Simulation modeling to assess performance of integrated healthcare systems: Literature review to characterize the field and visual aid to guide model selection

Larrain, Nicolás; Groene, Oliver

doi:10.1371/journal.pone.0254334

Cited by 9 publications

(3 citation statements)

References 67 publications

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“…System dynamics models can incorporate a broader set of boundaries, including variables that would otherwise be excluded in models relying on established empirical data sources or when data is sparse [1,9]. These models are also well suited for modeling continuous processes and capturing information flow and feedback loops, the structure of which can be easily presented in causal-loop and/or stock-and-flow diagrams [24,28].…”

Section: System Dynamics Modelingmentioning

confidence: 99%

System dynamics modeling for cancer prevention and control: A systematic review

Kenzie,

Seater,

Wakeland

et al. 2023

PLoS ONE

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Cancer prevention and control requires consideration of complex interactions between multilevel factors. System dynamics modeling, which consists of diagramming and simulation approaches for understanding and managing such complexity, is being increasingly applied to cancer prevention and control, but the breadth, characteristics, and quality of these studies is not known. We searched PubMed, Scopus, APA PsycInfo, and eight peer-reviewed journals to identify cancer-related studies that used system dynamics modeling. A dual review process was used to determine eligibility. Included studies were assessed using quality criteria adapted from prior literature and mapped onto the cancer control continuum. Characteristics of studies and models were abstracted and qualitatively synthesized. 32 studies met our inclusion criteria. A mix of simulation and diagramming approaches were used to address diverse topics, including chemotherapy treatments (16%), interventions to reduce tobacco or e-cigarettes use (16%), and cancer risk from environmental contamination (13%). Models spanned all focus areas of the cancer control continuum, with treatment (44%), prevention (34%), and detection (31%) being the most common. The quality assessment of studies was low, particularly for simulation approaches. Diagramming-only studies more often used participatory approaches. Involvement of participants, description of model development processes, and proper calibration and validation of models showed the greatest room for improvement. System dynamics modeling can illustrate complex interactions and help identify potential interventions across the cancer control continuum. Prior efforts have been hampered by a lack of rigor and transparency regarding model development and testing. Supportive infrastructure for increasing awareness, accessibility, and further development of best practices of system dynamics for multidisciplinary cancer research is needed.

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Section: System Dynamics Modelingmentioning

confidence: 99%

System dynamics modeling for cancer prevention and control: A systematic review

Kenzie,

Seater,

Wakeland

et al. 2023

PLoS ONE

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“…1,2 To assist healthcare decision-makers in optimizing their available resources, modeling and simulation techniques have been employed to assess hospital operations' efficiency. [3][4][5][6] These approaches not only evaluate the current strategies but also offer insights into the potential outcomes of future modifications to the healthcare system. Models serve as mathematical frameworks that aim to represent specific facets of reality in sufficient detail, enabling the estimation of the consequences of healthcare decisions.…”

Section: Introductionmentioning

confidence: 99%

Unlocking Efficient Hospital Operations: Discrete Event Simulation in R

Kamal,

Hassanain

2023

Preprint

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In healthcare systems, optimizing resource allocation without compromising patient care is paramount. This study employs a simulation-based approach to evaluate the efficiency of bed allocation within a hospital setting. Utilizing a patient arrival model with an exponential distribution, we simulated patient trajectories to examine system bottlenecks, particularly focusing on waiting times. Initial simulations painted a scenario of an "unstable" system, where waiting times and queue lengths surged due to the limited number of available beds. Through iterative simulations, we explored the operational research question: "What is the minimum number of beds required to stabilize the system?" Our results, visualized in a series of detailed metrics plots, suggest that the addition of a specific number of beds can significantly reduce patient waiting time and stabilize the system. This research offers insights for hospital management on resource optimization, potentially leading to improved patient care and reduced operational costs.

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“…Der Medical Research Council hat im September 2021 eine Überarbeitung seines Konzepts zur Evaluation komplexer Interventionen vorgelegt, das als Richtschnur für die Entwicklung von Evaluationskonzepten für regionale komplexe Versorgungsmodelle dienen kann 2 . Ebenfalls kürzlich hat auf europäischer Ebene das EU-finanzierte ‚Healthcare Performance Intelligence Network‘ Empfehlungen zur Evaluation von integrierten Versorgungsmodellen herausgegeben 3 . Zu einigen der in diesen Publikationen angesprochenen Aspekte wird im folgenden Positionspapier aus Sicht des Deutschen Netzwerks Versorgungsforschung Stellung genommen.…”

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