“A successful screening program consists of education, evaluation and adjustment and it is about the whole chain, equipment and professionals,” commented Prof. Ruud Pijnappel. “Evaluation can be very helpful for this purpose. If you can be helped by artificial intelligence and then you can educate to the point which they need it, it can be very beneficial and support what we are doing.”
In examining the technologist perspective, the panelists emphasized that minimizing the patient anxiety and discomfort caused when repeat exams are needed due to poor image quality should be a top priority. Furthermore, procuring high quality images the first time will minimize radiation exposure from a second exam when needed due to poor image quality and breast positioning. Their performance in these and many other areas are essential to compliance with the MQSA audit requirements.
From the radiologist’s perspective, breast positioning is essential to their ability to detect malignancies, as they can only assess the areas of the breast that are imaged and therefore could be at risk of overlooking cancers. As they rely on technologists to procure high-quality images, they must work together to address potential issues and areas for improvement whenever they are identified as part of training and quality initiatives. Panelists discussed the need for and effectiveness of training, teamwork and open communication to ensuring that optimal breast positioning is a continual, shared focus.
“What we're trying to create is not a blame and shame culture, but an improvement culture,” added Prof. Pijnappel. “We learn from our technicians and they learn from us, but they also learn from each other. The interactive part of the process is very, very important and brings you to a higher level.”
The panel also addressed the growing potential and evidence for AI to enhance the quality of mammography imaging. New AI-powered quality assurance tools can provide new ways of achieving optimal breast positioning and image quality. Certain solutions work by retrospectively reading mammography images and provide a detailed technical assessment of breast positioning and image quality. These outputs can then be used to drive training and continuous performance improvement for mammography technologists. They also can support screening services to comply with and prepare for external audits - thereby reducing costs and simplifying recurring requirements.