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Data and Automation rescues Radiology from decreasing margins

Fact: Margins in Radiology have been decreasing for years.

Other market forces, such as price competition and transition to value-based care, all weigh down on profit margins for Radiology groups. Site-neutral payments also threaten to further erode margins. What makes things more difficult, is that Radiology practices are constrained by a referral network in which patients typically are referred into the practice, rather than seeking out the facility themselves.

For Radiology groups to survive in the next five to ten years, they will need to better control costs, while increasing patient throughput. But given the constrained environment, how to better control costs while increasing patient throughput? The answer is automation.

Most groups feel their staff is already maxed out and overworked; however, when controlling for costs, it can be difficult to know whether to hire more people, purchase new technology, or keep going with what you have and what you know.

In Radiology, what our team has found using automation technology is that there are three different types of loss in the Patient Flow process:

Inflow loss: Where the patient is not ready when the doctor or provider is, there is a no-show or cancellation, or there are unfilled slots in the schedule.

Downtime loss: Where the patient is ready, the room is available, but the technician is unaware or unavailable that the patient is ready to go; or, there is an equipment breakdown.

Productivity loss: Less productive technician. The technician is less productive as compared to their own track record, or when compared to similar technicians in the clinic.

Using real-time data and analytics, our team is able to identify where there are the biggest losses as defined above. With our second layer of automation technology, our data showed that one particular organization could actually see an additional 5 patients per service lane (X-ray, MRI, Ultrasound, etc.) per day.

Up until this point, this organization did not know what their losses were and which ones were biggest. Without data, it was difficult to know where and how to make changes. As in this example, data can help organizations know exactly where and how to make changes to streamline and improve their process flow; that way, they can avoid unnecessary spending on tasks or activities that may not have the biggest payoff. Automation only becomes valuable when organizations know WHAT to automate.

In summary, given the cost-constrained environment that Radiology groups operate in, those organizations that survive in the next 5-10 years will be those that can use live data to quickly and easily identify where there are gaps in their workflow, and are able to successfully leverage technology to help them fill those gaps. These organizations can then better focus on what matters most in healthcare: delivering a quality experience to their patients.