Development and application of simulation modelling for orthopaedic elective resource planning in England.

OBJECTIVES: This study aimed to develop a simulation model to support orthopaedic elective capacity planning. METHODS: An open-source, generalisable discrete-event simulation was developed, including a web-based application. The model used anonymised patient records between 2016 and 2019 of elective orthopaedic procedures from a National Health Service (NHS) Trust in England. In this paper, it is used to investigate scenarios including resourcing (beds and theatres) and productivity (lengths of stay, delayed discharges and theatre activity) to support planning for meeting new NHS targets aimed at reducing elective orthopaedic surgical backlogs in a proposed ring-fenced orthopaedic surgical facility. The simulation is interactive and intended for use by health service planners and clinicians. RESULTS: A higher number of beds (65-70) than the proposed number (40 beds) will be required if lengths of stay and delayed discharge rates remain unchanged. Reducing lengths of stay in line with national benchmarks reduces bed utilisation to an estimated 60%, allowing for additional theatre activity such as weekend working. Further, reducing the proportion of patients with a delayed discharge by 75% reduces bed utilisation to below 40%, even with weekend working. A range of other scenarios can also be investigated directly by NHS planners using the interactive web app. CONCLUSIONS: The simulation model is intended to support capacity planning of orthopaedic elective services by identifying a balance of capacity across theatres and beds and predicting the impact of productivity measures on capacity requirements. It is applicable beyond the study site and can be adapted for other specialties.

Improving the usability of open health service delivery simulation models using Python and web apps.

One aim of Open Science is to increase the accessibility of research. Within health services research that uses discrete-event simulation, Free and Open Source Software (FOSS), such as Python, offers a way for research teams to share their models with other researchers and NHS decision makers. Although the code for healthcare discrete-event simulation models can be shared alongside publications, it may require specialist skills to use and run. This is a disincentive to researchers adopting Free and Open Source Software and open science practices. Building on work from other health data science disciplines, we propose that web apps offer a user-friendly interface for healthcare models that increase the accessibility of research to the NHS, and researchers from other disciplines. We focus on models coded in Python deployed as streamlit web apps. To increase uptake of these methods, we provide an approach to structuring discrete-event simulation model code in Python so that models are web app ready. The method is general across discrete-event simulation Python packages, and we include code for both simpy and ciw implementations of a simple urgent care call centre model. We then provide a step-by-step tutorial for linking the model to a streamlit web app interface, to enable other health data science researchers to reproduce and implement our method.

Simulation models provide a quantitative way for researchers to make predictions about complex health services, for example to assess the effects of changes to patient care pathways. The most common approach used for health services research is discrete-event simulation. Historically, research has used software that must be purchased and has restrictive licensing. This can make it difficult for other researchers, and NHS staff such as managers and clinicians, to use the model to help with their planning and resourcing decisions. One aim of Open Science is to increase the accessibility of research. Free and Open Source Software (FOSS) such as Python offers a way for research teams to share their models with other researchers and NHS decision makers. Although the code for simulation models can be shared alongside publications, it may require specialist skills to use and run. Building on work from other health disciplines, we propose that web apps offer a user-friendly interface for healthcare models that increase the accessibility of research. A web app runs in the browser of a computer and allows users to update model parameters, run different experiments, and explore the impact on the health service that is being studied. We focus on a package called streamlit. To increase uptake of these methods, we provide an approach to structuring model code in Python to enable the model to be easily integrated into streamlit. The method does not depend on a specific discrete-event simulation package. To illustrate this, we developed simulations using two Python packages called simpy and ciw of a simple urgent care call centre. We then provide a step-by-step tutorial for linking the model to the streamlit web app interface. This enables other health data science researchers to reproduce our method for their own simulation models and improve the accessibility and usability of their work.

Forecasting the daily demand for emergency medical ambulances in England and Wales: a benchmark model and external validation.

BACKGROUND: We aimed to select and externally validate a benchmark method for emergency ambulance services to use to forecast the daily number of calls that result in the dispatch of one or more ambulances. METHODS: The study was conducted using standard methods known to the UK's NHS to aid implementation in practice. We selected our benchmark model from a naive benchmark and 14 standard forecasting methods. Mean absolute scaled error and 80 and 95% prediction interval coverage over a 84 day horizon were evaluated using time series cross validation across eight time series from the South West of England. External validation was conducted by time series cross validation across 13 time series from London, Yorkshire and Welsh Ambulance Services. RESULTS: A model combining a simple average of Facebook's prophet and regression with ARIMA errors (1, 1, 3)(1, 0, 1, 7) was selected. Benchmark MASE, 80 and 95% prediction intervals were 0.68 (95% CI 0.67 - 0.69), 0.847 (95% CI 0.843 - 0.851), and 0.965 (95% CI 0.949 - 0.977), respectively. Performance in the validation set was within expected ranges for MASE, 0.73 (95% CI 0.72 - 0.74) 80% coverage (0.833; 95% CI 0.828-0.838), and 95% coverage (0.965; 95% CI 0.963-0.967). CONCLUSIONS: We provide a robust externally validated benchmark for future ambulance demand forecasting studies to improve on. Our benchmark forecasting model is high quality and usable by ambulance services. We provide a simple python framework to aid its implementation in practice. The results of this study were implemented in the South West of England.

