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EHRs, digital imaging and the slow evolution of interoperability

par Sean Ruck, Contributing Editor | September 02, 2019
Health IT
From the September 2019 issue of HealthCare Business News magazine


So it's clear that the technology at many sites isn't exactly delivering what's needed for interoperability with imaging. But how well are imaging departments complying? In a broad sense, not too well. Part of the blame lies with the vendors, with different vendors having different ways of labeling very similar techniques and different ways of capturing and incorporating images into the systems, meaning the task is exponentially harder the more systems the radiologist has to work with. Part of the blame lies with physicians and hospitals permitting imaging metadata variation.

Different scanner manufacturers call nearly identical sequences by different names. Different scanners within a hospital may or may not have similar imaging metadata, depending on who configured the scanner, the scanner age, and how long ago those metadata were last reviewed. Until recently, there was no ability to centrally modify and standardize the image data capture configurations on many devices at once. Integrating the Healthcare Enterprise Radiology recently created a profile called Management of Acquisition Protocols (IHE MAP) so that a leader technologist and physician can jointly and centrally review and modify the configurations of the entire scanner fleet, permitting consistent metadata capture, even across scanner vendors. "Most hospitals around the country keep scanners for more than five years, often up to 10 years," Roth says. "Some scans will always only have older sequences, and thus older metadata describing them unless the institution puts in the effort to standardize how they capture scans. IHE MAP can assist with this data standardization and should be requested of scanner manufacturers during the RFP process as a play toward imaging data interoperability."

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The inconsistencies create at least one additional source of friction. AI, which relies on accurate and consistent data as its lifeblood, suffers. A radiologist who verbally dictates in a less-structured way what they're seeing, either due to limited technology or to preference, isn't adding clean and clear data. On the other hand, a radiologist with current technology and a mind for innovation may use more structured terms computers can understand.

The tools are slowly being developed, but the question is whether they'll reach their potential anytime soon. After all, there's been about half a century to work out the kinks.

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