NURS 8210 Week 7 Blog Innovative Informatics Tools and Applications to Clinical Practice
Walden University NURS 8210 Week 7 Blog Innovative Informatics Tools and Applications to Clinical Practice-Step-By-Step Guide
This guide will demonstrate how to complete the Walden University NURS 8210 Week 7 Blog Innovative Informatics Tools and Applications to Clinical Practice assignment based on general principles of academic writing. Here, we will show you the A, B, Cs of completing an academic paper, irrespective of the instructions. After guiding you through what to do, the guide will leave one or two sample essays at the end to highlight the various sections discussed below.
How to Research and Prepare for NURS 8210 Week 7 Blog Innovative Informatics Tools and Applications to Clinical Practice
Whether one passes or fails an academic assignment such as the Walden University NURS 8210 Week 7 Blog Innovative Informatics Tools and Applications to Clinical Practice depends on the preparation done beforehand. The first thing to do once you receive an assignment is to quickly skim through the requirements. Once that is done, start going through the instructions one by one to clearly understand what the instructor wants. The most important thing here is to understand the required format—whether it is APA, MLA, Chicago, etc.
After understanding the requirements of the paper, the next phase is to gather relevant materials. The first place to start the research process is the weekly resources. Go through the resources provided in the instructions to determine which ones fit the assignment. After reviewing the provided resources, use the university library to search for additional resources. After gathering sufficient and necessary resources, you are now ready to start drafting your paper.
How to Write the Introduction for NURS 8210 Week 7 Blog Innovative Informatics Tools and Applications to Clinical Practice
The introduction for the Walden University NURS 8210 Week 7 Blog Innovative Informatics Tools and Applications to Clinical Practice is where you tell the instructor what your paper will encompass. In three to four statements, highlight the important points that will form the basis of your paper. Here, you can include statistics to show the importance of the topic you will be discussing. At the end of the introduction, write a clear purpose statement outlining what exactly will be contained in the paper. This statement will start with “The purpose of this paper…” and then proceed to outline the various sections of the instructions.
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How to Write the Body for NURS 8210 Week 7 Blog Innovative Informatics Tools and Applications to Clinical Practice
After the introduction, move into the main part of the NURS 8210 Week 7 Blog Innovative Informatics Tools and Applications to Clinical Practice assignment, which is the body. Given that the paper you will be writing is not experimental, the way you organize the headings and subheadings of your paper is critically important. In some cases, you might have to use more subheadings to properly organize the assignment. The organization will depend on the rubric provided. Carefully examine the rubric, as it will contain all the detailed requirements of the assignment. Sometimes, the rubric will have information that the normal instructions lack.
Another important factor to consider at this point is how to do citations. In-text citations are fundamental as they support the arguments and points you make in the paper. At this point, the resources gathered at the beginning will come in handy. Integrating the ideas of the authors with your own will ensure that you produce a comprehensive paper. Also, follow the given citation format. In most cases, APA 7 is the preferred format for nursing assignments.
How to Write the Conclusion for NURS 8210 Week 7 Blog Innovative Informatics Tools and Applications to Clinical Practice
After completing the main sections, write the conclusion of your paper. The conclusion is a summary of the main points you made in your paper. However, you need to rewrite the points and not simply copy and paste them. By restating the points from each subheading, you will provide a nuanced overview of the assignment to the reader.
How to Format the References List for NURS 8210 Week 7 Blog Innovative Informatics Tools and Applications to Clinical Practice
The very last part of your paper involves listing the sources used in your paper. These sources should be listed in alphabetical order and double-spaced. Additionally, use a hanging indent for each source that appears in this list. Lastly, only the sources cited within the body of the paper should appear here.
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Sample Answer for NURS 8210 Week 7 Blog Innovative Informatics Tools and Applications to Clinical Practice
From the five topics: AI, Machine Learning, Genomics, Precision Health, and Robotics, assess the applications of the technology, noting the potential benefits and potential challenges of the innovations. Be specific. Appraise the potential of the innovations to improve healthcare practice and related outcomes.
Artificial Intelligence
AI is developed by training computers to do human tasks (SAS, n.d.). It includes a range of processes and behaviors generated by models and algorithms (Chen & Decary, 2020). AI integrates big data when it is used in conjunction with these models and algorithms. AI is most frequently used in healthcare for clinical decision support processes in the form of alerts, guidelines, order sets, reports, templates, and workflow tools (Sutton et al, 2020. It is also used in natural language processing to extract information from notes, voice technology to simplify documentation, and robotics in surgical procedures (Chen & Decary, 2020). There are several challenges associated with AI. These challenges include an overarching lack of understanding about what AI is in healthcare, how to integrate it, a shortage of individuals who understand how to implement it, an incompatible healthcare technology infrastructure, and not enough good data in healthcare to support AI methods (Chen & Decary, 2020). It is also very costly to set up and maintain (Sutton et al, 2020). AI is used as a tool and when partnered with clinicians supports the delivery of safe quality care (Sutton et al, 2020). AI also has the potential to improve the triple aim (Chen & Decary, 2020).