The False Economy of Seeking to Eliminate Delayed Transfers of Care: Some Lessons from Queueing Theory.

BACKGROUND: It is a stated ambition of many healthcare systems to eliminate delayed transfers of care (DTOCs) between acute and step-down community services. OBJECTIVE: This study aims to demonstrate how, counter to intuition, pursual of such a policy is likely to be uneconomical, as it would require large amounts of community capacity to accommodate even the rarest of demand peaks, leaving much capacity unused for much of the time. METHODS: Some standard results from queueing theory-a mathematical discipline for considering the dynamics of queues and queueing systems-are used to provide a model of patient flow from the acute to community setting. While queueing models have a track record of application in healthcare, they have not before been used to address this question. RESULTS: Results show that 'eliminating' DTOCs is a false economy: the additional community costs required are greater than the possible acute cost saving. While a substantial proportion of DTOCs can be attributed to inefficient use of resources, the remainder can be considered economically essential to ensuring cost-efficient service operation. For England's National Health Service (NHS), our modelling estimates annual cost savings of £117m if DTOCs are reduced to the 12% of current levels that can be regarded as economically essential. CONCLUSION: This study discourages the use of 'zero DTOC' targets and instead supports an assessment based on the specific characteristics of the healthcare system considered.

Participatory design research for the development of real-time simulation models in healthcare.

The implementation challenges for modelling and simulation in health and social care are well-known and understood. Yet increasing availability of data and a better understanding of the value of Operational Research (OR) applications are strengthening opportunities to support healthcare delivery. Participative approaches in healthcare modelling have shown value through stakeholder engagement and commitment towards co-creation of models and knowledge but are limited in focus on model design and development. For simulation modelling, a participative design research methodology can support development for sustained use, emphasising model usefulness and usability using iterative cycles of development and evaluation. Within a structured methodology, measures of success are built into the design process, focusing on factors which contribute to success, with implicit goals of implementation and improvement. We illustrate this through a participative case study which demonstrates development of the component parts of a real-time simulation model aimed at reducing emergency department crowding.

Identification of risk factors associated with prolonged hospital stay following primary knee replacement surgery: a retrospective, longitudinal observational study.

OBJECTIVES: To identify risk factors associated with prolonged length of hospital stay and staying in hospital longer than medically necessary following primary knee replacement surgery. DESIGN: Retrospective, longitudinal observational study. SETTING: Elective knee replacement surgeries between 2016 and 2019 were identified using routinely collected data from an NHS Trust in England. PARTICIPANTS: There were 2295 knee replacement patients with complete data included in analysis. The mean age was 68 (SD 11) and 60% were female. OUTCOME MEASURES: We assessed a binary length of stay outcome (>7 days), a continuous length of stay outcome (≤30 days) and a binary measure of whether patients remained in hospital when they were medically fit for discharge. RESULTS: The mean length of stay was 5.0 days (SD 3.9), 15.4% of patients were in hospital for >7 days and 7.1% remained in hospital when they were medically fit for discharge. Longer length of stay was associated with older age (b=0.08, 95% CI 0.07 to 0.09), female sex (b=0.36, 95% CI 0.06 to 0.67), high deprivation (b=0.98, 95% CI 0.47 to 1.48) and more comorbidities (b=2.48, 95% CI 0.15 to 4.81). Remaining in hospital beyond being medically fit for discharge was associated with older age (OR=1.07, 95% CI 1.05 to 1.09), female sex (OR=1.71, 95% CI 1.19 to 2.47) and high deprivation (OR=2.27, 95% CI 1.27 to 4.06). CONCLUSIONS: The regression models could be used to identify which patients are likely to occupy hospital beds for longer. This could be helpful in scheduling operations to aid hospital efficiency by planning these patients' operations for when the hospital is less busy.

Optimising the balance of acute and intermediate care capacity for the complex discharge pathway: Computer modelling study during COVID-19 recovery in England.

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.

A systematic review of sleep deprivation and neurobehavioral function in young adults.

AIM: To examine the effect of sleep deprivation (total and partial) on neurobehavioral function compared to a healthy sleep opportunity (7-9 h) in young adults 18-30 years. BACKGROUND: More than one-third of young adults are sleep deprived, which negatively affects a range of neurobehavioral functions, including psychomotor vigilance performance (cognitive), affect, and daytime sleepiness. METHODS: A systematic review of randomized controlled trials (RCTs) on sleep deprivation and neurobehavioral function. Multiple electronic databases (Cochrane Central Registry of Controlled Trials [CENTRAL], PubMed, PsycINFO, CINAHL, and Web of Science) were searched for relevant RCTs published in English from the establishment of each database to December 31, 2020. RESULTS: Nineteen RCTs were selected (N = 766, mean age = 23.7 ± 3.1 years; 44.8% female). Seven were between-person (5 were parallel-group designs and 2 had multiple arms), and 12 were within-person designs (9 were cross over and 3 used a Latin square approach). Total sleep deprivation had the strongest detrimental effect on psychomotor vigilance performance, with the largest effects on vigilance tasks in young adults in the included studies. CONCLUSION: Acute sleep deprivation degrades multiple dimensions of neurobehavioral function including psychomotor vigilance performance, affect, and daytime sleepiness in young adults. The effect of chronic sleep deprivation on the developing brain and associated neurobehavioral functions in young adults remains unclear.

