Falls in Assisted Living Facilities: Can AI improve documentation and reduce injury?

BackgroundFalls among elderly residents in assisted living facilities (ALFs) are prevalent, costly, and frequently under-documented. AUGi, a wall-mounted device employing obfuscated computer vision, deep learning, and mobile integration, provides continuous monitoring while maintaining patient privacy. ObjectiveTo evaluate whether the use of AI-assisted detection of falls could improve documentation of falls and lead to prevention of subsequent falls and related injury. MethodsAn ITS design analyzed monthly fall documentation data collected nine months pre-installation and four months post-installation of AUGi in ALFs. The primary outcome was the monthly documented fall rate, with and without injuries. Segmented regression analysis assessed changes in fall documentation trends related to AUGi installation. ResultsSegmented regression revealed no statistically significant immediate change in total falls post-AUGi installation (p = 0.85) nor a significant trend increase over time post-intervention (p = 0.17). Documented falls with injury also showed no significant immediate (p = 0.99) or trend differences (p = 0.73). Falls without injury similarly showed no immediate (p = 0.62) or trend changes (p = 0.82). Injury rate slightly declined (Cohens d = -0.54), though not significantly. The power analysis indicated low statistical power (13%), yet the moderate effect size suggests clinical relevance. DiscussionThe findings highlight pre-installation under-documentation of falls. Although statistical significance was not achieved, increased documentation post-AUGi installation suggests improved surveillance accuracy and potential for enhanced patient safety through more timely interventions. Future research should explore longer-term outcomes, including reduced injury severity and hospitalization, linked to improved fall documentation. ConclusionITS analysis indicates AUGi effectively enhances documentation of falls, suggesting improved patient safety monitoring in ALFs. Surveillance technologies may significantly improve documentation and decrease costs. Future studies could examine cost-benefits as well as potential to reduce documentation burden using ambient surveillance data.