Written by Michelle Flatt.

Preventing Human Errors in Radiology

As they say, “to err is human.” Everyone makes mistakes. As medical practitioners, there is more at stake when we err than for the average person. A diagnostic error can delay needed treatment and harm patient outcome. Autopsy studies have found that there may be major diagnostic discrepancies in as many as 1 in 5 patients overall. For this reason, we must learn from our mistakes as fully as possible to avoid making them again.

One of the areas where mistakes can frequently take place is in radiology. Medical liability insurer Coverys found that almost 5% of claims directly named a radiologist. This happens in part because modern medicine relies so heavily on diagnostic imaging, but also due to the fact that many radiologic results are open to interpretation. This is an area where humans can not rely on artificial intelligence to do any of the interpretation for them. As much as medicine is a science, interpretation is up to the individual and therefore subject to cognitive bias, lack of experience or knowledge, or misleading clinical information.

For instance, in 2015, experienced abdominal imaging radiologists from Massachusetts General Hospital participated in a study wherein they studied abdominopelvic computed tomographic (CT) studies that had already been interpreted by their colleagues or themselves. Surprisingly, their results not only disagreed with each other’s over 30% of the time, their diagnosis actually disagreed with their own earlier read of the images more than 25% of the time!

There is a range of potential errors in radiology, from not detecting an existing problem, misinterpreting what the images show, or false positives, where an issue is diagnosed in an image of a healthy patient. Efforts have been made for many years to re-train working radiologists with continuing education, and to prevent errors caused by over-confidence or repetitious work. However, the education alone does not appear to bring the numbers down. Other efforts to reduce fatigue, stress, over-work, and eye-strain, all of which may contribute to incorrect diagnoses, have also not seemed to make a significant difference.

Going forward, the profession as a whole needs to continue to strive for vigilance. There are some potential growth areas where artificial intelligence and image enhancement could improve outcomes, but at its heart, radiology will always need the eyes and minds of human radiologists. The field needs to work toward a mindful, systematic approach to interpretation, relying heavily on communication between care providers, peer review, and methods for overcoming cognitive bias.

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