Artificial Intelligence (AI) is increasingly transforming healthcare systems; however, its structured integration into public-sector human resource management (HRM) remains limited. Polish public hospitals face persistent workforce shortages, recruitment inefficiencies, and regulatory constraints under the General Data Protection Regulation (GDPR) and the EU Artificial Intelligence Act. Building upon prior conceptual work on legitimacy-preserving AI governance architecture, this study advances an applied AI-enabled workforce governance model tailored to public healthcare HR systems. Using a structured conceptual-analytical framework development approach grounded in Institutional Theory, the Resource-Based View, Strategic Human Capital Theory, and algorithmic governance literature, the Public AI-HR Governance Framework (P-AIHR) integrates five operational governance pillars supported by a 36-month implementation roadmap and structured risk matrix. Scenario modelling calibrated against OECD workforce indicators and illustrated through a 300-bed hospital simulation suggests plausible reductions in recruitment cycle time (20–30%), turnover rates (3–6 percentage points), and overtime variability (10–18%) under governance-controlled AI deployment. Rather than presenting empirical outcomes, the model provides analytically bounded projections intended to demonstrate the operational plausibility of governance-aligned AI integration. The study contributes a governance-calibrated framework for high-risk regulatory environments and advances the literature on AI-enabled HR transformation in public healthcare systems.
Dawid Prestini (Sat,) studied this question.