Alignment of Big Data Perceptions Across Levels in Healthcare: The case of New Zealand
DOI:
https://doi.org/10.3127/ajis.v27i0.4067Keywords:
big data, New Zealand Healthcare, business-IT alignment, theory of sociotechnical representations, business-IT alignment taxonomyAbstract
Big data and related technologies have the potential to transform healthcare sectors by facilitating improvements to healthcare planning and delivery. Big data research highlights the importance of aligning big data implementations with business needs to achieve success. In one of the first studies to examine the influence of big data on business-IT alignment in the healthcare sector, this paper addresses the question: how do stakeholders’ perceptions of big data influence alignment between big data technologies and healthcare sector needs across macro, meso, and micro levels in the New Zealand (NZ) healthcare sector? A qualitative inquiry was conducted using semi-structured interviews to understand perceptions of big data across the NZ healthcare sector. An application of a novel theory, Theory of Sociotechnical Representations (TSR), is used to examine people’s perceptions of big data technologies and their applicability in their day-to-day work. These representations are analysed at each level and then across levels to evaluate the degree of alignment. A social dimension lens to alignment was used to explore mutual understanding of big data across the sector. The findings show alignment across the sector through the shared understanding of the importance of data quality, the increasing challenges of privacy and security, and the importance of utilising modern and new data in measuring health outcomes. Areas of misalignment include the differing definitions of big data, as well as perceptions around data ownership, data sharing, use of patient-generated data and interoperability. Both practical and theoretical contributions of the study are discussed.
References
Andreu-Perez, J., Poon, C. C., Merrifield, R. D., Wong, S. T., & Yang, G.-Z. (2015). Big data for health. IEEE journal of biomedical and health informatics, 19, 1193-1208.
Ashley, E. A. (2016). Towards precision medicine. Nature Reviews Genetics, 17, 507-522.
Bag, S., Dhamija, P., Luthra, S., & Huisingh, D. (2023). How big data analytics can help manufacturing companies strengthen supply chain resilience in the context of the COVID-19 pandemic. The International Journal of Logistics Management, 34, 1141-1164.
Bakker, L., Aarts, J., Uyl-De Groot, C., & Redekop, W. (2020). Economic evaluations of big data analytics for clinical decision-making: a scoping review. Journal of the American Medical Informatics Association, 27, 1466-1475.
Bates, D. W., Kuperman, G. J., Wang, S., Gandhi, T., Kittler, A., Volk, L., Spurr, C., Khorasani, R., Tanasijevic, M., & Middleton, B. (2003). Ten Commandments for Effective Clinical Decision Support: Making the Practice of Evidence-based Medicine a Reality. Journal of the American Medical Informatics Association, 10, 523-530.
Bean, R., & Kiron, D. (2013). Organizational Alignment is Key to Big Data Success. MIT Sloan Management Review Big Idea: Data & Analytics, 54.
Blasimme, A., Fadda, M., Schneider, M., & Vayena, E. (2018). Data Sharing For Precision Medicine: Policy Lessons And Future Directions. Health Affairs, 37, 702-709.
Burns, W. A. 2014. Healthcare Data's Perfect Storm: Why Healthcare Organizations Are Drowning in the Data They are Creating and Why They Need Even More Data to Weather this Storm [White Paper]. Available: https://s3.amazonaws.com/rdcms-himss/files/production/public/HIMSSorg/Content/files/HitachiDataSystems_BR-458_perfectStorm.pdf [Accessed 26 Feb 2016].
Cecez-Kecmanovic, D., Galliers, R. D., Henfridsson, O., Newell, S., & Vidgen, R. (2014). The sociomateriality of information systems. Management Information Systems Quarterly, 38, 809-830.
Chan, Y. E., & Reich, B. H. (2007). IT alignment: what have we learned? Journal of Information Technology, 22, 297-315.
Chawla, N., & Davis, D. (2013). Bringing Big Data to Personalized Healthcare: A Patient-Centered Framework. Journal of General Internal Medicine, 28, 660-665.
Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19, 171-209.
Collins, B. (2016). Big Data and Health Economics: Strengths, Weaknesses, Opportunities and Threats. Pharmaco Economics, 34, 101-106.
Cumming, J. (2011). Integrated care in New Zealand. International Journal of Integrated Care, 11, e138.
Dang, A., & Mendon, S. (2015). The value of big data in clinical decision making. International Journal of Computer Science and Information Technologies, 6, 3830-3835.
Davenport, T. H. (2013). Analytics 3.0: in the new era, big data will power consumer products and services [Online]. Harvard Business School Press. Available:
https://hbr.org/2013/12/analytics-30 [Accessed 12 Jun 2016].
