Detecting effective classes of medical incident reports based on linguistic analysis for common reporting system in Japan.
The analysis of medical incident reports is indispensable for patient safety. Most incident reports are composed from freely written descriptions, but an analysis of such free descriptions is not sufficient in the medical field. In this study, we aim to conduct new findings using incident information, to clarify improvements that should be made to solve the root cause of an accident, and to ensure safe medical treatment through such improvements. We employed natural language processing (NLP) and network analysis to identify effective classes of medical incident reports. Network analysis can find various relationships that are not only direct but also indirect. After that, we compared the clustering results between Jichi Medical University and Osaka City University Hospital. By finding the common and different parts in medical incident report' s classes, we could show new perspectives on proposing a common reporting systems in Japan for improving patient safety.
Facility of Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan.
Katsuhide
K
Fujita
Masanori
M
Akiyama
Nobuyuki
N
Toyama
Yasuko
Y
Kamemori
D057225:Data Mining / Q000379:methods*; D007564:Japan; D019300:Medical Errors / Q000145:classification*; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D061214:Patient Safety; D010363:Pattern Recognition, Automated / Q000379:methods; D012308:Risk Management / Q000145:classification*; D012660:Semantics; D009626:Terminology as Topic