The Role of the Internet of Things in Health Care: A Systematic and Comprehensive Study

Thilakarathne, Navod Neranjan; Kagita, Mohan Krishna; Gadekallu, Thippa Reddy

doi:10.31033/ijemr.10.4.22

Cited by 48 publications

(47 citation statements)

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“…To change the password, you need to enter the old password first, that is, the original password consistent with the work number; then enter the new password and then confirm the new password; the new password has to be entered twice before the password can be changed successfully. In this platform system, we use a graphical interface to display all hospital beds with 5 beads per row [ 19 ].…”

Section: Analysis Of Remote Clinical Care Technologies For Cloud Computingmentioning

confidence: 99%

A Study of Cloud-Based Remote Clinical Care Technology

Lin

Huang²

2021

Journal of Healthcare Engineering

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This paper uses cloud computing to build and design remote clinical care technology, and the study refines the evaluation approach for the elements and builds an evaluation prototype for the strategy, uses service design theory to improve the design of the service part of the assistive system, summarizes the list of requirements based on system design and service design, and designs a service design prototype. Through design practice, the detailed design of the software interaction interface and the auxiliary product of the care assistance system based on the prototype are investigated. Based on the user perspective, the strategy of meeting user expectations and improving user information literacy is proposed; based on the social network perspective, the strategy of establishing a long-term mechanism for smart medical operation and improving the information interaction network environment is proposed; and based on the system service perspective, the strategy of optimizing the system function design and innovating the service model is proposed. Compared with the traditional written patient handover, the application of MNIS under cloud computing can significantly shorten the handover time of surgical patients, improve the standardized rate of surgical safety verification execution and the qualified rate of nursing documents, while the rate of standardized application of prophylactic antibiotics is also significantly higher than that of the control group. The questionnaire survey of nursing staff in the operating room showed that clinical nursing staff was generally satisfied with the clinical application of MNIS under cloud computing, with an average satisfaction score of 64.5 to 11.3, and an average score of 3.58 to 0.54 for each item. Among them, pre-application training of MNIS, departmental support for MNIS, and its ease of verification in surgical patients were the three main factors favoring the clinical application of MNIS in the operating room with cloud computing, while barriers to wireless network connectivity, inconvenient PDA input, and small screen size were the three main drawbacks affecting its application. The determined clinical evaluation index system of MNIS in the operating room is innovative, which not only includes clinical care indicators but also covers general hardware and software indicators, which can effectively reflect the practical application capability of mobile terminal clinical and user experience feelings, and the evaluation index system is comprehensive.

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Section: Analysis Of Remote Clinical Care Technologies For Cloud Computingmentioning

confidence: 99%

A Study of Cloud-Based Remote Clinical Care Technology

Lin

Huang²

2021

Journal of Healthcare Engineering

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“…Several studies have been conducted to support Telemedicine such as the impact of IoT in healthcare using network sensors worn in body or embedded in the environment [15], the use of latest technologies like cloud computing and fog computing in IoT [16], the use of biosensors in saliva test on hormonal imbalance for women living in the rural areas [17], the used of Field Programmable Gate Array (FPGA)-based IoT architecture in remote monitoring of patients' vital signs [18] and symptoms of cognitive impairment [19]. The application of the IoT to the medical field is called IoMT or Internet of Medical Things that can be used in remote medical supervision [21], real-time monitoring and medical information system [22].…”

Section: Literature Review Methodology Results Discussion and Recommendationmentioning

confidence: 99%

“…It is also noteworthy that the data may be incomplete for some records [22] [48] so data handling and processing algorithm such as machine learning is needed to be utilized to better understand and use the data. Massive amount data will also need adequate power and memory [16]. The system set up should include power sources and availability of memory and redundancy for backup.…”

