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High-fidelity Whole-System Patient Flow Modelling to Assess Health Care Transformation Policies
Abstract
Health systems are continuously seeking ways of transforming their capacities and processes to provide care in novel ways that are aligned with emerging best practices and patient choice while remaining responsive to fiscal pressures. These transformation policy options call for interventions across multiple sectors and patient cohorts, and expect benefits to be realized across the care system, driving policy analysts to take a whole-system point of view in their assessments. This paper presents a system dynamics simulation for the assessment of healthcare transformation policies involving alterations to patient pathways and service levels. The model takes a whole-system, strategic perspective, and is designed to evaluate the direction and magnitude of patient flow changes resulting from transformation policy implementations. The strategic simulation model is developed through a collaborative process with decision-makers across the health system. It has a simple model structure while providing detailed breakdown of cross-sector flows through the use of patient-level clinical and demographic data. A use case is presented for the assessment of the impact of Ontario's proposed stroke best practices. The results indicate significant patient flow gains from the implementation of this policy, which are contingent on significant investments in the community care sector.
... On the other hand, exploring data processing is necessary since few works have considered workflows and teamwork. Some papers [16][17][18][19][20] propose an entity relationship diagram to define a database. Other authors established, Workflow for data management [17,[19][20]. ...
... Some papers [16][17][18][19][20] propose an entity relationship diagram to define a database. Other authors established, Workflow for data management [17,[19][20]. Three works [16][17] and [20] provide care in the subacute phase or hospital care. ...
... Other authors established, Workflow for data management [17,[19][20]. Three works [16][17] and [20] provide care in the subacute phase or hospital care. [19] provides care in the acute phase or primary care. ...
A person who has had a stroke needs rehabilitation to recover from the effects of the incident. A multidisciplinary team of experts performs rehabilitation, offering treatment from many fields, including neurology, nutrition, psychology, and physiotherapy. In the rehabilitation process, physicians interact with medical computing software and devices. The interactions represent medical activities that follow rehabilitation. Nevertheless, how specialists collaborate to do medical tasks is poorly understood using technologies since no particular means of communication enable interdisciplinary cooperation for integral rehabilitation of strokes. Therefore, we present a collaborative software architecture to assist and enable the monitoring of medical activities through multimodal human-computer interactions. The architecture has three layers: the first is to perceive interactions and monitor activities, the second is to manage information sharing and interdisciplinary access, and the third is to assess how well multidisciplinary activities were carried out. The physicians are assisted in their decision-making on the execution of the treatment plan by evaluating how the activities are carried out, which are recollected through the architecture proposed. As a result, we provide a prototype with a user-centered design that understands how the architecture supports human-computer interactions.
... Closer to the true system modelled, ABM can also incorporate ongoing learning from events whereby patients can be influenced by their interactions with other patients or health workers and by their own personal experience with the health system [21]. SDM has also been identified as a useful tool for simulating feedback and activity across the care continuum [27][28][29][30] and is highly adept at capturing changes to the system over time [31]. This is not possible with certain 'snapshot in time' modelling approaches such as DES [32]. ...
... The proportion of papers that modelled health systems in high, upper middle, lower middle and low income countries is presented in Fig. 2. Eighteen (18/28) papers that employed SDM simulated health systems in high income countries including England [33,36,43,45,50,54,56,57] and Canada [28,51,62]. Four SDM papers simulated upper middle income country health systems, including Turkey [52,59] and China [64], with a nominal number of papers (5/28) focussing on lower middle or low income countries (West Bank and Gaza [48,55], Indonesia [37], Afghanistan [30] and Uganda [60]). ...
... Twenty of the SDM papers selected in this review assessed the impact of health policy or interventions on the modelled system. Common policy targets included finding robust methods to relieve stretched healthcare services, ward occupancy and patient length of stay [28,31,36,43,49,50,54,58,62], reducing the time to patient admission [33,53,61], targeting undesirable patient health outcomes [47,58,60,63], optimising performance-based incentive health system policies [30,59] and reducing the total cost of care [33,54,61]. The remaining eight papers explored factors leading to undesirable emergency care system behaviour [56,57], simulating hospital waste management systems and predicting future waste generation [37,48,55], Al-Khatib (2016) [48] Assess the impact of key factors on the hospital waste management system and compare the future total waste output between private, charitable and government hospitals. ...
Background:
Mathematical modelling has been a vital research tool for exploring complex systems, most recently to aid understanding of health system functioning and optimisation. System dynamics models (SDM) and agent-based models (ABM) are two popular complementary methods, used to simulate macro- and micro-level health system behaviour. This systematic review aims to collate, compare and summarise the application of both methods in this field and to identify common healthcare settings and problems that have been modelled using SDM and ABM.
