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Hemşirelikte Yapay Zekâ ve Robot Teknolojilerinin Kullanımı

Yıl 2023, Cilt: 27 Sayı: 2, 118 - 127, 27.08.2023

Öz

Dünya nüfusu, hızla yaşlanmakta olup, Dünya’da 65 yaş üstü nüfus oranı %9,6’ya ulaşmıştır. Türkiye’de ise bu oran %9,7’dir. Kronik hastalıkların artması, doğumda beklenen yaşam süresinin uzaması, sağlık hizmet kullanıcılarının taleplerinin artması nedeniyle hasta bakımında önemli rolü olan hemşirelere ihtiyaç giderek artmaktadır. Dünya’da ve Türkiye’de nitelikli hemşire konusunda sayısal yetersizlikler bulunmaktadır. Türkiye’de yüz bin kişiye düşen hemşire sayısı üç yüz kırk üçtür. Bu durumla başa çıkmak, iş gücü eksikliğini giderebilmek için, gelişen teknolojilerin katkısıyla hemşirelik bakımı robotları geliştirmeye odaklanıldı. Hemşireler yeni teknolojileri çalışma ortamlarında kullanırken birtakım zorluklarla karşılaşmaktadır. Bunun sebebi yapay zekâ ve hemşire robotların hemşirelik mesleği için avantajlara sahip olduğu gibi dezavantajlara sahip olmasıdır. Bunlar; kişinin mahremiyeti, ülke ekonomilerine bağlı ayrımcılık ve eşitsizlik, sorumluluk sorunları, güvenlik sorunları, etik olarak sıralanabilir. Dezavantajlı yanları hemşirelerde bazı endişelere neden olurken, yapılan çalışmalarda, hemşirelerin gelecekte rutin işleri robotlara devredeceği, bununla beraber bakım gibi işlere daha çok zaman ayırabileceği yönünde sonuçlara da ulaşılmıştır. Hemşirelerin, teknoloji okuryazarlığı, bilişim sistemleri konusunda kendilerini geliştirmeleri, teknolojilerin gelişmesinde görev almaları ve hemşirelik disiplini açısından katkı sunmaları oldukça önemlidir. Hemşirelerin günümüz teknolojilerine uyum sağlamaları hasta bakımında kullanmaları hastaların klinik seyri ve mesleğin gelişimi açısından oldukça önemlidir.

Kaynakça

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Use of Artificial Intelligence and Robotic Technologies in Nursing

