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Lowering the likelihood of errors in radiology

Radiologists in Pennsylvania, as elsewhere, often suffer from long shifts and an excessive workload. When radiologists read an image, a cognitive bias may lead them to only look for those things they were trained to look for. All of these can contribute to missed diagnoses and other errors. Missed diagnoses resulting from a false-positive reading make up a startling 30% of all diagnoses involving CT scans and MRIs.

The best practices laid down by the American Journal of Roentgenology can help prevent radiology errors. They include a peer-review process that encourages diagnostic accuracy and fosters mutual respect. Feedback should, of course, remain anonymous. Continual education is another must and can be boosted by a learning management system or e-learning platform.

AI and machine learning technology can come into play when coming up with decision support systems. AI-driven automated analysis has been proven, for example, to outperform radiologists in the speedy detection of lung nodules on chest CT images.

Several changes can reduce the chances of burnout, including a shortening of work shifts, better structuring of break time and double reads. When incidental findings are discovered, there should be a timely follow-up. Lastly, structured reporting can prevent errors caused by cognitive bias and by the lack of communication between radiologists and the referring physicians.

Under medical malpractice law, errors in radiology or in any other medical field that harm a patient may form the basis for a claim. It all depends on proving the doctor’s negligence and connecting that negligence directly or indirectly to the injuries. There must be a preexisting doctor-patient relationship, and the patient must show that he or she followed all the doctor’s directions. To see whether the grounds for a claim are good, the victim may hire a lawyer for guidance.

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