Machine Learning
Machine Learning is a subset of AI which is used to look for patterns in big data and draw conclusions (SAS, n.d.) while learning from performance and improving over time (Bini, 2018). Machine learning incorporates seven steps which include the use of big data when developing predictive models or algorithms. These steps include asking a question and gathering data, prepping the data, choosing a model, and then splitting the data to train. Approximately 80% is used to train a model/algorithm and the other 20% is used to evaluate and test the model through hyperparameter tuning or feedback (Google Cloud Tech, 2017; SAS, n.d.). Machine learning has the ability to sift through large amounts of complex data, such as the data being produced by wearables, and transform the data into usable information which can be used to develop individualized care (Kwon et al, 2019). Integrated machine learning models are rare in healthcare because they are challenging to develop, prioritize, and implement (Sendak et al, 2019). Most frequently they are developed as knowledge-based decision support programs used to improve safety, clinical management, cost containment, automated administrative functions, diagnostic support, decision support, better documentation, and workflow improvements (Sutton et al, 2020).
Genomics
Genomics is the study of the interrelationship of all genes (Taylor & Barcelona de Mendoza,2017). Genomics uses technology and big data analytics to develop better precision in patient assessment, intervention, and evaluation processes (Corwin et al, 2019). Genomics integrates big data sets in the form of biospecimens, environment, and behavior in an attempt to determine their effects on health (Taylor & Barcelona de Mendoza, 2017). There are several challenges associated with using genomics in healthcare. There is a lack of understanding in nursing concerning genomics (Newcomb et al, 2019) and a need to apply ethical considerations at both individual and population levels (Williams & Anderson, 2018). These challenges are compounded by privacy, reproducibility, and translation issues (Corwin et al, 2019). GP testing has the potential to improve practice because it is an inexpensive and fast method used to prevent, diagnose, treat, and maintain patient care while transforming healthcare outcomes (Genomic Education Program, n.d.).
Precision Health
Precision health is an emerging field using big data to focus on the interplay of genomics, physiology, psychology, environment, and ethics which are used to improve health (Hacker et al, 2019; Hickey et al, 2019). The goal of precision health is to keep people from getting sick in the first place (Minor, 2016). Precision health is driving the development of therapies tailored to treat individual diseases (Minor, 2016) as well as to reduce the current racial and ethnic disparities in healthcare practice (Hacker et al, 2019; Taylor et al, 2017). There are currently challenges to obtaining funding and publishing the papers needed to share knowledge on this topic (Hacker et al, 2019). Partnerships between researchers at NINR Centers are currently needed to leverage experience and address gaps in the evolving science (Hickey et al. 2020) and this appears to be occurring in the form of yearly NINR boot camps.
Robotics
Robotics is the culmination of all artificial intelligence technology to date (Chen & Decary, 2020). Medical robots are currently being used to facilitate surgery in-house or at a distance, in the course of rehabilitation, for social interaction, in assisted living scenarios, and for delivering supplies (Chen & Decary, 2020). Sapci & Sapci (2019) identified home service robots as most applicable for older individuals aging in place with their family members. Robotics will become imperative to facilitate as life expectancy continues to increase and our aging population grows. The greatest challenge, aside from cost, to remember in regard to robotics is that computer intelligence enhances human intelligence, it was never designed to replace it (Chen & Decary, 2020).
Explain the difference between AI, Machine Learning, Data Mining and Deep Learning as presented in the Bini (2018) article.
Why do these differences matter and how relevant are they for Big Data?
Healthcare’s “new normal” is destined to become artificial intelligence using predictive analytics and/or cognitive computing. The technology has seen exponential growth and the cost is coming down. Advanced neural networks, the most complex of algorithms, now exist as 100 layers of stored and built information. These networks are so advanced they can be given data (even an output) and are capable of extracting features. There will soon no longer be a need to address the formatting of unstructured data for input which is currently prevalent in healthcare. Examples of their existence in healthcare practice today include the development of operating room block times and outpatient scheduling systems. Big data sets exist for researchers to mine, model, train, and enhance machines that ultimately aid human intelligence. Soon, healthcare will see these changes implemented more frequently in imaging diagnostics, optimized workflows, and resource allocation. AI trained on big data to learn will provide healthcare invaluable insights in the future.
References
Bini, S. A. (2018). Artificial intelligence, machine learning, deep learning, and cognitive computing: What do these terms mean and how will they impact healthcare? The Journal of Arthroplasty, 33(8), 2358-2361. https://doi:10.1016/j.arth.2018.02.067
Chen, M., & Decary, M. (2020). Artificial intelligence in healthcare: An essential guide for health leaders. Healthcare Management Forum, 33(1),10–18. https://doi:10.1177/0840470419873123
Corwin, E., Redeker, N. S., Richmond, T. S., Docherty, S. L., Rita, H., & Pickler, R. H.
(2019). Ways of knowing in precision health. Nursing Outlook, 67(4), 293–301. https://doi:10.1016/j.outlook.2019.05.011
Genomics Education Programme. (n.d.). Introducing genomics in healthcare. https://www.genomicseducation.hee.nhs.uk/education/videos/introducing-genomics-in- healthcare/
Google Cloud Tech. (2017, August 31). The 7 steps of machine learning [Video].
