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

Yıldız E.

JOURNAL OF PSYCHIATRIC AND MENTAL HEALTH NURSING, cilt.1, sa.1, ss.1-8, 2025 (SCI-Expanded, SSCI, Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 1 Sayı: 1
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1111/jpm.70038
  • Dergi Adı: JOURNAL OF PSYCHIATRIC AND MENTAL HEALTH NURSING
  • Derginin Tarandığı İndeksler: Scopus, Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Academic Search Premier, Abstracts in Social Gerontology, CINAHL, Communication & Mass Media Index, MEDLINE, Psycinfo, Public Affairs Index, DIALNET
  • Sayfa Sayıları: ss.1-8
  • İnönü Üniversitesi Adresli: Evet

Özet

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.