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.
- 1.Institute of Medicine, Committee on quality of health care in America. Crossing the quality chasm: a new health system for the 21st century. Institute of Medicine of the National Academies: Institute of Medicine of the National Academies; 2001.Google Scholar
- 2.Barnes K, Levy D, Lutz S. Customizing healthcare: how a new approach to diagnosis, care, and cure could transform employer benefits in a post reform world, in view. PwC Health Research Institute.Google Scholar
- 5.Marshall DA. Health care, Meet Xbox: the mass customization of medicine, in international society for pharmacoeconomics and outcomes research (ispor) connections. International Society for Pharmacoeconomics and Outcomes Research (ISPOR); 2013. p. 3–4.Google Scholar
- 6.Alexander L. NHS: everyone in UK with chronic condition to have a digital, personalized plan of care by 2015. 2013 [cited 2015 May 5]. http://medcitynews.com/2013/09/nhs-everyone-uk-chronic-condition-digital-personalized-plan-care-2015/. Accessed 5 May 2015.
- 7.National Health System—Commisioning Assembly, Technology Enabled Care Services 2015, NHS England: National Health System NHS England.Google Scholar
- 8.Marshall DA. Getting connected: systems solutions for generating maximal value from health care resources. In: International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Connections. 2012, International Society for Pharmacoeconomics and Outcomes Research (ISPOR). p. 3–4.Google Scholar
- 9.Laney, D., The Importance of’Big Data’: A Definition. Gartner. Retrieved, 2012. 21.Google Scholar
- 10.Gantz J, Reinsel D. Extracting value from chaos. IDC Iview. 2011;1142:9–10.Google Scholar
- 11.Onukwugha E. Big data and its role in health economics. PharmacoEconomics. 2015 (submitted).Google Scholar
- 16.Mayer-Schönberger V, Cukier K. Big data: a revolution that will transform how we live, work, and think. New York: Houghton Mifflin Harcourt; 2013.Google Scholar
- 21.Anderson C. The end of theory. Wired Mag. 2008;16(7):16-07.Google Scholar
- 23.Lazer D, et al. The parable of Google Flu: traps in big data analysis. Science. 2014;343(6176):1203–5.Google Scholar
- 27.Deshpande AD, Schootman M, Mayer A. Development of a claims-based algorithm to identify colorectal cancer recurrence. Ann Epidemiol. 2015.Google Scholar
- 31.Glouberman S, Zimmerman B. Complicated and complex systems: what would successful reform of medicare look like. Discussion paper number 8. Commission on the Future of Health Care in Canada; 2002.Google Scholar
- 32.Zimmerman B, Lindberg C, Plsek PE. A complexity science primer, in Edgeware, insights from complexity science for health care leaders. In: Zimmerman B, et al, editor. Irving: VHA Inc; 2001. p. 3–20.Google Scholar
- 38.Sokolowski JA, Banks CM. Principles of modeling and simulation: a multidisciplinary approach. Wiley; 2009.Google Scholar
- 39.Schein EH. How can organizations learn faster? The challenge of entering the green room. Sloan Manag Rev. 1993;34(2):85–92.Google Scholar
- 41.Brown G, Patrick T, Pasupathy KS. Health informatics: a systems perspective. Chicago; 2012.Google Scholar
- 45.Sterman JD. Business dynamics: systems thinking and modeling for a complex world. 1st ed. New York: McGraw-Hill; 2000.Google Scholar
- 49.Vasilakis C, et al. A simulation study of scheduling clinic appointments in surgical care: individual surgeon versus pooled lists. J Oper Res Soc. 2006;58(2):202–11.Google Scholar
- 50.Baldwin LP, et al. Using simulation for the economic evaluation of liver transplantation. In: Proceedings of the 32nd conference on Winter simulation. Orlando: Society for Computer Simulation International; 2000. p. 1963–1970.Google Scholar
- 55.Diamond D. iPhone 6: Apple And Mayo Clinic Partnership Could Be Smart Medicine. 2014 September 9 [cited 2015 January 20]. http://www.forbes.com/sites/dandiamond/2014/09/09/iphone-6-apple-and-mayo-clinic-partnership-could-be-smart-medicine-2/. Accessed 20 Jan 2015.
- 56.Osgood N. iEpi: a robust and versatile Smartphone-based Epidemiological data collection system. 2011 [cited 2015 March 30]. http://www.cs.usask.ca/~osgood/iEpi/iEpi.html. Accessed 30 Mar 2015.
- 58.Osgood N, Liu J. Towards closed loop modeling: evaluatng the prospects for creating recurrently regrounded aggregate simulation models using particle filtering. In: Proceedings of the 2014 Winter Simulation Conference. IEEE Press; 2014.Google Scholar
- 62.Knowles DL, Stanley KG, Osgood ND. A Field-validated architecture for the collection of health-relevant behavioural data. In: Healthcare Informatics (ICHI), 2014 IEEE International Conference on. 2014. IEEE.Google Scholar
- 65.Qian, W, Osgood ND, Stanley KG. Integrating epidemiological modeling and surveillance data feeds: a Kalman filter based approach, in Social Computing, Behavioral-Cultural Modeling and Prediction. Springer; 2014. p. 145–152.Google Scholar
- 67.Memorial Sloan Kettering Cancer Center. Memorial Sloan Kettering’s Collaboration with IBM Watson Featured on CBS This Morning. 2013 [cited 2015 March 24]. http://www.mskcc.org/blog/msk-s-collaboration-ibm-watson-featured-cbs-morning. Accessed 24 Mar 2015.
- 68.World Health Organization. FluNet [cited 2015 March 24]. http://www.who.int/influenza/gisrs_laboratory/flunet/en/. Accessed 24 Mar 2015.
- 69.Sage Bionetworks. Synapse [cited 2015 March 24]. http://sagebase.org/synapse/. Accessed 24 Mar 2015.
- 70.American Society of Clinical Oncology. CancerLinQ™. 2015 [cited 2015 September 6]. http://www.instituteforquality.org/cancerlinq. Accessed 6 Sept 2015.
- 71.Newhouse JP, Garber A. Geographic variation in health care spending and promotion of high-value care. Washington: National Academies Press; 2010.Google Scholar
- 72.The Clinical Practice Research Datalink. The Clinical Practice Research Datalink. [cited 2015 April 3]. http://www.cprd.com/.
- 73.The PHARMO Institute. PHARMO [cited 2015 April 3]. http://www.pharmo.nl/.
- 74.International Society for Pharmacoeconomics and Outcomes Research (ISPOR). International Society for Pharmacoeconomics and Outcomes Research (ISPOR) [cited 2015 April 3]. http://www.ispor.org.
- 77.Cragin MH, et al. An educational program on data curation; Illinois digital environment for access to learning and scholarship. University of Illinois at Urbana-Champaign. 2007.Google Scholar
- 79.Harman GCMDC. Quantifying mental health signals in twitter. ACL. 2014;2014:51.Google Scholar
- 80.Paul MJ, Wallace BC, Dredze M. What affects patient (dis) satisfaction? Analyzing online doctor ratings with a joint topic-sentiment model. In: AAAI Workshop on Expanding the Boundaries of Health Informatics Using AI (HIAI); 2013.Google Scholar
- 81.Elliott TE, et al. Data warehouse governance programs in healthcare settings: a literature review and a call to action. eGEMs (Gener Evid Methods Improve Patient Outcomes). 2013;1(1):15.Google Scholar