Ethical Big Data for Personalised Mental Health Nursing: A P4 and Systems View.
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| Titel: | Ethical Big Data for Personalised Mental Health Nursing: A P4 and Systems View. |
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| Autoren: | Yıldız E; Department of Psychiatric Nursing, Faculty of Nursing, Inonu University, Malatya, Türkiye. |
| Quelle: | Journal of psychiatric and mental health nursing [J Psychiatr Ment Health Nurs] 2025 Dec; Vol. 32 (6), pp. 1404-1411. Date of Electronic Publication: 2025 Oct 03. |
| Publikationsart: | Journal Article; Review |
| Sprache: | English |
| Info zur Zeitschrift: | Publisher: Blackwell Scientific Publications Country of Publication: England NLM ID: 9439514 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1365-2850 (Electronic) Linking ISSN: 13510126 NLM ISO Abbreviation: J Psychiatr Ment Health Nurs Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Oxford ; Boston : Blackwell Scientific Publications, c1994- |
| MeSH-Schlagworte: | Big Data* , Psychiatric Nursing*/ethics , Precision Medicine*/ethics , COVID-19*, Humans |
| Abstract: | Background: Mental health nursing faces transformation through big data and metadata integration. These technologies create new opportunities but introduce ethical and practical complexities. Digital adoption accelerated during COVID-19, making it essential to understand implications for nursing practice. Aim: This perspective paper aims to critically examine the transformative potential and ethical dilemmas of leveraging big data in mental health nursing, guided by systems biology and P4 (Predictive, Preventive, Personalised, and Participatory) medicine principles. It seeks to define the evolving roles of mental health nurses in this new digital landscape. Method: This perspective essay utilises a focused literature review of key studies in nursing, psychiatry, informatics, and ethics, alongside theoretical approaches including systems biology, P4 medicine, and a personalist ethical framework. The analysis explores the integration of big data, focusing on potential benefits, risks, and ethical considerations. Results: Big data contributes meaningfully to early diagnosis, personalised treatments, and prevention strategies. However, these contributions must supplement, not substitute, traditional nursing approaches. AI diagnostic tools and digital phenotyping for relapse prediction demonstrate practical applications. Excessive algorithmic dependence risks damaging patient-nurse relationships. Data privacy, algorithmic bias, and access inequities present significant ethical challenges requiring careful attention. Conclusion: Big data implementation should enhance, not replace, human interaction in mental health nursing. A new synthesis is proposed where data-driven insights support efficiency, allowing nurses more time for complex emotional needs. Key recommendations include strengthening data literacy in nursing education, developing robust data governance policies, and establishing comprehensive ethical principles to preserve the essential human dimension of care and ensure equitable access. (© 2025 John Wiley & Sons Ltd.) |
| References: | Ahmed, A., M. Agus, M. Alzubaidi, et al. 2022. “Overview of the Role of Big Data in Mental Health: A Scoping Review.” Computer Methods and Programs in Biomedicine Update 2: 100076. https://doi.org/10.1016/j.cmpbup.2022.100076. Beames, J. R., J. Han, A. Shvetcov, et al. 2024. “Use of Smartphone Sensor Data in Detecting and Predicting Depression and Anxiety in Young People (12–25 Years): A Scoping Review.” Heliyon 10, no. 15: e35472. https://doi.org/10.1016/j.heliyon.2024.e35472. Brennan, P. F., and S. Bakken. 2015. “Nursing Needs Big Data and Big Data Needs Nursing.” Journal of Nursing Scholarship 47, no. 5: 477–484. https://doi.org/10.1111/jnu.12159. Delaney, C. W., C. A. Weaver, J. J. Warren, T. R. Clancy, and R. L. Simpson. 2017. “Big Data‐Enabled Nursing.” Education, Research and Practice 2017: 3–488. Duah, H. O., S. Boch, S. Arter, N. Nidey, and J. Lambert. 2024. “A Guide to Understanding Big Data for the Nurse Scientist: A Discursive Paper.” Nursing Inquiry 31, no. 3: e12648. https://doi.org/10.1111/nin.12648. Gillan, C. M., and R. Whelan. 2017. “What Big Data Can Do for Treatment in Psychiatry.” Current Opinion in Behavioral Sciences 18: 34–42. https://doi.org/10.1016/j.cobeha.2017.07.003. Hood, L. 2013. “Systems Biology and p4 Medicine: Past, Present, and Future.” Rambam Maimonides Medical Journal 4, no. 2: e0012. https://doi.org/10.5041/rmmj.10112. Jilka, S., and D. Giacco. 2024. “Digital Phenotyping: How It Could Change Mental Health Care and Why We Should All Keep Up.” Journal of Mental Health 33, no. 4: 439–442. https://doi.org/10.1080/09638237.2024.2395537. Kemp, P., and J. D. Rendtorff. 2008. “The Barcelona Declaration.” Synthesis Phylosophica 46, no. 2: 239–251. Liang, Y., X. Zheng, and D. D. Zeng. 2019. “A Survey on Big Data‐Driven Digital Phenotyping of Mental Health.” Information Fusion 52: 290–307. https://doi.org/10.1016/j.inffus.2019.04.001. Mesarović, M. D. 1968. “Systems Theory and Biology—View of a Theoretician.” In Systems Theory and Biology, edited by M. D. Mesarović, 59–87. Springer. Milasan, L. H., and D. Scott‐Purdy. 