International Journal of Science Annals, Vol. 7, No. 2, 2024 рrint ISSN: 2617-2682; online ISSN: 2707-3637; DOI:10.26697/ijsa SOCIAL AND BEHAVIORAL SCIENCES. Health Care Sciences ORIGINAL RESEARCH Data Interoperability Assessment Model for Health Information System in South African Public Healthcare Authors’ Contribution: A – Study design; Rikhotso M.1 ABCDEF , Kalema B. M.2 ACDEF , B – Data collection; Seaba T. R.3 AEF C – Statistical analysis; 1 Tshwane University of Technology, South Africa D – Data interpretation; 2 University of Mpumalanga, South Africa E – Manuscript preparation; 3 Nelson Mandela University, South Africa F – Literature search; G – Funds collection Received: 19.06.2024; Accepted: 23.10.2024; Published: 25.12.2024 Abstract Background and The increasing use of information technologies in healthcare has enhanced Aim of Study: communication between its stakeholders and has also reduced health cost. As a result, data interoperability has become a priority which has increased the need to assess whether health information systems (HIS) used are interoperable enough to support this call. The aim of the study: to assess the data interoperability of the HIS used in the South African public healthcare. Material and Methods: Based on the conceptual model with the constructs of core, policy, societal, engagement as well as acceptance and use readiness and parameters of functional, syntactic and semantic interoperability, a measuring instrument in the form of closed-ended questionnaire was designed. Statistical data was collected from Information Technology personnel in three district hospitals of Gauteng Province in South Africa. Results: Hypotheses 1, 3 5, 6a and 6c predicted the influence of core readiness, societal readiness, use readiness functional interoperability and semantic interoperability on HIS data interoperability readiness respectively and were all accepted. Hypothesis 2, 4 6b predicted the influence of policy readiness, engagement readiness and syntactic interoperability on HIS data interoperability readiness and were all rejected. Conclusions: The developed model can be used to enhance research on data interoperability that is a major challenge in the use of information technology in healthcare. The sharing of information among different levels of medical personnel is essential for healthcare quality, efficiency, and safety of care provided to a patient. To enable this, systems should be able to connect and exchange information with each other without limitation. Such also enables better workflows, reduce ambiguity, and allows data transfer among systems and healthcare stakeholders. Keywords: health information systems, interoperability assessment, interoperability parameters, readiness assessment, South African healthcare Copyright: © 2024 Rikhotso M., Kalema B. M., Seaba T. R. Published by Archives of International Journal of Science Annals DOI: https://doi.org/10.26697/ijsa.2024.2.4 Conflict of interests: The authors declare that there is no conflict of interests Peer review: Double-blind review Source of support: This research did not receive any outside funding or support Information about Rikhotso Matimu – https://orcid.org/0009-0001-9665-664X; MComp, Tshwane the authors: University of Technology, Pretoria, South Africa. Kalema Billy Mathias – https://orcid.org/0000-0002-2405-9088; Doctor of Philosophy in Computer Science, Professor, University of Mpumalanga, Mbombela, South Africa. Seaba Tshinakaho Relebogile (Corresponding Author) – https://orcid.org/0000- 0002-5773-887X; Tshinakaho.Seaba@mandela.ac.za; Doctor of Computing in Informatics, Senior Lecturer, School of Information Technology, Nelson Mandela University, Gqeberha, South Africa. 67 International Journal of Science Annals, Vol. 7, No. 2, 2024 рrint ISSN: 2617-2682; online ISSN: 2707-3637; DOI:10.26697/ijsa Introduction In today’s information age with increasing digitization, The implementation of an Electronic National Health information technology (IT) has become an Insurance can be used to facilitate the tracking of indispensable part of healthcare institution. IT has the patients with the intention to enhance accuracy of data potential to improve the health of patients and the and completeness of healthcare. The importance of performance of providers. This will lead to improved health in society cannot be overemphasized, and the quality, cost savings, and greater patient engagement in corresponding data is expected to be extremely relevant their own healthcare (Richemond & Huggins-Jordan, and of good quality. According to Tsegaye and 2023). As result, health institutions in South Africa (SA) Flowerday (2021), it is important to understand patient are also implementing different IT solutions to improve data so that more prominent decisions may be taken to their health data management systems to enhance improve the use of integrated patient information. healthcare service delivery. However, this various Interoperability occurs only when there is interaction