Driven by the increased availability of electronic patient data, electronic HAI surveillance systems use more data, making systems more sensitive yet less specific, but also allow systems to be tailored to the needs of healthcare institutes' surveillance programs.

Abstract-Narrative reports and criminal records are stored digitally across individual police departments, enabling the collection of this data to compile a nation-wide database of criminals and the crimes they committed. The compilation of this data through the last years presents new possibilities of analyzing criminal activity through time. Augmenting the traditional, more socially oriented, approach of behavioral study of these criminals and traditional statistics, data mining methods like clustering and prediction enable police forces to get a clearer picture of criminal careers. This allows officers to recognize crucial spots in changing criminal behaviour and deploy resources to prevent these careers from unfolding.Four important factors play a role in the analysis of criminal careers: crime nature, frequency, duration and severity. We describe a tool that extracts these from the database and creates digital profiles for all offenders. It compares all individuals on these profiles by a new distance measure and clusters them accordingly. This method yields a visual clustering of these criminal careers and enables the identification of classes of criminals. The proposed method allows for several user-defined parameters.

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This study assessed the effectiveness of a fully automated surveillance system for the detection of healthcare-associated infections (HCAIs) in intensive care units. Manual ward surveillance (MS) and electronic surveillance (ES) were performed for two intensive care units of the Vienna General Hospital. All patients admitted for a period longer than 48 h between 13 November 2006 and 7 February 2007 were evaluated according to HELICS-defined rules for HCAI. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and personnel time spent per surveillance type were calculated. Ninety-three patient admissions were observed, whereby 30 HCAI episodes were taken as a reference standard. Results with MS versus ES were: sensitivity 40% versus 87%, specificity 94% versus 99%, PPV 71% versus 96%, NPV 80% versus 95%, and time spent per surveillance type 82.5 h versus 12.5 h. In conclusion, ES was found to be more effective than MS while consuming fewer personnel resources.

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