Design and analysis of an outpatient orthopaedic clinic performance with discrete event simulation and design of experiments

Considering that demand for healthcare services is constantly increasing, outpatient services must improve their performance. Being able to satisfy the demand with a limited outpatient service capacity is an important operational challenge. The objective of our research consists in studying the relationships and interactions between patient flows, resource capacity (number of consulting rooms and number of nurses) and appointment scheduling rules in order to improve an outpatient orthopaedic clinic performance. Discrete event simulation is used to model outpatient flows. An experimental design was developed to test how to assign consulting rooms and nurses to each orthopedist considering four appointment scheduling rules and three patient flow types of varied complexity. Analysis of variance and the Tukey test are used to evaluate the simulation results. Our conclusion is that in order to improve the outpatient orthopaedic clinic performance, resources (consulting rooms, nurses) and appointment scheduling rules must be adapted to the different patient flows.     

Using cognitive artifacts to understand distributed cognition

Studies of patient safety have identified gaps in current work including the need for research about communication and information sharing among healthcare providers. They have also encouraged the use of decision support tools to improve human performance. Distributed cognition is the shared awareness of goals, plans, and details that no single individual grasps. Cognitive artifacts are objects such as: schedules, display boards, lists, and worksheets that form part of a distributed cognition. Cognitive artifacts that are related to operating room (OR) scheduling include: the availabilities sheet, master schedule, OR graph, and OR board. All provide a "way in" to understand how teams in the acute care setting dynamically plan and manage the balance between demand for care and the resources available to provide it. This work has import for the way that information technology supports the organization, management, and use of healthcare resources. Better computer-supported cognitive artifacts will benefit patient safety by making teamwork processes, planning, communications, and resource management more resilient.     

Hospital Event Simulation Model: Arrivals to Discharge–Design,development and application

In this paper we outline the design, development and application of a hospital patient flow management support tool – Hospital Event Simulation Model: Arrivals to Discharge (HESMAD). The model captures the patterns of patient flows within Flinders Medical Centre, a teaching hospital located in South Australia, through extensive exploitation of an existing hospital patient journey database (PJD). HESMAD employs mathematical and statistical modelling techniques, as well as the concept of modular design, to construct functions and processes that are embedded in a discrete event simulation system. The current structure of HESMAD reflects many iterations of refinements based on feedback from relevant industry experts. It places great emphasis on providing an engaging visualisation of the dynamics of events, and a convenient interface for domain experts: doctors, hospital managers and other health care professionals. An illustrative example of HESMAD’s wider applicability is presented.     

Multi-agent Pareto appointment exchanging in hospital patient scheduling

We present a dynamic and distributed approach to the hospital patient scheduling problem, in which patients can have multiple appointments that have to be scheduled to different resources. To efficiently solve this problem we develop a multi-agent Pareto-improvement appointment exchanging algorithm: MPAEX. It respects the decentralization of scheduling authorities and continuously improves patient schedules in response to the dynamic environment. We present models of the hospital patient scheduling problem in terms of the health care cycle where a doctor repeatedly orders sets of activities to diagnose and/or treat a patient. We introduce the Theil index to the health care domain to characterize different hospital patient scheduling problems in terms of the degree of relative workload inequality between required resources. In experiments that simulate a broad range of hospital patient scheduling problems, we extensively compare the performance of MPAEX to a set of scheduling benchmarks. The distributed and dynamic MPAEX performs almost as good as the best centralized and static scheduling heuristic, and is robust for variations in the model settings. A preliminary version of this work has appeared as [1].     

Predictive resource management for meta-applications

This paper defines meta-applications as large, related collections of computational tasks, designed to achieve a specific overall result, running on a (possibly geographically) distributed, non-dedicated meta-computing platform. To carry out such applications in an industrial context, one requires resource management and job scheduling facilities (including capacity planning), to ensure that the application is feasible using the available resources, that each component job will be sent to an appropriate resource, and that everything will finish before the computing resources are needed for other purposes.

