Investigation of the application of KMS for diseases classifications: A study in a Taiwanese hospital

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Abstract

Recently, there has been much interest in knowledge management within leading businesses. A saying holds true in regard to knowledge management systems (KMS) “no measurement–no management”. Without accurate measurement it is impossible to gauge the actual performance and value of KMS. In this study, we have used a survey to investigate the effects of KMS for disease classification in a major Taiwanese medical center. Structural modeling techniques were applied to the data collected by questionnaires from 163 KMS system users. The results exhibited a good fit with the data and appeared to be superior in explaining the effectiveness and influence of KMS adoption. These results and conclusions serve as a reference to determine whether or not to further extend a full-scale KMS in both Medicare and administrative affairs.

Section snippets

Background

To paraphrase Drucker (1993) in the post-capitalist society, the basic economic resource are no longer capital, land, or labor; it is and will be knowledge. Value is now created by productivity and innovation. Knowledge has become the most valuable resource. For this reason, management of the tacit and explicit knowledge around corporate organizations will eventually become the top issue of management. The healthcare, financial and insurance industries all belong to information intensive

Knowledge management system

Knowledge management involves the strategies and process for identifying, capturing, structuring, sharing and applying an individual’s or an organization’s knowledge to extract competitive advantage and create sources of sustainable growth (Bose, 2003). Knowledge management system refers to a class of information systems applied to managing an organization’s knowledge. They are IT-based systems which are developed to support and enhance the process of knowledge management. Typically, these

Research framework

This study modified the 2003 D&M-IS success model by adding the construct of perceived usefulness from Technology Acceptance Model into our research framework to measure the success of the disease classification KMS (see Fig. 1). DeLone and McLean (1992) note that the interdependent nature of IS success requires careful attention to the definition and measurement of each aspect of their model. The selection of success dimensions and measures should be contingency variables on the context of the

Methodology

Since the diseases classification KMS is relative new to healthcare providers in Taiwan, very few hospitals develop or adopt the innovation. Therefore, this study used case study to validate the research framework.

Results

The survey instrument was then mailed to all the registered users of KMS. The survey lasted for approximately 6 weeks, and a follow-up phone call or reminder letter (E-mail) was sent to the subjects. The first round yielded 58 usable responses. The second round yielded an additional 105 responses, raising the total number to 163; this produced a final responses rate of 46%. Using The Mann–Whitney U statistic advocated by Siegal and Castellan (1998), non-response bias was evaluated by comparing

Conclusion

Uncovering the relationship between system adoption and the consequences is useful for hospital managers. The main contribution of the study is that it illustrates a real KMS case for disease classification and demonstrates functional requirements of a KMS in a healthcare domain. The research framework of this study demonstrated several benefits listed by Turban and Aronson (2001) for measuring the success of knowledge assets: (1) to discriminate between success and failure of KMS, (2) to help

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