, Volume 34, Issue 2, pp 115–126 | Cite as

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


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.


Healthcare Delivery Emergent Behaviour Clinical Practice Research Datalink Healthcare Delivery System Dynamic Simulation Modelling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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. 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. 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
  3. 3.
    Nugent R. Chronic diseases in developing countries. Ann N Y Acad Sci. 2008;1136(1):70–9.CrossRefPubMedGoogle Scholar
  4. 4.
    Ferguson T. Consumer health informatics. Healthc Forum J. 1995;38(1):28.PubMedGoogle Scholar
  5. 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. 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]. Accessed 5 May 2015.
  7. 7.
    National Health System—Commisioning Assembly, Technology Enabled Care Services 2015, NHS England: National Health System NHS England.Google Scholar
  8. 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. 9.
    Laney, D., The Importance of’Big Data’: A Definition. Gartner. Retrieved, 2012. 21.Google Scholar
  10. 10.
    Gantz J, Reinsel D. Extracting value from chaos. IDC Iview. 2011;1142:9–10.Google Scholar
  11. 11.
    Onukwugha E. Big data and its role in health economics. PharmacoEconomics. 2015 (submitted).Google Scholar
  12. 12.
    Marshall DA, et al. Selecting a dynamic simulation modeling method for health care delivery research—Part 2: report of the ISPOR Dynamic Simulation Modeling Emerging Good Practices Task Force. Value Health. 2015;18(2):147–60.CrossRefPubMedGoogle Scholar
  13. 13.
    Marshall DA, et al. Applying dynamic simulation modeling methods in health care delivery research—The SIMULATE checklist: report of the ISPOR Simulation Modeling Emerging Good Practices Task Force. Value Health. 2015;18(1):5–16.CrossRefPubMedGoogle Scholar
  14. 14.
    Grumbach K, Lucey CR, Johnston S. Transforming from centers of learning to learning health systems: the challenge for academic health centers. JAMA. 2014;311(11):1109–10.CrossRefPubMedGoogle Scholar
  15. 15.
    Krumholz HM. Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system. Health Aff. 2014;33(7):1163–70.CrossRefGoogle Scholar
  16. 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
  17. 17.
    Brill E. Processing natural language without natural language processing. In: Gelbukh A, editor. Computational linguistics and intelligent text processing. Berlin: Springer; 2003. pp. 360–9.CrossRefGoogle Scholar
  18. 18.
    Halevy A, Norvig P, Pereira F. The unreasonable effectiveness of data. Intell Syst IEEE. 2009;24(2):8–12.CrossRefGoogle Scholar
  19. 19.
    Murdoch TB, Detsky AS. The inevitable application of big data to health care. JAMA. 2013;309(13):1351–2.CrossRefPubMedGoogle Scholar
  20. 20.
    Matthews PM, et al. The emerging agenda of stratified medicine in neurology. Nat Rev Neurol. 2014;10(1):15–26.CrossRefPubMedGoogle Scholar
  21. 21.
    Anderson C. The end of theory. Wired Mag. 2008;16(7):16-07.Google Scholar
  22. 22.
    Ginsberg J, et al. Detecting influenza epidemics using search engine query data. Nature. 2009;457(7232):1012–4.CrossRefPubMedGoogle Scholar
  23. 23.
    Lazer D, et al. The parable of Google Flu: traps in big data analysis. Science. 2014;343(6176):1203–5.Google Scholar
  24. 24.
    Jensen PB, Jensen LJ, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet. 2012;13(6):395–405.CrossRefPubMedGoogle Scholar
  25. 25.
    Pollack CE, et al. Patient sharing and quality of care: measuring outcomes of care coordination using claims data. Med Care. 2015;53(4):317–23.PubMedGoogle Scholar
  26. 26.
