Analysis of factors affecting IoT-based smart hospital design

Uslu, Banu Çalış; Okay, Ertuğ; Dursun, Erkan

doi:10.1186/s13677-020-00215-5

Cited by 105 publications

(57 citation statements)

References 167 publications

(153 reference statements)

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“…Although the studies in developed countries are mostly conducted in a high quality way, another reason might also be the different opinion of some journals about the quality of data or authors from developing countries compared to the one in and developed countries. One other cause of this difference in quality can be large resource requirements to conduct studies in the eld of smart hospitals [34].…”

Section: Economicsmentioning

confidence: 99%

Smart hospitals around the world: a systematic review

Rasoulian-Kasrineh

Sharifzadeh

Tabatabaei

2021

Preprint

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Background It is almost impossible to think about a non-digital world today. digital technologies have changed our life style and they have also affected many domains including health domain. We are all aware that paper-based hospitals have changed a lot to be paper-less. Then a new term was born which is “smart hospital” and studies have been conducted in this field, so this study was designed to conduct a comprehensive review about smart hospitals. Methods 741 studies were identified using unique keywords through searching in the PubMed, ScienceDirect, Embase and Scopus databases. Overall, after applying the inclusion and exclusion criteria (647 based on abstract title and 33 after reading full text) and removing duplicates (43), 18 studies were included in this review. Results Geographically, most of the articles were from Asia (50%). The highest number of publications were observed in 2012. A multidisciplinary team were involved in 77% of the researches and 61.12% of them were conducted in more than one research center. The majority of articles have been published in Q1 quality journals (33.34%) and high-income countries accounted for the largest percentage (66.67) of publishing smart hospital related articles. 27.78% of the studies were aimed at patient care, which had the highest percentage. Among the technologies used, RFID was the most and 66.67% of the articles came from researches about the implementation of a smart hospital. Conclusions it should be noted that development or implementation of a smart device in a hospital should not be considered as implementing a smart hospital, while many studies have used the term smart hospital in their study. Many Asian researchers have used the term “smart hospital” in their articles, but on the other hand, most of related articles published in Q1 journals were from America and Europe. Also, it was also observed that most studies focused on the two concepts including resource management and patient care.

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Section: Economicsmentioning

confidence: 99%

Smart hospitals around the world: a systematic review

Rasoulian-Kasrineh

Sharifzadeh

Tabatabaei

2021

Preprint

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“…A review of conventional methods in IoT-DHSs, considering several aspects like monitoring mechanisms, communication and computing strategies, was presented in [15]. Yet another analysis factor affecting IoT-based smart hospital was proposed in [16]. A comprehensive survey of artificial intelligence-based classification for edge intelligence was presented in [17].…”

Section: Related Workmentioning

confidence: 99%

Sugeno Fuzzy Model Free Reinforced Transformation Based Data Processing for Healthcare Sources in IoT Environment

Sellam¹,

Natarajan²,

Gopu³

et al. 2021

Preprint

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Modern computer sciences and information technologies are anticipated to bring transformative influence in part that mobile communication technologies play in society. To completely take advantage of the services bestowed by modern computer sciences and information technologies, the evidence of the economic and business case is an essential prerequisite. Existing research engulfs several transformative computing methods based on sensors area obtainable as service contain optimize resource management, data processing/storage and security provisioning. With transformative computing being on edge, real-time data must be necessitated for healthcare data analytics. The conventional cloud server cannot address the latency requirements of healthcare IoT sensors. To survive with how to handle these services, we introduce a hybrid method integrating Sugeno Fuzzy Inference (SFI) and Model-free Reinforcement Learning to enhance healthcare IoT and cloud latency. The objective is to lessen high latency between healthcare IoT devices. The proposed Sugeno Fuzzy Model-free Reinforcement Learning Data Computing (SF-MRLDC) method uses a Sugeno Fuzzy Inference model integrated with a Model-free Reinforcement Learning model data computing in a healthcare IoT data analytics environment. The simulation results of the SF-MRLDC method show that it is computationally efficient in terms of latency by ensuring better response time.

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“…IoT devices are inter-communicable and can extract information about different environments. IoT devices are used, for example, to monitor patients in smart hospitals [1], to monitor smart homes [2], or to detect accidents in vehicular ad-hoc networks [3]. The Statista group 1 predicts that the installed IoT devices is projected to amount to 21.5 billion units worldwide by 2025.…”

Section: Introductionmentioning

confidence: 99%

“…IoT devices are used, for example, to monitor patients in smart hospitals [1], to monitor smart homes [2], or to detect accidents in vehicular ad-hoc networks [3]. The Statista group 1 predicts that the installed IoT devices is projected to amount to 21.5 billion units worldwide by 2025. Most of these devices will be located at the edge of the Internet and could provide new applications, changing many aspects of both traditional industrial productions and our everyday living.…”

Section: Introductionmentioning

confidence: 99%

“…A smart hospital is a concept that emerged as a result of rapid digitalization across the healthcare industries with the use of IoHT, data analytics, availability of personalized services, Artificial Intelligence (AI), and lastly the Cloud-Fog-Edge processing capacities [1,18]. Smart hospitals offer new opportunities to provide fast and accurate responses by obtaining relevant information.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance Evaluation of an Internet of Healthcare Things for Medical Monitoring Using M/M/c/K Queuing Models

Silva

Nguyen

Fé³

et al. 2021

IEEE Access

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Due to the non-stop and rapid spreading of virus pandemics all over the world, traditional healthcare monitoring capabilities of hospitals and/or medical centers are under a severe over-load. Modern computing infrastructures with the harmony of various layers of computing paradigms (e.g., cloud/fog/edge computing) for healthcare monitoring are apparently the essential computing backbone that help access and process instantly the medical data of every single patient at the very edge of the healthcare system to combat with global or regional virus contagion. Previous studies proposed different computing system architectures for healthcare monitoring but few works considered the evaluation of pure performance of medical data transmission in a comprehensive manner. In this paper, we proposed an M/M/c/K queuing network model for the performance evaluation of an Internet of Healthcare Things (IoHT) infrastructure in association with a three layer cloud/fog/edge computing continuum. The model considers a life cycle of medical data from body-attached IoT sensors in edge layer all the way to local clients (e.g., local medical doctors, physicians) through fog layer and to remote clients (e.g., medical professionals, patient's family members) through cloud layer. Furthermore, we also explore the impact of the alteration in system configuration and computing capability of computing layers in two scenarios on various performance metrics. Critical performance metrics related to quality of service are evaluated in a comprehensive manner, such as (i) mean response time of medical data transmission to fog (local) clients and to cloud (remote) clients, (ii) utilization of cloud/fog/edge computing layers, (iii) service throughput, (iv) number of medical messages in a period of time, and (v) drop rate. The simulation results pinpoint bottle-neck parameters and configurations of the IoHT infrastructure's system architecture in relation to the frequency of medical data collection for health check of patients. Thus, the findings of this study can help improve medical administration in hospitals and healthcare centers and help design computing infrastructures in accordance for medical monitoring in the severe circumstances of virus pandemics.

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Analysis of factors affecting IoT-based smart hospital design

Cited by 105 publications

References 167 publications

Smart hospitals around the world: a systematic review

Smart hospitals around the world: a systematic review

Sugeno Fuzzy Model Free Reinforced Transformation Based Data Processing for Healthcare Sources in IoT Environment

Performance Evaluation of an Internet of Healthcare Things for Medical Monitoring Using M/M/c/K Queuing Models

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