Metadata Engineering

Harmonizing CT Descriptors in Enterprise Imaging Systems

Abstract

Enterprise imaging environments accumulate years of heterogeneous, site-specific metadata that undermine both radiologist workflow and the reliability of downstream learning and inference pipelines. In multihospital health systems, drift in CT Study Description, Protocol Name, Body Part Examined, and Contrast indicators produces label fragmentation, mishangs, cross-site inconsistencies, and domain shift conditions that destabilize supervised learning and automated routing.

This paper presents a 12-month, enterprise-scale metadata engineering initiative across two major geographic regions in a large U.S. health system that harmonized hundreds of CT descriptor variants into a unified, AIready vocabulary. The harmonization layer was implemented within the metadata ingestion and worklist logic of a commercial PACS platform (Agfa Enterprise Imaging), using token parsing, fuzzy matching, controlled vocabularies, and rule-driven normalization.

Across ∼175 radiologists and more than 30 imaging facilities, harmonization reduced anatomical and protocol-label drift by over 90%, eliminated dozens of contrast-flag inconsistencies, and markedly reduced mis-hang-related workflow disruptions. Support tickets related to CT metadata dropped from double digit weekly volumes to near zero, and radiologists reported smoother reading flow and more consistent priors. These improvements contributed to broader efficiency gains previously measured as a 25-second reduction in turnaround time and over 1,180 hours saved monthly.

Standardized metadata enabled reproducible extraction of clean CT cohorts for AI development, including anatomy classification and contrast triage tasks that were previously infeasible due to label noise and regional drift. We present the harmonization architecture, drift analysis, and AI-systems implications, demonstrating how metadata engineering provides foundational infrastructure for scalable, trustworthy imaging AI.