AI-Driven Inpatient Fall Prevention Using Continuous Monitoring: From Early Detection to Workflow-Integrated Decision Support: A Scoping Review

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.