A framework for selecting analytics tools to improve healthcare big data usefulness in developing countries

Abstract

Background: In many developing countries including South Africa, there are challenges in understanding how the different networks of patients, diagnoses, and medical personnel are formed, as well as the types of big data that are generated. The challenges include the relationship and interaction that exist between the big data within the various networks. Some of the challenges manifest into different factors such as inaccuracy of data, inconsistency, incompleteness of data, and lack of cohesion. The trajectory of the challenge is the inability to select the most appropriate analytics tools for big data analysis.

Objectives: The objective of the study was to propose a solution that can be used to address the challenges in selecting analytics tools, to enhance big data usefulness for the improvement of healthcare services, particularly in developing countries.

Method: Literature of within 10 years of publication in the areas of big data, big data analytics and healthcare were gathered and used as data. The analysis of the data followed the hermeneutics approach within the interpretivist paradigm.

Results: From the analysis, factors that influence big data and analytics tools’ usefulness were found, based on which a solution (framework) is proposed. The solution is intended to contribute to the works of information systems and technologies (IS/IT) personnel, health practitioners and academics.

Conclusion: The relationships that exit between actors, and the actors’ interactions in the process of providing medical services contribute to the sources of big data. Therefore, it is necessary to holistically analyse healthcare big data from both technical and non-technical perspectives.