Personalized AI for workplace health promotion

Performance management and healthcare worker engagement through digital analytics

Bibliographic Data

ID22090048
AuthorsDaniele Virgillito (0009-0006-5547-0765, University of Catania), Caterina Ledda (0000-0003-0739-8798, University of Catania, corresponding author)
Year2026
Volume13
Pages1718474-1718474
Publication date2026-01-13
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2025.1718474
PMID41607882
OpenAlexW7123350338
LanguageEN
References cited103

Background: Artificial intelligence (AI) is increasingly being applied in healthcare work-places to promote worker wellbeing and optimize organizational performance. However, evidence on its effectiveness, adoption, and limitations remains fragmented. This scoping review aimed to systematically map the literature on AI-based digital technologies for workplace health promotion and performance management among healthcare workers. Methods: The review was reported in accordance with PRISMA-ScR guidelines and was conducted up to July 2025. Studies were screened and selected using the PCC (Population-Concept-Context) framework, and data were extracted on AI technology type, health promotion focus, and outcomes. Electronic searches were conducted in PubMed, Scopus, Web of Science, PsycINFO, IEEE Xplore, and Google Scholar. The search identified 351 records; after removing duplicates and non-eligible papers, 180 records were screened, 84 full texts assessed, and 21 studies included in the final synthesis. Results: Twenty-one studies were included, covering quantitative, qualitative, and mixed-method designs. Two major domains of application emerged: AI-enabled health monitoring and intervention and AI-driven performance optimization. Reported benefits included reductions in stress, burnout, anxiety, and musculoskeletal pain, as well as improvements in workflow efficiency, documentation quality, leadership support, and staff engagement. However, limitations included short study durations, methodological heterogeneity, privacy and ethical concerns, and variable adoption by healthcare staff. Conclusions: AI-based digital technologies show promise for enhancing both worker health and organizational sustainability. To ensure long-term impact, future research should prioritize rigorous study designs, standardized outcome measures, privacy-preserving frameworks, and human-centered approaches to technology integration

Analytics · Digital health · Employee engagement · Health care · Organizational culture · Organizational performance · Performance Management · Workplace health promotion · Artificial Intelligence in Healthcare and Education · Digital Mental Health Interventions · Workplace Health and Well-being

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