Transforming Patient Safety Event Data into Actionable Insights through Advanced Analytics

摘要 拟议研究的目标是开发一种创新的算法和软件工具来减少 对安全事件报告的负担进行分类和分析，以便将报告数据转化为 具有可操作性的见解。使安全事件数据更具可操作性，将支持主动识别安全 在患者受到伤害之前的危险。我们将通过以下方式实现我们的研究目标：(1)开发 将安全事件报告分类为可操作用药错误的自然语言处理算法 分类；(2)开发可自动对安全进行分类和可视化的原型软件 支持趋势识别的事件报告；以及(3)与医院和 患者安全组织安全分析员。 该项目利用了研究团队在人的因素和安全科学方面的广泛专业知识， 包括计算机科学，特别是关于信息检索和数据分类。我们的研究 团队包括患者安全组织以及与计算机科学部的合作，网址为 乔治城大学。该提案直接与AHRQ使医疗保健更安全的优先领域保持一致。 这项研究的贡献将包括扩大我们对自然语言处理的理解 及其在临床文本分类中的应用，视觉分析的进展，以及软件的开发 支持患者安全分析员的工具。这项研究的成果将服务于这两个医疗组织 和患者安全组织，使他们能够更高效和有效地分析安全报告数据。

Abstract The objective of the proposed research is to develop an innovative algorithms and a software tool to reduce the burden of safety event report classification and analysis so that report data can be transformed to actionable insights. Making safety event data more actionable will support the proactive identification of safety hazards before patients are harmed. We will achieve our research objective through (1) the development of natural language processing algorithms to classify safety event reports into actionable medication error categories; (2) the development of prototype software that will automatically categorize and visualize safety event reports to support trend identification; and (3) the pilot testing of prototype software with hospital and patient safety organization safety analysts. This project utilizes the extensive expertise of the research team in human factors and safety science, including computer science, specifically regarding information retrieval and data classification. Our research team includes patient safety organizations and collaboration with the computer science department at Georgetown University. The proposal is directly aligned with AHRQ’s priority area of making health care safer. Contributions from this research will include an expansion of our understanding of natural language processing and its application to categorizing clinical text, advances in visual analytics, and the development of a software tool to support patient safety analysts. The outputs of this research will serve both healthcare organizations and patient safety organizations allowing them to more efficiently and effectively analyze safety report data.