Diagnostic Reading #7: Five “Must Read” Articles on HIT and Radiology

Reading Time: 3 minutes read

Radiologist burnout and healthcare’s 6 top concerns are in the news.

This week’s articles in Diagnostic Reading include: six issues facing healthcare in 2019; reducing scatter to improve chest X-rays; machine learning appreciation is increasing; radiology burnout and how to handle it; and imaging setting influences follow-up participation.

Here are 6 major issues facing healthcare in 2019, according to PwC – Healthcare IT News

The U.S. healthcare industry is looking less like a special case—a large segment of the U.S. economy with its own unique quirks—and is beginning to behave like other industries, according to “Top health industry issues of 2019: The New Health Economy comes of age.” In its annual healthcare report, consulting giant PwC says connected care, upskilled workers, tax reform, a Southwest Airlines approach, private equity, and the Affordable Care Act all will impact healthcare organizations in 2019.

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Diagnostic reading helps radiologists, healthcare IT and others in the medical imaging profession stay up to date.

Improving the quality of chest X-rays – Everything Rad

Mobile chest X-rays are often performed on acutely ill patients who are too critical or unstable to be transported to an X-ray imaging room. The X-rays can be problematic because patients are often imaged while prone. Also, there is the likelihood of potential degradation of image quality due to radiation scatter. This phenomenon occurs most frequently when imaging thicker areas of the body – such as the chest. However, new software that reduces the damaging effects of scatter radiation in an image can help improve the contrast of the image.

Appreciation of machine learning on the rise among imaging professionals – Radiology Business

How significant is the hype surrounding artificial intelligence and machine learning in radiology? According to recent market research from Reaction Data, 77 percent of imaging professionals said they think machine learning is important when asked about it in 2018, up from 65 percent in 2017. The study states that “Machine Learning is one of the hottest topics in healthcare today, especially in medical imaging. It promises to provide efficiencies in several areas with the expectation of improving quality while decreasing costs.” Read the blog by Dr. Siegel on the future of AI in radiology.

7 reasons rads burn out—and how to cope – Diagnostic Imaging

Burnout is the reality for 50 percent of radiologists, according to the 2016 Medscape Physician Life Report. As a result, job satisfaction falls, patient care suffers, and workflow management becomes inefficient. Consequently, many industry leaders say it’s vital to identify why burnout occurs and pinpoint some methods, both individually and institutionally, to counteract the effects or side-step it altogether. Ignoring the problem, they say, could have grave consequences. Read the blog by a radiologist who practices meditation to help stay focused and precise.

Imaging setting impacts follow-up participation. Can health systems address the problem? – Health Imaging

When it comes to follow-up imaging, location matters. That’s what authors of a recent study found after comparing locations during initial imaging with the likelihood that patients came back for follow-up imaging. Findings from examinations performed in the ED setting would be less likely to be appropriately followed up compared with those performed in the outpatient and inpatient settings, according to the authors. By understanding variances in follow-up adherence, health systems may be able to identify organizational improvements that work across provider types, service lines and care sites to increase follow-up participation.

#diagnosticreading #radiologyburnout #AI

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