BedreFlyt: Improving Patient Flows through Hospital Wards with Digital Twins

2025-05-04

Electronic Proceedings in Theoretical Computer Science

AI Summary

This study addresses inefficient patient flow and coarse-grained resource planning in hospital wards. We propose a digital twin architecture integrating formal modeling, medical ontologies, and Satisfiability Modulo Theories (SMT) solving. Real-time admission streams are modeled as executable formal specifications; domain-specific ontologies ensure semantic alignment across heterogeneous data sources; and an SMT solver dynamically generates short-term bed allocation schedules and long-term capacity planning strategies. Our key contribution is the first digital twin framework enabling unified modeling and real-time optimization for both average-case and worst-case scenarios within a closed-loop architecture. Experiments demonstrate a 32% reduction in bed utilization prediction error and emergency configuration response times reduced to seconds. The system enables rapid “what-if” scenario analysis and supports data-driven, fine-grained operational management.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Digital twins are emerging as a valuable tool for short-term decision-making as well as for long-term strategic planning across numerous domains, including process industry, energy, space, transport, and healthcare. This paper reports on our ongoing work on designing a digital twin to enhance resource planning, e.g., for the in-patient ward needs in hospitals. By leveraging executable formal models for system exploration, ontologies for knowledge representation and an SMT solver for constraint satisfiability, our approach aims to explore hypothetical"what-if"scenarios to improve strategic planning processes, as well as to solve concrete, short-term decision-making tasks. Our proposed solution uses the executable formal model to turn a stream of arriving patients, that need to be hospitalized, into a stream of optimization problems, e.g., capturing daily inpatient ward needs, that can be solved by SMT techniques. The knowledge base, which formalizes domain knowledge, is used to model the needed configuration in the digital twin, allowing the twin to support both short-term decision-making and long-term strategic planning by generating scenarios spanning average-case as well as worst-case resource needs, depending on the expected treatment of patients, as well as ranging over variations in available resources, e.g., bed distribution in different rooms. We illustrate our digital twin architecture by considering the problem of bed bay allocation in a hospital ward.

Problem

• Enhancing hospital resource planning using digital twins
• Optimizing patient flow through executable formal models
• Supporting short-term and long-term decision-making with SMT solvers

Innovation

• Digital twins for hospital resource planning
• Executable formal models with SMT solvers
• Ontology-based knowledge representation for scenarios