Davenport, T. H., & Dyché, J. (2013). Big data in big companies. International Institute for Analytics. https://www.iqpc.com/media/7863/11710.pdf
Dhawan, R., Singh, K., & Tuteja, A. (2014). When big data goes lean. McKinsey Quarterly, 24, 97-102.
Dopfer, K., Foster, J., & Potts, J. (2004). Micro-meso-macro. Journal of Evolutionary Economics, 14, 263-279.
Dulipovici, A., & Robey, D. (2013). Strategic Alignment and Misalignment of Knowledge Management Systems: A Social Representation Perspective. Journal of Management Information Systems, 29, 103-126.
El-Mekawy, M., Rusu, L., & Perjons, E. (2015). An evaluation framework for comparing business-IT alignment models: A tool for supporting collaborative learning in organizations. Computers in Human Behavior, 51, 1229-1247.
Emani, C. K., Cullot, N., & Nicolle, C. (2015). Understandable Big Data: A survey. Computer Science Review, 17, 70-81.
Emery, F. E. (1959). Characteristics of Socio-Technical Systems. London, UK: Tavistock Institute.
Emery, F. E., & Trist, E. L. (1965). The Causal Texture of Organizational Environments. Human Relations, 18, 21-32.
Esposito, C., De Santis, A., Tortora, G., Chang, H., & Choo, K. R. (2018). Blockchain: A Panacea for Healthcare Cloud-Based Data Security and Privacy? IEEE Cloud Computing, 5, 31-37.
Eynon, R. (2013). The Rise of Big Data: what does it mean for education, technology, and media research? Learning, Media and Technology, 38, 237-240.
Fattah, F., & Arman, A. A. (2014). Business-IT Alignment: Strategic Alignment Model for healthcare (case study in hospital bandung area). 2014 International Conference on ICT For Smart Society (ICISS), Bandung, Indonesia, 2014, 256-259,
doi: 10.1109/ICTSS.2014.7013183.
Gal, U., & Berente, N. (2008). A social representations perspective on information systems implementation: Rethinking the concept of "frames". Information Technology & People, 21, 133-154.
Ginsburg, G. S., & Phillips, K. A. (2018). Precision Medicine: From Science To Value. Health Affairs, 37, 694-701.
Grant, G. (2010). Reconceptualizing the concept of business and IT alignment: from engineering to agriculture. European Journal of Information Systems, 19, 619-624.
Groves, P., Kayyali, B., Knott, D., & Van Kuiken, S. (2013). The 'Big Data' revolution in healthcare: Accelerating value and Innovation [Online]. Available:
Guo, C., & Chen, J. (2023). Big data analytics in healthcare. Knowledge Technology and Systems: Toward Establishing Knowledge Systems Science. Springer.
Halamka, J. D. (2014). Early Experiences With Big Data At An Academic Medical Center. Health Affairs, 33, 1132-1138.
He, K. Y., Ge, D., & He, M. M. (2017). Big Data Analytics for Genomic Medicine. International Journal of Molecular Sciences, 18, 412. https://doi.org/10.3390/ijms18020412
Henderson, J. C., & Venkatraman, N. (1992). Strategic Alignment: A Model for Organizational Transformation through Information Technology. In: Kochan, T. A., & Useem, M. (eds.) Transforming organizations. New York, USA: Oxford University Press.
Henderson, J. C., & Venkatraman, N. (1993). Strategic alignment: leveraging information technology for transforming organizations. IBM Systems Journal 32, 472-484.
Herland, M., Khoshgoftaar, T. M., & Wald, R. (2014). A review of data mining using big data in health informatics. Journal of Big Data, 1, 1-35.
Jameson, J. L., & Longo, D. L. (2015). Precision medicine - personalized, problematic, and promising. Obstetrical & Gynecological Survey, 70, 612-614.
Jenkin, T. A., & Chan, Y. E. (2010). IS project alignment - a process perspective. Journal of Information Technology, 25, 35-55.
Jia, Y., Wang, N., & Ge, S. (2018). Business-IT Alignment Literature Review: A Bibliometric Analysis. Information Resources Management Journal, 31, 34-53.
Jim, H. S., Hoogland, A. I., Brownstein, N. C., Barata, A., Dicker, A. P., Knoop, H., Gonzalez, B. D., Perkins, R., Rollison, D., & Gilbert, S. M. (2020). Innovations in research and clinical care using patient‐generated health data. CA: A Cancer Journal for Clinicians, 70, 182-199.
Kaisler, S., Armour, F., Espinosa, J. A., & Money, W. (2012). Big Data: Issues and Challenges Moving Forward. In Proceedings of the 46th Hawaii International Conference on System Sciences, 7-10 January 2013. 995-1004.
