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The role of imaging informatics in real world radiology departments

by John R. Fischer , Staff Reporter
From the May 2018 issue of DOTmed HealthCare Business News magazine


For all this promise and potential, the advent of machine learning has many skeptics asking if this spells the end of humans in radiology. Or worse, a giant step toward a future that resembles the plot of The Matrix, where computer intelligence enslaves mankind.

“Am I scared it’s going to make the radiologist’s job go away? No,” Cheryl Petersilge, the medical director of integrated content for information technology division at Cleveland Clinic, told HCB News. “Do I think it will change the radiologist’s job? Of course. It’s going to for any imager. I think we might have to be in the role of educating our colleagues about appropriate uses of imaging. We should be doing this now. I think we’re going to be the synthesizers of information that the computer presents to us rather than the gatherer of information and then the synthesizer of information.”
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Imaging technology experts believe that machine learning will act as an assistant, retrieving information that will aid radiologists in the assessment of a patient’s condition, but not making the final treatment or diagnostic determinations.

But in order for this to effectively happen, providers and practices must bring together players throughout the enterprise to form strategies ahead of time for when these technologies arrive. Such plans require input from nurses, radiologists, IT personnel and practice administrators, each of whom must become familiar with the ins and outs of their facility’s day-to-day activities and enterprise imaging workflow.

“How do they process the orders? How do they track information about that order?,” McEnery asked. “Any practice needs to understand their business in the context of the studies they’re doing and how those studies are interpreted. As the practice gets larger, there’s more complexity in that, because you have different expertise and radiologists and more subspecialty practice. You need to know how you get the study to the subspecialty person in the group.”

Aside from workflow considerations, there’s a much more fundamental factor that prevents some organizations from implementing certain imaging informatics tools: cost.

“It’s much easier for a luminary site or academic facility to invest in bleeding-edge technologies like AI than a small community hospital because they take not just considerable money to implement but a considerable amount of other resources too. These include IT and departmental support to radiologists who have extra bandwidth to evaluate the technology,” Michael Cannavo, an imaging IT consultant, told HCB News.
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