AI Summary & Key Takeaways
AI InsightOur proposed framework provides the overall degree of to what extent the user expectations are met.
📌 3 Key Takeaways
Abstract
Hospital information system (HIS) evaluation frameworks have largely been discussed in the literature. However, existing frameworks lack one important aspect: to what extent user expectations of HISs are met. In this study, user expectation data are collected by means of the 'expectation questionnaire'. The internal consistencies of the answers to the questionnaire are measured by Cronbach's alpha coefficient. Fuzzy logic methodologies are used to evaluate the expectation variables in the proposed evaluation framework. The evaluation variables are not represented in the results equally; they are reflected by the weights assigned by the users. Our proposed framework provides the overall degree of to what extent the user expectations are met. It also gives the opportunity to analyze to what extent each expectation is met and the degree of to what extent different user groups' expectations are met. The proposed framework is not a rival of but is rather an alternative or complementary to the existing frameworks. It is a different approach and has a different computation methodology, supported by fuzzy logic. The acceptable meeting ratio depends on the evaluator; we do not propose a threshold.
Özet
Hospital information system (HIS) evaluation frameworks have largely been discussed in the literature. However, existing frameworks lack one important aspect: to what extent user expectations of HISs are met. In this study, user expectation data are collected by means of the 'expectation questionnaire'. The internal consistencies of the answers to the questionnaire are measured by Cronbach's alpha coefficient. Fuzzy logic methodologies are used to evaluate the expectation variables in the proposed evaluation framework. The evaluation variables are not represented in the results equally; they are reflected by the weights assigned by the users. Our proposed framework provides the overall degree of to what extent the user expectations are met. It also gives the opportunity to analyze to what extent each expectation is met and the degree of to what extent different user groups' expectations are met. The proposed framework is not a rival of but is rather an alternative or complementary to the existing frameworks. It is a different approach and has a different computation methodology, supported by fuzzy logic. The acceptable meeting ratio depends on the evaluator; we do not propose a threshold.