ArticleJournal of multidisciplinary healthcare2026
Thoughts on Developing an Early Warning Model for Hospital Violence Risk Events Using Geographic Information Systems.
Article in Journal of multidisciplinary healthcare, 2026. 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
The risk of violence faced by healthcare workers is a widespread challenge in the global healthcare industry. This article examines the feasibility and practical value of developing a spatial prediction and early warning model for hospital violent injury incidents using Geographic Information System(GIS) technology. By integrating multi-source spatiotemporal data and employing spatiotemporal analysis, machine learning, and spatial interpolation methods, the model can identify hotspots and predict the risk of violent events at a micro scale, shifting from passive response to proactive prevention and control. This study not only offers a novel approach for the refined and scientific management of hospital safety but also provides valuable insights for the development of smart hospitals, the application of artificial intelligence, and interdisciplinary integration. Although challenges remain in data integration, privacy protection, and model generalization, advancements in technology and management optimization are expected to establish this technology as a core tool for security prevention and control in smart hospitals. This study is a preliminary exploration in this field.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.