Abstract
With advancements in artificial intelligence (AI) and machine learning (ML) new technologies are emerging which can assist the organizations in many ways from resource optimization to efficiently maintaining the facilities. Digital twin is such a technology where a virtual model is created to accurately reflect the physical object. It can be applied in the fields of construction, manufacturing, energy, automotive, and health care. Even though the technology is complex to understand but when properly implemented, it can be used to solve the complex problems efficiently. For resource optimization in healthcare management real-time data of hospital operations and its surrounding environment data related to number of patients suffering with a particular disease, its criticality and also cases related to accidents need to be captured. This helps the patients efficiently search the nearby hospitals for admission and better care. Digital twins enable the hospital management to detect the number of bed shortages. Digital twins can be used to replicate staffing systems, capacity planning, workflows, and care delivery models to improve efficiency, optimize costs, and anticipate future needs. In this paper, we study the architecture of building the digital twins for resource optimization in hospital. We also study the existing architecture, identify the gaps, and propose a novel architecture to efficiently optimize the hospital resources. We propose machine learning-based optimization techniques model for optimizing the resources. This will enable the patients to get assisted with better healthcare services.
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Rallapalli, S., Dileep, M.R., Navaneeth, A.V. (2023). Resource Optimization with Digital Twins Using Intelligent Techniques for Smart Healthcare Management. In: Ranganathan, G., Papakostas, G.A., Rocha, Á. (eds) Inventive Communication and Computational Technologies. ICICCT 2023. Lecture Notes in Networks and Systems, vol 757. Springer, Singapore. https://doi.org/10.1007/978-981-99-5166-6_20
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