Automated Scheduling in the Healthcare Sector
summary
A Munich software startup with ~70 staff automates hospital shift rostering using a genetic algorithm that replaces Excel, cuts coordination from 6.5 to 1.5 FTE at one customer, and handles realistic cases of ~8 employees and ~100 shifts. Experimenting with QUBO on D‑Wave shows millisecond solutions for tiny tests but failure on large, realistic plans; a quantum-assisted genetic algorithm hybrid refines classical schedules and avoids local optima. Estimate: ~16,000 qubits needed before full-scale quantum advantage for complex hospital schedules.
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chapters (10)
- 0:00Automated healthcare scheduling
- 1:06Manual shift planning problems
- 3:37Planario’s AI shift planning
- 7:48Genetic algorithms challenges
- 9:11Quantum computing potential
- 10:56Planck quantum AI platform
- 13:02Quantum proof of concept
- 13:44Hybrid quantum-classical approach
- 15:53Quantum limitations and needs
- 17:58Customer reactions and next steps
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