Delayed Diagnosis of Serious Pediatric Emergency Conditions

严重儿科紧急情况的延迟诊断

• 批准号: 10246799
• 负责人: Kenneth Michelson
• 金额: $ 15.34万
• 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/10246799
• 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.

项目摘要 诊断的延迟是急诊科（ED）环境中发病率的主要原因。孩子们 由于症状和严重性疾病的迹象，ED诊断延迟的风险可能会增加 可能是非特异性的，并且由于多达95％的儿童访问主要评估成年人的ED。率 急诊室中儿童的诊断延迟是未知的，但在各个情况下可能有很大差异。 由于延迟诊断是对患者安全的主要威胁，因此需要重点研究 确定访问EDS的儿童的事件，危险因素和结果。主要目标 项目是确定患者和医院属性，以增加延迟诊断的风险 访问ED的儿童中严重的条件，并确定患者预后的差异 当诊断延迟时。该项目的具体目的是（1）完善和验证算法 准确检测三种代表性严重疾病的延迟诊断（阑尾炎，新发炎 糖尿病性酮症酸中毒和败血症）在帐单索赔数据中； （2）确定事件，院间 变异性和延迟诊断的风险因素； （3）确定延迟特定条件的结果。 AIM 1将完善索赔检测算法并将其性能与手动记录审查进行比较；会 在多个合作中心进行。 AIM 2和3将使用该机构进行医疗保健研究和 质量（AHRQ）全州ED和住院数据库，以应用算法以识别算法 延迟诊断。将构建多级模型，以评估延迟和相关健康的预测指标 利用结果，包括对手术程序的需求，重症监护干预措施，长期医院 停留和死亡。这项研究将直接介绍AHRQ的顶级研究优先级，改善医疗保健患者 通过确定风险，危害和危害来安全。我们将专注于儿童 AHRQ优先人口。这项研究的结果将直接允许更多的宽度检测 以及对关键高危条件的诊断延迟的监视，以及蜂蜜方法扩展到 其他的。使用索赔检测方法的这种方法将允许对错误的监视类型 学习卫生系统的基本属性。首席调查员肯尼斯·米歇尔森（Kenneth Michelson）博士是 早期的职业生理科学家，在儿科和急诊医学方面具有强大的临床背景。这 奖项将促进他作为诊断错误的内容专业知识的研究人员的发展 使用多级建模，大型数据库分析和实施科学的结果分析。强壮 由经验丰富的生物统计学，定量，急诊医学和诊断的识别团队 错误专家支持该项目，并促进米歇尔森博士的职业发展朝着独立研究。