Clinical simulation in Australia and New Zealand: Through the lens of an advisory group

Across Australia, innovations in simulation to enhance learning in nursing have been occurring for three decades and nursing is, and needs to be, a leading player in simulation knowledge diffusion. However, expertise is unevenly distributed across health services and education providers. Rather than build on the expertise and achievements of others, there is a tendency for resource duplication and for trial and error problem solving, in part related to a failure to communicate achievements for the benefits of the professional collective. For nursing to become a leader in the use of simulation and drive ongoing development, as well as conducting high quality research and evaluation, academics need to collaborate, aggregate best practice in simulation learning, and disseminate that knowledge to educators working in health services and higher education sectors across the whole of Australia and New Zealand. To achieve this strategic intent, capacity development principles and committed action are necessary. In mid 2010 the opportunity to bring together nurse educators with simulation learning expertise within Australia and New Zealand became a reality. The Council of Deans of Nursing and Midwifery (CDNM) Australia and New Zealand decided to establish an expert reference group to reflect on the state of Australian nursing simulation, to pool expertise and to plan ways to share best practice knowledge on simulation more widely. This paper reflects on the achievements of the first 18 months since the group's establishment and considers future directions for the enhancement of simulation learning practice, research and development in Australian nursing

Serviço de emergência médica angolano : optimização utilizando sistemas multi-agente

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2015A temática da saúde é uma das que apresenta mais desafios em Angola. Os desafios são não só intrínsecos à própria área, mas resultam também de condicionantes externas. Uma das áreas mais problemáticas, dada a sua natureza complexa e multidisciplinar, é a dos serviços de emergências hospitalares. Visando um aumento de eficiência desses serviços, podem estudar e ensaiar-se várias políticas públicas, mas que, frequentemente, apenas podem ser avaliadas quando já se encontram implementadas. A simulação à priori dessas políticas apresenta vários benefícios: o design pode ser ajustado aos objectivos dos decisores políticos de forma mais exacta; as políticas podem reflectir melhor as motivações dos indivíduos envolvidos em diversos papéis (utilizadores, médicos, enfermeiros, funcionários públicos, auditores, decisores políticos); as ligações micro-macro e as mediações são representadas explicitamente; a simulação permite a melhoria sucessiva das políticas, de tal forma que as mesmas aquando da sua implementação estejam aperfeiçoadas; os decisores e intervenientes podem conhecer melhor o território de decisão tendo em vista uma economia de custos, um aumento da eficiência dos serviços, uma maior satisfação dos utentes e uma acção mais adequada em situações de contingência. Defendemos a simulação baseada em multi-agente como forma de orientar a especificação de políticas. Os sistemas multi-agente (SMA) permitem a representação de agentes racionais heterogéneos e fornecem uma abordagem para criar modelos dinâmicos complexos de fenómenos sociais. Ao longo dos últimos anos assistiu-se a um crescente interesse pela utilização dos SMA na área da prestação de cuidados de saúde. O potencial de flexibilidade, adaptabilidade e robustez dos SMA é amplamente considerado como uma mais-valia para a área da saúde em tópicos como o apoio à decisão médica, diagnóstico e monitorização de pacientes, prestação de cuidados remotos, gestão e coordenação de recursos ou aprendizagem e treino médicos. Nesta dissertação descreve-se como podemos atacar o problema de optimização das políticas de serviços de emergência médica, quando há uma diferença clara entre a concepção dessas políticas e o uso que as pessoas lhes dão. Apresenta-se o cenário e um modelo para a simulação, identificando os actores envolvidos, as medidas necessárias para avaliar os resultados multidimensionais da simulação e como se podem afinar as políticas e simulá-las antes da sua implementação no mundo real. Motivado pelo cenário mais eficiente resultante da simulação e por forma a validá-lo, implementou-se o protótipo SIEMA (Sistema Integrado de Emergências Médicas Angolanas) com a finalidade de apoiar a gestão de emergências médicas em Angola.Healthcare presents major challenges in Angola. These challenges are not only intrinsic to the area itself, but are also a consequence of external constraints. Medical emergency services, on account of their complex and multidisciplinary nature, are one of the most problematic areas. Aiming at an increase of efficiency of these services, various public policies can be studied and tested, but their results often can only be assessed when policies are already implemented. The simulation of these policies has several benefits: the design can be adjusted to the objectives of policy makers more accurately; policies can better reflect the motivations of the individuals involved in various roles (patients, doctors, nurses, hospital staff, auditors, policy makers); micro-macro links and mediations are represented explicitly. Simulation allows successive improvement of policies before their implementation; decision-makers and stakeholders can better understand the decision territory, namely concerning cost savings, increased service efficiency, greater user satisfaction and a more adequate action in contingency situations. We defend multi-agent based simulation as a way to guide the policy specification. Multi-agent systems (MAS) allow the representation of heterogeneous agents and provide a rational approach to create complex social phenomena dynamic models. The past few years have witnessed a growing interest in the use of MAS in health. The potential for flexibility, adaptability and robustness of MAS is widely regarded as an asset for healthcare on topics such as medical decision support, diagnosis and monitoring of patients, remote care, management and coordination of resources or learning and medical training. This thesis describes how we tackle the optimization of medical emergency services policies when there is a clear distance between the conception of policies and the use that people give them. We present the scenario and a model for the simulation, identify involved actors and fine-tuned and simulate policies before implementation in the real world. Motivated by the most efficient scenario resulting from the simulation and in order to validate it, we implemented a prototype (SIEMA) to support the management of medical emergencies in Angola

