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Objectives: Overcrowding in the emergency department (ED) is common in the UK as in other countries worldwide. Computer simulation is one approach used for understanding the causes of ED overcrowding and assessing the likely impact of changes to the delivery of emergency care. However, little is known about the usefulness of computer simulation for analysis of ED patient flow. We undertook a systematic review to investigate the different computer simulation methods and their contribution for analysis of patient flow within EDs in the UK., Methods: We searched eight bibliographic databases (MEDLINE, EMBASE, COCHRANE, WEB OF SCIENCE, CINAHL, INSPEC, MATHSCINET and ACM DIGITAL LIBRARY) from date of inception until 31 March 2016. Studies were included if they used a computer simulation method to capture patient progression within the ED of an established UK National Health Service hospital. Studies were summarised in terms of simulation method, key assumptions, input and output data, conclusions drawn and implementation of results., Results: Twenty-one studies met the inclusion criteria. Of these, 19 used discrete event simulation and 2 used system dynamics models. The purpose of many of these studies (n=16; 76%) centred on service redesign. Seven studies (33%) provided no details about the ED being investigated. Most studies (n=18; 86%) used specific hospital models of ED patient flow. Overall, the reporting of underlying modelling assumptions was poor. Nineteen studies (90%) considered patient waiting or throughput times as the key outcome measure. Twelve studies (57%) reported some involvement of stakeholders in the simulation study. However, only three studies (14%) reported on the implementation of changes supported by the simulation., Conclusions: We found that computer simulation can provide a means to pretest changes to ED care delivery before implementation in a safe and efficient manner. However, the evidence base is small and poorly developed. There are some methodological, data, stakeholder, implementation and reporting issues, which must be addressed by future studies., Competing Interests: Competing interests: None declared., (© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.)

Background: Traumatic hemorrhage is a leading preventable cause of mortality following mass casualty events (MCEs). Improving outcomes requires adequate in-hospital provision of high-volume red blood cell (RBC) transfusions. This study investigated strategies for optimizing RBC provision to casualties in MCEs using simulation modeling., Methods: A computerized simulation model of a UK major trauma center (TC) transfusion system was developed. The model used input data from past MCEs and civilian and military trauma registries. We simulated the effect of varying on-shelf RBC stock hold and the timing of externally restocking RBC supplies on TC treatment capacity across increasing loads of priority one (P1) and two (P2) casualties from an event., Results: Thirty-five thousand simulations were performed. A casualty load of 20 P1s and P2s under standard TC RBC stock conditions left 35% (95% confidence interval, 32-38%) of P1s and 7% (4-10%) of P2s inadequately treated for hemorrhage. Additionally, exhaustion of type O emergency RBC stocks (a surrogate for reaching surge capacity) occurred in a median of 10 hours (IQR, 5 to >12 hours). Doubling casualty load increased this to 60% (57-63%) and 30% (26-34%), respectively, with capacity reached in 2 hours (1-3 hours). The model identified a minimum requirement of 12 U of on-shelf RBCs per P1/P2 casualty received to prevent surge capacity being reached. Restocking supplies in an MCE versus greater permanent on-shelf RBC stock holds was considered at increasing hourly intervals. T-test analysis showed no difference between stock hold versus supply restocking with regard to overall outcomes for MCEs up to 80 P1s and P2s in size (p < 0.05), provided the restock occurred within 6 hours., Conclusion: Even limited-sized MCEs threaten to overwhelm TC transfusion systems. An early-automated push approach to restocking RBCs initiated by central suppliers can produce equivocal outcomes compared with holding excess stock permanently at TCs., Level of Evidence: Therapeutic/care management study, level IV.

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://www.bmj.com/company/products-services/rights-and-licensing/)

Computer simulation has been employed to evaluate proposed changes in the delivery of health care. However, little is known about the utility of simulation approaches for analysis of changes in the delivery of surgical care. We searched eight bibliographic databases for this comprehensive review of the literature published over the past five decades, and found 34 publications that reported on simulation models for the flow of surgical patients. The majority of these publications presented a description of the simulation approach: 91% outlined the underlying assumptions for modeling, 88% presented the system requirements, and 91% described the input and output data. However, only half of the publications reported that models were constructed to address the needs of policy-makers, and only 26% reported some involvement of health system managers and policy-makers in the simulation study. In addition, we found a wide variation in the presentation of assumptions, system requirements, input and output data, and results of simulation-based policy analysis.

