Instance Generation for Patient-to-room Assignment and Admission Scheduling Based on Real Hospital Data
arXiv:2507.03423v2 Announce Type: replace-cross Abstract: Developing algorithms for real-life problems that perform well in practice depends on the availability of realistic data for testing. Obtaining real-life data for optimization problems in health care, however, is often difficult, and such data typically cannot be published, which limits reproducibility by other researchers. This is especially true for patient-related problems because of data privacy policies such as the patient-to-room assignment problem. Therefore, artificially generated instances are commonly used. To improve the gene
The increasing availability of computational resources and advancements in AI/optimization techniques are enabling more sophisticated approaches to healthcare operational challenges, while data privacy concerns remain paramount.
This research addresses a critical bottleneck in developing robust AI solutions for healthcare: the lack of realistic, shareable data due to privacy concerns, highlighting a common problem across many sensitive AI applications.
The ability to generate high-quality synthetic data for complex healthcare optimization problems improves research reproducibility and accelerates the development and testing of practical algorithms.
- · Healthcare AI Developers
- · Hospitals and Healthcare Systems
- · Optimization Researchers
- · Patients (indirectly)
- · manual scheduling processes
- · inefficient resource allocation
Improved patient flow and resource utilization within hospitals will lead to more efficient healthcare operations.
Standardized synthetic data generation could foster collaborative development of AI solutions across various healthcare institutions and researchers.
Enhanced operational efficiency driven by AI could free up resources for other critical healthcare areas or improve patient care quality significantly.
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Read at arXiv cs.LG