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-猫眼课题宝

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：

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

项目状态：

已结题

起止时间：

2023-08-01 至 2024-07-31

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

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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.

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