A Different Perspective to Artificial Intelligence Technologies from Nursing Education and Nursing Process Perspective

Year 2022, Volume: 4 Issue: 3, 153 - 158, 17.12.2022

Funda Aslan , Abdülhamit Subaşı

https://doi.org/10.48071/sbuhemsirelik.1109187

Cited By: 6

Abstract

It is seen that artificial intelligence and nursing discussions are generally limited to the ethical dilemmas about the fact robots will replace nurses in the future and that this may create in health care. Increasing expectations of both service providers and service recipients for technologically-based individualized care in the transforming healthcare system make it more and more necessary for nurses to recognize artificial intelligence applications and to be involved in this process. Therefore, it is important to expand the boundaries of the topics discussed in the context of artificial intelligence and nursing and to develop a different perspective towards the current need. In this review, it is aimed to raise awareness about artificial intelligence applications from the perspective of nursing education and nursing process, to capture the relationship between the nursing process and artificial intelligence from a different point, and to present approaches on how nurses can be involved in the development and use of artificial intelligence applications from the education process.

Keywords

nursing, nursing education

Hemşirelik Eğitimi ve Hemşirelik Süreci Perspektifinden Yapay Zeka Teknolojilerine Farklı Bir Bakış

Abstract

Yapay zeka ve hemşirelik ile ilgili tartışmaların genel olarak robotların gelecekte hemşirelerin yerini alacağı ve bunun sağlık bakımı konusunda oluşturabileceği etik ikilimler ile sınırlı kaldığı görülmektedir. Dönüşen sağlık sisteminde hem hizmet sağlayıcıların hem de hizmet alanların teknolojik temelli bireyselleştirilmiş bakıma yönelik artan beklentileri, hemşirelerin yapay zeka uygulamalarını tanımalarını ve bu sürece dahil olmalarını her geçen gün daha gerekli hale getirmektedir. Dolayısı ile yapay zeka ve hemşirelik bağlamında tartışılan konuların sınırlarını genişletmek ve mevcut ihtiyaca yönelik farklı bir bakış açısı geliştirmek önem taşımaktadır. Bu derlemede hemşirelik eğitimi ve hemşirelik süreci perspektifinden yapay zeka uygulamalarına ilişkin farkındalığı artırmak, hemşirelik süreci ve yapay zeka ilişkisini farklı bir noktadan yakalamak, ve hemşirelerin, eğitim sürecinden itibaren yapay zeka uygulamalarının geliştirilme ve kullanılma sürecine nasıl dahil olabileceklerine ilişkin yaklaşımların sunulması amaçlanmıştır.

