In this workshop Ron will discuss the benefits of discrete-event simulation for Health Economic investigations that are conducive for decision-making by payers and providers, and talk through a "Real-World" application example.
Today more than ever governments and health providers are under extreme pressure to reduce costs while maintaining patient quality of life. Accuracy of information presented to them is critical for acceptance. This workshop will justify the use of discrete-event simulation by health economists as a means to provide accurate assessments of value for medical products or services.
5. Health Economic Modeling
The International Society for Pharmacoeconomics
and Outcomes Research (ISPOR) Task Force on Good
Research Practices – Modeling Studies:
"[...] an analytic methodology that accounts for events over
time and across populations, that is based on data drawn
from primary and/or secondary sources, and whose
purpose is to estimate the effects of an intervention on
valued health consequences and costs.“1
6. Health Economic Modeling
The aim of health economic modeling is to generate expected values for the
clinical and economic effects of therapeutic alternatives
20. References
1. Weinstein MC, et.al. Principles of good practice for decision analytic modeling in health-care
evaluation: report of the ISPOR Task Force on Good Research Practices--Modeling Studies. Value
Health. 2003, Jan-Feb;6(1):9-17.
2. Sun X, Faunce T. Decision-analytical modelling in health-care economic evaluations. Eur J Health
Econ, 2008, 9:313-323.
3. Siebert U, Alagoz O, Bayoumi AM, Jahn B, Owens K, Cohen D, Kunz KM. State- Transition Modeling:
A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-3. Value in Health, 2012,
(15):812-820.
4. Pidd M. Computer Simulation in Management Science (5th ed). New York: John Wiley & Sons, 2004.
5. Jacobson SH, Hall SN, Swisher JR. Discrete-event simulation of health care systems, patient flow:
reducing delay in healthcare delivery. Int Ser Oper Res Manag Sci 2006;91:211–52.
6. Figge MT. Stochastic discrete event simulation of germinal center reactions. Phys Rev E Stat Nonlin
Soft Matter Phys 2005;71:1–9.
7. Zand MS, Briggs BJ, Bose A, Vo T. Discrete event modeling of CD4 memory T cell generation. J
Immunol 2004;173:3763–72.
8. Coelli FC, Ferreira RB, Almeida RM, Pereira WC. Computer simulation and discrete-event models in
the analysis of a mammography clinic patient flow. Computer Methods Programs Biomed
2007;87:201–7.
21. References
9. Comas M, Castells X, Hoffmeister L, et al. Discrete-event simulation applied to the analysis of
waiting lists: evaluation of a prioritization system for cataract surgery. Value Health 2008;11:1203–
13.
10. Stahl JE, Rattner D, Wiklund R, et al. Reorganizing the system of care surrounding laparoscopic
surgery: a cost-effectiveness analysis using discrete-event simulation. Med Decis Making
2004;24:461–71.
11. Clark DE, Hahn DR, Hall RW, Quaker RE. Optimal location for a helicopter in a rural trauma system:
prediction using discrete-event computer simulation. Proc Annu Symp Comput Appl Med Care
1994;888 –92.
12. Chase D, Roderick P, Cooper K, et al. Using simulation to estimate the cost effectiveness of
improving ambulance and thrombolysis response times after myocardial infarction. Emerg Med J
2006;23:67–72.
13. Skolnik JM, Barrett JS, Jayaraman B, et al. Shortening the timeline of pediatric phase I trials: the
rolling six design. J Clin Oncol 2008;26:190–5.
14. Barth-Jones DC, Adams AL, Koopman JS. Monte Carlo simulation experiments for analysis of HIV
vaccine effects and vaccine trial design. Winter Simul Conf Proc 2000;2:1985–94.
15. Groothuis S, van Merode GG. Discrete event simulation in the health policy and management
program. Methods Inf Med 2000;39:339–42.
22. References
16. Mar J, Arrospide A, Comas M. Budget impact analysis of thrombolysis for stroke in Spain: a
discrete event simulation model. Value Health 2010;13:69 –76.
17. Stahl JE, Vacanti JP, Gazelle S. Assessing emerging technologies—the case of organ replacement
technologies: volume, durability, cost. Int J Technol Assess Health Care 2007;23:331– 6.
18. Caro JJ, Moller J, Getsios D. Discrete Event Simulation: The Preferred Technique for Health
Economic Evaluations? Value in Health. 2010, Vol 13(8):1056-1060.
19. Barton P, Bryan S, Robinson S (2004) Modelling in the economic evaluation of health care:
selecting the appropriate approach. J Health Serv Res Policy 9: 110–118.
20. Brennan A, Chick SE, Davies R. A taxonomy of model structures for economic evaluation of
health technologies. Health Econ. 15: 1295–1310 (2006).
21. Cairo JJ. Pharmacoeconomic Analyses Using Discrete Event Simulation. Pharmacoeconomics
2005; 23 (4): 323-332.
22. Weinstein MC. Recent developments in decision-analytic modelling for economic evaluation.
Pharmacoeconomics. 2006; 24(11):1043–53.
23. Karnon J. Alternative decision modelling techniques for the evaluation of health care
technologies: Markov processes versus discrete event simulation. Health Econ.
2003;12(10):837–48.
23. References
24. Kamon J, Brown J. Selecting a decision model for economic evaluation: a case study and
review. Health Care Management Science 1 (1998) 133–140.
25. Simpson KN, Strassburger A, Jones WJ, Dietz B, Rajagopalan R. Comparison of Markov Model
and Discrete-Event Simulation Techniques for HIV. Pharmacoeconomics 2009; 27 (2): 159-165.