Monitoring spread of epidemic diseases by using clinical data from multiple hospitals: A data warehouse approach

dc.contributor.authorRutatola, Edgar P.
dc.descriptionA dissertation submitted in partial fulfillment of the requirements for the Degree of Master’s in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and Technology
dc.description.abstractMany countries apply data science techniques to enhance their health sectors and the surveillance of diseases. The success of the innovations lies on the availability and quality of datasets to be analyzed. In Tanzania, while different Hospital Management Information Systems (HoMIS) like the Government of Tanzania Hospital Management Information System (GoT HoMIS) are installed in various hospitals, the data stored in the systems are not integrated. This causes unavailability of high quality, timely, anonymous, harmonized, and integrated datasets that can be shared and exhaustively analyzed for epidemic diseases surveillance. This study intended to develop a data warehouse to host patients’ demographic and clinical particulars essential for epidemic diseases surveillance from a multi-node GoT-HoMIS, and yield an integrated dataset that can be used for epidemic diseases surveillance. Interviews were conducted in three strategic health facilities and the Ministry responsible for Health in Tanzania. Documents were reviewed, and observation done on the patient’s registration process in the GoT-HoMIS. Thereafter, a data warehouse was developed to run under MariaDB database server, and using Hypertext Preprocessor an Extract, Transform, and Load (ETL) module was developed. The ETL module was deployed at six health facilities, and the resulting integrated dataset of 152 104 facts was visualized by using FusionCharts libraries. The study demonstrates a novel means to extract data straight from the GoT-HoMIS nodes, which has the potential to make available and provide timely data and integrated reports for decision-making on epidemics. By scaling the innovation to other health facilities, epidemics surveillance can be significantly enhanced
dc.description.sponsorshipVLIR-UOS GRE@T programme
dc.titleMonitoring spread of epidemic diseases by using clinical data from multiple hospitals: A data warehouse approach
dc.title.alternativeA data warehouse approach
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