Taking our data to the next level in 2015: Q & A with Keith Chew

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 - Keith Chew
Keith Chew, Senior Vice President, Integrated Radiology Partners

As senior vice president of Integrated Radiology Partners, (IRP) and also president of the Radiology Business Management Association, Keith Chew is a well-respected leader in our industry. He recently spent time with RadAnalytics to talk about his new role with IRP, the importance of applying analytics in radiology, and share his thoughts on radiology’s outlook for 2015.

RadAnalytics: Can you tell us a little about why you made the move to Integrated Radiology Partners?

Chew: I started with IRP October 1st, 2014, and one of the things that actually enticed me to make the move was IRP’s strategic vision on the impact of analytics for the future, especially within imaging, but within all of medical practice. That’s a concept to which I also ascribe. I think that imaging is right now at a bit of a crossroads, where we could truly become a commodity if we are not careful. Radiologists need to revert in some ways back to the truly consultative, valued member of the patient care team role that they were a number of years ago. To redevelop into that type of a position, it’s going to take analytics to demonstrate the value that medical imaging brings to the patient care continuum.

RadAnalytics: How can radiologists best accomplish that?

Chew: Well, I think it has got to follow more or less a step wise progression. First, you’ve got to be able to collect the data, start recognizing the patterns within the data, and then start understanding what those patterns are showing you. It’s a progression in the thought process.

To start with, radiology could begin by collecting certain types of superficial data reporting sets. We need to start reporting some very general, straightforward metrics now, such as physician productivity by work RVU, by total procedures, or by peer review activities.  

There’s a lot of information out there, but I don’t know that there is any large universe, a large n, or a large number of radiologists that are being set as the base for those studies. Instead, what we’re seeing is the study that comes out of one facility, reflecting one set workflow. Because there are workflow variances between all the different studies, you need to bring a very large number together to represent your statistical universe, the n within statistical analysis that will allow you to better develop a meaningful output. That’s your first step.

The next thing is to look at codified data within the system; whether it’s the CPT code, the ICD-9 or soon-to-be ICD-10 codes, and anything else you can actually find that puts a specific number to a piece of data. You can start looking at activities related to episodes of care, whether they are acute or chronic. How many X-rays does a normal patient in an episode of care for a hip replacement receive? We’ve got government data showing a standard based upon a very large n sampling and you can use data from your own facility to see how well you are adhering to that standard. That’s one good example there.

Then you move it into the next realm, which is the non-codified data, the data that is pulled from reports and notes within the EMRs that are really based upon an analysis of general syntax. From that data you can begin to do not only prospective but retrospective reviews to find out if the diagnostic and therapeutic imaging services you’re providing were provided at the right time. Did it have a positive outcome or did they have a positive impact on the patient’s overall outcome? 

It just continues to build from that very low level superficial-type analysis all the way up through the metadata development into the non-codified data that allows you to say we can actually now look at the whole concept of medical imaging in many terms just like imaging 3.0 is from the ACR, making certain that you get the right imaging at the right time, interpreted by the right radiologists. It allows through this analysis to get you to that point, but without the analytics, how do you get there? That to me is the big question, so that’s why I’m so strong behind analytics and that’s why I think that IRP is on the right path.

RadAnalytics: How do you feel that radiology can demonstrate value by either improving quality or decreasing cost in the particular care of a patient? 

Chew: As you’re able to collect greater data and utilize that data to generate information and demonstrate patterns, you’re going to understand better how that quality side of the formula is impacted. Part of the problem with medicine