ArticleJournal of imaging informatics in medicine2026
Integrating Radiology & Clinical Decision Support to Drive Management of Emergency Department Patients in a Large Academic Medical Center.
Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Clinical decision support (CDS) systems have increasingly optimized care pathways. Integrating imaging findings into CDS remains a challenge due to unstructured radiology outputs. To evaluate the role of imaging-based decision support within an integrated clinicoradiological pathway for determining emergency department (ED) disposition using modality-based assessment. A computer-assisted reporting/decision support (CAR/DS) tool was developed to standardize chest radiograph and CT interpretations and integrated with a CDS system. 9,036 adult patients with suspected viral pneumonia presenting to the ED (07/2020 -12/2021) were analyzed as a use case. Associations between CAR/DS outputs and ED disposition were assessed using correlation and logistic regression. Agreement between radiograph- and CT-derived outputs was also examined. Most exams were negative [70.9% (6,408/9,036)] and 3.1% typical (276/9,036). Higher CAR/DS likelihood was independently associated with increased odds of admission [all p < 0.001; Groups: radiograph only (OR = 1.86), CT only (OR = 1.83), or both (CT OR = 1.97, radiograph OR = 1.71)]. Radiograph and CT outputs agreement was moderate (Spearman's ρ = 0.43, p < 0.001). Most negative radiographs were followed by a negative CT (60.7%, 389/642), only 23.8% (24/101) of typical radiographs had a subsequent typical CT; most typical radiographs were followed by an indeterminate (37.6%, 38/101) or atypical (28.7%, 29/101) CT. CAR/DS outputs integrated into CDS systems provide actionable information that independently predicts ED disposition. CT added value by excluding suspected pneumonia on radiographs. This exemplifies how imaging data can be standardized and seamlessly incorporated into broader decision pathways, with potential applicability well beyond pandemic-related use cases.
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