Applying dynamic simulation modeling methods in health care delivery research - The SIMULATE checklist: Report of the ISPOR simulation modeling emerging good practices task force

Deborah A. Marshall, Lina Burgos-Liz, Maarten J. Ijzerman, Nathaniel D. Osgood, William V. Padula, Mitchell K. Higashi, Peter K. Wong, Kalyan S Pasupathy, William Crown

Research output: Contribution to journalArticle

75 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)5-16
Number of pages12
JournalValue in Health
Volume18
Issue number1
DOIs
StatePublished - Jan 1 2015

Fingerprint

Health Services Research
Advisory Committees
Checklist
Delivery of Health Care
Biomedical Technology Assessment
Research Personnel
Health
Systems Analysis
Facility Design and Construction

Keywords

  • decision making
  • dynamic simulation modeling
  • health care delivery
  • methods

ASJC Scopus subject areas

  • Health Policy
  • Public Health, Environmental and Occupational Health

Cite this

Applying dynamic simulation modeling methods in health care delivery research - The SIMULATE checklist : Report of the ISPOR simulation modeling emerging good practices task force. / Marshall, Deborah A.; Burgos-Liz, Lina; Ijzerman, Maarten J.; Osgood, Nathaniel D.; Padula, William V.; Higashi, Mitchell K.; Wong, Peter K.; Pasupathy, Kalyan S; Crown, William.

In: Value in Health, Vol. 18, No. 1, 01.01.2015, p. 5-16.

Research output: Contribution to journalArticle

Marshall, Deborah A. ; Burgos-Liz, Lina ; Ijzerman, Maarten J. ; Osgood, Nathaniel D. ; Padula, William V. ; Higashi, Mitchell K. ; Wong, Peter K. ; Pasupathy, Kalyan S ; Crown, William. / Applying dynamic simulation modeling methods in health care delivery research - The SIMULATE checklist : Report of the ISPOR simulation modeling emerging good practices task force. In: Value in Health. 2015 ; Vol. 18, No. 1. pp. 5-16.
@article{bad48cdea1c34baf9fe59eaa7e4d74a3,
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.",
keywords = "decision making, dynamic simulation modeling, health care delivery, methods",
author = "Marshall, {Deborah A.} and Lina Burgos-Liz and Ijzerman, {Maarten J.} and Osgood, {Nathaniel D.} and Padula, {William V.} and Higashi, {Mitchell K.} and Wong, {Peter K.} and Pasupathy, {Kalyan S} and William Crown",
year = "2015",
month = "1",
day = "1",
doi = "10.1016/j.jval.2014.12.001",
language = "English (US)",
volume = "18",
pages = "5--16",
journal = "Value in Health",
issn = "1098-3015",
publisher = "Elsevier Limited",
number = "1",

}

TY - JOUR

T1 - Applying dynamic simulation modeling methods in health care delivery research - The SIMULATE checklist

T2 - Report of the ISPOR simulation modeling emerging good practices task force

AU - Marshall, Deborah A.

AU - Burgos-Liz, Lina

AU - Ijzerman, Maarten J.

AU - Osgood, Nathaniel D.

AU - Padula, William V.

AU - Higashi, Mitchell K.

AU - Wong, Peter K.

AU - Pasupathy, Kalyan S

AU - Crown, William

PY - 2015/1/1

Y1 - 2015/1/1

N2 - 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.

AB - 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.

KW - decision making

KW - dynamic simulation modeling

KW - health care delivery

KW - methods

UR - http://www.scopus.com/inward/record.url?scp=84920809871&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84920809871&partnerID=8YFLogxK

U2 - 10.1016/j.jval.2014.12.001

DO - 10.1016/j.jval.2014.12.001

M3 - Article

C2 - 25595229

AN - SCOPUS:84920809871

VL - 18

SP - 5

EP - 16

JO - Value in Health

JF - Value in Health

SN - 1098-3015

IS - 1

ER -