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      Biomedical informatics and translational medicine

      review-article
      Author(s): 1 ,
      Publication date (Electronic):
      Journal: Journal of Translational Medicine
      Publisher: BioMed Central

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          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.

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          Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review.

          Amit X Garg, Neill K. J. Adhikari, Heather McDonald … (2005)
          Developers of health care software have attributed improvements in patient care to these applications. As with any health care intervention, such claims require confirmation in clinical trials. To review controlled trials assessing the effects of computerized clinical decision support systems (CDSSs) and to identify study characteristics predicting benefit. We updated our earlier reviews by searching the MEDLINE, EMBASE, Cochrane Library, Inspec, and ISI databases and consulting reference lists through September 2004. Authors of 64 primary studies confirmed data or provided additional information. We included randomized and nonrandomized controlled trials that evaluated the effect of a CDSS compared with care provided without a CDSS on practitioner performance or patient outcomes. Teams of 2 reviewers independently abstracted data on methods, setting, CDSS and patient characteristics, and outcomes. One hundred studies met our inclusion criteria. The number and methodologic quality of studies improved over time. The CDSS improved practitioner performance in 62 (64%) of the 97 studies assessing this outcome, including 4 (40%) of 10 diagnostic systems, 16 (76%) of 21 reminder systems, 23 (62%) of 37 disease management systems, and 19 (66%) of 29 drug-dosing or prescribing systems. Fifty-two trials assessed 1 or more patient outcomes, of which 7 trials (13%) reported improvements. Improved practitioner performance was associated with CDSSs that automatically prompted users compared with requiring users to activate the system (success in 73% of trials vs 47%; P = .02) and studies in which the authors also developed the CDSS software compared with studies in which the authors were not the developers (74% success vs 28%; respectively, P = .001). Many CDSSs improve practitioner performance. To date, the effects on patient outcomes remain understudied and, when studied, inconsistent.
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            Systematic review: impact of health information technology on quality, efficiency, and costs of medical care.

            Basit Chaudhry, Jerome K Wang, Shinyi Wu … (2006)
            Experts consider health information technology key to improving efficiency and quality of health care. To systematically review evidence on the effect of health information technology on quality, efficiency, and costs of health care. The authors systematically searched the English-language literature indexed in MEDLINE (1995 to January 2004), the Cochrane Central Register of Controlled Trials, the Cochrane Database of Abstracts of Reviews of Effects, and the Periodical Abstracts Database. We also added studies identified by experts up to April 2005. Descriptive and comparative studies and systematic reviews of health information technology. Two reviewers independently extracted information on system capabilities, design, effects on quality, system acquisition, implementation context, and costs. 257 studies met the inclusion criteria. Most studies addressed decision support systems or electronic health records. Approximately 25% of the studies were from 4 academic institutions that implemented internally developed systems; only 9 studies evaluated multifunctional, commercially developed systems. Three major benefits on quality were demonstrated: increased adherence to guideline-based care, enhanced surveillance and monitoring, and decreased medication errors. The primary domain of improvement was preventive health. The major efficiency benefit shown was decreased utilization of care. Data on another efficiency measure, time utilization, were mixed. Empirical cost data were limited. Available quantitative research was limited and was done by a small number of institutions. Systems were heterogeneous and sometimes incompletely described. Available financial and contextual data were limited. Four benchmark institutions have demonstrated the efficacy of health information technologies in improving quality and efficiency. Whether and how other institutions can achieve similar benefits, and at what costs, are unclear.
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              The meaning of translational research and why it matters.

              Steven H Woolf (2008)
              Article 0 comments Cited 417 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): J Transl Med
                Title: Journal of Translational Medicine
                Publisher: BioMed Central
                ISSN (Electronic): 1479-5876
                Publication date Collection: 2010
                Publication date (Electronic): 26 February 2010
                Volume: 8
                Page: 22
                Affiliations
                [1 ]Center for Clinical and Translational Science, Department of Microbiology and Molecular Genetics, & Department of Computer Science, University of Vermont, College of Medicine, 89 Beaumont Ave, Given Courtyard N309, Burlington, VT 05405 USA
                Article
                Publisher ID: 1479-5876-8-22
                DOI: 10.1186/1479-5876-8-22
                PMC ID: 2837642
                PubMed ID: 20187952
                SO-VID: 82b13fda-4ba5-4207-9a25-7e4f919efd12
                Copyright © Copyright ©2010 Sarkar; licensee BioMed Central Ltd.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 21 July 2009
                Date accepted : 26 February 2010
                Categories
                Subject: Review

                ScienceOpen disciplines: Medicine
                Data availability:
                ScienceOpen disciplines: Medicine

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