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. 2025 Nov 17;11:23779608251398113. doi: 10.1177/23779608251398113

Strengthening Ethical Practices of Patient Data Confidentiality and Sharing Among Nurses in the Artificial Intelligence-Driven Healthcare Era

Sawsan Abuhammad 1,2,✉
PMCID: PMC12623646  PMID: 41262122

Abstract

As artificial intelligence (AI) becomes increasingly integrated into healthcare systems, ethical challenges surrounding patient data confidentiality and informed data sharing have intensified—particularly for nurses, who serve as key custodians of health information. This commentary examines the current state of nurses’ knowledge and practices related to data confidentiality in the AI era, highlighting significant gaps in training, awareness, and institutional support. The commentary calls for urgent reforms in nursing education, policy, and global governance frameworks to ensure that technological innovation does not compromise patient trust or ethical standards.

Keywords: Artificial intelligence, nursing ethics, patient confidentiality, data sharing, digital

Introduction

It is imperative and clear that the integration of artificial intelligence (AI) into healthcare has created unprecedented opportunities for innovation in clinical practice, diagnostics, and personalized care (Albalawi et al., 2021; Meskó & Görög, 2020; Nashwan et al., 2025). Currently, nurses play a critical role in the collection, documentation, and communication of patient health information, and they are front of patient interaction and care coordination in all healthcare settings (Abuhammad et al., 2022). Adoption of AI-powered tools that rely on large-scale data processing, ethical concerns regarding patient confidentiality, and informed data sharing have gained considerable urgency, as the healthcare sector increasingly (Meskó & Görög, 2020; van Kolfschooten & de Ruijter, 2024). For clarity, in this paper “AI” refers to nurse-facing, electronic health record (EHR)-integrated systems—machine-learning clinical decision support, computer-vision monitoring/triage, and generative-AI tools for documentation/education; the authors do not address consumer chatbots (ANA, 2022; ICN, 2023; van Kolfschooten & de Ruijter, 2024; WHO, 2024). Recent international guidance underscores the global relevance of AI governance in health (Abuzaid et al., 2022). The World Health Organization's (2024) guidance on large multimodal models outlines over 40 recommendations for safe, equitable AI use in health systems (World Health Organization, 2024). The International Council of Nurses (ICN) calls on nurses worldwide to uphold ethical standards when using digital health technologies (International Council of Nurses, 2023), and the American Nurses Association provides ethical guidance specific to AI in nursing practice (American Nurses Association, 2022). Regulatory developments such as the European Union's Artificial Intelligence Act (2024) classify many clinical AI systems as high-risk, with obligations for risk management, transparency, and human oversight—changes that will influence global vendors and cross-border implementations (European Parliament, 2025; van Kolfschooten & de Ruijter, 2024). The aim of this commentary is to highlight the knowledge, ethical responsibilities, and practices of nurses concerning patient data confidentiality in the AI era.

Brief Review / Discussion of the Topic

The following brief review synthesizes current evidence and policy guidance on nursing confidentiality in AI-enabled care, showing how digital workflows expand traditional ethical duties and risks. It organizes the discussion into ethical foundations and the expanding role of nurses, persistent knowledge gaps, real-world consequences, legal and technical challenges, and the international context shaping practice

Ethical Foundations and the Expanding Role of Nurses

Based on ICN (ICN, 2023), confidentiality is a cornerstone of nursing ethics as embedded in their documents. It ensures that nurses’ responsibilities to safeguard patient privacy and manage information with discretion and integrity (ICN, 2023). Previously, nurses were involved in securing written records and limiting verbal disclosure, but this changed with AI-driven digital systems that are used for storing and analyzing data. There is an increasing risk of privacy breaches, unauthorized access, and re-identification (Gabriel, 2023). One of the nurses’ roles in healthcare system is digital management of personal health information (PHI), including entering data into EHRs and interact with AI decision-support systems.

Knowledge Gaps Among Nurses

Empirical studies indicate gaps in nurses’ knowledge and practice regarding confidentiality and data sharing, limited and uneven inclusion of AI/data-ethics content in curricula and continuing education, and low exposure to formal AI training; qualitative work highlights policy clarity, training, and workflow fit as decisive for safe adoption, while multi-professional surveys show inconsistent understanding of data-protection obligations around AI (Abuhammad et al., 2020a; Lifshits & Rosenberg, 2024; Park et al., 2024; Yalcinkaya et al., 2024; Kahraman et al., 2025; Ramadan et al., 2024; Mehta et al., 2023).

Real-World Consequences and Ethical Breaches

A well-known incident involved identifiable data from 1. 6 million UK patients shared without proper consent for development of the “Streams” app intended to detect acute kidney injury; the UK Information Commissioner concluded the Trust breached data-protection law (Hern, 2018). Frontline nurses interacted with the app and related workflows, illustrating how unclear governance can draw clinicians into ethical violations.

