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 and Nathaniel D. Osgood
Additional contact information
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

PharmacoEconomics, 2016, vol. 34, issue 2, No 5, 115-126

Abstract: 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.

Date: 2016
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DOI: 10.1007/s40273-015-0330-7

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