The future of artificial intelligence in nursing
Hemşirelik alanında yapay zekanın geleceği
DOI:
https://doi.org/10.14687/jhs.v19i2.6217Keywords:
artificial intelligence, technology, nurse, nursing, care, yapay zeka, teknoloji, hemşire, hemşirelik, bakımAbstract
The world is constantly experiencing social, economic, political, cultural and technological change. It is artificial intelligence that is expected to change all aspects of society, including science. The use of artificial intelligence in health services and its dissemination in the society will affect all aspects of the health field. Artificial intelligence will help nurses provide precise and personalized evidence-based care that meets patients' goals and priorities. The aim of this review is to define artificial intelligence and its sub-fields in the light of the literature, to make it understandable in the context of nursing and to explain the use of artificial intelligence in nursing. As a first step towards applying artificial intelligence to maintenance processes, we can start with questions about potential bias in data or algorithms, the suitability of artificial intelligence to predict real situations and outcomes. The concepts of machine learning and deep learning, which are sub-fields of artificial intelligence, should also be known by nurses. The first step for artificial intelligence to realize its potential in nursing is to make the various terms and definitions understandable. The more trained nurses are in artificial intelligence, the more familiar they will be with the technological language. In this way, they are effective in solving problems in maintenance, creating new algorithms, developing and using artificial intelligence.
Extended English summary is in the end of Full Text PDF (TURKISH) file.
Özet
Dünya sürekli olarak sosyal, ekonomik, politik, kültürel ve teknolojik değişim yaşamaktadır. Bilim de dahil olmak üzere toplumun tüm yönlerini değiştirmesi beklenen yapay zekadır. Yapay zekanın sağlık hizmetlerinde kullanımı, toplumda yaygınlaştırılması, sağlık alanının tüm yönlerini etkileyecektir. Yapay zeka, hemşirelerin hastaların hedeflerini ve önceliklerini karşılayan kesin ve kişiselleştirilmiş kanıta dayalı bakım sağlamasına yardımcı olacaktır. Bu derlemenin amacı, literatür ışığında yapay zeka ve alt alanlarını tanımlamak, hemşirelik bağlamında anlaşılır hale getirmek ve yapay zekanın hemşirelikte kullanımını açıklamaktır. Yapay zekanın bakım süreçlerine uygulanmasının ilk adımı olarak, veri veya algoritmalardaki potansiyel önyargı, gerçek durumları ve sonuçları tahmin etmek için yapay zekanın uygunluğu hakkında sorularla başlanabilir. Yapay zekanın alt alanlarından olan makine öğrenmesi ve derin öğrenme kavramlarının da hemşireler tarafından bilinmesi gerekir. Yapay zekanın hemşirelikte potansiyelini gerçekleştirmesi için ilk adım, çeşitli terimleri ve tanımları anlaşılır hale getirmektir. Hemşireler yapay zeka konusunda ne kadar eğitimli olurlarsa, teknolojik dile o kadar aşina olurlar. Bu sayede bakımda görülen sorunların çözümünde, yeni algoritmaları oluşturulmasında, yapay zeka geliştirilmesi ve kullanılmasında etkili olurlar.
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Abbasgholizadeh-Rahimi, S., Granikov, V., & Pluye, P. (2020, May). Current works and future directions on application of machine learning in primary care. In Proceedings of the 11th Augmented Human International Conference (pp. 1-2).
Allam, Z., Dey, G., Jones, D.S. (2020). Artificial Intelligence (AI) Provided Early Detection of the Coronavirus (COVID-19) in China and Will Influence Future Urban Health Policy Internationally. AI 1(2), 156-165.
Allam, Z., Tegally, H., & Thondoo, M. (2019). Redefining the use of big data in urban health for increased liveability in smart cities. Smart Cities 2019, 2, 259–268.
American Nurses Association [ANA]. (2015). Nursing: Scope and standards of practice (3rd edition). Silver Spring, MD: American Nurses Association.
Anderson J., Rainie L., & Luchsinger A. (2018). Artificial intelligence and the future of humans. Pew Research Center. Erişim adresi: https://www.pewinternet.org/2018/12/10/artificial-intelligence-and-the-future-of-humans/ Erişim tarihi: 19.08.2021.
