Artificial Intelligence-Enhanced Early Warning Systems for Early Detection of Clinical Deterioration in Hospitalized Patients: Current Clinical Applications and Future Directions.

Authors

Varvara Kakaidi, Mary Gouva, Konstantinos Tsimos, Athina Paschou, Efthalia Moustakli, Evangelia G Vryoni, Dimitra Vourda, Maria Kitsiou, Nikolaos-Stefanos Bastas, Aikaterini D Lianou

Published in

Maedica. Volume 21. Issue 3. Pages 767-775.

Abstract

Early recognition of clinical deterioration in hospitalized patients is essential for preventing adverse events such as cardiac arrest, sepsis, respiratory failure and unplanned intensive care unit (ICU) admission. Traditional Early Warning Systems (EWS), including the Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS), are widely used to support bedside assessment through standardized evaluation of physiological parameters. However, conventional EWS are limited by intermittent monitoring, manual data entry and variable clinical interpretation. Recent advances in electronic health records, continuous physiological monitoring and artificial intelligence (AI) have enabled the development of AI-enhanced Early Warning Systems capable of real-time risk assessment and prediction of clinical deterioration before overt clinical manifestations occur. Machine learning algorithms can analyze large volumes of clinical data, identify subtle physiological trends and support earlier clinical intervention. Emerging evidence suggests that AI-enhanced EWS may improve rapid response activation, reduce ICU transfers, decrease in-hospital mortality and optimize workflow efficiency. This narrative review examines the evolution of EWS from traditional scoring systems to AI-driven predictive models, focusing on their current clinical applications in hospitalized adult patients. Particular emphasis is placed on practical implementation in routine clinical care, the role of nurses and multidisciplinary teams, interpretability of AI algorithms, ethical considerations and barriers to adoption. In addition, the review discusses the integration of AI-enhanced systems into clinical decision support pathways and highlights the importance of maintaining clinical judgment alongside technological innovation. AI-enhanced EWS should be viewed as decision-support tools that complement rather than replace healthcare professionals. Successful clinical implementation requires robust validation, interdisciplinary collaboration, organizational readiness and continuous staff education. When appropriately integrated into clinical workflows, AI-supported predictive systems may significantly contribute to earlier intervention and improved patient safety in acute hospital settings.

PMID:
42859454
Bibliographic data and abstract were imported from PubMed on 11 Oct 2026.

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