ORCID Profile
0000-0003-2631-4481
Current Organisations
University of Southampton
,
University of Exeter
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Publisher: National Institute for Health and Care Research
Date: 05-10-2023
Publisher: Center for Open Science
Date: 14-07-2022
Abstract: This paper provides a framework for conceptualising levels of open science and open working within computer modelling and simulation. We aim to support researchers to share their models and working so that others are free to use, reproduce, adapt and build upon, and re-share their work. We introduce a six level framework of increasing complexity: not open, open access, open artefacts, open models, open environment and open infrastructure. For each we provide practical advice on what aspects of open science researchers must consider, what options are available to them, and what challenges they will need to overcome. We illustrate our open science framework using a stylised discrete-event simulation model. All code used in this paper is available, cloud executable and reusable under an MIT license.
Publisher: Center for Open Science
Date: 28-05-2021
Abstract: BackgroundWe 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. The study was conducted using standard methods known to the UK's NHS to aid implementation in practice.MethodsWe 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. ResultsA 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).ConclusionsWe 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.
Publisher: Cold Spring Harbor Laboratory
Date: 05-06-2023
DOI: 10.1101/2023.05.31.23290774
Abstract: To develop a simulation model to support orthopaedic elective capacity planning. An open-source, generalisable discrete-event simulation was developed, including a web-based application. The model used anonymised patient records between 2016-2019 of elective orthopaedic procedures from an 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, 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. 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. 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. The simulation model provides rapid quantitative estimates to support post-COVID elective services recovery toward medium-term elective targets. Parameter combinations include changes to both resourcing and productivity. The interactive web app enables intuitive parameter changes by users while underlying source code can be adapted or re-used for similar applications. Patient attributes such as complexity are not included in the model but are reflected in variables such as length-of-stay and delayed discharge rates. Theatre schedules are simplified, incorporating the five key orthopaedic elective surgical procedures.
Publisher: Center for Open Science
Date: 05-06-2023
Abstract: Objectives: Discrete-event simulation is a widely used computational method in health services and health economic studies. This systematic scoping review investigates to what extent authors share computer models, and audits if sharing adheres to best practice. Data sources: The Web of Science, Scopus, PubMed, and ACM Digital Library databases were searched between 1st January 2019 till 31st December 2022.Eligibility criteria for selecting studies: Cost-effectiveness, Health service research and methodology studies in a health context were included.Data extraction and synthesis: The data extraction and best practice audit were performed by two reviewers. We developed best practice audit criteria based on the Turing Way and other published reproducibility guides.Main outcomes and measures: We measured the proportion of literature that shared models we report analyses by publication type, year of publication, Covid-19 application and free and open source versus commercial software. Results: 47 (8.3\\%) of the 564 studies included cited a published DES computer model rising to 9.0\\% in 2022. Studies were more likely to share models if they had been developed using free and open source tools. Studies rarely followed best practice when sharing computer models.Conclusions: Although still in the minority, there is evidence that healthcare DES authors are increasingly sharing their computer model artifacts. Although commercial software dominates the DES literature, free and open source software plays a crucial role in sharing. The DES community can adopt many simple best practices to improve the quality of sharing.
Publisher: Center for Open Science
Date: 10-10-2023
Publisher: National Institute for Health and Care Research
Date: 03-2020
DOI: 10.3310/HSDR08160
Abstract: The Safer Nursing Care Tool is a system designed to guide decisions about nurse staffing requirements on hospital wards, in particular the number of nurses to employ (establishment). The Safer Nursing Care Tool is widely used in English hospitals but there is a lack of evidence about how effective and cost-effective nurse staffing tools are at providing the staffing levels needed for safe and quality patient care. To determine whether or not the Safer Nursing Care Tool corresponds to professional judgement, to assess a range of options for using the Safer Nursing Care Tool and to model the costs and consequences of various ward staffing policies based on Safer Nursing Care Tool acuity/dependency measure. This was an observational study on medical/surgical wards in four NHS hospital trusts using regression, computer simulations and economic modelling. We compared the effects and costs of a ‘high’ establishment (set to meet demand on 90% of days), the ‘standard’ (mean-based) establishment and a ‘flexible (low)’ establishment (80% of the mean) providing a core staff group that would be sufficient on days of low demand, with flexible staff re-deployed/hired to meet fluctuations in demand. Medical/surgical wards in four NHS hospital trusts. The main outcome measures were professional judgement of staffing adequacy and reports of omissions in care, shifts staffed more than 15% below the measured requirement, cost per patient-day and cost per life saved. The data sources were hospital administrative systems, staff reports and national reference costs. In total, 81 wards participated (85% response rate), with data linking Safer Nursing Care Tool ratings and staffing levels for 26,362 wards × days (96% response rate). According to Safer Nursing Care Tool measures, 26% of all ward-days were understaffed by ≥ 15%. Nurses reported that they had enough staff to provide quality care on 78% of shifts. When using the Safer Nursing Care Tool to set establishments, on average 60 days of observation would be needed for a 95% confidence interval spanning 1 whole-time equivalent either side of the mean. Staffing levels below the daily requirement estimated using the Safer Nursing Care Tool were associated with lower odds of nurses reporting ‘enough staff for quality’ and more reports of missed nursing care. However, the relationship was effectively linear, with staffing above the recommended level associated with further improvements. In simulation experiments, ‘flexible (low)’ establishments led to high rates of understaffing and adverse outcomes, even when temporary staff were readily available. Cost savings were small when high temporary staff availability was assumed. ‘High’ establishments were associated with substantial reductions in understaffing and improved outcomes but higher costs, although, under most assumptions, the cost per life saved was considerably less than £30,000. This was an observational study. Outcomes of staffing establishments are simulated. Understanding the effect on wards of variability of workload is important when planning staffing levels. The Safer Nursing Care Tool correlates with professional judgement but does not identify optimal staffing levels. Employing more permanent staff than recommended by the Safer Nursing Care Tool guidelines, meeting demand most days, could be cost-effective. Apparent cost savings from ‘flexible (low)’ establishments are achieved largely by below-adequate staffing. Cost savings are eroded under the conditions of high temporary staff availability that are required to make such policies function. Research is needed to identify cut-off points for required staffing. Prospective studies measuring patient outcomes and comparing the results of different systems are feasible. Current Controlled Trials ISRCTN12307968. This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research Vol. 8, No. 16. See the NIHR Journals Library website for further project information.
Publisher: Informa UK Limited
Date: 30-09-2023
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Thomas Monks.