Twin It to Win It: How BedreFlyt Reimagines Hospital Resource Planning

Hospitals often operate under intense pressure, juggling patient needs, staff availability, and limited resources. Now imagine an AI-powered assistant that anticipates those needs, simulates complex patient flows, and delivers optimized resource plans—without burning out the staff. That’s the promise of BedreFlyt, a modular, simulation-driven Digital Twin (DT) designed for hospital wards.

Developed at the University of Oslo, BedreFlyt isn’t just another simulation tool. It uniquely integrates:

  • Formal modeling (ABS) for process simulation,
  • Knowledge graphs for dynamic system representation, and
  • Z3 SMT solver for constraint-based optimization.

From Chaos to Coordination: The Hospital Ward Challenge

Hospital wards face a surprisingly complex scheduling problem: patients arrive unpredictably, stay for varying durations, and require beds that meet diverse care-level constraints (e.g., proximity to monitoring stations, gender, or infection isolation). These variables are typically managed manually by nurses—draining time and creating bottlenecks.

BedreFlyt tackles this by automating the conversion of live or historical patient data into a stream of optimization problems. Its architecture transforms patient flows into time-indexed bed allocation plans that respect all constraints—and minimize disruptive reallocations.

Architecture in Action: What Powers the Twin?

BedreFlyt’s architecture features five key components:

  1. Knowledge Base (OWL/RDF): Ontologies describe rooms, bed types, patient pathways, and treatment tasks.
  2. Simulation Driver + ABS: Time-aware actor-based modeling simulates patient progression through treatments.
  3. Local Data Store: Historical or real-time patient streams serve as simulation inputs.
  4. Constraint Formulation: Each timestep’s allocation problem is expressed as SMT logic.
  5. Z3 Solver: Computes feasible, minimal-change bed allocations.

Through this stack, BedreFlyt supports both short-term operational decisions and long-term “what-if” scenario exploration, including seasonal and risk-sensitive planning.

Real-World Impact: Proof-of-Concept and Performance

Using anonymized data from Rikshospitalet, Norway, BedreFlyt generated year-long bed allocation plans within practical compute times—e.g., 100 patients over 30 days simulated in ~30 seconds. Scalability tests show up to 2000 patients planned across 365 days in under 20 minutes.

The result? Fewer unnecessary patient moves, smarter use of ward capacity, and a scalable blueprint for future hospital AI deployment.

Why It Matters: From Static to Self-Adaptive Healthcare

Most planning tools are static. BedreFlyt evolves. Thanks to modularity and self-adaptive design via SMOL (a DT orchestration language), it’s future-proof. Upcoming iterations aim to:

  • Refine temporal granularity (e.g., down to minutes),
  • Integrate statistical distributions and live updates,
  • Expand to multi-ward or full-hospital ecosystems,
  • Include other hospital resources like staff, diagnostics, or equipment.

Final Diagnosis

BedreFlyt is more than a digital twin. It’s a digital doctor for operational efficiency. By combining ontological intelligence with executable logic and robust optimization, it’s set to become a critical actor in hospital decision-making.

Cognaptus: Automate the Present, Incubate the Future