TRANSFORMING NURSING
MODULE 3: AT A GLANCE
DATA SCIENCE AND DIGITAL HEALTH: ADVANCES IN TECHNOLOGY AND NURSING INFORMATICS
INTRODUCTION
Technology shapes all aspects of our lives, from mobile devices holding as much power and usage ability as a desktop computer, to cars that can alert a driver if they swerve out of a lane or are too close to another driver. Technological advancements drive society forward and provide opportunities for safety and growth. The healthcare field is no exception, from the use of wearable medical devices to offering telehealth appointments during a pandemic and beyond, technology has shaped how healthcare is delivered, studied, and monitored.
In this module, you will explore data science and digital health. Think about what areas of your practice might be affected by these advancements in technology, and how these technologies might shape the study of nursing informatics. What experience have you had with these advancements, and how might these advancements affect the patients or care in your area of practice?
WHAT’S HAPPENING THIS MODULE?
Module 3: Data Science and Digital health: Advances in Technology and Nursing Informatics is a 2-week module—Weeks 5 and 6 of the course—in which you will explore emerging technologies through advancements in data collection and digital health. You will also submit the first part of your nursing informatics project, in which you will demonstrate your understanding of nursing informatics through project creation.
DATA SCIENCE: APPLICATIONS FOR PRACTICE
INTRODUCTION
Data drives innovations in healthcare. Whether through exploring patient care practices, introducing new care techniques, or providing new lifesaving medicine, data drives the ability to offer these solutions in practice. Data is not compiled, applied, or analyzed using only one approach—therefore, it is important to explore the various strategies used and consider implications, barriers, and impact of data science on nursing practice.
This week, you will analyze the use of data science applications and processes for healthcare organizations and nursing practice. You will also consider and examine approaches for implementation of data science.
This week also serves as the week in which you will submit your proposed nursing informatics project. Remember, while using project management skills and techniques, the goal of this project is to demonstrate your understanding of nursing informatics through the implementation, or potential implementation, of your proposed nursing informatics project.
LEARNING OBJECTIVES
Students will:
Analyze data science applications and processes for healthcare organizations and nursing practice
Evaluate approaches for implementation of data science applications and processes for nursing practice
Analyze use of predictive analytics for clinical practice
DATA SCIENCE AND DIGITAL HEALTH: ADVANCES IN TECHNOLOGY AND NURSING INFORMATICS
**Learning Objectives:**
**Analyze data science applications and processes for healthcare organizations and nursing practice:**
– Understand the role of data science in healthcare.
– Explore various data science applications used in healthcare organizations.
– Examine how data science processes can enhance nursing practice and patient care.
**Evaluate approaches for implementation of data science applications and processes for nursing practice:**
– Assess different strategies for implementing data science applications in nursing practice.
– Consider the challenges and barriers associated with the implementation of data science in healthcare settings.
– Identify best practices for successful integration of data science into nursing practice.
**Analyze use of predictive analytics for clinical practice:**
– Understand the concept of predictive analytics and its relevance to healthcare.
– Explore how predictive analytics can be used to improve clinical decision-making and patient outcomes.
– Analyze real-world examples of predictive analytics applications in clinical practice and their impact on patient care.
**Module Overview:**
– **Week 5:** Data Science Applications and Processes
– Explore the role of data science in healthcare organizations and nursing practice.
– Analyze various data science applications and processes used in healthcare.
– Evaluate approaches for implementing data science in nursing practice.
– **Week 6:** Predictive Analytics for Clinical Practice
– Understand the concept of predictive analytics and its significance in healthcare.
– Analyze how predictive analytics can be utilized in clinical practice to improve patient care.
– Explore real-world examples of predictive analytics applications in clinical settings.
**Project Submission:**
– Submit the proposed nursing informatics project, demonstrating understanding of nursing informatics through project creation.
– Utilize project management skills and techniques to outline the implementation or potential implementation of the proposed project.
Throughout this module, students will gain insight into the evolving landscape of data science and digital health in nursing informatics, preparing them to leverage these advancements to enhance nursing practice and patient outcomes.
DATA SCIENCE APPLICATIONS AND PROCESSES
How might data compiled and analyzed in your healthcare organization or nursing practice help support efforts aimed at patient quality and safety? Why might it be important to consider the how’s and why’s of data collection, application, and implementation? How might these practices shape your nursing practice or even the future of nursing?
For this Discussion, you will explore various topics related to data and consider the process and application of each. Reflect on the use of these applications, but also consider the implications of how these applications might shape the future of nursing and healthcare practice.
RESOURCES
Be sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources.
WEEKLY RESOURCES
LEARNING RESOURCES
Required Readings
Begin your review of required Learning Resources with these quick media resources to define some of the many terms you will hear in Nursing Informatics and Project Management today. If you are more interested in a particular one, there are many longer videos available.