Methods:
We searched MEDLINE, EMBASE, Cochrane Library, MathSciNet, ACM Digital Library, HMIC, Econlit and Global Health databases to identify literature for this review. We described papers meeting the inclusion criteria using descriptive statistics and narrative synthesis, and made comparisons between the identified SDM and ABM literature.
Results:
We identified 28 papers using SDM methods and 11 papers using ABM methods, one of which used hybrid SDM-ABM to simulate health system behaviour. The majority of SDM, ABM and hybrid modelling papers simulated health systems based in high income countries. Emergency and acute care, and elderly care and long-term care services were the most frequently simulated health system settings, modelling the impact of health policies and interventions such as those targeting stretched and under resourced healthcare services, patient length of stay in healthcare facilities and undesirable patient outcomes.
Conclusions:
Future work should now turn to modelling health systems in low- and middle-income countries to aid our understanding of health system functioning in these settings and allow stakeholders and researchers to assess the impact of policies or interventions before implementation. Hybrid modelling of health systems is still relatively novel but with increasing software developments and a growing demand to account for both complex system feedback and heterogeneous behaviour exhibited by those who access or deliver healthcare, we expect a boost in their use to model health systems.
... The literature on clinical trials (Berkhemer et al., 2015;Hacke et al., 2008) and economic evaluations (Saka et al., 2009) typically consider one intervention at a time in a particular context. Similarly, the OR and simulation on stroke mostly investigates specific interventions; literature taking into the whole breadth of hyper-acute, acute and long-term as well as preventative care for people who have suffered a stroke is less common (Esensoy & Carter, 2018). The simulation literature on stroke care mainly comprises studies using discrete event simulation models to study specific interventions in the acute phase of stroke treatment (mostly thrombolysis to dissolve blood clots in the brain; e.g. ...
... Although system dynamics has been widely used in health care settings to study many strategic policy and planning questions (Atkinson et al., 2015) the use of system dynamics in the area of stroke care is limited. Esensoy and Carter (Esensoy & Carter, 2018) have used system dynamics to develop a whole-system patient flow model for stroke patients. They investigate the routes of different types of patients through the system and consider the relevant capacities in the system. ...
The paper demonstrates how a system dynamics approach can support strategic planning of health care services and can in particular help to balance cost-effectiveness considerations with budget impact considerations when assessing a comprehensive package of stroke care interventions in Singapore. A population-level system dynamics model is used to investigate 12 intervention scenarios based on six stroke interventions (a public information campaign, thrombolysis, endovascular therapy, acute stroke unit (ASU), out-of-hospital rehabilitation, and secondary prevention). Primary outcomes included cumulative discounted costs and quality-adjusted life years (QALYs) gained, as well as cumulative net monetary benefit by 2030. All intervention scenarios result in an increase in net monetary benefit by 2030; much of these gains were realized through improved post-acute care. Findings highlight the importance of coordination of care, and affirms the economic value of current stroke interventions.
... 28,29 This conceptual framework was developed to introduce the mapping symbols utilised in SD modelling as well as to generate dialogue surrounding the emerging pathways related to which factors expedite or delay patient flows. 30 It is hoped that these concepts will be communicated back to the executive management committee of the hospital to oversee potential adaptation assimilating these pathways. Other authors have also given credence to these presentations of patient data to influence managerial change. ...
