Transforming Patient Safety Event Data into Actionable Insights through Advanced Analytics

通过高级分析将患者安全事件数据转化为可行的见解

基本信息

批准号：
10633121
负责人：
Raj M Ratwani
金额：
$ 38.25万
依托单位：
MEDSTAR HEALTH RESEARCH INSTITUTE
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10633121
关键词：
Transforming Patient Safety Event Data

Transforming Patient Safety Event Data

项目摘要

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.

抽象的拟议研究的目的是开发一种创新算法和软件工具来减少安全事件报告的分类和分析的燃烧，以便将报告数据转换为可行的见解。使安全事件数据更加可行，将支持对安全的主动识别危害患者之前的危害。我们将通过（1）发展实现我们的研究目标自然语言处理算法将安全事件报告分类为可行的药物错误类别；（2）开发原型软件，该软件将自动对安全性进行分类和可视化事件报告以支持趋势识别；（3）与医院和患者安全组织安全分析师。该项目利用研究团队在人为因素和安全科学方面的广泛专业知识，包括计算机科学，特别是有关信息检索和数据分类的。我们的研究团队包括患者安全组织以及与计算机科学系的合作乔治敦大学。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我们对自然语言处理的理解的扩展及其在分类临床文本，视觉分析的进展和软件开发中的应用支持患者安全分析师的工具。这项研究的产出将为两个医疗组织服务和患者安全组织允许他们更有效，有效地分析安全报告数据。