Thomas Monks

Maximizing the Population Benefit From Thrombolysis in Acute Ischemic Stroke A Modeling Study of In-Hospital Delays

Background and Purpose— To maximize the benefits of thrombolysis, it is necessary not only to treat more patients, but to deliver treatment as early as possible. The aims of our study were to prospectively evaluate the clinical benefit from reducing delays in the emergency stroke pathway at our district hospital and examine outcomes from scenarios that include extension of the alteplase license. Methods— We developed a discrete-event simulation from prospective data for patients with stroke arriving at our large district hospital. We modeled current practice and assessed the impact on stroke outcomes of measures to reduce in-hospital delays to alteplase treatment and of extensions to the European license for alteplase from 3 to 4.5 hours and to people aged >80 years. Results— Extension of the time window to 4.5 hours increases the thrombolysis rate by 4%, yielding an additional 2 patients per year with minimal or no disability at 3 months. Time window extension is most effective when combined with a system of prealerts, achieving a thrombolysis rate of 15% and an additional 8 patients per year with minimal or no disability, increasing to 13 patients per year with extension of the license to patients >80 years. Conclusions— If implemented alone, extension of the time window for alteplase has only a modest additional population disability benefit, but this benefit can be increased 5-fold if time window extension is combined with substantial reductions to in-hospital delays.

Will Delays in Treatment Jeopardize the Population Benefit From Extending the Time Window for Stroke Thrombolysis

Background and Purpose— Pooled analyses show benefits of intravenous alteplase (recombinant tissue-type plasminogen activator) treatment for acute ischemic stroke up to 4.5 hours after onset despite marketing approval for up to 3 hours. However, the benefit from thrombolysis is critically time-dependent and if extending the time window reduces treatment urgency, this could reduce the population benefit from any extension. Methods— Based on 3830 UK patients registered between 2005 to 2010 in the Safe Implementation of Treatments in Stroke–International Stroke Thrombolysis Registry (SITS-ISTR), a Monte Carlo simulation was used to model recombinant tissue-type plasminogen activator treatment up to 4·5 hours from onset and assess the impact (numbers surviving with little or no disability) from changes in hospital treatment times associated with this extended time window. Results— We observed a significant relation between time remaining to treat and time taken to treat in the UK SITS-ISTR data set after adjustment for censoring. Simulation showed that as this “deadline effect” increases, an extended treatment time window entails that an increasing number of patients are treated at a progressively lower absolute benefit to a point where the population benefit from extending the time window is entirely negated. Conclusions— Despite the benefit for individual patients treated up to 4.5 hours after onset, the population benefit may be reduced or lost altogether if extending the time window results in more patients being treated but at a lower absolute benefit. A universally applied reduction in hospital arrival to treatment times of 8 minutes would confer a population benefit as large as the time window extension.

Systems modelling and simulation in health service design, delivery and decision making

The ever increasing pressures to ensure the most efficient and effective use of limited health service resources will, over time, encourage policy makers to turn to system modelling solutions. Such techniques have been available for decades, but despite ample research which demonstrates potential, their application in health services to date is limited. This article surveys the breadth of approaches available to support delivery and design across many areas and levels of healthcare planning. A case study in emergency stroke care is presented as an exemplar of an impactful application of health system modelling. This is followed by a discussion of the key issues surrounding the application of these methods in health, what barriers need to be overcome to ensure more effective implementation, as well as likely developments in the future.

Learning from discrete-event simulation: Exploring the high involvement hypothesis

Discussion of learning from discrete-event simulation often takes the form of a hypothesis stating that involving clients in model building provides much of the learning necessary to aid their decisions. Whilst practitioners of simulation may intuitively agree with this hypothesis they are simultaneously motivated to reduce the model building effort through model reuse. As simulation projects are typically limited by time, model reuse offers an alternative learning route for clients as the time saved can be used to conduct more experimentation. We detail a laboratory experiment to test the high involvement hypothesis empirically, identify mechanisms that explain how involvement in model building or model reuse affect learning and explore the factors that inhibit learning from models. Measurement of learning focuses on the management of resource utilisation in a case study of a hospital emergency department and through the choice of scenarios during experimentation. Participants who reused a model benefitted from the increased experimentation time available when learning about resource utilisation. However, participants who were involved in model building simulated a greater variety of scenarios including more validation type scenarios early on. These results suggest that there may be a learning trade-off between model reuse and model building when simulation projects have a fixed budget of time. Further work evaluating client learning in practice should track the origin and choice of variables used in experimentation; studies should also record the methods modellers find most effective in communicating the impact of resource utilisation on queuing.

