PO.PS01.06 · 人群科学
招募吸烟男性进行肺癌风险研究:使用电子健康记录和社区招募策略的经验教训
Recruiting men who smoke for lung cancer risk research: Lessons learned using the electronic health record and community recruitment strategies
该海报暂无可下载的资料
AACR 官方页面
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
引言:电子健康记录(Electronic Health Records, EHRs)被广泛用于研究招募;然而,所报告的社会史(如吸烟状态)报告不一致、不完整或不准确。本综述的目的是分析在为一项应激反应性研究招募男性中获得的经验教训,并比较EHR与社区策略在各种招募产出上的差异。
方法:在洛杉矶一家大型三级医疗系统中,我们于2024年1月至2025年6月开展了两轮EHR驱动的外联,针对21-75岁的非裔美国人/黑人和非西班牙裔白人男性。第一轮利用自然语言处理(natural language processing, NLP)查询,结合病历记录、吸烟相关术语和吸烟相关健康史。第二轮手动查询记录烟草使用的离散社会史字段。前两轮的招募经过标准化,通过多次短信、电话和电子邮件尝试联系潜在参与者。平行的社区招募使用链接到REDCap调查筛查器的定向社交媒体广告,加上重新联系此前同意参加未来研究的吸烟队列。主要测量结局为同意率、联系率和被错误分类为吸烟者的比例(通过EHR被识别为吸烟者但报告为"从不使用者"的人)。
结果:在EHR第一轮(NLP轮)中,接触了579名参与者,其中171名(29%)被联系到,16名同意参加(2.8%)。在拒绝者中,77名(49.6%)报告有从不吸烟史。EHR第二轮(离散字段)有更多参与者(1,374名患者):141名(10.2%)被联系到,16名(1.2%)同意加入研究。社区广告筛查了1,488名潜在参与者,404名符合条件(27.2%),110名同意参加研究活动(27.2%)。
结论:单独的EHR招募,尤其是NLP驱动时,被证明会错误分类推定的吸烟者并产生较低的同意率。第二轮(社会史离散字段)在特异性上有所改善,但在参与度上没有。社交媒体定向外联产生了最积极的招募活动;然而,这需要电话验证以确保个人真实性。多模式招募,结合标准化、纵向的EHR吸烟强度(以包年计)和吸烟史采集,对于具有代表性和准确性的入组以及提高肺癌筛查转诊接受度至关重要。
查看英文原文 English abstract
Introduction : Electronic Health Records (EHRs) are broadly used for research recruitment; however, reported social histories such as smoking status are inconsistently reported, incomplete, or inaccurate. The goal of this review is to analyze lessons learned in recruiting men for a stress reactivity study and compare various recruitment yields across EHR versus community strategies.
Methods : In a large Los Angeles tertiary health system, we conducted two EHR driven outreach waves from January 2024-June 2025 targeting African American/Black and non-Hispanic White men aged 21-75. The first wave utilized a natural language processing (NLP) query of a combination of notes, smoking-related terms, and smoking-related health history. The second wave manually queried discrete social history fields that recorded tobacco use. Recruitment from the first two waves was standardized, with potential participants contacted via multiple text, phone calls, and email attempts. Parallel community recruitment used targeted social media advertisements linked to a REDCap survey screener, plus recontact of a prior smoking cohort who consented to future studies. Primary measured outcomes were the consent rate, contact rate, and proportion misclassified as smokers (those who were identified via EHR as smokers but reported being “never-users”).
Results : In EHR wave 1 (NLP wave), 579 participants were approached, with 171 (29%) reached and 16 consented (2.8%). Among those who declined, 77 (49.6%) reported having a never-smoking history. EHR wave 2 (discrete fields) had more participants (1,374 patients): 141 (10.2%) were reached, and 16 (1.2%) consented to join the study. Community ads screened 1,488 potential participants, 404 were eligible (27.2%) and 110 consented to participate in study activities (27.2%).
Conclusions : EHR recruitment alone, particularly when NLP driven, was demonstrated to misclassify presumed smokers and yielded low consent rates. Wave 2 (discrete field of social history) yielded improvement in specificity but not engagement. Social media targeted outreach generated the most positive recruitment activity; however, this requires phone verification to ensure individual authenticity. Multimodal recruitment, coupled with standardized, longitudinal EHR capture of smoking intensity (in pack-years) and history is essential for representative and accurate enrollment and improving lung cancer screening referral uptake.
利益披露 Disclosure
T. A. Beard, None..
D. L. Morales, None..
K. Campbell, None..
A. L. Lindgren, None.
C. Hughes Halbert,
Merck ).