AbstractBackground & MotivationStroke outcomes following revascularization therapy (intravenous thrombolysis, IVT, and/or mechanical thrombectomy, MT) depend critically on time from stroke onset to treatment. Different service configurations may prioritise time to IVT or time to MT. In order to evaluate alternative acute stroke care configurations, it is necessary to estimate clinical outcomes given differing times to IVT and MT.MethodModel using an algorithm coded in Python. This is available at https://github.com/MichaelAllen1966/stroke_outcome_algorithmResultsWe demonstrate how the code may be used to estimate clinical outcomes given varying times to IVT and MT.ConclusionPython code has been developed and shared to enable estimation of clinical outcome given times to IVT and MT. Here we share pseudocode and links to full Python code.

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.

Objectives: to guide policy when planning reperfusion thrombolysis (IVT) and thrombectomy (MT) services for acute stroke in England, focussing on the choice between ‘mothership’ (direct conveyance to an MT centre) and ‘drip-and-ship' (secondary transfer for MT after local IVT) provision and the impact of bypassing local acute stroke centres.Methods: Computer modelling was used to estimate the likely outcomes from reperfusion therapies, along with admission numbers to units, based on expected times to IVT and MT.Results: Without pre-hospital selection for LAO, 94% of the population of England live in areas where the greatest clinical benefit accrues from direct conveyance to an IVT/MT centre. If this model was followed then net benefit from reperfusion is predicted to be increased from 31 to 34 additional disability-free outcomes / 1,000 admissions. However, this policy produces unsustainable admission numbers at these centres, and depletes all but 19 IVT-only units of all stroke admissions. Implementing a maximum permitted additional travel time to bypass an IVT-only unit, or using a pre-hospital test for LAO, both increase net benefit over the current drip-and-ship model, but produce a similar destabilising effect on acute systems of care. Use of IVT-only units manage admission numbers to IVT/MT centres.Conclusions: the mothership model reduces time to MT at the cost of increased time to IVT, but the benefit of faster MT is predicted to lead to a modest improvement in overall outcomes. Providing a sustainable national system of acute stroke care requires a hybrid of mothership and drip-and-ship provision.

ABSTRACTBackgroundThis study presents two simulation modelling tools to support the organisation of networks of dialysis services during the COVID-19 pandemic. These tools were developed to support renal services in the South of England (the Wessex region caring for 650 patients), but are applicable elsewhere.MethodsA discrete-event simulation was used to model a worst case spread of COVID-19 (80% infected over three months), to stress-test plans for dialysis provision throughout the COVID-19 outbreak. We investigated the ability of the system to manage the mix of COVID-19 positive and negative patients, and examined the likely effects on patients, outpatient workloads across all units, and inpatient workload at the centralised COVID-positive inpatient unit. A second Monte-Carlo vehicle routing model estimated the feasibility of patient transport plans and relaxing the current policy of single COVID-19 patient transport to allow up to four infected patients at a time.ResultsIf current outpatient capacity is maintained there is sufficient capacity in the South of England to keep COVID-19 negative/recovered and positive patients in separate sessions, but rapid reallocation of patients may be needed (as sessions are cleared of negative/recovered patients to enable that session to be dedicated to positive patients). Outpatient COVID-19 cases will spillover to a secondary site while other sites will experience a reduction in workload. The primary site chosen to manage infected patients will experience a significant increase in outpatients and in-patients. At the peak of infection, it is predicted there will be up to 140 COVID-19 positive patients with 40 to 90 of these as inpatients, likely breaching current inpatient capacity (and possibly leading to a need for temporary movement of dialysis equipment).Patient transport services will also come under considerable pressure. If patient transport operates on a policy of one positive patient at a time, and two-way transport is needed, a likely scenario estimates 80 ambulance drive time hours per day (not including fixed drop-off and ambulance cleaning times). Relaxing policies on individual patient transport to 2-4 patients per trip can save 40-60% of drive time. In mixed urban/rural geographies steps may need to be taken to temporarily accommodate renal COVID-19 positive patients closer to treatment facilities.ConclusionsDiscrete-event simulation simulation and Monte-Carlo vehicle routing model provides a useful method for stress-testing inpatient and outpatient clinical systems prior to peak COVID-19 workloads.

