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Medical big data applications: Intertwined effects and effective resource allocation strategies identified through IRA-NRM analysis

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  • Chen, Peng-Ting

Abstract

The development of information and communication technology has led to the rapid growth of medical data encountered by various players in healthcare industry. This evolution from a paper-based database to electronic records demonstrates the continuous advancement of medical information systems. Medical institutions are paying more attention to this issue and attempting to figure out the applications of big data. However, most of them have struggled to find pathways to apply big data adequately. Using hybrid methodologies and examining Taiwan's healthcare industry, this research aims to assess, forecast and summarize the major applications of medical big data, and establish strategic pathways for medical institutions to follow regarding different dimensions of applications. First, a review of literature related to the utility of medical big data and interviews with relevant stakeholders were conducted. Content analysis was subsequently done to extract the key applications, and DEMATEL was used to find out their Net Relation Map (NRM). With the Innovation Importance-Resistance Analysis (IRA), this study carried out IRA-NRM analysis to cultivate the strategy of medical big data development. This research concluded a IRA-NRM framework of 4 application categories and 16 factors. Suggestions for medical institutions regarding the use of medical big data are also provided.

Suggested Citation

  • Chen, Peng-Ting, 2018. "Medical big data applications: Intertwined effects and effective resource allocation strategies identified through IRA-NRM analysis," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 150-164.
    • Handle: RePEc:eee:tefoso:v:130:y:2018:i:c:p:150-164
    DOI: 10.1016/j.techfore.2018.01.033
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    References listed on IDEAS

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    1. Meltzer, David O. & Smith, Peter C., 2011. "Theoretical Issues Relevant to the Economic Evaluation of Health Technologies," Handbook of Health Economics, in: Mark V. Pauly & Thomas G. Mcguire & Pedro P. Barros (ed.), Handbook of Health Economics, volume 2, chapter 0, pages 433-469, Elsevier.
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    4. Cheng, Yu & Huang, Lucheng & Ramlogan, Ronnie & Li, Xin, 2017. "Forecasting of potential impacts of disruptive technology in promising technological areas: Elaborating the SIRS epidemic model in RFID technology," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 170-183.
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    Cited by:

    1. Wei-Chih Lu & I-Ching Tsai & Kuan-Chung Wang & Te-Ai Tang & Kuan-Chen Li & Ya-Ci Ke & Peng-Ting Chen, 2021. "Innovation Resistance and Resource Allocation Strategy of Medical Information Digitalization," Sustainability, MDPI, vol. 13(14), pages 1-20, July.
    2. Jiansheng Wu & Tengyun Yi & Han Wang & Hongliang Wang & Jiayi Fu & Yuhao Zhao, 2022. "Evaluation of Medical Carrying Capacity for Megacities from a Traffic Analysis Zone View: A Case Study in Shenzhen, China," Land, MDPI, vol. 11(6), pages 1-19, June.
    3. Tortorella, Guilherme Luz & Saurin, Tarcísio Abreu & Fogliatto, Flavio S. & Rosa, Valentina M. & Tonetto, Leandro M & Magrabi, Farah, 2021. "Impacts of Healthcare 4.0 digital technologies on the resilience of hospitals," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    4. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).

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