Digital Innovations in Nursing Workforce Management: A Scoping Review of Advanced Applications and Implementation Challenges

Abstract

Objective Artificial intelligence (AI) is increasingly being adopted to support healthcare workforce planning and operational decision‐making. However, evidence regarding its application specifically within nursing workforce management remains fragmented across diverse disciplines and publication types. This scoping review aimed to map the current evidence on AI applications in nursing workforce management, identify major application domains, synthesise reported organisational outcomes and implementation challenges and highlight priorities for future research. Design Scoping review. Methods The review was conducted in accordance with the Arksey and O’Malley methodological framework, subsequent methodological enhancements by Levac and colleagues, the Joanna Briggs Institute guidance and the PRISMA Extension for Scoping Reviews (PRISMA‐ScR). A comprehensive literature search was undertaken in PubMed, Scopus, Web of Science, CINAHL and IEEE Xplore from database inception to May 2025, with a supplementary top‐up search executed in August 2026 to ensure currency. Two reviewers independently screened studies, extracted data using a standardised data‐charting form and synthesised findings through descriptive numerical analysis and thematic synthesis. Results Twenty‐eight studies met the eligibility criteria. AI applications were identified across six principal workforce management domains: workforce planning and demand forecasting, nurse scheduling and rostering, workload optimisation, burnout and workforce sustainability, workforce analytics and managerial decision support. Most publications described conceptual models, pilot projects, qualitative investigations or review‐based evidence, whereas relatively few evaluated implemented AI systems in routine nursing management practice. Commonly reported organisational benefits included improvements in workforce allocation, scheduling transparency, operational efficiency and data‐informed managerial decision‐making. Frequently reported implementation challenges included ethical concerns, organisational readiness, data quality, interoperability, digital competence and user acceptance. Conclusion Current evidence suggests that AI has considerable potential to support nursing workforce management; however, empirical evidence regarding implementation effectiveness remains limited. Future research should prioritise prospective implementation studies, rigorous evaluation of workforce outcomes and development of transparent, nurse‐centred AI governance frameworks. Implications for Nursing Management AI‐enabled workforce management may assist nurse leaders in efforts to improve staffing decisions, potentially enhancing operational efficiency and supporting efforts toward more equitable workload distribution. Successful implementation requires organisational readiness, robust governance, multidisciplinary collaboration and meaningful involvement of nurses throughout system design and evaluation.