(2016, June 15). Defining data analyticsLinks to an external site.[Video]. YouTube. https://www.youtube.com/watch?v=RAw55JEcnEs
IDG TECHTalk. (2020, March 27). What is predictive analyticsLinks to an external site.? Transforming data into future insights [Video]. YouTube. https://www.youtube.com/watch?v=cVibCHRSxB0
(2016, March 11). Gantt charts, simplified – project management trainingLinks to an external site.[Video]. YouTube. https://www.youtube.com/watch?v=cGkHjby1xKM
(2017, August 3). Data science vs big data vs data analyticsLinks to an external site.[Video]. YouTube. https://www.youtube.com/watch?v=yR2wWQYiVKM
(2019, December 10). Big data in 5 minutesLinks to an external site.| What is big data?| introduction to big data | big data explained | simplilearn [Video]. YouTube. https://www.youtube.com/watch?v=bAyrObl7TYE
Required Media
Sipes, C. (2020). Project management for the advanced practice nurse(2nd ed.). Springer Publishing.
Chapter 4, “Planning: Project Management—Phase 2” (pp. 75–120)
American Nurses Association. (2015). Nursing informaticsLinks to an external site.: Scope and standards of practice(2nd ed.).
“Standard 3: Outcomes Identification” (p. 71)
“Standard 4: Planning” (p. 72)1
Brennan, P. F., & Bakken, S. (2015). Nursing needs big data and big data needs nursingLinks to an external site.. Journal of Nursing Scholarship, 47(5), 477–484. doi:10.1111/jnu.12159 National Institutes of Health, Office of Data Science Strategy. (2021). Data science.
National Institutes of Health, Office of Data ScienceLinks to an external site. (2021). Data science. https://datascience.nih.gov/
Zhu, R., Han, S., Su, Y., Zhang, C., Yu, Q., & Duan, Z. (2019). The application of big data and the development of nursing science: A discussion paperLinks to an external site.. International Journal of Nursing Sciences, 6(2), 229–234. doi:10.1016/j.ijnss.2019.03.001
Data analysis
Elsaleh, T., Enshaeifar, S., Rezvani, R., Acton, S. T., Janeiko, V., & Bermudez-Edo, M. (2020). IoT-stream: A lightweight ontology for internet of things data streams and its use with data analytics and event detection servicesLinks to an external site.. Sensors, 20(4), 953. doi:10.3390/s20040953
Parikh, R. B., Gdowski, A., Patt, D. A., Hertler, A., Mermel, C., & Bekelman, J. E. (2019). Using big data and predictive analytics to determine patient risk in oncology. American Society of Clinical Oncology Educational BookLinks to an external site., 39, e53–e58. doi:10.1200/EDBK_238891
Spachos, D., Siafis, S., Bamidis, P., 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 abuseLinks to an external site.. Health Informatics Journal, 26(3), 2265–2279. doi:10.1177/1460458219901232
Optional Resources
Mehta N., & Pandit, A. (2018). Concurrence of big data analytics and healthcare: A systematic review. International Journal of Medical InformaticsLinks to an external site., 114, 57–65. doi:10.1016/j.ijmedinf.2018.03.013
Ristevski, B., & Chen, M. (2018). Big data analytics in medicine and healthcare. Journal of Integrative BioinformaticsLinks to an external site., 15(3), 1–5. https://doi.org/10.1515/jib-2017-0030
Shea, K. D., Brewer, B. B., Carrington, J. M., Davis, M., Gephart, S., & Rosenfeld, A. (2018). A model to evaluate data science in nursing doctoral curricula. Nursing OutlookLinks to an external site., 67(1), 39–48. https://www.nursingoutlook.org/article/S0029-6554(18)30324-5/fulltext
Sheehan, J., Hirschfeld, S., Foster, E., Ghitza, U., Goetz, K., Karpinski, J., Lang, L., Moser. R. P., Odenkirchen, J., Reeves, D., Runinstein, Y., Werner, E., & Huerta, M. (2016). Improving the value of clinical research through the use of common data elements. Clinical Trials, 13(6), 671–676, doi:10.1177/ 1740774516653238
Topaz, M., & Pruinelli, L. (2017). Big data and nursing: Implications for the futureLinks to an external site.. Studies in Health Technology and Informatics, 232, 165–171.
Westra, B. L., Sylvia, M., Weinfurter, E. F., Pruinelli, L., Park, J. I., Dodd, D., Keenan, G. M., Senk, P., Richesson, R. L., Baukner, V., Cruz, C., Gao, G., Whittenburg, L., & Delaney, C. W. (2017). Big data science: A literature review of nursing research exemplarsLinks to an external site.. Nursing Outlook, 65(5), 549–561.
