1222020 Rubric Detail Blackboard Learnhttpsclasswaldenueduwe ✓ Solved

12/2/2020 Rubric Detail – Blackboard Learn 1/5 Rubric Detail Select Grid View or List View to change the rubric's layout. Excellent Good Fair Poor Quality of Work Submitted: The extent of which work meets the assignned criteria and work reects graduate level critical and analytic thinking. 27 (27%) - 30 (30%) Assignment exceeds expectations. All topics are addressed with a minimum of 75% containing exceptional breadth and depth about each of the assignment topics. 24 (24%) - 26 (26%) Assignment meets expectations.

All topics are addressed with a minimum of 50% containing good breadth and depth about each of the assignment topics. 21 (21%) - 23 (23%) Assignment meets most of the expectations. One required topic is either not addressed or inadequately addressed. 0 (0%) - 20 (20%) Assignment supercially meets some of the expectations. Two or more required topics are either not addressed or inadequately addressed.

Quality of Work Submitted: The purpose of the paper is clear. 5 (5%) - 5 (5%) A clear and comprehensive purpose statement is provided which delineates all required criteria. 4 (4%) - 4 (4%) Purpose of the assignment is stated, yet is brief and not descriptive. 3.5 (3.5%) - 3.5 (3.5%) Purpose of the assignment is vague or o topic. 0 (0%) - 3 (3%) No purpose statement was provided.

Name: NRNP_6670_Week2_Assignment_Rubric EXIT Grid View List View 12/2/2020 Rubric Detail – Blackboard Learn 2/5 Excellent Good Fair Poor Assimilation and Synthesis of Ideas: The extent to which the work reects the student's ability to: Understand and interpret the assignment's key concepts. 9 (9%) - 10 (10%) Demonstrates the ability to critically appraise and intellectually explore key concepts. 8 (8%) - 8 (8%) Demonstrates a clear understanding of key concepts. 7 (7%) - 7 (7%) Shows some degree of understanding of key concepts. 0 (0%) - 6 (6%) Shows a lack of understanding of key concepts, deviates from topics.

Assimilation and Synthesis of Ideas: The extent to which the work reects the student's ability to: Apply and integrate material in course rsources (i.e. video, required readings, and textook) and credible outside resources. 18 (18%) - 20 (20%) Demonstrates and applies exceptional support of major points and integrates 2 or more credible outside sources, in addition to 2-3 course resources to suppport point of view. 16 (16%) - 17 (17%) Integrates specic information from 1 credible outside resource and 2-3 course resources to support major points and point of view. 14 (14%) - 15 (15%) Minimally includes and integrates specic information from 2-3 resources to support major points and point of view.

0 (0%) - 13 (13%) Includes and integrates specic information from 0 to 1 resoruce to support major points and point of view. 12/2/2020 Rubric Detail – Blackboard Learn 3/5 Excellent Good Fair Poor Assimilation and Synthesis of Ideas: The extent to which the work reects the student's ability to: Synthesize (combines various components or dierent ideas into a new whole) material in course resources (i.e. video, required readings, textbook) and outside, credible resources by comparing dierent points of view and highlighting similarities, dierences, and connections. 18 (18%) - 20 (20%) Synthesizes and justies (defends, explains, validates, conrms) information gleaned from sources to support major points presented.

Applies meaning to the eld of advanced nursing practice. 16 (16%) - 17 (17%) Summarizes information gleaned from sources to support major points, but does not synthesize. 14 (14%) - 15 (15%) Identies but does not interpret or apply concepts, and/or strategies correctly; ideas unclear and/or underdeveloped. 0 (0%) - 13 (13%) Rarely or does not interpret, apply, and synthesize concepts, and/or strategies. 12/2/2020 Rubric Detail – Blackboard Learn 4/5 Excellent Good Fair Poor Written Expression and Formatting Paragraph and Sentence Structure: Paragraphs make clear points that support well developed ideas, ow logically, and demonstrate continuity of ideas.