YouTube. https://www.youtube.com/watch?v=nKW8Ndu7Mjw
Hacker, E. D., McCarthy, A. M, & DeVon, H. (2019). Precision health: Emerging science
for nursing research. Nursing Outlook, 67(4), 287–289. https://doi:10.1016/j.outlook.2019.06.008
Hickey, K. T., Bakken, S., Byrne, M. W., Bailey, D. C. E., Demiris, G., Docherty, S. L., Dorsey, S. G., Guthrie, B. J., Heitkemper, M. M., Jacelon, C. S., Kelechi, T. J., Moore, S. M., Redeker, N. S., Renn, C. L., Resnick, B., Starkweather, A., Thompson, H., Ward, T. M., McCloskey, D. J., Austin, J. K., & Grady, P. A. (2020). Corrigendum to precision health: Advancing symptom and self-management science. Nursing Outlook, 68(2), 139–140. https://doi:10.1016/j.outlook.2019.11.003
Kwon, J. Y., Karim, M. E., Topaz, M., & Currie, L. M. (2019). Nurses “seeing forest for
the trees” in the age of machine learning: Using nursing knowledge to improve relevance and performance. Computers, Informatics, Nursing, 37, 203–212. https://doi:10.1097/CIN.0000000000000508
Newcomb, P., Behan, D., Sleutel, M., Walsh, J., Baldwin, K., & Lockwood, S. (2019).
Are genetics/genomics competencies essential for all clinical nurses? Nursing, 49(7), 54– 60. https://doi:10.1097/01.NURSE.0000554278.87676.ad
Minor, L. (2016). We don’t just need precision medicine, we need precision health. Forbes. https://www.forbes.com/sites/valleyvoices/2016/01/06/we-dont-just-need-precision- medicine-we-need-precision-health/?sh=6f70d7896a92
SAS. (n.d.). Machine learning: What it is and why it matters [Video].
https://www.sas.com/en_us/insights/analytics/machine-learning.html
Sendak, M., Gao, M., Nichols, M., Lin, A., & Balu, S. (2019). Machine learning in health
care: A critical appraisal of challenges and opportunities. eGEMS, 7(1), 1. https://doi.org/10.5334/egems.287
Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker,
- I. (2020). An overview of clinical decision support systems: Benefits, risks, and strategies for success. NPJ Digital Medicine, 3(17). https://doi.org/10.1038/s41746-020- 0221-y
Taylor, J. Y., & Barcelona de Mendoza, V. (2017). Improving-omics-based research and precision health in minority populations: Recommendations for nurse scientists. Journal of Nursing Scholarship, 50(1), 11-19. https://doi:10.1111/jnu.12358
Williams, J. K., & Anderson, C. M. (2018). Omnics research ethics considerations. Nursing Outlook, 66(4), 383-393.
Sample Answer 2 for NURS 8210 Week 7 Blog Innovative Informatics Tools and Applications to Clinical Practice
Hello Susan. Thanks for the insightful discussion. From your discussion, I have learned that AI integrates big data when it is used in conjunction with these models and algorithms (Reddy, 2018). Artificial intelligence (AI) is changing the way healthcare is delivered by helping doctors make faster, more accurate diagnoses (Bini, 2018). Additionally, AI can help identify patients who are at risk for certain diseases and conditions, which allows for proactive treatment. Genomics is the study of genes and their interactions in order to understand how they influence health and disease. Genomic data can be used to personalize treatments for patients based on their specific genetic makeup (Williams et al., 2018). This information can also be used to develop new therapies and drugs. Together, AI and genomics are making it possible for healthcare providers to deliver more individualized care that is tailored to each patient’s unique needs. This leads to improved outcomes and a more cost-effective approach to healthcare. Robotics are being increasingly used in healthcare delivery processes. This is largely because of the many benefits that robotics provides. For example, robots can help to improve accuracy and precision while reducing the risk of human error. Additionally, they can help to speed up the process while ensuring consistent quality. There are a number of different types of robots that are being used in healthcare delivery processes. Some of these include surgical robots, hospital logistics robots, and pharmacy robots. Each type of robot has its own set of benefits that can be extremely helpful for healthcare providers and patients alike. It is important to note that as robotics technology continues to evolve, more and more types of robots will likely be introduced into the healthcare field.
References
Bini, S. A. (2018). Artificial intelligence, machine learning, deep learning, and cognitive computing: What do these terms mean and how will they impact healthcare? The Journal of Arthroplasty, 33(8), 2358-2361. https://doi:10.1016/j.arth.2018.02.067
Reddy, S. (2018). Use of artificial intelligence in healthcare delivery. In eHealth-Making Health Care Smarter. IntechOpen.
Williams, M. S., Buchanan, A. H., Davis, F. D., Faucett, W. A., Hallquist, M. L., Leader, J. B., … & Ledbetter, D. H. (2018). Patient-centered precision health in a learning health care system: Geisinger’s genomic medicine experience. Health Affairs, 37(5), 757-764. https://www.healthaffairs.org/doi/full/10.1377/hlthaff.2017.1557