AI mindset transformation needs assessment for early warning system integration among emergency nurses
DOI: https://doi.org/10.33024/min.v9i7.4327
Artificial Intelligence Emergency Nurse Early Warning System Hypovolemic Shock Scientific Clinical Culture
Abstract
Background: The increasing complexity of emergency care and the persistent challenges in early hypovolemic shock detection highlight the need for innovative approaches to support clinical decision-making. The integration of artificial intelligence (AI) and Early Warning Systems (EWS) requires not only technological readiness but also transformation in nurses’ mindset, knowledge, and clinical culture.
Purpose: To assess emergency nurses’ needs for AI mindset transformation through EWS integration and explore the role of scientific clinical culture in supporting future AI implementation in emergency nursing practice.
Method: An exploratory mixed-methods design with a convergent approach involving 164 emergency nurses from hospital emergency departments. Quantitative data were collected using a structured questionnaire assessing perceptions of AI, EWS utilization, scientific clinical culture, and professional development needs. Qualitative data were obtained through open-ended questions. Quantitative data were analyzed using descriptive statistics, while qualitative data were analyzed using thematic analysis. The findings from both approaches were integrated to provide a comprehensive interpretation.
Results: The findings indicated substantial needs for AI mindset transformation among emergency nurses was categorized as high. The highest priority needs included strengthening collaboration among nurses, researchers, and hospital management; improving EWS training; increasing nurse participation in innovation development; and implementing practice-based learning. Participants demonstrated positive attitudes toward AI and acknowledged its potential to support clinical decision-making and emergency response. However, gaps remained in AI literacy, practical experience, and decision-support competencies. Qualitative findings revealed challenges in early hypovolemic shock detection, the need for continuous competency development, and the importance of collaborative innovation. The integrated findings identified scientific clinical culture, EWS integration, professional development, and stakeholder collaboration as essential components for AI readiness among emergency nurses.
Conclusion: This study provides empirical evidence for the development of a Living Lab-based AI-enhanced EWS model to strengthen emergency nursing practice and support early hypovolemic shock detection.
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Ahn, B., Mupere, E., Irarrázaval, B., Opoka, R. O., Aanyu-Tukamuhebwa, H., Bills, C. B., & Glomb, N. W. (2021). Qualitative needs assessment for paediatric emergency care in Kampala, Uganda. African Journal of Emergency Medicine, 11(2), 277-282.
Antonacci, G., Benevento, E., Bonavitacola, S., Cannavacciuolo, L., Foglia, E., Fusi, G., & Stefanini, A. (2023). Healthcare professional and manager perceptions on drivers, benefits, and challenges of telemedicine: results from a cross-sectional survey in the Italian NHS. BMC health services research, 23(1), 1115.
Azimirad, M., Magnusson, C., Wiseman, A., Selander, T., Parviainen, I., & Turunen, H. (2022). Identifying teamwork‐related needs of the medical emergency team: Nurses' perspectives. Nursing in critical care, 27(6), 804-814.
Balkrishna, A., Sharma, N., Srivastava, D., Kukreti, A., Srivastava, S., & Arya, V. (2024). Exploring the safety, efficacy, and bioactivity of herbal medicines: bridging traditional wisdom and modern science in healthcare. Future Integrative Medicine, 3(1), 35-49.
Ben Ida, I., Balti, M., Chaabane, S., & Jemai, A. (2021). Adaptative vital signs monitoring system based on the early warning score approach in smart hospital context. IET Smart Cities, 3(1), 16-28.
Bonder, B., Martin, L., & Papadakis, A. (2024). Culture in clinical care: Strategies for competence. Taylor & Francis.
Day, B. A. (2003). Early warning system scores and response times: an audit. Nursing in critical care, 8(4), 156.
El Khatib, M., Hamidi, S., Al Ameeri, I., Al Zaabi, H., & Al Marqab, R. (2022). Digital disruption and big data in healthcare-opportunities and challenges. ClinicoEconomics and Outcomes Research, 563-574.
Fotis, T., Kioskli, K., Sundaralingam, A., Fasihi, A., & Mouratidis, H. (2023). Co-creation in a digital health living lab: a case study. Frontiers in public health, 10, 892930.
