Artificial intelligence in hospital fall prevention: Current applications, challenges, and future directions synthesizes an emerging, fragmented evidence base into a clinically usable map of what “AI for falls” actually means in hospitals, what it can realistically deliver today, and what must change for it to be trustworthy at scale. The authors frame hospital falls as a persistent patient safety problem where conventional tools are typically intermittent and subjective, while the operational reality of fall risk is dynamic and changes across hours, units, and care processes. Their central argument is that AI is not a single intervention but a family of technologies that can shift fall prevention from periodic scoring to continuous, data-driven risk management, provided implementation design is as rigorous as model design.
Methodologically, the paper is positioned as a narrative review supported by a systematic search, explicitly spanning multiple clinical and technical databases. This matters because “fall AI” research sits across nursing, informatics, engineering, and safety science, so a single-database approach systematically under-captures the field. The review’s stated purpose is practical: consolidate what exists, compare modalities, surface implementation barriers (not just AUROC), and identify research gaps that directly block safe deployment.
A key contribution is the four-domain typology of AI applications and the way the authors anchor each domain in performance ranges rather than hype. Machine-learning prediction models are reported with AUROC values in the 0.85–0.97 band, but with calibration inconsistently reported. Computer-vision systems are described as enabling real-time behavioural monitoring, with 94–97% detection accuracy in controlled settings. Sensor-based approaches report 89–96% accuracy with multi-sensor fusion. Natural language processing is positioned as a way to extract fall-relevant risk factors from clinical documentation, with sensitivity around 95% in selected studies. The paper is careful about what these numbers are and are not: they largely reflect single-site, retrospective work with limited external validation, so performance may not transfer when the patient mix, workflows, or documentation culture changes.
The implementation discussion is where the paper becomes unusually decision-useful for managers and quality leaders. Reported outcomes include fall reductions of 0.9–1.2 falls per 1,000 patient-days, corresponding to 15–40% relative reductions, but the authors explicitly warn that baseline rates vary (2.8 to 5.1 per 1,000 patient-days in the cited implementations), and heterogeneous study designs plus secular trends limit causal inference. In other words, “40% reduction” can be a rounding error in one unit and a major safety gain in another, so evaluations must report both absolute and relative change, baseline rate, and setting context to avoid misleading ROI narratives.
The paper is also direct about why many AI fall projects fail quietly after installation. Alarm fatigue is flagged as a first-order implementation risk, compounded by the fact that alert rates and positive predictive value are rarely reported, making it difficult to anticipate workload and trust erosion. The authors also elevate algorithmic bias as an operational problem, not an ethics footnote, recommending ongoing fairness audits rather than one-time model checks. They add pragmatic constraints that hospital leaders actually feel: liability when systems fail, data privacy constraints, integration complexity (especially EHR and workflow), clinical adaptation costs, and affordability barriers for smaller institutions. Future directions are correspondingly concrete: explainable AI, multisite external validation with standardized metrics (AUROC, AUPRC, calibration), federated learning to enable cross-site learning without centralizing data, and implementation trials that measure care-process outcomes as well as fall rates.
Mini glossary (atıflı)
Hospital falls: Unintentional descents to the floor or a lower surface occurring during hospitalization; the paper treats falls as a high-frequency safety outcome that is sensitive to unit context, workflow, and time-varying clinical status, which is why static scoring often underperforms. (Osonuga et al., 2026).
Predictive models, detection systems, prevention frameworks: The review separates three overlapping use cases: prediction estimates future fall risk over a time horizon, detection identifies fall events or high-risk behaviours in real time, and prevention frameworks connect prediction and detection to workflow triggers that activate interventions; this distinction matters because each layer requires different infrastructure, staffing responses, and evaluation logic. (Osonuga et al., 2026).
AUROC: Area under the receiver operating characteristic curve; used to summarize discrimination of predictive models, and the paper reports strong AUROC ranges for some fall prediction models while emphasizing that calibration and external validation are often missing, limiting deployability. (Osonuga et al., 2026).
AUPRC: Area under the precision–recall curve; highlighted as a needed standardized metric in future validation because falls are often relatively infrequent events (class imbalance), so AUROC alone can hide poor real-world alert usefulness. (Osonuga et al., 2026).
Calibration: Agreement between predicted risk and observed risk; the paper notes calibration is variably reported even when AUROC is high, which is risky because intervention thresholds depend on whether “20% risk” actually behaves like 20% risk in that unit. (Osonuga et al., 2026).
Computer vision in fall prevention: Video-based analytics used for real-time behavioural monitoring, often aiming to detect movements that precede a fall attempt; the review reports high controlled-setting detection accuracy but implies performance and acceptance depend on privacy design, workflow, and false-alarm burden. (Osonuga et al., 2026).
Sensor-based surveillance and multi-sensor fusion: Wearable/environmental sensors used for continuous monitoring, where combining multiple sensors can improve accuracy; the review reports 89–96% accuracy ranges and positions sensors as a pathway to continuous surveillance when video is impractical. (Osonuga et al., 2026).
Natural language processing: Methods that extract fall-relevant risk factors from unstructured clinical notes, enabling risk signals that conventional structured fields may miss; the paper reports high sensitivity in selected studies while implicitly warning that documentation habits vary by site. (Osonuga et al., 2026).
Alarm fatigue: A degradation of clinician attention and trust caused by frequent alerts, especially when many are non-actionable; the review flags this as a core barrier and points out that alert rates and PPV are rarely reported, which blocks realistic staffing and workflow planning. (Osonuga et al., 2026).
Positive predictive value (PPV): The proportion of alerts that are true positives; the paper treats PPV as central for operational feasibility (how many alerts nurses must respond to per prevented fall), yet notes PPV is often not reported, creating “invisible workload” risk. (Osonuga et al., 2026).
External validation (multisite validation): Testing model performance in different hospitals or units beyond the development site; the review repeatedly signals that limited external validation is a key reason reported performance may not generalize, and explicitly recommends multisite validation as a priority. (Osonuga et al., 2026).
Federated learning: A cross-institution learning approach where models are trained across sites without centralizing patient data; the review lists it as a future direction to reconcile the need for multisite generalizability with privacy and governance constraints. (Osonuga et al., 2026).
Explainable AI: Techniques that make model reasoning more interpretable to clinicians; the review prioritizes explainability as part of future development because adoption depends on trust, accountability, and threshold-setting, not just headline accuracy. (Osonuga et al., 2026).
References
Osonuga, A. A., Osonuga, A., Omeni, D., Okoye, G. C., Egbon, E., & Olawade, D. B. (2026). Artificial intelligence in hospital fall prevention: Current applications, challenges, and future directions. Safety Science, 196, 107104. https://doi.org/10.1016/j.ssci.2025.107104