EAI Endorsed Transactions on Pervasive Health and Technology

A Systematic Review on the Adoption of Blockchain Technology in the Healthcare Industry

Mahmood A. Bazel, Fathey Mohammed, Mazida Ahmad — Thu, 20 Apr 2023 00:00:00 +0000

INTRODUCTION: Blockchain technology is a distributed ledger, decentralized, and cryptographically secure technology which has garnered considerable interest in different sectors including healthcare. It can enable better trust, security, management, and transparency of healthcare data, processes, and transactions resulting improving quality of care. Despite the fact of the increasing number of research investigating the applications/potentials of blockchain in healthcare, there is a scarcity of comprehensive reviews that focuses on the factors that influence its adoption in the healthcare industry.

OBJECTIVES: This review aims to summarise existing studies regarding the adoption of blockchain technology in the healthcare industry. This review presents a detailed review of existing empirical studies investigating the factors influencing blockchain adoption in healthcare by highlighting the research methodologies, targeted stakeholders, adoption theories/models used, and the influential factors explored in each of these studies. Careful syntheses of these studies would enable researchers and partitioners to acquire a wide knowledge and understand various opportunities and challenges of blockchain implementation in healthcare.

METHODS: Inspired on “Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)” guidelines, the study's scope and research questions are established, Scopus database is selected as an information resource, search strategy, and inclusion and exclusion criteria for document selection is developed. This review was conducted in August 2022. From 223 articles found in the search, 12 met the eligibility criteria and were selected to be extensively analyzed in this review.

RESULTS: This review reveals that very few empirical studies exist that sought to explore the significant factors influencing blockchain adoption in healthcare. The qualitative method was the most method employed, healthcare providers were the most targeted stakeholders, and most of the studies were not based on adoption theories/models. Privacy, government regulation, and trust were the most influential factors investigated in the studies.

CONCLUSION: The utilization of blockchain can help handle many issues in healthcare systems and bring improved healthcare delivery. Little attention has been paid to highlight internal and external factors that would impact successful blockchain adoption in healthcare. Additionally, the evaluated research placed little attention on understanding how underlying factors interact, social structures and institutional mechanisms affect the adoption of blockchain in healthcare. The reasons why healthcare organizations are hesitant to implement blockchain are still not clear. There is a need to conduct more research to examine the factors influencing the decision of healthcare stakeholders to adopt blockchain by using adoption theories/models. The proposed framework of the factors in this study may contribute as a starting point for future blockchain adoption studies in the healthcare industry.

Telemedicine and mHealth Applications for Health Monitoring in Rural Communities in Colombia: A Systematic Review

Verenice Sánchez Castillo, Carlos Alberto Gómez Cano, Javier Gonzalez-Argote — Sat, 27 May 2023 00:00:00 +0000

INTRODUCTION: Telemedicine and mHealth applications constitute a central pillar in the digital transformation of healthcare.

OBJECTIVE: To describe the efficacy, applicability, and impact of telemedicine and mHealth applications on the monitoring and improvement of health in rural communities in Colombia.

METHODS: This research was carried out as a systematic review, a type of study that allows for a thorough and replicable evaluation of the existing literature in the databases PubMed, Scopus, Embase, Web of Science, Cochrane Library, CINAHL, and ERIC.

RESULTS: A total of 14 studies were included, which encompassed different types of research designs: two case-control studies, two randomized trials, four cross-sectional studies, two qualitative investigations, one consensus study, one retrospective cohort study, and two reviews. The sample size varied significantly among the studies, from 16 participants in the consensus study to 313,897 patients in one of the cross-sectional studies.

CONCLUSIONS: Telemedicine and mHealth applications are transforming the way medical care is delivered to rural communities in Colombia. These tools have proven to be valuable in improving the detection and management of chronic diseases such as cognitive decline and cardiovascular diseases. At the same time, the implementation of these technologies has shown to be effective in improving the quality of medical care, providing greater access to specialized medical services, and reducing the sense of isolation among health professionals in rural areas.

An Efficient Discrete Wavelet Transform Architecture with Low Power and Multiplier-Less Structure for Pervasive Biomedical Image Processing Application

Maram Anantha Guptha, Surampudi Srinivasa Rao, Ravindrakumar Selvaraj — Tue, 10 Jan 2023 00:00:00 +0000

INTRODUCTION: Over the past several years analysis of image has moved from larger system to pervasive portable devices. For example, in pervasive biomedical systems like PACS-Picture achieving and Communication system, computing is the main element. Image processing application for biomedical diagnosis needs efficient and fast algorithms and architecture for their functionality. Future pervasive systems designed for biomedical application should provide computational efficiency and portability. The discrete wavelet transform (DWT) designed in on-chip been used in several applications like data, audio signal processing and machine learning.
OBJECTIVES: The conventional convolution based scheme is easy to implement but occupies more memory , power and delay. The conventional lifting based architecture has multiplier blocks which increase the critical delay. Designing the wavelet transform without multiplier is a effective task especially for the 2-D image analysis. Without multiplier Daubechies wavelet implementation in forward and inverse transforms may find efficient. The objective of the work is on obtaining low power and less delay architecture.
METHODS: The proposed lifting scheme for two dimensional architecture reduces critical path through multiplier less and provides low power, area and high throughput. The proposed multiplier is delay efficient.
RESULTS: The architecture is Multiplier less in the predict and update stage and the implementation carried out in FPGA by the use of Quartus II 9.1 and it is found that there is reduction in consumption of power at approximately 56%. There is reduction in delay due to multiplier less architecture.
CONCLUSION: multiplier less architecture provides less delay and low power. The power observed is in milliwatts and suitable for high speed application due to low critical path delay.