Fox, A., & Elliott, N. (2015). Early warning scores: a sign of deterioration in patients and systems. Nursing Management, 22(1).
Gangl, C., & Krychtiuk, K. (2023). Digital health—high tech or high touch?. Wiener Medizinische Wochenschrift, 173(5), 115-124.
Gillett, G. (2006). Medical science, culture, and truth. Philosophy, Ethics, and Humanities in Medicine, 1(1), 13.
Giovanelli, A., Rowe, J., Taylor, M., Berna, M., Tebb, K. P., Penilla, C., & Ozer, E. M. (2023). Supporting adolescent engagement with artificial intelligence–driven digital health behavior change interventions. Journal of medical Internet research, 25, e40306.
Günther, C., Grieshaber, C., Helmer, D., & Röder, A. (2024). [論文摘要] Healthcare-platform-Inclusive working and living. In Conference of the International Society for Gerontechnology (pp. 1-1). 元智大學老人福祉科技研究中心.
Hou, J.-S., Hsu, B.-G., & Subeq, Y.-M. (2020). Early warning systems: Introduction and application. Journal of Nursing, 67(1), 6–11.
Jeon, J., & Park, S. (2021). An exploratory study to develop a virtual reality based simulation training program for hypovolemic shock nursing care: a qualitative study using focus group interview. In Healthcare (Vol. 9, No. 4, p. 417). MDPI.
Joore, P., Overdiek, A., Smeenk, W., & van Turnhout, K. (Eds.). (2024). Applied Design Research in Living Labs and Other Experimental Learning and Innovation Environments. CRC Press.
Kemp, E., Trigg, J., Beatty, L., Christensen, C., Dhillon, H. M., Maeder, A., & Koczwara, B. (2021). Health literacy, digital health literacy and the implementation of digital health technologies in cancer care: the need for a strategic approach. Health Promotion Journal of Australia, 32, 104-114.
Kim, Y., & Lee, W. (2018). Hypovolemic shock caused by delayed-onset superior gluteal artery rupture, successfully treated with arteriographic embolization. Acta Chirurgica Belgica, 118(6), 380-383.
König, L., & Suhr, R. (2023). The effectiveness of publicly available web-based interventions in promoting health app use, digital health literacy, and media literacy: pre-post evaluation study. Journal of Medical Internet Research, 25, e46336.
Kyriacos, U., Jelsma, J., & Jordan, S. (2011). Monitoring vital signs using early warning scoring systems: a review of the literature. Journal of nursing management, 19(3), 311-330.
Landesberg, G., Shamir, M., & Avidan, A. (2015). Early noninvasive detection of hypovolemia in trauma patients—are we there Yet?. Critical Care Medicine, 43(4), 907-908.
Lashen, H., St John, T. L., Almallah, Y. Z., Sasidhar, M., & Shamout, F. E. (2023). Machine learning models versus the national early warning score system for predicting deterioration: retrospective cohort study in the United Arab Emirates. JMIR AI, 2, e45257.
Marchezini, V., Trajber, R., Olivato, D., Munoz, V. A., de Oliveira Pereira, F., & Oliveira Luz, A. E. (2017). Participatory early warning systems: Youth, citizen science, and intergenerational dialogues on disaster risk reduction in Brazil. International Journal of Disaster Risk Science, 8(4), 390-401.
Mass-Hernández, L. M., Acevedo-Aguilar, L. M., Lozada-Martínez, I. D., Osorio-Agudelo, L. S., Maya-Betancourth, J. G. E. M., Paz-Echeverry, O. A., & Rahman, S. (2022). Undergraduate research in medicine: A summary of the evidence on problems, solutions and outcomes. Annals of Medicine and Surgery, 74, 103280.
Melhem, S. J., Nabhani-Gebara, S., & Kayyali, R. (2023). Digital trends, digital literacy, and e-health engagement predictors of breast and colorectal cancer survivors: a population-based cross-sectional survey. International Journal of Environmental Research and Public Health, 20(2), 1472.