BACKGROUND: The high burden of trauma in Durban results in longer elective surgery waiting periods, which exacerbates the in-patient hospital days and increases the average length of stay. Quantitative analyses of the data clearly demonstrate a growing list of elective patients awaiting surgery while the rate of acute trauma admissions continues to escalate. It has been demonstrated that interactions of patients between the various stages of care should be carefully studied in order for policymakers to identify limiting factors and leverage points Many public health interventions run aground and fail to actualise their initial objectives since the system is deconstructed and reduced to simplified autonomous components. A restorative undertaking to remedy this syndrome is to reconstitute the normative conventions of framing, mapping out and scrutinising deficiencies within healthcare systems. This paper explores a model of total patient flow through the orthopaedic service to test alternative major new structural options for relieving pressure on health services. METHODS: Qualitative data was collected using purposeful sampling to conduct 20 semi-structured interviews as well as including discourse analysis and ethnographic research. Participatory action research (PAR) was the main epistemological method driving the study under the auspices of a system dynamics framework RESULTS: Areas of potential improvements have been identified which can ameliorate the flow of patients between the different departments together with the challenges and uncertainties that are present in achieving this CONCLUSION: Efficient patient flow management is a cornerstone in optimising healthcare services; the failure of such a system burdens the entire health system Level of evidence: Level 5
... The second category-organizational modeling-includes studies of organizational processes that aim (1) to manage patient flows de Andrade et al., 2014;Esensoy & Carter, 2015;Esensoy & Carter, 2018;Gonzalez-Busto & Garcia, 1999;Hallberg et al., 2015;Kumar, 2011;Lane & Husemann, 2008;Lane et al., 2000;Lattimer et al., 2004;Lyons & Duggan, 2015;van Ackere & Smith, 1999;Vanderby & Carter, 2010;Wolstenholme, 1999;Wolstenholme et al., 2007;Wong et al., 2012;Worthington, 1991); (2) to improve organizational performance (Hovmand & Gillespie, 2010;Jalali et al., 2017;Santos et al., 2008;Schwaninger & Klocker, 2018;Smits, 2010); (3) to plan healthcare workforce such as workforce planning in the United Kingdom (Willis et al., 2018), as well as forecast manpower requirements for nurses (Abas et al., 2018;Chung et al., 2010), cardiac surgeons (Vanderby et al., 2014), physicians (Ishikawa et al., 2017;Ishikawa et al., 2013), medical specialists (Barber & Gonzalez Lopez-Valcarcel, 2010), radiology professionals (Taba et al., 2015), dentists (Brailsford & De Silva, 2015), and pediatricians ; and (4) to study or improve medical decision-making (Ghaffarzadegan, 2011;Ghaffarzadegan et al., 2013;Lim, 2018). The list of articles in this category is presented in the online supporting information, Table A1. ...
This article reports the first systematic literature review of system dynamics (SD) applications in health and medicine published between 1960 and 2018. We categorize SD contributions into three groups—disease‐related modeling, organizational modeling, and regional health modeling—and explore major trends and approaches. We then focus on disease‐related modeling and discuss (1) common structures underlying models of infectious and noninfectious diseases, (2) major findings and modeling insights, and (3) avenues for future modeling efforts. While application areas cover a wide range of contexts, a considerable level of quality variation is observable, particularly in regards to model documentation, use of data, and model validation. While these shortcomings are not specific to SD modeling—and other schools of modeling often suffer from similar problems—we invite the community to address the issues both as authors and reviewers. Our study provides a reference document for several exemplary SD models, which is especially useful for early career modelers.
© 2020 System Dynamics Society
... Trabajos más recientes en materia de flujo de pacientes utilizando dinámica de sistemas abordan el tema del estudio de la capacidad en áreas específicas de hospitales como Urgencias [33]- [35] y desde una mirada más estratégica, la reacción de sistemas hospitalarios ante la implementación de políticas de transformación del servicio [36]. ...
Introducción: La adecuada administración de los recursos disponibles es un factor que incide significativamente en la calidad de la atención en hospitales. Las nuevas tecnologías en salud, el envejecimiento de la población, y la escasa disponibilidad presupuestal conllevan a mejorar la administración de la capacidad hospitalaria para lograr mejores desempeños globales.
Objetivo: Construir herramientas que permitan estudiar los factores que inciden en los indicadores de eficiencia hospitalaria global (giro-cama, porcentaje ocupacional y estancia promedio) desde la perspectiva del flujo de pacientes.
Metodología: Se desarrolló un estudio utilizando Dinámica de sistemas en un hospital público de alta complejidad desde la perspectiva del flujo de pacientes, haciendo énfasis en la parametrización de condicionantes de capacidad y en la medición del impacto en indicadores de eficiencia global. La exploración de estrategias se desarrolló con análisis de varianzas y análisis de componentes principales.
Resultados: El porcentaje ocupacional reflejó mayor sensibilidad al uso de políticas sobre uso de camas y reducción de tiempos de espera. El giro cama fue el indicador con mayor velocidad de respuesta a los cambios implementados. El aumento de las horas-médico disponibles en urgencias no presentó impactos significativos en el flujo de pacientes ni en las medidas de desempeño global.
Conclusiones: La simulación continua y análisis de componentes principales permitió obtener herramientas para la planeación de capacidad en el mediano y largo plazo. En lugar de invertir en el aumento de capacidad instalada, la gerencia debe priorizar la reducción de desperdicios a través de metodologías como Lean healthcare.
