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October 5, 2025Journal of Psychiatric and Mental Health Nursing2 citations

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

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EYErman Yıldız

Key Points

  • Big data enhances early diagnosis and personalized treatments in mental health nursing, but must complement traditional practices.
  • AI diagnostic tools for relapse prediction demonstrate practical applications, underscoring a balancing act with human interaction.
  • Ethical challenges including data privacy and algorithmic bias must be addressed to ensure fair access and preserve patient relationships.
  • Strengthening data literacy and developing robust governance policies are essential recommendations for the evolving role of mental health nurses.

Abstract

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.

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Cite This Study

Erman Yıldız (2025) studied this question.

synapsesocial.com/papers/68e25378d6d66a53c2474086https://doi.org/10.1111/jpm.70038
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