Transforming Healthcare Delivery: Integrating Dynamic Simulation Modelling and Big Data in Health Economics and Outcomes Research
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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.
KeywordsHealthcare Delivery Emergent Behaviour Clinical Practice Research Datalink Healthcare Delivery System Dynamic Simulation Modelling
DAM led the conception and design of the work, drafting and critical revision of the manuscript. LBL contributed to the conception and design of the work, drafting and formatting, and critical revision of the manuscript. KSP, WVP, MJIJ, JE, WC, NDO and SW contributed to the conception and design of the work, drafting and critical revision of the manuscript. PKW and MKH contributed to the conception and design of the work, and to the critical revision of the manuscript. All authors have approved the final version of the article submitted and agree to be accountable for all aspects of the work. DAM is the guarantor.
Compliance with Ethical Standards
Deborah A. Marshall is supported by the Canada Research Chair, Health Services and Systems Research and the Arthur J.E. Child Chair in Rheumatology Outcomes Research. She undertakes ad hoc consulting to support health economics and outcomes research for various companies.
Lina Burgos-Liz: No conflicts of interest. Kalyan S. Pasupathy: The work on this manuscript was partly funded by Mayo Clinic’s Division of Health Care Policy & Research. No conflicts of interest. William V. Padula: No conflicts of interest to declare. William’s time for this manuscript was supported by an unrestricted Agency for Healthcare Research and Quality (AHRQ) F32 National Research Service Award (1 F32 HS023710-01). Maarten J. IJzerman: No conflicts of interest. Peter K. Wong: No conflicts of interest. Mitchell K. Higashi is employed by GE Healthcare. No conflicts of interest. Jordan Engbers: No conflicts of interest. Samuel Wiebe: Samuel Wiebe is supported by the Hopewell Professorship of Clinical Neurosciences Research at the Hotchkiss Brain Institute, and receives funding for projects from Alberta Innovates Health Solutions, and the American Brain Foundation. No conflicts of interest. William Crown: No conflicts of interest. Nathaniel D. Osgood: Consulting on applying AnyLogic software to health and software engineering.
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