This requirement has been addressed by the PAC in three major European collaborative projects: PROMENVIR, TOOLSHED and HPC-VAO, leading to the creation of job scheduling software, in which scheduling is brought together with performance modelling of applications and systems, to provide meta-applications management facilities. This software is described, focusing on the performance modelling approach which was needed to support it.

Early results from this approach are discussed, raising some new issues in performance modelling and software deployment for meta-applications. An indication is given about ongoing work at the PAC designed to overcome current limitations and address these outstanding issues.     

A new model of scheduling in manufacturing: tasks, roles, and monitoring

For over 3 decades there was a belief that computer-based solutions would "solve" complex industrial scheduling problems, yet most manufacturing organizations still require human contributions for effective scheduling performance. We present a new model of scheduling for the development and implementation of effective scheduling systems within manufacturing companies. The model derives from investigating the work of 7 schedulers in 4 manufacturing environments using a qualitative field study approach, for which novel field-based data collection and analysis methods were developed. The results show that scheduling in practice comprises task, role, and monitoring activities and that the business environment influences a scheduler at work. A new definition of scheduling is presented that includes the significant facilitation and implementation aspects of human scheduling ignored by many computer-based scheduling approaches. The implications for this model extend across the domains of human factors and operations management, especially for the analysis and improvement of existing and new production planning and control processes and enterprise information systems. Actual or potential applications of this research include the analysis, design, and management of planning, scheduling, and control processes in industry; the selection, training, and support of production schedulers; and the allocation of tasks to humans and computer systems in industrial planning, scheduling, and control processes.     

时间敏感网络业务流动态调度场景中的批量重配置算法

工业自动化领域广泛使用时间敏感网络技术. 该领域业务流的调度方式主要包含静态调度和动态调度. 静态调度一次计算所有业务流, 可以最大程度节省链路和时间资源, 但是计算时间长, 无法灵活处理新增业务流. 动态调度以增量的形式计算新增业务流, 计算时间短, 但是资源分配不够合理, 会产生时隙碎片. 全局流重配置机制可以定期对网络中所有业务流进行重新规划, 来优化链路和时间资源的分配, 但该机制只适用于拥有较少业务流的小型网络, 业务流数量的增多会引起计算时间的急剧增长, 影响后续到来的业务流. 本文在现有动态调度算法的基础上, 设计了批量重配置算法. 该算法给出了新的评价指标——网络吞吐率, 并在满足动态调度秒级响应时间的情况下, 定期重配置网络中的部分业务流, 优化网络资源配置. 此外, 算法给出了重配置业务流的选取标准, 并优化了流的路径选择标准和传输开始时间计算方式. 本文针对原算法和增加了批量重配置机制的改进算法进行了仿真实验, 实验结果表明, 改进算法可以在拥有数千条业务流的大型网络运行, 并在网络吞吐率和调度成功的流数量方面有16.5%和5.5%的提升, 同时保证了算法的秒级计算时间.     

Adaptive scheduling algorithm for media-optimized traffic management in software defined networks

Multi-policy resource management have been considered as an efficient methodology for delivering ready-to-use media-optimized applications in Software-Defined Networks (SDNs). Prioritized flow scheduling ensures high-speed communication in SDNs under large-scale distribution, heterogeneity of network resources, and exponential distribution of the flows granularity. The effectiveness of priority-based approaches depends usually on the control mechanism of the resource management. In this paper we improve the resource utilization by developing a novel adaptive scheduling strategy. We came with an effecting scheduling strategy to determine what resource to be allocated to a set of flows keeping their priority, increasing the average utilization of resources and, most importantly, establishing a virtual circuit for a specific flow over a network. Our theoretical remarks and extensive simulation results show that the proposed scheduling strategies can achieve the described goals.     

