PharmacoEconomics

, Volume 34, Issue 2, pp 115–126

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

  • 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 D. Osgood
Practical Application

DOI: 10.1007/s40273-015-0330-7

Cite this article as:
Marshall, D.A., Burgos-Liz, L., Pasupathy, K.S. et al. PharmacoEconomics (2016) 34: 115. doi:10.1007/s40273-015-0330-7

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.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Deborah A. Marshall
    • 1
  • Lina Burgos-Liz
    • 2
  • Kalyan S. Pasupathy
    • 3
  • William V. Padula
    • 4
  • Maarten J. IJzerman
    • 5
  • Peter K. Wong
    • 6
  • Mitchell K. Higashi
    • 7
  • Jordan Engbers
    • 8
  • Samuel Wiebe
    • 8
  • William Crown
    • 9
  • Nathaniel D. Osgood
    • 10
    • 11
  1. 1.Department of Community Health Sciences, Cumming School of MedicineUniversity of Calgary, Room 3C56 Health Research Innovation CentreCalgaryCanada
  2. 2.Department of Community Health Sciences, Cumming School of MedicineUniversity of Calgary, Room 3C58 Health Research Innovation CentreCalgaryCanada
  3. 3.Clinical Engineering Learning LabMayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care DeliveryRochesterUSA
  4. 4.Department of Health Policy and Management, Bloomberg School of Public HealthJohns Hopkins UniversityBaltimoreUSA
  5. 5.Department of Health Technology and Services ResearchUniversity of TwenteEnschedeThe Netherlands
  6. 6.Illinois Divisions and HSHS Medical GroupHospital Sisters Health System (HSHS)BellevilleUSA
  7. 7.GE HealthcareBarringtonUSA
  8. 8.Clinical Research UnitUniversity of CalgaryCalgaryCanada
  9. 9.Optum LabsBostonUSA
  10. 10.Department of Computer ScienceUniversity of SaskatchewanSaskatoonCanada
  11. 11.Department of Community Health & Epidemiology and Bioengineering DivisionUniversity of SaskatchewanSaskatoonCanada

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