This paper is based on the keynote address given by the paper's first author at EURO 2021. We draw on our experience over more than three decades to define the critical challenges of healthcare implementation. We do not address issues pertaining to technical quality of a solution. Rather, we focus on five general characteristics of the problem that should be carefully considered for any healthcare project that requires implementation. The problem needs an internal Champion; there should be a current Critical Issue; one must understand and adapt to the Cultural dynamics of the organization; appropriate Data exists; and we need to manage Expectations. We illustrate each with examples of our successes, failures, and mixed results. Finally, we summarize what short and long-term steps we believe the operational research community can take that will lead to improvement in each of these areas.
Demographic changes, particularly population ageing, and rising morbidity from chronic conditions contribute to ever-increasing pressures on health and care systems in developed countries. Partly as a response, new models of care and service innovations are being piloted and introduced. However, the effectiveness and sustainability of these complex health system transformations are often not well understood and most modelling studies fail to capture both system configuration and populating dynamics. In this paper, we present a comprehensive system dynamics modelling approach to capture both population ageing and the organisation of the health and care services from a whole system perspective. The development of the model was directly informed by an ambitious care system transformation project designed to offer a different pathway for those patients deemed to be complex. The model input parameters were populated using estimates from empirical data. A series of simulation experiments were conducted to inform the design of the new service and its sustainability. We found that, subject to the model’s limitations and assumptions, the new pathway could have a stabilising effect against increasing demand provided hospital readmission fractions and length of stay for complex patients can be managed effectively.
Health is a complex system since the appearance of illness and sickness has high uncertainty. In this regard, health economics is a complex system that the emergence of health care demand is uncertain. Thus, evaluation of health economics using classical mathematical methods produce insufficient and incorrect outcomes. Therefore, in this work, complex system tools and methods assessing the health economics and the health care market were investigated to illustrate the superiority of the simulation methods. In recent years, researchers tend to use simulation and modelling based applications to assess the health care market instead of classical methods such as cost-effectiveness and cost-utility. Hence, methods like discrete event simulation, system dynamic modelling, and agent-based modelling attract the attention of researchers. Such methods were used in the evaluation of different health care policies and the assessment of the simulation outputs. Moreover, simulation methods were also used in the minimization of health care expenditures and the assessment of health insurance repayment. It was concluded that researchers tend to use simulation methods to evaluate health economics rather than classical approaches.
Sağlık hizmetleri ve sağlık hizmetleri piyasası kompleks adaptif sistemdir. Dolayısıyla, klasik Newton matematiğini kullanan yöntemlerden elde edilen tek bir çözüm ile sağlık hizmetleri piyasasının değerlendirilmesi, analiz edilmesi ve çözüm önerilerinin geliştirilmesi yeterli değildir. Klasik matematiksel araçlar yerine, kompleks sistemi analiz eden araçların ve yöntemlerin kullanılması gerekir. Böylece sistem gerçeğe daha yakın modellenirken, genellikle tek bir çözüm yerine çözüm kümesi sunan yöntemler ile sorunlara daha etkin çözümler bulunabilmektedir. Bu bağlamda, bu derleme çalışmasında konu ile ilgili bir kılavuz olması açısından sağlık hizmetleri ve sağlık hizmetleri piyasasını değerlendirmede kompleks sistem analiz araçlarını kullanan çalışmalar araştırılmıştır. Çalışmada, öncelikle kompleks adaptif sistemleri analiz ederken en sık kullanılan ajan bazlı modelleme, sistem dinamiği ve ayrık olay simülasyonu gibi yöntemler kısaca açıklanmış ve sonrasında bu yöntemleri kullanılan literatüre yer verilmiştir.
Hospital information systems are increasingly used as part of decision support tools for planning at strategic, tactical and operational decision levels. Clinical pathways are an effective and efficient approach in standardising the progression of treatment, to support patient care and facilitate clinical decision making. This literature review proposes a taxonomy of problems related to clinical pathways and explores the intersection between Information Systems (IS), Operational Research (OR) and industrial engineering. A structured search identified 175 papers included in the taxonomy and analysed in this review. The findings suggest that future work should consider industrial engineering integrated with OR techniques, with an aim to improving the handling of multiple scopes within one model, while encouraging interaction between the disjoint care levels and with a more direct focus on patient outcomes. Achieving this would continue to bridge the gap between OR, IS and industrial engineering, for clinical pathways to aid decision support.
Purpose
The purpose of this paper is to present a new method and model for constructing a new decision-making paradigm of Medicare, which can not only satisfy the needs of the sick people but also reduce the possibility of people slipping back to poverty due to diseases under the policy of Targeted Poverty Alleviation (TPA) of China.