Bali, J., Garg, R., & Bali, R. (2019). Artificial intelligence (AI) in healthcare and biomedical research: Why a strong computational/AI bioethics framework is required?. Indian Journal of Ophthalmology, 67(1), 3.
Barnard, A. (2017). Technology and professional empowerment in nursing. In J. Daly, S. Speedy, & D. Jackson (Eds.), Contexts of nursing: An introduction, 5th ed. (pp. 235– 252). Chatswood, Australia: Elsevier Australia.
Barrera, A., Gee, C., Wood, A., Gibson, O., Bayley, D., & Geddes, J. (2020). Digital mental health: Introducing artificial intelligence in acute psychiatric inpatient care: qualitative study of its use to conduct nursing observations. Evidence-Based Mental Health, 23(1), 34.
Beam, A. L. & Kohane, I. S. (2018). Big Data and Machine Learning in Health Care. JAMA 319(13), 1317-1318.
Beauchet, O., Noublanche, F., Simon, R., Sekhon, H., Chabot, J., Levinoff, E. J., ... & Launay, C. P. (2018). Falls risk prediction for older inpatients in acute care medical wards: Is there an interest to combine an early nurse assessment and the artificial neural network analysis?. The journal of nutrition, health & aging, 22(1), 131-137.
Beedholm, K., Frederiksen, K., & Lomborg, K. (2015). What was (also) at stake when a robot bathtub was implemented in a Danish elder center: A constructivist secondary qualitative analysis. Qualitative Health Research, 26(10), 1424– 1433.
Belgiu, M & Drăgut, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing 114, 24-31.
Bini, S. A. (2018). Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care? The Journal of Arthroplasty 33(8), 2358-2361.
Booth, R. G. (2016). Informatics and Nursing in a Post-Nursing Informatics World: Future Directions for Nurses in an Automated, Artificially Intelligent, Social-Networked Healthcare Environment. Nursing Leadership (Toronto, Ont.), 28(4), 61-69.
Bose, E., Maganti, S., Bowles, K. H., Brueshoff, B. L., & Monsen, K. A. (2019). Machine learning methods for identifying critical data elements in nursing documentation. Nursing research, 68(1), 65-72.
Brenan, M. (2018). Nurses again outpace other professions for honesty, ethics. Gallup. Erişim adresi: https://news.gallup.com/poll/245597/nurses-again-outpace-professions-honesty-ethics.aspx Erişim tarihi: 19.08.2021.
Broad, W. J. (2020). A.I. Versus the Coronavirus. The New York Times, March 26th Erişim adresi: https://www.nytimes.com/2020/03/26/science/ai-versus-the-coronavirus.html Erişim tarihi: 28.04.2020.
Brom, H., Carthon, J. M. B., Ikeaba, U., & Chittams, J. (2020). Leveraging electronic health records and machine learning to tailor nursing Care for Patients at high risk for readmissions. Journal of nursing care quality, 35(1), 27.
Buchanan, C., Howitt, M. L., Wilson, R., Booth, R. G., Risling, T., & Bamford, M. (2020). Predicted influences of artificial intelligence on the domains of nursing: scoping review. JMIR Nursing, 3(1), e23939.
Buchanan, C., Howitt, M. L., Wilson, R., Booth, R. G., Risling, T., & Bamford, M. (2021). Predicted influences of artificial intelligence on nursing education: Scoping review. JMIR Nursing, 4(1), e23933.
Carroll, W. M. (2019). Artificial intelligence, critical thinking and the nursing process. On-Line Journal of Nursing Informatics, 23(1).
Cato, K. D., McGrow, K., & Rossetti, S. C. (2020). Transforming clinical data into wisdom. Nursing management, 51(11), 24.
Charte, D., Charte, F., García, S., del Jesus, M. J., & Herrera, F. (2018). A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines. Information Fusion, 44, 78-96.
Chawla, M. N. (2020). AI, IoT and wearable technology for smart healthcare–A review. Int J Green Energy, 7(1), 9-13.
