AIAPJun 17, 2014

Identifying roles of clinical pharmacy with survey evaluation

arXiv:1406.4287v12 citations
Originality Synthesis-oriented
AI Analysis

This work provides incremental insights for hospital managers to optimize clinical pharmacy integration, based on a specific survey dataset.

The authors tackled the problem of introducing clinical pharmacy services into hospitals by adapting a survey analysis method from marketing to evaluate user competences and predict successful adoption, achieving identification of wards with high cooperation probability from a small sample.

The 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 the introduction of clinical pharmacy services into hospital. We use two analytical approaches to extract relevant managerial consequences. With OrdEval algorithm we first evaluate the importance of competences for the users of clinical pharmacy and extract their nature according to the users expectations. Next, we build a model for predicting a successful introduction of clinical pharmacy to the clinical departments. We the wards with the highest probability of successful cooperation with a clinical pharmacist. We obtain useful managerially relevant information from a relatively small sample of highly relevant respondents. We show how the OrdEval algorithm exploits the information hidden in the ordering of class and attribute values and their inherent correlation. Its output can be effectively visualized and complemented with confidence intervals.

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