Improving follow-up of incidental pulmonary nodules in the emergency department using an artificial intelligence-supported workflow.

BACKGROUND

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.

OBJECTIVE

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).

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.