Agent-Based Modeling for Implementation Research: An Application to Tobacco Smoking Cessation for Persons with Serious Mental Illness.
Agent-Based Modeling for Implementation Research: An Application to Tobacco Smoking Cessation for Persons with Serious Mental Illness.
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DOI：
10.1177/26334895211010664
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发表时间：
2021-01
影响因子：
--
通讯作者：
Igusa T
中科院分区：
文献类型：
--
作者：
Huang W;Chang CH;Stuart EA;Daumit GL;Wang NY;McGinty EE;Dickerson FB;Igusa T
实施研究人员一直在寻找使用模拟来支持实施核心组件的方法，其中通常包括评估变革需求、设计实施策略、执行策略和评估结果。本文的目标是解释基于代理的建模如何实现这一角色。我们描述了基于代理的建模与其他仿真方法，已被用于实现科学，使用非技术语言，是广泛访问。然后，我们提供了一个逐步的程序开发基于代理的模型的实施过程。我们使用，作为一个案例研究来说明程序，实施循证戒烟的做法，为严重精神疾病（SMI）的人在社区精神卫生诊所。对于我们的案例研究，我们提出了激励研究问题的描述，用于回答这些问题的具体模型，以及可以从模型中获得的见解的摘要。在第一个例子中，我们使用一种简单形式的基于代理的建模来模拟在最近完成的试验（IDEAL，SMI患者综合心血管风险降低试验）中观察到的SMI患者的吸烟行为。在第二个例子中，我们说明了一个更复杂的基于代理的方法，包括患者，供应商和网站管理员之间的互动，可以用来提供指导的实施干预，包括培训和组织策略。这个例子部分基于一个正在进行的项目，该项目的重点是在马里兰州的社区精神卫生诊所扩大循证戒烟实践。在这篇文章中，我们解释了如何基于代理的模型可以用来解决实现科学的研究问题，并提供了一个建立仿真模型的过程。通过我们的例子，我们展示了如何假设场景可以在实施过程中进行检查，这是特别有用的自适应组件的实施框架。本文的目的是解释如何基于代理的建模可以作为一种辅助工具，以支持复杂的实施过程的组成部分。这些模型尚未在实施科学中得到广泛应用，部分原因是它们的开发并不简单。为了促进基于代理的建模的使用，我们使用非技术语言提供了一个分步程序，并强调模型和实施过程之间的关系。我们用两个详细的例子来证明我们提出的方法。在第一个例子中，我们模拟了在最近完成的试验（IDEAL，严重精神疾病患者综合心血管风险降低试验）中观察到的严重精神疾病患者的吸烟行为。在第二个例子中，我们说明了如何基于代理的模型，包括患者，供应商和网站管理员之间的互动，可以用来提供指导的实施干预，包括培训和组织策略。这个例子部分基于一个正在进行的项目，该项目的重点是在马里兰州的社区精神卫生诊所扩大循证戒烟实践。在这个例子中，我们展示了基于代理的模型的可视化用户界面如何以仪表板的形式出现，仪表板上的杠杆用于模拟假设场景，这些场景可用于指导实施决策。总之，本文展示了基于代理的模型如何在复杂的干预过程中提供见解，并指导实施决策，以改善社区精神卫生诊所循证实践的交付。
Implementation researchers have sought ways to use simulations to support the core components of implementation, which typically include assessing the need for change, designing implementation strategies, executing the strategies, and evaluating outcomes. The goal of this article is to explain how agent-based modeling could fulfill this role. We describe agent-based modeling with respect to other simulation methods that have been used in implementation science, using non-technical language that is broadly accessible. We then provide a stepwise procedure for developing agent-based models of implementation processes. We use, as a case study to illustrate the procedure, the implementation of evidence-based smoking cessation practices for persons with serious mental illness (SMI) in community mental health clinics. For our case study, we present descriptions of the motivating research questions, specific models used to answer these questions, and a summary of the insights that can be obtained from the models. In the first example, we use a simple form of agent-based modeling to simulate the observed smoking behaviors of persons with SMI in a recently completed trial (IDEAL, Comprehensive Cardiovascular Risk Reduction Trial in Persons with SMI). In the second example, we illustrate how a more complex agent-based approach that includes interactions between patients, providers, and site administrators can be used to provide guidance for an implementation intervention that includes training and organizational strategies. This example is based in part on an ongoing project focused on scaling up evidence-based tobacco smoking cessation practices in community mental health clinics in Maryland. In this article, we explain how agent-based models can be used to address implementation science research questions and provide a procedure for setting up simulation models. Through our examples, we show how what-if scenarios can be examined in the implementation process, which are particularly useful in implementation frameworks with adaptive components. The goal of this paper is to explain how agent-based modeling could be used as a supplementary tool to support the components of complex implementation processes. Such models have not yet been widely used in implementation science, partly because they are not straightforward to develop. To promote the use of agent-based modeling we provide a stepwise procedure using non-technical language and emphasizing the relationships between the model and implementation processes. We used two detailed examples to demonstrate our proposed approach. In the first example, we simulate the observed smoking behaviors of persons with serious mental illness in a recently completed trial (IDEAL, Comprehensive Cardiovascular Risk Reduction Trial in Persons with Serious Mental Illness). In the second example, we illustrate how agent-based models that include interactions between patients, providers and site administrators can be used to provide guidance for an implementation intervention that includes training and organizational strategies. This example is based in part on an ongoing project focused on scaling up evidence-based tobacco smoking cessation practices in community mental health clinics in Maryland. For this example, we show how the visual user interface of an agent-based model can be in the form of a dashboard with levers for simulating what-if scenarios that can be used to guide implementation decisions. In summary, this paper shows how agent-based models can provide insights into the processes in complex interventions, and guide implementation decisions for improving delivery of evidence-based practices in community mental health clinics.
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影响因子：
2.2
作者：
Brown CH;Medoff D;Dickerson FB;Fang LJ;Lucksted A;Goldberg RW;Kreyenbuhl J;Himelhoch S;Dixon LB
通讯作者：
Dixon LB
DOI：
10.1186/1748-5908-4-50
发表时间：
2009-08-07
期刊：
Implementation science : IS
影响因子：
--
作者：
Damschroder LJ;Aron DC;Keith RE;Kirsh SR;Alexander JA;Lowery JC
通讯作者：
Lowery JC
影响因子：
4.7
作者：
Dalcin, Arlene T.;Jerome, Gerald J.;Daumit, Gail L.
通讯作者：
Daumit, Gail L.
影响因子：
2.4
作者：
Ballas, D;Clarke, GP;Wiemers, E
通讯作者：
Wiemers, E
影响因子：
3.1
作者：
Camargo, Chico Q.
通讯作者：
Camargo, Chico Q.