Clavelle, J. T., Sweeney, C. D., Swartwout, E., Lefton, C., & Guney, S. (2019). Leveraging technology to sustain extraordinary care: a qualitative analysis of meaningful nurse recognition. JONA: The Journal of Nursing Administration, 49(6), 303-309.
Clipper, B., Batcheller, J., Thomaz, A. L., & Rozga, A. (2018). Artificial intelligence and robotics: A nurse leader's primer. Nurse Leader, 16(6), 379– 384.
Cooper, P. B., Hughes, B. J., Verghese, G. M., Just, J. S., & Markham, A. J. (2021). Implementation of an automated sepsis screening tool in a community hospital setting. Journal of Nursing Care Quality, 36(2), 132-136.
Dey, A. (2016). Machine learning algorithms: A review. International Journal of Computer Science and Information Technologies, 7(3), 1174-1179.
Doğan, F. & Türkoğlu, İ. (2019). Derin öğrenme modelleri ve uygulama alanlarına ilişkin bir derleme. DÜMF Mühendislik Dergisi 10(2), 409-445.
Doya, K. (2007). Reinforcement learning: Computational theory and biological mechanisms. HFSP journal, 1(1), 30.
Fogassi, L., Ferrari, P. F., Gesierich, B., Rozzi, S., Chersi, F. & Rizzolatti, G. (2005). Parietal lobe: from action organization to intention understanding. Science 308, 662–7.
Frith, K. H. (2019). Artificial intelligence: what does it mean for nursing?. Nursing education perspectives, 40(4), 261.
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.
Glasgow M. E. S., Colbert A., Viator J., & Cavanagh S. (2018). The nurse-engineer: A new role to improve nurse technology interface and patient care device innovations. Journal of Nursing Scholarship, 50(6), 601–611.
Glauser, W. (2017). Artificial intelligence, automation and the future of nursing. The Canadian nurse, 113(3), 24-26.
Gonçalves, L. S., Amaro, M. L. D. M., Romero, A. D. L. M., Schamne, F. K., Fressatto, J. L., & Bezerra, C. W. (2020). Implementation of an Artificial Intelligence Algorithm for sepsis detection. Revista brasileira de enfermagem, 73.
Greenbaum, N. R., Jernite, Y., Halpern, Y., Calder, S., Nathanson, L. A., Sontag, D. A., & Horng, S. (2019). Improving documentation of presenting problems in the emergency department using a domain-specific ontology and machine learning-driven user interfaces. International journal of medical informatics, 132, 103981.
Griner, T. E., Thompson, M., High, H., & Buckles, J. (2020). Artificial intelligence forecasting census and supporting early decisions. Nursing Administration Quarterly, 44(4), 316-328.
Guo, Y., Hao, Z., Zhao, S., Gong, J., & Yang, F. (2020). Artificial intelligence in health care: bibliometric analysis. Journal of Medical Internet Research, 22(7), e18228.
Hamilton, H. J. (2000). Advances in Artificial Intelligence: 13th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 2000 Montreal, Quebec, Canada, May 14-17, 2000 Proceedings (Vol. 13). Springer Science & Business Media. P. 278.
Huisman, C., & Kort, H. (2019). Two-year use of care robot Zora in Dutch nursing homes: An evaluation study. In Healthcare (Vol. 7, No. 1, p. 31). Multidisciplinary Digital Publishing Institute.
IBM (2021). Yapay Zeka. Erişim adresi: https://www.ibm.com/tr-tr/cloud/learn/what-is-artificial-intelligence Erişim tarihi: 27.05.2021.
Jeong, G. H. (2020). Artificial intelligence, machine learning, and deep learning in women’s health nursing. Korean J Women Health Nurs, 26(1), 5-9.
Joerin, A., Rauws, M., & Ackerman, M. L. (2019). Psychological artificial intelligence service, Tess: delivering on-demand support to patients and their caregivers: technical report. Cureus, 11(1).
Khanjankhani, K., Askari, R., Rafiei, S., Shahi, M., Hashemi, F., & Shafii, M. (2017). Applying artificial neural network approach to predict nurses' job performance based on personality traits and organizational factors. Annals of Tropical Medicine and Public Health, 10(5).
