Before joining GE Healthcare two years ago, I spent nearly 15 years as a technologist leading a radiology department.
I came through X-ray school as many sites were transitioning from film to CR or film to DR. Starting out with X-ray on film helped me learn early on how important image quality is and what can affect it, but also how much more latitude we have with CR and DR imaging. During the days of film, reject rates were determined by how full the reject bin was. Films were pulled one by one and evaluated. Fast-forward a few years …
As a supervisor, I was responsible for performing QAs, maintaining and updating protocols, working with radiologists to make sure their needs and expectations were met, and working with new and existing staff to implement the radiologists’ imaging expectations.
That included taking a closer look at what went wrong with rejected images. After all, repeatable image quality is critical to how well radiologists can diagnose patients accurately and efficiently, such as spotting a small fracture that needs proper care.
Successfully analyzing rejected images involves looking at the image processing components, the techniques used by a technologist, the physical condition of the patient and the access to the patient that is required to acquire the image.
But at my facility, we didn’t have software to help with this analysis. Once a month, I had to pull the data manually and go through it, image by image. I then had to meet separately with the acquiring technologist, then meet with the radiologist for their feedback and give more details of acquisition. I’d estimate that this process took me ONE HOUR PER MACHINE. It was time consuming and click heavy, for sure. However, at the time, many of us didn’t realize it could be done differently.
Now, the more I learn about GE Healthcare’s new, web-based X-ray Quality Application featuring Repeat/Reject Analytics (RRA), the more I see what we were and, in many cases, still are missing.
This web-based software automatically gathers, analyzes and compiles repeat/reject data in a convenient dashboard for quick and easy review, reporting and discussion. This automation will eliminate hours of manual work, while also providing deeper insights that help make better decisions about X-ray processes with respect to patient radiation dose and repeat images.
For example, many facilities rely on Exposure Index (EI) or Deviation Index (DI) ranges to determine when a repeat is required. But the truth is, depending on the patient and the situation, you may not need a re-exposure simply because the given image was out of range. A more in-depth look at the data may help identify those instances in which you don’t actually need repeats.
Furthermore, the RRA dashboard helps identify true reject rates out of the total images acquired (versus simply a total reject count that doesn’t tell you how big of a problem you have), which is a desired insight that has been cited by many QA technologists I talked to in the field.
Too bad the X-ray Quality Application didn’t exist when I was a technologist – it would have saved me a lot of time pulling reports and going through them with radiologists and staff. If you’re in the same boat today, I can actually help you find a way out …