NURS 8210 Week 7 blog: INNOVATIVE INFORMATICS TOOLS AND APPLICATIONS TO CLINICAL PRACTICE
INNOVATIVE INFORMATICS TOOLS AND APPLICATIONS TO CLINICAL
PRACTICE
New technology and tools will undoubtedly shape nursing practice. “Research suggests that
between 8% and 16% of nursing time is spent on non-nursing activities and tasks that should be
delegated to others” (Robert, 2019). As a result, new innovations may minimize the time spent
on these non-nursing activities and tasks to further support and strengthen patient care.
One such technology is the use of robots. While nursing robots are not yet readily available,
researchers have earned millions in grants over the last decade researching and developing AI
and robotic innovations to improve healthcare and nursing practice. From clinical practice to
patient support, the future seems endless with possibilities.
For this Discussion, you will explore various topics associated with innovative technology and
your healthcare organization or nursing practice. You will consider how you might utilize these
advancements, as well as consider how these advancements might influence nursing informatics.
Reference:
Robert, N. (2019). How artificial intelligence is changing nursing. Nursing Management, 50(9),
30–39. doi:10.1097/01.NUMA.0000578988.56622.21
TO PREPARE
Review the Learning Resources associated with the topics: AI, Machine Learning,
Genomics, Precision Health, and Robotics.
Consider the role of these technologies in your healthcare organization or nursing
practice.
Analyze the differences of these technologies as they may impact healthcare delivery
and nursing practice.
Reflect on the potential use of each of these topics and your personal experiences
with their implementation into practice.
BY DAY 3 OF WEEK 7
Post a response to your blog for each of the following:
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.
Explain whether these applications integrate Big Data? Why or why not?
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?
Resources
Sipes, C. (2020). Project management for the advanced practice nurse (2nd ed.).
Springer Publishing.
o Chapter 5, “Implementation/Execution: Phase 3” (pp. 121–146)
American Nurses Association. (2015). Nursing informaticsLinks to an external site.:
Scope and standards of practice (2nd ed.).
o “Standard 5: Implementation” (pp. 73–74)
o “Standard 5a: Coordination of Activities” (p. 75)
o “Standard 6: Evaluation” (p. 78)
o “Standard 11: Communication” (p. 86)
o “Standard 12: Leadership” (pp. 87–88)
o “Standard 15: Resource Utilization” (p. 92)
Chen, M., & Decary, M. (2020). Artificial intelligence in healthcare: An essential
guide for health leadersLinks to an external site.. Healthcare Management Forum,
33(1),10–18. doi:10.1177/0840470419873123
Dermody, G., & Fritz, R. (2019). A conceptual framework for clinicians working
with artificial intelligence and health-assistive Smart Homes. Nursing InquiryLinks to
an external site., 26(1), Article e12267. doi:10.1111/nin.12267
Lee, M. S., Grabowski, M. M., Habboub, G., & Mroz, T. E. (2020). The Impact of
artificial intelligence on quality and safetyLinks to an external site.. Global Spine
Journal, 10(1 Suppl), 99S–103S. https://doi.org/10.1177/2192568219878133
Sapci, A. H., & Sapci, H. A. (2019). Innovative assisted living tools, remote
monitoring technologies, artificial intelligence-driven solutions, and robotic systems
for aging societies: Systematic reviewLinks to an external site.. JMIR Aging, 2(2),
Article e15429.
Scudellari, M. (2020). AI recognizes COVID-19 in the sound of a coughLinks to an
external site.. IEEE Spectrum. https://spectrum.ieee.org/the-human-os/artificial-
intelligence/medical-ai/ai-recognizes-covid-19-in-the-sound-of-a-cough
Machine learning
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 performanceLinks to an external site.. Computers,
Informatics, Nursing, 37, 203–212. doi:10.1097/CIN.0000000000000508
Park, J. I., Bliss, D. Z., Chi, C. L., Delaney, C. W., & Westra, B. L.
(2020). Knowledge discovery with machine learning for hospital-acquired
catheter-associated urinary tract infectionsLinks to an external
site.. Computers, Informatics, Nursing, 38(1), 28–35.
https://doi.org/10.1097/CIN.0000000000000562
Sendak, M., Gao, M., Nichols, M., Lin, A., & Balu, S. (2019). Machine learning in
health care: A critical appraisal of challenges and opportunitiesLinks to an
external site.. eGEMS, 7(1), 1. https://doi.org/10.5334/egems.287
Precision medicine and Genomics
Burke, W., & Thummel, K. (2019). Precision medicine and health disparities: The
case of pediatric acute lymphoblastic leukemiaLinks to an external site.. Nursing
Outlook, 67(4), 331–336. doi:10.1016/j.outlook.2019.05.003
Corwin, E., Redeker, N. S., Richmond, T. S., Docherty, S. L., Rita, H., & Pickler, R.
H. (2019). Ways of knowing in precision healthLinks to an external site.. Nursing
Outlook, 67(4), 293–301. https://doi.org/10.1016/j.outlook.2019.05.011
Hacker, E. D., McCarthy, A. M, & DeVon, H. (2019). Precision health: Emerging
science for nursing researchLinks to an external site.. Nursing Outlook, 67(4),
287–289. 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
scienceLinks to an external site.. Nursing Outlook, 68(2), 139–140.
doi:10.1016/j.outlook.2019.11.003
Newcomb, P., Behan, D., Sleutel, M., Walsh, J., Baldwin, K., & Lockwood, S.
(2019). Are genetics/genomics competencies essential for all clinical nurses?Links to
an external site. Nursing, 49(7), 54–60.
doi:10.1097/01.NURSE.0000554278.87676.ad
Robotics
Frazier, R. M., Carter-Templeton, H., Wyatt, T. H., & Wu, L. (2019). Current trends
in robotics in nursing patents—a glimpse into emerging innovationsLinks to an
external site.. Computers, Informatics, Nursing, 37(6), 290–297.
doi:10.1097/CIN.0000000000000538
Song, S., & Collins, S. H. (2021).Optimizing exoskeleton assistance for faster self-
selected walking. IEEE Transactions on Neural Systems and Rehabilitation
EngineeringLinks to an external site., 29, 786–795.
doi:10.1109/TNSRE.2021.3074154
Yang, G.-Z., Nelson, B. J., Murphy, R. R, Choset, H., Christensen, H., Collins, S. H.,
Dario, P., Goldberg, K., Ikuta, K., Jacobstein, N., Kragic, D., Taylor, R. H., &
McNutt, M. (2020). Combating COVID-19—The role of robotics in managing public
health and infectious diseasesLinks to an external site.. Science Robotics, 5(40).
https://doi.org/10.1126/scirobotics.abb5589
Clinical decision support, digital medicine
Centers for Disease Control and Prevention. (n.d.). Implementing clinical decision
support systemsLinks to an external site.. Division for Heart Disease and Stroke
Prevention.
https://www.cdc.gov/dhdsp/pubs/docs/Best_Practice_Guide_CDSS_508.pdf
Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., &
Kroeker, K. I. (2020). An overview of clinical decision support systems: Benefits,
risks, and strategies for successLinks to an external site.. NPJ Digital Medicine,
3(17). https://doi.org/10.1038/s41746-020-0221-y
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