Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Apr 16:7:1548209.
doi: 10.3389/fdgth.2025.1548209. eCollection 2025.

Prevention of falls in hospitalized patients-evaluation of the effectiveness of a monitoring system (Verso Vision) developed with artificial intelligence

Corrado Gervasi  1 , Erik Perego  2 , Francesca Galli  3 , Valter Torri  4 , Massimo Castoldi  2 , Emilio Bombardieri  5
Affiliations

Prevention of falls in hospitalized patients-evaluation of the effectiveness of a monitoring system (Verso Vision) developed with artificial intelligence

Corrado Gervasi et al. Front Digit Health. .

Abstract

Introduction: The prevention of accidental falls in hospital is an important aspect of a healthcare management strategy, since they represent a relevant socio-economic problem. The Verso Vision System (VS) is an artificial intelligence-based system for accidental fall prevention and management, which uses computer vision algorithms to monitor environments and people in real time.

Methods: The efficacy of VS monitoring in terms of reduction of accidentals falls was retrospectively evaluated in a group of 362 hospitalized patients at Humanitas Gavazzeni Hospital.

Results: Of the 362 patients included in the analysis, 580 statistical units, 228 monitored with VS and 355 without VS were obtained splitting the observation of each patient based on the presence of VS monitoring and the Stratify score. The mean age of the 362 patients was 75.3 years and 150 were females (41.4%). The crude incidence rates per 1,000 person-time was 2.85 (95% CI 0.92-6.63, 5 accidental falls) and 6.65 (95% CI 3.72-10.96, 15 accidental falls) in the monitored with VS and unmonitored groups, respectively. At multivariable Poisson regression model, a statistically significant reduction of the risk of accidental falls was found in the monitored group compared to the unmonitored group [incidence rate ratio (IRR) 0.21, 95% CI 0.12-0.38, p < 0.0001]. The positive impact was supported by sensitivity analysis (IRR 0.22, 95% CI 0.13-0.35, p < 0.0001).

Conclusion: This analysis suggests that the VS can reduce the number of accidental falls in hospitalized patients. Nonetheless, further prospective analyses are needed to confirmed the efficacy of the VS.

Keywords: accidental falls; artificial intelligence; hospitalization; prevention; remote monitoring.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
On the left, a fixed camera monitors patients within the room's field of view and detects their movements. Anonymized data are processed by an AI system to analyze potentially risky situations and falls that may require notifying the nursing personnel. On the right, a remote mobile device receives and displays alerts sent to the nursing personnel.
See this image and copyright information in PMC

References

    1. Ministero Della Salute. Raccomandazione Per La Prevenzione E La Gestione Della Caduta Del Paziente Nelle Strutture Sanitarie: Raccomandazione N.13. (2011). Available online at: http://www.salute.gov.it/imgs/C_17_pubblicazioni_1639_allegato.pdf (accessed November 25, 2024).
    1. James SL, Lucchesi LR, Bisignano C, Castle CD, Dingels ZV, Fox JT, et al. The global burden of falls: global, regional and national estimates of morbidity and mortality from the global burden of disease study 2017. Inj Prev. (2020) 26(Supp 1):i3–11. 10.1136/injuryprev-2019-043286 - DOI - PMC - PubMed
    1. Montero-Odasso M, van der Velde N, Martin FC, Petrovic M, Tan MP, Ryg J, et al. World guidelines for falls prevention and management for older adults: a global initiative. Age Ageing. (2022) 51(9):afac205. 10.1093/ageing/afac205 - DOI - PMC - PubMed
    1. CDC. Available online at: https://www.cdc.gov/steadi/pdf/steadi-inpatient-guide-508.pdf (accessed November 25, 2024).
    1. Nice. Available online at: https://www.nice.org.uk/guidance/Cg161 (accessed November 25, 2024).

LinkOut - more resources