• Genomics & Informatics
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• v.11 no.4
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• pp.186-190
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• 2013

The advances in electronic medical records (EMRs) and bioinformatics (BI) represent two significant trends in healthcare. The widespread adoption of EMR systems and the completion of the Human Genome Project developed the technologies for data acquisition, analysis, and visualization in two different domains. The massive amount of data from both clinical and biology domains is expected to provide personalized, preventive, and predictive healthcare services in the near future. The integrated use of EMR and BI data needs to consider four key informatics areas: data modeling, analytics, standardization, and privacy. Bioclinical data warehouses integrating heterogeneous patient-related clinical or omics data should be considered. The representative standardization effort by the Clinical Bioinformatics Ontology (CBO) aims to provide uniquely identified concepts to include molecular pathology terminologies. Since individual genome data are easily used to predict current and future health status, different safeguards to ensure confidentiality should be considered. In this paper, we focused on the informatics aspects of integrating the EMR community and BI community by identifying opportunities, challenges, and approaches to provide the best possible care service for our patients and the population.

• Journal of Preventive Medicine and Public Health
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• v.35 no.2
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• pp.83-91
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• 2002

Bioinformatics is a rapidly emerging field of biomedical research. A flood of large-scale genomic and postgenomic data means that many of the challenges in biomedical research are now challenges in computational sciences. Clinical informatics has long developed methodologies to improve biomedical research and clinical care by integrating experimental and clinical information systems. The informatics revolutions both in bioinformatics and clinical informatics will eventually change the current practice of medicine, including diagnostics, therapeutics, and prognostics. Postgenome informatics, powered by high throughput technologies and genomic-scale databases, is likely to transform our biomedical understanding forever much the same way that biochemistry did a generation ago. The paper describes how these technologies will impact biomedical research and clinical care, emphasizing recent advances in biochip-based functional genomics and proteomics. Basic data preprocessing with normalization, primary pattern analysis, and machine learning algorithms will be presented. Use of integrated biochip informatics technologies, text mining of factual and literature databases, and integrated management of biomolecular databases will be discussed. Each step will be given with real examples in the context of clinical relevance. Issues of linking molecular genotype and clinical phenotype information will be discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.10
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• pp.1830-1840
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• 2011

CDISC (Clinical Data Interchanging Standards Consortium) standards are to support the acquisition, exchange, submission and archival of clinical trial and research data. SDTM (Study Data Tabulation Model) for Case Report Forms (CRFs) was recommended for U.S. Food and Drug Administration (FDA) regulatory submissions since 2004. Although the SDTM Implementation Guide gives a standardized and predefined collection of submission metadata 'domains' containing extensive variable collections, transforming CRFs to SDTM files for FDA submission is still a very hard and time-consuming task. For addressing this issue, we developed metadata based SDTM mapping rules. Using these mapping rules, we also developed a semi-automatic tool, named CDISC Transformer, for transforming clinical trial data to CDISC standard compliant data. The performance of CDISC Transformer with or without MDR support was evaluated using CDISC blank CRF as the 'gold standard'. Both MDR and user inquiry-supported transformation substantially improved the accuracy of our transformation rules. CDISC Transformer will greatly reduce the workloads and enhance standardized data entry and integration for clinical trial and research in various healthcare domains.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2001.10a
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• pp.1-9
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• 2001

scale genomic and postgenomic data means that many of the challenges in biomedical research are now challenges in computational sciences and information technology. The informatics revolutions both in clinical informatics and bioinformatics will change the current paradigm of biomedical sciences and practice of clinical medicine, including diagnostics, therapeutics, and prognostics. Postgenome informatics, powered by high throughput technologies and genomic-scale databases, is likely to transform our biomedical understanding forever much the same way that biochemistry did a generation ago. In this talk, 1 will describe how these technologies will in pact biomedical research and clinical care, emphasizing recent advances in biochip-based functional genomics. Basic data preprocessing with normalization and filtering, primary pattern analysis, and machine teaming algorithms will be presented. Issues of integrated biochip informatics technologies including multivariate data projection, gene-metabolic pathway mapping, automated biomolecular annotation, text mining of factual and literature databases, and integrated management of biomolecular databases will be discussed. Each step will be given with real examples from ongoing research activities in the context of clinical relevance. Issues of linking molecular genotype and clinical phenotype information will be discussed.

• KOGO NEWS
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• v.5 no.4
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• pp.9-16
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• 2005

Bioinformatics is a rapidly emerging field of biomedical research. A flood of large-scale genomic expression data transforms the challenges m biomedical research into ones in bioinformatics. Clinical informatics has long developed technologies to imp개ve biomedical research by integrating experimental and clinical information systems. Biomedical informatics, powered by high throughput techniques, genomic-scale databases and advanced clinical information system, is likely to transform our biomedical understanding forever much the same way that biochemistry did to biology a generation ago. The emergence of healthcare and biomedical informatics revolutionizing both bioinformatics and clinical informatics will eventually change the current practice of medicine, including diagnostics, therapeutics and prognostics.

