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Risk Mining: Mining Nurses’ Incident Factors and Application of Mining Results to Prevention of Incidents

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4259))

Abstract

To err is human. How can we avoid near misses and achieve medical safety? From this perspective, we analyzed the nurses’ incident data by data mining with the ”concept of quality control” that near misses are produced by the system rather than individuals. Nurses’ incident data were collected during the 18 months at the emergency room. Significant rules (If-then rules) indicated that the medication errors are likely to occur when mental concentration is disrupted by interruption of work, etc. Based on the results of the analysis, the nurses’ medication check system was improved. During the last 6 months, the check system was put into effect. The frequency of the medication errors decreased to about one-twenties or less. It was considered that the data mining analysis contributes the decision support on the improvement of incidents.

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© 2006 Springer-Verlag Berlin Heidelberg

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Tsumoto, S., Matsuoka, K., Yokoyama, S. (2006). Risk Mining: Mining Nurses’ Incident Factors and Application of Mining Results to Prevention of Incidents. In: Greco, S., et al. Rough Sets and Current Trends in Computing. RSCTC 2006. Lecture Notes in Computer Science(), vol 4259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11908029_73

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  • DOI: https://doi.org/10.1007/11908029_73

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-47693-1

  • Online ISBN: 978-3-540-49842-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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