Patterns embedded in large volumes of clinical data may provide important insights into the characteristics of patients or care delivery processes, but may be difficult to identify by traditional means. Data mining offers methods that can recognize patterns in these large data sets and make them actionable. We present an example of this capability in which we successfully applied data mining to hospital infection control. The Data Mining Surveillance System (DMSS) uses data from the clinical laboratory and hospital information systems to create association rules linking patients, sample types, locations, organisms, and antibiotic susceptibilities. Changes in association strength over time signal epidemiologic patterns potentially appropriate for follow-up, and additional heuristic methods identify the most informative of these patterns for alerting.
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