Original Research

Integration of social media with healthcare big data for improved service delivery

Sibulela Mgudlwa, Tiko Iyamu
SA Journal of Information Management | Vol 20, No 1 | a894 | DOI: https://doi.org/10.4102/sajim.v20i1.894 | © 2018 Tiko Iyamu | This work is licensed under CC Attribution 4.0
Submitted: 24 June 2017 | Published: 11 April 2018

About the author(s)

Sibulela Mgudlwa, Department of Information Technology, Cape Peninsula University of Technology, South Africa
Tiko Iyamu, Department of Information Technology, Cape Peninsula University of Technology, South Africa

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Background: In the last decade, social media users across the world have crossed 1 billion, making it one of the fastest growing sources of big data. Also, people needing healthcare continue to increase in every society. Through accessibility, communication and interaction between health practitioners and patients, this type of ever-growing, social media subscriber–based platform can be of significant use in improving healthcare delivery to society. However, users encounter serious challenges in their attempts to make use of social media and big data for health-related services. The challenges are primarily caused by factors such as integration, complexity, security and privacy. The challenges are mainly owing to the sensitive nature of the healthcare environment, as a result of personalisation and privacy of information.


Objectives: The objectives of the study were to examine and gain a better understanding of the complexities that are associated with the use of social media and healthcare big data, through influencing factors, and to develop a framework that can be used to improve health-related services to the patients.


Methods: The interpretivist approach was employed, within which qualitative data were collected. This included documents and existing literature in the areas of social media and healthcare big data. To have a good spread of both previous and current state of events within the phenomena being studied, literature published between 2006 and 2016 were gathered. The data were interpretively analysed.


Results: Based on the analysis of the data, factors of influence were found, which were used to develop a model. The model illustrates how the factors of influence can enable and at the same time constrain the use of social media for healthcare services. The factors were interpreted from which a framework was developed. The framework is intended to guide integration of social media with healthcare big data through which service delivery to patients can be improved.


Conclusion: This study can be used to guide integration of social media with healthcare big data by health facilities in the communities. The study contributes to healthcare workers’ awareness on how social media can possibly be used to improve the services that they provide to the needy. Also, the study will benefit information systems and technologies and academic domains, particularly from the health services’ perspective.


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