Developing cost-effective simulators for patient management: A modular approach

Alsalemi, Abdullah; Disi, Mohammed Al; Ahmed, Ibrahim; Alhomsi, Yahya; Bensaali, Fayçal; Amira, Abbes; Alinier, Guillaume

doi:10.1109/icabme.2017.8167552

Cited by 14 publications

(15 citation statements)

References 18 publications

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“…Problems that occur in the real world can all be solved by mathematical modeling, problems can be modeled into mathematical forms that become equations [21], Mathematical principles are not only used for systematization, but also function to maximize results or minimize costs [22]. Models that emphasize cost effectiveness focus on the physical accuracy that will be used [23], and violating capacity limits can be done by giving a penalty value [24]. In the GU et al Study, it introduced the Fog-Computing-Medical Cyber-Physical System (FC-MCPS) model, which presents mixed-integernonlinear-programming (MINLP) formulations on minimum cost issues with joint consideration of user associations, task distribution and VM deployment (virtual machine) [25].…”

Section: Methodsmentioning

confidence: 99%

Optimization of Health Care Services with Limited Resources

Pardede¹,

Mawengkang²,

Zarlis³

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Health services are an integral part of hospitals or health clinics. Maximum service can get if the availability of resources in the service center is very adequate, but the availability of resources cannot be ascertained that it will always be adequate, and excessive availability of resources can also result in waste. The problem that often occurs is the lack of optimal services provided to patients due to limited available resources. Various obstacles, such as services that are not permitted are repeated, uncertain service distances, and service time are optimal barriers to service. This study aims to solve the problem of optimizing health care services for patients in hospitals using a number of variables in the hospital environment such as available resources, namely doctors, nursing medical personnel, technicians, technical equipment. This study is subject to the aim of minimizing all costs incurred to perform services, namely travel time from places to provide health services to patients, medical staff costs to provide services of the type of service, and so on. These variables are explained in the form of mathematical models that are able to explain existing constraints and minimize costs and time when performing services. The modeling results were tested using Linear Ineraktive Discrete Optimizer (LINDO) programming to determine errors that might occur in the model. The test results provide information that the maximum value of the objective function is 88.00 at the 25th iteration step so that the new model is expected to optimize health services for hospital patients and existing health clinics.

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Section: Methodsmentioning

confidence: 99%

Optimization of Health Care Services with Limited Resources

Pardede¹,

Mawengkang²,

Zarlis³

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…The first subsystem is very innovative as it concentrates on simulating the ECMO machine itself using a modular design where the instructor has the control to simulate different complications. The main innovation of the simulator is the use of thermochromic ink as a substitute for animal blood which makes its maintenance cheaper while upholding high standards for realism in terms of oxygenation and deoxygenation color change [15]- [23].…”

Section: Proposed Design Description and Analysismentioning

confidence: 99%

Preliminary Implementation of the Next Generation Cannulation Simulator

Mahmoud

Hssain

Alinier

et al. 2019

2019 IEEE Student Conference on Research and Development (SCOReD)

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Extracorporeal Membrane Oxygenation (ECMO) is a highly complex/critical lifesaving procedure known to support patients with cardiac and respiratory issues. Patients on ECMO are monitored 24/7 by a team of highly trained ECMO team comprising nurses, physicians, respiratory therapists, and perfusionists promptly intervening to any potential emergency situation. Simulation-Based Training (SBT) allows clinicians to experience and practice realistic hands-on procedures and scenarios without any risk. In ECMO, cannulation is a critical procedure performed to externally reroute the blood flow so it can be re-oxygenated by the ECMO machine before being recirculated through the patient's body. In a close collaboration with Hamad Medical Corporation (HMC), this project aims to develop a cost effective, realistic, and user-friendly ECMO simulator focusing on the venous and arterial cannulation procedure, The main features of this simulator include cannulation emergencies caused by low pressure flow, excessive force, recirculation, or mispositioned wire/cannula. Therefore, the ECMO cannulation simulator will not only greatly contribute to the initial and ongoing local training of HMC ECMO clinicians but also contribute to improving patient care by lowering the risks associated with the cannulation process.

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“…Problems that occur in the real world can all be solved by mathematical modeling, problems can be modeled into mathematical forms that become equations [29], Mathematical principles are not only used for systematization but also function to maximize results or minimize costs [30]. Models that emphasize cost-effectiveness focus on the physical accuracy that will be used [31], and violating capacity limits can be done by giving a penalty value [32]. In the GU et al Study, it introduced the Fog-Computing-Medical Cyber-Physical System (FC-MCPS) model, which presents mixed-integer-nonlinear-programming (MINLP) formulations on minimum cost issues with joint consideration of user associations, task distribution and VM deployment (virtual machine) [33].…”

Section: State Of the Artmentioning

confidence: 99%

Limited Resources Optimization of Health Care Services With a Linear Integer Programming Approach

Pardede¹

2019

Preprint

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If the availability of resources in the service center is very adequate, services can be carried out optimally, but we cannot be sure in this situation and even if excessive resources can also result in wasteful costs. If the number of requests more than the number of resources available at the service center can result in non-fulfillment of service requests, this can lead to a long queue of service requests, and if the demand for health care comes from an emergency patient (prioritized patient) can definitely cause this problem to die need to be handled better so as not to cause harm to service providers and patients as service recipients. Health service requests by patients that provide information about the location and type of service desired will be received by the service provider / server connected to the internet service, so that health care request information can be scheduled by the server (hospital health). Communication between the resources available on the server allocates time for each health service that maximizes the limited use of resources to meet the patient's health service needs. The purpose of this study is to simulate the Smart Health model on health services using linear integer programming, so that the resulting model can solve the problem of limited available resources that can serve all requests so that services and services can be done quickly, so as to reduce the risk of losing lives or risks that can endanger the patient. From the new models produced, this maximizes health services and minimizes the time of arrival of medical personnel at the place of request, where when patients request treatment for medical care from the hospital, health care will be provided according to the patient's request.

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Developing cost-effective simulators for patient management: A modular approach

Cited by 14 publications

References 18 publications

Optimization of Health Care Services with Limited Resources

Optimization of Health Care Services with Limited Resources

Preliminary Implementation of the Next Generation Cannulation Simulator

Limited Resources Optimization of Health Care Services With a Linear Integer Programming Approach

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