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, A., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. O., Bourne, P., Bouwman, J., Brookes, A. J., Clark. T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C., Finkers, R., … González-Beltrán, A. (2016). The FAIR guiding principles for scientific data management and stewardship. Scientific DataLinks to an external site., 3, Article 160018, 1–9. doi:10.1038/sdata.2016.18
TO PREPARE
Review the Learning Resources for this week related to the topics: Big Data, Data Science, Data Mining, Data Analytics, and Machine Learning.
Consider the process and application of each topic.
Reflect on how each topic relates to nursing practice.
Post a summary on how predictive analytics might be used to support healthcare. Note: These topics may overlap as you will find in the readings (e.g., some processes require both Data Mining and Analytics).
In your post include the following:
Describe a practical application for predictive analytics in your nursing practice. What challenges and opportunities do you envision for the future of predictive analytics in healthcare?
Assignment Rubric DetailsClose
Rubric
NURS_8210_Week5_Discussion_Rubric
NURS_8210_Week5_Discussion_Rubric
Criteria
Ratings
Pts
This criterion is linked to a Learning OutcomeRESPONSIVENESS TO DISCUSSION QUESTION (20 possible points) Discussion post minimum requirements: The original posting must be completed by Day 3 at 10:59 pm CT. Two response postings to two different peer original posts, on two different days, are required by Day 6 at 10:59 pm CT. Faculty member inquiries require responses, which are not included in the peer posts. Your Discussion Board postings should be written in Standard Academic English and follow APA 7 style for format and grammar as closely as possible given the constraints of the online platform. Be sure to support the postings with specific citations from this week’s learning resources as well as resources available through the Walden University library and other credible online resources (guidelines, expert opinions etc.)
20 to >19.0 pts
Excellent
• Discussion postings and responses are responsive to and exceed the requirements of the Discussion instructions. • The student responds to the question/s being asked or the prompt/s provided. Goes beyond what is required in some meaningful way (e.g., the post contributes a new dimension, unearths something unanticipated) • Demonstrates that the student has read, viewed, and considered a variety of learning resources, as well as resources available through the Walden University library and other credible online resources (guidelines, expert opinions etc.) • Exceeds the minimum requirements for discussion posts.
19 to >15.0 pts
Good
• Discussion postings and responses are responsive to and meet the requirements of the Discussion instructions. • The student responds to the question/s being asked or the prompt/s provided. • Demonstrates that the student has read, viewed, and considered a variety of learning resources, as well as resources available through the Walden University library and other credible online resources (guidelines, expert opinions etc.) • Meets the minimum requirements for discussion posts.
15 to >12.0 pts
Fair
• Discussion postings and responses are somewhat responsive to the requirements of the Discussion instructions. • The student may not clearly address the objectives of the discussion or the question/s or prompt/s. • Minimally demonstrates that the student has read, viewed, and considered a variety of learning resources, as well as resources available through the Walden University library and other credible online resources (guidelines, expert opinions etc.) • Does not meet the minimum requirements for discussion posts; has not posted by the due date at least in part.
12 to >0 pts
Poor
• Discussion postings and responses are unresponsive to the requirements of the Discussion instructions. • Does not clearly address the objectives of the discussion or the question/s or prompt/s. • Does not demonstrate that the student has read, viewed, and considered a variety of learning resources, as well as resources available through the Walden University library and other credible online resources (guidelines, expert opinions etc.) • Does not meet the requirements for discussion posts; has not posted by the due date and did not discuss late post timing with faculty.
20 pts
This criterion is linked to a Learning OutcomeCONTENT REFLECTION and MASTERY: Initial Post (30 possible points)
30 to >29.0 pts
Excellent
Initial Discussion posting: • Post demonstrates mastery and thoughtful/accurate application of content and/or strategies presented in the course. • Posts are substantive and reflective, with critical analysis and synthesis representative of knowledge gained from the course readings and current credible evidence. • Initial post is supported by 3 or more relevant examples and research/evidence from a variety of scholarly sources including course and outside readings.
29 to >23.0 pts
Good
Initial Discussion posting: • Posts demonstrate some mastery and application of content, applicable skills, or strategies presented in the course. • Posts are substantive and reflective, with analysis and synthesis representative of knowledge gained from the course readings and current credible evidence. • Initial post is supported by 3 or more relevant examples and research/evidence from a variety of scholarly sources including course and outside readings.
23 to >18.0 pts
Fair
Initial Discussion posting: • Post may lack in depth, reflection, analysis, or synthesis but rely more on anecdotal than scholarly evidence. • Posts demonstrate minimal understanding of concepts and issues presented in the course, and, although generally accurate, display some omissions and/or errors. • There is a lack of support from relevant scholarly research/evidence.
