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Pediatri Hemşireliğinde Yapay Zeka

Yıl 2024, , 36 - 43, 25.04.2024
https://doi.org/10.59398/ahd.1346089

Öz

Yapay zeka herhangi bir canlı organizma olmadan bilgisayar teknolojilerinin insan benzeri davranışları gösterdiği bilgisayar biliminin bir alt dalıdır. Günümüzde yaygınlığı giderek artan yapay zekanın kullanım alanlarından biri de pediatri hemşireliğidir. Çocuk hastalarda yapay zeka ilk defa 1968'de Paycha'nın SHELP uygulamasını geliştirmesiyle başlamış, alanda yapılan çalışmaların çeşitliliği giderek artış göstermiştir. Voss ve arkadaşları “Otizm spektrum bozukluğu çocukların sosyal sonuçlarını iyileştirmek için yapay zeka odaklı giyilebilir davranışsal bir müdahale olan Superpower Glass'ın etkinliğini değerlendirmek’’ amacıyla giyilebilir bir cihaz geliştirmişlerdir. “Hemşirelerin Pediatri Ünitesinde Robotların Potansiyel Kullanımına İlişkin Görüşleri’’ adlı makalede robotların bakım kalitesini iyileştireceğinden, hemşirelerin iş yükünü azaltarak hasta bakımına ve hastaya ayrılan zamanın artacağı vurgulamıştır. Ortaya konan her yeni çalışma ve buluş hemşirelik bakım uygulamalarını güncellemekte ve yeni bakım kavramlarını ortaya çıkarmaktadır. Çocuk hastalarda ilaç uygulamalarında yapılabilecek en küçük hata geri dönüşümü olmayan risklerin ortaya çıkmasına neden olabilir. Çocuklar için hazırlanan ilaçlar için küçük doz hesaplamalarının yapılması ve ilaç uygulamalarının fazla dikkat gerektirmesi riskleri daha da arttırmaktadır. Bu riskler açısından hemşirelerin komplikasyonları gözlemlemesi, kaydetmesi, gerekli önlemleri almaları için çok fazla bilgi birikimine sahip olmaları ve hızlı kararlar vermeleri beklenir. İlaç uygulamalarında yapay zeka uygulamalarının kullanımı hemşirelere kolaylık sağlayabilir. Pediatri hemşireliğinde yapay zeka uygulamalarının henüz sınırlı sayıda ve geliştirme aşamasında olması nedeniyle, yapay zeka tabanlı uygulamaların uygun şekilde kullanıldığında; çocuk sağlığını koruma, geliştirme ve tedavi etme süreçlerinde hemşirelik uygulamaları üzerinde olumlu etkileri olacağı varsayılmaktadır. Klinik iş akışını iyileştirebileceği ve dolayısıyla bakım kalitesini artırabileceği düşünülmektedir.

Destekleyen Kurum

Destekleyen kurum yoktur.