Kulikowski C. A. (2019). Beginnings of artificial intelligence in medicine (AIM): Computational artifice assisting scientific inquiry and clinical art—With reflections on present AIM challenges. Yearbook of Medical Informatics.
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.
Li, H. L., Lin, S. W., & Hwang, Y. T. (2019). Using nursing information and data mining to explore the factors that predict pressure injuries for patients at the end of life. CIN: Computers, Informatics, Nursing, 37(3), 133-141.
Liang, H. F., Wu, K. M., Weng, C. H., & Hsieh, H. W. (2019). Nurses' views on the potential use of robots in the pediatric unit. Journal of pediatric nursing, 47, e58-e64.
Liao, P.-H., Hsu, P.-T., Chu, W., & Chu, W.-C. (2015). Applying artificial intelligence technology to support decision-making in nursing: A case study in Taiwan. Health Informatics Journal, 21(2), 137– 148.
Linnen, D. T., Javed, P. S., & D'Alfonso, J. N. (2019). Ripe for disruption? Adopting nurse-led data science and artificial intelligence to predict and reduce hospital-acquired outcomes in the learning health system. Nursing administration quarterly, 43(3), 246-255.
Liua, W., Wang, Z., Liua, X., Zeng, N., Liu, Y. & Alsaadid, F. E. (2017). A survey of deep neural network architectures and their applications. Neurocomputing 234, 11-26.
Lynn, L. A. (2019). Artificial intelligence systems for complex decision-making in acute care medicine: a review. Patient safety in Surgery, 13(1), 1-8.
Mak, K. K., Pichika, M. R. (2019). Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today, 24(3), 773–780.
McBride, S., Tietze, M., Robichaux, C., Stokes, L., & Weber, E. (2018). Identifying and addressing ethical issues with use of electronic health records. Online Journal of Issues in Nursing, 23(1).
McCarthy, J. (1956). What is artificial intelligence?. Stanford University. Erişim adresi: http://jmc.stanford.edu/artificial-intelligence/what-is-ai/index.html Erişim tarihi: 23.08.2021
Meehan A. (2017). Big data requires information governance. Journal of AHIMA, 88(3), 28–29.
Merriam-Webster Dictionary (2021). Artificial intelligence. Springfield, MA. Erişim adresi: https://www.merriam-webster.com/ Erişim tarihi: 20.08.2021.
Minvielle, L., & Audiffren, J. (2019). NurseNet: monitoring elderly levels of activity with a piezoelectric floor. Sensors, 19(18), 3851.
Mosavi, A., Ardabili, S. & Varkonyi-Koczy, A. R. (2019). List of Deep Learning Models. Preprints.
Nabiyev, V. V. (2012). Yapay zeka: insan-bilgisayar etkileşimi. Seçkin Yayıncılık.
Nevala K. (2017). Machine learning primer. Cary, NC: SAS Institute.
O'Connor, S., Waite, M., Duce, D., O'Donnell, A., & Ronquillo, C. (2020). Data visualization in health care: The Florence effect. Journal of Advanced Nursing, 76(7), 1488-1490.
Panch, T., Szolovits, P. & Atun, R. (2018). Artificial intelligence, machine learning and health systems. J Glob Health 5(2), 1-8.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12, 2825-2830.
Pepito, J. A., & Locsin, R. (2019). Can nurses remain relevant in a technologically advanced future?. International journal of nursing sciences, 6(1), 106-110.
Peterson, L. E. (2009). K-nearest neighbor. Scholarpedia, 4(2), 1883.
Pruinelli, L., Simon, G. J., Monsen, K. A., Pruett, T., Gross, C. R., Radosevich, D. M., & Westra, B. L. (2018). A holistic clustering methodology for liver transplantation survival. Nursing research, 67(4), 331.
Pruinelli, L., Stai, B., Ma, S., Pruett, T., & Simon, G. J. (2019a). A likelihood-based convolution approach to estimate event occurrences in large longitudinal incomplete clinical data. In 2019 IEEE International Conference on Healthcare Informatics (ICHI) (pp. 1-8). IEEE.
