According to the latest Duke University study exploring the efficacy of different Thyroid Imaging Reporting and Data System strategies, imaging for thyroid ultrasound sees a new dawn. By adopting an automated and fully structured reporting template, it is possible to minimize errors down to nil. The study was published in the recent JACR.
Testing four distinct reporting templates from free text to automated and fully structured – with software embedded and electronically summing up TI-RADS points, experts discovered that the latter mostly eliminated mistakes.
Benjamin Wildman-Tobriner, MD, Department of Radiology at the Duke University Hospital, along with colleagues from North Carolina Institution, on Aug 17, 2020, wrote, “Classification systems in radiology such as TI-RADS can be complicated and cumbersome. Readers’ data revealed less errors with the automated report in comparison to other types of templates. Moreover, readers did not commit any error during the use of advanced template.”
To conduct this investigation, the experienced research team engaged four radiologists with the American College of Radiology’s TI-RADS dictating 80 ultrasounds splitting evenly between four types of templates. Those templates ranged from the usual free text template to the minimally as well as a fully structured template, and lastly the souped-up template including automation. Thereafter, investigators tracked errors, their frequency, and changes in reporting time, and surveyed physicians involved about their experience.
The team identified that the combination of structured reporting and software reduced the rate of errors from 29 percent without automation, to zero for advanced models. Moreover, the software did not drag down the efficiency, ensures the dictation times remain flat, and all physicians were immensely satisfied with the survey results involving the care tool.
Wildman-Tobriner and his colleagues concluded by saying, “All of our readers subjectively preferred the automated template, likely in part because it concentrated their experience and skills on classifying nodule features. Once radiologiusts determined the features, results showed that software can also be used for generating TI-RADS suggestions and remove calculation errors.”
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