Factors Influencing the Use of Mobile Systems to Access Healthcare Big Data in a Namibian Public Hospital

Factors Influencing the Use of Mobile Systems to Access Healthcare Big Data in a Namibian Public Hospital

Tiko Iyamu, Irja Shaanika
Copyright: © 2020 |Pages: 19
DOI: 10.4018/IRMJ.2020070104
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The use of mobile systems to access healthcare big data is generally a challenge, but worse in Namibia because the influencing factors are not empirically known in the country. The objective of this study is to examine the factors that can determine and influence the use of mobile systems to access big data within the public healthcare in Namibia. Thus, a Namibian public hospital was used as a case in the study. Qualitative data was collected by using the semi-structured technique. Structuration theory was employed as a lens to guide the analysis of the data. The following factors—mobile systems ease of use, system user training, online consultation, medical history traceability, access to external facilities, practitioner's collaboration, systems decentralisation, and technology infrastructure flexibility—were found to influence the use of mobile systems in accessing healthcare big data for service delivery. Based on the findings, a model was developed. The model is intended to guide hospital managers in the use of mobile systems to access patient big data for service delivery.
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1. Introduction

In recent years, the use of mobile system has increasingly become dominant in healthcare organisations towards improving service delivery. Mobile systems that are used in healthcare are referred to as healthcare mobile systems such as tablets, laptops, and cellular phones (Wu, Wang & Wolter, 2013). Healthcare mobile systems are advancing developments in healthcare, and improving the quality of its service delivery. This has not been the case in developing countries, in that there are often challenges of data quality (Horner & Coleman, 2016). As a result, even the largest hospitals are still operating on paper-based systems (Lulembo & Silumbe, 2016). According to Karon (2016), many of the challenges experienced by Namibian healthcare are caused by both technical and non-technical factors. This gets worst as some healthcare facilities access patient’ big data for services delivery.

The concept of big data is not about size, it consists of characteristics (Daniel, 2015), which are volume, velocity and variety, often refer to as 3Vs (Acharjya & Kauser, 2016). Through diagnoses of patients’ health conditions, huge volume, velocity and variety of datasets are generated, in the forms of texts, digital images, and videos (Liu & Park, 2014). According to Patil and Seshadri (2014), the healthcare industry is witnessing an increase in sheer volume of datasets, which leads to complexity, diversity, and challenges of timeliness. This could be attributed to the fact that growth in healthcare data results in consumption of more storage space, increases in sources and velocity, making access more complex (Ohlhorst, 2012). Patient’s big data is highly critical and important in the delivering of healthcare services. Pastorino et al. (2019) argue that the use of big data improves delivery of healthcare services. This makes accessing of patient’s big data crucial. However, Kearny et al. (2016) argue that the increases in volume and diversity make accessing and management of datasets challenging. Fernández-Alemán et al. (2013) found that the increasing volumes of health data from various sources is problematic in terms access, and usefulness. In Namibia, the situation is even worse as the big data are often unstructured and scattered within public hospital environments. Dahal et al. (2016) suggest that the application of mobile systems for sharing of information in the forms of audio, video, and images has been overwhelming and challenging in such an environment.

The health care sector is challenged with gaining potential benefits that the big data concept presents (Wang, Kung & Byrd, 2018), which can be used to improve visualization and dissemination of patients’ datasets (Sahay, Rashidian & Doctor, 2020). In addition, the use of big data helps to reveal new insights into risk factors that lead to diseases (Pastorino et al., 2019. Access to patient’s big data helps to promptly mitigate some illnesses, at various levels (Clim, Zota & Tinica, 2019). The challenge is that many health practitioners particularly in developing countries do not know how to access patients’ big data by using technologies such mobile systems (Iyamu & Mgudlwa, 2018).

Due to the overwhelming nature of big data in the Namibian healthcare environment, practitioners prefer to employ manual approach rather explore the mobile system, in accessing patient’s information to improve services. Shaanika (2016) argues that this is a common challenge among Namibian public hospitals. The Namibian healthcare industry is considered to operate complex systems, which is influenced by its cultural and geographic spread and diversity (Iyamu, Hamunyela & Mkhomazi, 2014). Subsequently, governments invest technically and non-technically in their healthcare systems, with the overall aim of providing better and improved healthcare services. Despite the investment, healthcare systems in Namibia are characterised with challenges, which affect access to patient’s data-set (Karon et al., 2015). According to Iyamu et al. (2014:54), “healthcare records in Namibia public hospitals are not centralised and visualised, making accessibility into patient’s record difficult or impossible”. Another important factor affecting access to patients’ data-sets is sensitivity, which is currently challenging in the Namibian environment (Shaanika, 2016).

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