ABSTRACTBackgroundOpen science is a movement seeking to make scientific research accessible to all, including publication of code and data. Publishing patient-level data may, however, compromise the confidentiality of that data if there is any significant risk that data may later be associated with individuals. Use of synthetic data offers the potential to be able to release data that may be used to evaluate methods or perform preliminary research without risk to patient confidentiality.MethodsWe have tested five synthetic data methods:A technique based on Principal Component Analysis (PCA) which samples data from distributions derived from the transformed data.Synthetic Minority Oversampling Technique, SMOTE which is based on interpolation between near neighbours.Generative Adversarial Network, GAN, an artificial neural network approach with competing networks - a discriminator network trained to distinguish between synthetic and real data. and a generator network trained to produce data that can fool the discriminator network.CT-GAN, a refinement of GANs specifically for the production of structured tabular synthetic data.Variational Auto Encoders, VAE, a method of encoding data in a reduced number of dimensions, and sampling from distributions based on the encoded dimensions.Two data sets are used to evaluate the methods:The Wisconsin Breast Cancer data set, a histology data set where all features are continuous variables.A stroke thrombolysis pathway data set, a data set describing characteristics for patients where a decision is made whether to treat with clot-busting medication. Features are mostly categorical, binary, or integers.Methods are evaluated in three ways:The ability of synthetic data to train a logistic regression classification model.A comparison of means and standard deviations between original and synthetic data.A comparison of covariance between features in the original and synthetic data.ResultsUsing the Wisconsin Breast Cancer data set, the original data gave 98% accuracy in a logistic regression classification model. Synthetic data sets gave between 93% and 99% accuracy. Performance (best to worst) was SMOTE > PCA > GAN > CT-GAN = VAE. All methods produced a high accuracy in reproducing original data means and stabdard deviations (all R-square > 0.96 for all methods and data classes). CT-GAN and VAE suffered a significant loss of covariance between features in the synthetic data sets.Using the Stroke Pathway data set, the original data gave 82% accuracy in a logistic regression classification model. Synthetic data sets gave between 66% and 82% accuracy. Performance (best to worst) was SMOTE > PCA > CT-GAN > GAN > VAE. CT-GAN and VAE suffered loss of covariance between features in the synthetic data sets, though less pronounced than with the Wisconsin Breast Cancer data set.ConclusionsThe pilot work described here shows, as proof of concept, that synthetic data may be produced, which is of sufficient quality to publish with open methodology, to allow people to better understand and test methodology. The quality of the synthetic data also gives promise of data sets that may be used for screening of ideas, or for research project (perhaps especially in an education setting).More work is required to further refine and test methods across a broader range of patient-level data sets.

Abstract
. 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.
.

OBJECTIVES: to systematically review the accuracy of artificial intelligence (AI)-based systems for grading of fundus images in diabetic retinopathy (DR) screening. METHODS: We searched MEDLINE, EMBASE, the Cochrane Library and the ClinicalTrials.gov from 1st January 2000 to 27th August 2021. Accuracy studies published in English were included if they met the pre-specified inclusion criteria. Selection of studies for inclusion, data extraction and quality assessment were conducted by one author with a second reviewer independently screening and checking 20% of titles. Results were analysed narratively. RESULTS: Forty-three studies evaluating 15 deep learning (DL) and 4 machine learning (ML) systems were included. Nine systems were evaluated in a single study each. Most studies were judged to be at high or unclear risk of bias in at least one QUADAS-2 domain. Sensitivity for referable DR and higher grades was ≥85% while specificity varied and was

Introduction: the aim of this work was to understand between-hospital variation in thrombolysis use among emergency stroke admissions in England and Wales. Patients: a total of 88,928 patients who arrived at all 132 emergency stroke hospitals in England Wales within 4 h of stroke onset, from 2016 to 2018. Methods: Machine learning was applied to the Sentinel Stroke National Audit Programme (SSNAP) data set, to learn which patients in each hospital would likely receive thrombolysis. We used XGBoost machine learning models, coupled with a SHAP model for explainability; Shapley (SHAP) values, providing estimates of how patient features, and hospital identity, influence the odds of receiving thrombolysis. Results: Thrombolysis use in patients arriving within 4 h of known or estimated stroke onset ranged 7% -49% between hospitals. The odds of receiving thrombolysis reduced 9-fold over the first 120 min of arrival-to-scan time, varied 30-fold with stroke severity, reduced 3-fold with estimated rather than precise stroke onset time, fell 6-fold with increasing pre-stroke disability, fell 4-fold with onset during sleep, fell 5-fold with use of anticoagulants, fell 2-fold between 80 and 110 years of age, reduced 3-fold between 120 and 240 min of onset-to-arrival time and varied 13-fold between hospitals. The majority of between-hospital variance was explained by the hospital, rather than the differences in local patient populations. Conclusions: Using explainable machine learning, we identified that the majority of the between-hospital variation in thrombolysis use in England and Wales may be explained by differences in in-hospital processes and differences in attitudes to judging suitability for thrombolysis.

