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使用自然语言处理技术由护士导航员运行的项目成功识别出腹主动脉瘤患者。

Successful implementation of a nurse-navigator-run program using natural language processing identifying patients with an abdominal aortic aneurysm.

作者信息

Boitano Laura T, DeVivo Gabrielle, Robichaud Devon I, Okuhn Steven, Steppacher Robert C, Simons Jessica P, Aiello Francesco A, Jones Douglas, Judelson Dejah, Nguyen Tammy, Sorensen Caitlin, Schanzer Andres

机构信息

UMass Chan Medical School, Worcester, MA.

UMass Chan Medical School, Worcester, MA.

出版信息

J Vasc Surg. 2023 Mar;77(3):922-929. doi: 10.1016/j.jvs.2022.10.034. Epub 2022 Oct 31.

Abstract

BACKGROUND

Abdominal aortic aneurysms (AAA) are often identified incidentally on imaging studies. Patients and/or providers are frequently unaware of these AAA and the need for long-term follow-up. We sought to evaluate the outcome of a nurse-navigator-run AAA program that uses a natural language processing (NLP) algorithm applied to the electronic medical record (EMR) to identify patients with imaging report-identified AAA not being followed actively.

METHODS

A commercially available AAA-specific NLP system was run on EMR data at a large, academic, tertiary hospital with an 11-year historical look back (January 1, 2010, to June 2, 2021), to identify and characterize AAA. Beginning June 3, 2021, a direct link between the NLP system and the EMR enabled for real-time review of imaging reports for new AAA cases. A nurse-navigator (1.0 full-time equivalent) used software filters to categorize AAA according to predefined metrics, including repair status and adherence to Society for Vascular Surgery imaging surveillance protocol. The nurse-navigator then interfaced with patients and providers to reestablish care for patients not being followed actively. The nurse-navigator characterized patients as case closed (eg, deceased, appropriate follow-up elsewhere, refuses follow-up), cases awaiting review, and cases reviewed and placed in ongoing surveillance using AAA-specific software. The primary outcome measures were yield of surveillance imaging performed or scheduled, new clinic visits, and AAA operations for patients not being followed actively.

RESULTS

During the prospective study period (January 1, 2021, to December 30, 2021), 6,340,505 imaging reports were processed by the NLP. After filtering for studies likely to include abdominal aorta, 243,889 imaging reports were evaluated, resulting in the identification of 6495 patients with AAA. Of these, 2937 cases were reviewed and closed, 1183 were reviewed and placed in ongoing surveillance, and 2375 are awaiting review. When stratifying those reviewed and placed in ongoing surveillance by maximum aortic diameter, 258 were 2.5 to 3.4 cm, 163 were 3.5 to 3.9 cm, 213 were 4 to 5 cm, and 49 were larger than 5 cm; 36 were saccular, 86 previously underwent open repair, 274 previously underwent endovascular repair, and 104 were other. This process yielded 29 new patient clinic visits, 40 finalized imaging studies, 29 scheduled imaging studies, and 4 AAA operations in 3 patients among patients not being followed actively.

CONCLUSIONS

The application of an AAA program leveraging NLP successfully identifies patients with AAA not receiving appropriate surveillance or counseling and repair. This program offers an opportunity to improve best practice-based care across a large health system.

摘要

背景

腹主动脉瘤(AAA)常在影像学检查中偶然发现。患者和/或医疗服务提供者常常 unaware 这些 AAA 以及长期随访的必要性。我们试图评估一个由护士导航员管理的 AAA 项目的结果,该项目使用自然语言处理(NLP)算法应用于电子病历(EMR),以识别影像学报告发现但未得到积极随访的 AAA 患者。

方法

在一家大型学术三级医院,使用一个商用的特定于 AAA 的 NLP 系统对 EMR 数据进行分析,回顾 11 年的历史数据(2010 年 1 月 1 日至 2021 年 6 月 2 日),以识别和表征 AAA。从 2021 年 6 月 3 日开始,NLP 系统与 EMR 之间的直接链接实现了对新 AAA 病例影像学报告的实时审查。一名护士导航员(相当于 1.0 个全职人员)使用软件过滤器根据预定义的指标对 AAA 进行分类,包括修复状态和对血管外科学会影像学监测方案的依从性。然后,护士导航员与患者和医疗服务提供者进行沟通,为未得到积极随访的患者重新建立护理。护士导航员将患者分为病例已结束(如死亡、在其他地方接受适当随访、拒绝随访)、等待审查的病例以及使用特定于 AAA 的软件进行审查并纳入持续监测的病例。主要结局指标是为未得到积极随访的患者进行或安排的监测影像学检查、新的门诊就诊以及 AAA 手术。

结果

在前瞻性研究期间(2021 年 1 月 1 日至 2021 年 12 月 30 日),NLP 处理了 6340505 份影像学报告。在筛选可能包括腹主动脉的研究后,评估了 243889 份影像学报告,结果识别出 6495 例 AAA 患者。其中,2937 例病例经过审查并结束,1183 例经过审查并纳入持续监测,2375 例等待审查。当根据最大主动脉直径对纳入持续监测的病例进行分层时,258 例为 2.5 至 3.4 厘米,163 例为 3.5 至 3.9 厘米,213 例为 4 至 5 厘米,49 例大于 5 厘米;36 例为囊状,86 例先前接受过开放修复,274 例先前接受过血管内修复,104 例为其他情况。这一过程为未得到积极随访的患者带来了 29 次新的患者门诊就诊、40 项最终确定的影像学检查、29 项安排的影像学检查以及 3 例患者的 4 次 AAA 手术。

结论

利用 NLP 的 AAA 项目的应用成功识别出未接受适当监测、咨询和修复的 AAA 患者。该项目为在大型医疗系统中改善基于最佳实践的护理提供了机会。

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