International Journal of Healthcare Technology and Management (5 papers in press)
Patient perception of interactive mobile healthcare apps: a predictive model
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 Taiwan's national health insurance website quality and customers' loyalty
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
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.
Application of GIS and SPSS for prostate cancer and health disparity detection in Texas
by Jose Huerta, Gayle Prybutok, Victor Prybutok
Abstract: This study uses a geographic information system to create and analyse choropleth maps determining the distribution of prostate cancer in Texas and uses SPSS software to analyse social determinants of health that may explain prostate cancer mortality. The data, collected for period 19992009, was furnished by the Texas Health Rankings and VitalWeb. The dataset was for 19992004 and 20042009. It comprised age-adjusted data specific to the 2000 US Standard Population data, based on an age-distributed and -weighted methodology to create age adjustments for statistical purposes. The study found there was a statistically significant (P < .05) percentage of African Americans with age-adjusted prostate cancer mortality, but no statistically significant correlations were found in other races. The study indicates a number of ways in which medical communities and public health agencies can employ GIS and SPSS to screen for and treat prostate cancer more effectively.
Keywords: GIS; prostate cancer; social determinants of health; spatial patterns; SPSS; choropleth maps; geographic information systems.
Implementation of lean practices to reduce healthcare associated infections
by Anna Ferraro, Piera Centobelli, Roberto Cerchione, Maria Vincenza Di Cicco, Emma Montella, Eliana Raiola, Maria Triassi, Giovanni Improta
Abstract: In recent years, hospitals have had to implement strategies to contain costs, as have other companies. One of the major cost items is associated with healthcare complications. The most frequent and serious are nosocomial infections (HAI), which have not only economic but also health costs for both patients and hospitals. In this perspective, in this document the Lean Six Sigma (LSS) approach has been used as a tool to define, measure, analyse, improve and control (DMAIC) the onset of HAIs that affect patient safety in healthcare organisations. Lean and Six Sigma, and in particular the DMAIC cycle, have been applied to the processes of the Intensive Care Unit (ICU) for adults of the University Hospital 'Federico II' of Naples in order to reduce the number of positive patients on the sentinel bacterium test who are at high risk of developing HAI. The first data collection campaign starts on January 1, 2014, and ends on February 28, 2015, and involves 144 patients. The second data collection campaign took place from March 1, 2015, to February 28, 2016, and involved 154 patients. The results showed that the most present bacterium in the reference sample is Acinetobacter baumannii and that there is a link between the total number of patients with HAIs and the number of health procedures they undergo. The paper shows that with the application of the LSS method it is possible to decrease the total number of colonised hospitalised patients through the identification of different parameters that influence the process and distribution of the sentinel bacterium.
Keywords: DMAIC; healthcare-associated infections; lean thinking; lean management; quality improvement.