Monitoring Diagnostic Safety Risks in Emergency Departments: Protocol for a Machine Learning Study.
Monitoring Diagnostic Safety Risks in Emergency Departments: Protocol for a Machine Learning Study.
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DOI：
10.2196/24642
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发表时间：
2021-06-14
影响因子：
1.7
通讯作者：
Pasupathy KS
中科院分区：
文献类型：
--
作者：
Enayati M;Sir M;Zhang X;Parker SJ;Duffy E;Singh H;Mahajan P;Pasupathy KS
诊断决策，特别是在急诊科，是一个高度复杂的认知过程，涉及到不确定性和对错误的敏感性。多种因素的结合，包括患者因素（如病史、行为、复杂性和合并症）、提供者-护理团队因素（如认知负荷和信息收集与综合）和系统因素（如健康信息技术、拥挤、轮班工作和中断），可能导致诊断错误。使用电子触发器来识别具有某些护理模式（例如护理升级）的患者的记录对于筛查诊断错误是有用的。一旦发现错误，复杂的数据分析和机器学习技术可以应用于现有的电子健康记录（EHR）数据集，以揭示影响诊断决策的潜在风险因素。本研究旨在使用大规模EHR数据和机器学习技术识别与急诊科诊断错误相关的变量。这项研究计划在EHR数据存储库中使用触发算法来生成一个大型的记录数据集，这些记录被标记为阳性或阴性，这取决于它们是否符合某些标准。将使用病历审查对两个数据集的样本进行验证，我们预计在阳性子集中发现更多的诊断安全性事件。机器学习将用于评估某些患者因素，提供者护理团队因素，系统级风险因素和诊断安全信号之间的关系，这些风险因素和诊断安全信号在统计学上匹配的阳性和阴性图表组中。这项由联邦政府资助的研究得到了2个学术医疗中心及其附属社区医院的机构审查委员会的批准。两个组织都在开发触发查询，并将使用触发标记样本队列。机器学习技术，如关联规则挖掘、卡方自动交互检测、分类和回归树，将用于发现重要变量，这些变量可以纳入未来的临床决策支持系统，以帮助识别和降低导致诊断错误的风险。使用大型EHR数据集和机器学习来调查诊断过程中的风险因素（与患者，提供者护理团队和系统级相关）可能有助于创建未来的诊断安全监控机制。DERR1-10.2196/24642
Diagnostic decision making, especially in emergency departments, is a highly complex cognitive process that involves uncertainty and susceptibility to errors. A combination of factors, including patient factors (eg, history, behaviors, complexity, and comorbidity), provider-care team factors (eg, cognitive load and information gathering and synthesis), and system factors (eg, health information technology, crowding, shift-based work, and interruptions) may contribute to diagnostic errors. Using electronic triggers to identify records of patients with certain patterns of care, such as escalation of care, has been useful to screen for diagnostic errors. Once errors are identified, sophisticated data analytics and machine learning techniques can be applied to existing electronic health record (EHR) data sets to shed light on potential risk factors influencing diagnostic decision making. This study aims to identify variables associated with diagnostic errors in emergency departments using large-scale EHR data and machine learning techniques. This study plans to use trigger algorithms within EHR data repositories to generate a large data set of records that are labeled trigger-positive or trigger-negative, depending on whether they meet certain criteria. Samples from both data sets will be validated using medical record reviews, upon which we expect to find a higher number of diagnostic safety events in the trigger-positive subset. Machine learning will be used to evaluate relationships between certain patient factors, provider-care team factors, and system-level risk factors and diagnostic safety signals in the statistically matched groups of trigger-positive and trigger-negative charts. This federally funded study was approved by the institutional review board of 2 academic medical centers with affiliated community hospitals. Trigger queries are being developed at both organizations, and sample cohorts will be labeled using the triggers. Machine learning techniques such as association rule mining, chi-square automated interaction detection, and classification and regression trees will be used to discover important variables that could be incorporated within future clinical decision support systems to help identify and reduce risks that contribute to diagnostic errors. The use of large EHR data sets and machine learning to investigate risk factors (related to the patient, provider-care team, and system-level) in the diagnostic process may help create future mechanisms for monitoring diagnostic safety. DERR1-10.2196/24642
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影响因子：
8.5
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通讯作者：
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