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Enhancing Service Quality in Hospitals: Mining Multiple Data Sources
Abstract
Large organizations use multiple data sources, centralize processing in these organizations require analysis of huge database originating from various locations. Data mining association rules help perform exploration and analysis of large amounts of data to discover meaningful patterns which can facilitate effective decision-making. The objective of this article is to enhance service quality in a hospital using data mining. The improvement in service quality will help to create hygienic environment and enhance technical competence among staff members which will generate value to patients. A weighting model is proposed to identify valid rules among large number of forwarded rules from various data sources. This model is applied to rank the rules based on patient perceived service parameters in a hospital. Results show that this weighting model is efficient. The proposed model can be used effectively for determining the patient’s perspective on hospital services like technical competence, reliability and hygiene conditions under a distributed environment.
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