At Akron Children’s Hospital, the road to fully realized patient-centered care for kids leads to a scenario in which all patient information—including consent forms, admissions documents, diagnostic images and multimedia files—is readily accessible through the facility’s EHR.

If your hospital or healthcare system is like most others in the U.S. today, you have an EHR that’s proving expensive to maintain while working well below its potential for centralized, cost-saving image sharing. You’re fretting over non-DICOM images acquired with smartphones and insecurely siloed in numerous clinical departments. And you’re also talking a lot about enterprise imaging (EI) as a way to broach both those touchy topics and a host of others.

Communication with referring physicians is essential in radiology, but it often becomes stressful and frustrating for radiologists when they struggle to track down the right person. This can lead to workflow delays that chip away at various quality metrics and have a negative impact on patient care. In a recent case study published in the Journal of the American College of Radiology, Eduardo J. Matta, MD, University of Texas Health Science Center at Houston, and colleagues worked to improve communication between radiologists and referring physicians by implementing a new, PACS-based software solution.

An artificial intelligence (AI) algorithm predicted whether or not a 6-month-old child would develop autism with 96 percent accuracy in a study on a group of almost 60 infants.

AHRA’s annual meeting was held in Nashville, Tenn., in 2016, but this year, it’s trading in cowboy boots and country music for sunshine and that cartoon mouse with the famous laugh. AHRA President Jason Newmark, CRA, took a break from making final preparations for AHRA 2017 in Anaheim, Calif., to speak about some of the biggest issues impacting both the present and future of radiology.

Over the last 10 to 15 years, awareness of the risks of radiation exposure in medical imaging and efforts to reduce dose have escalated exponentially. Imaging equipment vendors have answered the call with dose-reducing strategies that include more sensitive image receptors, better image reconstruction techniques, dose alerts and post-processing software. Radiologists, technologists and physicists have been hard at work as well, edging down dose without compromising image quality. So where do we stand? Are we as low as we can go or is there more that can be done?

The American College of Radiology announced the formation of the Data Science Institute (DSI), an inter-disciplinary organization aiming to guide the implementation of artificial intelligence tools in radiology.

Surgeons and Specialists View Imaging Studies from Their Mobile Devices To Make Treatment and Transfer Decisions