Kaynakça

  • Würtz GMF, Jensen CS, Egerod I. International Perspectives On The Pediatric Nurse Practitioner Role. Journal of the American Association of Nurse Practitioners. 2019; 31(12):773-781.
  • Yehene E, Goldzweig, G, Simana H, Brezner A. Mind the gap: Exploring pediatric nurses perceptions of the theory and practice of caring for children and families. Journal of Pediatric Nursing. 2022; 64: 84-94.
  • Requejo J, Strong K, Agweyu A, Billah SK. Measuring and monitoring child health and wellbeing: recommendations for tracking progress with a core set of indicators in the Sustainable Development Goals era. The Lancet Child Adolescent Health. 2022; 6(5): 345-352.
  • Lee DH, Yoon SN. Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges. International Journal of Environmental Research and Public Health. 2021; 18(1): 271.
  • Arslan G, Tokem Y, İşler Y. Hemşirelikte Sanal Gerçeklik Uygulamaları. Akıllı Sistemler ve Uygulamaları Dergisi. 2020; 3:122-129.
  • Liang HF, Wu KM, Weng CH, Hsieh HW. Nurses' Views On The Potential Use Of Robots İn The Pediatric Unit. Journal of Pediatric Nurse. 2019;47: 58-64.
  • Robert N. How Artificial İntelligence İs Changing Nursing. Nursing Management. 2019; 50 (9):30-39.
  • Bhbosale S, Pujari V, Multani Z. Advantages and Disadvantages of Artificial Intelligence. Aayushi International Interdisciplinary Research Journal. 2020; 77: 227–230.
  • Pepito JA, Locsin R. Can nurses remain relevant in a technologically advanced future?, International Journal of Nursing Sciences. 2019; 6(1): 106-110.
  • Akalın B, Veranyurt Ü. Sağlıkta Dijitalleşme ve Yapay Zekâ. SDÜ Sağlık Yönetimi Dergisi. 2020; 2: 131-141.
  • Şener LT, Bozkaya DN, Kıtır T. COVID-19 Sürecinde Yapay Zeka, Dijital Sağlık Tanı ve Tedavisindeki Gelişmeler: COVID-19 Sürecindeki Gelişmeler. Sağlık Bilimlerinde Yapay Zeka Dergisi. (Journal of Artificial Intelligence in Health Sciences).2022; 2(1): 13-20.
  • Alazzam MB, Tayyib N, Alshawwa, S Z, Ahmed MK Nursing care systematization with case-based reasoning and artificial intelligence. Journal of Healthcare Engineering, vol. 2022, Article ID 1959371, 9 pages, 2022.
  • Betriana F, Tanioka R, Gunawan J, Locsin RC. Healthcare robots and human generations: Consequences for nursing and healthcare. Collegian, 2022; 29(5): 767-773.
  • American Nurse Official Journal. Artificial İntellingence İn Nursing. Available from: https://myamericann u r s e . c o m / h o w - a r t i f i c i a l - i n t e l l i - gence-is-transforming-the-future-of-nursing [Accessed 15th April 2023].
  • Li Z, Moran P, Dong Q, Shaw RJ, Hauser K. Development of a tele-nursing mobile manipulator for remote care-giving in quarantine areas. IEEE International Conference on Robotics and Automation. 2017; 3581-3586.
  • Bakas T, Sampsel D, Israel J, Chamnikar A, Bodnarik B, Clark JG, et al. Using Telehealth To Optimize Healthy Independent Living For Older Adults: A Feasibility Study. Geriatric Nursing, 2018;39(5):566-573.
  • Gerich VH, Moen H, Blok LJ, Chu CH, DeForest H, Hobensack M. Et al. Artificial Intelligence- based technologies in nursing: A scoping literature review of the evidence. International journal of nursing studies. 2022; 127:104153.
  • Merih YD, Akdoğan E. Hemşirelikte Yapay Zekâ. In: 4th International Eurasian Conference on Biological and Chemical Sciences (EurasianBioChem 2021) 2021; p. 24-26.
  • Akgerman A, Yavuz E, Kavaslar İ, Güngör S. Yapay Zeka Ve Hemşirelik. Sağlık Bilimlerinde Yapay Zeka Dergisi. 2022; 2(1): 21-27.
  • Aslan F, Subaşı A. Hemşirelik Eğitimi ve Hemşirelik Süreci Perspektifinden Yapay Zeka Teknolojilerine Farklı Bir Bakış. Sağlık Bilimleri Üniversitesi Hemşirelik Dergisi, 2022; 4(3): 153-158.
  • Su J, Zhao S. An Interactive Nursing Knowledge System Based on Artificial Intelligence and Its Implications for Neonatal Care Management. Wireless Communications and Mobile Computing. 2022; Article ID 2992851, 8 pages 2022.
  • Choudhury A, Urena E. Artificial Intelligence İn Nıcu And Pıcu: A Need For Ecological Validity, Accountability, And Human Factors. Healthcare (Basel). 2022;10(5):952.
  • Yigit D, Acikgoz A. Evaluation of Comfort Behavior Levels of Newborn by Artificial Intelligence Techniques. J Perinat Neonatal Nurs. 2023; 28(9).
  • Yang RL, Yang YL, Wang T, Xu WZ, Yu G, Yang JB. et al. Establishment Of An Auxiliary Diagnosis System Of Newborn Screening For İnherited Metabolic Diseases Based On Artificial İntelligence Technology And A Clinical Trial. Zhonghua Er Ke Za Zhi Chinese Journal Of Pediatrics. 2021;59(4):286-293.
  • Thompson R, Christine L. Kuryla, Kim J, Golden C, Crocetti M. Use Of Telemedicine With An Artificial Intelligence-Enabled Stethoscope To Support Decision-Making And Reduce Inappropriate Use Of Echocardiography İn Children With Heart Murmurs. Pediatrics. 2021;147 (3): 989.
  • Shimizu N, Motomura M, Saito O, Ikeyama T. Affinity of health care providers for artificially intelligent robots at bedside in the pediatric intensive care unit. Chiba Med J. 2019; 95(2): 17-9.
  • Voss C, Schwartz J, Daniels J, Kline A, Haber N, Washington P, et al. Effect Of Wearable Digital Intervention For Improving Socialization İn Children With Autism Spectrum Disorder: A Randomized Clinical Trial. Jama Pediatr. 2019;173(5):446-454.
  • Ali S, Manaloor R, Ma K, Sivakumar M, Beran T, Scott SD, et al. A randomized trial of robot-based distraction to reduce children’s distress and pain during intravenous insertion in the emergency department. Canadian Journal of Emergency Medicine. 2021; 23: 85-93.
  • Jin M, Kim J. A Survey of Nurses' Need for Care Robots in Children's Hospitals: Combining Robot-Care, Game-Care, and Edu-Care. CIN: Computers, Informatics, Nursing 2020;38(7): 349-357.
  • Zhao Y, Hu J, Gu Y, Wan Y, Liu F, Ye C, et al. Development and Implementation of a Pediatric Nursing-Clinical Decision Support System for Hyperthermia: A Pre- and Post-test. Computers, informatics, nursing: CIN. 2021;40(2): 131–137.

