Integrating Big Data and Artificial Intelligence in Nursing Management of Immune-Mediated Diseases: Associations with Clinical Decision-Making and Patient Monitoring.
Abstract
Introduction. Immune-mediated diseases (IMDs) require individualized nursing management because of heterogeneous clinical manifestations and complex biological mechanisms. This study examined associations between Big Data and Artificial Intelligence (AI)-supported nursing management, clinical outcomes, patient monitoring, clinical decision-making, and nurse usability. Methodology. A multicenter cross-sectional study was conducted in five public referral hospitals in West Sulawesi, Indonesia, from February to December 2024. Participants included 300 adults with rheumatoid arthritis, systemic lupus erythematosus, or Crohn’s disease and 55 registered nurses. An AI-enabled decision-support platform integrated electronic health records, laboratory data, wearable-device metrics, and molecular biomarkers. Clinical outcomes and nurse perceptions were assessed using standardized measures and the Technology Acceptance Model. Results. AI-supported nursing management was associated with shorter symptom-resolution time (8.0 ± 2.1 vs. 13.0 ± 3.5 days; p < 0.001) and lower hospital readmission (25.3% vs. 34.7%; p < 0.001). The AI model demonstrated 91.2% predictive accuracy for disease-flare risk. Among nurses, 89.1% rated the system highly useful, 85.5% found it easy to operate, 83.6% reported strong decision-making support, and 90.9% intended to continue using it. Discussion. Integrating clinical, molecular, and wearable data through AI may strengthen symptom monitoring, early recognition, and evidence-informed nursing decisions. High nurse acceptance indicates favorable usability. However, organizational readiness, digital literacy, interoperability, and ethical governance remain important. Conclusions. AI-supported nursing management was associated with favorable clinical and professional outcomes. AI may support precision nursing when complementing professional judgment. Longitudinal multicenter studies are needed to confirm clinical effectiveness.
References
1. Tsokos GC. Systemic lupus erythematosus. N Engl J Med [Internet]. 2011;365(22):2110–2121. doi: https://doi.org/10.1056/nejmra1100359
2. Smolen JS, Aletaha D, McInnes IB. Rheumatoid arthritis. Lancet [Internet]. 2016;388(10055):2023–2038. doi: https://doi.org/10.1016/s0140-6736(16)30173-8
3. Rieke N, Hancox J, Li W, Milletari F, Roth HR, Albarqouni S, et al. The future of digital health with federated learning. NPJ Digit Med [Internet]. 2020;3:119. doi: https://doi.org/10.1038/s41746-020-00323-1
4. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med [Internet]. 2019;25(1):44-56. doi: https://doi.org/10.1038/s41591-018-0300-7
5. Kitano H. Systems biology: a brief overview. Science [Internet]. 2002;295(5560):1662-1664. doi: https://doi.org/10.1126/science.1069492
6. Zhernakova DV, Deelen P, Vermaat M, van Iterson M, van Galen M, Arindrarto W, et al. Identification of context-dependent expression quantitative trait loci in whole blood. Nat Genet 2017;49(1):139–145. doi: https://doi.org/10.1038/ng.3737
7. Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nat Med [Internet]. 2019;25(1):24-29. doi: https://doi.org/10.1038/s41591-018-0316-z
8. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med [Internet]. 2019;380(14):1347-1358. doi: https://doi.org/10.1056/nejmra1814259
9. Miotto R, Li L, Kidd BA, Dudley JT. Deep Patient: An unsupervised representation to predict the future of patients from electronic health records. Sci Rep [Internet]. 2016;6:26094. doi: https://doi.org/10.1038/srep26094
10. Ronquillo CE, Peltonen LM, Pruinelli L, Chu CH, Bakken S, Beduschi A, et al. Artificial intelligence in nursing: priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative. J Adv Nurs [Internet]. 2021;77(9):3707-3717. doi: https://doi.org/10.1111/jan.14855
11. Buchanan C, Howitt ML, Wilson R, Booth RG, Risling T, Bamford M. Predicted influences of artificial intelligence on nursing education: scoping review. JMIR Nurs [Internet]. 2021;4(1):e23933. doi: https://doi.org/10.2196/23933
12. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol [Internet]. 2017;2(4):230-243. doi: https://doi.org/10.1136/svn-2017-000101
13. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J [Internet]. 2019;6(2):94-98. doi: https://doi.org/10.7861/futurehosp.6-2-94
14. Booth RG, Strudwick G, McBride S, O'Connor S, Solano-López AL. How the nursing profession should adapt for a digital future. BMJ [Internet]. 2021;373:n1190. doi: https://doi.org/10.1136/bmj.n1190
15. Paprica PA, Nunes-de Melo M, Schull MJ. Social license and the general public's attitudes toward research based on linked administrative health data: a qualitative study. CMAJ Open [Internet]. 2019;7(1):E40–E46. doi: https://doi.org/10.9778/cmajo.20180099
16. Topol, E.J. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25, 44–56 (2019). https://doi.org/10.1038/s41591-018-0300-7
17. Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng [Internet]. 2018;2(10):719-731. doi: https://doi.org/10.1038/s41551-018-0305-z
18. Meskó B, Drobni Z, Bényei É, Gergely B, Győrffy Z. Digital health is a cultural transformation of traditional healthcare. Mhealth [Internet]. 2017;3:38. doi: https://doi.org/10.21037/mhealth.2017.08.07
19. Pant Pai N, Abbasgholizadeh Rahimi S, Tulsi J, Bartlett S, Grover S, Sejdic E. Envisioning digital health ecosystem transformation in Canada «a conceptual foundation en Neuf Etapes.». FACETS. [Internet]. 2026;11:1-17. doi: https://doi.org/10.1139/facets-2025-0142
20. Dilsizian SE, Siegel EL. Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Curr Cardiol Rep [Internet]. 2014;16(1):441. doi: https://doi.org/10.1007/s11886-013-0441-8
21. Kaplan B. How should health data be used? Camb Q Healthc Ethics [Internet]. 2016;25(2):312-329. doi: https://doi.org/10.1017/s0963180115000614
22. van der Schaar M, Alaa AM, Floto A, Gimson A, Scholtes S, Wood A, et al. How artificial intelligence and machine learning can help healthcare systems respond to COVID-19. Mach Learn [Internet]. 2021;110:1-14. doi: https://doi.org/10.1007/s10994-020-05928-x
23. Ibrahim S. The Digital Doctor: Hope, Hype and Harm at the Dawn of Medicine’s Computer Age [Book Review]. Malays Orthop J [Internet]. 2018 Jul;12(2):75. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC6092535/.
24. Vayena E, Blasimme A, Cohen IG. Machine learning in medicine: addressing ethical challenges. PLOS Med 2018;15(11):e1002689. doi: https://doi.org/10.1371/journal.pmed.1002689
25. Bohr A, Memarzadeh K, editors. Artificial Intelligence in Healthcare [Internet]. London: Academic Press; 2020. doi:10.1016/C2018-0-04097-9. Available from: https://books.google.com.co/books?id=FDLXDwAAQBAJ
26. Morley J, Machado CCV, Burr C, Cowls J, Joshi I, Taddeo M, et al. The ethics of AI in health care: A mapping review. Soc Sci Med [Internet]. 2020;260:113172. doi: https://doi.org/10.1016/j.socscimed.2020.113172
27. Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc [Internet]. 2020;92(4):807-812. doi: https://doi.org/10.1016/j.gie.2020.06.040
28. Shilo S, Rossman H, Segal E. Axes of a revolution: challenges and promises of big data in healthcare. Nat Med [Internet]. 2020;26(1):29-38. doi: https://doi.org/10.1038/s41591-019-0727-5
29. Murdoch TB, Detsky AS. The inevitable application of big data to health care. JAMA [Internet]. 2013;309(13):1351-1352. doi: https://doi.org/10.1001/jama.2013.393
30. Booth RG, Strudwick G, McBride S, O’Connor S, Solano-López AL. How the nursing profession should adapt for a digital future. BMJ [Internet]. 2021;373:n1190. doi: https://doi.org/10.1136/bmj.n1190
31. Sutton RT, Pincock D, Winterstein DA, et al. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med [Internet]. 2020;3:17. doi: https://doi.org/10.1038/s41746-020-0221-y
32. Christina J, Ford K, Menz B, Sorich M, Hopkins A, Ramsey I, et al. Integrating the Caring Life Course Theory and artificial intelligence applications to enhance cancer care across the continuum. Semin Oncol Nurs. [Internet]. 2025;41(6):152040. doi: https://doi.org/10.1016/j.soncn.2025.152040
33. Lee P, Bubeck S, Petro J. Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine. N Engl J Med [Internet]. 2023;388(13):1233-1239. doi: https://doi.org/10.1056/nejmsr2214184
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