health information technology they implement run between the systems at three levels: functional, independently and lack uniform data standards as syntactic, and semantic interoperability (Blobel & Scott, different suppliers provide them and, thus, have 2018). Despite the three-level view of interoperability, different architectures, databases, and infrastructures drivers thereof should be taken into consideration. (Torab-Miandoab et al., 2023). Management issues are of the utmost importance for South Africa, classified as a middle-income country, aligning interoperability initiatives with national grapples with legacy systems functioning in isolation, priorities in the healthcare sector. This includes presenting challenges in safeguarding sensitive investments in interoperability initiatives, strategies, information, including patient privacy (Peng & policies, service, standards, and infrastructure (Savage Goswami, 2019). Hence, this fragmented approach to & Savage, 2020). data management poses significant obstacles to Accessibility to large quantities of accurate health data safeguarding patient privacy and protecting sensitive is required to understand medical and scientific information. Health information systems are not information in real-time, evaluate public health integrated, which underpins the fact that information measures before, during, and after times of crisis as well systems are operating in silos (Torab-Miandoab et al., as preventing medical errors. Much as this is so, there 2023). South Africa’s health information systems are are challenges towards easy accessibility and sharing of not integrated, and although they currently use schemas health data (Savage & Savage, 2020). Among these that could potentially help patient information be shared, challenges is the lack of proper guidance is the the issue of systems working in silos makes it difficult functional interoperability in the healthcare sector for patient information to be shared. This creates a (Szarfman et al., 2022). Additionally, Tsegaye and serious challenge with data interoperability. As a result, Flowerday (2021) also note that there is limited research a number of these electronic health information systems on addressing interoperability when implementing (HIS) used in some hospitals are unable to interoperate technologies in healthcare. They indicate that although with each other for data synchronization and exchange the South African healthcare institutions use schemas, (Savage & Savage, 2020). health information systems are not interoperable as they According to Torab-Miandoab et al. (2023), although do not exchange information among each other. the adoption of HIS has improved the quality of There is a plethora of literature on the use of healthcare information and services, the interoperability technologies in health that has been conducted of these systems still requires attention. The inability to worldwide and in the South African perspectives. Kante allow the interoperability of health data and to have a and Ndayizigamiye (2021) work was on the analysis on comprehensive, interoperable supporting infrastructure the national digital health strategy for South Africa can be addressed through standardization. relating to the use of the Internet of Medical Things Standardization in this context enables automatic data (IoMT) in healthcare. Their study focused on examining interchanges to enhance smart hospitals and improve situational, structural, cultural, and environmental decision-making. Further, the high rate, speed, and factors. Their study revealed that most research has been volume of big data further shows the need for concentrating on the adoption of technologies in health standardized formats that in turn enable systems to but paying little attention on their interoperability. They interoperate (Richemond & Huggins-Jordan, 2023). It is recommended that national digital health strategy therefore critical that healthcare address standards to should provide a framework for the adoption and use of improve and address the current data fragmentation. HIS as well as the interoperability and compatibility of Dixon et al. (2020) highlight that interoperability these systems with the existing technologies. improves effective organizational communication and The study of Mbunge et al. (2022) on the virtual the integration of efforts. This shows that healthcare services and digital health in South Africa interoperability of health information systems is a major indicated that six factors, namely perceived usefulness, factor for enabling healthcare institutions to improve perceived ease-of-use, organization, environment, medical service delivery. technology, innovation, and vendor management Currently, SA is working on initiatives to standardize influence readiness of private health sectors to adopt the National Health Insurance System (NHI) to improve HIS. They however noted that challenges of data quality and exchanges of data (Naidoo et al., 2023). infrastructural