New research shows radiologists – regardless of experience level – fall victim to inattentional blindness, overlooking obvious unrelated findings when they are trying to answer a specific diagnostic question.
Radiologists make roughly 40 million mistakes every year, according to an Insights Imaging study. But, a significant portion of those errors can be attributed to inattentional blindness – the failure to see something clearly in front of you when you are focused on something else. And, it affects all radiologists equally.
You might remember the 2013 study where a team of researchers from the University of Utah, led by Trafton Drew, Ph.D., assistant professor of psychology, asked radiologists to examine chest CT scans that contained an image of a gorilla for lung nodules. Eighty-three percent of radiologists literally missed the gorilla in the room.
But, missing something that has no business being in a diagnostic scan does not necessarily equate to a radiologist overlooking a medical finding that could impact a patient’s health or alter a course of treatment. So, in a new study, published in Psychonomic Bulletin & Review, Drew’s team looked into how often providers might miss something that falls outside the question they have been asked to answer.
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“We’ve known for a long time that many errors in radiology are retrospectively visible,” he said. “This means if something goes wrong with a patient, you can often go back to the imaging for that patient and see that there were visible signs – say, a lung nodule – on something like a chest CT.”
This time, Drew’s team asked 50 radiologists to evaluate seven chest CT scans for lung cancer. Those images also included two clear abnormalities – a significant breast mass and a lymphadenopathy. According to the radiologists’ performances, two-thirds of providers did not see the breast mass, and one-third overlooked the lymphadenopathy.
“Like anyone that experiences inattentional blindness, I think many radiologists were simply surprised to learn they had missed something,” said Lauren Williams, a recent Utah graduate and current post-doctoral student at the University of California, San Diego. “Our intuition tells us that if something is fully visible, we’ll detect it, but we’ve all experienced the feeling of missing important information that is retrospectively obvious when our attention is focused elsewhere.”
Just as important as the number of providers who missed potentially critical findings is the fact that years of experience made no difference, the team said. Veteran and early-career radiologists both made these mistakes. This suggests not only that experience does not outweigh the existence and impact of inattentional blindness, but also that missing abnormalities is not a reflection on the skill or competence of the provider.
But, the team went further to see if broader instructions could make an impact on performance. In a second experiment, the team asked the same radiologists to review the same scans and report on a range of abnormalities. This time, the team said, only 3 percent of providers overlooked the breast mass, and 10 percent missed the lymphadenopathy.
But, simply keeping tabs on how often providers miss things in plain sight leaves an important piece of the puzzle unanswered. What are radiologists looking for when they breeze past things in plain sight?
“Our research demonstrates that focusing narrowly on one task may cause radiologists to miss unexpected abnormalities, even if those abnormalities are critical for patient outcomes,” Williams said. “However, focused attention is probably beneficial when the abnormalities match the radiologist’s expectations.”
Small clinical changes, such as conducting a general assessment of the scan before examining it for specifics or using a checklist for commonly missed things, could be beneficial, she said. These tactics will likely be more effective than artificial intelligence (AI) tools that do a much better job of honing in on specific findings rather than detecting everything that might be in a scan.
“Radiologists might benefit from being thoughtful about what they are looking for rather [than] assuming that if they see it, they will perceive it,” Drew said. “AI has, in some ways, the same limitation. It’s only going to be good at detecting what it has been taught to detect.”
Ultimately, Drew said, these results will provide a greater understanding about how often people only see the things they are looking for.
“Everyone, even experts, can miss things that seem really obvious if we are not looking for them,” he said. “If you’ve searched through your whole apartment for your phone, you might assume you would have noticed your keys during that search. Our research suggests a reason why you will probably have to search again specifically for the keys.”
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