Investigating the Diffusion of Agent‐based Modelling and System Dynamics Modelling in Population Health and Healthcare Research

Liu, Shiyong; Xue, Hong; Li, Yan; Xu, Judy

doi:10.1002/sres.2460

Cited by 21 publications

(18 citation statements)

References 16 publications

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“…human decision-making and heterogeneous interactions between individuals), SD models address macro-level system behaviours (i.e. changes or movement of resources in complex systems over time) [ 13 ]. Using differential equations to model changing variables over a period of time while allowing for feedback and various interactions and delays, SD models also address the issues of simultaneity or the mutual causation of systems behaviours [ 10 ].…”

Section: Methodsmentioning

confidence: 99%

System dynamics modelling of health workforce planning to address future challenges of Thailand’s Universal Health Coverage

et al. 2021

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Background System dynamics (SD) modelling can inform policy decisions under Thailand's Universal Health Coverage. We report on this thinking approach to Thailand's strategic health workforce planning for the next 20 years (2018–2037). Methods A series of group model building (GMB) sessions involving 110 participants from multi-sectors of Thailand's health systems was conducted in 2017 and 2018. We facilitated policymakers, administrators, practitioners and other stakeholders to co-create a causal loop diagram (CLD) representing a shared understanding of why the health workforce's demands and supplies in Thailand were mismatched. A stock and flow diagram (SFD) was also co-created for testing the consequences of policy options by simulation modelling. Results The simulation modelling found hospital utilisation created a vicious cycle of constantly increasing demands for hospital care and a constant shortage of healthcare providers. Moreover, hospital care was not designed for effectively dealing with the future demands of ageing populations and prevalent chronic illness. Hence, shifting emphasis to professions that can provide primary care, intermediate care, long-term care, palliative care, and end-of-life care can be more effective. Conclusions Our SD modelling confirmed that shifting the care models to address the changing health demands can be a high-leverage policy of health workforce planning, although very difficult to implement in the short term. of health workforce planning, although very difficult to implement in the short term.

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Section: Methodsmentioning

confidence: 99%

System dynamics modelling of health workforce planning to address future challenges of Thailand’s Universal Health Coverage

et al. 2021

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“…This composite nature of the concept is often expressed as a division between hard and soft approaches (11). While some authors focus on the advantages offered by computational and analytical complexity methods (5,(12)(13)(14), others point out that a complexity approach enables new organizational practices, which improve the working experience (15,16). The interests of this second group of scholars widely overlap with Systems Theory (17).…”

Section: Complexity Approaches and Health Improvementmentioning

confidence: 99%

Appraising the Implementation of Complexity Approaches Within the Public Health Sector in Scotland. An Assessment Framework for Pre-Implementation Policy Evaluation

Zucca

Long

Hilton

et al. 2021

Front. Public Health

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Complexity approaches have gained international attention as potentially effective strategies to address population health challenges. In light of this, the Scottish government (Scot. Gov.) set the implementation of these approaches as the recommended practice for its public health sector organizations. This study evaluates the opportunity and feasibility of implementing complexity approaches in public health Scotland employees' everyday routine by employing a qualitative study that involves 20 stakeholders, representative of different organizations and roles. We made use of an assessment framework based on Soft Systems Methodology (SSm) and Normalization Process Theory (NPT) comprised of five phases: Phase One defines the boundaries, aims, and goals of the issue under study; Phase Two consists of data collection, drawing on the e-Health Implementation Toolkit (e-HIT); Phase Three involves short presentations and breakout group activities to provide information on the new policy; Phase Four employs system thinking tasks to structure debate and builds shared understanding among participants; Phase Five applies NPT to appraise the organizational position around complexity based on information from the preceding steps. We found two main obstacles to implementing complexity approaches: (1) a lack of a shared understanding of the key concepts in complexity and their practical implications; (2) stakeholders' fear of significant disruption to work routines and power relationships. We recommend addressing these issues with appropriate training and customization of goals and tools that may enable complexity approaches to succeed within the Scottish public health context. Our assessment framework allows the recognition of key mechanisms to support how Scotland's Public Health body can enhance the implementation of complexity approaches. The appraisal framework could be used to study early-stage policy implementation in other contexts.

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“…Still, it is possible that our review may have missed pertinent SD applications to injury problems. In Liu et al's (2018) [69] review of SD applications in the population health literature, they highlight the fact that many SD researchers are employed in the private sector, which could result in SD applications appearing less frequently in the published literature. Finally, as with any review, there is a possibility that relevant studies were missed in our review of potential articles or that we did not correctly capture specific study details during data abstraction.…”

Section: Limitations Of Reviewmentioning

confidence: 99%

System Dynamics Applications to Injury and Violence Prevention: a Systematic Review

et al. 2019

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Purpose of review: System dynamics (SD) is an approach to solving problems in the context of dynamic complexity. The purpose of this review was to summarize SD applications in injury prevention and highlight opportunities for SD to contribute to injury prevention research and practice. Recent findings: While SD has been increasingly used to study public health problems over the last few decades, uptake in the injury field has been slow. We identified 18 studies, mostly conducted in the last 10 years. Applications covered a range of topics (e.g., road traffic injury; overdose; violence), employed different types of SD tools (i.e., qualitative and quantitative), and served a variety of research and practice purposes (e.g., deepen understanding of a problem, policy analysis). Summary: Given the many ways that SD can add value and complement traditional research and practice approaches (e.g., through novel stakeholder engagement and policy analysis tools), increased investment in SD-related capacity building and opportunities that support SD use are warranted.

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Investigating the Diffusion of Agent‐based Modelling and System Dynamics Modelling in Population Health and Healthcare Research

Cited by 21 publications

References 16 publications

System dynamics modelling of health workforce planning to address future challenges of Thailand’s Universal Health Coverage

System dynamics modelling of health workforce planning to address future challenges of Thailand’s Universal Health Coverage

Appraising the Implementation of Complexity Approaches Within the Public Health Sector in Scotland. An Assessment Framework for Pre-Implementation Policy Evaluation

System Dynamics Applications to Injury and Violence Prevention: a Systematic Review

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