Biomedical informatics and translational medicine

Abstract

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.

Figure 1

The synergistic relationship across the biomedical informatics and translational medicine continua. Major areas of translational medicine (along the top; innovation, validation, and adoption) are depicted relative to core focus areas of biomedical informatics (along the bottom; molecules and cells, tissues and organs, individuals, and populations). The crossing of translational barriers (T1, T2, and T3) can be enabled using translational bioinformatics and clinical research informatics approaches, which are comprised of methodologies from across the sub-disciplines of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics).

Figure 2

The role of the biomedical informatician in a translational medicine team. Biomedical informaticians interact with key stakeholders across the translational medicine spectrum (e.g., biologists, clinicians/clinical researchers, epidemiologists, and health services researchers). The suite of methods as described in this manuscript and depicted as the shadowed region enable the transformation of data from bench, bedside, community, and policy based data sources (shown in blocks).