The first phase consists of two steps, namely to identify the purpose of the SLR and to draft the protocol. The intention of the research is to answer the question posed from the perspective of previously published data on the topic. The RQ determined the focus of the planning stage. The study used a selection of keywords and phrases as search criteria in Google Scholar. The researcher based the search strings and keywords on the RQ to retrieve as many papers as possible. This process resulted in 142 published papers, which included journal articles, conference papers and e-resources. The researcher stored all the screened articles in Mendeley for bibliography and in-text citations (Harpur 2018). Mendeley is a tool that allows researchers to manage PDFs, documents and citations through a desktop client version (Parabhoi, Seth & Pathy 2017).

The planning step requires a clear identification of the purpose and intended goals (Okoli 2015). A review protocol provides a clear review of the procedure to be followed where a confined strategy assists to select primary studies and to conduct the SLR (Kitchenham 2004). It supports the replication of the SLR for further studies, and it minimises the bias of the search (Okoli 2015). This study applied the review protocol in the field of DG in HIS in South Africa. From the Planning – Phase 1, iteration 1 led to a group of 142 articles. The Selection – Phase 2 shows the articles that went through the selection process. The Planning phase included the use of ATLAS.ti V8 for data analysis, followed by the Extraction – Phase 3. Finally, Execution – Phase 4 sets the stage for writing the results of an SLR.

This is the second phase of the SLR strategy, which consists of two steps, namely application of a practical screen and the search for literature. This step is also called the screening for inclusion, whereby certain studies were considered for review and other studies were eliminated (Okoli 2015). The study excluded papers not relevant to this SLR through abstract reading. Excluded are non-English publications relevant to HIS, as well as those that are not full papers. Re-reviewing of papers for the second time, using keywords and abstracts, focuses on the RQ and the objective of the study. Screen-published studies on DG in HIS that provided broader information in healthcare, based on titles, abstracts and date were studied. Only literature published from 2010–2020 was analysed to determine the status of DG in HIS within the South African context. A total number of 142 papers were collected.

The study explained and justified the details of the literature according to how they assured the search’s comprehensiveness (Okoli 2015). Boell and Cecez-Kecmanovic (2010) highlighted that a successful search procedure is not one that occurs in high recollection but rather one that results in high accuracy. Searching for literature provides a clear, in-depth understanding of the field of study. Furthermore, it also improves the way to search literature. Pertinent papers from digital databases and the web search engines were covered. The study reviewed studies published from 2010 to 2020. The investigation assisted in providing a picture of the current state of DG in HIS research in South Africa.

The researcher used ATLAS.ti V8, a computer-assisted qualitative data analysis software (CAQDAS) tool, to import the last selection of articles for the SLR. ATLAS.ti V8 helped the researcher to link different codes of quotations to create networks (Lewis 2015). Selection of articles included themes that emerged from the research topic, problem, RQ and objectives. After the inclusion of all the identified studies for the review, the researcher systematically extracted appropriate information from each study (Okoli 2015). The data extraction was according to the relevant publications, correctly recording the information acquired from the selected publications. The study extracted phrases, words and quotations from the selected articles during the extraction phase.

The researcher screened the extracted articles for exclusion, whereby quality-oriented criteria were used to determine which studies were included and which were not (Okoli 2015). It is not easy to determine values for all used concepts when extracting data, because the values depend on the contents and studies (Staples & Niazi 2007). The researcher created a codebook from the articles in ATLAS.ti V8 using Code in vivo and Open coding. The researcher grouped the codes in themes, sorted the themes in alphabetical order and prefixed each theme with the theme abbreviation and code number, for example (DG01). After the codebook creation, the researcher moved each code to each relevant code group. The researcher gleaned six concepts from the literature sources that led to the proposal of six categories below:

The list of the categorised items is as follows:

Figure 1 illustrates an example of a network diagram that links the category to a theme as well as items and authors that contributed to each theme.

Figure 1 is the analysis of the code snippets (quotations) from the articles. From the analysis of the codes, the researcher was able to create a visual representation of the themes, items and associated authors. The blue dotted lines show a link for the authors who contributed to each item. The purple dotted lines illustrate that the items, ‘DG01 Big Data’, ‘DG02 Healthcare Challenges’, ‘DG03 Improved DG’ and ‘DGO4 Internet of Things’ are associated with Category C that is directly connected to the theme DG through the link of the red dotted lines.

Execution is the fourth phase, comprising two steps, namely the synthesis of studies and the writing of the review. Facts are extracted from the studies are combined by using a qualitative or a quantitative technique or both (Okoli 2015). This step collects, combines and summarises the results of the selected publications. In an SLR, the process needs a full detailed report so that other researchers can reproduce the review results (Okoli 2015).

Competitive advantage is the feature that identifies the organisation to outdo its competitors. Increasing the competitive advantage is one of the challenges in healthcare (Saeidi et al. 2019). The digital transformation is a key to tackle these challenges (Gujral, Shivarama & Mariappan 2019). Although digital transformation has begun in many healthcare organisations, few of them have reached maturity (Gopal et al. 2019). The improvement of technology would lead to a greater competitive advantage.

The entire organisation needs to align its goals to DG. Organisations are increasingly developing advanced DG capabilities (Janssen et al. 2020). The only way to solve the data problem is the implementation of effective DG (Al-ruithe, Benkhelifa & Hameed 2018). Data governance assists organisations to ensure data quality and to maintain the value of data as an organisational asset. Many companies see DG as a promising approach to ensuring data quality (Otto 2011a, 2011b). Successful DG may answer certain data challenges of many organisations.

A contingency is an upcoming event, which is potential but not easily predicted. The most appropriate contingency factors are recognised as culture, industry, maturity and structure (Pereira & Silva 2012). Organisations with high contingency fit are less vulnerable to deviation from the formation of organisation performance (Volberda et al. 2012). Data governance is necessary to safely manage organisational data and success (Lee, Zhu & Jeffery 2018b). Organisations need greater levels of innovativeness to be successful (Boso et al. 2013).

A HIS serves as a bridge between IS and the business processes in healthcare in order to bring better healthcare services (Almunawar & Anshari 2012). The proper collection and utilisation of electronic healthcare record (EHR) is the foundation of the digital healthcare (Yang et al. 2015). Electronic healthcare record serves as the main driver of modern healthcare. The effect of technological, social and political factors changed the nature of the healthcare industry eventually (Almunawar & Anshari 2012). This change led organisations to seek organisational change through digital transformation.

Performance strategy is a method that organisations use to help implement their strategy into their organisation to achieve all goals. The digitisation of patient records opens rich possibilities for medical professionals (Atasoy, Greenwood & Mccullough 2019). The strategies for sharing information on goals, organisational structure and overall performance have a significant positive effect on performance. Managerial competences play a very important role on organisational performance (Vainieri et al. 2019). Healthcare professionals and organisations must be prepared to change (Wiljer & Hakim 2019). Develop human and organisational skills to adopt cultures and accept changes that promote readiness to face unexpected and expected challenges (Alsharif et al. 2018).

Process harmonisation refers to organising and applying standards for a business process to achieve targeted business requirements. Data harmonisation is an important intervention to strengthen health system’s functioning (Schmidt et al. 2018). It is a key intervention to give strength to the functioning of health systems (Schmidt et al. 2018). Data harmonisation enhances the accessibility, production and usability of standard health information for service management and clinical decision-making. Harmonised data quality assessment terms, methods and reporting practices can establish a common understanding (Kahn et al. 2016).