A Comprehensive and Improved Definition for Hospital-Acquired Pressure Injury Classification Based on Electronic Health Records: Comparative Study.
A Comprehensive and Improved Definition for Hospital-Acquired Pressure Injury Classification Based on Electronic Health Records: Comparative Study.
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DOI：
10.2196/40672
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发表时间：
2023-02-23
影响因子：
3.2
通讯作者：
Ho, Joyce C.
中科院分区：
文献类型：
--
作者：
Sotoodeh, Mani;Zhang, Wenhui;Simpson, Roy L.;Hertzberg, Vicki Stover;Ho, Joyce C.
关键词：
患者在医院中因活动能力低、局部受压、循环状况以及其他诱发因素而发生压疮（PI）。每年有超过250万美国人患压疮。医疗保险和医疗补助中心认为医院获得性压疮（HAPI）是最常见的可预防事件，并且是诉讼中第二常见的索赔原因。随着医院越来越多地使用电子健康记录（EHR），存在建立机器学习模型来识别和预测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于高质量的HAPI数据标签。不幸的是，EHR内的不同数据源可能对同一患者的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定义也彼此不一致。这种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。
本项目有三个目标。我们旨在识别EHR内HAPI来源的差异，利用所有EHR来源的数据为HAPI分类制定一个全面的定义，并阐明改进的HAPI定义的重要性。
我们评估了重症监护医学信息集市III数据库中的临床记录、诊断代码、操作代码和图表事件中所记录的HAPI发生情况之间的一致性。我们分析了现有的3种HAPI定义所使用的标准以及它们对监管指南的遵循情况。我们提出了埃默里HAPI（EHAPI），这是一个改进且更全面的HAPI定义。然后我们使用基于树的和序列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，只有不到13%的住院病例至少有3个PI指征被记录。尽管图表事件是最常见的指标，但在超过49%的住院病例中，它是唯一的PI记录。我们证明了现有的HAPI定义与EHAPI之间缺乏一致性，只有219例住院病例有一致的阳性标签。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一小部分由护士标注的手动标注集以及所有定义都对标签达成一致的共识集上表现优于其他分类器。
标准化的HAPI定义对于准确评估HAPI护理质量指标以及确定用于预防措施的HAPI发病率非常重要。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。
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.
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影响因子：
3.2
作者：
Jiang M;Ma Y;Guo S;Jin L;Lv L;Han L;An N
通讯作者：
An N
DOI：
10.5334/egems.307
发表时间：
2019-09-05
期刊：
EGEMS (Washington, DC)
影响因子：
--
作者：
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通讯作者：
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DOI：
10.1093/jamia/ocaa336
发表时间：
2021-02-01
影响因子：
6.4
作者：
Song, Wenyu;Kang, Min-Jeoung;Dykes, Patricia C.
通讯作者：
Dykes, Patricia C.
影响因子：
9.8
作者：
Johnson AE;Pollard TJ;Shen L;Lehman LW;Feng M;Ghassemi M;Moody B;Szolovits P;Celi LA;Mark RG
通讯作者：
Mark RG
影响因子：
3.2
作者：
Hyun, Sookyung;Moffatt-Bruce, Susan;Kaewprag, Pacharmon
通讯作者：
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