Keywords

Hemşirelik, hemşirelik eğitimi, hemşirelik süreci, yapay zeka

References

• Buchanan, C., Howitt, M. L., Wilson, R., Booth, R. G., Risling, T., & Bamford, M. (2020). Nursing in the age of artificial intelligence: Protocol for a scoping review. Journal of Medical Internet Research, 9(4), doi:10.2196/17490.
• Calo, R. (2018). Artificial intelligence policy: A primer and roadmap. University of Bologna Law Review, 3(2), 180–218.
• Carroll, W. M. (2019). Artificial intelligence, critical thinking and the nursing process. Online Journal of Nursing Informatics, 23(1).
• Cummins, M. R., Gundlapalli, A. V., Murray, P., Park, H. A., & Lehmann, C. U. (2016). Nursing informatics certification worldwide: History, pathway, roles, and motivation. Yearbook of medical informatics, 25(01), 264-271. doi:10.15265/IY-2016-039.
• Darvish, A., Bahramnezhad, F., Keyhanian, S., & Navidhamidi, M. (2014). The role of nursing informatics on promoting quality of health care and the need for appropriate education. Global Journal of Health Science, 6(6), 11. doi:10.5539/gjhs.v6n6p11.
• Dobrev, D. (2012). A definition of artificial intelligence. arXiv preprint arXiv:1210.1568.
• Fritz, R. L., & Dermody, G. (2019). A nurse-driven method for developing artificial intelligence in “smart” homes for aging-in-place. Nursing Outlook, 67(2), 140–153. doi:10.1016/j.outlook.2018.11.004.
• Górriz, J. M., Ramírez, J., Ortíz, A., Martínez-Murcia, F. J., Segovia, F., Suckling, J., … Ferrández, J. M. (2020). Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications. Neurocomputing, 410, 237–270. doi:10.1016/j.neucom.2020.05.078.
• Gurses, A. P., & Xiao, Y. (2006). A systematic review of the literature on multidisciplinary rounds to design information technology. Journal of the American Medical Informatics Association, 13(3), 267–276. doi:10.1197/jamıa.m1992.
• Im, E. O., & Ju Chang, S. (2012). Current trends in nursing theories. Journal of Nursing Scholarship, 44(2), 156–164. doi:10.1111/j.1547-5069.2012.01440.x.
• Jago, R., van der Gaag, A., Stathis, K., Petej, I., Lertvittayakumjorn, P., Krishnamurthy, Y., Gao, Y., Silva, J. C., Webster, M., Gallagher, A., & Austin, Z. (2021). Use of artificial intelligence in regulatory decision-making. Journal of Nursing Regulation, 12(3), 11–19. doi:10.1016/S2155-8256(21)00112-5.
• 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 performance. CIN: Computers Informatics Nursing, 37(4), 203–212. doi:10.1097/cin.0000000000000508.
• McGrow, K. (2019). Artificial intelligence: Essentials for nursing. Nursing, 49(9), 46–49. doi:10.1097/01.nurse.0000577716.57052.8d.
• Murphy, J., Honey, M., Newbold, S., Weber, P., & Wu, Y. (2018). Forecasting informatics competencies for nurses in the future of connected health. Studies in Health Technology and Informatics, 250, 58–59. doi: 10.3233/978-1-61499-872-3-58.
• Murray, T. A. (2018). Nursing education: Our iceberg is melting. Journal of Nursing Education, 57(10), 575–576. doi:10.3928/01484834-20180921-01.
• Nagle, L. M., Kleib, M., & Furlong, K. (2020). Digital health in canadian schools of nursing part a: Nurse educators’ perspectives. Quality Advancement in Nursing Education - Avancées En Formation Infirmière, 6(1), 4. doi:10.17483/2368-6669.1229.
• NHS. (2019). Preparing the healthcare workforce to deliver the digital future. The Topol Review. An independent report on behalf of the Secretary of State for Health and Social Care. Nhs, February, 102. Retrievent From (01.04.2022): https://topol.hee.nhs.uk/wp-content/uploads/HEE-Topol-Review-2019.pdf.
• O’Connor, S., Hubner, U., Shaw, T., Blake, R., & Ball, M. (2017). Time for TIGER to ROAR! Technology informatics guiding education reform. Nurse Education Today, 58, 78–81. doi: 10.1016/J.NEDT.2017.07.014. Olgun, Ş. (2019). Türkiye’de ve Asya-Avrupa ülkelerinde bilişim hemşireliği. Van Sağlık Bilimleri Dergisi, 12(3), 35-40.
• Peltonen, L.M., Pruinelli, L., Lewis, A., Block, L., Topaz, M., von Gerich, H., & Ronquillo, C. (2021). Will artificial intelligence replace nurses? A debate. Studies in Health Technology and Informatics, 284. doi: 10.3233/SHTI210740.
• Peltonen, L.M., Topaz, M., Ronquillo, C., Pruinelli, L., Sarmiento, R. F., Badger, M. K., … Alhuwail, D. (2016). Nursing informatics research priorities for the future: Recommendations from an international survey. Nursing Informatics, 225, 222–226. doi:10.3233/978-1-61499-658-3-222.
• Pepito, J. A., & Locsin, R. (2019). Can nurses remain relevant in a technologically advanced future? International Journal of Nursing Sciences, 6(1), 106–110. doi:10.1016/J.IJNSS.2018.09.013.
• Risling, T. (2017). Educating the nurses of 2025: Technology trends of the next decade. Nurse Education in Practice, 22, 89–92. doi:10.1016/j.nepr.2016.12.007.
• Ronquillo, C. E., Peltonen, L. M., Pruinelli, L., Chu, C. H., Bakken, S., Beduschi, …Topaz, M. (2021). Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the nursing and artificial intelligence leadership collaborative. Journal of Advanced Nursing, 77(9), 3707–3717. doi:10.1111/jan.14855.
• Shortliffe, E. H., & Sepúlveda, M. J. (2018). Clinical decision support in the era of artificial intelligence. Journal of the American Medical Association, 320(21), 2199–2200. doi:10.1001/jama.2018.17163.
• Stokes, F., & Palmer, A. (2020). Artificial intelligence and robotics in nursing: Ethics of caring as a guide to dividing tasks between AI and humans. Nursing Philosophy, 21(4). doi:10.1111/nup.12306.
• Subasi, A. (2021). Disease Prediction Using Artificial Intelligence: A case study on epileptic seizure prediction. In Enhanced Telemedicine and e-Health (pp. 289-314). Springer, Cham.
• Topaz, M., Koleck, T. A., Onorato, N., Smaldone, A., & Bakken, S. (2021). Nursing documentation of symptoms is associated with higher risk of emergency department visits and hospitalizations in homecare patients. Nursing Outlook, 69(3), 435–446. doi:10.1016/j.outlook.2020.12.007.
• Topaz, M., Murga, L., Gaddis, K. M., McDonald, M. V., Bar-Bachar, O., Goldberg, Y., & Bowles, K. H. (2019). Mining fall-related information in clinical notes: Comparison of rule-based and novel word embedding-based machine learning approaches. Journal of Biomedical Informatics, 90, 103103. doi:10.1016/j.jbı.2019.103103.
• Topaz, M., Woo, K., Ryvicker, M., Zolnoori, M., & Cato, K. (2020). Home healthcare clinical notes predict patient hospitalization and emergency department visits. Nursing Research, 69(6), 448–454. doi:10.1097/nnr.0000000000000470.
• Villumsen, S., Elsberg, S., Løvgren, C., Vinther, K., Klarholt Busk, L., Vest Arler, S., & Rian, O. (2021). Capacity building in preparing the health workforce to deliver the digital future. Studies in Health Technology and Informatics, 286, 43–47. doi:10.3233/SHTI210634.
• Yılmaz, Y., Uzelli Yılmaz, D., Yıldırım, D., Akın Korhan, E., ve Özer Kaya, D. (2021). Yapay zeka ve sağlıkta yapay zekanın kullanımına yönelik sağlık bilimleri fakültesi öğrencilerinin görüşleri. Süleyman Demirel Üniversitesi Sağlık Bilimleri Dergisi, 12(3), 297-308. doi:10.22312/sdusbed.950372.
• Zhou, L., Pan, S., Wang, J., & Vasilakos, A. V. (2017). Machine learning on big data: Opportunities and challenges. Neurocomputing, 237, 350–361. doi:10.1016/J.NEUCOM.2017.01.026.