A practical data mining method to link hospital microbiology and an infection control database

This system achieves electronic surveillance, generates monthly or more frequent reports as required, provides patient diagnosis and culture history, offers unit-specific rates, and provides rapid identification of patients with multidrug-resistant organisms.

Abstract

BackgroundData mining is the process of data exploration, selection, and transformation. Data mining has developed as an important tool in health care for converting immense amounts of data into powerful information to support surveillance, detect and assist with investigation of microbial clusters, and collaborate data for research. Additionally, data mining supports ICPs' commitment to providing quality health care.MethodsRealizing the existing potential of a current infection control database, current software has been transformed into a system capable of data mining with the addition of a single laboratory information system interface.ResultsCommercial data mining software products are available but at significant cost. With a laboratory interface to a current infection control database, a system capable of data mining was created. Similar to other data mining products, this system achieves electronic surveillance, generates monthly or more frequent reports as required, provides patient diagnosis and culture history, offers unit-specific rates, and provides rapid identification of patients with multidrug-resistant organisms. Additionally, health care system-wide custom report-writing features are highly effective tools used to assist with outbreak investigations and research projects.ConclusionPurchasing new software products can be expensive; however, current resources may be used to meet data mining needs. Although weekly queries are optimal, data mining software must provide, at a minimum, monthly reports to track infection-related events in a health care facility. The capability to detect a sudden increase in any specific microorganism and the ability to monitor a known problematic microorganism is a necessity. In addition, data mining software products must include graphing capabilities and ease of use custom report-writing features. Data mining is the process of data exploration, selection, and transformation. Data mining has developed as an important tool in health care for converting immense amounts of data into powerful information to support surveillance, detect and assist with investigation of microbial clusters, and collaborate data for research. Additionally, data mining supports ICPs' commitment to providing quality health care. Realizing the existing potential of a current infection control database, current software has been transformed into a system capable of data mining with the addition of a single laboratory information system interface. Commercial data mining software products are available but at significant cost. With a laboratory interface to a current infection control database, a system capable of data mining was created. Similar to other data mining products, this system achieves electronic surveillance, generates monthly or more frequent reports as required, provides patient diagnosis and culture history, offers unit-specific rates, and provides rapid identification of patients with multidrug-resistant organisms. Additionally, health care system-wide custom report-writing features are highly effective tools used to assist with outbreak investigations and research projects. Purchasing new software products can be expensive; however, current resources may be used to meet data mining needs. Although weekly queries are optimal, data mining software must provide, at a minimum, monthly reports to track infection-related events in a health care facility. The capability to detect a sudden increase in any specific microorganism and the ability to monitor a known problematic microorganism is a necessity. In addition, data mining software products must include graphing capabilities and ease of use custom report-writing features.