The Application of Machine Learning to Quality Improvement Through the Lens of the Radiology Value Network

Valeria Makeeva; Judy Gichoya; C Matthew Hawkins; Alexander J Towbin; Marta Heilbrun; Adam Prater

doi:10.1016/j.jacr.2019.05.039

The Application of Machine Learning to Quality Improvement Through the Lens of the Radiology Value Network

J Am Coll Radiol. 2019 Sep;16(9 Pt B):1254-1258. doi: 10.1016/j.jacr.2019.05.039.

Authors

Valeria Makeeva¹, Judy Gichoya², C Matthew Hawkins², Alexander J Towbin³, Marta Heilbrun², Adam Prater²

Affiliations

¹ Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia. Electronic address: [email protected].
² Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia.
³ Department of Radiology and Medical Imaging, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.

PMID: 31492403
DOI: 10.1016/j.jacr.2019.05.039

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.

Keywords: Machine learning; artificial intelligence; data aggregation; radiology quality improvement; radiology value network.

Publication types

Review

MeSH terms

Algorithms
Artificial Intelligence*
Automation
Data Collection
Forecasting
Humans
Machine Learning*
Quality Improvement*
Radiology / methods
Radiology / trends*
Workflow