Towards the Holy Grail: combining system dynamics and discrete-event simulation in healthcare

The idea of combining discrete-event simulation and system dynamics has been a topic of debate in theoperations research community for over a decade. Many authors have considered the potential benefits ofsuch an approach from a methodological or practical standpoint. However, despite numerous examples ofmodels with both discrete and continuous parameters in the computer science and engineering literature,nobody in the OR field has yet succeeded in developing a genuinely hybrid approach which truly integratesthe philosophical approach and technical merits of both DES and SD in a single model. In this paperwe consider some of the reasons for this and describe two practical healthcare examples of combinedDES/SD models, which nevertheless fall short of the “holy grail” which has been so widely discussed inthe literature over the past decade

Technique for improving care integration models

Recent developments in technologies and improved life style have had a positive impact on prolonging human life contributing to the increasing elderly population. As a consequence, many countries (particularly developed ones) started to experience higher proportions of elderly people (over 65). This has consequently generated the need for care for the elderly that is necessitating the integration of health and social care to accommodate their complex needs. A number of modelling methods have been employed to assist those concerned to cope with health and social care but albeit separately. The literatures so far, identified several techniques that have been employed mostly to model the care integration. However, literatures also suggest that there are some challenges still persist when modelling integrated care. It can be argued that these techniques are not capable of handling the complexities associated with the requirements of integrated systems. This paper attempts to prove the reason why despite the fact that many models of integrated care have been developed, problems are still exist. Based on the literatures, the problems exist due to the unsuitable techniques used to model the IC systems as most of the developed models are using single technique. Therefore, new technique to improve the care integration model is suggested

A toolkit of designs for mixing discrete event simulation and system dynamics

In recent years there has been significant interest in multimethodology and the mixing of OR/MS methods, including Discrete Event Simulation (DES) with System Dynamics (SD). Several examples of mixing DES and SD are described in the literature but there is no overarching framework which characterises the spectrum of options available to modellers. This paper draws on a sample of published case studies, in conjunction with the theoretical literature on mixing methods, to propose a toolkit of designs for mixing DES and SD which can be implemented as a set of questions which a modeller should ask in order to guide the choice of design and inform the associated project methodology. The impetus for this work was the perceived need to transfer insight from reported practice in order to formalise how the two methods can be and have been mixed

From Hybrid Simulation to Hybrid Systems Modelling

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.Hybrid Simulation (HS) is the combined application of simulation approaches like SD, DES and ABS in the model implementation stage of a simulation study. Its objective is to better represent the system under scrutiny. Hybrid Systems Modelling (HSM), on the other hand, is the combined application of simulation with methods and techniques from disciplines such as Applied Computing, Computer Science, Engineering and the wider OR. HSM can be applied to multiple stages of a simulation study. In this paper, we present a classification of HS and extend it to include HSM approaches which use simulation with other OR techniques. The paper contributes to the debate on what constitutes HS and offers a unifying conceptual representation for mixing simulation approaches with HSM methods and techniques

A generic framework for hybrid simulation in healthcare

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 11/01/2010.Healthcare problems are complex; they exhibit both detail and dynamic complexity. It has been argued that Discrete Event Simulation (DES), with its ability to capture detail, is ideal for problems exhibiting this type of complexity. On the other hand, System Dynamics (SD) with its focus on feedback and nonlinear relationships lends itself naturally to comprehend dynamic complexity. Although these modelling paradigms provide valuable insights, neither of them are proficient in capturing both detail and dynamic complexity to the same extent. It has been argued in literature that a hybrid approach, wherein SD and DES are integrated symbiotically, will provide more realistic picture of complex systems with fewer assumptions and less complexity. In spite of wide recognition of healthcare as a complex multi- dimensional system, there has not been any reported study which utilises hybrid simulation. This could be attributed to the fact that due to fundamental differences, the mixing of methodologies is quite challenging. In order to overcome these challenges a generic theoretical framework for hybrid simulation is required. However, there is presently no such generic framework which provides guidance about integration of SD and DES to form hybrid models. This research has attempted to provide such a framework for hybrid simulation which can be utilised in healthcare domain. On the basis of knowledge induced from literature, three requirements for the generic framework have been established. It is argued that the framework for hybrid simulation should be able to provide answers to Why (why hybrid simulation is required), What (what information is exchanged between SD and DES models) and How (how SD and DES models are going to interact with each other over the time to exchange information) within the context of implementation of hybrid simulation to different problem scenarios. In order to meet these requirements, a three-phase generic framework for hybrid simulation has been proposed. Each phase of the framework is mapped to an established requirement and provides guidelines for addressing that requirement. The proposed framework is then evaluated theoretically based on its ability to meet these requirements by using multiple cases, and accordingly modified. It is further evaluated empirically with a single case study comprising of Accident and Emergency department of a London district general hospital. The purpose of this empirical evaluation is to identify the limitations of the framework with regard to the implementation of hybrid models. It is realised during implementation that the modified framework has certain limitations pertaining to the exchange of information between SD and DES models. These limitations are reflected upon and addressed in the final framework. The main contribution of this thesis is the generic framework for hybrid simulation which has been applied within healthcare context. Through an extensive review of existing literature in hybrid simulation, the thesis has also contributed to knowledge in multi-method approaches. A further contribution is that this research has attempted to quantify the impact of intangible benefits of information systems into tangible business process improvements. It is expected that this work will encourage those engaged in simulation (e.g., researchers, practitioners, decision makers) to realise the potential of cross-fertilisation of the two simulation paradigms