Legal and Technical Challenges

AI-driven systems pose unique challenges to data confidentiality. These include: Algorithmic opacity relates to receiving clinical recommendations from AI tools among nurses without understanding how decisions were made, which raises concerns about accountability and informed consent (Hussain et al., 2024). Secondary data use without clear patient consent or nurse awareness, since patient data entered by nurses may be used for research or commercial AI training (Park et al., 2024). Re-identification risks especially in small or unique populations, since AI models may be able to reconstruct personal identities from anonymized data (Rocher et al., 2019).

International Context and Relevance

Across regions, evidence shows uneven preparedness and training for ethical AI use in nursing. Qualitative study from Saudi Arabia highlights facilitators and barriers to adopting AI in nursing practice, including the need for training and clear policies (Ramadan et al., 2024). A scoping reviews document both opportunities and gaps in AI-enabled nursing education and GenAI literacy (Lifshits & Rosenberg, 2024; Park et al., 2024). These findings align with global policy directions—WHO's evolving AI guidance (2024), ICN's position on digital health (2023), and the EU AI Act (2024)—all emphasizing privacy protection, transparency, accountability, and inclusion.

Current Insights and Interpretations

Building on this review, the current insights translate the literature into practical, nurse-centered actions that education programs and institutions can implement now to safeguard confidentiality in AI-mediated practice.

Integrate Artificial Intelligence and Data Ethics into Nursing Education

The nurses have important role in preparing nurses to critically evaluate technology and uphold ethical standards in practice; nursing education programs should embed content on AI technologies, digital data ethics, and cybersecurity within undergraduate and continuing education (Rony et al., 2024). Case studies and simulation-based learning could be used to understand real-world ethical challenges posed by AI in clinical care (Meskó & Görög, 2020). Evidence from recent scoping reviews in nursing education and GenAI indicates curricular integration improves confidence and ethical awareness (Lifshits & Piyan, 2024; Park et al., 2024). For example, a generative-AI ambient scribe drafts notes from audio in a step-down unit; a short case module trains nurses to spot PHI leakage and correct misattributions before signing (Meskó & Görög, 2020; Park et al., 2024).

Provide Institutional Data Governance Training

Studies mentioned that nurses have many responsibilities that include offering regular training focused on legal frameworks (e.g., GDPR, HIPAA), securing data handling, and integrating AI in clinical systems for all employees who participated in patient care. To bridge knowledge gaps and reduce legal and ethical risks, institutional support is crucial, studies show that nurses with training are significantly more likely to adhere to data confidentiality best practices (Abuhammad et al., 2020b; Karasneh et al., 2021). Studies across settings link structured training to better adherence to confidentiality and safer AI adoption (Ramadan et al., 2024). For example, in outpatient oncology a risk-prediction tool flags early sepsis; quarterly sessions review audit logs, role-based access, and secondary-use policies (Abuhammad et al., 2020a). Include nurses in data ethics committees. Global professional guidance emphasizes nursing participation in governance and oversight (ANA, 2022; ICN, 2023). Shaping practical and ethically sound data governance policies required nurses clinical experience and patient advocacy roles, since they should be involved in institutional data ethics and AI oversight committees (ANA, 2022).

Promote Transparent Consent Processes

Simplified, culturally appropriate language is vital for transparency; AI systems often use patient data beyond initial clinical care purposes. This need nurses to be trained to explain how data may be used in AI applications and ensure patients give informed, voluntary consent. International frameworks stress explainability and patient-facing transparency for secondary data use (WHO, 2024). Privacy-preserving analytics (e.g., federated learning, differential privacy) can reduce re-identification risks while enabling multi-site learning (Rocher et al., 2019; Yang et al., 2019). These principles should be reflected in nursing policies and practices to reflect international ethical frameworks like the World Health Organization's (2024) AI guidance. The EU AI Act's high-risk provisions and WHO guidance provide actionable guardrails for clinical AI systems (van Kolfschooten & de Ruijter, 2024; WHO, 2024).

Conclusions/Importance to Nursing Profession

In conclusion, nurses must be empowered with the knowledge, tools, and institutional support necessary to protect patient confidentiality and ensure ethical data sharing practices, as AI systems become more pervasive. Currently, nurses play a critical role in managing patient data that is undermined by insufficient training and unclear ethical guidance since nurses stand at the intersection of clinical care and digital innovation in the era of AI-enabled healthcare. Nurses could continue to fulfill their professional obligations while embracing the future of intelligent healthcare systems with upholding patient trust and autonomy in this technologically complex environment which requires a concerted effort to revise nursing education, reform institutional policies, and implement rigorous ethical oversight.

Footnotes

ORCID iD: Sawsan Abuhammad https://orcid.org/0000-0001-5817-8950

Funding: The author received no financial support for the research, authorship, and/or publication of this article.

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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