Sentences are clearly structured and carefully focused--neither long and rambling nor short and lacking substance. 5 (5%) - 5 (5%) Paragraphs and sentences follow writing standards for structure, ow, continuity and clarity 4 (4%) - 4 (4%) Paragraphs and sentences follow writing standards for structure, ow, continuity and clarity 80% of the time. 3.5 (3.5%) - 3.5 (3.5%) Paragraphs and sentences follow writing standards for structure, ow, continuity and clarity 60%- 79% of the time. 0 (0%) - 3 (3%) Paragraphs and sentences follow writing standards for structure, ow, continuity and clarity < 60% of the time. Written Expression and Formatting English writing standards: Correct grammar, mechanics, and proper punctuation 5 (5%) - 5 (5%) Uses correct grammar, spelling, and punctuation with no errors.

4 (4%) - 4 (4%) Contains a few (1-2) grammar, spelling, and punctuation errors. 3.5 (3.5%) - 3.5 (3.5%) Contains several (3-4) grammar, spelling, and punctuation errors. 0 (0%) - 3 (3%) Contains many (≥ 5) grammar, spelling, and punctuation errors that interfere with the reader’s understanding. 12/2/2020 Rubric Detail – Blackboard Learn 5/5 Excellent Good Fair Poor Written Expression and Formatting The paper follows correct APA format for title page, headings, font, spacing, margins, indentations, parenthetical/in- text citations, and reference list. 5 (5%) - 5 (5%) Uses correct APA format with no errors 4 (4%) - 4 (4%) Contains a few (1-2) APA format errors.

3.5 (3.5%) - 3.5 (3.5%) Contains several (3-4) APA format errors. 0 (0%) - 3 (3%) Contains many (≥ 5) APA format errors. Total Points: 100 Name: NRNP_6670_Week2_Assignment_Rubric EXIT

Paper for above instructions

Introduction

The healthcare landscape is rapidly evolving with the integration of technology and data analytics. This revolution has been particularly transformative for nursing practice, where data-driven decision-making is becoming increasingly vital (McGonigle & Mastrian, 2021). This assignment will delve into the significance of data analytics in advanced nursing practice, exploring its benefits, challenges, and implications for patient care, nursing leadership, and healthcare policy.

Significance of Data Analytics in Nursing

Data analytics refers to the systematic computational analysis of data to reveal patterns, trends, and relationships within the information (HIMSS, 2020). In nursing, data analytics is pivotal for enhancing patient outcomes, improving efficiency, and fostering evidence-based practice.
1. Improving Patient Outcomes
One of the most significant advantages of data analytics in nursing is its potential to improve patient outcomes. According to a study by Pogue et al. (2020), analytical tools can help nurses identify at-risk patients by analyzing clinical data trends. For instance, early warning systems can predict patient deterioration by monitoring vital signs, leading to timely interventions and reduced hospital stays.
2. Enhancing Evidence-based Practice
Utilizing data analytics allows nurses to apply evidence-based practices effectively. The integration of clinical data with research findings can help in creating guidelines that are not only based on theoretical frameworks but are backed by real-time evidence (Stevens, 2013). This synthesis of data empowers nurses to make informed clinical decisions, ultimately enhancing quality of care and patient satisfaction (Ellingson et al., 2020).
3. Resource Optimization
Data analytics can also optimize resource allocation within healthcare settings. By analyzing admission and discharge data, nurses can forecast patient influx and adjust staffing levels accordingly, which is crucial for maintaining operational efficiency and reducing burnout (MacLeod, 2019). In addition, analytics can assist in managing supplies and medication effectively to minimize wastage while ensuring adequate availability for patient needs.

Challenges in Implementing Data Analytics

While the benefits of data analytics are substantial, several challenges hinder its effective implementation in nursing practice.
1. Data Overload
One of the significant challenges facing healthcare professionals is the overwhelming volume of data available. Nurses may struggle to filter relevant information from the sea of data, leading to analysis paralysis (Gonzalez et al., 2021). This overload can compromise clinical decision-making as nurses may find it challenging to focus on critical patient information.
2. Training and Education
The successful integration of data analytics into nursing practice requires adequate training and education. A report by Becker's Hospital Review (2021) indicated that many nursing curricula do not sufficiently cover data analytics. Consequently, nurses may lack the skills necessary to interpret data effectively, limiting their ability to engage with analytical tools to enhance patient care.
3. Ethical Considerations
The ethical implications of utilizing data analytics in nursing cannot be overlooked. The use of sensitive patient data raises concerns regarding privacy and consent (Dinev et al., 2019). Nurses must navigate the challenges of data security while ensuring that patient confidentiality is maintained. Developing robust ethical frameworks is essential for guiding the responsible use of data in nursing practice.