Pruinelli, L., Westra, B. L., Pruett, T., Monsen, K. A., Gross, C. R., Radosevich, D. R., ... & Simon, G. J. (2019b). A multi-dimensional general health status concept to predict liver transplant mortality. In 2019 IEEE International Conference on Healthcare Informatics (ICHI) (pp. 1-5). IEEE.
Ronquillo, C. E., Peltonen, L. M., Pruinelli, L., Chu, C. H., Bakken, S., Beduschi, A., ... & 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.
Samuel, A. L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of research and development, 3(3), 210-229.
Schork, N. J. (2019). Artificial intelligence and personalized medicine. In Precision Medicine in Cancer Therapy (pp. 265-283). Springer, Cham.
Sensmeier, J. (2017). Harnessing the power of artificial intelligence. Nursing management, 48(11), 14-19.
Shaban-Nejad, A., Michalowski, M., & Buckeridge, D. L. (2018). Health intelligence: how artificial intelligence transforms population and personalized health.
Shang, Z. (2021). A Concept Analysis on the Use of Artificial Intelligence in Nursing. Cureus, 13(5).
Shorey, S., Ang, E., Yap, J., Ng, E. D., Lau, S. T., & Chui, C. K. (2019). A virtual counseling application using artificial intelligence for communication skills training in nursing education: development study. Journal of medical Internet research, 21(10), e14658.
Sitterding, M. C., Raab, D. L., Saupe, J. L., & Israel, K. J. (2019). Using artificial intelligence and gaming to improve new nurse transition. Nurse Leader, 17(2), 125-130.
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), e12306.
Şeker, A., Diri, B. & Balık, H. H. (2017). Derin Öğrenme Yöntemleri ve Uygulamaları Hakkında Bir İnceleme. Gazi Mühendislik Bilimleri Dergisi, 3(3), 47-64.
Tanioka, T., Yasuhara, Y., Dino, M. J. S., Kai, Y., Locsin, R. C., & Schoenhofer, S. O. (2019). Disruptive engagements with technologies, robotics, and caring: advancing the transactive relationship theory of nursing. Nursing administration quarterly, 43(4), 313-321.
Taulli, T. (2020). AI (Artificial Intelligence) Companies That Are Combating The COVID-19 Pandemic. Forbes, 28 March.
Topaz, M., Murga, L., Bar-Bachar, O., Cato, K., & Collins, S. (2019b). Extracting alcohol and substance abuse status from clinical notes: The added value of nursing data. In MEDINFO 2019: Health and Wellbeing e-Networks for All (pp. 1056-1060). IOS Press.
Topaz, M., Murga, L., Gaddis, K. M., McDonald, M. V., Bar-Bachar, O., Goldberg, Y., & Bowles, K. H. (2019a). 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.
Topol, E. (2018). Health education England. The Topol review. preparing the healthcare workforce to deliver the digital future. London, Unitied Kingdom: NHS, 1-48.
Ulupınar, F., & Toygar, Ş. A. (2020). Hemşirelik Eğitiminde Teknoloji Kullanımı ve Örnek Uygulamalar. Fiscaoeconomia, 4(2), 524-537.
Van Achterberg, T., Schoonhoven, L., & Grol, R. (2008). Nursing implementation science: how evidence‐based nursing requires evidence‐based implementation. Journal of nursing scholarship, 40(4), 302-310.
Yang, X., Bian, J., Fang, R., Bjarnadottir, R. I., Hogan, W. R., & Wu, Y. (2020). Identifying relations of medications with adverse drug events using recurrent convolutional neural networks and gradient boosting. Journal of the American Medical Informatics Association, 27(1), 65-72.
Yu, J. Y., Jeong, G. Y., Jeong, O. S., Chang, D. K., & Cha, W. C. (2020). Machine learning and initial nursing assessment-based triage system for emergency department. Healthcare informatics research, 26(1), 13-19.
Zachariah, P., Sanabria, E., Liu, J., Cohen, B., Yao, D., & Larson, E. (2020). Novel strategies for predicting healthcare-associated infections at admission: implications for nursing care. Nursing Research, 69(5), 399-403.
Zhang XD. (2020) Machine Learning. In: A Matrix Algebra Approach to Artificial Intelligence. Springer, Singapore.
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