用连续AI监测更准估算床椅跌倒率，发现椅子跌倒风险是床的2.35倍。
Exposure-Normalized Bed and Chair Fall Rates via Continuous AI Monitoring
- 按暴露时长而非床位天数计算跌倒率，更精准反映真实风险。
- 椅子每千小时暴露有17.8次跌倒，床为4.3次，椅子风险更高。
- 建议优化椅子脚踏设计，适合医疗护理场景研究者参考。
本回顾性队列研究利用连续人工智能监测，基于暴露时间而非占用床位天数估算跌倒率。2024年8月至2025年12月期间，3,980个符合条件的监测单元提供了292,914条每小时数据，计算得椅子每千小时暴露有17.8次跌倒，床为每千小时4.3次。研究期内共43起经判定的跌倒事件匹配监测流程，其中40起关联到可纳入主泊松模型的暴露时段，调整后椅子与床的跌倒率比为2.35（95%置信区间0.87至6.33；p=0.0907）。在另一更广泛观察队列（n=32起去重事件）中，7起直接椅子跌倒中有6起涉及脚踏位置失误。由于这是单一医疗系统内的观察性研究，结果仍具假设性，支持测试更安全的椅子配置，而非减少使用椅子。
原文摘要 · Abstract (English)
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.
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