Section: ) Big Data Managementmentioning

confidence: 99%

Telemedicine Implementation Challenges in Underserved Areas of the Philippines

Cruz¹,

Tolentino²

2021

IJETAE

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During these difficult times of global pandemic, mobility restrictions and lockdowns were put in place to prevent an outbreak. However, patients with medical needs must deal with regular face-to-face consultations with physicians for their prescriptions and medications. It is important to review the various telemedicine research in the Philippines and abroad and how to formulate a national framework for telemedicine so the program can be implemented in the entire country. As a developing nation and an archipelago, the Philippines needs to work on massive infrastructure project for national broadband and health care facilities. The increase of medical professionals per 1000 of population and prevent them from migrations will help implement such innovation in medical field. A national policy to support the program in terms of funding and its transparency, establishment of processes and standards, and safeguarding digital information should be put in place. The country will need to address these gaps in technology and processes and encourage the population to partake in national health plans and initiatives to increase awareness and engagement of all stakeholders. Keywords— Challenges, Developing Countries, eHealth, Implementation, IoMT, Philippines, Rural, Telehealth, Telemedicine, Underserved

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“…However, the optimal cutoff value of SILA for differentiation was different from the initial suggested value, and the reasons for these differences must be studied further. In addition, by incorporating a similar approach of CANARY with the emerging automated machine learning method to increase the efficiency and the internet of things technology to simplify the process [26,28], the research could be extended to other carcinomas of the lung or even to other organs, and related research is underway.…”

Section: Plos Onementioning

confidence: 99%

Evaluation of Computer-Aided Nodule Assessment and Risk Yield (CANARY) in Korean patients for prediction of invasiveness of ground-glass opacity nodule

et al. 2021

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Differentiating the invasiveness of ground-glass nodules (GGN) is clinically important, and several institutions have attempted to develop their own solutions by using computed tomography images. The purpose of this study is to evaluate Computer-Aided Analysis of Risk Yield (CANARY), a validated virtual biopsy and risk-stratification machine-learning tool for lung adenocarcinomas, in a Korean patient population. To this end, a total of 380 GGNs from 360 patients who underwent pulmonary resection in a single institution were reviewed. Based on the Score Indicative of Lung Cancer Aggression (SILA), a quantitative indicator of CANARY analysis results, all of the GGNs were classified as “indolent” (atypical adenomatous hyperplasia, adenocarcinomas in situ, or minimally invasive adenocarcinoma) or “invasive” (invasive adenocarcinoma) and compared with the pathology reports. By considering the possibility of uneven class distribution, statistical analysis was performed on the 1) entire cohort and 2) randomly extracted six sets of class-balanced samples. For each trial, the optimal cutoff SILA was obtained from the receiver operating characteristic curve. The classification results were evaluated using several binary classification metrics. Of a total of 380 GGNs, the mean SILA for 65 (17.1%) indolent and 315 (82.9%) invasive lesions were 0.195±0.124 and 0.391±0.208 (p < 0.0001). The area under the curve (AUC) of each trial was 0.814 and 0.809, with an optimal threshold SILA of 0.229 for both. The macro F1-score and geometric mean were found to be 0.675 and 0.745 for the entire cohort, while both scored 0.741 in the class-equalized dataset. From these results, CANARY could be confirmed acceptable in classifying GGN for Korean patients after the cutoff SILA was calibrated. We found that adjusting the cutoff SILA is needed to use CANARY in other countries or races, and geometric mean could be more objective than F1-score or AUC in the binary classification of imbalanced data.

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The Role of the Internet of Things in Health Care: A Systematic and Comprehensive Study

Cited by 48 publications

References 0 publications

A Study of Cloud-Based Remote Clinical Care Technology

A Study of Cloud-Based Remote Clinical Care Technology

Telemedicine Implementation Challenges in Underserved Areas of the Philippines

Evaluation of Computer-Aided Nodule Assessment and Risk Yield (CANARY) in Korean patients for prediction of invasiveness of ground-glass opacity nodule

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