18 to >0 pts
Poor
Initial Discussion posting: • Post lacks in substance, reflection, analysis, or synthesis. • Posts do not generalize, extend thinking or evaluate concepts and issues within the topic or context of the discussion. • Relevant examples and scholarly resources are not provided.
30 pts
This criterion is linked to a Learning OutcomeCONTRIBUTION TO THE DISCUSSION: First Response (20 possible points)
20 to >19.0 pts
Excellent
Discussion response: • Significantly contributes to the quality of the discussion/interaction and thinking and learning. • Provides rich and relevant examples and thought-provoking ideas that demonstrates new perspectives, and synthesis of ideas supported by the literature. • Scholarly sources are correctly cited and formatted. • First response is supported by 2 or more relevant examples and research/evidence from a variety of scholarly sources including course and outside readings. • Responds to questions posed by faculty.
19 to >15.0 pts
Good
Discussion response: • Contributes to the quality of the interaction/discussion and learning. • Provides relevant examples and/or thought-provoking ideas • Scholarly sources are correctly cited and formatted. • First response is supported by 2 or more relevant examples and research/evidence from a variety of scholarly sources including course and outside readings. • Responds to questions posed by faculty.
15 to >12.0 pts
Fair
Discussion response: • Minimally contributes to the quality of the interaction/discussion and learning. • Provides few examples to support thoughts. • Information provided lacks evidence of critical thinking or synthesis of ideas. • There is a lack of support from relevant scholarly research/evidence. • No response to questions posed by faculty.
12 to >0 pts
Poor
Discussion response: • Does not contribute to the quality of the interaction/discussion and learning. • Lacks relevant examples or ideas. • There is a lack of support from relevant scholarly research/evidence. • No response to questions posed by faculty.
20 pts
This criterion is linked to a Learning OutcomeCONTRIBUTION TO THE DISCUSSION: Second Response (20 possible points)
20 to >19.0 pts
Excellent
Discussion response: • Significantly contributes to the quality of the discussion/interaction and thinking and learning. • Provides relevant examples and thought-provoking ideas that demonstrates new perspectives, and extensive synthesis of ideas supported by the literature. • Second response is supported by 2 or more relevant examples and research/evidence from a variety of scholarly sources including course and outside readings. • Scholarly sources are correctly cited and formatted. • Responds to questions posed by faculty.
19 to >15.0 pts
Good
Discussion response: • Contributes to the quality of the interaction/discussion and learning. • Provides relevant examples and/or thought-provoking ideas • Second response is supported by 2 or more relevant examples and research/evidence from a variety of scholarly sources including course and outside readings. • Scholarly sources are correctly cited and formatted. • Responds to questions posed by faculty.
15 to >12.0 pts
Fair
Discussion response: • Minimally contributes to the quality of the interaction/discussion and learning. • Provides few examples to support thoughts. • Information provided lacks evidence of critical thinking or synthesis of ideas. • Minimal scholarly sources provided to support post. • Does not respond to questions posed by faculty.
12 to >0 pts
Poor
Discussion response: • Does not contribute to the quality of the interaction/discussion and learning. • Lacks relevant examples or ideas. • No sources provided. • Does not respond to questions posed by faculty.
20 pts
This criterion is linked to a Learning OutcomeQUALITY OF WRITING (10 possible points)
10 to >9.0 pts
Excellent
Discussion postings and responses exceed doctoral level writing expectations: • Use Standard Academic English that is clear, concise, and appropriate to doctoral level writing. • Make few if any errors in spelling, grammar, that does not affect clear communication. • Uses correct APA 7 format as closely as possible given the constraints of the online platform. • Are positive, courteous, and respectful when offering suggestions, constructive feedback, or opposing viewpoints.
9 to >8.0 pts
Good
Discussion postings and responses meet doctoral level writing expectations: • Use Standard Academic English that is clear and appropriate to doctoral level writing • Makes a few errors in spelling, grammar, that does not affect clear communication. • Uses correct APA 7 format as closely as possible given the constraints of the online platform. • Are courteous and respectful when offering suggestions, constructive feedback, or opposing viewpoints.
8 to >6.0 pts
Fair
Discussion postings and responses are somewhat below doctoral level writing expectations: • Posts contains multiple spelling, grammar, and/or punctuation deviations from Standard Academic English that affect clear communication. • Numerous errors in APA 7 format • May be less than courteous and respectful when offering suggestions, feedback, or opposing viewpoints.
6 to >0 pts
Poor
Discussion postings and responses are well below doctoral level writing expectations: • Posts contains multiple spelling, grammar, and/or punctuation deviations from Standard Academic English that affect clear communication. • Uses incorrect APA 7 format • Are discourteous and disrespectful when offering suggestions, feedback, or opposing viewpoints.
10 pts
Total Points: 100
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