Scheduling Patients in Emergency Department by Considering Material Resources

Harzi, Marwa; Condotta, Jean‐François; Nouaouri, Issam; Krichen, Saoussen

doi:10.1016/j.procs.2017.08.153

Cited by 10 publications

(3 citation statements)

References 9 publications

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“…Many previous studies explore various resource planning approaches for EDs, ranging from static and predictive to dynamic and online scheduling. Harzi et al [8] develop predictive patient scheduling in ED using Mixed Integer Linear Programming (MILP) that significantly reduces the patients' waiting time. Luscombe et al [10] propose a dynamic scheduling framework that provides real-time support for managing the scarce resources of an ED.…”

Section: Resource Scheduling In An Edmentioning

confidence: 99%

Exploration of (De-)centralising Scheduling in an Emergency Department

Florencia,

Moyaux,

Trilling

et al. 2024

Studies in Computational Intelligence

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: Resource Scheduling In An Edmentioning

confidence: 99%

Exploration of (De-)centralising Scheduling in an Emergency Department

Florencia,

Moyaux,

Trilling

et al. 2024

Studies in Computational Intelligence

View full text Add to dashboard Cite

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“…Several works have focused on minimizing the overall patient time in the ED. For example, Harzi et al [ 19 ] used a mixed integer linear program approach to derive optimal scheduling of patients for a given scenario (specified by the patient’s arrival times and the number of physicians). Unfortunately, this approach is limited by the number of patients (the authors were unable to evaluate it with 25 patients or more).…”

Section: Related Workmentioning

confidence: 99%

LBA: Online Learning-Based Assignment of Patients to Medical Professionals

Rosemarin

Rosenfeld

Lapp

et al. 2021

Sensors

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Central to any medical domain is the challenging patient to medical professional assignment task, aimed at getting the right patient to the right medical professional at the right time. This task is highly complex and involves partially conflicting objectives such as minimizing patient wait-time while providing maximal level of care. To tackle this challenge, medical institutions apply common scheduling heuristics to guide their decisions. These generic heuristics often do not align with the expectations of each specific medical institution. In this article, we propose a novel learning-based online optimization approach we term Learning-Based Assignment (LBA), which provides decision makers with a tailored, data-centered decision support algorithm that facilitates dynamic, institution-specific multi-variate decisions, without altering existing medical workflows. We adapt our generic approach to two medical settings: (1) the assignment of patients to caregivers in an emergency department; and (2) the assignment of medical scans to radiologists. In an extensive empirical evaluation, using real-world data and medical experts’ input from two distinctive medical domains, we show that our proposed approach provides a dynamic, robust and configurable data-driven solution which can significantly improve upon existing medical practices.

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“…Despite their success, in recent years, researchers seek to improve the efficiency of emergency material scheduling. Harzi et al presented a mixed integer linear programming approach to schedule medical resources respecting emergency degrees of patients and the availability of resources [3]. Further, considering travel time, road capacity, and material supply and demand, Jincheng Jiang et al designed an effective multi-objective multi-dynamic-constraint emergency material vehicle dispatching and routing model [4].…”

Section: Introductionmentioning

confidence: 99%

An AI Planning Approach to Emergency Material Scheduling Using Numerical PDDL

Yang¹,

Liang²

2022

Atlantis Highlights in Intelligent Systems

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Emergency material planning and scheduling aims to schedule emergency material to destination efficiently to reduce property losses and personal casualties caused by material shortage. Existing researches usually rely on optimization of transportation routes. They ignore the rich scenarios in which human and material resources participate in scheduling simultaneously. Moreover, sudden emergencies may involve the supply of multiple material demand points. To address these issues, we propose an AI planning approach to model emergency material scheduling domain and construct a standard planning task using PDDL. Then a state-of-the-art planner is employed to solve the generated planning task. Experimental results show that the proposed approach can fit the actual situation of emergencies and give high-quality instruction to guide emergency material planning and scheduling.

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Scheduling Patients in Emergency Department by Considering Material Resources

Cited by 10 publications

References 9 publications

Exploration of (De-)centralising Scheduling in an Emergency Department

Exploration of (De-)centralising Scheduling in an Emergency Department

LBA: Online Learning-Based Assignment of Patients to Medical Professionals

An AI Planning Approach to Emergency Material Scheduling Using Numerical PDDL

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