Hyperacute stroke care and NHS England’s business plan

Computer simulation, coupled with high quality data, can help in decision making

Can involving clients in simulation studies help them solve their future problems? A transfer of learning experiment

It is often stated that involving the client in operational research studies increases conceptual learning about a system which can then be applied repeatedly to other, similar, systems. Our study provides a novel measurement approach for behavioural OR studies that aim to analyse the impact of modelling in long term problem solving and decision making. In particular, our approach is the first to operationalise the measurement of transfer of learning from modelling using the concepts of close and far transfer, and overconfidence. We investigate learning in discrete-event simulation (DES) projects through an experimental study. Participants were trained to manage queuing problems by varying the degree to which they were involved in building and using a DES model of a hospital emergency department. They were then asked to transfer learning to a set of analogous problems. Findings demonstrate that transfer of learning from a simulation study is difficult, but possible. However, this learning is only accessible when sufficient time is provided for clients to process the structural behaviour of the model. Overconfidence is also an issue when the clients who were involved in model building attempt to transfer their learning without the aid of a new model. Behavioural OR studies that aim to understand learning from modelling can ultimately improve our modelling interactions with clients; helping to ensure the benefits for a longer term; and enabling modelling efforts to become more sustainable.

Modelling self-management pathways for people with diabetes in primary care

BackgroundSelf-management support to facilitate people with type 2 diabetes to effectively manage their condition is complex to implement. Organisational and system elements operating in relation to providing optimal self-management support in primary care are poorly understood. We have applied operational research techniques to model pathways in primary care to explore and illuminate the processes and points where people struggle to find self-management support.MethodsPrimary care clinicians and support staff in 21 NHS general practices created maps to represent their experience of patients’ progress through the system following diagnosis. These were collated into a combined pathway. Following consideration of how patients reduce dependency on the system to become enhanced self-managers, a model was created to show the influences on patients’ pathways to self-management.ResultsFollowing establishment of diagnosis and treatment, appointment frequency decreases and patient self-management is expected to increase. However, capacity to consistently assess self-management capabilities; provide self-management support; or enhance patient-led self-care activities is missing from the pathways. Appointment frequencies are orientated to bio-medical monitoring rather than increasing the ability to mobilise resources or undertake self-management activities.ConclusionsThe model provides a clear visual picture of the complexities implicated in achieving optimal self-management support. Self-management is quickly hidden from view in a system orientated to treatment delivery rather than to enhancing patient self-management. The model created highlights the limited self-management support currently provided and illuminates points where service change might impact on providing support for self-management. Ensuring professionals are aware of locally available support and people’s existing network support has potential to provide appropriate and timely direction to community facilities and the mobilisation of resources.

A note on the use of multiple comparison scenario techniques in education and practice

Our main aim in this paper is to highlight current practice and education in multiple scenario comparison within DES experimentation and to illustrate the possible benefits of employing false discovery rate (FDR) control as opposed to strict family-wise error rate (FWER) control when comparing large numbers of scenarios in an exploratory manner. We present the results of a small survey into the current practice of scenario analysis by simulation practitioners and academics. The results indicated that the range of scenarios used in DES studies may prohibit the use of FWER control methods such as the Bonferroni Correction referred to in DES textbooks. Furthermore, 80% of our sample were not familiar with any of the multiple comparison control procedures presented to them. We provide a practical example of the FDR in action and argue that it is preferable to employ FDR instead of no multiple comparison control in exploratory style studies.

