We Can Remember It For You Wholesale: A Data Warehouse Supporting The Real-time Syndromic Surveillance Team (RESST)
PHE ePoster Library. Loveridge P. 09/12/17; 186480; 148
Mr. Paul Loveridge
Mr. Paul Loveridge
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Abstract
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Abstract IntroductionRESST has developed a suite of surveillance systems that collect syndromic data from a number of health care sources. Currently routine surveillance and responses to health protection incidents require separate interrogations of up to four databases (over 200 million records). Consolidating these data into a central repository enables more efficient surveillance, supporting monitoring of infectious diseases and the health impact of environmental incidents in England. MethodsA prototype data warehouse created from syndromic databases is partitioned into "Facts" (numeric values) and "Dimensions" (reference data that give context to the facts). A 'Fact' table is populated from 'Dimension' tables e.g. dates (from 01/01/2005), demographic measures (age, sex), geographical measures (PHE centre and upper tier local authority) and syndromic system (GP Out Of Hours, GP In Hours, NHS 111 and Emergency Department). The Fact Table contains 'foreign' keys which correspond to 'candidate' keys in the Dimension Tables. Dimension table rows are uniquely identified by a single key field.A Microsoft Excel 'front end' employing the 'Power BI' desktop platform provides a range of data visualisations to aid easier surveillance. ResultsA working interactive dashboard with charts, maps and tables shows one year's syndromic data broken down by system, age group, sex and geographical location. The dimensional approach improves performance. ConclusionsThis will be extended to all available syndromic data and used in parallel with existing surveillance tools to assess the added value. It should enable analysis of many years of historical data, which is not feasible with the current set up.
Abstract IntroductionRESST has developed a suite of surveillance systems that collect syndromic data from a number of health care sources. Currently routine surveillance and responses to health protection incidents require separate interrogations of up to four databases (over 200 million records). Consolidating these data into a central repository enables more efficient surveillance, supporting monitoring of infectious diseases and the health impact of environmental incidents in England. MethodsA prototype data warehouse created from syndromic databases is partitioned into "Facts" (numeric values) and "Dimensions" (reference data that give context to the facts). A 'Fact' table is populated from 'Dimension' tables e.g. dates (from 01/01/2005), demographic measures (age, sex), geographical measures (PHE centre and upper tier local authority) and syndromic system (GP Out Of Hours, GP In Hours, NHS 111 and Emergency Department). The Fact Table contains 'foreign' keys which correspond to 'candidate' keys in the Dimension Tables. Dimension table rows are uniquely identified by a single key field.A Microsoft Excel 'front end' employing the 'Power BI' desktop platform provides a range of data visualisations to aid easier surveillance. ResultsA working interactive dashboard with charts, maps and tables shows one year's syndromic data broken down by system, age group, sex and geographical location. The dimensional approach improves performance. ConclusionsThis will be extended to all available syndromic data and used in parallel with existing surveillance tools to assess the added value. It should enable analysis of many years of historical data, which is not feasible with the current set up.
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