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Transforming Healthcare Delivery: Integrating Dynamic Simulation Modelling and Big Data in Health Economics and Outcomes Research

Author

Listed:
  • Deborah A. Marshall

    (University of Calgary, Room 3C56 Health Research Innovation Centre)

  • Lina Burgos-Liz

    (University of Calgary, Room 3C58 Health Research Innovation Centre)

  • Kalyan S. Pasupathy

    (Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery)

  • William V. Padula

    (Johns Hopkins University)

  • Maarten J. IJzerman

    (University of Twente)

  • Peter K. Wong

    (Hospital Sisters Health System (HSHS))

  • Mitchell K. Higashi

    (GE Healthcare)

  • Jordan Engbers

    (University of Calgary)

  • Samuel Wiebe

    (University of Calgary)

  • William Crown

    (Optum Labs)

  • Nathaniel D. Osgood

    (University of Saskatchewan
    University of Saskatchewan)

Abstract

In the era of the Information Age and personalized medicine, healthcare delivery systems need to be efficient and patient-centred. The health system must be responsive to individual patient choices and preferences about their care, while considering the system consequences. While dynamic simulation modelling (DSM) and big data share characteristics, they present distinct and complementary value in healthcare. Big data and DSM are synergistic—big data offer support to enhance the application of dynamic models, but DSM also can greatly enhance the value conferred by big data. Big data can inform patient-centred care with its high velocity, volume, and variety (the three Vs) over traditional data analytics; however, big data are not sufficient to extract meaningful insights to inform approaches to improve healthcare delivery. DSM can serve as a natural bridge between the wealth of evidence offered by big data and informed decision making as a means of faster, deeper, more consistent learning from that evidence. We discuss the synergies between big data and DSM, practical considerations and challenges, and how integrating big data and DSM can be useful to decision makers to address complex, systemic health economics and outcomes questions and to transform healthcare delivery.

Suggested Citation

  • Deborah A. Marshall & Lina Burgos-Liz & Kalyan S. Pasupathy & William V. Padula & Maarten J. IJzerman & Peter K. Wong & Mitchell K. Higashi & Jordan Engbers & Samuel Wiebe & William Crown & Nathaniel , 2016. "Transforming Healthcare Delivery: Integrating Dynamic Simulation Modelling and Big Data in Health Economics and Outcomes Research," PharmacoEconomics, Springer, vol. 34(2), pages 115-126, February.
    • Handle: RePEc:spr:pharme:v:34:y:2016:i:2:d:10.1007_s40273-015-0330-7
    DOI: 10.1007/s40273-015-0330-7
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    References listed on IDEAS

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    1. C Vasilakis & B G Sobolev & L Kuramoto & A R Levy, 2007. "A simulation study of scheduling clinic appointments in surgical care: individual surgeon versus pooled lists," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(2), pages 202-211, February.
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