2025. “The Future of Artificial Intelligence in Mental Health Nursing Practice: An Integrative Review.” International Journal of Mental Health Nursing 34, no. 1: e70003. https://doi.org/10.1111/inm.70003. Milton, C. L. 2017. “The Ethics of Big Data and Nursing Science.” Nursing Science Quarterly 30, no. 4: 300–302. https://doi.org/10.1177/0894318417724474. Olawade, D. B., O. Z. Wada, A. Odetayo, A. C. David‐Olawade, F. Asaolu, and J. Eberhardt. 2024. “Enhancing Mental Health With Artificial Intelligence: Current Trends and Future Prospects.” Journal of Medicine, Surgery, and Public Health 3: 100099. https://doi.org/10.1016/j.glmedi.2024.100099. Peirce, A. G., S. Elie, A. George, M. Gold, K. O'Hara, and W. Rose‐Facey. 2020. “Knowledge Development, Technology and Questions of Nursing Ethics.” Nursing Ethics 27, no. 1: 77–87. https://doi.org/10.1177/0969733019840752. Rendtorff, J. D. 2002. “Basic Ethical Principles in European Bioethics and Biolaw: Autonomy, Dignity, Integrity and Vulnerability – Towards a Foundation of Bioethics and Biolaw.” Medicine, Health Care and Philosophy 5, no. 3: 235–244. https://doi.org/10.1023/A:1021132602330. Rogan, J., S. Bucci, and J. Firth. 2024. “Health Care Professionals' Views on the Use of Passive Sensing, AI, and Machine Learning in Mental Health Care: Systematic Review With Meta‐Synthesis.” JMIR Mental Health 11: e49577. https://doi.org/10.2196/49577. Rosenfeld, A., D. Benrimoh, C. Armstrong, et al. 2021. “6 – Big Data Analytics and Artificial Intelligence in Mental Healthcare.” In Applications of Big Data in Healthcare, edited by A. Khanna, D. Gupta, and N. Dey, 137–171. Academic Press. https://doi.org/10.1016/B978‐0‐12‐820203‐6.00001‐1. Stewart, R., and K. Davis. 2016. “‘Big Data’ in Mental Health Research: Current Status and Emerging Possibilities.” Social Psychiatry and Psychiatric Epidemiology 51, no. 8: 1055–1072. https://doi.org/10.1007/s00127‐016‐1266‐8. World Health Organization. 2022. “Big Data Analytics and Artificial Intelligence in Mental Health.” Access. https://www.who.int/europe/news‐room/events/item/2022/12/07/default‐calendar/big‐data‐analytics‐and‐artificial‐intelligence‐in‐mental‐health. World Health Organization. 2023. “Artificial Intelligence in Mental Health Research: New WHO Study on Applications and Challenges.” Access. https://www.who.int/europe/news/item/06‐02‐2023‐artificial‐intelligence‐in‐mental‐health‐research‐‐new‐who‐study‐on‐applications‐and‐challenges. Yıldız, E. 2025. “Artificial Intelligence in Mental Health Nursing: Balancing Clinical Efficiency and the Human Touch—A Quest for a New Synthesis.” Journal of Psychiatric and Mental Health Nursing 32, no. 4: 946–952. https://doi.org/10.1111/jpm.13173. |
| Contributed Indexing: | Keywords: P4 medicine; data literacy; ethics; mental health nursing; metadata; personalised care; systems biology big data |
| Entry Date(s): | Date Created: 20251004 Date Completed: 20251112 Latest Revision: 20251112 |
| Update Code: | 20251113 |
| DOI: | 10.1111/jpm.70038 |
| PMID: | 41044982 |
| Datenbank: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstract: | Background: Mental health nursing faces transformation through big data and metadata integration. These technologies create new opportunities but introduce ethical and practical complexities. Digital adoption accelerated during COVID-19, making it essential to understand implications for nursing practice.<br />Aim: This perspective paper aims to critically examine the transformative potential and ethical dilemmas of leveraging big data in mental health nursing, guided by systems biology and P4 (Predictive, Preventive, Personalised, and Participatory) medicine principles. It seeks to define the evolving roles of mental health nurses in this new digital landscape.<br />Method: This perspective essay utilises a focused literature review of key studies in nursing, psychiatry, informatics, and ethics, alongside theoretical approaches including systems biology, P4 medicine, and a personalist ethical framework. The analysis explores the integration of big data, focusing on potential benefits, risks, and ethical considerations.<br />Results: Big data contributes meaningfully to early diagnosis, personalised treatments, and prevention strategies. However, these contributions must supplement, not substitute, traditional nursing approaches. AI diagnostic tools and digital phenotyping for relapse prediction demonstrate practical applications. Excessive algorithmic dependence risks damaging patient-nurse relationships. Data privacy, algorithmic bias, and access inequities present significant ethical challenges requiring careful attention.<br />Conclusion: Big data implementation should enhance, not replace, human interaction in mental health nursing. A new synthesis is proposed where data-driven insights support efficiency, allowing nurses more time for complex emotional needs. Key recommendations include strengthening data literacy in nursing education, developing robust data governance policies, and establishing comprehensive ethical principles to preserve the essential human dimension of care and ensure equitable access.<br /> (© 2025 John Wiley & Sons Ltd.) |
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| ISSN: | 1365-2850 |
| DOI: | 10.1111/jpm.70038 |