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AbstractThis article reports the first systematic literature review of system dynamics (SD) applications in health and medicine published between 1960 and 2018. We categorize SD contributions into three groups—disease‐related modeling, organizational modeling, and regional health modeling—and explore major trends and approaches. We then focus on disease‐related modeling and discuss (1) common structures underlying models of infectious and noninfectious diseases, (2) major findings and modeling insights, and (3) avenues for future modeling efforts. While application areas cover a wide range of contexts, a considerable level of quality variation is observable, particularly in regards to model documentation, use of data, and model validation. While these shortcomings are not specific to SD modeling—and other schools of modeling often suffer from similar problems—we invite the community to address the issues both as authors and reviewers. Our study provides a reference document for several exemplary SD models, which is especially useful for early career modelers.© 2020 System Dynamics Society

Lack of healthcare insurance is considered one of the drivers of the rise in emergency department (ED) use in the United States. Using survey‐based, individual‐level data, we compare ED use after the 2006 Massachusetts health insurance reform with ED use before the reform both in Massachusetts and nearby states. We find that the reform increased the insurance rate significantly by 5.29 percentage points. We do not find a statistically significant effect on ED visits but do not have enough power to rule out potentially relevant effects. It seems that policy makers hoping that insurance reform will dramatically decrease ED overcrowding are likely to be disappointed.

Emergency department (ED) utilization has increased nationally, leading to overcrowding, which in turn increases wait times, adverse clinical outcomes, and costs. We constructed a state‐level panel dataset of 50 U.S. states and the District of Columbia from 1999 to 2011 and used a first‐differencing regression model to investigate the extent to which changes in public and private health insurance coverage could account for increased ED utilization. We found that changes in health insurance coverage explained some of the variations in ED utilization; however, the magnitudes of health insurance coverage variables were very small relative to the mean of ED utilization. This suggests that anticipated increases in private health insurance coverage and the Medicaid expansion due to the Affordable Care Act will not drive large changes in ED utilization.

AbstractWe discuss the emerging new opportunity for building feedback‐rich computational models of social systems using generative artificial intelligence. Referred to as generative agent‐based models (GABMs), such individual‐level models utilize large language models to represent human decision‐making in social settings. We provide a GABM case in which human behavior can be incorporated into simulation models by coupling a mechanistic model of human interactions with a pre‐trained large language model. This is achieved by introducing a simple GABM of social norm diffusion in an organization. For educational purposes, the model is intentionally kept simple. We examine a wide range of scenarios and the sensitivity of the results to several changes in the prompt. We hope the article and the model serve as a guide for building useful dynamic models of various social systems that include realistic human reasoning and decision‐making. © 2024 System Dynamics Society.

Systems modelling of health issues enriches analysis of health care policies by exploring interdependencies among system components. Considering a wide range of stakeholder behavior is a requisite for developing a useful model. Our paper shows how to systematically identify stakeholder behavior, formulate them in a model and test their impact on health outcomes. Stakeholder behavior are explained in the context of a simulation‐based analysis of infant mortality in Ohio. Using group model building techniques, we identify three levels of stakeholder behavior—individual, organizational and policy behaviors—and discuss how these behaviors are reflected in the model formulation and drive the key health outcomes. Specifically, we show how individual and policy behaviors are formulated to examine their influence on the impact of progesterone therapy—one of the interventions identified in the group model building session—on infant mortality. We conclude with recommendations for incorporating diverse stakeholder perspectives in systems modelling of health issues. © 2018 John Wiley & Sons, Ltd.

AbstractThere is limited methodological guidance for estimating system dynamics (SD) models using datasets common to social sciences that include few data points over time for many units under analysis. Here, we introduce indirect inference, a simulation‐based estimation method that can be applied to common datasets and is applicable to SD models that often include intractable likelihood functions. In this method, the model parameters are found by ensuring that simulated data from the model and available empirical data produce similar auxiliary statistics. The method requires few assumptions about the structure of the model and error‐generating processes and thus can be used in a variety of applications. We demonstrate the method in estimating an SD model of depression and rumination using a panel dataset. The overall results suggest that indirect inference can extend the application of SD models to new topics and leverage common panel datasets to provide unique insights. Copyright © 2016 System Dynamics Society

AbstractWe introduce the Lake Urmia Vignette (LUV) as a tool to assess individuals' understanding of complexity in socio‐environmental systems. LUV is based on a real‐world case and includes a short vignette describing an environmental catastrophe involving a lake. Over a few decades, significant issues have manifested themselves at the lake because of various social, political, economic, and environmental factors. We design a rubric for assessing responses to a prompt. A pilot test with a sample of 30 engineering graduate students is conducted. We compare responses to LUV with other measures. Our findings suggest that students' understanding of complexity is positively associated with their understanding of systems concepts such as feedback loops but not with other possible variables such as self‐reported systems thinking skills or systems‐related coursework. Based on the provided instructions, researchers can use LUV as a novel assessment tool to examine understanding of complexity in socio‐environmental systems.© 2020 System Dynamics Society