Design/methodology/approach
This paper uses the traditional supply chain theory to analyze the Medicare of impoverished people with the policy of TPA of China and transforms it into a multi-layer supply chain optimization decision-making problem. First, a nonlinear integer programming model for poor people’s Medicare decision with opportunity constraints is constructed. To facilitate the solution of the optimal decision scheme, the abovementioned model is transformed into a linear integer programming model with opportunity constraints by using the Newsvendor model for reference. Meanwhile, the scope of the inventory model is discussed, for it can be combined with the construction of the medical insurance system better. Second, the theoretical model is applied to the practical problem. Finally, based on the results of the theoretical model applying the practical problem, we give further improvement and modification of the theoretical model applies it to the actual situation further.
Findings
This paper presents a theoretical model about determine the optimal the inventory, under the framework of traditional supply chain decision-making, for it can be combined with the construction of the medical insurance system better. The theoretical model is applied to the practical problem of the fight against poverty in XX County, China. By using the actual data and MATLAB, optimal decision scheme is obtained.
Originality/value
There are two aspects of value. On the one hand, this paper provides a new way to construct a Medicare system of impoverished people with TPA of China. On the other hand, this paper tries making a new way to handle the storage of medicines and related medical devices at basic standard clinics decision-making problems based on above mentioned Medicare system.
Like many other critical infrastructures, the public health systems of most countries have grown to high complexity and broad interdependence. Complex adaptive systems of this kind have been shown to evolve into a precarious state between order and disorder in which remote triggers can lead to cascading failure. The healthcare stresses of ageing populations, emerging infectious diseases and treatment-resistant pathogens continue to grow. However, the economic drive for leaner systems and supply chains, and their increasing interdependencies (power, water, food, fuel etc.), all contribute to the growing fragility of public health. When systems are stressed (during emergencies or disasters), accountability for effective resource use requires that health decision making switch from optimal care of individuals to a resource-conserving mode designed to maximise outcomes for the population. Understanding when and how to operate under such conditions, demands that decision makers have a strong grasp of system capacities, limitations and the beneficial or deleterious effects of parameters they are able to manipulate. These difficulties lie behind a growing effort to model health systems to inform planning and (more aspirationally) support real-time decision making. However, modelling efforts to date have been less than optimally founded and structured. Our purpose in this paper is to reframe conceptual approaches to the strategic modelling of health systems and their dependencies in order to point the way to a more productive path for health sector decision-support modelling.
This paper describes how system dynamics was used as a central part of a whole-system review of emergency and on-demand health care in Nottingham, England. Based on interviews with 30 key individuals across health and social care, a ‘conceptual map’ of the system was developed, showing potential patient pathways through the system. This was used to construct a stock-flow model, populated with current activity data, in order to simulate patient flows and to identify system bottle-necks. Without intervention, assuming current trends continue, Nottingham hospitals are unlikely to reach elective admission targets or achieve the government target of 82% bed occupancy. Admissions from general practice had the greatest influence on occupancy rates. Preventing a small number of emergency admissions in elderly patients showed a substantial effect, reducing bed occupancy by 1% per annum over 5 years. Modelling indicated a range of undesirable outcomes associated with continued growth in demand for emergency care, but also considerable potential to intervene to alleviate these problems, in particular by increasing the care options available in the community.Journal of the Operational Research Society (2004) 55, 34–42. doi:10.1057/palgrave.jors.2601667
Rising expenditures spur healthcare organizations to organize their processes more efficiently and effectively. Unfortunately, healthcare planning and control lags behind manufacturing planning and control. We analyze existing planning and control concepts or frameworks for healthcare operations management and find that they do not address various important planning and control problems. We conclude that they only focus on hospitals and are too narrow, focusing on a single managerial area, such as resource capacity planning, or ignoring hierarchical levels. We propose a modern framework for healthcare planning and control that integrates all managerial areas in healthcare delivery operations and all hierarchical levels of control, to ensure completeness and coherence of responsibilities for every managerial area. The framework can be used to structure the various planning and control functions and their interaction. It is applicable to an individual department, an entire healthcare organization, and to a complete supply chain of cure and care providers. The framework can be used to identify and position various types of managerial problems, to demarcate the scope of organization interventions and to facilitate a dialogue between clinical staff and managers.
