Translational Research And Population Health Management Essay

Translational Research And Population Health Management Essay

Translational Research And Population Health Management Essay

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A. Define how translational research plays a role in influencing policy? Provide an example of a local health care policy that has been recently enacted and or is awaiting legislative passage that has been influenced by research.Translational Research And Population Health Management Essay


B. As an advanced registered nurse, what will your role be in advocating for equitable population health services and policies? Do you anticipate any challenges or barriers to “population advocacy”?

Translational Research for Practice and Populations Name Institution In 2009 unspecified electronic survey was conducted with factors such as type, frequency, perpetrators, and professional/personal concerns on bullying identified (Quine, 2001). The results revealed that out of 330 RN respondents, 72% reported positive to bullying at various occasions in line of their career. Of this segment, clear hostility seemed most frequent in surgical/medical, operational rooms, emergency, obstetrical areas of care and adolescent residential behavioral/ mental health units. The main culprits to these act are non-other than; charge nurses, senior nurses, physicians and nurse managers. This is impartial research among others that have been…show more content…
Individual assaults might be through criticizing, belittling, and intimidation, isolation, degradation and malice. For instance, one way of bullying could be a case where managers can ask their staffs to perform duties which they well know are beyond the staff’s ability since they have no knowledge of what to be done on such duties. Furthermore, the managers may not pay attention when the employees bully each and at the same time they may take advantage of the bullying as way of improving and managing their organization. Additional nurse types of bullying include: o The super nurse – the nurse that shows how powerful they are o he resentful nurse – the nurse who hates others o The put down gossip and rumors nurse – the nurse that speaks ill about others o The backstabbing nurse – the nurse who pretend to have others back yet they are against them o The cliquish nurse – the nurse who isolates themselves o The green with envy nurse o – the nurse who snobs others and tends to favor others (Dellasega, 2009). In most cases, no one enjoys to be bulled but it happens and one has to find a way of dealing with. Besides, Bullying is not too far from may differ somehow since harassment might be nfluenced by differences in race gender biasness, age  Translational Research And Population Health Management Essay

Rapid growth of genomics data, increased patient expectations of quality of care, and financial
pressures to improve operational efficiencies have created fundamental challenges and opportunities
for healthcare and life sciences organizations to develop and implement biomarker therapies. One
of the major promises of personalized medicine is an increase in drug efficacy. In 2008 and 2009,
success rates for new drugs in Phase II clinical trials from 16 pharmaceutical companies decreased
10%. Of these failures, 51% were due to insufficient efficacy and 19% due to preclinical safety.1
Although many organizations, including healthcare providers, insurers, pharmaceutical companies,
and diagnostic laboratories, realize the potential of personalized medicine, they still lack adequate
implementation of an informatics solution.
The main obstacles to adoption of the new paradigm include:
• Lack of seamlessly integrated, productized, and scalable technology infrastructure to support data
management, analysis, and reporting
• Vendor- and modality-specific omics data are difficult to interrelate, and isolated data in clusters
(that is, silo omics) are insufficient to provide biological insights
• Difficulty in jointly analyzing public domain data and data generated in-house
• Disparate clinical data sources requiring heavy post processing
• Lack of flexible and HIPPA-compliant deployment models
As the cost of DNA sequencing continues to decrease and technological advances make Translational Research And Population Health Management Essay
sequencing both easier and more robust, the bottleneck in translational research has shifted
from data gathering to data analysis. Many researchers are finding insurmountable analytical
hurdles in testing simple hypotheses without the ad hoc assistance of a bioinformatician. For
bioinformaticians, poor infrastructure design has made it difficult to build tools to enable
researchers to be self-sufficient in their standard analyses. This has created an environment where
bioinformaticians are required to perform mundane tasks, diverting their time and focus away
from challenges and innovations. This ad hoc analysis paradigm often leads to scattered data
and analysis files across multiple storage devices. As a result, it is difficult to reproduce results
and transfer knowledge to external collaborators in accordance with regulatory requirements. In
the past, some institutions have contracted consultants to build customized solutions, which is
very time-intensive. The customized solution also becomes a maintenance challenge due to the
high cost of long-term contracts for external expertise and a disconnect in knowledge between
contractors and in-house resources.
1 Arrowsmith, J., Trial watch: Phase II failures: 2008–2010. Nat Rev Drug Discov, 2011. 10(5):
p. 328–329.
Oracle Health Sciences Translational Research Center: A Translational Medicine Platform to Address the Big Data Challenge
Most informatics infrastructure today focuses on a single omics technology vendor (for example,Translational Research And Population Health Management Essay
Illumina, Life Technologies, and others) or in a single omics data modality (for example,
genome versus transcriptome). While useful, this approach provides a fragmented picture of the
underlying biological processes. Overwhelming evidence has shown that an integrated approach
is the key to identifying the root cause of a disease based on gene structure, expression, and
regulation across multiple omics modalities.2–345
For instance, an integrated analysis of cross-modality
glioblastoma (GBM) data, including DNA copy number, gene expression, and DNA methylation
aberrations, helped dissect genome-wide regulatory mechanisms for further investigation into
the identification of candidate biomarkers for GBM tumors and potential therapeutic targets.6
A holistic view of cross-vendor and cross-modality data is thus critical for the development and
delivery of targeted medicine.
The research community has devoted significant resources to make large-scale genomics data
available to the public. For example, The Cancer Genome Atlas project (TCGA) and the
International Cancer Genome Consortium (ICGC) offer comprehensive sets of complementary
resources for researchers to understand the complete repertoire of mechanisms contributing to
tumor initiation and progression. Similarly, the 1000 Genomes project provides a detailed catalogue
of human genetic variation. These efforts have provided a great opportunity for researchers to
combine public data with the data generated in their own laboratories; to simulate the testing of new
types of hypotheses, increase statistical power of tests, and sub-sequentially generate new biological
insights. Given these public data, researchers and clinicians can theoretically map their own patients’
molecular profiles against the public data to predict disease progression and treatment responses.Translational Research And Population Health Management Essay
However, this requires daunting manual steps or complex programming skills. For example, when
verifying whether a particular mutation occurs in TCGA GBM patients, a researcher needs to pass
through several files on the TCGA website to identify the relevant ones, download them to the
local storage, write script (using bioinformatician’s help), and finally perform the analysis to make a
prediction. As a result, most public data are grossly under-utilized. Furthermore, this approach also
leads to redundant copies of the same public data in an institution as researchers in different labs
independently manage their own copy – multiplying data storage and labor costs. Translational Research And Population Health Management Essay


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