Transforming Patient Safety Event Data into Actionable Insights through Advanced Analytics

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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.