An investigation into modeling and simulation approaches for sustainable operations management

Modeling and simulation (M&S) studies have been widely used in industry to gain insights into existing or proposed systems of interest. The majority of these studies focus on productivity-related measures to evaluate systems' performance. This paradigm, however, needs to be shifted to cope with the advent of sustainability, as it is increasingly becoming an important issue in the managerial and the organizational agendas. The application of M&S to evaluate the often-competing metrics associated with sustainable operations management (SOM) is likely to be a challenge. The aim of this review is to investigate the underlying characteristics of SOM that lend towards modeling of production and service systems, and further to present an informed discussion on the suitability of specific modeling techniques in meeting the competing metrics for SOM. The triple bottom line, which is a widely used concept in sustainability and includes environmental, social, and economic aspects, is used as a benchmark for assessing this. Findings from our research suggest that a hybrid (combined) M&S approach could be an appropriate method for SOM analysis; however, it has its challenges.This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors

Conceptual System Dynamics and Agent-Based Modelling Simulation of Interorganisational Fairness in Food Value Chains: Research Agenda and Case Studies

open16siThe research on which this paper is based formed part of the VALUMICS project “Understanding Food Value Chain and Network Dynamics” funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 727243System dynamics and agent-based simulation modelling approaches have a potential as tools to evaluate the impact of policy related decision making in food value chains. The context is that a food value chain involves flows of multiple products, financial flows and decision making among the food value chain players. Each decision may be viewed from the level of independent actors, each with their own motivations and agenda, but responding to externalities and to the behaviours of other actors. The focus is to show how simulation modelling can be applied to problems such as fairness and power asymmetries in European food value chains by evaluating the outcome of interventions in terms of relevant operational indicators of interorganisational fairness (e.g., profit distribution, market power, bargaining power). The main concepts of system dynamics and agent-based modelling are introduced and the applicability of a hybrid of these methods to food value chains is justified. This approach is outlined as a research agenda, and it is demonstrated how cognitive maps can help in the initial conceptual model building when implemented for specific food value chains studied in the EU Horizon 2020 VALUMICS project. The French wheat to bread chain has many characteristics of food value chains in general and is applied as an example to formulate a model that can be extended to capture the functioning of European FVCs. This work is to be further progressed in a subsequent stream of research for the other food value chain case studies with different governance modes and market organisation, in particular, farmed salmon to fillet, dairy cows to milk and raw tomato to processed tomato.openMcGarraghy S.; Olafsdottir G.; Kazakov R.; Huber E.; Loveluck W.; Gudbrandsdottir I.Y.; Cechura L.; Esposito G.; Samoggia A.; Aubert P.-M.; Barling D.; Duric I.; Jaghdani T.J.; Thakur M.; Saviolidis N.M.; Bogason S.G.McGarraghy S.; Olafsdottir G.; Kazakov R.; Huber E.; Loveluck W.; Gudbrandsdottir I.Y.; Cechura L.; Esposito G.; Samoggia A.; Aubert P.-M.; Barling D.; Duric I.; Jaghdani T.J.; Thakur M.; Saviolidis N.M.; Bogason S.G

Towards More Nuanced Patient Management: Decomposing Readmission Risk with Survival Models

Unplanned hospital readmissions are costly and associated with poorer patient outcomes. Overall readmission rates have also come to be used as performance metrics in reimbursement in healthcare policy, further motivating hospitals to identify and manage high-risk patients. Many models predicting readmission risk have been developed to facilitate the equitable measurement of readmission rates and to support hospital decision-makers in prioritising patients for interventions. However, these models consider the overall risk of readmission and are often restricted to a single time point. This work aims to develop the use of survival models to better support hospital decision-makers in managing readmission risk. First, semi-parametric statistical and nonparametric machine learning models are applied to adult patients admitted via the emergency department at Gold Coast University Hospital (n = 46,659) and Robina Hospital (n = 23,976) in Queensland, Australia. Overall model performance is assessed based on discrimination and calibration, as measured by time-dependent concordance and D-calibration. Second, a framework based on iterative hypothesis development and model fitting is proposed for decomposing readmission risk into persistent, patient-specific baselines and transient, care-related components using a sum of exponential hazards structure. Third, criteria for patient prioritisation based on the duration and magnitude of care-related risk components are developed. The extensibility of the framework and subsequent prioritisation criteria are considered for alternative populations, such as outpatient admissions and specific diagnosis groups, and different modelling techniques. Time-to-event models have rarely been applied for readmission modelling but can provide a rich description of the evolution of readmission risk post-discharge and support more nuanced patient management decisions than simple classification models

STAMINA: Bioinformatics Platform for Monitoring and Mitigating Pandemic Outbreaks

This paper presents the components and integrated outcome of a system that aims to achieve early detection, monitoring and mitigation of pandemic outbreaks. The architecture of the platform aims at providing a number of pandemic-response-related services, on a modular basis, that allows for the easy customization of the platform to address user’s needs per case. This customization is achieved through its ability to deploy only the necessary, loosely coupled services and tools for each case, and by providing a common authentication, data storage and data exchange infrastructure. This way, the platform can provide the necessary services without the burden of additional services that are not of use in the current deployment (e.g., predictive models for pathogens that are not endemic to the deployment area). All the decisions taken for the communication and integration of the tools that compose the platform adhere to this basic principle. The tools presented here as well as their integration is part of the project STAMINA