Alignment of big data perceptions in New Zealand healthcare : a thesis presented in partial fulfilment of the requirement for the degree of Doctor of Philosophy in Management at Massey University, Albany, New Zealand

Abstract

The growing use of information systems (IS) in the healthcare sector, on top of increasing patient populations, diseases and complicated medication regimens, is generating enormous amounts of unstructured and complex data that have the characteristics of ‘big data’. Until recent times data driven approaches in healthcare to make use of large volumes of complex healthcare data were considered difficult, if not impossible, because available technology was not mature enough to handle such data. However, recent technological developments around big data have opened promising avenues for healthcare to make use of its big-healthcare-data for more effective healthcare delivery, in areas such as measuring outcomes, population health analysis, precision medicine, clinical care and research and development. Being a recent IT phenomenon, big data research has leaned towards technical dynamics such as analytics, data security and infrastructure. However, to date, the social dynamics of big data (such as peoples’ understanding and their perceptions of its value, application, challenges and the like) have not been adequately researched. This thesis addresses the research gap through exploring the social dynamics around the concept of big data at the level of policy-makers (identified as the macro level), funders and planners (identified as the meso level), and clinicians (identified as the micro level) in the New Zealand (NZ) healthcare sector. Investigating and comparing social dynamics of big data across these levels is important, as big data research has highlighted the importance of business-IT alignment to the successful implementation of big data technologies. Business-IT alignment is important and can be investigated through many different dimensions. This thesis adopts a social dimension lens to alignment, which promotes investigating alignment through people’s understanding of big data and its role in their work. Taking a social dimension lens to alignment fits well with the aim of this thesis, which is to understand perceptions around the notion of big data technologies that could influence the alignment of big data in healthcare policy and practice. With this understanding, the research question addressed is: 𝘩𝘰𝘸 𝘥𝘰 𝘱𝘦𝘳𝘤𝘦𝘱𝘵𝘪𝘰𝘯𝘴 𝘰𝘧 𝘣𝘪𝘨 𝘥𝘢𝘵𝘢 𝘪𝘯𝘧𝘭𝘶𝘦𝘯𝘤𝘦 𝘢𝘭𝘪𝘨𝘯𝘮𝘦𝘯𝘵 𝘢𝘤𝘳𝘰𝘴𝘴 𝘮𝘢𝘤𝘳𝘰, 𝘮𝘦𝘴𝘰, 𝘢𝘯𝘥 𝘮𝘪𝘤𝘳𝘰 𝘭𝘦𝘷𝘦𝘭𝘴 𝘪𝘯 𝘵𝘩𝘦 𝘕𝘡 𝘩𝘦𝘢𝘭𝘵𝘩𝘤𝘢𝘳𝘦 𝘴𝘦𝘤𝘵𝘰𝘳? This thesis is by publication with four research articles that answer these questions as a body of knowledge. A qualitative exploratory approach was taken to conduct an empirical study. Thirty-two in-depth interviews with policy makers, senior managers and physicians were conducted across the NZ healthcare sector. Purposive and snowball sampling techniques were used. The interviews were transcribed verbatim and analysed using general inductive thematic analysis. Data were first analysed within each group (macro, meso, and micro) to understand perceptions of big data, then across groups to understand alignment. In order to investigate perceptions, Social Representations Theory (SRT), a theory from social psychology, was used as the basis for data collection. However, data analysis led to the decision to integrate SRT with Sociotechnical Systems Theory (SST), a well-known IS theory. This integration of SRT with SST developed the Theory of Sociotechnical Representations (TSR), which is a key theoretical contribution of this research. The thesis presents the concept and application of TSR, by using it to frame the study’s findings around perceptions of big data across macro, meso and micro levels of the NZ healthcare sector. The practical contribution of this thesis is the demonstration of areas of alignment and misalignment of big data perceptions across the healthcare sector. Across the three levels, alignment was found in the shared understanding of the importance of data quality, the increasing challenges of privacy and security, and the importance of new types of data in measuring health outcomes. Aspects of misalignment included the differing definitions of big data, as well as perceptions around data ownership, data sharing, use of patient-generated data and interoperability. While participants identified measuring outcomes, clinical decision making, population health, and precision medicine as potential areas of application for big data technologies, the three groups expressed varying levels of interest, which could cause misalignment issues with implications for policy and practice.