2024 ARRS ANNUAL MEETING - ABSTRACTS

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E1009. Streamlining Radiology Workflow by Creating Custom-Made Plug-ins for Radiology Desktop Applications
Authors
  1. Tomer Nawrocki; Staten Island University Hospital
  2. David Sarkany; Staten Island University Hospital
  3. Mark Raden; Staten Island University Hospital
Background
QuickMacros (QM) is a computer coding platform for Microsoft Windows. Algorithms written in QM adds functionality, or plug-ins, to existing native radiology desktop applications. Added functionality includes automating routine tasks, which eliminates repetitive actions encountered by radiologists which are otherwise not addressed by native desktop radiology applications. It also allows for the transfer of data between different radiology applications, serving as an integrative platform between the picture archiving and communication system (PACS), radiology worklist applications, dictation software, and clinical applications that are commonly used in practice. This unique interplay improves workflow efficiency, decreases dictation time, and enhances user experience. All this is accomplished without the need to modify any of the native application source code. Rather, QM uses already accessible data that is vendor-nonspecific to complete these tasks.

Educational Goals / Teaching Points
Understand how custom-made computer code can interact with existing native radiology desktop applications. Become familiar with some examples of custom-made QM algorithms for radiology applications. Realize the potential of QM and how it can enhance the end-user experience, especially in practice settings that utilizes many clinical applications.

Key Anatomic/Physiologic Issues and Imaging Findings/Techniques
Algorithms written in QM can automatically populate sections of dictation templates with ultrasound measurements, eliminating the need for manual entry. Other algorithms can also be triggered when certain conditions are met, such as when a report is finalized. By analyzing the final report, predefined key phrases such as “mediastinal mass” generates an email to the cardiothoracic team, or the phrase “pulmonary embolism” to intervention radiology so that appropriate action is taken to expedite patient care. Additional functionality includes automatically linking accession numbers of studies that are meant to be combined (e.g., complete chest, abdomen, and pelvis).

Conclusion
Algorithms written in QM can be quite versatile, allowing for customized tasks to be performed within commonly used radiology applications such as dictation platforms, PACS, clinical applications, and radiologist worklists. By serving as an integrative platform between these native radiology desktop applications, QM increases workflow efficiency and enables the radiologist to focus on the images and the substance of the report. By decreasing the time spent on performing tedious, repetitive tasks, radiologists can dedicate more time to patient care and improve dictation accuracy.