Forthcoming articles

International Journal of Healthcare Technology and Management

International Journal of Healthcare Technology and Management (IJHTM)

These articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Healthcare Technology and Management (3 papers in press)

Regular Issues

  • Patient perception of interactive mobile healthcare apps: a predictive model   Order a copy of this article
    by Mattie Milner, Scott Winter, Rian Mehta, Stephen Rice, Matthew Pierce, Emily Anania, Karla Candelaria-Oquendo, Diego Garcia, Nathan Walters 
    Abstract: Many professionals connect with consumers through mobile app technology. It is then no surprise that healthcare providers have begun exploring this technology as a tool to reach their patients. Despite increasing accessibility, the willingness to use mobile technology, such as healthcare apps, can be affected by several different factors. This study aims to determine what factors predict a persons willingness to use this type of technology. Four hundred and five participants completed the study over two stages, which included a hypothetical scenario using a mobile healthcare app and a survey identifying their willingness to use, knowledge of, and privacy concerns regarding mobile healthcare applications. A backward stepwise regression analysis revealed two significant predictors: privacy concerns and likelihood to use the internet. There was good model fit, highlighting the predictive power of the regression model on a new dataset. As technology becomes more prevalent among consumer e-health options, this research may help key stakeholder groups, such as healthcare providers, doctors, and patients, better understand patients willingness to use mobile health applications.
    Keywords: e-health; willingness to use; privacy; health care; mobile apps; regression.

  • Examining Taiwans national health insurance website quality and customers loyalty   Order a copy of this article
    by Jengchung Victor Chen, Timothy McBush Hiele, Mei-Tsui Lin 
    Abstract: The favourable impact on the assessment of website quality and response from customers can explain and justify the manifestation and significance of an online healthcare platform. This study applies the information systems (IS) success model and technology acceptance model (TAM) to assess Taiwans bureau of national health insurance (BNHI) online platform. In particular, this study presents and empirically tests the research framework by using a SmartPLS path analysis through the use and validation of Taiwans BNHI customers feedback. Overall, this study sheds light on the value and importance of a medical institution and/or organisation to offer better and quality information technology (IT) platform that can serve and retain its loyal customers.
    Keywords: customer loyalty; e-government; IS success model; Taiwan national health insurance; website quality.

  • General analytics limitations with coronavirus healthcare big data   Order a copy of this article
    by Kenneth David Strang 
    Abstract: The goal of this study was to reveal factual big data statistical general analytics issues in the healthcare industry using COVID-19 coronavirus as an empirical example. Search engines and the SPSS Python R extension were used to analyse healthcare big data information stored on the internet. The research question was focused on what were the significant limitations of statistical techniques when analysing the effect of publicly available healthcare big data, using the coronavirus as an example. The sample was a manageable subset of dynamic information from the internet time-stamped to midnight of April 14, 2020 with a filter set for coronavirus confirmed cases or deaths in Wuhan in Hubei province in China, New York State in USA and New South Wales, Australia. There were surprising results, indicating using general analytics that the healthcare big data were not reliable. Nevertheless, interesting relationships were detected when linking foreign property ownership to the two Australian cities of Sydney and Melbourne experiencing the largest coronavirus related fatalities. During this study several useful and practical general analytics effect size equations were shown and proven to help detect reliability limitations when examining healthcare big data.
    Keywords: healthcare big data problems; privacy; security; systems thinking action research.