Modelos de Simulação em Saúde: Promoção de Estilos de Vida Saudáveis

As doenças crónicas são um problema de saúde pública global, sendo responsá-veis por cerca de 70% do número total de mortes, em todo o mundo. Cada vez mais, é necessário promover estilos de vida saudáveis na sociedade, através da alteração dos comportamentos de risco, permitindo a prevenção, autogestão e controlo de doenças crónicas. Esta matéria tem sido alvo de diversos estudos, na tentativa de compreender o impacto da alteração de estilos de vida na saúde dos indivíduos. Contudo, os modelos desenvolvidos não permitem compreender o problema de forma global. Assim, através da modelação e simulação baseada em agentes (ABMS), o presente estudo propõe de-senvolver uma ferramenta de apoio em saúde pública, que permita estimar o efeito de diferentes intervenções em estilos de vida, na população em geral. O modelo desenvolvido avalia o impacto individual e combinado de interven-ções de dieta, exercício físico e apoio psicológico, no índice de massa corporal, na hemo-globina glicada A1c e na qualidade de vida dos participantes. Esta ferramenta de simu-lação permite testar diferentes políticas de promoção de estilos de vida saudáveis, como se de um “laboratório” se tratasse. Nesta investigação verificou-se que a intervenção individual com maior impacto no índice de massa corporal foi a dieta, e na hemoglobina glicada A1c foi a atividade física. As intervenções combinadas são mais complexas, tendo por isso, uma eficiência superior. A situação ideal com mais influência na saúde dos participantes, foi interven-ção combinada da dieta, atividade física e apoio psicológico, na qual um maior número de participantes alterou o seu estado de risco. O modelo necessita de validação para poder ser utilizado como ferramenta de apoio à decisão médica

Modelling the Impacts of Demographic Ageing on the Demand for Health Care Services

This thesis presents a methodology that predicts the number of individuals aged 50 and older who have one or more of three morbidities within each English local authority district to 2031. The three morbidities are cardiovascular disease, diabetes or high blood sugar and respiratory illnesses. The methodology uses spatial microsimulation to create a representative 2011 base population in each district. This population is then dynamically simulated through time using a process that: ages the population, changes its morbidity status, restructures its composition along demographic lines and replenishes the population at younger ages. An accounting system is used to examine how the demographic changes within each district influence its health status. In terms of prevalence counts and rates the prediction is for significant reductions in both these measures for CVD. For respiratory illness, the prevalence count remains fairly constant but due the increases in the size of the population at risk, the prevalence rate decreases. With diabetes or high blood sugar, both the prevalence counts and rates increase. Examination of the demographic changes affecting these prevalences shows that for the more ethnically diverse districts the changing ethnic structure has a large impact whilst for the more prospering districts the changing age structure has the largest impact. These results suggest that public health messages on circulatory and heart conditions and the reduction in smoking will have beneficial health effects in the future, which will help to mitigate the strains placed on the health care system in England. The prospects for diabetes or high blood sugar are however not so good and some considerations on how best to utilise scarce resources to prevent or treat this morbidity are urgently required

Workforce behaviour in healthcare systems

This thesis investigates the behavioural dynamics that emerge at the interface of Emergency Departments (EDs) and the Emergency Medical Service (EMS). The focus is on the impact that time-targets may have on staff behaviour and patient well-being. This research is structured into two main parts: the first part is the development of a queueing theoretic representation of an ED and the second part is the development of a game theoretic model between two EDs and the EMS that distributes ambulances to them. This thesis uses a variety of mathematical and computational fields such as linear algebra, game theory, queueing theory, graph theory, optimisation, probability theory, agent-based simulation and reinforcement learning. The queueing model is developed using both a discrete event simulation and a Markov chain approach. The queueing network consists of two queueing nodes where there is some strategic managerial behaviour that relates to how two types of individuals are routed between the two nodes. The first node acts as a buffer for one type of individuals before moving to the second node, while the second node consists of a waiting room and a service centre. Both approaches are used to obtain performance measures of the queueing system and explicit formulas are derived for the mean waiting time, the mean blocking time and the proportion of individuals within a given target time. In addition, some numeric results are presented that compare the Markov chain and discrete event simulation approaches. Consequently, this thesis describes the development and application of a 3-player game theoretic model between two such queueing networks and a service that distributes individuals to them. In particular the game is then reduced to a 2-player normal-form game. The resultant model is used to explore dynamics between all players. A backwards induction technique is used to get the utilities of the normal-form game between the two queueing systems. The particular game is then applied to a healthcare scenario to capture the emergent behaviour between the EMS and two EDs. The results and outcomes that are produced ii by various instances of the game are then analysed and discussed. The learning algorithm replicator dynamics is used to explore the evolutionary behaviours that emerge in the game. In particular, the behaviour that naturally emerges from the game seems to be one that causes more blockage and includes less cooperation. Several ways to escape this learned inefficient behaviour are discussed. Finally, the thesis explores an extension of the queueing theoretic model that allows servers to choose their own service speed. This is implemented using an agent-based simulation approach. The agent-based model is then used in conjunction with a reinforcement learning algorithm to explore the effect that the servers’ behaviour has on the overall performance of the system