NURS 8210 WEEK 5 DATA SCIENCE APPLICATIONS AND PROCESSES

Walden University NURS 8210 WEEK 5 DATA SCIENCE APPLICATIONS AND PROCESSES-Step-By-Step Guide

This guide will demonstrate how to complete the Walden University NURS 8210 WEEK 5 DATA SCIENCE APPLICATIONS AND PROCESSES 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 5 DATA SCIENCE APPLICATIONS AND PROCESSES                           

Whether one passes or fails an academic assignment such as the Walden University NURS 8210 WEEK 5 DATA SCIENCE APPLICATIONS AND PROCESSES 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 5 DATA SCIENCE APPLICATIONS AND PROCESSES                         

The introduction for the Walden University NURS 8210 WEEK 5 DATA SCIENCE APPLICATIONS AND PROCESSES 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.

How to Write the Body for NURS 8210 WEEK 5 DATA SCIENCE APPLICATIONS AND PROCESSES                         

After the introduction, move into the main part of the NURS 8210 WEEK 5 DATA SCIENCE APPLICATIONS AND PROCESSES 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 5 DATA SCIENCE APPLICATIONS AND PROCESSES                         

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 5 DATA SCIENCE APPLICATIONS AND PROCESSES                           

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 5 DATA SCIENCE APPLICATIONS AND PROCESSES

PREDICTIVE ANALYTICS

Data analytics examines raw data to identify patterns and trends and predict future processes and outcomes. Predictions of these improvements come from identifying the issue, identifying why the problem occurred, predicting what will happen, and identifying ways to make the solution happen (GovLoop, 2016). Predictive analytics uses past and present data and transforms that data into insight to make predictions of future success and improved healthcare outcomes(IDG TECHTalk, 2020).

Predictive Analytics to Support Healthcare

Predictive analytics has the potential to turn information into profit. Predictive analytics aims to promote a proactive rather than reactive healthcare model. Using past and present data, the healthcare system can anticipate negative patterns and reduce the chance of continuing the same problems by implementing new and improved processes. Predictive analysis uses various tools, such as machine learning and data science. Data science is the brain for applications such as Big Data. Big Data can accommodate large amounts of data, but data science organizes, processes, and translates the data to help solve problems. Machine learning then uses this data to learn from and apply the data in various ways, such as robotic surgeries (Mathur, 2023).

Practical Application for Predictive Analytics

Ataman et al. (2023) conducted a study about the effects of diagnostic testing and consultations on emergency department wait times (WT) and length of stay (LOS). The study used t-test and ANOVA inferential statistics. By conducting the study, several variables were identified as contributing factors to extended WT and LOS. A retrospective review using patient data from electronic charts was used to gather data (Ataman et al., 2023). An effective throughput model is essential for the flow of emergency department productivity. Delays cause overcrowding, decreased satisfaction scores, compromised patient outcomes, and loss of revenue. Predictive analytics provides the structure to use this information to predict why delays are persistent, the outcome due to delays, and what changes to make to prevent the delays and improve WT and LOS.

Challenges and Opportunities for the Future of Predictive Analytics in Healthcare

With any successful process, challenges are always a concern. Although technology can produce algorithms, human error is possible. Skill and knowledge to appropriately interpret and share data are essential to the implementation of changes in healthcare. Predictive analytics is based on past and present data but does not account for unpredictable changes that may affect outcomes.

In the world of technological advances, predictive analytics plays a significant role. Early disease detection, production of time and cost-saving processes, and enhancement of medical decision-making techniques are expected outcomes of predictive analytics. As problems are identified, predictive analytics can change the healthcare system to a preventative healthcare model.

References

Ataman, M. G., Sariyer, G., Saglam, C., Karagoz, A., & Unluer, E. E. (2023). Factors relating to decision delay in the Emergency Department: Effects of diagnostic tests and consultations. Open Access Emergency Medicine, 15, 119–131.

GovLoop. (2016, June 15). Defining data analytics. [Video]. YouTube. https://www.youtube.com/watch?v=RAw55JEcnEs

IDG TECHTalk. (2020, March 27). What is predictive analytics? Transforming data into future insights [Video]. YouTube. https://www.youtube.com/watch?v=cVibCHRSxB0

Mathur, G. (2023). IBM. Data science versus machine learning: What’s the difference? https://www.ibm.com/blog/data-science-vs-machine-learning-whats-the-difference/

Sample Answer 2 for NURS 8210 WEEK 5 DATA SCIENCE APPLICATIONS AND PROCESSES

The essay provides a comprehensive overview of predictive analytics in healthcare, highlighting its potential to transform the industry by including past and present data to predict future outcomes and improve patient care. To further expand the discussion, it is essential to emphasize the multidimensional nature of predictive analytics and its implications for various aspects of healthcare delivery (Zhang, 2020).

Predictive analytics offers a proactive approach to healthcare by identifying patterns and trends that can help anticipate and prevent adverse outcomes (Spachos et al., 2020). However, it is crucial to recognize the broader context in which predictive analytics operates. For instance, beyond just improving wait times and length of stay in emergency departments, predictive analytics can also be applied to optimize resource allocation, enhance patient triage, and even predict disease outbreaks or epidemics.

Furthermore, while the essay touches upon the challenges associated with predictive analytics, such as the potential for human error and the limitations of past and present data, it is essential to delve deeper into these issues. For instance, addressing data privacy and security concerns is paramount, especially given the sensitive nature of healthcare data (Spachos et al., 2020). Moreover, ensuring the interpretability and transparency of predictive models is crucial for building trust among healthcare professionals and patients alike.

Looking ahead, the opportunities presented by predictive analytics in healthcare are vast and multifaceted. From personalized medicine and early disease detection to streamlining administrative processes and reducing healthcare costs, predictive analytics can revolutionize how healthcare is delivered and experienced. However, realizing these opportunities requires a concerted effort to overcome challenges, foster collaboration across disciplines, and prioritize ethical considerations in implementing predictive analytics solutions.

References

Spachos, D., Siafis, S., Bamidis, P. D., Kouvelas, D., & Papazisis, G. (2020). Combining big data search analytics and the FDA Adverse Event Reporting System database to detect a potential safety signal of mirtazapine abuse. Health Informatics Journal, 26(3), 2265–2279. https://doi.org/10.1177/1460458219901232Links to an external site.

Zhang, Z. (2020). Predictive analytics in the era of big data: Opportunities and challenges. Annals of Translational Medicine, 8(4), 68. https://doi.org/10.21037/atm.2019.10.97Links to an external site.