Improving Follow-Up of Incidental Pulmonary Nodules in the Emergency Department Using an Artificial Intelligence-Supported Workflow.

Authors

Peter P Olivieri, Anika Markan, Darwin Ashbaker, Jason J Heavner, Jennifer Emel, McKenzie E Bedra, Alyssa Kisielewski, Sarah Nyarko, Melanie Dobrovic, Tyler Goldberg, Deborah Talley, Patrice Downs, Sofia Prieto, Gavin Henry, Tricia Roesch, Alisa Larbalestrier, Ashutosh Sachdeva, Calvin Ferrier, Warren D D'Souza, Jeffrey D Marshall

Published in

Respiratory medicine. Pages 109045. Jul 25, 2026. Epub Jul 25, 2026.

Abstract

Incidental pulmonary nodules (IPNs) are frequently identified on computed tomography (CT) scans but are often associated with poor rates of patient notification and follow-up, limiting opportunities for early lung cancer detection.
To evaluate whether implementation of an artificial intelligence (AI)-supported workflow improves patient notification and follow-up of IPNs detected in the emergency department (ED).
We conducted a retrospective pre-post cohort study at an academic-affiliated community hospital. The pre-intervention cohort included ED patients undergoing chest CT between January and March 2023. The post-intervention cohort included ED patients undergoing chest CT between June and August 2025, in which an AI-based natural language processing system identified potential IPNs from radiology reports, and patients were contacted to facilitate follow-up. Primary outcomes were rates of patient notification about their IPN and nodule-specific follow-up.
With implementation of the AI-supported workflow, patient notification increased from 171/228 (75%) to 223/252 (88.4%) p = 0.0001, and nodule-specific follow-up increased from 122/228 (53.5%) to 171/252 (67.9%) p = 0.0012. Inability to reach patients by phone after their ED visit was identified as a significant barrier to follow-up. There was no significant difference in lung cancer stage at diagnosis between cohorts.
An AI-supported IPN identification and outreach workflow improved patient notification and follow-up. Proactive communication strategies, facilitated by AI, represent a feasible approach to addressing care gaps and enhancing early lung cancer detection.

PMID:
42501874
Bibliographic data and abstract were imported from PubMed on 26 Jul 2026.

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