Automated Substance Use Detection from Electronic Health Records in the Pediatric Setting

从儿科电子健康记录中自动检测药物使用情况

• 批准号: 10447967
• 负责人: Sarah Beal
• 金额: $ 7.95万
• 依托单位: CINCINNATI CHILDRENS HOSP MED CTR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10447967
• 关键词: Address; Adolescence; Adolescent; Adolescent Development; Adult; Algorithms; Artificial Intelligence; Caring; Child; Childhood; Chronic Disease; Clinical; Clinical Research; Data; Data Sources; Detection; Disease Management; Documentation; Electronic Health Record; Ethnic Origin; Evidence based program; Family; Feedback; Funding; Future; Gender; Goals; Healthcare; Healthcare Systems; Individual; Insurance; Intervention; Laboratories; Language; Lead; Longevity; Machine Learning; Medical; Methods; Modeling; Modernization; Monitor; Morbidity - disease rate; National Institute of Drug Abuse; Natural Language Processing; Outpatients; Patients; Pattern; Pediatrics; Performance; Pharmaceutical Preparations; Policies; Population; Prevention; Prevention program; Primary Health Care; Provider; Public Health; Publishing; Race; Reporting; Research; Research Personnel; Retrieval; Risk; Sampling; Screening Result; Sensitivity and Specificity; Structure; Substance Use Disorder; System; Systems Analysis; Technology; Testing; Text; Time; United States; Visit; Work; Youth; adolescent patient; adolescent substance use; artificial intelligence algorithm; clinical care; computerized; critical period; detection platform; electronic structure; evidence base; health care settings; high risk; innovation; insight; medical specialties; mortality; population health; preference; prevent; provider behavior; public health relevance; racism; reduced substance use; screening; screening, brief intervention, referral, and treatment; social stigma; structured data; substance use; substance use prevention; substance using adolescents; tv watching; unstructured data

Project Summary The majority of adults with substance use disorders (SUD) report beginning to use substances as adolescents; thus, adolescence represents a critical time for screening of substance use initiation and implementing interventions to prevent or reduce use. The healthcare system prioritizes substance use screening, including during adolescence, with the goal of identifying when substance use is occurring and monitoring use to determine necessary intervention. Unfortunately, the majority of this information is documented in unstructured clinical notes, making it difficult for providers to monitor change in substance use for an adolescent over encounters. Published studies also suggest bias around substance use screening such as laboratory tests exists. Both limitations prevent electronic health record (EHR) data from being used to study the contexts and consequences of substance use in populations of adolescents. Rather than changing clinician behavior, which can be challenging, this study utilizes automated artificial intelligence algorithms to detect substance use screening occurrences and results in the EHRs. Our work could allow current provider-led preferences and practices in substance use documentation to continue while simultaneously increasing access to documented information and mitigating screening bias to avoid perpetuating racism and inequity in healthcare. As a result, the study has the potential to aid in long-term efforts to target prevention, intervention, and referral for treatment in adolescence and ultimately reduce risk of SUD across the lifespan. Our work will be completed through accomplishing the following aims: Aim 1: Examine the generalizability of an automated substance use detection system in a sample of ~5,000 adolescent patients who receive well child and/or outpatient specialty visits, maximizing contexts where substance use screening is most likely to occur; and Aim 2: Assess differences in substance use screening and positive screening results by gender, insurance type, minoritized race and ethnicity status, and clinical context, evaluating whether bias is detected in structured data, unstructured data, or both data sources. In addition, participatory research principles will be used to solicit feedback from clinicians and researchers about the application of findings to clinical care. By the end of the funding period, we will have validated the performance of the automated system, assessed bias in identifying substance use screening results, and gained insights from clinician feedback about application to clinical care.

项目摘要 大多数患有药物使用障碍的成年人报告说，他们在青少年时期就开始使用药物; 因此，青春期是筛查物质使用开始和实施的关键时期， 采取干预措施，防止或减少使用。医疗保健系统优先考虑物质使用筛查，包括 在青春期，目的是确定何时使用药物，并监测使用情况， 确定必要的干预措施。不幸的是，这些信息的大部分都是以非结构化的 临床记录，使供应商难以监测青少年药物使用的变化， 遭遇已发表的研究还表明，实验室检查等物质使用筛查存在偏见 存在.这两个限制都阻止了电子健康记录（EHR）数据被用于研究环境， 青少年群体中物质使用的后果。而不是改变临床医生的行为， 这项研究利用自动人工智能算法来检测物质使用情况， 筛选EHR中的事件和结果。我们的工作可以允许当前提供者主导的偏好， 继续实行物质使用记录方面的做法，同时增加获得记录在案的 信息和减轻筛选偏见，以避免在医疗保健中延续种族主义和不平等。因此，在本发明中， 这项研究有可能有助于长期的预防、干预和转诊工作， 在青春期进行治疗，并最终降低整个生命周期中SUD的风险。我们的工作将完成 通过实现以下目标：目标1：检查自动化物质使用的普遍性 在接受良好儿童和/或门诊专科治疗的约5，000名青少年患者样本中使用检测系统 访问，最大限度地扩大最有可能进行药物使用筛查的背景;以及目标2：评估 按性别、保险类型、少数族裔分列的物质使用筛查和阳性筛查结果的差异 人种和种族状态以及临床背景，评价是否在结构化数据中检测到偏倚， 非结构化数据，或这两种数据源。此外，参与性研究原则将用于征求 来自临床医生和研究人员关于将研究结果应用于临床护理的反馈。结束时 在资助期间，我们将验证自动化系统的性能，评估识别偏差， 物质使用筛查结果，并从临床医生反馈中获得有关临床护理应用的见解。