A simulator to improve waiting times at a Medical Imaging Center

Ramis,; Baesler,; Berho,; Neriz,; Sepulveda,

doi:10.1109/wsc.2008.4736239

Cited by 12 publications

(7 citation statements)

References 7 publications

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“…the different types of diagnostic imaging and their benefits were discussed [12]., In a new Markov model were discussed [13]. A Queue Selector that can be used for the improvement of Utilization of the server [14].How queuing theory can be applied in bank sector to optimize the service is being discussed in [15]. M/Mc model is the discussion topic in the paper [16,17] CONCLUSION Diagnostic imaging contributes a major part in planning for treatment.…”

Section: Literature Surveymentioning

confidence: 99%

A Survey on Usage Analytics for Imaging Diagnostic Equipments

Verghese

Parvathi

2017

Asian J Pharm Clin Res

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The main aim of the paper is to find the best predictive algorithm that can be used to predict the usage of the imaging diagnostic equipment in the near future. The growing number of patients are not able to receive the image diagnostic test within a short waiting time. The goal is to keep the waiting time low. The rising demand for the image diagnostics equipment has to be estimated to improve the service.

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Section: Literature Surveymentioning

confidence: 99%

A Survey on Usage Analytics for Imaging Diagnostic Equipments

Verghese

Parvathi

2017

Asian J Pharm Clin Res

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“…For a comprehensive healthcare simulation review see (Eldabi et al, 2007), (Young et al, 2009) and (Neriz et al, 2011). Specific examples of simulation of imaging centers can be found in (Ramis et al, 2008(Ramis et al, , 2009, where simulation focuses on providing an alternative to reduce the waiting time of the patients, improve the use of physical and human resources, evaluate different alternatives of plant layout, scheduling patients or simply, to study and better understand the inner working of the system.…”

Section: Process Simulationmentioning

confidence: 99%

Tools to Improve the Patient’s Processes at Imaging Centers

Neriz¹,

Ramis²

2011

Medical Imaging

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There is a worldwide concern for increased efficiency and cost effectiveness in healthcare delivery which in the USA represents about 16% of the Gross Domestic Product. The variability and complexity of the processes within healthcare systems demand the use of more sophisticated management tools, such as the recommended by a joint study by the National Academy of Engineering and the Institute of Medicine in the USA (NAE 2005), to improve the healthcare delivery system. In this chapter it is seen the integration of three management tools (Activity Based Costing, Lean Healthcare and Process Simulation) to facilitate the development of Medical Imaging Center (MIC) studies in order to improve the patient´s processes, which departs from traditional modeling because it uses a pull paradigm for the patients. Activity Based Costing (ABC Costing) is used to identify improvement opportunities from the managers' point of view, Lean Healthcare (Lean) to identify opportunities from the Customers´ point of view and Process Simulation (Simulation) to test the potential impact of any proposed alternative identified with ABC Costing or Lean, prior to its implementation. The center to be modeled might be as simple as having one x-ray machine, or, more complex having all kind of imaging machines (magnetic resonance, CT, x-ray, digestive radiology, ultrasound scan, angiography, mammography and others). Medical Imaging Centers are important units in every hospital or medical center as they are a relevant link in generating a patient's diagnostic. Because of this, it is necessary that these centers be managed with high quality standards but also with efficiency. As an application, an integrated example is provided where ABC costing, Lean and Simulation are applied to an imaging center to improve the patient's waiting times and process costs. Medical Imaging Centers have made an important investment in PACS systems, which provide data comprising from the reception of patients to the taking of the image. This kind of system gives relevant and detailed information, such as: number and kind of patient, frequency, type of exam, supplies and drugs utilized and personnel involved, all of these entry data are significantly useful to the proposed tools, and, furthermore complement and support the information for the management of MIC.

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“…For many years, simulators have been proposed in numerous application fields and work situations, such as transportation [49], medicine [44] and ecology [39]. Most simulations make use of a standard software and hardware architecture.…”

Section: Introductionmentioning

confidence: 99%

A model for managing interactions between tangible and virtual agents on an RFID interactive tabletop: Case study in traffic simulation

Lebrun

Adam

Mandiau

et al. 2015

Journal of Computer and System Sciences

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Please cite this article in press as: Y. Lebrun et al., A model for managing interactions between tangible and virtual agents on an RFID interactive tabletop: case study in traffic simulation, J. Comput. System Sci. (2015), http://dx. AbstractInteractive tables are more frequently being suggested to support collaborative and co-localized applications. They can be tactile, tangible or mixed. New sensory entries enable users to manipulate different types of tangible objects. The purpose of this article is to propose a global interaction model that associates multiagent system concepts with interactive table entities. The model incorporates simultaneously usable entities: virtual and tangible entities. The virtual entities can be seen through video projection or the use of a screen integrated into the interactive table. The tangible entities are physical objects that can be manipulated by one or more users around the table. These objects are detected through dedicated sensors. A case study illustrates the proposal. The study incorporates the management of a road traffic simulator using the TangiSense interactive table; this table is equipped with RFID technology. The illustrations mainly show communication between the different agents involved in the simulation.

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A simulator to improve waiting times at a Medical Imaging Center

Cited by 12 publications

References 7 publications

A Survey on Usage Analytics for Imaging Diagnostic Equipments

A Survey on Usage Analytics for Imaging Diagnostic Equipments

Tools to Improve the Patient’s Processes at Imaging Centers

A model for managing interactions between tangible and virtual agents on an RFID interactive tabletop: Case study in traffic simulation

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