"Feeling Anxious about Catching COVID": Facilitators and Barriers of Sleep Health among Young Adults with Type 1 Diabetes.

BACKGROUND: The COVID-19 pandemic has imposed pervasive stress and significant disruptions in sleep health in young adults. The purpose of this study was to describe the perceived facilitators and barriers of sleep health among young adults with type 1 diabetes during the COVID-19 pandemic. PARTICIPANTS: Thirty-two young adults with T1D (87.5% female; mean age = 21.5, SD = 2.0) participated in an online survey between January and July 2021. Young adults between the ages of 18-25 years with T1D for at least 6 months were eligible to participate, while those who had a previous OSA diagnosis, were currently pregnant, or worked the night shift were not eligible to participate. METHODS: A qualitative descriptive approach was used to explore the perceived facilitators and barriers to sleep among a convenience sample. Qualitative content was analyzed and coded for themes using qualitative content analysis. Responses were coded using an in vivo approach. RESULTS: Young adults with T1D identified changes in facilitators and barriers of sufficient sleep from before the COVID-19 pandemic to during the pandemic. Three overarching barriers and facilitators were identified: (1) general, (2) diabetes-specific, and (3) COVID-19 specific. CONCLUSIONS: Our findings can inform future educational and cognitive-behavioral interventions to promote sleep health in young adults with T1D and other complex chronic conditions. When designing sleep-promoting interventions for young adults with T1D in the COVID-19 pandemic and post-pandemic, researchers should consider T1D as well as COVID-specific barriers and facilitators.

Hybrid models as transdisciplinary research enablers.

Modelling and simulation (M&S) techniques are frequently used in Operations Research (OR) to aid decision-making. With growing complexity of systems to be modelled, an increasing number of studies now apply multiple M&S techniques or hybrid simulation (HS) to represent the underlying system of interest. A parallel but related theme of research is extending the HS approach to include the development of hybrid models (HM). HM extends the M&S discipline by combining theories, methods and tools from across disciplines and applying multidisciplinary, interdisciplinary and transdisciplinary solutions to practice. In the broader OR literature, there are numerous examples of cross-disciplinary approaches in model development. However, within M&S, there is limited evidence of the application of conjoined methods for building HM. Where a stream of such research does exist, the integration of approaches is mostly at a technical level. In this paper, we argue that HM requires cross-disciplinary research engagement and a conceptual framework. The framework will enable the synthesis of discipline-specific methods and techniques, further cross-disciplinary research within the M&S community, and will serve as a transcending framework for the transdisciplinary alignment of M&S research with domain knowledge, hypotheses and theories from diverse disciplines. The framework will support the development of new composable HM methods, tools and applications. Although our framework is built around M&S literature, it is generally applicable to other disciplines, especially those with a computational element. The objective is to motivate a transdisciplinarity-enabling framework that supports the collaboration of research efforts from multiple disciplines, allowing them to grow into transdisciplinary research.

Food cravings during acute hypoglycaemia in adults with Type 1 diabetes.

BACKGROUND: Food craving is defined as an intense desire to eat a specific foodstuff. It may be distinguished from the sensation of hunger, which is relieved by nonspecific foodstuffs. No controlled studies have examined the effects of hypoglycaemia on food cravings. Thus, the aim of this study was to examine change in cravings for eight specified food types during euglycaemia and insulin-induced hypoglycaemia. METHODS: Thirteen adults with Type 1 diabetes attended two experimental sessions where acute hypoglycaemia was induced with either insulin aspart or human soluble insulin. Food cravings were assessed, using a questionnaire, at baseline and at the onset of the autonomic reaction to hypoglycaemia ("R"). RESULTS: The mean arterialised blood glucose concentration at baseline was 6.0+/-0.3 mmol l(-1) and at R was 1.9+/-0.4 mmol l(-1) (P<.01). Fifteen percent of subjects reported having a craving for food during euglycaemia. The prevalence increased to 65% during hypoglycaemia (P<.01). Cravings for seven of the eight food types increased during hypoglycaemia, but the greatest effect sizes (>1.0 standard deviations) were observed for three food types that had a high carbohydrate content. CONCLUSIONS: In people with Type 1 diabetes, acute hypoglycaemia produces a highly reliable generalised increase in cravings for food, particularly foodstuffs with a high content of carbohydrate. The mechanisms behind this response remain to be elicited.