Artificial Intelligence in Pediatric Nursing

Yıl 2024, , 36 - 43, 25.04.2024
https://doi.org/10.59398/ahd.1346089

Öz

Artificial intelligence is a sub-branch of computer science in which computer technologies demonstrate human-like behavior without any living organism. One of the areas of use of artificial intelligence, which is increasingly common today, is pediatric nursing. Artificial intelligence in pediatric patients first started in 1968, when Paycha developed the SHELP application, and the diversity of studies in the field has gradually increased. Voss et al. They developed a wearable device to “evaluate the effectiveness of Superpower Glass, an artificial intelligence-driven wearable behavioral intervention, to improve social outcomes of children with autism spectrum disorder.” In the article titled "Nurses' Views on the Potential Use of Robots in the Pediatrics Unit", it is emphasized that since robots will improve the quality of care, the time devoted to patient care and the patient will increase by reducing the workload of nurses. Every new study and invention that is revealed updates nursing care practices and reveals new care concepts. The slightest mistake in drug administration in pediatric patients may lead to irreversible risks. Small dose calculations for drugs prepared for children and the need for extreme caution in drug administration further increase the risks. In terms of these risks, nurses are expected to have a lot of knowledge and make quick decisions to observe and record complications, take the necessary precautions. The use of artificial intelligence applications in pharmaceutical applications can provide convenience to nurses. Since artificial intelligence applications in pediatric nursing are still limited in number and in the development stage, when artificial intelligence-based applications are used appropriately; It is assumed that it will have positive effects on nursing practices in the processes of protecting, developing and treating child health. It is thought that it may improve clinical workflow and therefore improve the quality of care.

