Precision nursing in mental health: A narrative review.

چکیده اصلی

BACKGROUND: The gradual development of omic sciences and personalised care has led to the establishment of precision nursing as a response to the need to establish new models of care that allow for greater optimisation and individualisation of therapy, through the use of omic biomarkers, psychosocial factors and lifestyles. Precision models are particularly relevant in disorders of multifactorial origin and complex approach, especially neuropsychiatric disorders. OBJECTIVES: To highlight the determinants and recommendations in the implementation of precision mental health nursing. It also aims to clarify future lines of research and areas for improvement. MATERIAL AND METHODS: A narrative review of the literature (period: February-May 2025, inclusive), using Pubmed, Scopus, Web of Science, CINAHL and UpToDate as main databases. INCLUSION CRITERIA: Spanish/English language, original and full-text publications, period: last 5 years (2021-2025), human study. EXCLUSION CRITERIA: databases without scientific evidence or derived reliability, being part of the grey literature, no inclusion criteria. RESULTS: A total of 22 articles were selected. The evidence establishes precision nursing as the backbone of achieving optimal health outcomes through holistic linkage and stratification of mental health risk scores. Clinical decision-making is aided by the implementation of computational algorithms and the use of m-Health. The gradual implementation of personalised care requires a paradigm shift from current care models, requiring more training, digital investment and reallocation of care roles according to clinical contexts and diagnostic categories. CONCLUSIONS: The implementation of precision nursing shows promising results in prevention and therapeutic optimisation in mental health. Further institutional support and professional training in omic sciences is needed. The reallocation of roles in precision health care shapes nursing as a cornerstone of the model's effectiveness.

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