Every percentage point drop in hospital readmissions represents not just cost savings but real patients avoiding preventable deterioration. A systematic review examining AI-assisted clinical decision-making in nursing settings quantifies these gains with striking specificity, offering a concrete data foundation for healthcare systems weighing technology investments in frontline care.
Drawing on eight experimental studies published between 2005 and 2024 — identified through five major databases including PubMed and Scopus using JBI methodological guidelines — the review maps AI tools across several high-stakes nursing domains. A discharge support algorithm reduced 30-day readmission rates from 22.2% to 9.4%, a reduction of more than half. A patient deterioration algorithm meaningfully shortened the time nurses took to escalate to senior clinicians and initiate diagnostic testing. In neonatal resuscitation, AI-assisted decision support lifted accuracy rates to 94–95% compared with 55–80% in control conditions. Additional improvements were documented in seizure assessment confidence, pressure ulcer prevention protocols, wound classification, and clinical documentation quality — all reaching statistical significance.
This review arrives at an inflection point in nursing informatics. Prior research has focused heavily on physician-facing AI tools, leaving a gap in evidence specifically addressing nursing workflow integration. The breadth of domains covered here — from neonatal emergencies to chronic wound care — suggests AI utility in nursing is not niche but systemic. However, critical limitations temper enthusiasm: only eight studies met inclusion criteria, cohort sizes are not detailed in available excerpts, and the review spans two decades during which AI capability changed dramatically. Heterogeneity across study designs makes effect-size pooling unreliable. Most findings remain single-institution or small-scale, meaning generalizability to diverse healthcare environments is unproven. For health-conscious adults, the practical implication is that the nurses managing their care may increasingly rely on algorithmic support — a development that demands transparency, rigorous validation, and equity-aware implementation before widespread adoption.