title: 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.
abstract: In a previous report, the ISPOR Task Force on Dynamic Simulation Modeling Applications in Health Care Delivery Research Emerging Good Practices introduced the fundamentals of dynamic simulation modeling and identified the types of health care delivery problems for which dynamic simulation modeling can be used more effectively than other modeling methods. The hierarchical relationship between the health care delivery system, providers, patients, and other stakeholders exhibits a level of complexity that ought to be captured using dynamic simulation modeling methods. As a tool to help researchers decide whether dynamic simulation modeling is an appropriate method for modeling the effects of an intervention on a health care system, we presented the System, Interactions, Multilevel, Understanding, Loops, Agents, Time, Emergence (SIMULATE) checklist consisting of eight elements. This report builds on the previous work, systematically comparing each of the three most commonly used dynamic simulation modeling methods-system dynamics, discrete-event simulation, and agent-based modeling. We review criteria for selecting the most suitable method depending on 1) the purpose-type of problem and research questions being investigated, 2) the object-scope of the model, and 3) the method to model the object to achieve the purpose. Finally, we provide guidance for emerging good practices for dynamic simulation modeling in the health sector, covering all aspects, from the engagement of decision makers in the model design through model maintenance and upkeep. We conclude by providing some recommendations about the application of these methods to add value to informed decision making, with an emphasis on stakeholder engagement, starting with the problem definition. Finally, we identify areas in which further methodological development will likely occur given the growing "volume, velocity and variety" and availability of "big data" to provide empirical evidence and techniques such as machine learning for parameter estimation in dynamic simulation models. Upon reviewing this report in addition to using the SIMULATE checklist, the readers should be able to identify whether dynamic simulation modeling methods are appropriate to address the problem at hand and to recognize the differences of these methods from those of other, more traditional modeling approaches such as Markov models and decision trees. This report provides an overview of these modeling methods and examples of health care system problems in which such methods have been useful. The primary aim of the report was to aid decisions as to whether these simulation methods are appropriate to address specific health systems problems. The report directs readers to other resources for further education on these individual modeling methods for system interventions in the emerging field of health care delivery science and implementation.
authors.affiliation: Health Services & Systems Research, Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. Electronic address: damarsha@ucalgary.ca.
authors.forename: Deborah A
authors.initials: DA
authors.lastname: Marshall
authors.affiliation: 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: Optum Labs, Boston, MA, USA.
authors.forename: William
authors.initials: W
authors.lastname: Crown
authors.affiliation: Section of Hospital Medicine, University of Chicago, Chicago, IL, USA.
authors.forename: William V
authors.initials: WV
authors.lastname: Padula
authors.affiliation: HSHS Illinois Divisions and 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: GE Healthcare, Barrington, IL, USA.
authors.forename: Mitchell K
authors.initials: MK
authors.lastname: Higashi
authors.affiliation: Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada; Department of Community Health & Epidemiology and Bioengineering Division, Saskatoon, Saskatchewan, Canada.
authors.forename: Nathaniel D
authors.initials: ND
authors.lastname: Osgood
mesh_terms: D026683:Advisory Committees / Q000639:trends; D003695:Delivery of Health Care / Q000379:methods* / Q000639:trends; D006291:Health Policy / Q000639:trends; D006302:Health Services Research / Q000379:methods* / Q000639:trends; D006801:Humans; D008962:Models, Theoretical; D058028:Research Report
keywords: decision making; dynamic simulation modeling; health care delivery; methods