Yıl 2023, Cilt: 27 Sayı: 2, 118 - 127, 27.08.2023

Öz

Kaynakça

  • 1. Dünya Sağlık Örgütü. Yaşlanma ve Sağlık. 2021. Çevrimiçi olarak kullanılabilir: https://www.who.int/news-room/fact-sheets/detail/ageing-and-health (erişim tarihi: 03 Nisan 2023).
  • 2. Sağlık istatistikleri yıllığı. 2021. Sağlık Bilgi Sistemleri Genel Müdürlüğü. Ankara. https://www.saglik.gov.tr/TR,95109/saglik-istatistikleri-yilligi-2021-yayinlanmistir.html (erişim tarihi: 03 Nisan 2023).
  • 3. Topakkaya, Eyibaş, AY. Yapay zekâ ve etik ilişkisi. Felsefe Dünyası, 2019;(70),81-99. Retrieved from https://dergipark.org.tr/tr/pub/felsefedunyasi/issue/58339/850694
  • 4. Oksanen A, Savela N, Latikka R, Koivula A. Trust toward robots and artificial ıntelligence: an experimental approach to human-technology ınteractions online. Front Psychol. 2020 Dec 3;11:568256. doi: 10.3389/fpsyg.2020.568256. PMID: 33343447; PMCID: PMC7744307
  • 5. Zhao Z, Ma Y, Mushtaq A, Rajper AMA, Shehab M, Heybourne A et al. Applications of robotics, artificial ıntelligence, and digital technologies during COVID-19: A Review. Disaster Med Public Health Prep. 2022 Aug;16(4):1634-1644. doi: 10.1017/dmp.2021.9. Epub 2021 Jan 8. PMID: 33413717; PMCID: PMC8027549.
  • 6. Frazier RM, Carter-Templeton H, Wyatt TH, Wu L. Current trends in robotics in nursing patents-a glimpse ınto emerging ınnovations. Comput Inform Nurs. 2019 Jun;37(6):290-297. doi: 10.1097/CIN.0000000000000538. PMID: 31135470.
  • 7. Lee J.Y., Şarkı Y.A., Jung J.Y., Kim H.J., Kim B.R., Do H.K. et al. Nurses' needs for care robots in integrated nursing care services. J Adv Nurs. 2018 May 13. doi: 10.1111/jan.13711. Epub ahead of print. PMID: 29754395.
  • 8. Anghel I, Cioara T, Moldovaca D, Antal C, Pop C.D, Salomie I. et al. Smart environments and social robots for age-friendly ıntegrated care services. Int J Environ Res Public Health. 2020 May 27;17(11):3801. doi: 10.3390/ijerph17113801. PMID: 32471108; PMCID: PMC7312538.
  • 9. Frennert, S, Aminoff H & Östlund B. Technological frames and care robots in eldercare. Int J of Soc Robotics, 2021;(13):311–325. https://doi.org/10.1007/s12369-020-00641-0
  • 10. Locsin RC. The co-existence of technology and caring in the theory of technological competency as caring in nursing. J Med Invest. 2017;64(1.2):160-164. doi: 10.2152/jmi.64.160. PMID: 28373615.
  • 11. Aymerich-Franch L, Ferrer I. Liaison, safeguard and well-being: Analyzing the role of social robots during the COVID-19 pandemic. Technol Soc. 2022 Aug;70:101993. doi: 10.1016/j.techsoc.2022.101993. Epub 2022 May 18. PMID: 35607651; PMCID: PMC9116979
  • 12. Christoforou EG, Avgousti S, Ramdani N, Novales C, Panayides AS. The upcoming role for nursing and assistive robotics: opportunities and challenges ahead. Front Digit Health. 2020 Dec 1;2:585656. doi: 10.3389/fdgth.2020.585656. PMID: 34713058; PMCID: PMC8521866.
  • 13. Khan ZH, Siddique A, Lee CW. Robotics utilization for healthcare digitization in global covıd-19 management. Int J Environ Res Public Health. 2020 May 28;17(11):3819. doi: 10.3390/ijerph17113819. PMID: 32481547; PMCID: PMC7312924.
  • 14. Miyagawa M, Kai Y, Yasuhara Y, Ito H, Betriana, F, Tanioka, T et al. Consideration of safety management when using pepper, a humanoid robot for care of older adults. ıntelligent control and automation, 2020; (11):15-24. doi: 10.4236/ica.2020.111002.
  • 15. Erikson H, Salzmann Erikson M. Future challenges of robotics and artificial intelligence in nursing: What can we learn from monsters in popular culture? The Permanente Journal,2016;20(3):15-17. http://dx.doi.org/10.7812/TPP/15-243
