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On-line Analytical Processing (OLAP); a step forward in surveillance technology ML Heginbothom on behalf of the Welsh Antimicrobial Study Group CDSC, NPHS for Wales, Abton House, Wedal Road, Cardiff, CF14 3QX Aim Data Sources Telepath METHODS OLAP technology OLAP technology was used to develop an integrated data warehouse to include microbiology, pharmacy and bed data. An ‘OLAP cube’ is the processed multidimensional database view of the underlying data warehouse. OLAP provides interactive access to aggregated data optimised for fast data output to support data analytics and reporting. OLAP empowers the user with the ability to view data from different perspectives by creating dynamic views interactively. The net effect is that most user queries can be answered almost instantaneously. DTS EDS, JAC, AscribeOLAP Data Access Data Warehouse Methodologies for automated collection and analysis of standardised microbiology data from laboratory information management systems and standardised, denominatorDT based antibiotic prescribing data from pharmacy systems S DataStore were developed using On-line Analytical Processing (OLAP) technology. Data Integration OLAP technology provides a simple method to integrate, interrogate, browse and display data from disparate hospital IT systems; enabling correlations of antibiotic resistance with antibiotic prescribing at ward, directorate and hospital S level. The application of OLAP technology to hospital DT infection surveillance willMedusa be discussed and presented. Data Marts OLAP PivotTable Service Data was imported from various hospital data sources into the Microsoft SQL server staging area. The staging area runs the data transformation services (DTS) – these are processes that clean, transform, combine, and prepare the data as data marts for use in the data warehouse Analysis Services software uses OLAP to aggregate the data as a multidimensional structure that can be viewed via client software (Figures 1 & 2). OLAP OnOn-line Analytical Processing Microbiology Pharmacy Method Data Warehouse Microsoft® Microsoft® SQL Server™ Server™ Client Application(s) On-line Analytical Processing (OLAP) technology was developed for surveillance purposes as part of a current Department of Health funded project (DH: 0410015); the main aim of which was to develop cost-effective IT strategies for automated collection and analysis of surveillance data on hospital antibiotic resistance and factors that may affect its occurrence. Staging Area Hospital MSSQL Server ESR, Oracle, PIMS SSAS 2005 CONCLUSION There is a wealth of data, routinely collected by hospitals in electronic format that may provide direct or surrogate measures of factors relevant to the occurrence of antibiotic resistant infections. Much of this data could be extracted economically with IT automation. We propose to undertake a scoping study that will give an assessment of the viability and economics of automated collation and presentation of these data sets. Other Data Source(s) KEY DTS: Data Transformation Services Microsoft® Microsoft® SQL Server™ Server™ 2005 Analysis Services (SSAS 2005) On-line analytical processing or OLAP is technology that uses a multidimensional view of aggregate data to provide quick access to strategic information for further analysis. Figure 1: Schematic drawing of On-line Analytical Processing (OLAP) OLAP REPORTING USING OFFICE WEB COMPONENTS The main component of OLAP is the OLAP server which acts as the intermediary between the client/user and the traditional database management system (DBMS). Conclusion OLAP systems provide a multidimensional view of the data, extending far beyond the ability of the relational database management system or spreadsheet. OLAP technology provides a simple method to integrate, interrogate, browse and display data from disparate hospital IT systems; enabling correlations of antibiotic resistance with antibiotic prescribing at ward, directorate and hospital level (Figure 2). A practical demonstration of OLAP technology and it’s application to surveillance solutions is available. For further information contact Maggie Heginbothom during the FIS conference or contact on 02920 524284 or email [email protected] Figure 2: Example of OLAP data viewed using the Office Web Component