Internet of Things in Emergency Medical Care and Services

Edoh, Thierry Oscar

doi:10.5772/intechopen.76974

Cited by 20 publications

(7 citation statements)

References 39 publications

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“…(EHR) also called federated EMR [12] that includes patient's medical records, digital imaging and archiving systems, e-prescribing, e-booking, and clinical administration systems. Despite that, the use of EMR/EHR could positively impact patient's health outcomes and make the patient care safer [7].…”

Section: Electronic Medical Records (Emr): It Is a Sub-set Of Electromentioning

confidence: 99%

Primary Health-Care Service Delivery and Accessibility in the Digital Age

Edoh¹

2021

Recent Advances in Digital System Diagnosis and Management of Healthcare

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The primary care is within a health-care system, the first contact and main point for people requiring health and medical care. Patients requiring specialized health and medical care are directed to the appropriate specialists by a general physician (GP) who coordinates the needed specialist care. GPs base their decisions partially on patient-centered information and partially on the results of medical examinations. Many health-IT systems for primary health care are available today. Their first aims are to assist GPs in their daily duties and the patient in collecting his medical data and to self-manage his conditions. IT systems enabling the patient to collect accurate information on his condition to self-manage his condition provide accurate patientcentric data, which shows the potential to outperform patient-centered information, which in turn is based on the patient's personal feeling and perception. Patientcentered information are biased. Beyond providing patient-centric information, health-IT systems can facilitate access to health-care services, increase the quality, efficiency, and effectiveness of health-care services, and can contribute to reducing medical expenses. This chapter aims to paint down the global trend of health-IT systems and the supporting technology. The chapter will further present some existing health-IT systems and discuss their role in the health-care accessibility, particularly in rural regions.

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Section: Electronic Medical Records (Emr): It Is a Sub-set Of Electromentioning

confidence: 99%

Primary Health-Care Service Delivery and Accessibility in the Digital Age

Edoh¹

2021

Recent Advances in Digital System Diagnosis and Management of Healthcare

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“…The few data there produce is patient-centered. It is though known that patient-centered data are subjective, incomplete, and sometimes biased [27,35].…”

Section: Challenges and Issues Facedmentioning

confidence: 99%

“…Though in a smart home automation enabled healthcare solution for "aging well," collecting patientcentric data in an autonomous way is mandatory. In a previous study [35], various advantages of collecting patient-centric data were discussed. The healthcare personal gets a complete picture of the patient's health condition and can thus pose the right diagnosis.…”

Section: Data Gatheringmentioning

confidence: 99%

IoT-Enabled Health Monitoring and Assistive Systems for in Place Aging Dementia Patient and Elderly

Edoh¹,

Dégila²

2019

IoT and Smart Home Automation [Working Title]

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Hospitals and nursing care homes are facing severe challenges such as lack of skilled workforces and cost explosion, among others. Especially, the western healthcare systems are headed over a cliff. German nursing care houses, hospitals, and government are working hard on solutions to overcome the crucial workforce's crises. Therefore, they are planning to hire nursing-workforces from abroad. They would also like to motivate families with monetary incentives, such as taxreduction, if they can care for their elderly and/or dementia family members at home. However, caring for a sick person at home is challenging for a working family member especially in the case the patient requests around-the-clock nursing care since elderly with cognitive impairments, people with physical disabilities, and people with dementia need (medical) assistance around the clock. The present study reveals an increasing tendency for family members to care for their sick family member at home despite the challenges and issues faced like the lack of time to intensively care for the sick person due to today's family lifestyle, challenging employment market, financial constraints, etc. The study reported in this chapter aims at providing family members willing to care for their sick member at home as long as possible with smart home automation enabled Patient@Home solution to effectively and efficiently care for their sick member despite working full time. People living in nursing care homes can also take benefit of the proposed system. Evidence shows the potential of the proposed system to effectively and efficiently assist in caring for people requesting nursing care around the clock. Though the health-related quality of life is positively impacted, patient's satisfaction is increased (better quality of experience).

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“…As the IoT has gained great popularity in different fields of the modern life, efforts in the context of biomedical has caused appearance of a unique name: Internet of Health Things (IoHT). Thanks to the IoHT, networks of smart medical devices are designed for improving real-time healthcare processes [14] , [15] , [16] . Currently, there is a remarkable variety of medical applications done via IoHT [17] , [18] , [19] .…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of heart diseases by a secure Internet of Health Things system based on Autoencoder Deep Neural Network

Deperlioğlu

Köse

Gupta

et al. 2020

Computer Communications

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Objective of this study is to introduce a secure IoHT system, which acts as a clinical decision support system with the diagnosis of cardiovascular diseases. In this sense, it was emphasized that the accuracy rate of diagnosis (classification) can be improved via deep learning algorithms, by needing no hybrid-complex models, and a secure data processing can be achieved with a multi-authentication and Tangle based approach. In detail, heart sounds were classified with Autoencoder Neural Networks (AEN) and the IoHT system was built for supporting doctors in real-time. For developing the diagnosis infrastructure by the AEN, PASCAL B-Training and Physiobank-PhysioNet A-Training heart sound datasets were used accordingly. For the PASCAL dataset, the AEN provided a diagnosis-classification performance with the accuracy of 100%, sensitivity of 100%, and the specificity of 100% whereas the rates were respectively 99.8%, 99.65%, and 99.13% for the PhysioNet dataset. It was seen that the findings by the developed AEN based solution were better than the alternative solutions from the literature. Additionally, usability of the whole IoHT system was found positive by the doctors, and according to the 479 real-case applications, the system was able to achieve accuracy rates of 96.03% for normal heart sounds, 91.91% for extrasystole, and 90.11% for murmur. In terms of security approach, the system was also robust against several attacking methods including synthetic data impute as well as trying to penetrating to the system via central system or mobile devices.

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Internet of Things in Emergency Medical Care and Services

Cited by 20 publications

References 39 publications

Primary Health-Care Service Delivery and Accessibility in the Digital Age

Primary Health-Care Service Delivery and Accessibility in the Digital Age

IoT-Enabled Health Monitoring and Assistive Systems for in Place Aging Dementia Patient and Elderly

Diagnosis of heart diseases by a secure Internet of Health Things system based on Autoencoder Deep Neural Network

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