We conducted an analysis of incident reports from Shimane University School of Medicine Hospital using text mining techniques to efficiently analyze the cases based on the keywords they contained. The analysis targeted the text data of incident reports filed between fiscal years 2017 and 2022, with a particular focus on reports related to personal information. We employed morphological analysis on the collected text data, organizing it word by word to facilitate easier analysis. Subsequently, techniques such as word clouds and cluster analysis were utilized. Based on the results obtained, we conducted a thorough analysis and evaluation of the issues highlighted in the incidents. It was found that incidents related to USB memory often involved them being inadvertently placed in pockets and sent for laundering. Additionally, document-related incidents could be categorized into six distinct clusters.
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Analysis of Medical Incident Reports using Text Mining*
Semantic Scholar · Medicine · 2023
Abstract
We conducted an analysis of incident reports from Shimane University School of Medicine Hospital using text mining techniques to efficiently analyze the cases based on the keywords they contained. The analysis targeted the text data of incident reports filed between fiscal years 2017 and 2022, with a particular focus on reports related to personal information. We employed morphological analysis on the collected text data, organizing it word by word to facilitate easier analysis. Subsequently, techniques such as word clouds and cluster analysis were utilized. Based on the results obtained, we conducted a thorough analysis and evaluation of the issues highlighted in the incidents. It was found that incidents related to USB memory often involved them being inadvertently placed in pockets and sent for laundering. Additionally, document-related incidents could be categorized into six distinct clusters.