The development and application of CFD models for water treatment flocculators

The use of CFD to simulate turbulent flows in laboratory and full scale flocculation processes commonly found at water treatment works is reported. The paper considers a range of modelling strategies and simulation techniques including, inter alia, steady and unsteady flow, two-equation and Reynolds stress turbulence modelling, sliding mesh and multiple reference frames approaches to rotational flow simulation, and mesh density optimisation. Through analysis of turbulence dissipation rate, this work considers the development of models for environmental engineering problem solving and demonstrates clearly the benefits to be gained from the use of CFD in flocculation vessels.     

Integrated clinical pathway management for medical quality improvement – based on a semiotically inspired systems architecture

Clinical pathway is an approach to standardise care processes to support the implementations of clinical guidelines and protocols. It is designed to support the management of treatment processes including clinical and non-clinical activities, resources and also financial aspects. It provides detailed guidance for each stage in the management of a patient with the aim of improving the continuity and coordination of care across different disciplines and sectors. However, in the practical treatment process, the lack of knowledge sharing and information accuracy of paper-based clinical pathways burden health-care staff with a large amount of paper work. This will often result in medical errors, inefficient treatment process and thus poor quality medical services. This paper first presents a theoretical underpinning and a co-design research methodology for integrated pathway management by drawing input from organisational semiotics. An approach to integrated clinical pathway management is then proposed, which aims to embed pathway knowledge into treatment processes and existing hospital information systems. The capability of this approach has been demonstrated through the case study in one of the largest hospitals in China. The outcome reveals that medical quality can be improved significantly by the classified clinical pathway knowledge and seamless integration with hospital information systems.     

A multilevel integrative approach to hospital case mix and capacity planning

Hospital case mix and capacity planning involves the decision making both on patient volumes that can be taken care of at a hospital and on resource requirements and capacity management. In this research, to advance both the hospital resource efficiency and the health care service level, a multilevel integrative approach to the planning problem is proposed on the basis of mathematical programming modeling and simulation analysis. It consists of three stages, namely the case mix planning phase, the master surgery scheduling phase and the operational performance evaluation phase. At the case mix planning phase, a hospital is assumed to choose the optimal patient mix and volume that can bring the maximum overall financial contribution under the given resource capacity. Then, in order to improve the patient service level potentially, the total expected bed shortage due to the variable length of stay of patients is minimized through reallocating the bed capacity and building balanced master surgery schedules at the master surgery scheduling phase. After that, the performance evaluation is carried out at the operational stage through simulation analysis, and a few effective operational policies are suggested and analyzed to enhance the trade-offs between resource efficiency and service level. The three stages are interacting and are combined in an iterative way to make sound decisions both on the patient case mix and on the resource allocation.     

A smart healthcare reward model for resource allocation in smart city

Today, cities face many significant challenges, and the smart city concept is a promising means to address typical traditional city problems. The wireless e-health technologies is an evolving topic in the area of telemedicine nowadays. Mobile telecommunication and the use of multimedia technologies are the core of providing better access to healthcare personnel on the move. These technologies provide equal access to medical information and expert care leading to a better and a more efficient use of resources. Mobile and Fog computing technologies can also cope with many challenges in smart healthcare resources of mobility, scalability, efficiency, and reliability. Optimal healthcare systems are particularly critical in cities, due to the highly concentrated populations. This high population increases the potential for harm and damage in the case of negligence or improper treatment. This can lead to infections and disease outbreaks, which could become epidemic situations and require containment, which is very costly. Motivated by the need for better usage and management of healthcare resources, which is crucial for reliable healthcare delivery, this paper introduces a model that can provide improved delivery and utilization of resources. The quality reward-based model was developed to study and react to the satisfaction factors of healthcare systems, and proposes an optimization-based algorithm called the Maximum Reward Algorithm (MRA), that enhances the use and delivery of healthcare resources. The algorithm has been tested with multiple experiments and simulations, and has proved that it can provide reliability, efficiency and achieves 50.1% to 77.2% performance improvement.

