Forthcoming and Online First Articles

International Journal of Electronic Healthcare

International Journal of Electronic Healthcare (IJEH)

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International Journal of Electronic Healthcare (8 papers in press)

Regular Issues

  • A Review on Information Communication Technology (ICT) Adoption in the Nigerian Healthcare Sector   Order a copy of this article
    by Ayeyi Victoria Gyebo, Amiruddin Ahamat, Siti Norbaya Yahaya 
    Abstract: Studies on information communication technology (ICT) adoption in the Nigerian healthcare sector posit that the objective of encouraging information sharing throughout all public hospitals in Nigeria is still farfetched following the limited number of hospitals that utilise ICT in their everyday clinical and administrative operations. The proposed study is framed under four objectives, which seek to critically examine existing literature on the issue, recognise obstacles to ICT adoption in Nigeria, consider the context of the administrative policy during and post ICT adoption, and suggest potential solutions that can be implemented by the Federal Ministry of Health Nigeria as part of the effort to introduce ICT to improve the service quality within the sector. This study conducted a systematic review of relevant literature using the ATLAS.ti 9 software. The result demonstrates that the current environment recognises the value of information and communication technology to improve the healthcare sector.
    Keywords: healthcare sector; Nigerian healthcare; information communication technology; ICT; hospital management.
    DOI: 10.1504/IJEH.2022.10047174
  • Healthcare analytics in big data: current trends and prospects   Order a copy of this article
    by Upasana Talukdar 
    Abstract: Healthcare analytics is a research area where many healthcare services operate on huge amounts of heterogeneous data. This leads to the generation of big healthcare-data. This paper performed a survey to answer six questions related to healthcare analytics: what are the different perspectives on the concepts of big healthcare-data? What major big data analytics approaches, tactics, tools, and strategies are being used in healthcare applications? What are the vital sources of big healthcare-data? What are the different architectures for modelling big healthcare-data? What are the leading terms that constitute the research field of healthcare analytics in big data and how rapidly is this research field expanding? Where is healthcare analytics in big data headed to the future? The analysis inferred interesting patterns, identified popular big data analytical techniques, tactics, tools, the vital sources of big healthcare-data, big healthcare-data processing architectures, growth patterns of the research field, and future trends.
    Keywords: healthcare; big data; analytics; big healthcare data; applications.
    DOI: 10.1504/IJEH.2022.10047641
  • Internet of Medical Things and Cloud Enabled Brain Tumor Diagnosis Model using Deep Learning with Kernel Extreme Learning Machine   Order a copy of this article
    by Ganesan M, Sivakumar N, Thirumaran M, Vengattaraman T 
    Abstract: Presently, internet of things (IoT) and cloud-based e-health services offer various decision support systems in the medical field. In this view, this paper introduces a new internet of medical things (IoMT) and cloud-enabled brain tumour (BT) diagnosis and classification using deep learning-based Inception model with the kernel extreme learning machine (KELM), named DLIM-KELM. The proposed DLIM-KELM undergoes a series of steps namely data acquisition, preprocessing, optimal multi-level threshold-based segmentation, Inception v3-based feature extraction, and KELM-based classification. Besides, firefly (FF) algorithm is applied for the selection of optimal threshold value in Tsallis entropy-based segmentation technique. The application of Inception v3 and KELM models helps to effectively diagnose and classify the occurrence of BT from magnetic resonance imaging (MRI) images. The DLIM-KELM model is tested using the BRATS2015 dataset and it has attained maximum sensitivity of 98.45%, specificity of 98.34%, and accuracy of 98.91%.
    Keywords: internet of medical things; IoMT; cloud computing; e-healthcare; electronic healthcare; deep learning; feature extraction.
    DOI: 10.1504/IJEH.2022.10047823
  • Privacy Policies of IoT-based Healthcare: An Empirical Analysis Study of the Data Collection Practices of Existing IoT Medical Applications   Order a copy of this article
    by Mohamed Sarrab, Fatma Alshohoumi, Abdulla AlHamadani 
    Abstract: In healthcare, the internet of things (IoT) has accelerated the process of gathering patients’ data. However, data processing is a significant violation of patients’ privacy. This research critically examines to what extent the privacy policies of IoT medical apps provide sufficient details about their data collection practices with respect to personal data. It aims to explore the various medical data collected by IoT medical apps. An empirical analysis investigated the privacy policies of 21 of IoT medical apps. The findings revealed that these apps had provided sufficient details related to some of the data collection practices. However, details related to the security of personal data were more general. The findings indicate that most of these apps offer no guarantee regarding securing personal data. Such results open new research to be extended to validate to what extent the service providers are committed to what is explained in their privacy policies.