    Steinbusch PJ, et al. The risk of upcoding in casemix systems: a comparative study. Health Policy. 2007;81(2):289–99.CrossRefPubMedGoogle Scholar
  27. 27.
    Deshpande AD, Schootman M, Mayer A. Development of a claims-based algorithm to identify colorectal cancer recurrence. Ann Epidemiol. 2015.Google Scholar
  28. 28.
    Appelboom G, et al. The quantified patient: a patient participatory culture. Curr Med Res Opin. 2014;30(12):2585–7.CrossRefPubMedGoogle Scholar
  29. 29.
    Hussain M, et al. Cloud-based Smart CDSS for chronic diseases. Health Technol. 2013;3(2):153–75.CrossRefGoogle Scholar
  30. 30.
    Denecke K, et al. How to exploit twitter for public health monitoring? Methods Inf Med. 2013;52(4):326–39.CrossRefPubMedGoogle Scholar
  31. 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. 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
  33. 33.
    Ackoff R. OR: after the post mortem. Syst Dyn Rev. 2001;17(4):341–6.CrossRefGoogle Scholar
  34. 34.
    Plsek PE, Greenhalgh T. The challenge of complexity in health care. BMJ 2001;323(7313):625–8.PubMedCentralCrossRefPubMedGoogle Scholar
  35. 35.
    Padula WV, et al. Integrating systems engineering practice with health-care delivery. Health Syst. 2014;3(3):159–64.CrossRefGoogle Scholar
  36. 36.
    Harrison JR, et al. Simulation modeling in organizational and management research. Acad Manag Rev. 2007;32(4):1229–45.CrossRefGoogle Scholar
  37. 37.
    Banks J. Handbook of simulation. USA: Wiley; 1998.CrossRefGoogle Scholar
  38. 38.
    Sokolowski JA, Banks CM. Principles of modeling and simulation: a multidisciplinary approach. Wiley; 2009.Google Scholar
  39. 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
  40. 40.
    Pasupathy KS. Transforming healthcare: leveraging the complementarities of health informatics and systems engineering. Int J Healthc Deliv Reform Initiat (IJHDRI). 2010;2(2):35–55.CrossRefGoogle Scholar
  41. 41.
    Brown G, Patrick T, Pasupathy KS. Health informatics: a systems perspective. Chicago; 2012.Google Scholar
  42. 42.
    Madon T, et al. Implementation science. Science. 2007;318(5857):1728–9.CrossRefPubMedGoogle Scholar
  43. 43.
    Brailsford SC, et al. An analysis of the academic literature on simulation and modelling in health care. J Simul. 2009;3(3):130–40.CrossRefGoogle Scholar
  44. 44.
    Forrester J. System dynamics—a personal view of the first fifty years. Syst Dyn Rev. 2007;23(2–3):345–58.CrossRefGoogle Scholar
  45. 45.
    Sterman JD. Business dynamics: systems thinking and modeling for a complex world. 1st ed. New York: McGraw-Hill; 2000.Google Scholar
  46. 46.
    Hollocks B. Forty years of discrete-event simulation—a personal reflection. J Oper Res Soc. 2006;57(12):1383–99.CrossRefGoogle Scholar
  47. 47.
    Siebers PO, et al. Discrete-event simulation is dead, long live agent-based simulation! J Simul. 2010;4(3):204–10.CrossRefGoogle Scholar
  48. 48.
    Troy PM, Rosenberg L. Using simulation to determine the need for ICU beds for surgery patients. Surgery. 2009;146(4):608–17.CrossRefPubMedGoogle Scholar
  49. 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. 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
  51. 51.
    Ratcliffe J, et al. A simulation modelling approach to evaluating alternative policies for the management of the waiting list for liver transplantation. Health Care Manag Sci. 2001;4(2):117–24.CrossRefPubMedGoogle Scholar
  52. 52.
    Gunal MM. A guide for building hospital simulation models. Health Systems. 2012;1(1):17–25.CrossRefGoogle Scholar
  53. 53.
    Macal CM, et al. Modeling the transmission of community-associated methicillin-resistant Staphylococcus aureus: a dynamic agent-based simulation. J Transl Med. 2014;12:124.PubMedCentralCrossRefPubMedGoogle Scholar
  54. 54.
