ArticleSensors (Basel, Switzerland)2018
An Edge Computing Based Smart Healthcare Framework for Resource Management.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Methodological Approaches to Support Process Improvement in Emergency Departments: A Systematic Review.International journal of environmental research and public health · 2020Pooled it
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- Willingness and influencing factors of adults receiving Hemodialysis to use mobile healthcare apps: a cross-sectional study.BMC nephrology · 2025Article
- Medical Big Data Storage in Precision Medicine: A Systematic Review.Journal of biomedical physics & engineering · 2025Article
- A Markov decision optimization of medical service resources for two-class patient queues in emergency departments via particle swarm optimization algorithm.Scientific reports · 2025Article
- An Analytical Model of IaaS Architecture for Determining Resource Utilization.Sensors (Basel, Switzerland) · 2024Article
- Internet of Things-Based ECG and Vitals Healthcare Monitoring System.Micromachines · 2022Article
- Ubiquitous and smart healthcare monitoring frameworks based on machine learning: A comprehensive review.Artificial intelligence in medicine · 2022Review
- A robust multi-objective model for healthcare resource management and location planning during pandemics.Annals of operations research · 2022Article
- Model-Driven Impact Quantification of Energy Resource Redundancy and Server Rejuvenation on the Dependability of Medical Sensor Networks in Smart Hospitals.Sensors (Basel, Switzerland) · 2022Article
- An Intelligent IoT Monitoring and Prediction System for Health Critical Conditions.Mobile networks and applications : MONET · 2022Article
- Fog Computing Service in the Healthcare Monitoring System for Managing the Real-Time Notification.Journal of healthcare engineering · 2022Article
- Ensemble of Deep Learning Based Clinical Decision Support System for Chronic Kidney Disease Diagnosis in Medical Internet of Things Environment.Computational intelligence and neuroscience · 2021Article
- An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare.Sensors (Basel, Switzerland) · 2020Article
- AI Techniques for COVID-19.IEEE access : practical innovations, open solutions · 2020Article
- Nanosystems, Edge Computing, and the Next Generation Computing Systems.Sensors (Basel, Switzerland) · 2019Review
- Real-Time Massive Vector Field Data Processing in Edge Computing.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The revolution in information technologies, and the spread of the Internet of Things (IoT) and smart city industrial systems, have fostered widespread use of smart systems. As a complex, 24/7 service, healthcare requires efficient and reliable follow-up on daily operations, service and resources. Cloud and edge computing are essential for smart and efficient healthcare systems in smart cities. Emergency departments (ED) are real-time systems with complex dynamic behavior, and they require tailored techniques to model, simulate and optimize system resources and service flow. ED issues are mainly due to resource shortage and resource assignment efficiency. In this paper, we propose a resource preservation net (RPN) framework using Petri net, integrated with custom cloud and edge computing suitable for ED systems. The proposed framework is designed to model non-consumable resources and is theoretically described and validated. RPN is applicable to a real-life scenario where key performance indicators such as patient length of stay (LoS), resource utilization rate and average patient waiting time are modeled and optimized. As the system must be reliable, efficient and secure, the use of cloud and edge computing is critical. The proposed framework is simulated, which highlights significant improvements in LoS, resource utilization and patient waiting time.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.