Objective: Survey data sets are important sources of data, and their successful exploitation is of key importance for informed policy decision-making. We present how a survey analysis approach initially developed for customer satisfaction research in marketing can be adapted for an introduction of clinical pharmacy services into a hospital.
Methods and material: We use a data mining analytical approach to extract relevant managerial consequences. We evaluate the importance of competences for users of a clinical pharmacy with the OrdEval algorithm and determine their nature according to the users' expectations. For this, we need substantially fewer questions than are required by the Kano approach.
Results: From 52 clinical pharmacy activities we were able to identify seven activities with a substantial negative impact (i.e., negative reinforcement) on the overall satisfaction of clinical pharmacy services, and two activities with a strong positive impact (upward reinforcement). Using analysis of individual feature values, we identified six performance, 10 excitement, and one basic clinical pharmacists' activity.
Conclusions: We show how the OrdEval algorithm can exploit the information hidden in the ordering of class and attribute values, and their inherent correlation using a small sample of highly relevant respondents. The visualization of the outputs turns out highly useful in our clinical pharmacy research case study.
Keywords: Clinical pharmacy; Human resource management; Kano model; OrdEval algorithm; Survey analysis.
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