How next-generation real-time data analytics can drive agile radiology departments

Chris Meenan, Philips: In the recent survey we did with over 100 AHRA members, 98% of them said that, with reimbursement increasingly tied to metrics, they feel pressure to drive efficiency across organizational boundaries. And, 91% of respondents believed that better access to data would positively change the way they run their practice overall and tie into larger organizational goals or value-based care. That’s great validation of the need to help radiology departments better utilize real-time data analytics. What are your thoughts on this?

AHRA President, Chris Tomlinson: You’re right there is tremendous pressure to drive efficiency across organization boundaries but I believe it’s for a different reason. In the larger ecosystem of a hospital, there has been a massive investment of IT and financial resources into Electronic Medical Records (EMRs). This might make you think – wow, that will help radiology leaders better leverage data! But actually, what’s happened is that investments in that radiology infrastructure, whether that’s PACS or analytics, hasn’t kept up with it as a result. 

Now, for many hospitals, there is a consolidation of IT resources into a central informatics or IT group that is focused on large data warehouses for the masses. Consequently, radiology-centric data that you really need is often very difficult to get or not detailed enough to use. Often, you only get a piece of the data you need from your Radiology Information System (RIS) and sometimes it’s not even clean data. That is not enough to drive efficiency and that is where the pressure comes in for radiology administrators. With an enterprise focus on EMRs, it’s harder to mine our imaging data at the modality or machine level, but we need that sequence level detailed data, in order to run our radiology practices more effectively. 

Chris Meenan, Philips: In the AHRA survey, the majority of respondents (66.96%) said their decision making was based on historical data (spreadsheet reports from the past month or more). This confirms what we’ve seen that the typical process of data analysis in radiology is limited in scope and very much manually driven. As a result, reporting is very labor and time intensive, not standardized, costly, and most importantly – delayed. Delayed reporting doesn’t make radiology departments agile. What’s your experience on using historical versus real-time data? 

AHRA President, Chris Tomlinson: In my experience as a radiology administrator, to improve efficiency in a radiology department you need to reduce variability and improve turnaround time and, to do that, we really need modality-level or machine-level data. By that I mean, we need to have data on things like: when a patient entered the room; or how long it took to set up the scan; and, how much time was there between sequences.  

If I can squeeze 5-10 minutes out of each MRI sequence and reduce variability – that’s huge – but that’s based on historical data. Historical data helps you find out where your problems are but then, how do you recover those operational issues all the time? How do you repair the system? What radiology administrators need is a way to make it easier to see it happening in real-time, and make adjustments for it and get back on course as quickly as possible. 

Most radiology leaders are more focused on the retrospective data than the real-time ‘repair’ data. However, if you can turn retrospective data into a useable, predictive format and couple it with the real-time ‘repair’ ability, that’s really where you can make a difference because you can re-allocate resources or move across scanners as needed, when needed. I believe that’s really where the opportunity is because it enables radiology leaders to run the department more strategically.

Chris Meenan, Philips: Exactly! Let’s talk a bit about ‘waste’ in imaging. Research suggests that 60-65% of the annual spend in radiology is in operations and that up to $10-12 billion of that is potential waste (such as wrong test, repeat exams, poor image quality, etc) [1]. We know that real-time data-driven practice management actually saves costs in various areas such as reducing the need for repeat imaging or rescans. And, it can even increase revenue by providing insights that help radiology administrators better understand which types of services are most needed for a hospital’s particular patient population. Interestingly, the results of our AHRA survey show that more than half (54.6%) of respondents said cost was the key obstacle for implementing real-time data-driven practice management strategies.  What are your thoughts on how can we best reduce ‘waste’ in imaging and support the adoption of data analytics solutions to achieve cost savings that can be reinvested in areas such as patient care? 

AHRA President, Chris Tomlinson: Most healthcare organizations want enterprise-class solutions so the cost of any radiology-centric data management solution is an obstacle, unless it aligns with the broader clinical care themes such as Length-of-Stay (LOS) or turnaround time that align with care pathways that resonate with CIOs. In terms of ‘waste’ in imaging, I see that a lot of the spend in imaging is due to over ordering or inappropriate imaging studies. Right now, if someone orders an MRI of the brain and then someone orders a CTA on top of that – I would argue that defensive medicine is being used and we need to push back on that to reduce waste and annual spend in radiology. 

As a radiology administrator, if I have the ability from an imaging perspective to readily look at data or metrics and see how many inappropriate studies were ordered and how much of the time are we able to redirect orders to reduce waste or cost – that’s very valuable insight. It enables me to look beyond the data and optimize by knowing how many studies can we move from in-patient to out-patient to reduce cost. As an example, I would say LOS is one really good metric that can be used and one that radiology can really impact positively. Radiology can see things from upstream systems and push back or help prioritize next steps for patients which correlates with LOS and impacts cost of care overall.