Ethical challenges in the algorithmic era: a systematic rapid review of risk insights and governance pathways for nursing predictive analytics and early warning systems

Abstract

BACKGROUND: Predictive analytics and early warning systems are now widely used in nursing practice worldwide. While these tools can improve efficiency and patient safety, but at the same time posing ethical challenges related to data privacy, algorithmic fairness, accountability, professional autonomy, and patient rights. Through a systematic rapid review, we identify the major ethical risks in nursing contexts and propose actionable governance pathways to inform clinical practice and policy. METHODS: This study used a systematic rapid review, searching eight databases-PubMed, Embase, Web of Science, Scopus, Cochrane Library, Ovid, EBSCOhost, and ProQuest-for English-language articles published from 2015 through May 2025. Two reviewers independently screened records and extracted data, with a third reviewer resolving disagreements, yielding 22 included studies. Using inductive thematic analysis, we summarized the ethical-risk dimensions and governance pathways of predictive analytics and early warning systems in nursing practice, and conducted an overall quality appraisal of the included literature. RESULTS: The included studies came from 11 countries, with publication volume rising markedly in recent years-reflecting growing attention to ethical issues in nursing. Most were reviews or commentaries, with fewer qualitative and mixed-methods studies. Thematic analysis identified five ethical-risk dimensions: (i) Data- and Algorithm-Related Ethical Risks; (ii) Professional Role and Responsibility Attribution Risks; (iii) Patient Rights and Humane-Care Ethical Risks; (iv) Ethical-Governance and Misuse Risks; and (v) Technological Accessibility and Social Acceptance Barriers. In response, the literature proposes four governance pathways-Technical-Data Governance, Clinical Human-Machine Collaboration, Organizational-Capacity Building, and Institutional-Policy Regulation-with concrete measures including privacy protection, algorithmic-bias monitoring and fairness audits, transparency and explainability enhancement, nurse training and digital literacy, interdisciplinary collaboration and co-creation, and policy and regulatory guidelines. CONCLUSIONS: Predictive analytics and early warning systems in nursing practice show substantial promise yet are accompanied by multidimensional ethical risks. For the first time in a nursing context, this study proposes a "five ethical-risk dimensions-four governance pathways" framework, offering actionable ethical-governance guidance for nurses, administrators, and policymakers. Future work should pursue interdisciplinary, multicenter empirical studies to evaluate the framework's feasibility and effectiveness and to align technological benefits with ethical values, thereby improving nursing quality and patient safety.

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