AZmed im AGAPLESION ELISABETHENSTIFT im Einsatz

Burnout in radiology is a well-documented syndrome of emotional exhaustion, depersonalization, and diminished professional efficacy. Although the sources of stressors may differ, they all relate to increased workload. During any given shift, many radiologists may interpret several hundred examinations, which, depending on the number of images per study, could amount to thousands of images. Night shifts and on-call duties only compound this experience. Hospitals now demand time-sensitive reporting, with expectations for prompt turnaround and clinical disposition, as part of the workload.

Radiology departments worldwide are facing increased numbers of imaging studies than ever. On the other hand, they are also facing reduced staff, increased caseloads, and increased risks of making mistakes. Therefore, it is no surprise that many departments are looking to install artificial intelligence in radiology to assist with speeding up diagnosis. But while speed is crucial, it should not be at the cost of diagnostic precision. The question most medical professionals are asking is straightforward: Can AI enable radiologists to speed up diagnosis yet maintain identical high levels of patient care?

The first key performance indicator (KPI) concerned with patient care and safety is accuracy, and it's the first KPI every radiologist is concerned with. In the emergency and trauma settings, simply overlooking one fracture or missing a pleural effusion can lead to consequences such as delayed treatment and worse patient outcomes. In emergent situations, radiologists often live with the knowledge that they missed a subtle pathology due to fatigue or simply because of the volume of work they are going through.

‍