Computer simulation of patient flow has been used extensively to assess the impacts of changes in the management of surgical care. However, little research is available on the utility of existing modeling techniques. The purpose of this paper is to examine the capacity of Statecharts, a system of graphical specification, for constructing a discrete-event simulation model of the perioperative process. The Statecharts specification paradigm was originally developed for representing reactive systems by extending the formalism of finite-state machines through notions of hierarchy, parallelism, and event broadcasting. Hierarchy permits subordination between states so that one state may contain other states. Parallelism permits more than one state to be active at any given time. Broadcasting of events allows one state to detect changes in another state. In the context of the peri-operative process, hierarchy provides the means to describe steps within activities and to cluster related activities, parallelism provides the means to specify concurrent activities, and event broadcasting provides the means to trigger a series of actions in one activity according to transitions that occur in another activity. Combined with hierarchy and parallelism, event broadcasting offers a convenient way to describe the interaction of concurrent activities. We applied the Statecharts formalism to describe the progress of individual patients through surgical care as a series of asynchronous updates in patient records generated in reaction to events produced by parallel finite-state machines representing concurrent clinical and managerial activities. We conclude that Statecharts capture successfully the behavioral aspects of surgical care delivery by specifying permissible chronology of events, conditions, and actions.

Modelling patient flow in health care systems is vital in understanding the system activity and may therefore prove to be useful in improving their functionality. An extensively used measure is the average length of stay which, although easy to calculate and quantify, is not considered appropriate when the distribution is very long-tailed. In fact, simple deterministic models are generally considered inadequate because of the necessity for models to reflect the complex, variable, dynamic and multidimensional nature of the systems. This paper focuses on modelling length of stay and flow of patients. An overview of such modelling techniques is provided, with particular attention to their impact and suitability in managing a hospital service.

Transferring individuals for treatment outside their geographic area occurs when healthcare demand exceeds local supply. This can result in significant financial cost while impacting patient outcomes and experience. The aim of this study was to assess initiatives to reduce psychiatric intensive care unit (PICU) out-of-area bed placements within a major healthcare system in South West England. Discrete event computer simulation was used to model patient flow across the healthcare system's three PICUs. A scenario analysis was performed to estimate the impact of management plans to decrease admissions and length of stay. The amount of capacity required to minimise total cost was also considered. Without increasing in-area capacity, mean out-of-area bed requirement can be reduced by 25.6% and 19.1% respectively through plausible initiatives to decrease admissions and length of stay. Reductions of 34.7% are possible if both initiatives are employed. Adjusting the in-area bed capacity can also lead to aggregate cost savings. This study supports the likely effectiveness of particular initiatives in reducing out-of-area placements for high-acuity bedded psychiatric care. This study also demonstrates the value of computer simulation in an area that has seen little such attention to date. [ABSTRACT FROM AUTHOR]

Objectives: While there has been significant research on the pressures facing acute hospitals during the COVID-19 pandemic, there has been less interest in downstream community services which have also been challenged in meeting demand. This study aimed to estimate the theoretical cost-optimal capacity requirement for 'step down' intermediate care services within a major healthcare system in England, at a time when considerable uncertainty remained regarding vaccination uptake and the easing of societal restrictions. Methods: Demand for intermediate care was projected using an epidemiological model (for COVID-19 demand) and regressing upon public mobility (for non-COVID-19 demand). These were inputted to a computer simulation model of patient flow from acute discharge readiness to bedded and home-based Discharge to Assess (D2A) intermediate care services. Cost-optimal capacity was defined as that which yielded the lowest total cost of intermediate care provision and corresponding acute discharge delays. Results: Increased intermediate care capacity is likely to bring about lower system-level costs, with the additional D2A investment more than offset by substantial reductions in costly acute discharge delays (leading also to improved patient outcome and experience). Results suggest that completely eliminating acute 'bed blocking' is unlikely economical (requiring large amounts of downstream capacity), and that health systems should instead target an appropriate tolerance based upon the specific characteristics of the pathway. Conclusions: Computer modelling can be a valuable asset for determining optimal capacity allocation along the complex care pathway. With results supporting a Business Case for increased downstream capacity, this study demonstrates how modelling can be applied in practice and provides a blueprint for use alongside the freely-available model code. [ABSTRACT FROM AUTHOR]

