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

Improving care pathways through evidence-based modeling strategies: a scoping review

OBJECTIVES

Noncommunicable and communicable diseases represent significant public health problems, heavily straining healthcare systems. The care pathway (CP) concept has emerged as a promising framework to improve care coordination and delivery, but its complexity often hinders implementation. Modeling, with its various methodologies, represents a valuable approach to address this challenge. Systematizing these methodologies is essential for enhancing CPs. This scoping review aims to describe and analyze CP modeling methodologies.

STUDY DESIGN

Scoping review.

METHODS

Following PRISMA-ScR guidelines, searches were performed in PubMed, Web of science and Embase. Inclusion criteria were: (i) publications in English; (ii) human studies, (iii) published between January 1, 2019 and April 3, 2024 and (iv) use of modeling to analyze CPs. For each publication included, data were extracted and categorized based on modeling goals, methods used, functions of the techniques and their respective strengths and limitations.

RESULTS

Analysis of the 41 included articles revealed that the main goals of CP modeling were quality of care (46.3 %), continuous improvement (31.7 %), and process optimization (22.0 %). The methods used for modeling were qualitative (41.5 %), quantitative (34.1 %), or mixed (24.4 %). Technical goals were description (48.8 %), decision support (36.6 %), and prediction (14.6 %). Qualitative methods (68.5 %) were common in studies focused on quality of care. Only 11 articles shared similar methodologies across at least two studies. Key weaknesses of CP modeling were data availability and implementation acceptance.

CONCLUSIONS

This scoping review identified key categories and commonly used methodologies in CP modeling, offering a framework to help researchers and healthcare professionals improve CP design and implementation, leading to better patient outcomes and more efficient healthcare systems.

Incorporating Complexity and System Dynamics into Economic Modelling for Mental Health Policy and Planning

Care as usual has failed to stem the tide of mental health challenges in children and young people. Transformed models of care and prevention are required, including targeting the social determinants of mental health. Robust economic evidence is crucial to guide investment towards prioritised interventions that are effective and cost-effective to optimise health outcomes and ensure value for money. Mental healthcare and prevention exhibit the characteristics of complex dynamic systems, yet dynamic simulation modelling has to date only rarely been used to conduct economic evaluation in this area. This article proposes an integrated decision-making and planning framework for mental health that includes system dynamics modelling, cost-effectiveness analysis, and participatory model-building methods, in a circular process that is constantly reviewed and updated in a 'living model' ecosystem. We describe a case study of this approach for mental health system policy and planning that synergises the unique attributes of a system dynamics approach within the context of economic evaluation. This kind of approach can help decision makers make the most of precious, limited resources in healthcare. The application of modelling to organise and enable better responses to the youth mental health crisis offers positive benefits for individuals and their families, as well as for taxpayers.

Improving resource allocation in the precision medicine Era: a simulation-based approach using R

The application of personalized medicine in developing countries is a major challenge, especially for those with poor economic status. A critical factor in improving the application of personalized medicine is the efficient allocation of resources. In healthcare systems, optimizing resource allocation without compromising patient care is paramount. This tutorial 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. This research offers insights for hospital management on resource optimization leading to improved patient care.

Crisis-ready educational skills of life support in newborns and adults' scenarios: the impact of simulation-based training on student proficiency

OBJECTIVE

Aim: To assess the impact of multidisciplinary simulation training on the educational outcomes of medical students in the emergency care of adults and newborns and implement changes in the curriculum to master simulation scenarios more.

PATIENTS AND METHODS

Materials and Methods: To assess the differences in learning outcomes between medical students who study the same curriculum without simulation interventions and those who undergo multidisciplinary emergency care simulation training. A quasi-experimental approach was used to assign students to the Intervention Group or the Control Group.

RESULTS

Results: According to individual criteria, the lowest scores in both groups were obtained for the stages that required the greatest accuracy and correct technique. After the appropriate cycle of initiation, the results in both groups improved significantly, but the results of students from the first group were significantly higher than those of students from the second group. Despite the absence of a significant difference in the average overall score for the skills, students in the first group significantly improved the accuracy and correctness of the criteria that assess the technical aspects of performance, while students in the second group mainly improved the quality of the descriptive and communicative parts of the practical skill.

CONCLUSION

Conclusions: We believe that reallocating curricular time to additional hours dedicated to simulation scenarios will better prepare aspiring healthcare professionals for the demanding and dynamic nature of their career, as we continue to increase our understanding of the potential of simulation-based education.

The role of university clinics in advancing higher medical education

OBJECTIVE

Aim: To explore the multifaceted role of university clinics in shaping medical professionals, advancing medical knowledge, and improving healthcare delivery. Special attention is given to their function as primary platforms for practical training, the development of professional competencies, and the implementation of innovative teaching methods in medical education..

PATIENTS AND METHODS

Materials and Methods: This article employs an analysis of the organizational structure, educational programs, and research initiatives of university clinics. Case studies, including the experience of the University Clinic of Taras Shevchenko National University of Kyiv, are utilized to provide insights into their operational challenges, successes, and future prospects.

CONCLUSION

Conclusions: University clinics play a crucial role in medical education by fostering professional skills, clinical competencies, and ethical standards in future physicians. They significantly contribute to the evolution of medical education and healthcare improvement through innovative teaching methods and impactful research.

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

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.