A Novel Framework for Extracting Knowledge Management from Business Intelligence Log Files in Hospitals

Abstract

This paper proposes a framework to extract knowledge-management elements from business systems in healthcare organizations. According to results of in-depth interviews with experts in the field, a framework is defined, and software was developed to generate log files. Following the application of the Bag of Words (BoW) method on log files of 455 days for feature extraction, the k-means algorithm was used to cluster the feature vectors. The framework was tested with queries for confirmation. The developed framework successfully clustered the generated reports at operational, tactical, and strategic levels to extract knowledge-management elements. This study provides evidence for the knowledge-management pyramid by finding that the generated reports are reviewed mostly at the operational level, then tactical, and then the least at the strategic level. Our framework has the potential to be used not only in the health sector, but also in banking, insurance, and other businesses using business intelligence, especially in accordance with the organization’s goals at operational, tactical, and strategic levels of the knowledge-management pyramid.

Keywords

• knowledge management
• business-intelligence systems
• clustering
• text mining
• healthcare business intelligence
• application