    Keywords: healthcare; privacy policy; data collection practices; privacy concerns; IoT medical apps.
    DOI: 10.1504/IJEH.2022.10047862
  • The attitudes of medical practitioners towards the actionability of performance data   Order a copy of this article
    by Anna Janssen, Tim Shaw 
    Abstract: The collection and feedback of electronic health data has been increasing over the last decade. In spite of the large amount of data collected the health sector, significant questions remain about the actionability of this data for key stakeholders such as health professionals. This study aimed to understand the attitudes of specialist medical practitioners towards the collection of electronic health data, and its feedback for practice reflection. Interviews were conducted with medical practitioners to answer this aim. Data from these interviews was analysed by researchers familiar with undertaking qualitative analysis of interview data. Findings indicate specialist medical practitioners are interested in having greater access to their clinical data. However, in order to make the data actionable to clinicians simply presenting the data is not sufficient. Digital technologies for presenting health data need to scaffold the presentation of data in order to make it actionable by end-users in the healthcare.
    Keywords: electronic health data; digital health; actionable data; qualitative research.
    DOI: 10.1504/IJEH.2022.10047863
  • Social media use by governments for COVID-19   Order a copy of this article
    by Seungil Yum 
    Abstract: This study explores how governments use social media for COVID-19 to communicate with the public according to government accounts by employing social network analysis for Twitter. First, this study finds that government accounts have different characteristics of key players. For instance, the US key players play an important role in the Donald Trump networks, whereas international key players play a significant role in the President of the United States (POTUS) networks. Second, Trump, POTUS, and the White House show a similar pattern, whereas the US Government reveals a unique shape for the social networks. Third, the US Government networks also exhibit unique characteristics of group networks against other government accounts. Fourth, citizens reply differently to the government accounts for the COVID-19 issue. For example, Donald Trump shows overwhelming replies over other tweeters in the Donald trump networks, whereas POTUS ranks second after Donald Trump in the POTUS networks.
    Keywords: coronavirus; COVID-19; government; social media use; Twitter.
    DOI: 10.1504/IJEH.2022.10048357
  • Health Public Crisis Impact on Non-Life Insurance: The case of Covid-19   Order a copy of this article
    by Marta Costa, Rui Gonçalves, Renato Lopes Da Costa, Leandro Pereira, Álvaro Lopes Dias 
    Abstract: The insurance industry plays a crucial role in developing a country’s economy, thus becoming one of the main focuses when studying the possible impacts of the current public health crisis. Accordingly, the main objective of this study is to investigate the COVID-19 impact on non-life insurance profitability of the Portuguese insurance sector. This study also intends to assess the explaining factors of insurance companies’ profitability on the period between 2004/2020. The methodology implemented was a multiple linear regression, using a panel data model with random effects. The sample consists of a total of 238 observations from 14 non-life insurers over 17 years. The results revealed that COVID-19 positively impacted the profitability of the insurance companies presented. However, this impact was only significant on the return on assets. Furthermore, the results showed an inverse and meaningful relationship between the profitability ratios and the variables leverage and loss ratio.
    Keywords: COVID-19; insurance sector; panel data; profitability.
    DOI: 10.1504/IJEH.2022.10048433
  • The use of mobile application (Shlonik) to control the spread of the Covid-19 pandemic in Kuwait.   Order a copy of this article
    by Faisal Almutairi, Ibtisam L. F. H. Almutairi, Bodoor F. Alazemi 
    Abstract: The purpose of this paper is to investigate factors influencing the use of the surveillance system (Shlonik app) used to track people quarantined in their homes during the COVID-19 pandemic in Kuwait. The researcher used Technology acceptance model (TAM) to predict Shlonik app usage (SU) by perceived usefulness (PU) and perceived ease of use (PEOU). The model also was extended with privacy concern, trust of government, and user awareness as predictors to Shlonik app usage (SU), which found to be significant and influential variables in the relevant past literature. The data was conducted through quantitative research method with a sample of 160 Shlonik app users in Kuwait and analysed using SmartPLS. The result of the study revealed that perceived ease of use, perceived usefulness, user awareness, and Privacy concerns have a positive influence on the SU. However, the trust of government showed no significant effect on the use of SU. The study was limited only to Kuwait and only to the health sector. Hence, based on the findings and limitations of this study, several recommendations have been given for future research in this area.
    Keywords: COVID-19; mobile applications usage; Kuwait; TAM; user awareness; privacy concerns; trust; surveillance system.
    DOI: 10.1504/IJEH.2022.10048435