     

An environmental assessment system for environmental technologies

A new model for the environmental assessment of environmental technologies, EASETECH, has been developed. The primary aim of EASETECH is to perform life-cycle assessment (LCA) of complex systems handling heterogeneous material flows. The objectives of this paper are to describe the EASETECH framework and the calculation structure. The main novelties compared to other LCA software are as follows. First, the focus is put on material flow modelling, as each flow is characterised as a mix of material fractions with different properties and flow compositions are computed as a basis for the LCA calculations. Second, the tool has been designed to allow for the easy set-up of scenarios by using a toolbox, the processes within which can handle heterogeneous material flows in different ways and have different emission calculations. Finally, tools for uncertainty analysis are provided, enabling the user to parameterise systems fully and propagate probability distributions through Monte Carlo analysis.     

支持公平服务的CICQ分层混合调度策略

为到达业务提供性能保障是衡量一个交换系统性能的重要参考.针对现有联合输入交叉点排队交换结构(CICQ)调度策略缺乏基于流的服务质量保障,探讨了在CICQ交换结构实施基于"流"调度的可能性,提出了一种能够为到达业务流的提供公平服务的分层混合调度策略(HSFS).HSFS采用分层的混合调度机制,每个输入、输出端口可独立地进行变长分组交换,其复杂度为O(1),具有良好可扩展特性.理论分析结果表明,HSFS无需加速便能为到达业务提供时延上限、速率和公平性保障.最后,基于SPES对HSFS的性能进行了评估.     

Achieving fair service with a hybrid scheduling scheme for CICQ switches

Providing performance guarantees for arriving traffic flows has become an important measure for today’s routing and switching systems. However, none of current scheduling algorithms built on CICQ (combined input and cross-point buffered) switches can provide flow level performance guarantees. Aiming at meeting this requirement, the feasibility of implementing flow level scheduling is discussed thoroughly. Then, based on the discussion, it comes up with a hybrid and stratified fair scheduling (HSFS) scheme, which is hierarchical and hybrid, for CICQ switches. With HSFS, each input port and output port can schedule variable length packets independently with a complexity of O(1). Theoretical analysis show that HSFS can provide delay bound, service rate and fair performance guarantees without speedup. Finally, we implement HSFS in SPES (switch performance evaluation system) to verify the analytical results.     

A simulation tool for managing environmental flows in regulated rivers

Environmental flows provide river flow regimes to restore and conserve aquatic ecosystems, creating considerably different demands compared to conventional water extraction. With increasing incorporation of environmental flows in water planning worldwide, governments require decision support tools to manage these flows in regulated rivers. We developed the Environmental Water Allocation Simulator with Hydrology (eWASH), a fast, flexible and user-friendly scenario-based hydrological modelling tool, supporting environmental flow management decisions for single- or multi-reservoir systems. Environmental flow demands and management rules are easily specified via the graphical user interface, and batch processing functions aid in uncertainty assessment. eWASH modelled main processes of complex regulated rivers and the tool is widely applicable. We calibrated eWASH for the Gwydir and Macquarie Rivers of Australia's Murray–Darling Basin. Modelled monthly environmental flow allocations exhibited Nash–Sutcliffe efficiencies of 0.55 for the Gwydir and 0.72 for the Macquarie catchments respectively when validated.     

Scalable services via egress admission control

Allocating resources for multimedia traffic flows with real-time performance requirements is an important challenge for future packet networks. However, in large-scale networks, individually managing each traffic flow on each of its traversed routers has fundamental scalability limitations, in both the control plane's requirements for signaling, state management, and admission control, and the data plane's requirements for per-flow scheduling mechanisms. In this paper, we develop a scalable architecture and algorithm for quality-of-service management termed egress admission control. In our approach, resource management and admission control are performed only at egress routers, without any coordination among backbone nodes or per-flow management. Our key technique is to develop a framework for admission control under a general “black box” model, which allows for cross traffic that cannot be directly measured, and scheduling policies that may be ill-described across many network nodes. By monitoring and controlling egress routers' class-based arrival and service envelopes, we show how network services can be provisioned via scalable control at the network edge. We illustrate the performance of our approach with a set of simulation experiments using highly bursty traffic flows and find that despite our use of distributed admission control, our approach is able to accurately control the system's admissible region under a wide range of conditions     

