Digital Bed Management in Austria: Interfaces, AI Tools, and Regional Integration

Background: Hospital bed management in Austria faces persistent structural pressure from an ageing population, fragmented cross-sectoral IT infrastructure, and limited real-time information exchange between primary care, nursing homes, and hospitals. A substantial share of unplanned hospital transfers from long-term care is considered potentially avoidable, yet the data and coordination mechanisms required to prevent them rarely cross institutional boundaries. Objective: We develop a practice-oriented framework for AI-driven, cross-sectoral bed management bridging primary care, long-term care, and hospital settings within a privacy-preserving, decentralised architecture, and report on the technical implementation of a minimal viable product deployed in a sandbox environment at the University Hospital Wiener Neustadt (UKWN), Austria. Methods: We combined a narrative review of international evidence on digital bed management, AI-based patient flow prediction, and interoperability frameworks (HL7 FHIR, European Health Data Space) with structured mapping of data flows and organisational interfaces across care sectors in an Austrian pilot region. A modular sandbox architecture was designed on privacy-by-design principles, implementing a container-based machine learning pipeline (DVC-managed versioning, PyTorch neural network for binary length-of-stay classification) trained on 200 synthetic cardiology cases. Results: The end-to-end pipeline executed reproducibly across all stages. A systematic audit of the synthetic training data revealed properties precluding meaningful performance evaluation; we therefore report infrastructure readiness rather than classification performance. Conclusions: A decentralised, privacy-preserving AI architecture for cross-sectoral bed management is technically feasible in the Austrian healthcare context. Validated infrastructure provides the foundation for evaluation with prospective real-world data and cross-sectoral extension pending ethics approval.