Applied Sciences, Vol. 16, Pages 3383: Ai-driven Inpatient Fall Prevention Using Continuous Monitoring: From Early Detection To Workflow-integrated Decision Support: A Scoping Review

Applied Sciences, Vol. 16, Pages 3383: AI-Driven Inpatient Fall Prevention Using Continuous Monitoring: From Early Detection to Workflow-Integrated Decision Support: A Scoping Review

Applied Sciences doi: 10.3390/app16073383

Authors: Kazumi Kubota Satoko Tsuda Anna Kubota

Inpatient falls often occur at the bedside during unsupervised bed egress or bed exit attempts. Many artificial intelligence methods predict fall risk, but clinical value depends on workflow-ready decision support. This scoping review, reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR), mapped AI-driven inpatient fall prevention systems using continuous monitoring data that generate explicit action triggers. We searched PubMed, Scopus, and Web of Science Core Collection for English-language studies published between 2016 and 2026 on 15 February 2026. Of 200 records identified, 32 duplicates were removed, and 168 records were screened. Eighty-three full-text reports were assessed for eligibility. Thirty-eight studies were included in the Tier 1 synthesis as action-trigger decision support systems, and 20 were classified as Tier 2 prediction or detection only to characterize evidence gaps. Tier 1 systems clustered into room-based monitoring with direct nurse alerting, wearable or batteryless sensing for bed egress and bed or chair exit alarms, and bed-centered early warning. Reporting was often incomplete for implementation-critical metrics such as alert burden, false alarms, response times, alert routing, and downstream actions. We propose a minimum operational reporting set to support clearer evaluation and comparison of AI-enabled inpatient fall-prevention systems in real-world ward settings.