Previously submitted to: JMIR Medical Informatics (no longer under consideration since Jun 01, 2026)
Date Submitted: May 22, 2026
Open Peer Review Period: Jun 1, 2026 - Jun 1, 2026
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AI-Driven Workforce Optimisation in NHS Healthcare: Conceptual Framework and Implementation Guidance
ABSTRACT
NHS organisations face a workforce crisis that goes deeper than vacancy numbers. Even trusts with reasonable headcount face weekly rota gaps, ward managers spend hours building schedules manually, and experienced staff leave in part because the operational systems around them feel chaotic and unfair. Artificial intelligence (AI) offers a credible set of capabilities to improve this picture—through intelligent rostering, predictive staffing, and administrative automation—but NHS adoption has been uneven, governance frameworks are underdeveloped, and the implementation evidence base remains thin. This viewpoint synthesises evidence and conceptual frameworks to make a practical argument about how NHS organisations can use AI to improve workforce management, and what they need to get right to make those improvements sustainable. Drawing on peer-reviewed literature in digital health, operations research, and implementation science, and applying three theoretical frameworks—the NASSS framework, Normalisation Process Theory, and UK algorithmic accountability frameworks—the paper examines intelligent rostering, predictive staffing, and workflow automation, and proposes a five-workstream implementation model covering outcome measurement, data readiness, solution delivery, workforce co-design, and algorithmic governance. AI-assisted rostering reduces rota construction from hours to minutes while distributing shifts more equitably. Predictive models identify demand-supply gaps weeks in advance. Automation recovers clinical time currently lost to paperwork. Across all three domains, data quality, staff trust, and governance readiness determine outcomes more reliably than technical sophistication. The five-workstream framework offers NHS leaders and policymakers a structured, actionable implementation model grounded in implementation science and NHS-specific governance obligations. AI is not a structural solution to the NHS workforce crisis, but it can materially improve how available capacity is used—if organisations invest seriously in the conditions that make it work.
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