An Empirical Study on Classification of Monkeypox Skin Lesion Detection

B. V. CHANDRAHAAS, Sachi Nandan Mohanty, Sujit Kumar Panda, Michael G. — Thu, 25 May 2023 00:00:00 +0000

INTRODUCTION: After the covid-19 outbreak, Monkeypox has become a global pandemic putting people’s lives in jeopardy. Monkeypox has become a major concern in 40+ countries apart from Africa as scientists are struggling to clinically diagnose the virus as it looks similar with chickenpox and measles. As a part of our research, we found that to get the clinically tested result of monkey pox through polymerase chain reaction (PCR) test would take 3-4 days which is a lengthy process.

OBJECTIVES: The objective of this paper is to provide a rapid identification solution which can instantly detect monkeypox virus with the help of computer vision architectures. This can be considered for preliminary examination of skin lesions and help the victim isolate themselves so that they would be cautious and can stop the spreading of virus.

METHODS: Many studies have been conducted to identify the monkeypox with the help of Deep Learning models but in this study, we compare the test results obtained by deep learning CNN models AlexNet, GoogLeNet using transfer learning approach and determine the efficient model[2].

RESULTS: Testing the algorithms by changing the batch sizes and number of epochs we have obtained a highest accuracy of 83.61% for AlexNet and 82.64% for GoogLeNet.

CONCLUSION: AlexNet was outperforming GoogLeNet architecture in terms of validation accuracy thus providing better results.

Automated Cardiovascular Disease Prediction Models: A Comparative Analysis

Taffazul Choudhury, Bismita Choudhury — Mon, 29 May 2023 00:00:00 +0000

INTRODUCTION: Cardiovascular disease (CVD) is one of the primary causes of the increased mortality rate universally. Therefore, automated methods for early prediction of CVD are of utmost importance to prevent the disease.

OBJECTIVES: In this study, we have pointed out the major advantages, drawbacks, and the scope of enhancing the prediction accuracy of the existing automated cardiovascular disease prediction methods. In addition to that, we have analyzed various combinations of attributes that can help in prediction at the earliest.

METHODS: We have exploited various machine learning models to analyse their performances in predicting the CVD at the earliest.

RESULTS: For a publicly available database, the Artificial Neural Network attained the highest accuracy of 88.5% and recall of 90%.

CONCLUSION: We justified the notion that it will be beneficial to identify potential physiological and behavioural attributes to predict CVD accurately as early as possible.

Multivariate Multiscale Entropy: An Approach to Estimating Vigilance of Driver

Kawser Ahammed, Mosabber Uddin Ahmed — Fri, 09 Jun 2023 00:00:00 +0000

Various driver’s vigilance estimation techniques currently exist in the literature. But none of them estimates the driver’s vigilance in the complexity domain. In this research, we propose the recently introduced multivariate multiscale entropy method to fill the above mentioned research gap. We apply this technique to differential entropy features of electroencephalogram and electrooculogram signals to detect driver’s vigilance. Also, we employ it to the percentage of eye closure values to analyse the driver’s cognitive states (awake, tired and drowsy) in the complexity domain. The contribution of this research is to efficiently classify the driver’s cognitive states using a new feature based on multivariate multiscale entropy. The experimental complexity profile curves show the statistically significant differences (p < 0.01) among brain electroencephalogram, forehead electroencephalogram and electrooculogram signals. Moreover, the difference in the multivariate sample entropy across all scales in awake (1.0828 ± 0.4664), tired (0.7841 ± 0.3183) and drowsy (0.2938 ± 0.1664) states are statistically significant (p <0.01). Also, the support vector machine, a machine learning technique, discriminates the driver’s cognitive states with a promising classification accuracy of 76.2%. Therefore, the complexity profile of driver’s cognitive states could be an indicator for vigilance estimation.

Use of real-time graphics in health education: A systematic review

Tue, 04 Apr 2023 00:00:00 +0000

Introduction: Using real-time graphics in health education is particularly relevant in technical skill development and knowledge acquisition in surgery, emergency medicine, and nursing.

Objective: To systematize the literature on using real-time graphics in health education.

Methods: A systematic review was conducted in the databases: PubMed, Scopus, Embase, Web of Science, Cochrane Library, CINAHL, and ERIC.

Results: The impact of real-time graphics use, including virtual reality (VR), in health education was examined, covering disciplines such as medicine, nursing, and other related professions. The findings of the selected studies for this review and existing literature suggest that implementing real-time graphics technologies in health education can significantly improve learning and the acquisition of clinical skills compared to traditional approaches.

Conclusions: Virtual reality was found to be particularly effective in training technical skills and surgical procedures and improving the quality of teaching in various disciplines. These findings support experiential learning theory and the idea that repeated practice and immediate feedback in a safe and controlled environment are essential for skill acquisition.