Objectives: to guide policy when planning thrombolysis (IVT) and thrombectomy (MT) services for acute stroke in England, focussing on the choice between ‘mothership’ (direct conveyance to an MT centre) and ‘drip-and-ship’ (secondary transfer) provision and the impact of bypassing local acute stroke centres. Design: Outcome-based modelling study. Setting: 107 acute stroke centres in England, 24 of which provide IVT and MT (IVT/MT centres) and 83 provide only IVT (IVT-only units). Participants: 242,874 emergency admissions with acute stroke over 3 years (2015–2017). Intervention: Reperfusion delivered by drip-and-ship, mothership or ‘hybrid’ models; impact of additional travel time to directly access an IVT/MT centre by bypassing a more local IVT-only unit; effect of pre-hospital selection for large artery occlusion (LAO). Main outcome measures: Population benefit from reperfusion, time to IVT and MT, admission numbers to IVT-only units and IVT/MT centres. Results: Without pre-hospital selection for LAO, 94% of the population of England live in areas where the greatest clinical benefit, assuming unknown patient status, accrues from direct conveyance to an IVT/MT centre. However, this policy produces unsustainable admission numbers at these centres, with 78 out of 83 IVT-only units receiving fewer than 300 admissions per year (compared to 3 with drip-and-ship). Implementing a maximum permitted additional travel time to bypass an IVT-only unit, using a pre-hospital test for LAO, and selecting patients based on stroke onset time, all help to mitigate the destabilising effect but there is still some significant disruption to admission numbers, and improved selection of patients suitable for MT selectively reduces the number of patients who would receive IVT at IVT-only centres, challenging the sustainability of IVT expertise in IVT-only centres. Conclusions: Implementation of reperfusion for acute stroke based solely on achieving the maximum population benefit potentially leads to destabilisation of the emergency stroke care system. Careful planning is required to create a sustainable system, and modelling may be used to help planners maximise benefit from reperfusion while creating a sustainable emergency stroke care system.

BACKGROUND: Expert opinion is that about 20% of emergency stroke patients should receive thrombolysis. Currently, 11% to 12% of patients in England and Wales receive thrombolysis, ranging from 2% to 24% between hospitals. The aim of this study was to assess how much variation is due to differences in local patient populations, and how much is due to differences in clinical decision-making and stroke pathway performance, while estimating a realistic target thrombolysis use. METHODS: Anonymised data for 246 676 emergency stroke admissions to 132 acute hospitals in England and Wales between 2016 and 2018 was obtained from the Sentinel Stroke National Audit Programme data. We used machine learning to learn decisions on who to give thrombolysis to at each hospital. We used clinical pathway simulation to model effects of changing pathway performance. Qualitative research was used to assess clinician attitudes to these methods. Three changes were modeled: (1) arrival-to-treatment in 30 minutes, (2) proportion of patients with determined stroke onset times set to at least the national upper quartile, (3) thrombolysis decisions made based on majority vote of a benchmark set of hospitals. RESULTS: of the modeled changes, any single change was predicted to increase national thrombolysis use from 11.6% to between 12.3% to 14.5% (clinical decision-making having the most effect). Combined, these changes would be expected to increase thrombolysis to 18.3%, but there would still be significant variation between hospitals depending on local patient population. Clinicians engaged well with the modeling, but those from hospitals with lower thrombolysis use were most cautious about the methods. CONCLUSIONS: Machine learning and clinical pathway simulation may be applied at scale to national stroke audit data, allowing extended use and analysis of audit data. Stroke thrombolysis rates of at least 18% look achievable in England and Wales, but each hospital should have its own target.