  • 16. Pepito JA, Locsin R. Can nurses remain relevant in a technologically advanced future? Int J Nurs Sci. 2018 Oct 4;6(1):106-110. doi: 10.1016/j.ijnss.2018.09.013. PMID: 31406875; PMCID: PMC6608671.
  • 17. Tanioka, T., Osaka, K., Locsin, R., Yasuhara, Y., & Ito, H. "Recommended design and direction of development for humanoid nursing robots perspective from nursing researchers." Intelligent Control and Automation 8.2 (2017): 96-110.
  • 18. Thuemmler C and Bai C (Eds.). Health 4.0: How virtualization and big data are revolutionizing healthcare. Cham Switzerland: Springer International Publishing, 2017; pp. 2168-2194
  • 19. Büyükgöze S. Sağlık 4.0’da giyilebilir teknolojilerden sensör yamalar üzerine bir inceleme. Avrupa Bilim ve Teknoloji Dergisi. 2019; 0(17): 1239 - 1247. 10.31590/ejosat.658020
  • 20. Carroll W. Artificial intelligence, nurses and the quadruple aim. Online Journal of Nursing Informatics, 2018; 22(2).
  • 21. Davis TM and Olff C. Tele-ICU Today: Connecting care through ınnovation. Nursing 2018 critical care, 2015;10(5):15-8.
  • 22. Khunlertkit A, Carayon P. Contributions of tele-intensive care unit (Tele-ICU) technology to quality of care and patient safety. J Crit Care. 2013 Jun;28(3):315.e1-12. doi: 10.1016/j.jcrc.2012.10.005. Epub 2012 Nov 14. PMID: 23159139.
  • 23. Desautels T, Calvert J, Hoffman J, Jay M, Kerem Y, Shieh L et al. Prediction of sepsis in the ıntensive care unit with minimal electronic health record data: A machine learning approach. JMIR Med Inform. 2016 Sep 30;4(3):e28. doi: 10.2196/medinform.5909. PMID: 27694098; PMCID: PMC5065680.
  • 24. Nemati S, Holder A, Razmi F, Stanley MD, Clifford GD, Buchman TG. An ınterpretable machine learning model for accurate prediction of sepsis in the ICU. Crit Care Med. 2018 Apr;46(4):547-553. doi: 10.1097/CCM.0000000000002936. PMID: 29286945; PMCID: PMC5851825.
  • 25. Kamaleswaran R, Akbilgic O, Hallman MA, West AN, Davis RL, Shah SH. Applying artificial ıntelligence to ıdentify physiomarkers predicting severe sepsis in the PICU. Pediatr Crit Care Med. 2018 Oct;19(10):e495-e503. doi: 10.1097/PCC.0000000000001666. PMID: 30052552.
  • 26. Yu C, Liu J, Zhao H. Inverse reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units. BMC Med Inform Decis Mak. 2019 Apr 9;19(Suppl 2):57. doi: 10.1186/s12911-019-0763-6. PMID: 30961594; PMCID: PMC6454602.
  • 27. Parreco J, Hidalgo A, Parks JJ, Kozol R, Rattan R. Using artificial intelligence to predict prolonged mechanical ventilation and tracheostomy placement. J Surg Res. 2018 Aug;228:179-187. doi: 10.1016/j.jss.2018.03.028. Epub 2018 Apr 11. PMID: 29907209.
  • 28. Davoudi A, Malhotra KR, Shickel B, Siegel S, Williams S, Ruppert M et al. Intelligent ICU for autonomous patient monitoring using pervasive sensing and deep learning. Sci Rep. 2019 May 29;9(1):8020. doi: 10.1038/s41598-019-44004-w. PMID: 31142754; PMCID: PMC6541714.
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  • 32. Şendir M, Şimşekoğlu N, Kaya A, Sümer K. Geleceğin teknolojisinde hemşirelik. Sağlık Bilimleri Üniversitesi Hemşirelik Dergisi. 2019; 1(3): 209-214.
  • 33. Baloğlu A, Kaplancalı UT ve Kılıç S. Bakıma ihtiyaç duyan yaşlılar için yardımcı sosyal robot araştırması ve analizi. Avrupa Bilim ve Teknoloji Dergisi , 2019;1-8. https://doi.org/10.31590/ejosat.626045
