利用人工智能支持的流程改善急诊科偶发性肺结节的随访
Improving follow-up of incidental pulmonary nodules in the emergency department using an artificial intelligence-supported workflow.
作者
作者单位
- Department of Medicine, Sub-Department of Pulmonary and Critical Care, University of Maryland Baltimore Washington Medical Center, 301 Hospital Drive, Second Floor, Glen Burnie, MD, 21061, United States. Electronic address: polivieri@umm.edu.
- Touro College of Osteopathic Medicine, 230 W 125th St, New York, NY, 10027, United States.
- Department of Medicine, Sub-Department of Pulmonary and Critical Care, University of Maryland Baltimore Washington Medical Center, 301 Hospital Drive, Second Floor, Glen Burnie, MD, 21061, United States.
- Department of Clinical Research, University of Maryland Baltimore Washington Medical Center, 305 Hospital Drive, Third Floor, Glen Burnie, MD, 21061, United States.
- Department of Thoracic Surgery, University of Maryland Baltimore Washington Medical Center, 305 Hospital Drive, Third Floor, Glen Burnie, MD, 21061, United States.
- Population Health and Data Analytics, University of Maryland Baltimore Washington Medical Center, 301 Hospital Drive, Glen Burnie, MD, 21061, United States.
- Department of Medicine, Division of Pulmonary, Critical Care & Sleep Medicine, University of Maryland School of Medicine, 655 W. Baltimore Street Baltimore, MD, 21201, United States.
- Department of Radiology, University of Maryland Baltimore Washington Medical Center, 301 Hospital Drive, Glen Burnie, MD, 21061, United States.
- Department of Radiation Oncology, University of Maryland School of Medicine, 655 W. Baltimore Street Baltimore, MD, 21201, United States.
摘要
中文
偶发性肺结节(IPNs)经常在 CT 扫描中发现,但通常与较差的患者通知率和随访率相关,限制了早期肺癌检测的机会。本研究旨在评估人工智能(AI)支持的流程实施是否能改善急诊科(ED)发现的 IPNs 的患者通知和随访。我们在大学附属社区医院进行了回顾性前后队列研究。干预前队列包括 2023 年 1 月至 3 月在急诊科接受胸部 CT 的患者。干预后队列包括 2025 年 6 月至 8 月在急诊科接受胸部 CT 的患者,其中基于 AI 的自然语言处理系统从放射学报告中识别潜在 IPNs,并联系患者以促进随访。主要结局是 IPN 患者通知率和结节特异性随访率。实施 AI 支持流程后,患者通知率从 171/228(75%)增加到 223/252(88.4%),p=0.0001,结节特异性随访率从 122/228(53.5%)增加到 171/252(67.9%),p=0.0012。急诊就诊后电话联系不到患者是随访的重要障碍。两组之间诊断时肺癌分期无显著差异。AI 支持的 IPN 识别与外展流程改善了患者通知和随访。由 AI 促动的主动沟通策略代表了弥合护理差距和加强早期肺癌检测的可行方法。
English
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.
分类与指标
- 研究类型
- AI/ML
- 病种
- 肺癌
- JCR 分区
- Q2
- 影响因子
- 3.3
- 新锐分区
- 3区