ArticleAnnals of operations research2023
Machine learning based integrated scheduling and rescheduling for elective and emergency patients in the operating theatre.
Article in Annals of operations research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Operating Room Management.Journal of medical systems · 2024Pooled it
- Explainable AI for Equitable Nurse Scheduling: Pragmatic Pre-Post Implementation Study.JMIR nursing · 2026Article
- Accurate surgery time prediction (ASTP) strategy based on artificial intelligence techniques.Scientific reports · 2026Article
- Operations Research for Pediatric Elective Surgery Planning: Example of a Mathematical Model.Bioengineering (Basel, Switzerland) · 2026Article
- A Reproducible Post-Valve-Replacement EHR Cohort for Comparative AI Studies.Diagnostics (Basel, Switzerland) · 2026Article
- Hierarchical Reinforcement Learning for Fair Operating Room Scheduling in Plastic Surgery.Plastic and reconstructive surgery. Global open · 2025Article
- Article
- Large language models in perioperative medicine-applications and future prospects: a narrative review.Canadian journal of anaesthesia = Journal canadien d'anesthesie · 2025Review
- Review
- Review
- Artificial Intelligence in Perioperative Care: Opportunities and Challenges.Anesthesiology · 2024Article
- Factors affecting operating room scheduling accuracy for primary and revision total hip arthroplasty: a retrospective study.Archives of orthopaedic and trauma surgery · 2024Article
- Two-stage multi-objective optimization for ICU bed allocation under multiple sources of uncertainty.Scientific reports · 2023Article
- WindowSHAP: An efficient framework for explaining time-series classifiers based on Shapley values.Journal of biomedical informatics · 2023Article
- Genetic Algorithm for Solving the No-Wait Three-Stage Surgery Scheduling Problem.Healthcare (Basel, Switzerland) · 2023Article
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4 authors.
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Abstract
As the only largest source of revenue and cost in a hospital, the operation room (OR) scheduling problem is a hot research topic. Nonetheless, an integrated model is the missing key to managing and improving the efficiency of ORs. This paper presents a fully integrated model regarding three concepts: meditating elective patients and emergency patients together, considering ORs and downstream units, and proposing hierarchical weekly, daily, and rescheduling models. Due to the inherent randomness in emergency patient arrival, a random forest machine learning model and geographical information systems are used to obtain the emergency patient surgery duration and arrival time, respectively. According to the machine learning model in weekly and daily scheduling, initially, fixed capacity is reserved for emergency patients. When an emergency patient arrives, the surgery starts if a reserved OR is available. Otherwise, the first available OR will be dedicated to the patient due to an emergency patient's higher priority than an elective patient. In this case, it is needed to reschedule the OT schedule for the remaining patient. Moreover, the three-phase model guarantees that an emergency patient assigns to an OR within a specific time limit. To solve the models, genetic algorithm and particle swarm optimization are developed and compared. In addition, a real-world case study is undertaken at a hospital. The results of comparing the proposed approach to the hospital's current scheduling show that the three-phase model had a considerable positive effect on the ORs schedule.
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