Radiologists are trained to be fearful, on alert for disease, and our fears are multilayered. Perhaps we’d be less fearful if our profession was secure, nonthreatening, yet still rewarding.
Fear. It’s practically become a bad word, implying that the individual experiencing it is deficient in some way. You can have stress, even anxiety (as long as you are using documented coping skills or are in some kind of therapy for it), but if you admit to having fear it’s tantamount to a character-flaw.
As physicians, we’re trained to be fearful, and given constant refreshers on the subject. If a patient has a 3 percent chance of pathology, we focus on the 97 percent likelihood of disease absence at our peril. Some progress notes (and, yes, radiology reports) even try to convey a wisp of our fear to show what good doctors we are, talking about “concerning” or “worrisome” findings.
Part of this fearful bias is learned from a desire to do as good a job as possible; One is generally more tolerated for being an “over-caller” than an “under-caller.” Even patients annoyed with recommended follow-up appointments or imaging studies will often speak well of their docs: “He’s just very cautious.” But part of it, also learned during training, is the recognized ever-present danger of our runaway med-mal system. Anything at any time, no matter how blameless, can get you sued.
Which leads to an extra layer of complexity: Our fears are nested, multilayered. We’re not so much fearful of getting served with papers, but rather the cascade of fear-inducing events which follow: Questioning whether we really performed poorly. Months or years of floating in legal Limbo. Costs of missing work as a result of the case’s proceedings, both financial and in terms of “political capital” with colleagues who will have to provide coverage for us. Another layer of documentation for licensing, credentialing, and job application purposes. Financial overhead not covered by our med-mal policies. The possibility of a bad verdict, leading to state board action, etc.
We fear losing dissatisfied patients, referrers, or contracts if we make a wrong move, or if we fail to make the right one. We fear reductions in reimbursement or increases in overhead in response to actions taken by entities external to our practices - government, insurers, vendors - or internal agents such as employees not performing up to snuff.
There’s even a fear of not having enough fear, that an insufficient level of it might lead to not just comfort but complacency. I’ve heard rads speak of going too long without a diagnostic “miss,” which, when it happens, shocks them back into a more adaptive sense of vigilance for their next gazillion cases.
Fear does, of course, have a biological purpose, and perhaps it is because we no longer commonly find ourselves being chased by large predators that we have learned so many new ways to make use of the emotion. Maybe, if some brilliant breakthroughs in our healthcare (or, for that matter, societal) system came along and transformed our professional lives into secure, nonthreatening, yet still rewarding experiences, we’d just move on to routinely frighten ourselves with other things.
Then again, fear of change (sometimes rationalized a la “better the devil you know”) might just prevent us from finding out.
Study Reaffirms Low Risk for csPCa with Biopsy Omission After Negative Prostate MRI
December 19th 2024In a new study involving nearly 600 biopsy-naïve men, researchers found that only 4 percent of those with negative prostate MRI had clinically significant prostate cancer after three years of active monitoring.
Study Examines Impact of Deep Learning on Fast MRI Protocols for Knee Pain
December 17th 2024Ten-minute and five-minute knee MRI exams with compressed sequences facilitated by deep learning offered nearly equivalent sensitivity and specificity as an 18-minute conventional MRI knee exam, according to research presented recently at the RSNA conference.
Can AI Enhance PET/MRI Assessment for Extraprostatic Tumor Extension in Patients with PCa?
December 17th 2024The use of an adjunctive machine learning model led to 17 and 21 percent improvements in the AUC and sensitivity rate, respectively, for PET/MRI in diagnosing extraprostatic tumor extension in patients with primary prostate cancer.