MedStar Authors catalog › Details for: PathBot: A Radiology-Pathology Correlation Dashboard.
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PathBot: A Radiology-Pathology Correlation Dashboard.

by Kelahan, Linda C; Kalaria, Amit D; Filice, Ross W.
Citation: Journal of Digital Imaging. 30(6):681-686, 2017 Dec..Journal: Journal of digital imaging.Published: 2017ISSN: 0897-1889.Full author list: Kelahan LC; Kalaria AD; Filice RW.UI/PMID: 28374195.Subject(s): Algorithms | Efficiency, Organizational | Health Records, Personal | Humans | Image-Guided Biopsy | *Information Storage and Retrieval/mt [Methods] | *Pathology | *Radiology Information Systems | User-Computer Interface | WorkflowInstitution(s): MedStar Washington Hospital CenterDepartment(s): RadiologyActivity type: Journal Article.Medline article type(s): Journal ArticleOnline resources: Click here to access online Digital Object Identifier: (Click here) Abbreviated citation: J Digit Imaging. 30(6):681-686, 2017 Dec.Abstract: Pathology is considered the "gold standard" of diagnostic medicine. The importance of radiology-pathology correlation is seen in interdepartmental patient conferences such as "tumor boards" and by the tradition of radiology resident immersion in a radiologic-pathology course at the American Institute of Radiologic Pathology. In practice, consistent pathology follow-up can be difficult due to time constraints and cumbersome electronic medical records. We present a radiology-pathology correlation dashboard that presents radiologists with pathology reports matched to their dictations, for both diagnostic imaging and image-guided procedures. In creating our dashboard, we utilized the RadLex ontology and National Center for Biomedical Ontology (NCBO) Annotator to identify anatomic concepts in pathology reports that could subsequently be mapped to relevant radiology reports, providing an automated method to match related radiology and pathology reports. Radiology-pathology matches are presented to the radiologist on a web-based dashboard. We found that our algorithm was highly specific in detecting matches. Our sensitivity was slightly lower than expected and could be attributed to missing anatomy concepts in the RadLex ontology, as well as limitations in our parent term hierarchical mapping and synonym recognition algorithms. By automating radiology-pathology correlation and presenting matches in a user-friendly dashboard format, we hope to encourage pathology follow-up in clinical radiology practice for purposes of self-education and to augment peer review. We also hope to provide a tool to facilitate the production of quality teaching files, lectures, and publications. Diagnostic images have a richer educational value when they are backed up by the gold standard of pathology.

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