通过持续AI监测实现的基于暴露时间标准化的床与椅子跌倒率研究 / Exposure-Normalized Bed and Chair Fall Rates via Continuous AI Monitoring
1️⃣ 一句话总结
这项研究利用人工智能持续监控发现,在考虑了实际使用时间后,病人从椅子上跌倒的风险是从床上跌倒的2倍多,并且多数椅子跌倒与脚踏板位置不当有关,提示应改进椅子设计而非减少使用。
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.
通过持续AI监测实现的基于暴露时间标准化的床与椅子跌倒率研究 / Exposure-Normalized Bed and Chair Fall Rates via Continuous AI Monitoring
这项研究利用人工智能持续监控发现,在考虑了实际使用时间后,病人从椅子上跌倒的风险是从床上跌倒的2倍多,并且多数椅子跌倒与脚踏板位置不当有关,提示应改进椅子设计而非减少使用。
源自 arXiv: 2603.22785