Towards a National Diagnostic Excellence Dashboard - Partnering with Stakeholders to Construct Evidence-Based Operational Measures of Misdiagnosis-Related Harms

迈向国家卓越诊断仪表板 - 与利益相关者合作，构建基于证据的误诊相关危害的操作措施

• 批准号: 10613492
• 负责人: DAVID NEWMAN-TOKER
• 金额: $ 39.85万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10613492
• 关键词: Towards; National; Diagnostic; Excellence; Dashboard

Project Summary (Abstract) This four-year project combines the assets of a leading academic medical center (Johns Hopkins Medicine) with those of a recognized international leader in diagnostic excellence (Society to Improve Diagnosis in Medicine [SIDM]) and a major physician specialty society (American College of Emergency Physicians [ACEP]) to break new ground in operational measurement of patient harms linked to diagnostic error. To achieve the goal of improved patient outcomes through diagnostic excellence, it is essential to be able to measure diagnostic performance. Diagnostic errors are the largest cause of preventable harms in US medical care, affecting an estimated 12 million people each year, causing permanent disability or death in at least 0.5 million. Diagnostic safety is a priority research area for AHRQ and the National Academy of Medicine (NAM). A key impediment to “moving the needle” on reducing harms from diagnostic error is the lack of measures that matter to both patients and clinicians, yet can be fully operationalized (i.e., routinely monitored in the existing workflow). Impactful diagnostic outcome measures would assess serious morbidity and mortality in clinical contexts where diagnostic errors are known to occur. Ideal measures would be specific, valid, precise, and comparable across institutions to facilitate benchmarking that identifies both low and high outlier performers. This proposal uses a novel approach to constructing evidence-based diagnostic outcome measures with readily-available administrative and claims data sets. The Symptom-disease Pair Analysis of Diagnostic Error (“SPADE”) method first identifies a clinically-plausible relationship between a common presenting symptom and a dangerous underlying disease (e.g., chest pain-heart attack, fever-sepsis, dizziness-stroke). It then searches for a statistically-valid pattern of unexpected adverse events (e.g., observed greater than expected short-term inpatient hospitalization following a treat-and-release emergency department [ED] visit). Once such patterns are confirmed, they can be monitored to assess the impact of interventions to improve diagnosis. This proposal seeks to mature a partially-developed SPADE measure (for dizziness-stroke, a frequent cause of serious misdiagnosis-related harms) to the point of readiness for use in national benchmarking of hospital-level diagnostic performance for quality improvement. This SPADE pair has been validated through detailed chart review and statistical testing using data from four Johns Hopkins hospitals, and the National Quality Forum (NQF) has named this measure as a top priority for immediate development. This project will advance the measure towards broad adoption with two Specific Aims: (1) engage key national stakeholders to optimize attributes of the missed stroke measure (via expert panel and emergency physician survey) and (2) measure diagnostic performance of US hospital EDs using the refined missed stroke measure (via Medicare data analysis). These Aims will address stroke misdiagnosis now and, also, yield generalizable scientific insights. This will streamline future development of new measures of harm for other important symptom-disease pairs.

项目摘要(摘要) 这个为期四年的项目结合了一个领先的学术医学中心(约翰·霍普金斯医学)的资产。 与公认的国际卓越诊断领先者(学会改善诊断 医学[SIDM])和主要医师专业学会(美国急诊医师学会 [ACEP])在与诊断错误相关的患者伤害的操作测量方面开辟了新的天地。 要通过卓越的诊断来实现改善患者预后的目标，关键是能够 测量诊断性能。诊断错误是美国医学界可预防伤害的最大原因 护理，估计每年影响1200万人，造成至少0.5%的永久性残疾或死亡 百万美元。诊断安全性是AHRQ和美国国家医学研究院(NAM)的优先研究领域。 在减少诊断错误造成的危害方面，一个关键的障碍是缺乏措施， 对患者和临床医生都有影响，但可以完全操作(即，在现有的 工作流)。有效的诊断结果衡量标准将评估临床上的严重发病率和死亡率 已知会发生诊断错误的上下文。理想的衡量标准应该是具体、有效、准确和 各机构之间的可比性，以便于进行基准测试，以确定低和高异常值表现者。 这项建议使用了一种新的方法来构建基于证据的诊断结果度量， 随时可用的管理和索赔数据集。诊断差错的症状-疾病配对分析 (“黑桃”)方法首先确定临床上可信的关系，在一个常见的表现症状 以及危险的潜在疾病(例如，胸痛-心脏病发作、发热-败血症、头晕-中风)。然后它 搜索意外不良事件的统计有效模式(例如，观察到的事件大于预期 急诊科就诊后的短期住院治疗)。曾经是这样 模式得到确认后，可以对其进行监测，以评估干预措施的影响，以改善诊断。 这项提议寻求成熟部分开发的黑桃措施(用于头晕-中风，一种常见的 严重误诊相关危害)到可用于医院级国家基准的程度 用于质量改进的诊断性能。这对黑桃已经通过了详细的图表验证 使用来自四家约翰·霍普金斯医院和国家质量论坛的数据进行审查和统计测试 (NQF)已将这一措施列为当前发展的首要任务。这一项目将推进 为广泛采用而采取的措施有两个具体目标：(1)让国家主要利益攸关方参与优化 缺失中风测量的属性(通过专家小组和急诊医生调查)和(2)测量 使用改进的未命中中风测量(通过Medicare数据)的美国医院急救人员的诊断性能 分析)。这些目标现在将解决中风的误诊问题，并产生可推广的科学见解。 这将简化其他重要症状-疾病对的新危害措施的未来发展。

项目成果

Misdiagnosis-related harm quantification through mixture models and harm measures.

通过混合模型和危害措施对误诊相关危害进行量化。

• DOI: 10.1111/biom.13759
• 发表时间: 2023
• 期刊: Biometrics
• 影响因子: 1.9
• 作者: Zhu,Yuxin;Wang,Zheyu;Newman-Toker,David
• 通讯作者: Newman-Toker,David

Characterizing the relationship between diagnostic intensity and quality of care.

描述诊断强度和护理质量之间的关系。

• DOI: 10.1515/dx-2021-0062
• 发表时间: 2021
• 期刊: Diagnosis (Berlin, Germany)
• 作者: Ellenbogen,MichaelI;Prichett,Laura;Newman-Toker,DavidE;Brotman,DanielJ
• 通讯作者: Brotman,DanielJ

Statistical insights for crude-rate-based operational measures of misdiagnosis-related harms.

• DOI: 10.1002/sim.9039
• 发表时间: 2021-09-10
• 期刊: Statistics in medicine
• 影响因子: 2
• 作者: Zhu Y;Wang Z;Liberman AL;Chang TP;Newman-Toker D
• 通讯作者: Newman-Toker D

Optimizing measurement of misdiagnosis-related harms using symptom-disease pair analysis of diagnostic error (SPADE): comparison groups to maximize SPADE validity.

使用诊断错误的症状-疾病对分析 (SPADE) 优化误诊相关危害的测量：比较组以最大限度地提高 SPADE 有效性。

• DOI: 10.1515/dx-2022-0130
• 发表时间: 2023
• 期刊: Diagnosis (Berlin, Germany)
• 作者: Liberman,AvaL;Wang,Zheyu;Zhu,Yuxin;Hassoon,Ahmed;Choi,Justin;Austin,JMatthew;Johansen,MichelleC;Newman-Toker,DavidE
• 通讯作者: Newman-Toker,DavidE