Data mining and infection control.

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 costs of nosocomial infections.

Background: Although nosocomial infections (NIs) are widely regarded as expensive complications of healthcare delivery, their costs have not been rigorously quantified in large-scale studies. Additionally, problems that can bias cost estimates have often gone unaddressed. For example, are NIs more likely to cause significant extra length of stay (LOS) and costs, or are they more likely to be relatively inexpensive and inevitable consequences of long and expensive hospitalizations? This study is the largest of its kind to provide a rigorous analysis of the costs of NIs.