Biomedical informatics and translational medicine

Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records) and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians") can be essential members of translational medicine teams

Data Mining for the Health System Pharmacist

Data mining is an application of computer technology that allows pharmacists to analyze the large amounts of clinical and administrative data related to patient care that health care organizations accumulate. Data mining is the process of taking the raw material (data) from the data warehouse and converting it to information that can be used by decision makers. Online analytical processing (OLAP) is the tool that accomplishes the data mining process. This article introduces the concept of data mining and related technologies, including the data warehouse, data mart, and OLAP - concepts that are closely related to one another. The benefits of applying data mining to pharmacy practice, with a focus on the point of care, are discussed. Successful data mining often requires an enterprise-wide information system as a prerequisite. Data can then be collected electronically at all points in the health care supply chain. A strategic view of the information flow in an acute care setting is provided. To further illustrate the application of data mining, several examples of OLAP integration are given. In addition, some unique data sources and a pharmacy specific reporting technology are described