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Big data analytics for clinical decision-making: Understanding health sector perceptions of policy and practice

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  • Weerasinghe, Kasuni
  • Scahill, Shane L.
  • Pauleen, David J.
  • Taskin, Nazim

Abstract

The introduction and use of ‘big data and analytics’ is an on-going issue of discussion in health sectors globally. Healthcare systems of developed countries are trying to create more value and better healthcare through data and use of big data technologies. With an increasing number of articles identifying the value creation of big data and analytics for clinical decision-making, this paper examines how big data is applied, or not applied, in clinical practice. Using social representation theory as a theoretical foundation the paper explores people's perceptions of big data across all levels (policy making, planning, funding, and clinical care) of the New Zealand healthcare sector. The findings show that although adoption of big data technologies is planned for population health and health management, the potential of big data for clinical care has yet to be explored in the New Zealand context. The findings also highlight concern over data quality. The paper provides recommendations for policy and practice particularly around the need for engagement and participation of all levels to discuss data quality as well as big-data-based changes such as precision medicine and technology-assisted clinical decision-making tools. Future avenues of research are suggested.

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  • Weerasinghe, Kasuni & Scahill, Shane L. & Pauleen, David J. & Taskin, Nazim, 2022. "Big data analytics for clinical decision-making: Understanding health sector perceptions of policy and practice," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    • Handle: RePEc:eee:tefoso:v:174:y:2022:i:c:s0040162521006557
    DOI: 10.1016/j.techfore.2021.121222
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    References listed on IDEAS

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    2. Yogesh K. Dwivedi & A. Sharma & Nripendra P. Rana & M. Giannakis & P. Goel & Vincent Dutot, 2023. "Evolution of Artificial Intelligence Research in Technological Forecasting and Social Change: Research Topics, Trends, and Future Directions," Post-Print hal-04292607, HAL.
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    6. Benoit, Dries F. & Tsang, Wai Kit & Coussement, Kristof & Raes, Annelies, 2024. "High-stake student drop-out prediction using hidden Markov models in fully asynchronous subscription-based MOOCs," Technological Forecasting and Social Change, Elsevier, vol. 198(C).

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