Performance analysis of priority scheduling mechanisms under heterogeneous network traffic

Priority scheduling principle plays a crucial role in the Differentiated Services (DiffServ) architecture for the provisioning of Quality-of-Service (QoS) of network-based applications. Analytical modelling and performance evaluation of priority queuing systems have received significant attention and research efforts. However, most existing work has primarily focused on the analysis of priority queuing under either Short Range Dependent (SRD) or Long Range Dependent (LRD) traffic only. Recent studies have shown that realistic traffic reveals heterogeneous nature within modern multi-service networks. With the aim of investigating the impact of heterogeneous traffic on the design and performance of network-based systems, this paper proposes a novel analytical model for priority queuing systems subject to heterogeneous LRD self-similar and SRD Poisson traffic. The key contribution of the paper is to extend the application of the generalized Schilder's theorem (originally a large deviation principle for handling Gaussian processes only) to deal with heterogeneous traffic and further develop the analytical upper and lower bounds of the queue length distributions for individual traffic flows. The validity and accuracy of the model demonstrated through extensive comparisons between analytical bounds and simulation results make it a practical and cost-effective evaluation tool for investigating the performance behaviour of priority queuing systems under heterogeneous traffic with various parameter settings.     

Process mining using BPMN: relating event logs and process models

Process-aware information systems (PAIS) are systems relying on processes, which involve human and software resources to achieve concrete goals. There is a need to develop approaches for modeling, analysis, improvement and monitoring processes within PAIS. These approaches include process mining techniques used to discover process models from event logs, find log and model deviations, and analyze performance characteristics of processes. The representational bias (a way to model processes) plays an important role in process mining. The BPMN 2.0 (Business Process Model and Notation) standard is widely used and allows to build conventional and understandable process models. In addition to the flat control flow perspective, subprocesses, data flows, resources can be integrated within one BPMN diagram. This makes BPMN very attractive for both process miners and business users, since the control flow perspective can be integrated with data and resource perspectives discovered from event logs. In this paper, we describe and justify robust control flow conversion algorithms, which provide the basis for more advanced BPMN-based discovery and conformance checking algorithms. Thus, on the basis of these conversion algorithms low-level models (such as Petri nets, causal nets and process trees) discovered from event logs using existing approaches can be represented in terms of BPMN. Moreover, we establish behavioral relations between Petri nets and BPMN models and use them to adopt existing conformance checking and performance analysis techniques in order to visualize conformance and performance information within a BPMN diagram. We believe that the results presented in this paper can be used for a wide variety of BPMN mining and conformance checking algorithms. We also provide metrics for the processes discovered before and after the conversion to BPMN structures. Cases for which conversion algorithms produce more compact or more complicated BPMN models in comparison with the initial models are identified.     

Modelling priority queuing systems with varying service capacity

Many studies have been conducted to investigate the performance of priority queuing (PQ) systems with constant service capacity. However, due to the time-varying nature of wireless channels in wireless communication networks, the service capacity of queuing systemsmay vary over time. Therefore, it is necessary to investigate the performance of PQ systems in the presence of varying service capacity. In addition, self-similar traffic has been discovered to be a ubiquitous phenomenon in various communication networks, which poses great challenges to performance modelling of scheduling systems due to its fractal-like nature. Therefore, this paper develops a flow-decomposition based approach to performance modelling of PQ systems subject to self-similar traffic and varying service capacity. It specifically proposes an analytical model to investigate queue length distributions of individual traffic flows. The validity and accuracy of the model is demonstrated via extensive simulation experiments.