Diagnostic Reading #48: Five “Must Read” Articles on HIT and Radiology
Artificial intelligence, data-enabled reports, and innovative imaging make this week’s headlines.
This week’s articles in Diagnostic Reading include: what impact advances in AI may have on radiology; radiologists can provide added value with data-enabled reports; RSNA 2017 president urges radiologists to be proactive in advancing new technology that can enhance the value of medicine; a five-category system for triaging ER patients based on their radiology reports delivers good results; and an analysis of nearly 3 million radiology exams has confirmed prior research showing that diagnostic accuracy for radiologists diminishes when they’re working especially long shifts or reading from long work lists.
Video from RSNA 2017: How will AI change radiology? – AuntMinnie
The past year has witnessed huge advances in the development of artificial intelligence (AI) for healthcare applications. What impact will this new generation of AI software have on radiology? Dr. Paul Chang of the University of Chicago offers his thoughts in this video interview.
RSNA 2017: How to provide value by creating data-enabled radiology reports – Radiology Business
Radiologists have been working to improve radiology reports so they can provide more value and bring significant improvements to patient care. At RSNA 2017 Tarik Alkasab, MD, PhD, radiology service chief of informatics and IT at Massachusetts General Hospital in Boston said: “We need to find a way to extract the value we get from the very rich imaging data we are seeing and make it more accessible in the context of the data-driven, team-oriented practice of medicine we are seeing in the 21st century. He proposes that radiologists think of their reports as two layers—a text layer and a layer that is essentially structured data that conveys the kind of information they are extracting.
Radiologists must make a continuous effort to reinvent radiology and be proactive in advancing new technology to enhance the value of medicine, RSNA 2017 President Dr. Richard Ehman said in his opening address. Advancements in machine learning, highly focused protocols, and value-focused engineering indicate improvements in effectively caring for patients in a new world of population-based care.
New emergency radiology report system passes test – Health Imaging
Radiologists and ED physicians at Brown University have developed a simple, five-category system for triaging imaged emergency patients based on their radiology reports, and the team’s test of the system has shown good results. Radiologist David Swenson, MD, emergency medicine physician David Portelli, MD, and colleagues describe their work in an article published online Oct. 8 in Emergency Radiology.
Long hours, high volumes escalate likelihood of radiologist error – Health Imaging
An analysis of nearly 3 million radiologic exams has confirmed prior research showing that diagnostic accuracy for radiologists diminishes when they’re working especially long shifts and/or plowing through long worklists. The findings were published in Radiology magazine on Nov. 20.
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