  • 34. Doğan Merih Y. ve Akdoğan E. Hemşirelikte yapay zekâ. 4th International Eurasian Conference on Biological and Chemical Sciences, Ankara;2021.
  • 35. Çoban N, Göktaş S ve Gezginci E. Surgical procedures in covid-19 patients. In Duran, N., & Demir, H. (Eds). Current Researches in Health Sciences, Gece Akademi, 2020; pp. 211-222 ISBN:978-9949- 46-028-0
  • 36. Bacaksız FE, Yılmaz M, Ezizi K, ve Alan H. Sağlık hizmetlerinde robotları yönetmek. Sağlık ve Hemşirelik Yönetimi Dergisi, 2020; 3(7):458-465. https://dx.doi.org/10.5222/SHYD.2020.59455
  • 37. Gonzalez-Jimenez H. Taking the fiction out of science fiction:(self-aware) Robots and what they mean for society, retailers and marketers. Futures, 2018;98:49-56. https://doi.org/10.1016/j.futures.2018.01.004
  • 38. Clipper B, Batcheller J, Thomaz AL, & Rozga A. (2018). Artificial intelligence and robotics: A nurse leader's primer. Nurse Leader, 2018;16(6):379-384. https://doi.org/10.1016/j.mnl.2018.07.015
  • 39. Bekker M, Coetzee SK, Klopper HC, Ellis SM. Non-nursing tasks, nursing tasks left undone and job satisfaction among professional nurses in South African hospitals. J Nurs Manag. 2015 Nov;23(8):1115-25. doi: 10.1111/jonm.12261. Epub 2014 Oct 27. PMID: 25345386.
  • 40. Erat Ş, Korkmaz M, Çimen V, Yahyaoğlu G. Hemşirelerin iş yaşam kalitesinin motivasyona etkisi. Uluslararası Hakemli Akademik Spor Sağlık ve Tıp Bilimleri Dergisi . 2011
  • 41. Chang HY, Huang TL, Wong MK, Ho LH, Wu CN, Teng CI. How robots help nurses focus on professional task engagement and reduce nurses' turnover intention. J Nurs Scholarsh. 2021 Mar;53(2):237-245. doi: 10.1111/jnu.12629. Epub 2021 Feb 10. PMID: 33567145.
  • 42. Saadatzi MN, Logsdon MC, Abubakar S, Das S, Jankoski P, Mitchell H et al. Acceptability of using a robotic nursing assistant in health care environments: Experimental pilot study. J Med Internet Res. 2020 Nov 12;22(11):e17509. doi: 10.2196/17509. PMID: 33180024; PMCID: PMC7691087.
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  • 54. Güvercin CH. Tıpta yapay zekâ ve etik. Ekmekci PE, editör. Yapay Zekâ ve Tıp Etiği. 1. Baskı. Ankara: Türkiye Klinikleri, 2020; p.7-13
  • 55. Bitirim Okmeydan S. Yeni iletişim teknolojilerini sorgulamak: Etik, güvenlik ve mahremiyetin kesiştiği nokta. Gümüşhane Üniversitesi İletişim Fakültesi Elektronik Dergisi. 2017; 5(1): 347-372
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  • 58. Bioethics briefing note: Artificial intelligence (AI) in healthcare and research. Nuffield Council on Bioethics 2018. http://nuffieldbioethics. org/wp-content/uploads/Artificial-Inte lligence-AI-in-healthcare-and-research.pdf Erişim tarihi 03.04.2023.
  • 59. Lin P, Abney K and Bekey G. Robot ethics: Mapping the issues for a mechanized world. Artificial Intelligence, 2011;175(5-6): 942-949.
  • 60. Van Wynsberghe, Aimee. Healthcare robots: Ethics, design and implementation. Routledge, 2016.
  • 61. Rigby MJ. Ethical dimensions of using artificial ıntelligence in health care. AMA Journal of Ethics. 2019;21(2):E121-124. doi: 10.1001/amajethics.2019.121.
Toplam 61 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Yoğun Bakım Hemşireliği
Bölüm Derleme
Yazarlar

Fatma Kandemir 0009-0005-2828-1101

Fatma Azizoğlu 0000-0002-7102-9797

Banu Terzi 0000-0002-9500-6872

Yayımlanma Tarihi 27 Ağustos 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 27 Sayı: 2

Kaynak Göster

Bu derginin içeriği Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı kapsamında lisanslanmıştır.

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