From the November 2021 issue of HealthCare Business News magazine

“One substantial change from the past year was how comfortable we all became with operating digitally; we got used to living in a virtually connected world. For hospitals, the pandemic forced many of their major revenue streams to cease, and they needed to figure out how to do things differently to stay afloat,” Dr. Jeremy Friese, president of payer market at Olive, told HCB News.

A 2019 study by Sage Growth Partners found only half of hospital leaders were familiar with the concept of AI, and that more than half were unable to name an AI vendor or solution.


With AI, researchers teach robots to stay out of the way in busy ERs
Computer scientists at the University of California San Diego announced in May they had created a navigation system that enables robots in the ER to move through hectic clinical environments without getting in the way of busy clinicians as they tend to patients in critical or serious condition.

Known as the Safety Critical Deep Q-Network (SafeDQN), the navigation system is built with an algorithm that tracks how many people are together in a space and how quickly or abruptly they are moving. Its purpose is to help robots continue supporting emergency workflow by delivering supplies and materials to physicians and nurses without distracting clinicians caring for high-acuity patients, according to professor Laurel Riek.

"To our knowledge, this is the first work that presents an acuity-aware navigation method for robots in safety-critical settings," Riek, professor of Computer Science and Engineering and Emergency Medicine at UC San Diego, told HCB News.

The algorithm was trained on more than 700 YouTube videos of documentaries and reality shows, such as "Trauma: Life in the ER" and "Boston EMS". The entire set of videos has been made available for other research teams to train their algorithms and robots.

They plan to test the system on a physical robot in a realistic environment next and will also partner with UC San Diego Health researchers to operate the campus’ healthcare training and simulation center.

Researchers fuse MR and CT images using deep learning technique
In May, researchers in China began touting a new deep learning-based process that “fuses” multi-modal scans to create a higher quality medical image that can improve clinical diagnosis and patient outcomes.

Known as image fusion, the technique automatically identifies and combines information of scans from different modalities to produce a single high-quality image. “Experimental results indicate that the proposed method achieves state-of-the-art performance in terms of both visual quality and quantitative evaluation metrics,” said author Yi Li, with Qingdao University’s College of Data Science and Software Engineering in Qingdao, China, in a statement.

November 2021 Magazine

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