Promoting Universal Screening and Early Identification of Child ADHD via Integrated Automatic EHR Supports in Primary Care

通过初级保健中的集成自动 EHR 支持促进儿童 ADHD 的普遍筛查和早期识别

• 批准号: 10526794
• 负责人: Guodong Gao
• 金额: $ 25.01万
• 依托单位: UNIV OF MARYLAND, COLLEGE PARK
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2022-09-16
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10526794
• 关键词: Academy; Age; Aging; Algorithms; American; Area; Attention deficit hyperactivity disorder; Binge Eating; Bipolar Disorder; Brain; Brain imaging; Caring; Child; Child Care; Childhood; Clinical; Data; Development; Diagnosis; Disease; Early identification; Early treatment; Electronic Health Record; Emergency department visit; Ethnic Origin; Family; Feedback; Feeling; Frequencies; Gender; Genetic; Goals; Guilt; Hospitalization; Hospitals; Individual; Inferior; Infrastructure; Insurance Coverage; International Classification of Disease Codes; Intervention; Language; Left; Life; Literature; Machine Learning; Mental Health; Monitor; National Institute of Mental Health; Natural Language Processing; Obsessive compulsive behavior; Parents; Patients; Pattern; Pediatric Hospitals; Pediatrics; Pharmaceutical Preparations; Phenotype; Practice Guidelines; Primary Health Care; Privacy; Provider; Race; Recording of previous events; Research Personnel; Research Support; Risk; Service delivery model; Shame; Structure; Suicide; Surveys; Symptoms; System; Techniques; Text; Time; Underserved Population; Uninsured; Well Child Visits; Youth; autism spectrum disorder; base; behavioral health; cost; criminal behavior; demographics; disparity reduction; health care service utilization; health disparity; health equity; implementation barriers; improved; internalized stigma; literacy; low socioeconomic status; machine learning algorithm; machine learning method; novel; phenotyping algorithm; screening; screening guidelines; structured data; substance use; suicidal; suicidal behavior; treatment as usual; urban children

Abstract ADHD is among the most common behavioral health conditions presented in pediatric primary care. When left untreated, ADHD is associated with negative consequences including suicide, criminal behavior, and serious substance use. The American Academy of Pediatrics recommends screening for ADHD in primary care for children ages 4-18. Unfortunately, compliance with practice guidelines and real-world implementation of behavioral health screening is highly variable. Even with universal behavioral health screening infrastructure in place, screening rates can remain below 50%. Developing an electronic health record (EHR) algorithm to identify children at risk for ADHD has the potential to realize universal screening and facilitate early identification and linkage to care. The proposed project will: 1) Describe disparities in the frequency of ADHD screening, diagnosis, and healthcare utilization for children with ADHD, 2) Develop an algorithm to predict ADHD phenotypes earlier than the typical age of diagnosis using EHR structured and text data, and 3) Collaborate with stakeholders to develop an implementation roadmap for the phenotyping algorithm in pediatric primary care. Researchers have successfully applied Natural Language Processing (NLP) techniques to EHR data to identify patients with behavioral health conditions, including suicidal behaviors, autism, and bipolar disorder, but NLP has not been applied to the identification of ADHD. The resulting phenotyping algorithm holds potential to be integrated into EHR in pediatric primary care to automatically flag children at risk for ADHD in real-time to trigger closer monitoring, reduce disparities in screening and diagnosis, and initiate earlier treatment. The resulting phenotyping algorithm and implementation roadmap will set the stage for a R01 trial to evaluate the clinical utility of an automated EHR phenotyping algorithm in pediatric primary care.

摘要 ADHD是儿童中最常见的行为健康状况之一 初级保健。如果不治疗，ADHD会带来负面后果，包括 自杀、犯罪行为和严重的药物使用。美国儿科学会 建议在4-18岁儿童的初级保健中进行ADHD筛查。不幸的是， 遵守实践指南并在现实世界中实施行为健康 筛查是高度可变的。即使有通用的行为健康筛查基础设施 在当地，筛查率可以保持在50%以下。开发电子健康记录(EHR) 识别ADHD高危儿童的算法有可能实现普遍筛查 并有助于及早识别和联系护理。 拟议的项目将：1)描述ADHD筛查频率的差异， ADHD儿童的诊断和医疗保健利用，2)开发一种算法来预测 使用EHR结构化和文本数据，ADHD表型早于典型诊断年龄， 以及3)与利益攸关方合作，为 儿科初级保健中的表型算法。研究人员成功地将Natural应用于 语言处理(NLP)技术对EHR数据进行识别，以确定患者的行为健康 条件，包括自杀行为，自闭症和双相情感障碍，但NLP还没有 应用于ADHD的鉴定。由此产生的表型算法有可能成为 集成到儿科初级保健的电子健康记录中，自动标记有ADHD风险的儿童 实时触发更密切的监控，减少筛查和诊断方面的差异，并启动 更早的治疗。由此产生的表型算法和实现路线图将设置 R01试验阶段，以评估自动EHR表型算法在 儿科初级保健。