背景:患者由于行动不便而在医院中获得压力伤害(PI),暴露于局部压力,循环条件,和其他诱发因素。每年有超过250万美国人遭受压力伤害。医疗保险和医疗补助中心认为医院获得性压力伤(HAPI)是最常见的可预防事件。它们是诉讼中第二常见的索赔。随着医院对电子健康记录(EHR)的利用越来越多,存在建立机器学习模型来识别和预测HAPI的机会,而不是依靠人类专家偶尔的人工评估。然而,准确的计算模型依赖于高质量的数据HAPI标签。不幸的是,EHR中的不同数据源可能会提供同一患者HAPI发生的相互矛盾的信息.此外,HAPI的现有定义彼此不一致,即使在同一患者群体中。不一致的标准使得无法对机器学习方法进行基准测试来预测HAPI。
目的:该项目的目标有三个方面:1)确定EHR中HAPI来源的差异;2)使用所有EHR来源的数据制定HAPI分类的综合定义;3)说明改进HAPI定义的重要性。
方法:我们评估了临床记录中记录的HAPI事件之间的一致性,诊断代码,程序代码,和图表事件从医疗信息集市重症监护III(MIMIC-III)数据库。我们分析了三种现有HAPI定义的标准及其对监管指南的遵守情况。我们提出了埃默里HAPI(EHAPI),改进和更全面的HAPI定义。然后,我们使用基于树的和顺序的神经网络分类器来评估标签在训练HAPI分类模型中的重要性。
结果:我们说明了定义HAPI的复杂性,在4个数据源中,少于13%的住院时间至少记录了3个PI适应症。虽然图表事件是最常见的指标,这是超过49%的住宿的唯一PI文档。我们证明了现有HAPI定义和EHAPI之间缺乏一致性,只有219个停留在有共识积极标签的地方。我们的分析强调了我们改进的HAPI定义的重要性,使用我们的标签训练的分类器在来自护士注释器的小的手动标记集上以及在标签上所有定义都同意的共识集上表现优于其他人。
结论:标准化的HAPI定义对于准确的HAPI护理质量度量评估和确定预防措施的HAPI发生率很重要。我们证明了在存在冲突和不完整的EHR数据的情况下定义HAPI事件的复杂性。我们的EHAPI定义具有良好的特性,使其成为HAPI分类任务的合适候选者。
BACKGROUND: Patients develop pressure injuries (PIs) in the hospital owing to low mobility, exposure to localized pressure, circulatory conditions, and other predisposing factors. Over 2.5 million Americans develop PIs annually. The Center for Medicare and Medicaid considers hospital-acquired PIs (HAPIs) as the most frequent preventable event, and they are the second most common claim in lawsuits. With the growing use of electronic health records (EHRs) in hospitals, an opportunity exists to build machine learning models to identify and predict HAPI rather than relying on occasional manual assessments by human experts. However, accurate computational models rely on high-quality HAPI data labels. Unfortunately, the different data sources within EHRs can provide conflicting information on HAPI occurrence in the same patient. Furthermore, the existing definitions of HAPI disagree with each other, even within the same patient population. The inconsistent criteria make it impossible to benchmark machine learning methods to predict HAPI.
OBJECTIVE: The objective of this project was threefold. We aimed to identify discrepancies in HAPI sources within EHRs, to develop a comprehensive definition for HAPI classification using data from all EHR sources, and to illustrate the importance of an improved HAPI definition.
METHODS: We assessed the congruence among HAPI occurrences documented in clinical notes, diagnosis codes, procedure codes, and chart events from the Medical Information Mart for Intensive Care III database. We analyzed the criteria used for the 3 existing HAPI definitions and their adherence to the regulatory guidelines. We proposed the Emory HAPI (EHAPI), which is an improved and more comprehensive HAPI definition. We then evaluated the importance of the labels in training a HAPI classification model using tree-based and sequential neural network classifiers.
RESULTS: We illustrate the complexity of defining HAPI, with <13% of hospital stays having at least 3 PI indications documented across 4 data sources. Although chart events were the most common indicator, it was the only PI documentation for >49% of the stays. We demonstrate a lack of congruence across existing HAPI definitions and EHAPI, with only 219 stays having a consensus positive label. Our analysis highlights the importance of our improved HAPI definition, with classifiers trained using our labels outperforming others on a small manually labeled set from nurse annotators and a consensus set in which all definitions agreed on the label.
CONCLUSIONS: Standardized HAPI definitions are important for accurately assessing HAPI nursing quality metric and determining HAPI incidence for preventive measures. We demonstrate the complexity of defining an occurrence of HAPI, given the conflicting and incomplete EHR data. Our EHAPI definition has favorable properties, making it a suitable candidate for HAPI classification tasks.