Model validation constitutes a very important step in system dynamics methodology. Yet, both published and informal evidence indicates that there has been little effort in system dynamics community explicitly devoted to model validity and validation. Validation is a prolonged and complicated pro-cess, involving both formal/quantitative tools and informal/ qualitative ones. This paper focuses on the formal aspects of validation and presents a taxonomy of various aspects and steps of formal model validation. First, there is a very brief discussion of the philosophical issues involved in model validation, followed by a flowchart that describes the logical sequence in which various validation activities must be carried out. The crucial nature of structure validity in system dynamics (causal-descriptive) models is emphasized. Then examples are given of specific validity tests used in each of the three major stages of model validation: Structural tests.
Accident and Emergency (A&E) units provide a route for patients requiring urgent admission to acute hospitals. Public concern over long waiting times for admissions motivated this study, whose aim is to explore the factors which contribute to such delays. The paper discusses the formulation and calibration of a system dynamics model of the interaction of demand pattern, A&E resource deployment, other hospital processes and bed numbers; and the outputs of policy analysis runs of the model which vary a number of the key parameters. Two significant findings have policy implications. One is that while some delays to patients are unavoidable, reductions can be achieved by selective augmentation of resources within, and relating to, the A&E unit. The second is that reductions in bed numbers do not increase waiting times for emergency admissions, their effect instead being to increase sharply the number of cancellations of admissions for elective surgery. This suggests that basing A&E policy solely on any single criterion will merely succeed in transferring the effects of a resource deficit to a different patient group.
Stroke is a leading cause of death and long-term severe disability. A major difficulty facing stroke care provision in the UK is the lack of service integration between the many authorities, professionals and stakeholders involved in the process. The objective of this article is to describe a prototype model to support integrative planning for local stroke care services.The model maps the flow of care in the acute and community segments of the care pathway for stroke patients and allows exploring alternatives for care provision. Simulation modelling can help to develop an understanding of the systemic impact of service change and improve the design and targeting of future services.
System dynamics models are becoming increasingly common in the analysis of policy and managerial issues. The usefulness of these models is predicated on their ability to link observable patterns of behavior to micro-level structure and decision-making processes. This paper posits that model calibration––the process of estimating the model parameters (structure) to obtain a match between observed and simulated structures and behaviors––is a stringent test of a hypothesis linking structure to behavior, and proposes a framework to use calibration as a form of model testing. It tackles the issue at three levels: theoretical, methodological, and technical. First, it explores the nature of model testing, and suggests that the modeling process be recast as an experimental approach to gain confidence in the hypothesis articulated in the model. At the methodological level, it proposes heuristics to guide the testing strategy, and to take advantage of the strengths of automated calibration algorithms. Finally, it presents a set of techniques to support the hypothesis testing process. The paper concludes with an example and a summary of the argument for the proposed approach.
Conceptual models serve as abstractions of user's perceptions of a system. The choice and detailing of these abstractions are key to model use and understanding for analyst and project stakeholders. In tins article we consider guidance for the analyst in his creative job of conceptual modeling. More in particular, we discuss guidance offered by diagramming techniques and simulation tools. Therefore we "unhide" their underlying engineering principles. The notion of such principles is helpful in judging techniques and tools for their conceptual richness and completeness, in educating simulation engineers and as a basis for a more structured approach towards conceptual model engineering.
We provide a framework for simulating the entire patient journey across different phases (such as diagnosis, treatment, rehabilitation and long-term care) and different sectors (such as GP, hospital, social and community services), with the aim of providing better understanding of such processes and facilitating evaluation of alternative clinical and care strategies. A phase-type modelling approach is used to promote better modelling and management of the specific elements of a patient pathway, using performance measures such as clinical outcomes, patient quality of life, and cost. The approach is illustrated using stroke disease. Approximately 5% of the United Kingdom National Health Service budget is spent treating stroke disease each year. There is an urgent need to assess whether existing services are cost-effective or new interventions could increase efficiency. This assessment can be made using models across primary and secondary care; in particular we evaluate the cost-effectiveness of thrombolysis (clot busting therapy), using discrete event simulation. Using our model, patient quality of life and the costs of thrombolysis are compared under different regimes. In addition, our simulation framework is used to illustrate the impact of internal discharge queues, which can develop while patients are awaiting placement. Probabilistic Sensitivity Analysis of the value parameters is also carried out. © 2016 Operational Research Society Ltd. All rights reserved. 0160-5682/16.
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.
Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
Global population ageing is creating immense pressures on hospitals and other healthcare services, compromising their abilities to meet the growing demand from elderly patients. Current demand-supply gaps result in prolonged waiting times in emergency departments (EDs), and several studies have focused on improving ED performance. However, the overcrowding in EDs generally stems from delayed patient flows to inpatient wards - which are congested with inpatients waiting for beds in post-acute facilities. This problem of bed blocking in acute hospitals causes substantial cost burdens on hospitals. This study presents a system dynamics methodology to model the dynamic flow of elderly patients in the Irish healthcare system aimed at gaining a better understanding of the dynamic complexity caused by the system's various parameters. The model evaluates the stock and flow interventions that Irish healthcare executives have proposed to address the problem of delayed discharges, and ultimately reduce costs. The anticipated growth in the nation's demography is also incorporated in the model. Policy makers can also use the model to identify the potential strategic risks that might arise from the unintended consequences of new policies designed to overcome the problem of the delayed discharge of elderly patients.
In a previous report, the ISPOR Task Force on Dynamic Simulation Modeling Applications in Health Care Delivery Research Emerging Good Practices introduced the fundamentals of dynamic simulation modeling and identified the types of health care delivery problems for which dynamic simulation modeling can be used more effectively than other modeling methods. The hierarchical relationship between the health care delivery system, providers, patients, and other stakeholders exhibits a level of complexity that ought to be captured using dynamic simulation modeling methods. As a tool to help researchers decide whether dynamic simulation modeling is an appropriate method for modeling the effects of an intervention on a health care system, we presented the System, Interactions, Multilevel, Understanding, Loops, Agents, Time, Emergence (SIMULATE) checklist consisting of eight elements. This report builds on the previous work, systematically comparing each of the three most commonly used dynamic simulation modeling methods-system dynamics, discrete-event simulation, and agent-based modeling. We review criteria for selecting the most suitable method depending on 1) the purpose-type of problem and research questions being investigated, 2) the object-scope of the model, and 3) the method to model the object to achieve the purpose. Finally, we provide guidance for emerging good practices for dynamic simulation modeling in the health sector, covering all aspects, from the engagement of decision makers in the model design through model maintenance and upkeep. We conclude by providing some recommendations about the application of these methods to add value to informed decision making, with an emphasis on stakeholder engagement, starting with the problem definition. Finally, we identify areas in which further methodological development will likely occur given the growing "volume, velocity and variety" and availability of "big data" to provide empirical evidence and techniques such as machine learning for parameter estimation in dynamic simulation models. Upon reviewing this report in addition to using the SIMULATE checklist, the readers should be able to identify whether dynamic simulation modeling methods are appropriate to address the problem at hand and to recognize the differences of these methods from those of other, more traditional modeling approaches such as Markov models and decision trees. This report provides an overview of these modeling methods and examples of health care system problems in which such methods have been useful. The primary aim of the report was to aid decisions as to whether these simulation methods are appropriate to address specific health systems problems. The report directs readers to other resources for further education on these individual modeling methods for system interventions in the emerging field of health care delivery science and implementation.
Copyright © 2015. Published by Elsevier Inc.
Health care delivery systems are inherently complex, consisting of multiple tiers of interdependent subsystems and processes that are adaptive to changes in the environment and behave in a nonlinear fashion. Traditional health technology assessment and modeling methods often neglect the wider health system impacts that can be critical for achieving desired health system goals and are often of limited usefulness when applied to complex health systems. Researchers and health care decision makers can either underestimate or fail to consider the interactions among the people, processes, technology, and facility designs. Health care delivery system interventions need to incorporate the dynamics and complexities of the health care system context in which the intervention is delivered. This report provides an overview of common dynamic simulation modeling methods and examples of health care system interventions in which such methods could be useful. Three dynamic simulation modeling methods are presented to evaluate system interventions for health care delivery: system dynamics, discrete event simulation, and agent-based modeling. In contrast to conventional evaluations, a dynamic systems approach incorporates the complexity of the system and anticipates the upstream and downstream consequences of changes in complex health care delivery systems. This report assists researchers and decision makers in deciding whether these simulation methods are appropriate to address specific health system problems through an eight-point checklist referred to as the SIMULATE (System, Interactions, Multilevel, Understanding, Loops, Agents, Time, Emergence) tool. It is a primer for researchers and decision makers working in health care delivery and implementation sciences who face complex challenges in delivering effective and efficient care that can be addressed with system interventions. On reviewing this report, the readers should be able to identify whether these simulation modeling methods are appropriate to answer the problem they are addressing and to recognize the differences of these methods from other modeling approaches used typically in health technology assessment applications.
Copyright © 2015 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.