Kaynakça

  • Würtz GMF, Jensen CS, Egerod I. International Perspectives On The Pediatric Nurse Practitioner Role. Journal of the American Association of Nurse Practitioners. 2019; 31(12):773-781.
  • Yehene E, Goldzweig, G, Simana H, Brezner A. Mind the gap: Exploring pediatric nurses perceptions of the theory and practice of caring for children and families. Journal of Pediatric Nursing. 2022; 64: 84-94.
  • Requejo J, Strong K, Agweyu A, Billah SK. Measuring and monitoring child health and wellbeing: recommendations for tracking progress with a core set of indicators in the Sustainable Development Goals era. The Lancet Child Adolescent Health. 2022; 6(5): 345-352.
  • Lee DH, Yoon SN. Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges. International Journal of Environmental Research and Public Health. 2021; 18(1): 271.
  • Arslan G, Tokem Y, İşler Y. Hemşirelikte Sanal Gerçeklik Uygulamaları. Akıllı Sistemler ve Uygulamaları Dergisi. 2020; 3:122-129.
  • Liang HF, Wu KM, Weng CH, Hsieh HW. Nurses' Views On The Potential Use Of Robots İn The Pediatric Unit. Journal of Pediatric Nurse. 2019;47: 58-64.
  • Robert N. How Artificial İntelligence İs Changing Nursing. Nursing Management. 2019; 50 (9):30-39.
  • Bhbosale S, Pujari V, Multani Z. Advantages and Disadvantages of Artificial Intelligence. Aayushi International Interdisciplinary Research Journal. 2020; 77: 227–230.
  • Pepito JA, Locsin R. Can nurses remain relevant in a technologically advanced future?, International Journal of Nursing Sciences. 2019; 6(1): 106-110.
  • Akalın B, Veranyurt Ü. Sağlıkta Dijitalleşme ve Yapay Zekâ. SDÜ Sağlık Yönetimi Dergisi. 2020; 2: 131-141.
  • Şener LT, Bozkaya DN, Kıtır T. COVID-19 Sürecinde Yapay Zeka, Dijital Sağlık Tanı ve Tedavisindeki Gelişmeler: COVID-19 Sürecindeki Gelişmeler. Sağlık Bilimlerinde Yapay Zeka Dergisi. (Journal of Artificial Intelligence in Health Sciences).2022; 2(1): 13-20.
  • Alazzam MB, Tayyib N, Alshawwa, S Z, Ahmed MK Nursing care systematization with case-based reasoning and artificial intelligence. Journal of Healthcare Engineering, vol. 2022, Article ID 1959371, 9 pages, 2022.
  • Betriana F, Tanioka R, Gunawan J, Locsin RC. Healthcare robots and human generations: Consequences for nursing and healthcare. Collegian, 2022; 29(5): 767-773.
  • American Nurse Official Journal. Artificial İntellingence İn Nursing. Available from: https://myamericann u r s e . c o m / h o w - a r t i f i c i a l - i n t e l l i - gence-is-transforming-the-future-of-nursing [Accessed 15th April 2023].
  • Li Z, Moran P, Dong Q, Shaw RJ, Hauser K. Development of a tele-nursing mobile manipulator for remote care-giving in quarantine areas. IEEE International Conference on Robotics and Automation. 2017; 3581-3586.
  • Bakas T, Sampsel D, Israel J, Chamnikar A, Bodnarik B, Clark JG, et al. Using Telehealth To Optimize Healthy Independent Living For Older Adults: A Feasibility Study. Geriatric Nursing, 2018;39(5):566-573.
  • Gerich VH, Moen H, Blok LJ, Chu CH, DeForest H, Hobensack M. Et al. Artificial Intelligence- based technologies in nursing: A scoping literature review of the evidence. International journal of nursing studies. 2022; 127:104153.
