Factors affecting the implementation of an enterprise data warehouse for the Kenyan health care industry: a case study of Narok county hospital

Abstract

Most health organizations face a common problem that is, having a huge volume of unstructured and unorganized data, in separate systems. Synthesizing this information to support decision-making processes becomes a tedious process, hence, these organizations cannot leverage the knowledge stored such repositories. Data Warehousing has been deployed in other industries to provide this, where it has been successful. Despite the advantages that data warehousing brings on board, there is no major application for it in the Kenyan Health Care industry. The purpose of the study was to identify obstacles to Data Warehousing in the Kenyan Health Care System. The objectives of this research were to identify the main obstacles in the data warehousing within Kenya’s health sector, to establish how data warehousing can be used to improve healthcare efficiency, to establish an evaluation framework that can be used by Kenyan Health-based organizations in conducting data warehousing needs, and to identify a suitable data warehousing model for the Kenyan Health Care Industry. The scope of the study was the Ministry of Health, Narok County Hospital. The proposed theoretical framework for use is the late-binding architecture. This architecture proposed the delaying of business rules such as data cleansing, data normalization and data aggregations for as long as possible. The preferred conceptual framework for use, in the model to be identified, was Unified Modelling Language. This study has identified that the Kenyan Health Care sector is not yet mature enough for Data Warehousing and there are organizational, technological and implementation issues that negatively affect the deployment of Data Warehousing. The study found that 63.3% of the outlined organizational factors, 34.1% of the examined technological factors and 46.9% of the examined implementation factors can be attributed to the understanding of Data Warehousing.