and technology, organizational and 68 International Journal of Science Annals, Vol. 7, No. 2, 2024 рrint ISSN: 2617-2682; online ISSN: 2707-3637; DOI:10.26697/ijsa financial issues, policy and regulatory challenges, attributes: Socio-political, technical, and regulatory cultural and resistance as well as interoperability impede factors, Legislature and political economy (Kouroubalia successful implementation of HIS. They recommended et al., 2019; Pypenko & Melnyk, 2021; Tsegaye & for the need to adjust eHealth policies to accommodate Flowerday, 2021). Societal readiness attributes: effective use of innovative technologies in healthcare Sociocultural factors, Interaction among members, that enables resources sharing. However, this can only Local communities (Yusif et al., 2020; Ilorah et al., be achieved if the implemented HIS are interoperable 2017). Engagement readiness (Yusif et al., 2020; Ilorah enough to enable the sharing of resources among health et al., 2017) attributes: Physical accessibility, and facilities. acceptability of services, Communication experiences Achieng and Ruhode’s (2023) investigated the context- (Ennis-Cole, Cullum and Iwundu, 2018), Socio- based factors that influence HIS implementation in economic (Ogundeji, Ohiri and Agidani, 2018), resource-constrained public hospitals. Their study Resistance to change/ Need to change. The last readiness identified factors including implementation of policies, construct is Acceptance and Use Readiness with planning and support strategies, analysis of healthcare attributes: Education and training, Willingness to information systems suitability as well as change, Training of users, Cultural settings of diverse interoperability. The study observed that population groups in the society (Yusif et al., 2020; interoperability is essential plays a role of standards, Ilorah et al., 2017). protocols, technologies, and mechanisms that allow data Tsegaye and Flowerday (2021) suggested that to flow between diverse systems with minimal human interoperability levels due to functional interoperability intervention since it enables diverse systems to will influence the interoperability readiness of health communicate with each other and share information in information system data. In terms of semantic real time. Their study recommended for more studies to interoperability, a health system that is semantically investigate the compatibility and interoperability of integrated allows the exchange of data among health information systems for successful organizations and their internal ecosystems by ensuring implementation, especially in public sectors. that the data exchanged is interpreted correctly and does Several theoretical models have been developed to not miss its meaning (de Mello et al., 2022).The explain users’ behavioural intentions to accept and use syntactic level on the other hand enables the exchange technologies. Consequently, various research studies of data by supporting the same protocol in a have been conducted to address interoperability standardized format (Villarreal et al., 2023). readiness in healthcare (Achieng & Ruhode, 2023). These constructs are presented in the Figure 1, showing Additionally, there are frameworks and models that the hypothesis derived from the constructs. have been developed specifically to inform technology The aim of the study. To assess the data interoperability readiness. Among them are Technology Reading Index of the Health Information System used in the South (TRI) (Parasuraman, 2000), that explains the overall African public healthcare. state of mind resulting from a gestalt of mental enablers and inhibitors that collectively determine a person’s Materials and Methods predisposition to use new technologies (Bakirta & Based on the conceptual model, a close-ended Akkas, 2020). questionnaire was developed to collect data from three Other studies have depended on TRI either by district hospitals in Gauteng province, South Africa. The replicating or extending it to conduct research on questionnaire was distributed online using Survey technology readiness. In each studies, some factors have Monkey. For ethical purposes, and to protect privacy been added either from the literature or other theories and anonymity, a link was sent to the contact person at and frameworks of technology acceptance and use each district hospital who then distributed it to the (Robin et al., 2020). respondents using their mailing lists. Respondents filled Researchers Nilsen et al. (2020) used the TRI by the questionnaire and on completion they clicked the introducing new factors such altitude, education and submit button that delivered the completed training, technology compatibility to address issues of questionnaire in the Survey Mokey database. interoperability, inadequate infrastructure, and lack of Population and Sampling standardization. The targeted population for this study consisted of Five constructs of core readiness, policy readiness, individuals who were