Formulation of a model for automating infection surveillance: algorithmic detection of central-line associated bloodstream infection

Table 1

Hospitals, database systems and challenges encountered in automated bloodstream infection surveillance

Hospital	Database system	Challenges	Solutions
Hospital A	Microsoft SQL Server 2000	Leveraging knowledge dimensions	Consultation among knowledge experts
Hospital B	None—extract of data given to Hospital A	Free text, unstructured microbiology data	Use of templates to import data to flat files (Datawatch Monarch)
		Ad hoc requests needed for individual data extracts	Python script to standardize free text data
			Bed information obtained from billing data warehouse
Hospital C	SYBASE	Negative cultures not available	Could not run 1 of 5 rules (Rule C)
Hospital D (30)	Oracle	Free text, unstructured microbiology data	Natural language processing
			Review of business processes of microbiology reporting
		SQL language version incompatibility (ie, T-SQL vs PL/SQL)	Algorithm rewritten from conceptual flowchart

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