Operational research as implementation science: definitions, challenges and research priorities

BackgroundOperational research (OR) is the discipline of using models, either quantitative or qualitative, to aid decision-making in complex implementation problems. The methods of OR have been used in healthcare since the 1950s in diverse areas such as emergency medicine and the interface between acute and community care; hospital performance; scheduling and management of patient home visits; scheduling of patient appointments; and many other complex implementation problems of an operational or logistical nature.DiscussionTo date, there has been limited debate about the role that operational research should take within implementation science. I detail three such roles for OR all grounded in upfront system thinking: structuring implementation problems, prospective evaluation of improvement interventions, and strategic reconfiguration. Case studies from mental health, emergency medicine, and stroke care are used to illustrate each role. I then describe the challenges for applied OR within implementation science at the organisational, interventional, and disciplinary levels. Two key challenges include the difficulty faced in achieving a position of mutual understanding between implementation scientists and research users and a stark lack of evaluation of OR interventions. To address these challenges, I propose a research agenda to evaluate applied OR through the lens of implementation science, the liberation of OR from the specialist research and consultancy environment, and co-design of models with service users.SummaryOperational research is a mature discipline that has developed a significant volume of methodology to improve health services. OR offers implementation scientists the opportunity to do more upfront system thinking before committing resources or taking risks. OR has three roles within implementation science: structuring an implementation problem, prospective evaluation of implementation problems, and a tool for strategic reconfiguration of health services. Challenges facing OR as implementation science include limited evidence and evaluation of impact, limited service user involvement, a lack of managerial awareness, effective communication between research users and OR modellers, and availability of healthcare data. To progress the science, a focus is needed in three key areas: evaluation of OR interventions, embedding the knowledge of OR in health services, and educating OR modellers about the aims and benefits of service user involvement.

Systems modelling for improving health care

Table of contentsKEYNOTE PRESENTATIONSK1 Researching complex interventions: the need for robust approachesPeter CraigK2 Complex intervention studies: an important step in developing knowledge for practiceIngalill Rahm-HallbergK3 Public and patient involvement in research: what, why and how?Nicky BrittenK4 Mixed methods in health service research – where do we go from here?Gunilla BorglinSPEAKER PRESENTATIONSS1 Exploring complexity in systematic reviews of complex interventionsGabriele Meyer, Sascha Köpke, Jane Noyes, Jackie ChandlerS2 Can complex health interventions be optimised before moving to a definitive RCT? Strategies and methods currently in useSara LevatiS3 A systematic approach to develop theory based implementation interventionsAnne SalesS4 Pilot studies and feasibility studies for complex interventions: an introductionLehana Thabane, Lora GiangregorioS5 What can be done to pilot complex interventions?Nancy Feeley, Sylvie CossetteS6 Using feasibility and pilot trials to test alternative methodologies and methodological procedures prior to full scale trialsRod TaylorS7 A mixed methods feasibility study in practiceJacqueline Hill, David A Richards, Willem KuykenS8 Non-standard experimental designs and preference designsLouise von EssenS9 Evaluation gone wild: using natural experimental approaches to evaluate complex interventionsAndrew WilliamsS10 The stepped wedge cluster randomised trial: an opportunity to increase the quality of evaluations of service delivery and public policy interventionsKarla Hemming, Richard Lilford, Alan Girling, Monica TaljaardS11 Adaptive designs in confirmatory clinical trials: opportunities in investigating complex interventionsMunyaradzi DimairoS12 Processes, contexts and outcomes in complex interventions, and the implications for evaluationMark PetticrewS13 Processes, contexts and outcomes in complex interventions, and the implications for evaluationJanis Baird, Graham MooreS14 Qualitative evaluation alongside RCTs: what to consider to get relevant and valuable resultsWillem Odendaal, Salla Atkins, Elizabeth Lutge, Natalie Leon, Simon LewinS15 Using economic evaluations to understand the value of complex interventions: when maximising health status is not sufficientKatherine PayneS16 How to arrive at an implementation planTheo van AchterbergS17 Modelling process and outcomes in complex interventionsWalter SermeusS18 Systems modelling for improving health careMartin Pitt, Thomas Monks