Methodology for profiling literature in healthcare simulation

The publications that relate to the application of simulation to healthcare have steadily increased over the years. These publications are scattered amongst various journals that belong to several subject categories, including Operational Research, Health Economics and Pharmacokinetics. The simulation techniques that are applied to the study of healthcare problems are also varied. The aim of this study is to present a methodology for profiling literature in healthcare simulation. In our methodology, we have considered papers on healthcare that have been published between 1970 and 2007 in journals with impact factors that belonging to various subject categories reporting on the application of four simulation techniques, namely, Monte Carlo Simulation, Discrete-Event Simulation, System Dynamics and Agent-Based Simulation. The methodology has the following objectives: (a) to categorise the papers under the different simulation techniques and identify the healthcare problems that each technique is employed to investigate; (b) to profile, within our dataset, variables such as authors, article citations, etc.; (c) to identify turning point (strategically important) papers and authors through co-citation analysis of references cited by the papers in our dataset. The focus of the paper is on the literature profiling methodology, and not the results that have been derived through the application of this methodology. The authors hope that the methodology presented here will be used to conduct similar work in not only healthcare but also other research domains

BCAS: A Web-enabled and GIS-based Decision Support System for the Diagnosis and Treatment of Breast Cancer

For decades, geographical variations in cancer rates have been observed but the precise determinants of such geographic differences in breast cancer development are unclear. Various statistical models have been proposed. Applications of these models, however, require that the data be assembled from a variety of sources, converted into the statistical models’ parameters and delivered effectively to researchers and policy makers. A web-enabled and GIS-based system can be developed to provide the needed functionality. This article overviews the conceptual web-enabled and GIS-based system (BCAS), illustrates the system’s use in diagnosing and treating breast cancer and examines the potential benefits and implications for breast cancer research and practice

Birth asphyxia as the major complication in newborns: Moving towards improved individual outcomes by prediction, targeted prevention and tailored medical care

Perinatal Asphyxia—oxygen deficit at delivery—can lead to severe hypoxic ischaemic organ damage in newborns followed by a fatal outcome or severe life-long pathologies. The severe insults often cause neurodegenerative diseases, mental retardation and epilepsies. The mild insults lead to so-called “minimal brain-damage disorders” such as attention deficits and hyperactivity, but can also be associated with the development of schizophrenia and life-long functional psychotic syndromes. Asphyxia followed by re-oxygenation can potentially lead to development of several neurodegenerative pathologies, diabetes type 2 and cancer. The task of individual prediction, targeted prevention and personalised treatments before a manifestation of the life-long chronic pathologies usually developed by newborns with asphyxic deficits, should be given the extraordinary priority in neonatology and paediatrics. Socio-economical impacts of educational measures and advanced strategies in development of robust diagnostic approaches targeted at effected molecular pathways, biomarker-candidates and potential drug-targets for tailored treatments are reviewed in the pap

Stakeholder engagement in sustainable housing refurbishment in the UK

The UK government is committed to effectively implement a viable sustainable agenda in the social housing sector. To this end housing associations and local authorities are being encouraged to improve the environmental performance of their new and existing homes. Whilst much attention has been focused on new housing (e.g. the Code for Sustainable Homes) little effort has been focussed on improving the 3.9 (approx) million homes maintained and managed by the public sector (in England), which, given the low rate of new build and demolition (<1% in England), will represent approximately 70% of the public housing stock in 2050. Thus, if UK is to achieve sustainable public housing the major effort will have to focus on the existing stock. However, interpreting the sustainability agenda for an existing housing portfolio is not a straight foreword activity. In addition to finding a ‘technical’ solution, landlords also haveto address the socio-economic issues that balance quality of expectations of tenants with the economic realities of funding social housing refurbishment. This paper will report the findings of a qualitative study (participatory approach) that examined the processes by which a large public landlord sought to develop a long-term sustainable housing strategy. Through a series of individual meetings and group workshops the research team identified: committed leadership; attitudes towards technology; social awareness; and collective understanding of the sustainability agenda as key issues that the organisation needed to address in developing a robust and defendable refurbishment strategy. The paper concludes that the challenges faced by the landlord in improving the sustainability of their existing stock are not primarily technical, but socio-economic. Further, while the economic challenges: initial capital cost; lack of funding; and pay-back periods can be overcome, if the political will exists, by fiscal measures; the social challenges: health & wellbeing; poverty; security; space needs; behaviour change; education; and trust; are much more complex in nature and will require a coordinated approach from all the stakeholders involved in the wider community if they are to be effectively addressed. The key challenge to public housing landlords is to develop mechanisms that can identify and interpret the complex nature of the social sustainability agenda in a way that reflects local aspirations (although the authors believe the factors will exist in all social housing communities, their relative importance is likely to vary between communities) whilst addressing Government agendas