actively involved or uses HIS and societal readiness, engagement readiness, and use and are somehow knowledgeable about the data sharing acceptance readiness were identified and derived from between health facilities. These were basically IT literature. Additionally, interoperability levels were professionals, data quality mentors, medical reconceptualized into three perspectives and included in professionals, and administration professionals. From the conceptual model these are Functional, Syntactic, the pre-exploratory study conducted, it was revealed that and Semantic interoperability. The attributes of the there are approximately fifty individuals in each hospital readiness factors were derived from literature in this that form the category of the participants of this study, manner: a. Core readiness attributes: Need to change, making the overall population of this study to be 150. Education and training, Awareness, Willingness to Based on the Krejcie and Morgan (1970) tool for change, E-health project planning, Trust on the use of determining the sample size of the finite population, a technology (Yusif et al., 2020). Policies readiness sample size of 108 respondents was needed for data 69 International Journal of Science Annals, Vol. 7, No. 2, 2024 рrint ISSN: 2617-2682; online ISSN: 2707-3637; DOI:10.26697/ijsa collection. Simple random sampling was then used to as ERead1 – ERead4, Acceptance and Use Readiness as distribute the Survey Monkey link to the respondents. AURead and its three attributes as AURead1 – Questionnaire Coding AURead3. The Functional Interoperability was coded as Before data analysis was conducted, the questionnaire FunInt and its three attributes as FunInt1 – FunInt3, was coded to allow easy transcription in the statical Semantic Interoperability as SemInt and its three package. Analysis was conducted using the Statistical attributes as SemInt1 – SemInt3 and the Syntactic Package for Social Scientists (SPSSv25). The Interoperability also known as Data Ontology was coded questionnaire coding was as follows. Core Readiness as SynInt and its three attributes as SynInt1 – SynInt3. was coded as CRead and its four attributes as CRead – CRead4, Policy Readiness as PRead and its three Results and Discussion attributes as PRead1 – PRead3, Societal Readiness as Table 1 presents a detailed analysis of frequencies of the SRead and its three attributes as SRead1 – Sread3, respondents’ demographics and situational variables. Engagement Readiness as Eread and its four attributes Table 1 Frequencies of Respondents’ Demographics 70 International Journal of Science Annals, Vol. 7, No. 2, 2024 рrint ISSN: 2617-2682; online ISSN: 2707-3637; DOI:10.26697/ijsa Regression Analysis Regression analysis explains the relationship between Table 2 presents results of the regression analysis. The two or more variables of interest (Creswell & Creswell, regression analysis explains each construct’s 2018). From the model summary, the overall prediction contribution to the overall prediction of the model. of the model to inform HIS data interoperability readiness assessment was 86.1% (R2=0.861). Table 2 Regression Analysis Note. *Dependent variable – HISDIRead; VIF – variance inflation factor; PRead – policy readiness; CRead – core readiness; SRead – societal readiness; ERead – engagement readiness; AURead – acceptance and use readiness; FUNInt – functional interoperability; SEMInt – semantic interoperability; SYNInt – syntactic interoperability. Results in Table 2 indicates that with the exception of below the recommended value for multicollinearity to policy readiness (PRead), engagement readiness ERead exist implies that there was no multicollinearity. By and syntactical interoperability the rest of the constructs using the critical ration t-value demonstrated in Table 2, showed that they have a significant contribution to the the testing of the hypotheses was deduced as presented overall prediction of the model. Additionally, all the in Table 3. values of the Variance Inflation Factor (VIF) were Table 3 Hypotheses Testing The final model with the constructs of core, societal, functional and semantic interoperability is shown in acceptance and readiness, and the parameters of Figure 2. 71 International Journal of Science Annals, Vol. 7, No. 2, 2024 рrint ISSN: 2617-2682; online ISSN: 2707-3637; DOI:10.26697/ijsa Figure 1 The Conceptual Model for Data Interoperability Assessment for Health Information System in South African Public Healthcare Figure 2 The Final Model for Data Interoperability Assessment Model for Health Information System in South African Public Healthcare This study sought to assess interoperability readiness in interoperability readiness in South African public South African public health. Interoperability plays a hospitals. major role in today’s interconnected world, as it enables The first hypothesis (H1) theorized that core readiness health institutions to communicate and exchange data has a direct influence on HIS data interoperability