Adherence rates for timely imaging follow-up are usually low due to low rates of diligence by referring physicians and/or patients with following recommendations for follow-up imaging. This can lead to delayed treatment, poor patient outcomes, unnecessary testing, and legal liability. Existing follow-up recommendation detection methods are often disease- and modality-specific. To address some of these limitations, we present a generic radiology report processing pipeline that can be used to extract follow-up imaging recommendations by anatomy using an ontology-based approach. Using a large dataset from three hospitals, we discuss our methodology in the context of identifying follow-up imaging recommendations that are related to lung, adrenal and/or thyroid conditions. The algorithm has 99% accuracy (95% CI: 95.8-99%). We also present an interactive dashboard that can be used to understand trends related to follow-up recommendations.
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