護理教育的未來—學習人工智慧之必要元素與實施策略

The Future of Nursing Education: Necessary Elements and Implementation Strategies for Learning Artificial Intelligence

10.6224/JN.202404_71(2).05

林承霈(Cheng-Pei LIN)；陳俞琪(Yu-Chi CHEN)；陳律言(Lu-Yen Anny CHEN)

人工智慧 ； 護理教育 ； 機器學習 ； artificial intelligence (AI) ； nursing education ； machine learning

護理雜誌

71卷2期（2024 / 04 / 01）

26 - 33

繁體中文;英文

隨著人口老化平均餘命的延長，多重共病與照護複雜度，不僅增加醫療負載，更對照護體系帶來沉重的負擔；資源不足所面臨嚴重挑戰是亟待克服的難題。人工智慧（artificial intelligence, AI）包括調查、整合、學習、預測和決策等功能，透過AI在臨床照護中的應用，不僅改善工作流程提高效率，也提升照護品質與降低人力需求。雖然AI的應用在健康照護實務中日益蓬勃，智慧健康科技導入更是醫療健康衛生政策的趨勢，但醫護教育領域對於AI的運用與訓練相對不足。當前護理教育必須積極面對AI世代的來臨，培養具備理解與應用AI能力的護理師，將AI知能的培訓整合入醫護課程和臨床實習，讓第一線護理人員能善用AI技術，創造高品質、高效能且安全的照護。因此，本文彙整文獻常見的人工智慧模式、護理人工智慧教育所應培訓之六大核心能力，以及相對應學習與實踐之面向，提供護理教育與實務在職培訓之參考。期能在醫療照護困境之際，護理人員能成為創新改革照護體系的尖兵，成為引領照護轉型的先驅。

As populations age, average life expectancy increases and the complexity of diseases rises, leading to nursing care and healthcare systems facing severe challenges related to inadequate resources. Artificial intelligence (AI), including elements such as investigation, integration, learning, prediction, and decision-making, holds significant potential for application in clinical care not only to enhance care quality but also to help guide the future direction of healthcare. AI applications are already being increasingly utilized to improve the quality of clinical care and to streamline workflows. However, because nursing education has lagged behind in terms of adopting AI, greater attention must be given to training up nursing students with AI-related knowledge and application skills. AI technologies should be integrated into nursing curricula and clinical internships to adapt to the rapidly changing high-tech healthcare environment, enabling the more-effective use of AI technology in providing high-quality and safe nursing care.

