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

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,  < 0.0001]. The positive impact was supported by sensitivity analysis (IRR 0.22, 95% CI 0.13-0.35,  < 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.