Assessment of surveys for the management of hospital clinical pharmacy services.

Abstract

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. 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. 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. 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.