In the news: medical devices might pose HIT risk; increase in radiology jobs

Articles include: medical devices offer risks for authorized access; ACR projects 16% job growth for radiologists in 2016; Stanford’s radiology department uses patient input to improve processes; FDA issued updated requirements Image of Healthcare Network Access

regarding 510(k) submissions for medical devices and software changes; and RSNA teams with The Sequoia Project to support the electronic exchange of medical images and related diagnostic reports.

Medical devices offer new risks for network access – Health Data Management

Hospitals typically have hundreds of medical devices, which represent an easy gateway for hackers. Newer medical devices might be more robust in the types and amounts of data they collect, and they might connect not only to the core network but also through Wi-Fi networks. A security consulting firm recommends healthcare facilities use “network segmentation,” so that devices are linked to a separate network.

ACR projects 16% job growth for radiologists in 2016 – Auntminnie

The number of new jobs available for radiologists in 2016 will be 16% higher than those available in 2015, according to the fifth annual workforce survey by the Commission on Human Resources at the American College of Radiology (ACR). The study was published online August 3 in the Journal of the American College of Radiology.

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As a research scientist at Carestream Health, my recent work has focused on pediatric X-ray imaging – specifically, on the goal of achieving high-quality diagnostic images while minimizing dose. To explore potential solutions to this challenge, I initiated a study in collaboration with Samuel Richard, PhD, a fellow researcher at Carestream, and Sosamma T. Methratta, MD, of the Milton S. Hershey Medical Center in Hershey, PA. The study had two specific goals: 1) Determine the impact of a simulated reduced dose rendering on the detection of skeletal fractures in children, and 2) Evaluate the effect of enhanced skeletal processing on the same detection task. The methodology and results of this study were on display at RSNA 2015.

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