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Advanced Processing & Visualization Workflow and Tools on
Display at Live Workflow Demonstration
Friday morning’s general
session demonstrated current processes for advanced processing & visualization
and a look toward the future and how workflow and tools might be improved.
Katherine Andriole, PhD, Paul Chang, MD, and Luciano Prevedello, MD, play acted
roles in a typical scenario to illustrate how complicated the current situation
is.
Andriole said that the advent of Multidetector CT and the maturation of PACS
have been key to advancing post-processing applications. Andriole played the
role of the CT technologist, Chang the radiologist, and Prevedello doubled as
the surgeon and lab technologist as the team spotlighted different pain points
in the process and how advanced processing & visualization fits into the
workflow. The team used a hypothetical scenario to demonstrate an “embellished”
version of a radiology department.
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 In the current general department workflow – ordering, scheduling,
acquisition, interpretation, and reporting – advanced processing & visualization
doesn’t really have a logical place. In their demonstration, the team
illustrated how it is often unclear where APV fits. Communication between
departments, uploading and transferring studies and images, and disparate
schedules often combine for a process that is less than efficient. Current
workflow issues include lack of automation, orchestration of all tasks,
coordinating standalone systems, increased network traffic, poor communication
among all players, what images should be sent to PACS, and what images should be
archived. There are implications with large data sets, policy and procedure
issues, medico-legal issues, and the health care payer environment.The team proposed a post-processing workflow of the future, which they called
“the Magic Box.” Each person would log in at their own station to complete their
steps in the process. Requirements for success include no disruption to the
radiologist workflow, no negative impact on PACS performance as a result of
thin-slice data, and must be able to validate if images can be approximately
read from the thin-slice data set.
As these tools become more available, said Prevedello, education must be in
place to support the new workflow.
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