title: Applying dynamic simulation modeling methods in health care delivery research-the SIMULATE checklist: report of the ISPOR simulation modeling emerging good practices task force.
abstract: Health care delivery systems are inherently complex, consisting of multiple tiers of interdependent subsystems and processes that are adaptive to changes in the environment and behave in a nonlinear fashion. Traditional health technology assessment and modeling methods often neglect the wider health system impacts that can be critical for achieving desired health system goals and are often of limited usefulness when applied to complex health systems. Researchers and health care decision makers can either underestimate or fail to consider the interactions among the people, processes, technology, and facility designs. Health care delivery system interventions need to incorporate the dynamics and complexities of the health care system context in which the intervention is delivered. This report provides an overview of common dynamic simulation modeling methods and examples of health care system interventions in which such methods could be useful. Three dynamic simulation modeling methods are presented to evaluate system interventions for health care delivery: system dynamics, discrete event simulation, and agent-based modeling. In contrast to conventional evaluations, a dynamic systems approach incorporates the complexity of the system and anticipates the upstream and downstream consequences of changes in complex health care delivery systems. This report assists researchers and decision makers in deciding whether these simulation methods are appropriate to address specific health system problems through an eight-point checklist referred to as the SIMULATE (System, Interactions, Multilevel, Understanding, Loops, Agents, Time, Emergence) tool. It is a primer for researchers and decision makers working in health care delivery and implementation sciences who face complex challenges in delivering effective and efficient care that can be addressed with system interventions. On reviewing this report, the readers should be able to identify whether these simulation modeling methods are appropriate to answer the problem they are addressing and to recognize the differences of these methods from other modeling approaches used typically in health technology assessment applications.
authors.affiliation: Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Division of Rheumatology, Department of Medicine, and the McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, AB, Canada; Alberta Bone & Joint Health Institute, University of Calgary, Calgary, AB, Canada. Electronic address: damarsha@ucalgary.ca.
authors.forename: Deborah A
authors.initials: DA
authors.lastname: Marshall
authors.affiliation: Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
authors.forename: Lina
authors.initials: L
authors.lastname: Burgos-Liz
authors.affiliation: Department of Health Technology & Services Research, University of Twente, Enschede, The Netherlands.
authors.forename: Maarten J
authors.initials: MJ
authors.lastname: IJzerman
authors.affiliation: Department of Computer Science, Health & Epidemiology and Bioengineering Division, University of Saskatchewan, Saskatoon, SK, Canada; Department of Community, Health & Epidemiology and Bioengineering Division, University of Saskatchewan, Saskatoon, SK, Canada.
authors.forename: Nathaniel D
authors.initials: ND
authors.lastname: Osgood
authors.affiliation: Section of Hospital Medicine, University of Chicago, Chicago, IL, USA.
authors.forename: William V
authors.initials: WV
authors.lastname: Padula
authors.affiliation: GE Health care, Barrington, IL, USA.
authors.forename: Mitchell K
authors.initials: MK
authors.lastname: Higashi
authors.affiliation: HSHS Illinois Divisions & Medical Group, Hospital Sisters Health System, Belleville, IL, USA.
authors.forename: Peter K
authors.initials: PK
authors.lastname: Wong
authors.affiliation: Health Care Systems Engineering Program, Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Rochester, MN, USA.
authors.forename: Kalyan S
authors.initials: KS
authors.lastname: Pasupathy
authors.affiliation: Health Care Policy & Research, Health Care Systems Engineering Program, Mayo Clinic, Rochester, MN, USA.
authors.forename: William
authors.initials: W
authors.lastname: Crown
mesh_terms: D026683:Advisory Committees / Q000191:economics* / Q000639:trends; D057189:Checklist / Q000191:economics* / Q000639:trends; D003198:Computer Simulation / Q000191:economics* / Q000639:trends; D003226:Congresses as Topic / Q000639:trends; D003695:Delivery of Health Care / Q000191:economics* / Q000639:trends; D006801:Humans; D018803:Models, Economic; D058028:Research Report / Q000639:trends
keywords: decision making; dynamic simulation modeling; health care delivery; methods