Karkouch, A., Mousannif, H., Al Moatassime, H., & Noel, T. (2016). Data quality in internet of things: A state-of-the-art survey. Journal of Network and Computer Applications, 73, 57-81.
Krey, M. (2018). Facing business-IT-alignment in healthcare. In Proceedings of 51st Hawaii International Conference on System Sciences (HICSS), Waikoloa Village HI, USA, 3-6 January 2018. 3090-3099.
Kyriazis, D., & Varvarigou, T. (2013). Smart, Autonomous and Reliable Internet of Things. Procedia Computer Science, 21, 442-448.
Leonardi, P. M. (2011). When flexible routines meet flexible technologies: Affordance, constraint, and the imbrication of human and material agencies. MIS quarterly, 147-167.
Liamputtong, P., & Ezzy, D. (2005). Qualitative Research Methods, Oxford, UK: Oxford University Press.
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic Inquiry, London, UK: SAGE Publications.
Luftman, J. (1996) . Competing in the Information Age : strategic alignment in practice, New York, USA: Oxford University Press.
Lv, Z. & Qiao, L. (2020). Analysis of healthcare big data. Future Generation Computer Systems, 109, 103-110.
Mason, M. (2010). Sample Size and Saturation in PhD Studies Using Qualitative Interviews. Forum: Qualitative Social Research, 11, 1-19.
McAfee, A., & Brynjolfsson, E. (2012). Big Data: The Management Revolution. (cover story). Harvard Business Review, 90, 60-68.
Menon, N. M., Yaylacicegi, U., & Cezar, A. (2009). Differential Effects of the Two Types of Information Systems: A Hospital-Based Study. Journal of Management Information Systems, 26, 297-316.
Merriam, S. B. (2009). Qualitative research: a guide to design and implementation, San Francisco,, CA, USA: Jossey-Bass.
Miles, M. B., Huberman, A. M., & Saldana, J. (2014). Qualitative data analysis: a methods sourcebook, Thousand Oaks, CA, USA: SAGE Publications.
Minister of Health (2016). New Zealand Health Strategy: Future Direction. Wellington, New Zealand: Ministry of Health
Ministry of Health (2014). Briefing to the Incoming Minister 2014. Wellington, New Zealand. https://www.treasury.govt.nz/publications/bim/briefing-incoming-minister-health-2014
Ministry of Health (2017). Overview of the Health System [Online]. New Zealand: Ministry of Health. Available: http://www.health.govt.nz/new-zealand-health-system/overview-health-system [Accessed 29 Jan 2019].
Moscovici, S. (1984). The Phenomenon of Social Representation. In: Farr, R. M., & Moscovici, S. (eds.) Social representations: European Studies in Social Psychology. Cambridge, UK: Cambridge University Press.
Moscovici, S. (1988). Notes Towards a Description of Social Representations. European Journal of Social Psychology, 18, 211-250.
Nash, D. B. (2014). Harnessing the Power of Big Data in Healthcare. American Health & Drug Benefits.
O'driscoll, A., Daugelaite, J., & Sleator, R. D. (2013). ‘Big data’, Hadoop and cloud computing in genomics. Journal of Biomedical Informatics, 46, 774-781.
Orlikowski, W. J., & Scott, S. V. (2008). Sociomateriality: challenging the separation of technology, work and organization. Academy of Management Annals, 2, 433-474.
Paré, G., Sicotte, C., Jaana, M., & Girouard, D. (2008). Prioritizing Clinical Information System Project Risk Factors: A Delphi Study. In Proceedings of the 41st Annual Hawaii International Conference on System Sciences, Waikoloa, HI, USA, 2008. doi: 10.1109/HICSS.2008.354.
Patton, M. Q. (2015). Qualitative research & evaluation methods : integrating theory and practice, Los Angeles, CA: SAGE Publications.
Petersen, C., & Demuro, P. (2015). Legal and regulatory considerations associated with use of patient-generated health data from social media and mobile health (mHealth) devices. Applied Clinical Informatics, 6, 16-26.
Protti, D., & Bowden, T. (2010). Electronic Medical Record Adoption in New Zealand Primary Care Physician Offices. Commonwealth Fund: Issues in International Health Policy, 96.
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 2, 3.
Reich, B. H., & Benbasat, I. (1996). Measuring the Linkage Between Business and Information Technology Objectives. MIS Quarterly, 20, 55-81.
Roski, J., Bo-Linn, G. W., & Andrews, T. A. (2014). Creating Value In Health Care Through Big Data: Opportunities And Policy Implications. Health Affairs, 33, 1115-1122.
Russom, P. (2011). Big data analytics. TDWI Best Practices Report, Fourth Quarter.
