Transformation of healthcare big data through the lens of actor network theory

ABSTRACT Ontologically, what we do know is that healthcare big data do exist across South African health facilities. Epistemologically, the patients and some health practitioners do not know how the big data exist and, most importantly, what to expect from the data sets. This is due to the rawness and complexity of big data evolution, which many health practitioners do not know how to socio-technically analyze, and get an understanding of why things happen in the way that they do within patients’ big data. Thus, the objective of this study was to develop a framework, which could be used to guide analysis that can translate and transform big data into a more useful and purposeful resource for health practitioners, thereby improving services that they provide to the patients. The qualitative approach methods were employed, following the interpretivist approach. Existing literature relating to the study was gathered. Actor network theory was applied as a lens to guide the analysis of the data. Based on the analysis, a model and framework were developed. The model creates an analysis platform that enacts the use of actor network theory (ANT) as lens for data analysis. ANT offers a different type of analysis through its focus on the relational effect that is shaped by interaction that happen between humans and non-humans in their heterogeneous networks. The framework provides a guide for the transformation of healthcare big data from ontological to epistemological positions, towards improving patients’ care. Through the use of ANT for analysis and transformation of healthcare big data, the challenges of analytics of healthcare big data in technological, social, and policy barriers can be addressed, making this study useful to both the health profession and academia.

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