Journal of Information Systems Engineering and Management

Journal of Information Systems Engineering and Management, 2024 - Volume 9 Issue 3, Article No: 25684
https://doi.org/10.55267/iadt.07.14926

Published Online: 26 Jul 2024

Views: 58 | Downloads: 28

The creation of Artificial Intelligence (AI) in healthcare has initiated exceptional modifications in service transport and affected person care. However, the specific effect and integration of AI within children's hospitals have no longer been drastically explored. Pediatric healthcare presents specific demanding situations and requires tailored AI applications to cope with its various needs. The goal of this study is to fill this gap by inspecting the role of AI in improving provider design in children's hospitals. It investigates how AI-pushed innovations can improve affected person consequences, streamline medical institution operations, and address the precise challenges of pediatric care. Utilizing a case examine technique, the study accrued qualitative insights from numerous stakeholders in deciding on main children's hospitals. The research concerned analyzing AI implementations across diagnostic approaches, remedy making plans, and patient engagement, in conjunction with evaluating the moral and practical implications. The findings reveal that AI drastically improves diagnostic accuracy and treatment efficacy, main to higher patient outcomes. Ethical issues, specifically regarding facts privations, emerged as crucial in AI adoption. The study underscores the want for comprehensive AI integration strategies which are sensitive to the precise requirements of pediatric sufferers. This research contributes to the literature by providing empirical information on AI's impact in a pediatric context, providing a unique AI-integrated service layout version. It gives authentic insights into the scalability and ethical integration of AI, underscoring the ability of AI to revolutionize pediatric healthcare transport.

AI-driven Service Design Ethical Considerations in AI Healthcare Operational Efficiency Patient Outcomes Pediatric Healthcare AI Integration