The Role of Nursing Leadership

Nursing leadership plays a crucial role in the adoption and implementation of data analytics within healthcare organizations. By fostering a culture that values data-driven decision-making, nursing leaders can help integrate analytics into daily practice.
1. Advocating for Education
Nursing leaders are responsible for advocating for educational programs that incorporate data analytics training. By creating partnerships with educational institutions, they can ensure that nursing curricula prepare future nurses to utilize analytical tools effectively (Murphy et al., 2020).
2. Investing in Technology
Leadership must also prioritize technology investments that support data analytics initiatives. This includes providing the necessary tools and software that facilitate data collection and analysis, allowing nurses to leverage these resources to enhance patient care (Vogel, 2021).
3. Promoting a Data-Driven Culture
Cultivating a data-driven culture within healthcare organizations requires leaders to model the use of data in decision-making processes. By encouraging teams to incorporate analytics in their practice, leaders can create an environment where data is valued and utilized to improve patient outcomes.

Policy Implications

The implementation of data analytics in nursing practice has significant implications for healthcare policy. Policymakers must recognize the importance of supporting data analytics initiatives to improve the quality of healthcare services.
1. Standardization of Data Practices
To facilitate effective data analytics, standardization of data collection and reporting processes is necessary. Policymakers can advocate for policies that establish uniform data standards across healthcare organizations, promoting interoperability and robust data sharing practices (HIMSS, 2020).
2. Funding and Resources
Policies should also prioritize funding and resources for data analytics in healthcare settings. Investments in analytics technologies, training programs, and infrastructure can significantly enhance the implementation of evidence-based practices across the nursing profession (MacLeod, 2019).
3. Regulatory Frameworks
Developing regulatory frameworks that address privacy and ethical concerns regarding data use is critical. Policymakers must ensure that patient data is utilized responsibly, balancing the benefits of analytics with the need to protect patient rights and confidentiality (Dinev et al., 2019).

Conclusion

In conclusion, data analytics is a transformative tool that holds immense potential for enhancing advanced nursing practice. Its ability to improve patient outcomes, promote evidence-based practices, and optimize resource management underscores its significance in the nursing field. However, challenges such as data overload, the need for education, and ethical considerations must be addressed to maximize its effectiveness. Nursing leadership plays a pivotal role in advocating for educational advancements and technology investments, while policymakers must create supportive frameworks for the sustainable integration of data analytics into healthcare systems. Embracing data analytics will not only enhance nursing practice but ultimately lead to improved patient care and health outcomes.

References

1. Becker's Hospital Review. (2021). Closing the nursing data science skills gap. https://www.beckershospitalreview.com/nursing/closing-the-nursing-data-science-skills-gap.html
2. Dinev, T., Jin, Y., & Hu, Q. (2019). Information privacy in healthcare: Factors affecting healthcare professionals’ willingness to share data. Journal of Medical Internet Research, 21(1), e10442.
3. Ellingson, K. D., et al. (2020). Patient outcomes as a function of nurse engagement with evidence-based practice. Nursing Research, 69(5), 336-343.
4. Gonzalez, M. C., et al. (2021). Data overload in healthcare: Navigating challenges in big data analytics. Journal of Health Management, 23(2), 260-270.
5. HIMSS. (2020). The importance of data analytics in healthcare. https://www.himss.org/library/dataanalytics
6. MacLeod, A. (2019). Optimizing healthcare resource allocation through data analytics. Health Services Research, 54(3), 714-725.
7. McGonigle, D., & Mastrian, K. (2021). Nursing informatics and the foundation of knowledge. Jones & Bartlett Learning.
8. Murphy, E., et al. (2020). The role of nurse leadership in advancing data analytics in nursing education. Journal of Nursing Education, 59(3), 123-130.
9. Pogue, S. L., et al. (2020). The potential of big data in nursing: Improving patient outcomes through analytics. Nursing Management, 51(1), 20-25.
10. Stevens, K. R. (2013). The importance of evidence-based practice in nursing education and practice. Journal of Professional Nursing, 29(1), 1-6.