Saporito, P. (2013). The 5 V's of big data: value and veracity join three more crucial attributes that carriers should consider when developing a big data vision [Online]. A.M. Best Company, Inc. Available: https://www.thefreelibrary.com/The+5+V%27s+of+big+data%3A+value+and+veracity+join+three+more+crucial...-a0350676739 [Accessed 7].
Sathi, A. (2012). Big data analytics: Disruptive technologies for changing the game. Boise, USA: Mc Press.
Scahill, S. L. (2012). The 'Way things are around here' : organisational culture is a concept missing from New Zealand healthcare policy, development, implementation, and research. New Zealand medical journal (Online).
Shailaja, K., Seetharamulu, B., & Jabbar, M. (2018). Machine learning in healthcare: A review. In Proceedings of the 2nd International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2018. IEEE Xplore, 910-914.
Shapiro, M., Johnston, D., Wald, J., & Mon, D. (2012). Patient-generated health data [White Paper]. RTI International, April. https://www.rti.org/publication/patient-generated-health-data-white-paper.
Shin, D. H. (2015). Demystifying big data: Anatomy of big data developmental process. Telecommunications Policy, 40, 837-854.
Shin, D. H., & Choi, M. J. 2015. Ecological views of big data: Perspectives and issues. Telematics and Informatics, 32, 311-320.
Smith, J. A., Flowers, P., & Larkin, M. 2009. Interpretative phenomenological analysis : theory, method and research, London, UK: SAGE Publications.
Strome, T. L. 2014. Healthcare analytics for quality and performance improvement, Hoboken, NJ, USA: John Wiley & Sons.
Thomas, D. R. 2006. A General Inductive Approach for Analyzing Qualitative Evaluation Data. American Journal of Evaluation, 27, 237-246.
Tormay, P. (2015). Big Data in Pharmaceutical R&D: Creating a Sustainable R&D Engine. Pharmaceutical Medicine - New Zealand, 29, 87-92.
Ward, M. J., Marsolo, K. A., & Froehle, C. M. 2014. Applications of business analytics in healthcare. Business Horizons, 57, 571-582.
Watson, H. J. (2014). Tutorial: Big data analytics: Concepts, technologies, and applications. Communications of the Association for Information Systems, 34, 1247-1268.
Weerasinghe, K. (2019). Transformation through Big Data Analytics: a Qualitative Enquiry in Health. 30th Australasian Conference on Information Systems. Perth, Australia.
Weerasinghe, K., Pauleen, D., Scahill, S., & Taskin, N. (2018a). Development of a Theoretical Framework to Investigate Alignment of Big Data in Healthcare through a Social Representation Lens. Australasian Journal of Information Systems, 22.
Weerasinghe, K., Pauleen, D. J., Scahill, S. & Taskin, N. (2022a). Theory of Sociotechnical Representations: A Novel Approach to Understanding Technology Perspectives. Pacific Asia Conference on Information Systems, 5-9 July 2022 Taipei/Sydney. Association for Information Systems. https://aisel.aisnet.org/pacis2022/314
Weerasinghe, K., Scahill, S. L., Pauleen, D. J., & Taskin, N. (2022b). Big data analytics for clinical decision-making: Understanding health sector perceptions of policy and practice. Technological Forecasting and Social Change, 174, 121222.
Weerasinghe, K., Scahill, S. L., Taskin, N., & Pauleen, D. J. (2018b). Development of a Taxonomy to be used by Business-IT Alignment Researchers. 22nd Pacific Asia Conference on Information Systems. Yokohama, Japan: AIS Electronic Library.
Weli, A. R. (2018). Precision Medicine. Health Affairs, 37, 687-687.
Williams, M. S., Buchanan, A. H., Davis, F. D., Faucett, W. A., Hallquist, M. L. G., Leader, J. B., Martin, C. L., Mccormick, C. Z., Meyer, M. N., Murray, M. F., Rahm, A. K., Schwartz, M. L. B., Sturm, A. C., Wagner, J. K., Williams, J. L., Willard, H. F., & Ledbetter, D. H. (2018). Patient-Centered Precision Health In A Learning Health Care System: Geisinger’s Genomic Medicine Experience. Health Affairs, 37, 757-764.
Wyber, R., Vaillancourt, S., Perry, W., Mannava, P., Folaranmi, T., & Celi, L. A. (2015). Big data in global health: improving health in low- and middle-income countries. Bulletin of the World Health Organization, 93, 203-208.
Yin, R. K. (2014). Case study research : design and methods, Los Angeles, CA: SAGE Publications.
Zadeh, A. H., Zolbanin, H. M., Sharda, R., & Delen, D. (2019). Social Media for Nowcasting Flu Activity: Spatio-Temporal Big Data Analysis. Information Systems Frontiers, 1-18.
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