effectively and efficiently. With interoperability, health readiness. This hypothesis was accepted. The acceptance institutions may have improved data sharing and of this hypothesis implies that with the increasing collaboration, enhanced data quality, increased digitization it is almost becoming impossible for health efficiency, lower costs, improved user experience, and institutions to operate without the use of technology. better security and privacy (Savage & Savage, 2020; results of the study are in agreement with those of other Torab-Miandoab et al., 2023). To maximumly benefit researchers such as (Achieng & Ruhode, 2023; Khubone from interoperability, health institutions need to focus on et al., 2020; van Heerden & Young, 2020) who indicated creating a culture of collaboration and data sharing and that digital solutions in health should be implemented investing in technology solutions that enable seamless with interoperability in mind to enable collaborations and data exchange between different systems and devices. information sharing especially in the resources This section discusses the results of the study in relation constrained areas. Hence, they emphasized the role of to the five hypotheses that were set to assess the core readiness in achieving interoperability readiness. 72 International Journal of Science Annals, Vol. 7, No. 2, 2024 рrint ISSN: 2617-2682; online ISSN: 2707-3637; DOI:10.26697/ijsa The second hypothesis (H2) predicted the influence of the level of system simplicity and user-friendliness. They policy readiness on HIS data interoperability readiness. indicated that, if the HIS is effectively used This hypothesis was rejected. Policy readiness which implementation of interoperability will be faster as users refers to government commitment regarding governance, will be eager to share information and collaborate with standards, and legal infrastructure. The implementation others. of technology in healthcare is often frequently expected The interoperability levels were based on to hypothesize to raise the standard of healthcare services. The rejection three relationships. H6a predicted that interoperability of this hypothesis implies that policies are paramount for levels due to functional interoperability will influence the implementation of HIS but may not have a role in the HIS data interoperability readiness. This hypothesis was architecture and operation of the system as many policy accepted. The acceptance of this hypothesis may imply makers are not actually the users of the system. The that fragmented data fail to achieve the full potential of findings of this study don’t align with those of other digital health, therefore today’s world healthcare researchers such as (Achieng and Ruhode, 2023; Kgasi facilities do their best to deliver the best patient & Kalema, 2014; Tsegaye & Flowerday, 2021) who experience. found policy significant and indicated that good policies Without a proper interoperability structure, exchanging should set standards that should be followed before. patient-related data becomes impossible in such cases. During and after the implementation of HIS. The foundational level is a basic level of exchange of data The third hypothesis (H3) predicted the influence of hence, the foundational level will assist in improving societal readiness on HIS data interoperability readiness. patient information. The findings of this study agree This hypothesis was accepted. The acceptance of this those of Rajkumar et al. (2022); Tsegaye and Flowerday hypothesis emphasizes the need to involve users when (2021) who suggested that interoperability levels due to implementing a technological innovation. Such functional interoperability will influence the involvement is key for ensuring that high quality interoperability readiness of health information system healthcare and reliable services are implemented to meet data. This level of interoperability only ensures that the users day to day needs. It also ensures trust, and information is transmitted and does not indicate anything confidence during use and the planning of the suitable about data representation. training for the users. The findings of this study concur Hypothesis H6b predicted the influence of with those of previous researchers such as (Khubone et interoperability levels due to syntactic interoperability to al., 2020; Robin et al., 2020; Udekwe et al., 2021) who have an influence on HIS data interoperability readiness. found society readiness significant and indicated that This hypothesis was rejected. During sharing and technological systems may fail when they meet collaboration, it is anticipated that there will be an resistance originating from users’ negative attitudes exchange of information. These exchanged messages towards the technology especially when the users were would need to be transmitted using a structure and syntax not involved in the implementation process. that are recognized by both the sender and the receiver The fourth hypothesis (H4) predicted the direct influence systems. As a result, there must be an agreed on uniform of engagement readiness on HIS data interoperability data format for sharing and integrating different readiness. This hypothesis was rejected. The rejection of applications based on their respective structures (Lehne this hypothesis implies that health institutions do not et al. 2019). The findings of this study are contrary to need to plan for engagement as it should be part and those of other researchers, such as Tsegaye and partial of the implementation process. When users are Flowerday (2021) and Rajkumar et al. (2022) who involved, engagement comes automatically as each user indicated that to achieve a meaningful exchange of health will feel that he/she is part of the whole process. The data, it is essential to have semantic and syntactic findings of this study concur with those of many other interoperability along with technical interoperability. researchers such as (Udekwe et al., 2021; Villarreal et al., The last hypothesis H6c predicted the influence of 2023) who also note that much as engagement readiness semantic interoperability on HIS data interoperability stimulates effective implementation planning that avoids readiness. This hypothesis was accepted. The acceptance financial losses, effort, time delays and, dissatisfaction of this hypothesis suggests that semantic interoperability among stakeholders. Its role may be reduced if users is the foundation of healthcare and focuses on clear and involvement is taken as part of the implementation unambiguous semantics and standardized medical process. terminologies. Hypothesis H5 theorized the influence of the acceptance Therefore, it is always better to use data with a clear and and use readiness on HIS data interoperability readiness. well-defined structure. To ensure the security of the This hypothesis was accepted. The acceptance of this exchanged data, semantic interaction is the best choice, hypothesis implies that any form of technology needs to as it allows interoperability at the highest level. The be accepted, adapted, adopted and then used. Acceptance findings of this study are consistent with those of other and use are very critical in the technology researchers - such as Tsegaye and Flowerday (2021) who implementation journal regardless of what technology is suggested that semantic interoperability levels will being implemented. The findings of this study are in influence HIS data interoperability readiness since to agreement with those other researchers such as (Achieng allows the exchange of data among health institutions & Ruhode, 2023; Naidoo & Naidoo, 2021; Robin et al., there is a need to ensure that data exchanged is 2020) who indicated that the use of HIS is influenced by interpreted correctly and does not miss its meaning. 73 International Journal of Science Annals, Vol. 7, No. 2, 2024 рrint ISSN: 2617-2682; online ISSN: 2707-3637; DOI:10.26697/ijsa Conclusions Dixon, B. E., Rahurkar, S., & Apathy, N. C. (2020). Interoperability in the field of health care is still in its Interoperability and health information exchange infancy and such has led to many legacy systems that for public health. In Magnuson, J., & Dixon, B. worsens the already existing information silos and has (Eds.), Public Health Informatics and Information consequently led to skyrocketing of healthcare costs, Systems. Health Informatics (pp. 307–324). poor health service delivery due to delayed decision Springer, Cham. https://doi.org/10.1007/978-3- making. However, implementing interoperability in 030-41215-9_18 healthcare systems faces numerous challenges ranges Ennis-Cole, D. L., Cullum, P. M., & Iwundu, C. (2018). from privacy and security Concerns to data quality and Physicians as operational leaders: Cost, integrity. Overcoming the barriers health institutions curriculum, technology, and organizational need to leverage models like the one developed in this challenges. TechTrends, 62, 239–249. study to have a better understanding of how best https://doi.org/10.1007/s11528-018-0273-x interoperability could be implemented with minimal Kgasi, M. R., & Kalema, B. M. (2014). Assessment E- challenges. health readiness for rural South African areas. 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Journal of Healthcare share health data electronically under HIPAA, and Informatics Research, 4, 189–214. sharing with patients and patients third-party https://doi.org/10.1007/s41666-020-00070-8 health apps is consistent: Interoperability and Cite this article as: Rikhotso, M., Kalema, B. M., & Seaba, T. R. (2024). Data interoperability assessment model for health information system in South African public healthcare. International Journal of Science Annals, 7(2), 67–75. https://doi.org/10.26697/ijsa.2024.2.4 The electronic version of this article is complete. It can be found online in the IJSA Archive https://ijsa.culturehealth.org/en/arhiv This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/deed.en). 75