This research is motivated by the desire to incorporate system-modelling tools in the policy-making process to facilitate a better understanding of the system-wide effects of patient flow related interventions. We discuss the development and use of a whole-system qualitative model for the Ontario Ministry of Health and Long-term care aimed to design patient flow policies, and to generate hypotheses on their intended and unintended consequences. We present a multi-panel expert knowledge elicitation approach based on group model building principles. A use case concerning changes to rehabilitation patient flows in the hospital and community settings is discussed. It is concluded that qualitative whole-system modelling is a highly valuable but resource intensive approach to facilitate systems thinking for policy development.
Stroke is one of the three most common causes of death around the world and the sixth most common cause of disability worldwide. Building effective and efficient stroke care systems is a critical step in improving patient outcomes in the prevention, treatment, and rehabilitation of stroke. Despite what seems like a great potential for Operations Research (often referred to as The Science of Better) to contribute to the design and operation of effective and efficient stroke care systems, OR contribution so far has been limited. The objectives of this paper are to review the field of stroke care systems for OR professionals, to illustrate existing OR contribution to stroke care systems and to propose an agenda for how The Science of Better could better contribute to the effort of designing and operating stroke care systems.
This article describes how the Operational Research group in the Department of Health in England has used system dynamics modelling in several areas of health care policy and programme development and implementation. It outlines applications in disease screening and in developing emergency care. It discusses the strengths and weaknesses we have found in its application and suggests some opportunities for its development and use in the future. Copyright © 1999 John Wiley & Sons, Ltd.
Simulation has been used for modeling healthcare systems for over forty years. In many respects it is the ideal approach for addressing healthcare issues, yet the relatively small number of successful implementations would suggest that (outside academia) it has been underused in the health sector, compared with manufacturing industry or defense. In this paper we present a review of applications of simulation in healthcare, focusing on successful implementations, and we discuss some possible reasons why simulation has arguably failed to fulfill its potential. We describe recent advances in the area and identify opportunities for further research and new developments.
In the past decade there has been an explosion in the use of system dynamics modeling in healthcare. Despite this, the approach is still far less well known than discrete-event simulation in the mainstream Operations Research community. This paper contains an introduction to system dynamics, illustrated by several examples in the field of healthcare, and discusses some of the possible reasons for the growth in the popularity of this approach for healthcare modeling.
Because stroke management is aimed at facilitating community reintegration, it would be logical that the sooner the patient can be discharged home, the sooner reintegration can commence. The purpose of this study was to determine the effectiveness of prompt discharge combined with home rehabilitation on function, community reintegration, and health-related quality of life during the first 3 months after stroke.
A randomized trial was carried out involving patients who required rehabilitation services and who had a caregiver at home. When medically ready for discharge, persons with stroke were randomized to either the home intervention group (n=58) or the usual care group (n=56). The home group received a 4-week, tailor-made home program of rehabilitation and nursing services; persons randomized to the usual care group received services provided through a variety of mechanisms, depending on institutional, care provider, and personal preference. The main outcome measure was the Physical Health component of the Measuring Outcomes Study Short-Form-36 (SF-36). Associated outcomes measures included the Timed Up & Go (TUG), Barthel Index (BI), the Older Americans Resource Scale for instrumental activities of daily living (OARS-IADL), Reintegration to Normal Living (RNL), and the SF-36 Mental Health component.
The total length of stay for the home group was, on average, 10 days, 6 days shorter than that for the usual care group. There were no differences between the 2 groups on the BI or on the TUG at either 1 or 3 months after stroke; however, there was a significantly beneficial impact of the home intervention on IADL and reintegration (RNL). By 3 months after stroke, the home intervention group showed a significantly higher score on the SF-36 Physical Health component than the usual care group. The total number of services received by the home group was actually lower than that received by the usual care group.
Prompt discharge combined with home rehabilitation appeared to translate motor and functional gains that occur through natural recovery and rehabilitation into a greater degree of higher-level function and satisfaction with community reintegration, and these in turn were translated into a better physical health.
In industrialised countries, stroke is one of the most common causes of death and handicap, and the costs for stroke services are high. However, rational planning of stroke services and estimation of the costs of their provision are complex, even when generic pathways for stroke diagnosis and treatment are well understood. The reason is the chronic nature of cerebro-vascular disease and the cumulative effect of disabling brain injury. In this paper we describe development of a computer model for estimating the costs of stroke services, intended for use by planners and purchasers of stroke care services. The model operates by incrementing patients' experience of stroke events and their outcomes in annual steps, and is calibrated using Swedish data. We demonstrate the cost consequences by simulating three different policy changes. The model facilitates comparisons between stroke prevention, treatment and rehabilitation, and we conclude that by combining the three policy options it is possible to reduce the costs for stroke services markedly.