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

基本信息

批准号：
10883975
负责人：
Guodong Gao
金额：
$ 21.25万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10883975
关键词：
Academy Age Algorithms American Area Attention deficit hyperactivity disorder Binge eating disorder Bipolar Disorder Brain imaging Caring Child Child Care Childhood Clinical Collaborations Data Development Diagnosis Disease Disparity Early identification Early treatment Electronic Health Record Emergency department visit Ethnic Origin Family Feedback Feeling Frequencies Gender Genetic Goals Guilt Hospitalization Hospitals Individual Infrastructure Insurance Coverage International Classification of Disease Codes Intervention Language Left Life Life Expectancy 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 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 behavioral health cost criminal behavior demographics disparity reduction ethnic minority ethnic minority population 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 social stigma structured data substance use suicidal behavior treatment as usual urban children

项目摘要

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.

抽象的 ADHD 是儿科最常见的行为健康状况之一初级保健。如果不及时治疗，多动症会带来负面后果，包括自杀、犯罪行为和严重的药物滥用。美国儿科学会建议在初级保健中对 4-18 岁儿童进行 ADHD 筛查。很遗憾，遵守行为健康的实践指南和现实世界的实施筛查的差异很大。即使拥有普遍的行为健康筛查基础设施地方，筛选率可以保持在 50% 以下。开发电子健康记录 (EHR) 识别多动症风险儿童的算法有可能实现普遍筛查并促进早期识别和护理联系。拟议的项目将： 1) 描述多动症筛查频率的差异， 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 结构化和文本数据发现 ADHD 表型早于典型诊断年龄， 3) 与利益相关者合作制定实施路线图儿科初级保健中的表型分析算法。研究人员成功地将自然 EHR 数据的语言处理 (NLP) 技术可识别行为健康的患者包括自杀行为、自闭症和双相情感障碍，但 NLP 尚未被广泛应用。应用于ADHD的识别。由此产生的表型分析算法有可能成为纳入儿科初级保健的 EHR，自动标记有 ADHD 风险的儿童实时触发更密切的监测，减少筛查和诊断方面的差异，并启动及早治疗。由此产生的表型分析算法和实施路线图将设定 R01 试验阶段，旨在评估自动化 EHR 表型分析算法的临床效用儿科初级保健。