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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

32 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)115-126
Number of pages12
Issue number2
Publication statusPublished - Feb 2016
Externally publishedYes

Bibliographical note

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© 2015, Springer International Publishing Switzerland.


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