{"title":"个性化心理健康护理的伦理大数据：P4和系统视图。","authors":"Erman Yıldız","doi":"10.1111/jpm.70038","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Aim: </strong>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.</p><p><strong>Method: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":50076,"journal":{"name":"Journal of Psychiatric and Mental Health Nursing","volume":" ","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Ethical Big Data for Personalised Mental Health Nursing: A P4 and Systems View.\",\"authors\":\"Erman Yıldız\",\"doi\":\"10.1111/jpm.70038\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Aim: </strong>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.</p><p><strong>Method: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>\",\"PeriodicalId\":50076,\"journal\":{\"name\":\"Journal of Psychiatric and Mental Health Nursing\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2025-10-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Psychiatric and Mental Health Nursing\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1111/jpm.70038\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"NURSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Psychiatric and Mental Health Nursing","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/jpm.70038","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NURSING","Score":null,"Total":0}
Ethical Big Data for Personalised Mental Health Nursing: A P4 and Systems View.
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.
期刊介绍：
The Journal of Psychiatric and Mental Health Nursing is an international journal which publishes research and scholarly papers that advance the development of policy, practice, research and education in all aspects of mental health nursing. We publish rigorously conducted research, literature reviews, essays and debates, and consumer practitioner narratives; all of which add new knowledge and advance practice globally.
All papers must have clear implications for mental health nursing either solely or part of multidisciplinary practice. Papers are welcomed which draw on single or multiple research and academic disciplines. We give space to practitioner and consumer perspectives and ensure research published in the journal can be understood by a wide audience. We encourage critical debate and exchange of ideas and therefore welcome letters to the editor and essays and debates in mental health.
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