Tasks related to these processes include data transfer of large volumes of compressed data, data back up to disk for disaster recovery efforts, data validation, and extensive data processing in SQL. These steps lead to clean, restructured data suitable for analysis with standard epidemiological tools. Additionally, EDC programmers have developed specialized methods to utilize these data for automated case finding, detailed tracking of influenza related tests and prescriptions, and reporting of trends in antibiotic resistance throughout the MHS beneficiary population. Case finding and influenza methodologies have been leveraged for the rapid development of a daily report for the identification of presumptive H1N1 influenza cases early in the outbreak.
Utility of the MHS laboratory data for public health surveillance purposes depends on proper infrastructure and resources for the set up, storage and retrieval of data. This infrastructure will be described, including details of the methods utilized in these processed, including SQL Server Agent jobs and SQL Server Integration Services (SSIS) packages for the generation, cleaning and validation of data and the automation of data transfer onto established directories.
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