Ethical Big Data for Personalised Mental Health Nursing: A P4 and Systems View

Basit Öğe Kaydı
dc.contributor.authorYildiz, Erman
dc.date.accessioned2026-04-04T13:33:22Z
dc.date.available2026-04-04T13:33:22Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractBackground: 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.
dc.identifier.doi10.1111/jpm.70038
dc.identifier.endpage1411
dc.identifier.issn1351-0126
dc.identifier.issn1365-2850
dc.identifier.issue6
dc.identifier.orcid0000-0002-6544-4847
dc.identifier.pmid41044982
dc.identifier.scopus2-s2.0-105018316384
dc.identifier.scopusqualityQ1
dc.identifier.startpage1404
dc.identifier.urihttps://doi.org/10.1111/jpm.70038
dc.identifier.urihttps://hdl.handle.net/11616/109086
dc.identifier.volume32
dc.identifier.wosWOS:001587442400001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorYildiz, Erman
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofJournal of Psychiatric and Mental Health Nursing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250329
dc.subjectdata literacy
dc.subjectethics
dc.subjectmental health nursing
dc.subjectmetadata
dc.subjectP4 medicine
dc.subjectpersonalised care
dc.subjectsystems biology big data
dc.titleEthical Big Data for Personalised Mental Health Nursing: A P4 and Systems View
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayın Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu