(untitled) (The Review of Diabetic Studies, 2024)
Background: The healthcare sector grapples with workforce shortages, rising costs, and post-COVID demands, necessitating digital transformation and AI to boost efficiency across paramedics, nursing, pharmacy, and infection control. This review synthesizes 2015-2026 evidence on technological evolution, theoretical frameworks like TAM/UTAUT and Donabedian’s model, and applications enhancing performance and quality.​ Objective: To evaluate digital transformation and AI’s impact on healthcare workforce efficiency and service quality, addressing gaps in multidisciplinary integration for non-physician roles. Methods: Narrative synthesis of peer-reviewed literature from PubMed, Scopus, and WHO sources (2015-2026), focusing on AI in triage, EHRs, predictive analytics, and barriers like bias and interoperability. Theoretical models (TAM, Diffusion of Innovations, socio-technical systems) frame adoption and outcomes.​ Results: AI automates tasks (e.g., RPA cuts scheduling errors 40%), improves diagnostics (95%+ accuracy in imaging), and enhances safety (20% sepsis mortality drop). Digital tools like telemedicine and IoT reduce administrative burdens by 20-30%, though barriers (data silos, resistance) limit gains in low-resource settings. Future trends include generative AI and metaverse training.​ Conclusions: Digital transformation and AI substantially elevate workforce efficiency and service quality via automation and prediction, but require human-centered design, ethical governance, and infrastructure to overcome adoption hurdles. Multidisciplinary strategies will maximize benefits in emergency and infection control contexts.​