Hierarchical causal variance decomposition for institution and provider comparisons in healthcare

Disease-specific quality indicators (QIs) are used to compare institutions and health care providers in terms processes or outcomes relevant to treatment of a particular condition. In the context of surgical cancer treatments, the performance variations can be due to hospital and/or surgeon level differences, creating a hierarchical clustering. We consider how the observed variation in care received at patient level can be decomposed into that causally explained by the hospital performance, surgeon performance within hospital, patient case-mix, and unexplained (residual) variation. For this purpose, we derive a four-way variance decomposition, with particular attention to the causal interpretation of the components. For estimation, we use inputs from a mixed-effect model with nested random hospital/surgeon-specific effects, and a multinomial logistic model for the hospital/surgeon-specific patient populations. We investigate the performance of our methods in a simulation study

Comparing conventional and distributed approaches to simulation in complex supply-chain health systems

Decision making in modern supply chains can be extremely daunting due to their complex nature. Discrete-event simulation is a technique that can support decision making by providing what-if analysis and evaluation of quantitative data. However, modelling supply chain systems can result in massively large and complicated models that can take a very long time to run even with today's powerful desktop computers. Distributed simulation has been suggested as a possible solution to this problem, by enabling the use of multiple computers to run models. To investigate this claim, this paper presents experiences in implementing a simulation model with a 'conventional' approach and with a distributed approach. This study takes place in a healthcare setting, the supply chain of blood from donor to recipient. The study compares conventional and distributed model execution times of a supply chain model simulated in the simulation package Simul8. The results show that the execution time of the conventional approach increases almost linearly with the size of the system and also the simulation run period. However, the distributed approach to this problem follows a more linear distribution of the execution time in terms of system size and run time and appears to offer a practical alternative. On the basis of this, the paper concludes that distributed simulation can be successfully applied in certain situations

Estimating healthcare demand for an aging population: a flexible and robust bayesian joint model

In this paper, we analyse two frequently used measures of the demand for health care, namely hospital visits and out-of-pocket health care expenditure, which have been analysed separately in the existing literature. Given that these two measures of healthcare demand are highly likely to be closely correlated, we propose a framework to jointly model hospital visits and out-of-pocket medical expenditure. Furthermore, the joint framework allows for the presence of non-linear effects of covariates using splines to capture the effects of aging on healthcare demand. Sample heterogeneity is modelled robustly with the random effects following Dirichlet process priors with explicit cross-part correlation. The findings of our empirical analysis of the U.S. Health and Retirement Survey indicate that the demand for healthcare varies with age and gender and exhibits significant cross-part correlation that provides a rich understanding of how aging affects health care demand, which is of particular policy relevance in the context of an aging population

SCS: 60 years and counting! A time to reflect on the Society's scholarly contribution to M&S from the turn of the millennium.

The Society for Modeling and Simulation International (SCS) is celebrating its 60th anniversary this year. Since its inception, the Society has widely disseminated the advancements in the field of modeling and simulation (M&S) through its peer-reviewed journals. In this paper we profile research that has been published in the journal SIMULATION: Transactions of the Society for Modeling and Simulation International from the turn of the millennium to 2010; the objective is to acknowledge the contribution of the authors and their seminal research papers, their respective universities/departments and the geographical diversity of the authors' affiliations. Yet another objective is to contribute towards the understanding of the overall evolution of the discipline of M&S; this is achieved through the classification of M&S techniques and its frequency of use, analysis of the sectors that have seen the predomination application of M&S and the context of its application. It is expected that this paper will lead to further appreciation of the contribution of the Society in influencing the growth of M&S as a discipline and, indeed, in steering its future direction