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Published in: Journal of Digital Imaging 1/2020

01-02-2020 | Original Paper

Just in Time Radiology Decision Support Using Real-time Data Feeds

Authors: John L. Burns, Dan Hasting, Judy W. Gichoya, Ben McKibben III, Lindsey Shea, Mark Frank

Published in: Journal of Imaging Informatics in Medicine | Issue 1/2020

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Abstract

Ready access to relevant real-time information in medical imaging offers several potential benefits. Knowing both when important information will be available and that important information is available can facilitate optimization of workflow and management of time. Unexpected findings, as well as deficiencies in reporting and documentation, can be immediately managed. Herein, we present our experience developing and implementing a real-time web-centric dashboard system for radiologists, clinicians, and support staff. The dashboards are driven by multi-sourced HL7 message streams that are monitored, analyzed, aggregated, and transformed into multiple real-time displays to improve operations within our department. We call this framework Pipeline. Ruby on Rails, JavaScript, HTML, and SQL serve as the foundations of the Pipeline application. HL7 messages are processed in real-time by a Mirth interface engine which posts exam data into SQL. Users utilize web browsers to visit the Ruby on Rails–based dashboards on any device connected to our hospital network. The dashboards will automatically refresh every 30 seconds using JavaScript. The Pipeline application has been well received by clinicians and radiologists.
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Metadata
Title
Just in Time Radiology Decision Support Using Real-time Data Feeds
Authors
John L. Burns
Dan Hasting
Judy W. Gichoya
Ben McKibben III
Lindsey Shea
Mark Frank
Publication date
01-02-2020
Publisher
Springer International Publishing
Published in
Journal of Imaging Informatics in Medicine / Issue 1/2020
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI
https://doi.org/10.1007/s10278-019-00268-2

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