Sotoodeh Mani, Zhang Wenhui, Simpson Roy L, Hertzberg Vicki Stover, Ho Joyce C
Public Health Research Institute of University of Montreal, University of Montreal, Montreal, QC, Canada.
School of Nursing, Emory University, Atlanta, GA, United States.
JMIR Med Inform. 2023 Feb 23;11:e40672. doi: 10.2196/40672.
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.
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.
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.
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.
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.
由于活动能力低下、局部受压、循环状况及其他诱发因素，患者在医院会发生压力性损伤（PI）。每年有超过250万美国人发生PI。医疗保险和医疗补助服务中心将医院获得性压力性损伤（HAPI）视为最常见的可预防事件，且它们是诉讼中第二常见的索赔项目。随着医院中电子健康记录（EHR）的使用日益增加，存在构建机器学习模型以识别和预测HAPI的机会，而不是依赖人类专家偶尔的手动评估。然而，准确的计算模型依赖高质量的HAPI数据标签。不幸的是，EHR中的不同数据源可能就同一患者的HAPI发生情况提供相互矛盾的信息。此外，即使在同一患者群体中，现有的HAPI定义也相互不一致。这些不一致的标准使得无法对预测HAPI的机器学习方法进行基准测试。
本项目的目的有三个方面。我们旨在识别EHR中HAPI来源的差异，使用来自所有EHR来源的数据为HAPI分类制定一个全面的定义，并说明改进HAPI定义的重要性。
我们评估了重症监护医学信息数据库III中临床记录、诊断代码、程序代码和图表事件中记录的HAPI发生情况之间的一致性。我们分析了3种现有HAPI定义所使用的标准及其对监管指南的遵循情况。我们提出了埃默里HAPI（EHAPI），这是一个改进的、更全面的HAPI定义。然后，我们使用基于树的和顺序神经网络分类器评估了标签在训练HAPI分类模型中的重要性。
我们说明了定义HAPI的复杂性，在4个数据源中记录有至少3个PI指征的住院时间不到13%。虽然图表事件是最常见的指征，但在超过49%的住院时间里它是唯一的PI记录。我们证明了现有HAPI定义与EHAPI之间缺乏一致性，只有219次住院有一致的阳性标签。我们的分析突出了我们改进的HAPI定义的重要性，使用我们的标签训练的分类器在护士注释的一个小的手动标记集和所有定义在标签上达成一致的共识集上优于其他分类器。
标准化的HAPI定义对于准确评估HAPI护理质量指标和确定预防措施的HAPI发生率很重要。鉴于EHR数据相互矛盾且不完整，我们证明了定义HAPI发生情况的复杂性。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。
