Diagnostic Reading #12: Five “Must Read” Articles on HIT and Radiology
Reading Time: 3 minutes read
Call for more education of radiographers in Spain; and “selfie elbows” are in the news.
This week’s articles in Diagnostic Reading include: breast cancer prediction models more effective with family data; educator calls for more training for radiographers in Spain; radiologists are “mildly happy” at work; and specialists see an increase in stress injuries from smartphone and tablet overuse.
Breast cancer prediction models more effective when they include family history data –Radiology Business
Breast cancer prediction models based on family history are more effective than those that do not focus on that information, according to a recent study published in The Lancet Oncology. The researchers calculated 10-year risk scores for the women, comparing the results of four different breast cancer risk models: the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) model, the Breast Cancer Risk Assessment Tool (BCRAT), the International Breast Cancer Intervention Study model (IBIS), and BRCAPRO.
Educator says Spain’s education plan for radiographers is “completely obsolete”- Everything Rad
Spain has one of the shortest curricula in the world for radiographers: two years. Salvador Pedraza Gutiérrez, director of the Diagnostic Imaging Institute and an associate professor, says advanced imaging and the continuously rising demand for imaging studies require more extensive education.
Only 25% of radiologists are happy at work, survey finds – Health Imaging
Radiologists are mildly happy at work compared to other physician specialties, according to Medscape’s 2019 Radiology Lifestyle, Happiness & Burnout Report. Twenty-five percent claimed to be “very or extremely happy” in the workplace. In their recently published radiology-specific report, radiology ranked 20th among all specialties in terms of workplace happiness.
AI predicts ovarian cancer survival rates from CT scans – Radiology Business
AI can predict a woman’s survival rate and response to treatments for ovarian cancer more accurately than current methods, according to research published online in Nature Communications. The machine learning software could help clinicians administer the most appropriate treatments to patients more quickly, and might be used to classify patients into groups based on subtle differences in textures of their cancer shown on CT scans.
Selfie elbow—yes, this is actually a thing – Healthcare in Europe
Shooting selfie after selfie can strain forearm muscles, resulting in trauma to the part of the tendon that connects to the elbow joint.
POST A COMMENT