• The Journal of Korean Academic Society of Nursing Education
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• v.26 no.3
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• pp.299-310
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• 2020

Purpose: This study is a content analysis to understand the nursing informatics competence of clinical nurses. Methods: Focus group interviews were used to collect data. Two focus group interviews were held with a total of nine clinical nurses. All interviews were recorded and transcribed. Content analysis was used to analyze data. Results: The five main categories of nursing informatics competence that emerged are 1) software program use, 2) use of nursing information, 3) use of information communication technology in nursing, 4) professional responsibilities and ethics, and 5) active attitudes and recognition. Next, there are three strategies to improve nursing informatics competence: 1) organizational approach, 2) opportunity of continuous education, 3) presentation of standards in nursing informatics competence. Conclusion: Further studies such as educational program development and evaluation tool development are necessary. Moreover, there is a need to enhance clinical nurses' nursing informatics competence by using the proposed strategies.

• Genomics & Informatics
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• v.13 no.2
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• pp.31-39
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• 2015

Sequencing depth, which is directly related to the cost and time required for the generation, processing, and maintenance of next-generation sequencing data, is an important factor in the practical utilization of such data in clinical fields. Unfortunately, identifying an exome sequencing depth adequate for clinical use is a challenge that has not been addressed extensively. Here, we investigate the effect of exome sequencing depth on the discovery of sequence variants for clinical use. Toward this, we sequenced ten germ-line blood samples from breast cancer patients on the Illumina platform GAII(x) at a high depth of ${\sim}200{\times}$. We observed that most function-related diverse variants in the human exonic regions could be detected at a sequencing depth of $120{\times}$. Furthermore, investigation using a diagnostic gene set showed that the number of clinical variants identified using exome sequencing reached a plateau at an average sequencing depth of about $120{\times}$. Moreover, the phenomena were consistent across the breast cancer samples.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.29-34
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• 2005

DNA microarray becomes a major tool for the investigation of global gene expression in all aspects of cancer and biomedical research. DNA microarray experiment generates enormous amounts of data and they are meaningful only in the context of a detailed description of microarrays, biomaterials, and conditions under which they were generated. MicroArray Gene Expression Data (MGED) society has established microarray standard for structured management of these diverse and large amount data. MGED MAGE-OM (MicroArray Gene Expression Object Model) is an object oriented data model, which attempts to define standard objects for gene expression. To assess the relevance of DNA microarray analysis of cancer research it is required to combine clinical and genomics data. MAGE-OM, however, does not have an appropriate structure to describe clinical information of cancer. For systematic integration of gene expression and clinical data, we create a new model, Cancer Genomics Object Model.

• The Journal of Korean Academic Society of Nursing Education
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• v.23 no.2
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• pp.146-155
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• 2017

Purpose: The purpose of this study was to investigate the nursing informatics competencies, problem-solving ability, and nursing performance ability of nurses, and to determine factors that affect their nursing performance ability. Methods: Data were collected from 210 clinical nurses employed by a general hospital having more than 500 beds in Seoul. The data were collected from June to October, 2014. The questionnaires included a nursing informatics questionnaire, the Korea problem solving process inventory, and a nurse performance appraisal tool. The data were analyzed using descriptive statistics, t-test, ANOVA, Pearson correlation coefficient, and stepwise multiple regression. Results: Nursing performance ability had statistically significant correlation with nursing informatics competencies (r=.49, p<.001) and problem-solving ability (r=.66, p<.001). Factors influencing nursing performance ability were problem-solving ability, nursing informatics competencies, work experience, and educational status, accounting for 54% of the variance. Conclusion: Findings indicate that nursing informatics competencies and problem-solving ability have important influences on the nursing performance ability of clinical nurses. Thus, in order to provide an improvement in nursing performance ability, educational programs towards nurses' problem-solving ability and nursing informatics competencies should be provided.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.13 no.1
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• pp.45-53
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• 2009

Background and purpose : We previously developed questionnaire of Guibi-tang symtom on the Delphi method through the pathogenesis analysis. But developed a questionnaire was not verified in the clinical. So, to ensure objectivity, quantification and validity, verification is needed for questionnaire items before applying a clinical. On this study, we looked at whether questionnaire items had been validity in the clinical. Methods : Participants of this study were outpatients in eleven clinics. The resources were collected from 200 patients. SPSS 15.0 for Windows was used for statistical analysis : reliability analysis, factor analysis were used to verify the results Results and Conclusions : 16 items were selected through reliability analysis perforfed on about 22 items. After factor analysis, we have four component. Veryfy research of the Guibi-tang Patternization Questionnaire is needed in the future. Also I think that research should proceed about a lot of people.