Aim: To identify, simulate and evaluate the formal and informal patient‐level and unit‐level factors that nurse managers use to determine the number of nurses for each shift. Background: Nurse staffing schedules are commonly set based on metrics such as midnight census that do not account for seasonality or midday turnover, resulting in last‐minute adjustments or inappropriate staffing levels. Methods: Staffing schedules at a paediatric intensive care unit (PICU) were simulated based on nurse‐to‐patient assignment rules from interviews with nursing management. Multivariate regression modelled the discrepancies between scheduled and historical staffing levels and constructed rules to reduce these discrepancies. The primary outcome was the median difference between simulated and historical staffing levels. Results: Nurse‐to‐patient ratios underestimated staffing by a median of 1.5 nurses per shift. Multivariate regression identified patient turnover as the primary factor accounting for this difference and subgroup analysis revealed that patient age and weight were also important. New rules reduced the difference to a median of 0.07 nurses per shift. Conclusion: Measurable, predictable indicators of patient acuity and historical trends may allow for schedules that better match demand. Implications for Nursing Management: Data‐driven methods can quantify what drives unit demand and generate nurse schedules that require fewer last‐minute adjustments. [ABSTRACT FROM AUTHOR]

Background: During the COVID-19 pandemic, many intensive care units have been overwhelmed by unprecedented levels of demand. Notwithstanding ethical considerations, the prioritization of patients with better prognoses may support a more effective use of available capacity in maximizing aggregate outcomes. This has prompted various proposed triage criteria, although in none of these has an objective assessment been made in terms of impact on number of lives and life-years saved. Design: An open-source computer simulation model was constructed for approximating the intensive care admission and discharge dynamics under triage. The model was calibrated from observational data for 9505 patient admissions to UK intensive care units. To explore triage efficacy under various conditions, scenario analysis was performed using a range of demand trajectories corresponding to differing nonpharmaceutical interventions. Results: Triaging patients at the point of expressed demand had negligible effect on deaths but reduces life-years lost by up to 8.4% (95% confidence interval: 2.6% to 18.7%). Greater value may be possible through "reverse triage", that is, promptly discharging any patient not meeting the criteria if admission cannot otherwise be guaranteed for one who does. Under such policy, life-years lost can be reduced by 11.7% (2.8% to 25.8%), which represents 23.0% (5.4% to 50.1%) of what is operationally feasible with no limit on capacity and in the absence of improved clinical treatments. Conclusions: The effect of simple triage is limited by a tradeoff between reduced deaths within intensive care (due to improved outcomes) and increased deaths resulting from declined admission (due to lower throughput given the longer lengths of stay of survivors). Improvements can be found through reverse triage, at the expense of potentially complex ethical considerations. [ABSTRACT FROM AUTHOR]

Background Obesity care services are often faced with the need to adapt their resources to rising levels of demand. The main focus of this study was to help prioritise planned investments in new capacity allowing the service to improve patient experience and meet future anticipated demand. Methods We developed computer models of patient flows in an obesity service in an Academic Health Science Centre that provides lifestyle, pharmacotherapy and surgery treatment options for the UK's National Health Service. Using these models we experiment with different scenarios to investigate the likely impact of alternative resource configurations on patient waiting times. Results Simulation results show that the timing and combination of adding extra resources (eg, surgeons and physicians) to the service are important. For example, increasing the capacity of the pharmacotherapy clinics equivalent to adding one physician reduced the relevant waiting list size and waiting times, but it then led to increased waiting times for surgical patients. Better service levels were achieved when the service operates with the resource capacity of two physicians and three surgeons. The results obtained from this study had an impact on the planning and organisation of the obesity service. Conclusions Resource configuration combined with demand management (reduction in referral rates) along the care service can help improve patient waiting time targets for obesity services, such as the 18 week target of UK's National Health Service. The use of simulation models can help stakeholders understand the interconnectedness of the multiple microsystems (eg, clinics) comprising a complex clinical service for the same patient population, therefore, making stakeholders aware of the likely impact of resourcing decisions on the different microsystems. [ABSTRACT FROM AUTHOR]

• Considers demand and capacity management at a COVID-19 mass vaccination centre. • Computer simulation used to inform a safe and effective operating model. • Inputs obtained through data collected from a live exercise conducted in December 2020. • Outputs directly informed the initial configuration of the site under consideration. • Demonstrates the value of Operational Research within the mass vaccination setting. [ABSTRACT FROM AUTHOR]