System Dynamics Modeling provides invaluable insights into understanding the dynamic complexity present in healthcare systems due to the interconnections between factors including health behaviors, environmental and socio-economic conditions, human service delivery infrastructures, medical innovations, health and social policy, disease progression and outcomes, and other health-related determinants, as these change in relation to one another over time.
The goal of this Research Topic is to highlight innovative approaches – including stakeholder-engaged approaches – to simulation modeling, data analysis, and data use that improve our understanding of the behavior of complex systems, predict responses to policy interventions, and transform findings and discussion into actionable insights.
In particular, we are interested in – but not limited to – the following subtopics:
- The use of system dynamics modeling with decision-making groups for scenario assessment and improved decision-making.
- Representation of healthcare and public health contexts and problems as dynamically complex systems to enable evidence-based decision-making.
- Assessments of unintended consequences of interventions in semi- or fully-unstructured contexts, inherent biases, and implications for policy and practice.
- Adoptions of Natural Language Processing (NLP) and multiple data streams to develop reasonable estimates of stocks, flows, feedback loop factors, and other measures essential for system dynamics modeling.
- Applications of systems science methods to take advantage of burgeoning AI and machine learning technologies and massive data sets.
- Support for non-technical audiences around the use of system dynamics tools and systems thinking more generally for challenging assumptions, improving individual and organizational learning, and exploring potential solutions to healthcare problems.
- Other topics that will advance the implementation and dissemination of system dynamics modeling along with necessary cautions and advisory around the prudent use of models.
System Dynamics Modeling provides invaluable insights into understanding the dynamic complexity present in healthcare systems due to the interconnections between factors including health behaviors, environmental and socio-economic conditions, human service delivery infrastructures, medical innovations, health and social policy, disease progression and outcomes, and other health-related determinants, as these change in relation to one another over time.
The goal of this Research Topic is to highlight innovative approaches – including stakeholder-engaged approaches – to simulation modeling, data analysis, and data use that improve our understanding of the behavior of complex systems, predict responses to policy interventions, and transform findings and discussion into actionable insights.
In particular, we are interested in – but not limited to – the following subtopics:
- The use of system dynamics modeling with decision-making groups for scenario assessment and improved decision-making.
- Representation of healthcare and public health contexts and problems as dynamically complex systems to enable evidence-based decision-making.
- Assessments of unintended consequences of interventions in semi- or fully-unstructured contexts, inherent biases, and implications for policy and practice.
- Adoptions of Natural Language Processing (NLP) and multiple data streams to develop reasonable estimates of stocks, flows, feedback loop factors, and other measures essential for system dynamics modeling.
- Applications of systems science methods to take advantage of burgeoning AI and machine learning technologies and massive data sets.
- Support for non-technical audiences around the use of system dynamics tools and systems thinking more generally for challenging assumptions, improving individual and organizational learning, and exploring potential solutions to healthcare problems.
- Other topics that will advance the implementation and dissemination of system dynamics modeling along with necessary cautions and advisory around the prudent use of models.