• Aifah, A., Okeke, N. L., Rentrope, C. R., Schexnayder, J., Bloomfield, G. S., Bosworth, H., . . . Vedanthan, R. (2020). Use of a human-centered design approach to adapt a nurse-led cardiovascular disease prevention intervention in HIV clinics. Progress in Cardiovascular Diseases, 63(2), 92-100.
• Almazroui, K. (2023). Learning as the best medicine: Proposal for SMART schooling for hospitalized children. Heliyon, 9(6), e16845.
• Ashinyo, M. E., Duti, V., Dubik, S. D., Amegah, K. E., & Alhassan, R. K. (2023). Experiences of postnatal mothers with quality of care including water, sanitation and hygiene amenities during the outbreak of COVID-19 in Ghana: An institutional cross-sectional study. Public Health in Practice, 5, 100361.
• Barnett, A., Savic, M., Pienaar, K., Carter, A., Warren, N., Sandral, E., . . . Lubman, D. I. (2021). Enacting ‘more-than-human’ care: Clients’ and counsellors’ views on the multiple affordances of chatbots in alcohol and other drug counselling. International Journal of Drug Policy, 94, 102910.
• Bertl, M., Ross, P., & Draheim, D. (2023). Systematic AI support for decision-making in the healthcare sector: Obstacles and success factors. Health Policy and Technology, 100748.
• Chandra, S., & Mohammadnezhad, M. (2020). Investigating factors influencing patient trust in a developing Pacific Island Country, Fiji, 2018. Heliyon, 6(12), e05680.
• de Marinis, R., Marigi, E. M., Atwan, Y., Yang, L., Oeding, J. F., Gupta, P., . . . Sperling, J. W. (2023). Current clinical applications of artificial intelligence in shoulder surgery: What the busy shoulder surgeon needs to know and what’s coming next. JSES Reviews, Reports, and Techniques. https://doi.org/10.1016/j.xrrt.2023.07.008
• Doo, F. X., Parekh, V. S., Kanhere, A., Savani, D., Tejani, A. S., Sapkota, A., & Yi, P. H. (2023). Evaluation of climate-aware metrics tools for radiology informatics and artificial intelligence: Towards a potential radiology eco-label. Journal of the American College of Radiology. https://doi.org/10.1016/j.jacr.2023.11.019
• Fawaz, P., Sayegh, P. El, & Vannet, B. Vande. (2023). What is the current state of artificial intelligence applications in dentistry and orthodontics?. Journal of Stomatology, Oral and Maxillofacial Surgery, 101524.
• Goktas, P., Karakaya, G., Kalyoncu, A. F., & Damadoglu, E. (2023). Artificial intelligence chatbots in allergy and immunology practice: Where have we been and where are we going?. The Journal of Allergy and Clinical Immunology: In Practice. https://doi.org/10.1016/j.jaip.2023.05.042
• Guo, H., Liang, H., Zhao, M., Xiao, Y., Wu, T., Xue, J., & Zhu, L. (2023). Privacy-preserving fine-grained redaction with policy fuzzy matching in blockchain-based mobile crowdsensing. Electronics, 12(16). https://doi.org/10.3390/ELECTRONICS12163416
• Haley, L. C., Boyd, A. K., Hebballi, N. B., Reynolds, E. W., Smith, K. G., Scully, P. T., . . . Li, L. T. (2024). Attitudes on artificial intelligence use in pediatric care from parents of hospitalized children. Journal of Surgical Research, 295, 158-167.
• Jadczyk, T., Wojakowski, W., Tendera, M., Henry, T. D., Egnaczyk, G., & Shreenivas, S. (2021). Artificial intelligence can improve patient management at the time of a pandemic: The role of voice technology. Journal of Medical Internet Research, 23(5). https://doi.org/10.2196/22959
• Johnson, K. B., Wei, W., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., . . . Snowdon, J. L. (2021). Precision Medicine, AI, and the Future of Personalized Health Care, 86-93.
• Kong, L. (2021). A study on the AI-based online triage model for hospitals in sustainable smart city. Future Generation Computer Systems, 125, 59-70.
• Kumar, A., Nanthaamornphong, A., Selvi, R., Venkatesh, J., Alsharif, M. H., Uthansakul, P., & Uthansakul, M. (2023). Evaluation of 5G techniques affecting the deployment of smart hospital infrastructure: Understanding 5G, AI and IoT role in smart hospital. Alexandria Engineering Journal, 83, 335-354.
• Li, L. T., Haley, L. C., Boyd, A. K., & Bernstam, E. V. (2023). Technical/Algorithm, Stakeholder, and Society (TASS) barriers to the application of artificial intelligence in medicine: A systematic review. Journal of Biomedical Informatics, 147, 104531.
• Li, X., Zhang, S., Luo, X., Gao, G., Luo, X., Wang, S., . . . Wu, N. (2023). Accuracy and efficiency of an artificial intelligence-based pulmonary broncho-vascular three-dimensional reconstruction system supporting thoracic surgery: Retrospective and prospective validation study. EBioMedicine, 87, 104422.
• Liu, X., He, X., Wang, M., & Shen, H. (2022). What influences patients’ continuance intention to use AI-powered service robots at hospitals?. The role of individual characteristics. Technology in Society, 70, 101996.
• Moser, E. C., & Narayan, G. (2020). Improving breast cancer care coordination and symptom management by using AI driven predictive toolkits. The Breast, 50, 25-29.
• Nakayama, L. F., Ribeiro, L. Z., Dychiao, R. G., Zamora, Y. F., Regatieri, C. V. S., Celi, L. A., . . . Belfort, R. (2023). Artificial intelligence in uveitis: A comprehensive review. Survey of Ophthalmology, 68(4), 669-677.
• Niecikowski, A., Gupta, S., Suarez, G., Kim, J., Chen, H., Guo, F., . . . Deng, J. (2022). A multi-modal deep learning-based decision support system for individualized radiotherapy of non-small cell lung cancer. International Journal of Radiation Oncology, Biology, Physics, 114(3), e100-e101.
• Ordu, M., Demir, E., Tofallis, C., & Gunal, M. M. (2023). A comprehensive and integrated hospital decision support system for efficient and effective healthcare services delivery using discrete event simulation. Healthcare Analytics, 4, 100248.
• Öztürk Şahin, Ö., Aközlü, Z., & Taşdelen, Y. (2023). Pediatric nursing students’ self-efficacy regarding medication administration and clinical comfort and worry: A pre-posttest comparative study of nurse mentoring versus peer mentoring. Nurse Education in Practice, 71, 103712.
• Panton, J., Beaulieu-Jones, B. R., Marwaha, J. S., Woods, A. P., Nakikj, D., Gehlenborg, N., & Brat, G. A. (2023). How surgeons use risk calculators and non-clinical factors for informed consent and shared decision making: A qualitative study. The American Journal of Surgery. https://doi.org/10.1016/j.amjsurg.2023.07.017
• Parker, C., Kellaway, J., & Stockton, K. (2020). Analysis of falls within paediatric hospital and community healthcare settings. Journal of Pediatric Nursing, 50, 31-36.
• Pepito, J. A., & Locsin, R. (2019). Can nurses remain relevant in a technologically advanced future?. International Journal of Nursing Sciences, 6(1), 106-110.
• Pham, P., Zhang, H., Gao, W., & Zhu, X. (2024). Determinants and performance outcomes of artificial intelligence adoption: Evidence from U.S. Hospitals. Journal of Business Research, 172, 114402.
• Rabie, S., Laurenzi, C. A., Field, S., Skeen, S., & Honikman, S. (2022). A mixed-methods feasibility study of Nyamekela4Care: An intervention to support improved quality of care among service providers in low-resource settings. SSM-Mental Health, 2, 100154.
• Roosan, D., Padua, P., Khan, R., Khan, H., Verzosa, C., & Wu, Y. (2023). Effectiveness of ChatGPT in clinical pharmacy and the Role of Artificial Intelligence in medication therapy management. Journal of the American Pharmacists Association. https://doi.org/10.1016/j.japh.2023.11.023
• Shepherd, D. A., & Majchrzak, A. (2022). Machines augmenting entrepreneurs: Opportunities (and threats) at the Nexus of artificial intelligence and entrepreneurship. Journal of Business Venturing, 37(4), 106227.
• Taj, M., Brenner, M., Sulaiman, Z., & Pandian, V. (2022). Sepsis protocols to reduce mortality in resource-restricted settings: A systematic review. Intensive and Critical Care Nursing, 72, 103255.
• Tran, Z., Byun, J., Lee, H. Y., Boggs, H., Tomihama, E. Y., & Kiang, S. C. (2023). Bias in Artificial Intelligence in Vascular Surgery. Seminars in Vascular Surgery. https://doi.org/10.1053/j.semvascsurg.2023.07.003
• Vidal, D. E., Loufek, B., Kim, Y. H., & Vidal, N. Y. (2023). Navigating US regulation of artificial intelligence in medicine—A primer for physicians. Mayo Clinic Proceedings: Digital Health, 1(1), 31-39.
• Weerakoon, B. S., & Chandrasiri, N. R. (2023). Knowledge and utilisation of information and communication technology among radiographers in a lower-middle-income country. Radiography, 29(1), 227-233.
• Yin, Robert K. (2014). Case study research: Design and methods. Los Angeles, CA: Sage.
• Zhong, B. L., Xu, Y. M., & Li, Y. (2022). Prevalence and unmet need for mental healthcare of major depressive disorder in community-dwelling Chinese people living with vision disability. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.900425

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