Optimisation of system dynamics models

1. Definition of the Subject and Its Importance 2. Optimisation as calibration 3. Optimisation of performance (policy optimisation) 4. Examples of SD optimisation reported in the literature 5. Future directions in SD optimisation 6. Reference

Modelling the feedback effects of reconfiguring health services

The shift in the balance of health care, bringing services ‘closer to home’, is a well-established trend, which has been motivated by the desire to improve the provision of services. However, these efforts may be undermined by the improvements in access stimulating demand. Existing analyses of this trend have been limited to isolated parts of the system with calls to control demand with stricter clinical guidelines or to meet demand with capacity increases. By failing to appreciate the underlying feedback mechanisms, these interventions may only have a limited effect. We demonstrate the contribution offered by system dynamics modelling by presenting a study of two cases of the shift in cardiac catheterization services in the UK. We hypothesize the effects of the shifts in services and produce model output that is not inconsistent with real world data. Our model encompasses several mechanisms by which demand is stimulated. We use the model to clarify the roles for stricter clinical guidelines and capacity increases, and to demonstrate the potential benefits of changing the goals that drive activity

A system dynamics-based simulation study for managing clinical governance and pathways in a hospital

This paper examines the development of clinical pathways in a hospital in Australia based on empirical clinical data of patient episodes. A system dynamics (SD)-based decision support system (DSS) is developed and analyzed for this purpose. System dynamics was used as the simulation modeling tool because of its rigorous approach in capturing interrelationships among variables and in handling dynamic aspects of the system behavior in managing healthcare. The study highlights the scenarios that will help hospital administrators to redistribute caseloads amongst admitting clinicians with a focus on multiple Diagnostic Related Groups (DRG’s) as the means to improve the patient turnaround and hospital throughput without compromising quality patient care. DRG’s are the best known classification system used in a casemix funding model. The classification system groups inpatient stays into clinically meaningful categories of similar levels of complexity that consume similar amounts of resources. Policy explorations reveal various combinations of the dominant policies that hospital management can adopt. The analyses act as a scratch pad for the executives as they understand what can be feasibly achieved by the implementation of clinical pathways given a number of constraints. With the use of visual interfaces, executives can manipulate the DSS to test various scenarios. Experimental evidence based on focus groups demonstrated that the DSS can enhance group learning processes and improve decision making. The simulation model findings support recent studies of CP implementation on various DRG’s published in the medical literature. These studies showed substantial reductions in length of stay, costs and resource utilization

Some results from a system dynamics model of construction sector competitiveness

Despite government-led good practice initiatives aimed to improve competitiveness in the U.K. construction sector, fluctuations in growth-driven demand, investment and constant regulatory revisions make it very difficult for an enterprise to plan strategically and remain competitive over a timescale exceeding 2 to 3 years. Research has been carried out to understand the historical evolution and changing face of the construction sector and the dynamic capabilities needed for an enterprise to secure a more sustainable competitive future. A dynamic model of a typical contracting firm has been created based upon extensive knowledge capture arising from fieldwork in collaborating firms together with a detailed review of the literature. A construct called the competitive index is used to model contract allocation in a stylised market. The simulations presented enable contracting enterprises to reflect strategically with a view to remaining competitive over a much longer time horizon of between 15 and 20 years. The rehearsal of strategy through simulated scenarios helps to minimise unexpected behaviour and offers insights about how endogenous behaviour can shape the future of the enterprise. To date, work on construction competitiveness has been either of a static nature or set predominantly at the level of the project. This study offers a new perspective by providing a dynamic tool to analyse competitiveness. It creates a new paradigm to support enhanced construction sector performance

A dynamic policy model to manage temporal performance amongst contracting firms in a competitive situation

Studies have been conducted to measure competitiveness in the construction industry. Such research has focused on all levels from the national picture to individual projects. While useful, the results are limited in that they present a snapshot picture at one point in time. Moreover, they do not suggest how under-performance might be improved. The research reported here is part of a large collaborative study to evaluate sustained competitiveness in the UK construction industry. It enhances previous research in that a system dynamics model of contracting firms operating in competition is used to not only measure each firm’s temporal performance by means of a dynamic competitive index, but it can also suggest high leverage policies which mitigate against under-performance. The model structure is described and simulated scenario runs presented. Besides the contribution to strategic policy making at the level of the contracting firm, the exemplar shows that the system dynamics methodology could have significant utility in the field of construction management

Simulation analysis of the consequences of shifting the balance of health care: a system dynamics approach

Objectives: The shift in the balance of health care, bringing services 'closer to home', is a well-established trend. This study sought to provide insight into the consequences of this trend, in particular the stimulation of demand, by exploring the underlying feedback structure. Methods: We constructed a simulation model using the system dynamics method, which is specifically designed for the analysis of feedback structure. The model was calibrated to two cases of the shift in cardiac catheterization services in the UK. Data sources included archival data, observations and interviews with senior health care professionals. Key model outputs were the basic trends displayed by waiting lists, average waiting times, cumulative patient referrals, cumulative patient activity and cumulative overall costs. Results: Demand was stimulated in both cases via several different mechanisms. We revealed the roles for clinical guidelines and capacity changes, and the typical responses to imbalances between supply and demand. Our analysis also demonstrated the potential benefits of changing the goals that drive activity by seeking a waiting list goal rather than a waiting time goal. Conclusions: Appreciating the wider consequences of shifting the balance of care is essential if services are to be improved overall. The underlying feedback mechanisms of both intended and unintended effects need to be understood. Using a systemic approach, more effective policies may be designed through coordinated programmes rather than isolated initiatives, which may have only a limited impact