AI-Driven and Algorithm-Supported Decision Support Using Continuous, Remote, and Self-Monitoring Patient Data for Early Deterioration Detection and Escalation: A Scoping Review

Continuous ward monitoring, remote patient monitoring, and self-monitoring can generate high-frequency physiological data streams, yet clinical benefit depends on whether signals lead to timely escalation without excessive non-actionable alerts and workflow burden. 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 and algorithm-supported decision support approaches using continuous, remote, or self-monitoring patient data for early deterioration detection or prediction and escalation support, with emphasis on nursing relevance, workflow integration, alert burden, and implementation outcomes. PubMed (MEDLINE), Ovid MEDLINE, Web of Science Core Collection, and Scopus were searched on 14 February 2026. The search identified 47 records; 12 duplicates were removed; 35 records were screened; 28 were excluded; and 7 full-text reports were included. The included evidence comprised two original studies, two protocol/design papers, and three reviews. Within these included sources, decision support was commonly described as linking monitoring inputs to interpretive outputs, such as tiered alerts or risk predictions, and then to escalation-related actions or response pathways. Because the evidence base was small and heterogeneous, the review should be interpreted as exploratory evidence mapping rather than as a basis for broad generalization. Within the included studies, key reporting gaps included inconsistent description of escalation endpoints, limited standardized reporting of alert burden and acknowledgment patterns, incomplete workflow descriptions in some remote monitoring evidence, and limited attention to maintenance risks such as dataset shift.