Abstract
Recent advances in machine learning and artificial intelligence offer promising applications
to radiology quality improvement initiatives as they relate to the radiology value
network. Coordination within the interlocking web of systems, events, and stakeholders
in the radiology value network may be mitigated though standardization, automation,
and a focus on workflow efficiency. In this article the authors present applications
of these various strategies via use cases for quality improvement projects at different
points in the radiology value network. In addition, the authors discuss opportunities
for machine-learning applications in data aggregation as opposed to traditional applications
in data extraction.
Key Words
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Article Info
Footnotes
Dr Hawkins is the associate editor for practice management at JACR, a member of the ACR Board of Chancellors, a member of the RADPAD Board of Directors, an alternate CPT adviser for the Society of Interventional Radiology , and sole proprietor of Hawkins Healthcare Consulting. Dr Towbin has received grants from Guerbet, Siemens , and the Cystic Fibrosis Foundation ; has received personal fees from and is an advisory board member for IBM Watson Health; is an advisory board member for KLAS; has received personal fees from Applied Radiology; and has received author royalties from Elsevier. Dr Heilbrun is a member of the RSNA Radiology Informatics Committee and the Quality Improvement Committee and of the ACR Data Science Institute Panel for Non-Interpretive Skills. All other authors state that they have no conflict of interest related to the material discussed in this article.
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