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：
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
项目类别：
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

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.

抽象的多动症是小儿最常见的行为健康状况之一初级保健。当不进行治疗时，多动症与负面后果有关自杀，犯罪行为和严重的物质使用。美国儿科学会建议对4-18岁儿童的初级保健中的多动症进行筛查。很遗憾，遵守实践指南和行为健康的现实实施筛选是高度可变的。即使有普遍的行为健康筛查基础设施位置，筛查率可以保持在50％以下。开发电子健康记录（EHR）识别有多动症风险的儿童算法有可能实现普遍筛查并促进早期识别和与护理的联系。拟议的项目将：1）描述ADHD筛选频率的差异，多动症儿童的诊断和医疗保健利用，2）开发一种算法以预测使用EHR结构和文本数据的典型诊断年龄早于ADHD表型， 3）与利益相关者合作开发实施路线图小儿初级保健中的表型算法。研究人员成功地应用了自然 EHR数据的语言处理（NLP）技术以识别行为健康的患者疾病，包括自杀行为，自闭症和躁郁症，但NLP尚未应用于多动症的识别。由此产生的表型算法有可能在小儿初级保健中集成到EHR中实时以触发更紧密的监视，减少筛查和诊断的差异并启动较早的治疗。由此产生的表型算法和实施路线图将设置 R01试验的阶段，以评估自动EHR表型算法的临床实用性小儿初级保健。