way that you deliver care.

Q: What can radiologists do about these challenges?

A: Our specialty is health care IT and we believe that the way to address challenges like this is with intelligent systems. We are pushing for creation of systems that will aggregate large amounts of data from multiple sources, know how to filter them and sort them out, and really present the data filtered in an easily digestible format for radiologists and fitting in the imaging process in their workflow. Systems will need to adjust to those challenges and will need to be smarter, able to crunch large datasets, and present the data. Systems will need to understand and connect both the radiology side and the EMR side to provide the best information to the radiologist and, again, will need to be able to track and support quality workflows. We are used to having just one workflow and reading all of the procedures, now all of a sudden we are seeing whole new workflows introduced into the radiology domain, different quality workflows and quality checks and improvement, and things of that nature. Systems will need to be able to support those workflows and provide insight on what is a quality decision — how do we track it? If we track it, how do we present the data to have it conclude different actions?

The system would need to provide collaboration or easier collaboration for the radiologist. When we say the radiologist needs to be part of the care giving team, we obviously mean that literally, they need to be part of the team meeting and need to be there, but they also want to allow virtual collaboration — the ability to ask a question or leave a comment or connect with the referring physician.

For more information about McKesson’s radiology solutions, visit our web site.

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