Merino-Barbancho, B., Abril Jiménez, P., Mallo, I., Lombroni, I., Cea, G., López Nebreda, C., & Arredondo, M. T. (2023). Innovation through the Quintuple Helix in living labs: lessons learned for a transformation from lab to ecosystem. Frontiers in public health, 11, 1176598.
Münter, L., Drachmann, D., Ghanem, M., Prinzellner, Y., Smits, C., Werner, K., & Faber, N. H. (2023). Transforming health systems with design health literacy: Presenting the 40-20-40 model for digital development. Computer Methods and Programs in Biomedicine Update, 4, 100122.
Muralitharan, S., Nelson, W., Di, S., McGillion, M., Devereaux, P. J., Barr, N. G., & Petch, J. (2021). Machine learning–based early warning systems for clinical deterioration: systematic scoping review. Journal of medical Internet research, 23(2), e25187.
Nie, G., Zhao, Q., Tang, G., Li, Y., & Hong, S. (2025). Artificial intelligence-derived photoplethysmography age as a digital biomarker for cardiovascular health. Communications Medicine, 5(1), 481.
Rahman Jabin, M. S., & Hammar, T. (2022). Issues with the Swedish e-prescribing system–an analysis of health information technology-related incident reports using an existing classification system. Digital Health, 8, 20552076221131139.
Ren Kon, M. W., Abisheganaden, J., & Ang, G. Y. (2026). Adoption of artificial intelligence in healthcare: A strategic transformation framework. Hong Kong Journal of Emergency Medicine, 33(1), e70077.
Rowland, S. P., Fitzgerald, J. E., Lungren, M., Lee, E., Harned, Z., & McGregor, A. H. (2022). Digital health technology-specific risks for medical malpractice liability. NPJ digital medicine, 5(1), 157.
Sahu, M. L., Atulkar, M., Ahirwal, M. K., & Ahamad, A. (2024). Internet-of-Things-Enabled Early Warning Score System for Patient Monitoring. IETE Journal of Research, 70(1), 425-436.
Sangiorgi, L., Boarini, M., Westerheim, I., Skarberg, R. T., Clancy, J., Wang, V., & Mordenti, M. (2024). Project SATURN–a real-world evidence data collaboration with existing European datasets in Osteogenesis Imperfecta to support future therapies. Orphanet Journal of Rare Diseases, 19(1), 184.
Sarbadhikari, S. N., & Pradhan, K. B. (2020). The need for developing technology-enabled, safe, and ethical workforce for healthcare delivery. Safety and Health at Work, 11(4), 533-536.
Sari, F. I., Purwaningsih, P., & Qur’aniati, N. (2024). Strategies to Improve Emergency Nurses' Response Time Through Training Programs: Strategi untuk Meningkatkan Waktu Tanggap Perawat Gawat Darurat Melalui Program Pelatihan. Indonesian Journal of Innovation Studies, 25(4), 10-21070.
Sriharan, A., Sekercioglu, N., Mitchell, C., Senkaiahliyan, S., Hertelendy, A., Porter, T., & Banaszak-Holl, J. (2024). Leadership for AI transformation in health care organization: scoping review. Journal of Medical Internet Research, 26, e54556.
Suresh, M. R. (2022). The early detection of hypovolemic shock and shifting the focus to compensation. Journal of Intensive Care Medicine, 37(12), 1673-1675.
Veldhuis, L. I., & Nanayakkara, P. W. (2021). Early warning score to detect deterioration in the hospital: Role of staffing and alarm fatigue. Neth J Crit Care, 29, 3.
Wachter, R. (2026). A Giant Leap: How AI Is Transforming Healthcare and What That Means for Our Future. Penguin Group.
Weile, J., Nebsbjerg, M. A., Ovesen, S. H., Paltved, C., & Ingeman, M. L. (2021). Simulation-based team training in time-critical clinical presentations in emergency medicine and critical care: a review of the literature. Advances in Simulation, 6(1), 3.
Wiedermann, C. J., Mahlknecht, A., Piccoliori, G., & Engl, A. (2023). Redesigning primary care: the emergence of artificial-intelligence-driven symptom diagnostic tools. Journal of Personalized Medicine, 13(9), 1379.
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