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

1. 1Collect data from continuous AI monitoring systems
2. 2Calculate exposure hours for beds and chairs
3. 3Estimate fall rates using probability-weighted rates
4. 4Compare fall rates between beds and chairs to identify areas for improvement

Data scientists and AI engineers on a healthcare team can benefit from this study as it provides a new approach to estimating fall rates, while healthcare professionals can use this data to improve patient safety

💡 Exposure-normalized fall rates provide a more accurate estimate of fall risk than traditional occupied bed-day measures

💡 AI monitoring reduces fall rates: 17.8 falls/1,000 chair hrs & 4.3 falls/1,000 bed hrs