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