# Exposure-Normalized Bed and Chair Fall Rates via Continuous AI Monitoring

arXiv: https://arxiv.org/abs/2603.22785 · HTML: https://mlsanity.com/paper/2603.22785/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 21 references that are in the map.
- First posted on arXiv 24 Mar 2026.

## Builds on (most cited first, 1 of 1)

- [Continuous Patient Monitoring with AI: Real-Time Analysis of Video in Hospital Care Settings](https://mlsanity.com/paper/2412.13152/) (2024)

## Abstract (arXiv)

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.