ReviewFrontiers in public health2025
Personalized AI for workplace health promotion: performance management and healthcare worker engagement through digital analytics.
Review in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.