  • Merih YD, Akdoğan E. Hemşirelikte Yapay Zekâ. In: 4th International Eurasian Conference on Biological and Chemical Sciences (EurasianBioChem 2021) 2021; p. 24-26.
  • Akgerman A, Yavuz E, Kavaslar İ, Güngör S. Yapay Zeka Ve Hemşirelik. Sağlık Bilimlerinde Yapay Zeka Dergisi. 2022; 2(1): 21-27.
  • Aslan F, Subaşı A. Hemşirelik Eğitimi ve Hemşirelik Süreci Perspektifinden Yapay Zeka Teknolojilerine Farklı Bir Bakış. Sağlık Bilimleri Üniversitesi Hemşirelik Dergisi, 2022; 4(3): 153-158.
  • Su J, Zhao S. An Interactive Nursing Knowledge System Based on Artificial Intelligence and Its Implications for Neonatal Care Management. Wireless Communications and Mobile Computing. 2022; Article ID 2992851, 8 pages 2022.
  • Choudhury A, Urena E. Artificial Intelligence İn Nıcu And Pıcu: A Need For Ecological Validity, Accountability, And Human Factors. Healthcare (Basel). 2022;10(5):952.
  • Yigit D, Acikgoz A. Evaluation of Comfort Behavior Levels of Newborn by Artificial Intelligence Techniques. J Perinat Neonatal Nurs. 2023; 28(9).
  • Yang RL, Yang YL, Wang T, Xu WZ, Yu G, Yang JB. et al. Establishment Of An Auxiliary Diagnosis System Of Newborn Screening For İnherited Metabolic Diseases Based On Artificial İntelligence Technology And A Clinical Trial. Zhonghua Er Ke Za Zhi Chinese Journal Of Pediatrics. 2021;59(4):286-293.
  • Thompson R, Christine L. Kuryla, Kim J, Golden C, Crocetti M. Use Of Telemedicine With An Artificial Intelligence-Enabled Stethoscope To Support Decision-Making And Reduce Inappropriate Use Of Echocardiography İn Children With Heart Murmurs. Pediatrics. 2021;147 (3): 989.
  • Shimizu N, Motomura M, Saito O, Ikeyama T. Affinity of health care providers for artificially intelligent robots at bedside in the pediatric intensive care unit. Chiba Med J. 2019; 95(2): 17-9.
  • Voss C, Schwartz J, Daniels J, Kline A, Haber N, Washington P, et al. Effect Of Wearable Digital Intervention For Improving Socialization İn Children With Autism Spectrum Disorder: A Randomized Clinical Trial. Jama Pediatr. 2019;173(5):446-454.
  • Ali S, Manaloor R, Ma K, Sivakumar M, Beran T, Scott SD, et al. A randomized trial of robot-based distraction to reduce children’s distress and pain during intravenous insertion in the emergency department. Canadian Journal of Emergency Medicine. 2021; 23: 85-93.
  • Jin M, Kim J. A Survey of Nurses' Need for Care Robots in Children's Hospitals: Combining Robot-Care, Game-Care, and Edu-Care. CIN: Computers, Informatics, Nursing 2020;38(7): 349-357.
  • Zhao Y, Hu J, Gu Y, Wan Y, Liu F, Ye C, et al. Development and Implementation of a Pediatric Nursing-Clinical Decision Support System for Hyperthermia: A Pre- and Post-test. Computers, informatics, nursing: CIN. 2021;40(2): 131–137.
Toplam 30 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Çocuk Sağlığı ve Hastalıkları Hemşireliği
Bölüm Derlemeler
Yazarlar

Ayşe Sevim Ünal 0000-0003-1044-6164

Aydın Avcı 0000-0002-9226-2651

Yayımlanma Tarihi 25 Nisan 2024
Gönderilme Tarihi 19 Ağustos 2023
Yayımlandığı Sayı Yıl 2024

Kaynak Göster

Vancouver Ünal AS, Avcı A. Pediatri Hemşireliğinde Yapay Zeka. Akd Hemsirelik D. 2024;3(1):36-43.