The more you know: Insights from integrated pre-visit surveys in a pediatric environmental health center.

Shalini H Shah,Alan D Woolf, Kimberly Manning,Faye Holder-Niles, Bridget Tully, Shelby Flanagan, Matthew C Spence,Marissa Hauptman

International public health journal(2023)

引用 0|浏览0
暂无评分
摘要
The Pediatric Environmental Health Center (PEHC) at Boston Children's Hospital is a specialty referral clinic that provides consultation for approximately 250 patients annually. Identifying environmental hazards is key for clinical management. Exposure concerns include lead, mold, pesticides, perfluoroalkyl substances (PFAS), impaired air quality, and more. Our goal was to identify concerns and visit priorities of our patient population to guide visits. A 47-question pre-visit survey was created exploring potential environmental hazards and administered prior to visits using a platform integrated into the electronic medical record (EMR). The study group was a convenience sample of patients from June 2021 to June 2022. Of 204 total visits, 101 surveys were submitted, yielding a response rate of 49.5%. 66/101 (65.3%) were surveys from initial consultations used for descriptive analysis. The majority of patients were seen for a chief complaint of lead exposure (90.1%). Most respondents had concerns about peeling paint (40.0%), and those reporting peeling paint were more likely to report additional concerns [75.0%, p < 0.001]. Other concerns highlighted were mold (15.2%), pests (15.2%), asbestos (10.6%), air pollution (9.1%), temperature regulation (7.6%), pesticides (6.1%), PFAS (4.5%), and formaldehyde (4.5%). A knowledge gap was identified; 45.5% (30/66) respondents responded "no" to the question asking if the Poison Control Center phone number was stored in their phone. This study illustrates how the implementation of a pre-visit EMR integrated survey engages families, informs clinical care, and serves as a point-of-care education tool for specific knowledge gaps. Findings will guide development of future environmental health screeners.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要