Collaborative Research: Statistical Algorithms for Anomaly Detection and Patterns Recognition in Patient Care and Safety Event Reports

项目摘要 医疗差错已被证明是美国第三大死因。美国国家科学研究院 医学和几个州立法机构建议使用患者安全事件报告系统 (PSR)，以更好地了解和改善安全隐患。患者安全事件(PSE)报告通常包括 结构化数据元素和非结构化数据元素。结构化数据是预定义的固定字段，用于请求 有关活动的具体信息。非结构化数据字段通常包括自由文本字段，其中 记者可以输入事件的文本描述。文本描述通常是一个丰富的数据源，因为 记者不局限于有限的类别或选择选项，能够自由地描述 这件事。 这个项目的目标是开发新的统计方法来分析非结构化文本，如患者安全 医疗保健中产生的事件报告，这可以显著改善患者的安全并使 适时的干预策略。我们解决三个问题：(A)建立现实和有意义的基线模型 对于险些发生的失误，并检测到相对于这些基线的不利结果的系统性恶化；(B) 了解导致险些失手的关键因素并量化结果的严重性；以及(C)确定 记录感兴趣的组。我们将使用结合自然语言的新的统计方法 利用统计过程监控、统计网络分析和时空建模进行处理 构建一个可泛化的工具箱，以解决医疗保健中的这些问题。我们还将发布开源软件 通过R Packages和GitHub提供的软件，这将使医护人员和研究人员能够执行我们的方法 在他们的数据集上。 新冠肺炎大流行导致患者数量增加，患者敏锐度提高，导致 给美国各地的许多医疗机构带来了过重的负担。这极大地增加了患者的风险 医疗设备故障或药物不良反应引起的安全后果。至 在这场危机中确保患者安全和最高质量的医疗保健，我们需要一个快速反应系统来 对特定于COVID的安全问题进行大规模建模和分析，并将结果快速传播给医疗保健 设施，以便在护理时减轻这些风险。在本补充资料中，我们建议通过以下方式实现这一点 (A)挖掘公共数据库和电子病历，以确定用于治疗COVID的设备/药物，以及(B) 应用我们的方法(基于NLP、SPC和SPM)来了解与这些项目相关的风险。这 信息将在全国范围内传播到所有医疗机构，以便能够整合到电子健康记录中 在护理时提醒临床医生。

Project Summary Medical errors have been shown to be the third leading cause of death in the United States. The Institute of Medicine and several state legislatures have recommended the use of patient safety event reporting systems (PSRS) to better understand and improve safety hazards. A patient safety event (PSE) report generally consists of both structured and unstructured data elements. Structured data are pre-defined, fixed fields that solicit specific information about the event. The unstructured data fields generally include a free text field where the reporter can enter a text description of the event. The text descriptions are often a rich data source in that the reporter is not constrained to limited categories or selection options and is able to freely describe the details of the event. The goal of this project is to develop novel statistical methods to analyze unstructured text like patient safety event reports arising in healthcare, which can lead to significant improvements to patient safety and enable timely intervention strategies. We address three problems: (a) Building realistic and meaningful baseline models for near misses, and detecting systematic deterioration of adverse outcomes relative to such baselines; (b) Understanding critical factors that lead to near misses & quantifying severity of outcomes; and (c) Identifying document groups of interest. We will use novel statistical approaches that combine Natural Language Processing with Statistical Process Monitoring, Statistical Networks Analysis, and Spatio-temporal Modeling to build a generalizable toolbox that can address these issues in healthcare. We will also release open source software via R packages & GitHub, which will enable healthcare staff and researchers to execute our methods on their datasets. The COVID-19 pandemic has resulted in increased patient volumes and increased patient acuity, leading to an excessive burden on many healthcare facilities across the United States. This greatly increases the risk of patient safety consequences arising from malfunctioning medical equipment or adverse reaction to medication. To ensure patient safety and the highest quality of healthcare during this crisis, we need a rapid response system to model and analyze COVID-specific safety issues at scale, and quickly disseminate the results to healthcare facilities, so that these risks can be mitigated at the point of care. In this supplement, we propose to do this by (a) mining public databases and EHRs to identify devices/medication being used for treating COVID and (b) applying our methods (based on NLP, SPC, and SPM) to understand risks associated with these items. This information will be disseminated nationally to all healthcare facilities so that it can be integrated into the EHR at the point of care to alert clinicians.