    Sterman JD. Learning from evidence in a complex world. Am J Public Health. 2006;96(3):505–14.PubMedCentralCrossRefPubMedGoogle Scholar
  55. 55.
    Diamond D. iPhone 6: Apple And Mayo Clinic Partnership Could Be Smart Medicine. 2014 September 9 [cited 2015 January 20]. Accessed 20 Jan 2015.
  56. 56.
    Osgood N. iEpi: a robust and versatile Smartphone-based Epidemiological data collection system. 2011 [cited 2015 March 30]. Accessed 30 Mar 2015.
  57. 57.
    Ong JBS, et al. Real-time epidemic monitoring and forecasting of H1N1-2009 using influenza-like illness from general practice and family doctor clinics in Singapore. PLoS One. 2010;5(4):e10036.PubMedCentralCrossRefPubMedGoogle Scholar
  58. 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
  59. 59.
    Lee BY, et al. The impact of making vaccines thermostable in Niger’s vaccine supply chain. Vaccine. 2012;30(38):5637–43.PubMedCentralCrossRefPubMedGoogle Scholar
  60. 60.
    Wallace PJ, et al. Optum labs: building a novel node in the learning health care system. Health Aff. 2014;33(7):1187–94.CrossRefGoogle Scholar
  61. 61.
    Gottesman O, et al. The Electronic Medical Records and Genomics (eMERGE) network: past, present, and future. Genet Med. 2013;15(10):761–71.PubMedCentralCrossRefPubMedGoogle Scholar
  62. 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
  63. 63.
    Hashemian M, et al. Temporal aggregation impacts on epidemiological simulations employing microcontact data. BMC Med Inform Decis Mak. 2012;12(1):132.PubMedCentralCrossRefPubMedGoogle Scholar
  64. 64.
    Hashemian M, Stanley K, Osgood N. Leveraging H1N1 infection transmission modeling with proximity sensor microdata. BMC Med Inform Decis Mak. 2012;12(1):35.PubMedCentralCrossRefPubMedGoogle Scholar
  65. 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
  66. 66.
    Kho AN, et al. CAPriCORN: Chicago area patient-centered outcomes research network. J Am Med Inform Assoc. 2014;21(4):607–11.PubMedCentralCrossRefPubMedGoogle Scholar
  67. 67.
    Memorial Sloan Kettering Cancer Center. Memorial Sloan Kettering’s Collaboration with IBM Watson Featured on CBS This Morning. 2013 [cited 2015 March 24]. Accessed 24 Mar 2015.
  68. 68.
    World Health Organization. FluNet [cited 2015 March 24]. Accessed 24 Mar 2015.
  69. 69.
    Sage Bionetworks. Synapse [cited 2015 March 24]. Accessed 24 Mar 2015.
  70. 70.
    American Society of Clinical Oncology. CancerLinQ™. 2015 [cited 2015 September 6]. Accessed 6 Sept 2015.
  71. 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. 72.
    The Clinical Practice Research Datalink. The Clinical Practice Research Datalink. [cited 2015 April 3].
  73. 73.
    The PHARMO Institute. PHARMO [cited 2015 April 3].
  74. 74.
    International Society for Pharmacoeconomics and Outcomes Research (ISPOR). International Society for Pharmacoeconomics and Outcomes Research (ISPOR) [cited 2015 April 3].
  75. 75.
    Selby JV, Krumholz HM, Kuntz RE, Collins FS. Network news: powering clinical research. Sci Transl Med. 2013;5:182fs13.CrossRefPubMedGoogle Scholar
  76. 76.
    van Walraven C, Austin P. Administrative database research has unique characteristics that can risk biased results. J Clin Epidemiol. 2012;65(2):126–31.CrossRefPubMedGoogle Scholar
  77. 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
  78. 78.
    Ayers JW, Althouse BM, Dredze M. Could behavioral medicine lead the web data revolution? JAMA. 2014;311(14):1399–400.PubMedCentralCrossRefPubMedGoogle Scholar
  79. 79.
    Harman GCMDC. Quantifying mental health signals in twitter. ACL. 2014;2014:51.Google Scholar
  80. 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. 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

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

Personalised recommendations