Systemic fragility in European total intravenous anesthesia delivery and opportunities for resilient real-time decision support
Rattachement africain : be, tr, it, fr, ro. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Total intravenous anesthesia (TIVA) is a well-established technique for general anesthesia and is increasingly relevant to Europe’s transition toward more digital, sustainable, and patient-centred healthcare. However, its use remains uneven across centres and still depends heavily on population-based drug models, variable monitoring practices, and repeated clinician adjustment. Differences in training, data access, device compatibility, patient response, and institutional resources can limit safe and consistent personalization. In this Review, we examine the main sources of fragility in current TIVA practice and discuss how interoperable perioperative data, digital patient simulators, patient-adaptive models, multimodal monitoring, and closed-loop decision support could improve safety and resilience. We emphasize that these technologies should support, not replace, the anesthesiologist, and should remain transparent, clinically supervised, and easy to override. We conclude by outlining priorities for European collaboration in data infrastructure, training, validation, regulation, and evaluation of AI-enabled tools. Ionescu et al. outline a European roadmap for strengthening Total Intravenous Anesthesia Delivery through resilient, human-in-the-loop real-time decision support. Current practice remains uneven and fragile, while interoperable data, validated simulators, adaptive models, robust control, and clear regulation enable safer personalization.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Systemic fragility in European total intravenous anesthesia delivery and opportunities for resilient real-time decision support
- Date Crossref
- 01/08/2026
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Les institutions déclarées
Une affiliation ne permet pas de déduire la nationalité d’un auteur.