The factors affecting a data harmonisation innovation in the Western Cape, South Africa
Doctoral Thesis
2019
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Abstract
Lack of coordination and integration between routine electronic databases can limit effective data production and utilisation to support health management decision-making. There is currently a need to strengthen data support structures through the harmonisation of multiple databases across different types of health services and organisations. Data harmonisation (DH) is an innovative process of copying existing electronic data captured in various databases into a centralised data repository where the data is integrated and then transformed into useable formats for data users. However, there is limited evidence about the wide range of factors (especially social factors) that impact on DH innovations, such as historical factors, stakeholder relationships and institutional terrain. This doctoral research aimed to identify and explore the factors affecting a DH initiative currently underway in the Western Cape Province of South Africa. The research was conducted using three methodological approaches, namely a historical analysis and synthesis, a scoping review and an ethnographic case study. For the historical analysis, relevant articles were identified through literature searches and data were collected through document reviews and interviews with two key informants. Data were first organised chronologically according to key events that took place in the health information system (HIS). Text from websites, journal articles, internal documents, standard operating procedures and interview notes were then synthesised according to key themes related to HIS interventions. For the scoping review, systematic literature searches were conducted to identify studies that met the eligibility criteria of the review. Two review authors (one being the doctoral student) screened titles, abstracts and full-texts and then sampled studies based on the range, variation and similarities or differences in definitions and concepts and intervention descriptions. Manual coding and the filter option in Excel were used to provide (a) numerical analysis of the characteristics of included studies; (b) narrative synthesis of the different DH definitions, components and processes, as well as intentions, suggestions and/or explanations of how DH may lead to improved health management decision-making. For the ethnographic case study, data were collected using participant observation (including conversations, meeting attendance and telephone and email communication), document reviews and in-depth interviews. Participants included data clerks, facility managers, health information staff and managers, DH innovators, researchers, public health specialists and database managers. Raw data were collected in the form of meeting minutes, field notes, interview notes and document extracts. Data analysis was conducted using thematic data analysis. The doctoral student manually coded data by highlighting recurring themes and evidence, and by extracting prominent themes from the various sources of data. As a strategy for testing the validity of emerging themes, the doctoral student used triangulation of different data sources; including looking for consistencies or inconsistencies between data sources. Five main findings emerged from the doctoral research. The first finding affirms that DH is a multi-faceted intervention. In the literature, it is defined and described using different terms for similar aims and activities (such as record linkage, data warehousing, health information exchange). Key characteristics emerging from a synthesis of DH studies include: a process of multiple steps to integrate electronic data; different types of databases, institutions and technical activities; integrating data involves using unique patient identifiers; and framing interventions or activities around a specific scope or purpose (such as geographic area, disease surveillance and treatment management). DH interventions contributed to three levels of health management decision-making, namely clinical support, operational and strategic management, and populationlevel disease surveillance. The second finding relates to the concept of ‘cultivation’. Cultivation is an ongoing and iterative social process to deal with problems between people, institutions and technology as they engage with each other in the context of an emerging innovation. The third finding is about striking a balance between the role of champions in designing and piloting innovations and the role of institutions in operationalising innovations and incorporating them into the broader health system for acceptance amongst implementers and users and for sustainability in the future. The fourth finding is about the motivations and opportunities that contributed to the emergence of a DH initiative in the Western Cape Province of South Africa. Opportunities for the new DH initiative include well-developed individual electronic databases, a government-university collaboration, and the positive attitude of frontline health workers towards DH projects. The new initiative faced design and operational challenges such as difficulty to access data from different health authorities and the incompleteness of electronic data. However, new data access and transfer procedures and existing social relationships were important for dealing with the changes that occurred as DH projects were being operationalised. The last finding highlights tensions that emerged between DH innovators and other health information technology (HIT) stakeholders because of institutional and conceptual differences (such as different approaches to data access and governance, differences in conceptualisations of the value of data, and misunderstandings about the purpose of formal data procedures). DH innovators were able to navigate conflicts emerging from institutional and conceptual differences because of their strong leadership and team setup, institutional positioning and stakeholder engagement activities, to become institutionalised within the health system. These findings provide health system, information technology and research stakeholders with a broader understanding of the range of social factors that impact on DH innovations. This research promotes a more comprehensive approach in designing, implementing and evaluating DH innovations to limit poor outcomes of innovations and wasted resources.
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Reference:
Schmidt, B. 2019. The factors affecting a data harmonisation innovation in the Western Cape, South Africa.