Delayed Diagnosis of Serious Pediatric Emergency Conditions

严重儿科紧急情况的延迟诊断

• 批准号: 10001484
• 负责人: Kenneth Michelson
• 金额: $ 15.36万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10001484
• 关键词: Delayed; Diagnosis; Serious; Pediatric; Emergency

PROJECT SUMMARY Delays in diagnosis represent a major cause of morbidity in the emergency department (ED) setting. Children are likely at increased risk of delayed diagnosis in the ED because symptoms and signs of serious disease may be nonspecific, and because as many as 95% of children visit EDs that primarily evaluate adults. Rates of delayed diagnosis among children in the ED are unknown, but likely vary substantially across settings. Because delayed diagnosis represents a major threat to patient safety, focused research is needed to determine its incidence, risk factors, and outcomes in children visiting EDs. The major objective of this project is to determine patient and hospital attributes that increase the risk for delayed diagnosis of serious conditions among children visiting EDs, and to determine the differences in patient outcomes when delayed diagnosis occurs. The project’s specific aims are (1) to refine and validate an algorithm for accurately detecting delayed diagnosis of three representative serious conditions (appendicitis, new-onset diabetic ketoacidosis, and sepsis) in billing claims data; (2) to determine the incidence, between-hospital variability, and risk factors for delayed diagnosis; and (3) to determine condition-specific outcomes of delay. Aim 1 will refine the claims detection algorithm and compare its performance with manual record review; it will be conducted at multiple collaborating centers. Aims 2 and 3 will use the Agency for Healthcare Research and Quality (AHRQ) Statewide ED and Inpatient Databases for multiple states to apply the algorithm to identify delayed diagnoses. Multilevel models will be constructed to assess predictors of delay and related health and utilization outcomes including need for surgical procedures, critical care interventions, prolonged hospital stays, and death. This study will directly address a top AHRQ research priority, improving health care patient safety through identification of risks, hazards, and harms. We will focus exclusively on children, an AHRQ priority population. The results of this research will directly allow more widespread detection and surveillance of diagnostic delays for critical high-risk conditions, and hone methods to expand to others. This approach utilizing a claims detection method would allow the kind of monitoring of error that is a fundamental property of a learning health system. The Principal Investigator, Dr. Kenneth Michelson, is an early career physician-scientist with a strong clinical background in Pediatrics and Emergency Medicine. This award will foster his development as a researcher with content expertise in diagnostic error, sophisticated outcomes analysis using multilevel modeling, large database analytics, and implementation science. A strong mentorship team composed of experienced biostatistical, quantitative, emergency medicine, and diagnostic error experts support the project and foster Dr. Michelson’s career development toward independent research.

项目总结 诊断延误是急诊科(ED)环境中发病率的主要原因。儿童 急诊科延误诊断的风险可能会增加，因为有严重疾病的症状和体征 可能是非特定的，因为多达95%的儿童访问主要评估成人的急诊室。收费率 急诊室儿童的延迟诊断是未知的，但可能在不同的环境中有很大差异。 由于延迟诊断是对患者安全的主要威胁，需要有重点的研究来 确定其发病率、危险因素和儿童到急诊室就诊的结果。这项工作的主要目标是 项目是确定增加延迟诊断风险的患者和医院属性 去急诊室就诊的儿童的严重情况，并确定患者预后的差异 当出现延迟诊断时。该项目的具体目标是(1)改进和验证算法 准确检测三种具有代表性的严重疾病(阑尾炎、新发病例)的延迟诊断 糖尿病酮症酸中毒和败血症)在账单索赔数据中；(2)确定医院之间的发病率 变异性和延迟诊断的风险因素；以及(3)确定延迟的特定条件的结果。 目标1将改进索赔检测算法，并将其性能与手动记录审查进行比较；它将 在多个协作中心进行。AIMS 2和3将使用医疗保健研究机构和 质量(AHRQ)全州急诊室和住院患者数据库针对多个州应用该算法进行识别 延误诊断。将建立多水平模型，以评估延误和相关健康的预测因素 利用结果包括需要外科手术、重症监护干预、延长住院时间 留下来，然后死亡。这项研究将直接解决AHRQ研究的首要任务，改善患者的医疗保健 通过识别风险、危险和危害来确保安全。我们将专门关注儿童，以及 AHRQ优先人口。这项研究的结果将直接允许更广泛的检测 以及对关键高危情况的诊断延迟进行监测，并磨练方法以扩展到 其他。这种利用索赔检测方法的方法将允许对错误的类型进行监视，即 学习型卫生系统的基本属性。首席调查员Kenneth Michelson博士是一名 早期职业内科医生和科学家，在儿科和急诊医学方面有很强的临床背景。这 该奖项将促进他成为一名研究人员，在诊断错误、复杂的内容方面拥有专业知识 使用多层次建模、大型数据库分析和实施科学进行结果分析。一个坚强的人 由经验丰富的生物统计、定量、急诊医学和诊断人员组成的导师团队 错误专家支持该项目，并促进了迈克尔逊博士向独立研究方向的职业发展。