Exposure-Normalized Bed and Chair Fall Rates via Continuous AI Monitoring
It addresses fall risk assessment in healthcare settings by providing exposure-normalized rates, though it is incremental as it applies existing AI monitoring to a new data analysis approach.
This study used continuous AI monitoring to estimate fall rates by exposure time, finding 17.8 falls per 1,000 chair exposure-hours and 4.3 per 1,000 bed exposure-hours, with an adjusted chair-versus-bed rate ratio of 2.35.
This retrospective cohort study used continuous AI monitoring to estimate fall rates by exposure time rather than occupied bed-days. From August 2024 to December 2025, 3,980 eligible monitoring units contributed 292,914 hourly rows, yielding probability-weighted rates of 17.8 falls per 1,000 chair exposure-hours and 4.3 per 1,000 bed exposure-hours. Within the study window, 43 adjudicated falls matched the monitoring pipeline, and 40 linked to eligible exposure hours for the primary Poisson model, producing an adjusted chair-versus-bed rate ratio of 2.35 (95% confidence interval 0.87 to 6.33; p=0.0907). In a separate broader observation cohort (n=32 deduplicated events), 6 of 7 direct chair falls involved footrest-positioning failures. Because this was an observational study in a single health system, these findings remain hypothesis-generating and support testing safer chair setups rather than using chairs less.