1. 黃筑榆、杜清敏、鄭夙芬（2021）．護理教育省思－人工智慧時代護理專業之準備與因應．護理雜誌，68（6），25–31。[Huang, C.-Y., Duh, C.-M., & Cheng, S.-F. (2021). A reflection on nursing education: Assuring the readiness of the nursing profession for the age of artificial intelligence. The Journal of Nursing, 68(6), 25–31.] https://doi.org/10.6224/JN.202112_68(6).05
   連結：
2. Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthcare Journal, 8(2), e188–e194. https://doi.org/10.7861/fhj.2021-0095
   連結：
3. Barakat-Johnson, M., Jones, A., Burger, M., Leong, T., Frotjold, A., Randall, S., Kim, B., Fethney, J., & Coyer, F. (2022). Reshaping wound care: Evaluation of an artificial intelligence app to improve wound assessment and management amid the COVID‐19 pandemic. International Wound Journal, 19(6), 1561–1577. https://doi.org/10.1111/iwj.13755
   連結：
4. 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. https://doi.org/10.1097/NNR.0000000000000315
   連結：
5. 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), Article e23933. https://doi.org/10.2196/23933
   連結：
6. Chen, L.-Y. A., & Fawcett, T. N. (2016). Using data mining strategies in clinical decision making: A literature review. CIN: Computers, Informatics, Nursing, 34(10), 448–454. https://doi.org/10.1097/CIN.0000000000000282
   連結：
7. Chen, Y., Moreira, P., Liu, W.-W., Monachino, M., Nguyen, T. L. H., & Wang, A. (2022). Is there a gap between artificial intelligence applications and priorities in health care and nursing management? Journal of Nursing Management, 30(8), 3736–3742. https://doi.org/10.1111/jonm.13851
   連結：
8. Clancy, T. R. (2020). Artificial intelligence and nursing: The future is now. JONA: The Journal of Nursing Administration, 50(3), 125–127. https://doi.org/10.1097/NNA.0000000000000855
   連結：
9. Howell, R. S., Liu, H. H., Khan, A. A., Woods, J. S., Lin, L. J., Saxena, M., Saxena, H., Castellano, M., Petrone, P., Slone, E., Chiu, E. S., Gillette, B. M., & Gorenstein, S. A. (2021). Development of a method for clinical evaluation of artificial intelligence–Based digital wound assessment tools. JAMA Network Open, 4(5), Article e217234. https://doi.org/10.1001/jamanetworkopen.2021.7234
   連結：
10. Huang, K., Jiao, Z., Cai, Y., & Zhong, Z. (2022). Artificial intelligence‐based intelligent surveillance for reducing nurses’ working hours in nurse–patient interaction: A two‐wave study. Journal of Nursing Management, 30(8), 3817–3826. https://doi.org/10.1111/jonm.13787
    連結：
11. Jiang, T., Gradus, J. L., & Rosellini, A. J. (2020). Supervised machine learning: A brief primer. Behavior Therapy, 51(5), 675–687. https://doi.org/10.1016/j.beth.2020.05.002
    連結：
12. Jones, O. T., Matin, R. N., van der Schaar, M., Prathivadi Bhayankaram, K., Ranmuthu, C. K. I., Islam, M. S., Behiyat, D., Boscott, R., Calanzani, N., Emery, J., Williams, H. C., & Walter, F. M. (2022). Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: A systematic review. The Lancet. Digital Health, 4(6), e466–e476. https://doi.org/10.1016/S2589-7500(22)00023-1
    連結：
13. Koleck, T. A., Dreisbach, C., Bourne, P. E., & Bakken, S. (2019). Natural language processing of symptoms documented in free-text narratives of electronic health records: A systematic review. Journal of the American Medical Informatics Association, 26(4), 364–379. https://doi.org/10.1093/jamia/ocy173
    連結：
14. Lauritsen, S. M., Kristensen, M., Olsen, M. V., Larsen, M. S., Lauritsen, K. M., Jørgensen, M. J., Lange, J., & Thiesson, B. (2020). Explainable artificial intelligence model to predict acute critical illness from electronic health records. Nature Communications, 11(1), Article 3852. https://doi.org/10.1038/s41467-020-17431-x
    連結：
15. Li, N., Shepperd, M., & Guo, Y. (2020). A systematic review of unsupervised learning techniques for software defect prediction. Information and Software Technology, 122, Article 106287. https://doi.org/10.1016/j.infsof.2020.106287
    連結：
16. Li, X., Jiang, M. Y.-C., Jong, M. S.-Y., Zhang, X., & Chai, C.-S. (2022). Understanding medical students’ perceptions of and behavioral intentions toward learning artificial intelligence: A survey study. International Journal of Environmental Research and Public Health, 19(14), Article 8733. https://doi.org/10.3390/ijerph19148733
    連結：
17. Liaw, W., Kueper, J. K., Lin, S., Bazemore, A., & Kakadiaris, I. (2022). Competencies for the use of artificial intelligence in primary care. Annals of Family Medicine, 20(6), 559–563. https://doi.org/10.1370/afm.2887
    連結：
18. Ng, Z. Q. P., Ling, L. Y. J., Chew, H. S. J., & Lau, Y. (2022). The role of artificial intelligence in enhancing clinical nursing care: A scoping review. Journal of Nursing Management, 30(8), 3654–3674. https://doi.org/10.1111/jonm.13425
    連結：
19. O’Connor, S., Peltonen, L.-M., Topaz, M., Chen, L.-Y. A., Michalowski, M., Ronquillo, C., Stiglic, G., Chu, C. H., Hui, V., & Denis-Lalonde, D. (2024). Prompt engineering when using generative AI in nursing education. Nurse Education in Practice, 74, Article 103825. https://doi.org/10.1016/j.nepr.2023.103825
    連結：
20. O’Connor, S., Yan, Y., Thilo, F. J. S., Felzmann, H., Dowding, D., & Lee, J. J. (2023). Artificial intelligence in nursing and midwifery: A systematic review. Journal of Clinical Nursing, 32(13-14), 2951–2968. https://doi.org/10.1111/jocn.16478
    連結：
21. Sarker, S., Jamal, L., Ahmed, S. F., & Irtisam, N. (2021). Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review. Robotics and Autonomous Systems, 146, Article 103902. https://doi.org/10.1016/j.robot.2021.103902
    連結：
22. Seibert, K., Domhoff, D., Bruch, D., Schulte-Althoff, M., Fürstenau, D., Biessmann, F., & Wolf-Ostermann, K. (2021). Application scenarios for artificial intelligence in nursing care: Rapid review. Journal of Medical Internet Research, 23(11), Article e26522. https://doi.org/10.2196/26522
    連結：
23. Sun, T. Q., & Medaglia, R. (2019). Mapping the challenges of artificial intelligence in the public sector: Evidence from public healthcare. Government Information Quarterly, 36(2), 368–383. https://doi.org/10.1016/j.giq.2018.09.008
    連結：
24. The Lancet Regional Health – Europe. (2023). Embracing generative AI in health care. The Lancet Regional Health – Europe, 30, Article 100677. https://doi.org/10.1016/j.lanepe.2023.100677
    連結：
25. Wang, X., Lin, X., & Dang, X. (2020). Supervised learning in spiking neural networks: A review of algorithms and evaluations. Neural Networks, 125, 258–280. https://doi.org/10.1016/j.neunet.2020.02.011
    連結：
26. Acar, O. A. (2023, June 15). Are your students ready for AI? Harvard Business Publishing Education. https://hbsp.harvard.edu/inspiring-minds/are-your-students-ready-for-ai
27. Kasula, B. Y. (2023). AI applications in healthcare a comprehensive review of advancements and challenges. International Journal of Managment Education for Sustainable Development, 6(6). https://ijsdcs.com/index.php/IJMESD/article/view/400
28. Nova, K. (2023). Generative AI in healthcare: Advancements in electronic health records, facilitating medical languages, and personalized patient care. Journal of Advanced Analytics in Healthcare Management, 7(1), 115–131.