Identifying Latent Similarities among Near-Miss Incident Records Using a Text-Mining Method and a Scenario-Based Approach

This research focuses on supporting an analyst’s activity of interpreting the contents of existing incident reports. During this activity, analysts are always predicting expected scenarios of the incidents at hand in comparing that with the actual development of the incidents reported therein. In order to learn lessons from a particular prior experience, analysts should be aware of the latent similarities among the incidents and should experience a breakdown called "expectation-failure" to let that incident be surely printed in their memory. To let the human analysts experience this breakdown, our system introduces a theory of Memory Organization Packets (MOPs) as a framework for explaining the dynamic memory structure of the human. By utilizing this idea as a basis for scenario-based expectation of human analysts and by integrating this idea with a text-mining method, a system for supporting an incident analysis is developed for a domain of medical incidents. Results of the experiments using our proposing system are presented, where the subjects are nurses working for a hospital. Based on those results, effectiveness of the system is discussed from various viewpoints by investigating into the protocols gathered from the subjects of the experiments.

Authors and Affiliations

1. Graduate School of Engineering, Kyoto University, Yoshida Honmachi, Sakyo, Kyoto, 606-8501, Japan

   Tetsuo Sawaragi, Kouichi Ito, Yukio Horiguchi & Hiroaki Nakanishi

Editors and Affiliations

1. Purdue University, Grissom Hall, Room 263, 315 North Grant Street, 47907-2023, West Lafayette, IN, USA

   Gavriel Salvendy

2. Department of Industrial and Systems Engineering, University of Wisconsin, 459 Mechanical Engineering Building, 1513 University Avenue, WI 53706, Madison, USA

   Michael J. Smith

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