Andreja Čufar1, Aleš Mrhar2, Marko Robnik-Šikonja3. 1. Pharmacy, University Medical Centre Ljubljana, Zaloška 7, 1000 Ljubljana, Slovenia. Electronic address: andreja.cufar@kclj.si. 2. Faculty of Pharmacy, University of Ljubljana, Aškerčeva 7, 1000 Ljubljana, Slovenia. Electronic address: ales.mrhar@ffa.uni-lj.si. 3. Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia. Electronic address: marko.robnik@fri.uni-lj.si.
Abstract
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.
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.
Authors: Ruomeng Yang; Qian Li; Khezar Hayat; Panpan Zhai; Wenchen Liu; Chen Chen; Amna Saeed; Jie Chang; Pengchao Li; Qianqian Du; Sen Xu; Jun Wen; Yu Fang Journal: Front Public Health Date: 2022-06-15