Using Machine Learning to find a life saving needle in a haystack of children's emergencies

利用机器学习在儿童紧急情况的大海捞针中找到救生针

• 批准号: 10341239
• 负责人: JEANNE-MARIE GUISE
• 金额: $ 70.29万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10341239
• 关键词: Academy; Adult; Adverse event; Algorithms; American; American Heart Association; Caring; Cessation of life; Child; Child Care; Child Health; Childhood; Clinical; Collection; Complex; Computing Methodologies; Data; Data Set; Detection; Development; Elements; Emergency Care; Emergency Situation; Emergency medical service; Encapsulated; Environment; Epidemiology; Evaluation; Event; Foundations; Future; Goals; Gold; Guidelines; Health; Healthcare Systems; Heart Arrest; Hospitals; Incidence; Informatics; Information Systems; Infrastructure; Injury; Interdisciplinary Study; Knowledge; Learning; Life; Logic; Machine Learning; Manuals; Medical; Medical Errors; Medicine; Methods; Modeling; Modernization; Monitor; Morbidity - disease rate; Natural Language Processing; Ontology; Oregon; Outcome; Output; Pediatric Hospitals; Play; Population; Pre-hospitalization care; Procedures; Provider; Quality of Care; Records; Research; Research Personnel; Role; Safety; Sampling; Savings; Scientist; Secure; Structure; Supervision; Survival Rate; System; Techniques; Testing; Text; Transcend; United States; Utah; Visualization software; Work; base; care costs; care outcomes; care systems; computerized tools; data visualization; deep learning; effective therapy; evidence base; experience; health care delivery; high risk; improved; mortality; multidisciplinary; novel; out-of-hospital cardiac arrest; patient safety; pediatric emergency; pediatric patients; point of care; prevent; prospective; relating to nervous system; screening; sequence learning; service providers; structured data; tool; trauma care

SUMMARY Adverse safety events (ASEs) resulting from medical care are a leading cause of preventable injury and death in the United States. The National Academy of Medicine recommends that hospital and Emergency Medical Services (EMS) systems “implement evidence-based approaches to reduce errors in emergency and trauma care for children,” but acknowledges that implementation is limited by the “paucity of high-quality data on the epidemiology of medical errors in children, particularly within the emergency care system.” Our research team developed and validated an EMS chart review tool to identify ASEs in the care of children and has begun to describe the epidemiology of these events. We have identified pediatric out-of-hospital cardiac arrest (OHCA) as a particularly high-risk condition for ASEs and poor survival. EMS plays a critical role in the health and outcomes of Americans during cardiac arrests. Receipt of effective treatment in the first few minutes of cardiac arrest can double or triple survival. However, while survival from adult OHCAs and in-hospital pediatric OHCAs have both increased significantly over the last 10-15 years, survival from pediatric OHCA remains largely unchanged. We focus on identifying preventable ASEs occurring over the entire episode of OHCA, recognized to be a major contributor to mortality and morbidity. The status quo, manual chart reviews, considered the gold standard for evaluating safety and quality of care, are costly and labor-intensive. The main goal of this proposal is to computationally detect ASEs associated with pediatric OHCA at a population level from electronic EMS charts through the following Study Aims: Aim 1. Identify adverse safety events in the prehospital care of children with OHCA via rules- and regression-based computational processing of structured data in pediatric EMS charts. Aim 2. Extract cardiac arrest-related indicators from EMS chart narrative text using deep learning NLP techniques and weak supervision techniques to augment the rules-and regression--based automatic screening of EMS charts. Aim 3. Prospectively demonstrate the scalability of automated detection of ASEs in OHCA at the scale of statewide populations. This proposal leverages the strengths of an experienced multidisciplinary research team that includes informaticians and clinician-scientists with expertise in pediatric patient safety and American Heart Association Guideline development. Successful completion of the project aims will create the foundational elements of an automated tool capable of screening EMS charts on a large scale to identify, monitor, and ultimately mitigate preventable pediatric prehospital patient safety events. Additionally, the computational tools and annotated dataset created in the course of this project will serve as valuable infrastructure to support future clinical and computational research.

概括 医疗引起的不良安全事件 (ASE) 是可预防伤害和死亡的主要原因 在美国。美国国家医学院建议医院和紧急医疗机构 服务（EMS）系统“实施基于证据的方法来减少紧急情况和创伤中的错误 照顾儿童”，但承认实施受到“缺乏有关儿童的高质量数据的限制”。 儿童医疗失误的流行病学，特别是在紧急护理系统内。”我们的研究团队 开发并验证了 EMS 图表审查工具，以识别儿童护理中的 ASE，并已开始 描述这些事件的流行病学。我们已经发现儿科院外心脏骤停 (OHCA) 作为 ASE 的一种特别高风险的疾病并且生存率低。 EMS 在健康和健康方面发挥着至关重要的作用 美国人在心脏骤停期间的结果。在心脏病发作的最初几分钟内接受有效的治疗 逮捕可以使生存率增加一倍或三倍。然而，虽然成人 OHCA 和住院儿童 OHCA 的生存率 在过去 10-15 年中，两者均显着增加，儿科 OHCA 的生存率仍然在很大程度上 不变。我们专注于识别 OHCA 整个发作期间发生的可预防的 ASE，并已认识到 成为死亡率和发病率的主要因素。现状，手动图表评论，认为是黄金 评估护理安全和质量的标准是昂贵且劳动密集型的。本提案的主要目标 是通过电子 EMS 在人群水平上计算检测与儿科 OHCA 相关的 ASE 通过以下研究目标绘制图表： 目标 1. 识别院前护理中的不良安全事件 通过基于规则和回归的儿科结构化数据计算处理来治疗 OHCA 儿童 EMS 图表。目标 2. 使用深度学习从 EMS 图表叙述文本中提取心脏骤停相关指标 NLP 技术和弱监督技术增强基于规则和回归的自动 EMS图表的筛选。目标 3. 前瞻性地证明 ASE 自动检测的可扩展性 全州人口规模的 OHCA。该提案充分利用了经验丰富的专家的优势 多学科研究团队，包括具有儿科专业知识的信息学家和临床科学家 患者安全和美国心脏协会指南的制定。项目顺利完成 目标将创建一个自动化工具的基本要素，该工具能够在大范围内筛选 EMS 图表。 规模来识别、监测并最终减轻可预防的儿科院前患者安全事件。 此外，在该项目过程中创建的计算工具和带注释的数据集将用作 支持未来临床和计算研究的宝贵基础设施。