Collaborative Research: Statistical algorithms for anomaly detection and patterns recognition in patient care and safety event reports

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)，以更好地了解和改进安全隐患。许多医疗保健提供者都有 采用了这些系统，为医疗保健服务人员报告患者安全提供了一个框架 事件。还创建了像Maude和VAERS这样的公共数据库来收集和趋势安全 医疗保健系统中的事件。患者安全事件(PSE)报告通常由结构化的 和非结构化数据元素。结构化数据是预定义的固定字段，用于请求特定信息 关于这件事。非结构化数据字段通常包括自由文本字段，记者可以在其中输入 事件的文本说明。文本描述通常是一个丰富的数据源，因为记者不是 仅限于有限的类别或选择选项，并能够自由描述事件的细节。 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 医疗保健中产生的安全事件报告，这可以显著提高患者的安全性和 启用及时干预策略。我们解决了三个问题：(A)建立现实和有意义的 险些失手的基线模型，并检测与以下方面相关的不良后果的系统性恶化 此类基线；(B)了解导致险些失手的关键因素并量化严重程度 结果；和(C)记录感兴趣的群体。我们将使用新的统计方法， 将自然语言处理与统计过程监控、统计网络分析、 和时空建模，以构建可泛化的工具箱，以解决医疗保健中的这些问题。 我们研究团队的一个重要优势是医疗保健领域专家的参与和访问 前线员工，我们将利用这一优势来开发我们的算法。我们工作的一个主要特点是 我们方法的普适性，这将适用于产生于 非常多样化的领域，如患者安全和设备故障报告、电子健康 记录、不良药物或疫苗报告等。我们还将通过R包发布开源软件& GitHub，这将使医疗保健人员和研究人员能够在他们的数据集上执行我们的方法。

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. Numerous healthcare providers have adopted these systems, which provide a framework for healthcare provlder staff to report patient safety events. Public databases like MAUDE and VAERS have also been created to collect and trend safety events across healthcare systems. 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 ls not constrained to limited categories or selection options and is able to freely descrlbe 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) ldentifylng 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. An important advantage of our research team is the involvement of healthcare domain experts and access to frontline staff, and we will leverage this strength to develop our algorithms. A key feature of our work is the generalizability of our methods, which will be applicable to biomedical documents arising across a remarkable variety of areas, such as patient safety and equipment malfunction reports, electronic health records, adverse drug or vaccine reports, etc. 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.