Translational Biomedical Informatics and Public Health

Introduction

Over the last decade, there has been significant development of biomedical research in the field of genomics, the cellular basis of the pathogenesis of diseases, and the definition of methods for their drug correction. Nevertheless, the gap between practical health care and traditional methods of diagnosis and theoretical information continues to grow. The existence of this increasing gap predetermined the emergence of translational medicine (Shortliffe & Cimino, 2014). Its essence lies in establishing professional contact between healthcare professionals and scientists so that it is possible to translate research data into effective medical patient care. The purpose of this paper is to analyze the way various technologies offered through translational biomedical informatics will influence population health.

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Analysis

Translational biomedical informatics must have a major role in optimizing the mechanisms for transferring the results of biomedical research into diagnostics, preventive care, and therapeutic technologies. The emerging methods of visualization will enable further progress in medicine and healthcare (Conte, 2016). In particular, this approach allows the creation of new biologically active substances through the coordination of research between research organizations, pharmaceutical and biotechnology institutions, and the search for new ways to increase the effectiveness of existing drugs and promotion of innovations in the pharmaceutical market.

Also, the enhanced methods of interpreting big data paired with the existing data from EMR and PHR will allow more comprehensive furnishing care. Moreover, this compilation will allow eliciting the patterns of more complex and rare diseases (Shortliffe & Cimino, 2014). For instance, individual data models that use integrated approaches will assist both specialists and the population to comprehend, treat, and control illnesses.

It can be assumed that special models will emerge that will serve as community standards. Because more people start employing health-monitoring tools, the need for translating the data from EMRs and PHRs will continue growing (Wachter, 2015). This tendency will enable creating tools for inspecting unique health traits. The transmission of health data and observational data into the systems coupled with interventions will allow eliminating the delays in transferring patients from disease to well-being.

Notably, the research outcomes will be used to elaborate on system-level interventions. They will be developed to provide optimization of existing policies. It can be implemented through the use of systems such as EHR and clinical decision support. Also, remote patient monitoring can be a useful tool as well. This system can be utilized for protocol and medication adherence. However, more importantly, translational biomedical informatics will allow the provision of personalized care. It will imply the compilation of individual-scale data with knowledge-based information (Shortliffe & Cimino, 2014). Such a combination will promote care provision through the introduction of rules-engines including EMR. Using the support systems, health care specialists will be able to evaluate individual rules and define their adaptability in a particular setting.

Conclusion

Thus, various technologies offered through translational biomedical informatics will allow combining the achievements in scientific research, clinical studies, and diagnostic approaches and will enhance the effectiveness of interventions. Consequently, they will contribute to improving the quality of life of the population. Translational biomedical informatics will also reduce the gap between the available clinical/scientific data and health policies (Holzinger, 2014). At this stage of development of this branch, the most important guideline is the maximum introduction of new biomedical scientific and theoretical positions and methods in the area of clinical use.

References

Conte, M. (2016). Translating expertise. Lanham, MA: Rowman & Littlefield Publishers.

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Holzinger, A. (2014). Biomedical informatics. New York, NY: Springer.

Shortliffe, E., & Cimino, J. (2014). Biomedical informatics (4th ed.). New York, NY: Springer.

Wachter, R. (2015). The digital doctor. New York, NY: McGraw Hill Professional.

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StudyCorgi. (2021, January 8). Translational Biomedical Informatics and Public Health. Retrieved from https://studycorgi.com/translational-biomedical-informatics-and-public-health/

Work Cited

"Translational Biomedical Informatics and Public Health." StudyCorgi, 8 Jan. 2021, studycorgi.com/translational-biomedical-informatics-and-public-health/.

1. StudyCorgi. "Translational Biomedical Informatics and Public Health." January 8, 2021. https://studycorgi.com/translational-biomedical-informatics-and-public-health/.


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StudyCorgi. "Translational Biomedical Informatics and Public Health." January 8, 2021. https://studycorgi.com/translational-biomedical-informatics-and-public-health/.

References

StudyCorgi. 2021. "Translational Biomedical Informatics and Public Health." January 8, 2021. https://studycorgi.com/translational-biomedical-informatics-and-public-health/.

References

StudyCorgi. (2021) 'Translational Biomedical Informatics and Public Health'. 8 January.

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