BackgroundStroke is a common cause of adult disability. Expert opinion is that about 20% of patients should receive thrombolysis to break up a clot causing the stroke. Currently, 11–12% of patients in England and Wales receive this treatment, ranging between 2% and 24% between hospitals.ObjectivesWe sought to enhance the national stroke audit by providing further analysis of the key sources of inter-hospital variation to determine how a target of 20% of stroke patients receiving thrombolysis may be reached.DesignWe modelled three aspects of the thrombolysis pathway, using machine learning and clinical pathway simulation. In addition, the project had a qualitative research arm, with the objective of understanding clinicians’ attitudes to use of modelling and machine learning applied to the national stroke audit.Participants and data sourceAnonymised data were collected for 246,676 emergency stroke admissions to acute stroke teams in England and Wales between 2016 and 2018, obtained from the Sentinel Stroke National Audit Programme.ResultsUse of thrombolysis could be predicted with 85% accuracy for those patients with a chance of receiving thrombolysis (i.e. those arriving within 4 hours of stroke onset). Machine learning models allowed prediction of likely treatment choice for each patient at all hospitals. A clinical pathway simulation predicted hospital thrombolysis use with an average absolute error of 0.5 percentage points. We found that about half of the inter-hospital variation in thrombolysis use came from differences in local patient populations, and half from in-hospital processes and decision-making. Three changes were applied to all hospitals in the model: (1) arrival to treatment in 30 minutes, (2) proportion of patients with determined stroke onset times set to at least the national upper quartile and (3) thrombolysis decisions made based on majority vote of a benchmark set of 30 hospitals. Any single change alone was predicted to increase national thrombolysis use from 11.6% to between 12.3% and 14.5% (with clinical decision-making having the most effect). Combined, these changes would be expected to increase thrombolysis to 18.3% (and to double the clinical benefit of thrombolysis, as speed increases also improve clinical benefit independently of the proportion of patients receiving thrombolysis); however, there would still be significant variation between hospitals depending on local patient population. For each hospital, the effect of each change could be predicted alone or in combination. Qualitative research with 19 clinicians showed that engagement with, and trust in, the model was greatest in physicians from units with higher thrombolysis rates. Physicians also wanted to see a machine learning model predicting outcome with probability of adverse effect of thrombolysis to counter a fear that driving thrombolysis use up may cause more harm than good.LimitationsModels may be built using data available in the Sentinel Stroke National Audit Programme only. Not all factors affecting use of thrombolysis are contained in Sentinel Stroke National Audit Programme data and the model, therefore, provides information on patterns of thrombolysis use in hospitals, but is not suitable for, or intended as, a decision aid to thrombolysis.ConclusionsMachine learning and clinical pathway simulation may be applied at scale to national audit data, allowing extended use and analysis of audit data. Stroke thrombolysis rates of at least 18% look achievable in England and Wales, but each hospital should have its own target.Future workFuture studies should extend machine learning modelling to predict the patient-level outcome and probability of adverse effects of thrombolysis, and apply co-production techniques, with clinicians and other stakeholders, to communicate model outputs.FundingThis project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme and will be published in full inHealth and Social Care Delivery Research; Vol. 10, No. 31. See the NIHR Journals Library website for further project information.

Background and motivation: Deep Reinforcement Learning (Deep RL) is a rapidly
developing field. Historically most application has been made to games (such as
chess, Atari games, and go). Deep RL is now reaching the stage where it may
offer value in real world problems, including optimisation of healthcare
systems. One such problem is where to locate ambulances between calls in order
to minimise time from emergency call to ambulance on-scene. This is known as
the Ambulance Location problem.
. Aim: to develop an OpenAI Gym-compatible framework and simulation environment
for testing Deep RL agents.
. Methods: a custom ambulance dispatch simulation environment was developed
using OpenAI Gym and SimPy. Deep RL agents were built using PyTorch. The
environment is a simplification of the real world, but allows control over the
number of clusters of incident locations, number of possible dispatch
locations, number of hospitals, and creating incidents that occur at different
locations throughout each day.
. Results: a range of Deep RL agents based on Deep Q networks were tested in
this custom environment. All reduced time to respond to emergency calls
compared with random allocation to dispatch points. Bagging Noisy Duelling Deep
Q networks gave the most consistence performance. All methods had a tendency to
lose performance if trained for too long, and so agents were saved at their
optimal performance (and tested on independent simulation runs).
. Conclusions: Deep RL agents, developed using simulated environments, have the
potential to offer a novel approach to optimise the Ambulance Location problem.
Creating open simulation environments should allow more rapid progress in this
field.

ObjectiveThe Royal College of Obstetricians and Gynaecologists has advised that consolidation of birth centres, where reasonable, into birth centres of at least 6000 admissions per year should allow constant consultant presence. Currently, only 17% of mothers attend such birth centres. The objective of this work was to examine the feasibility of consolidation of birth centres, from the perspectives of birth centre size and travel times for mothers.DesignComputer-based optimisation.SettingHospital-based births.Population or sample1.91 million admissions in 2014–2016.MethodsA multiple-objective genetic algorithm.Main outcome measuresTravel time for mothers and size of birth centres.ResultsCurrently, with 161 birth centres, 17% of women attend a birth centre with at least 6000 admissions per year. We estimate that 95% of women have a travel time of 30 min or less. An example scenario, with 100 birth centres, could provide 75% of care in birth centres with at least 6000 admissions per year, with 95% of women travelling 35 min or less to their closest birth centre. Planning at local level leads to reduced ability to meet admission and travel time targets.ConclusionsWhile it seems unrealistic to have all births in birth centres with at least 6000 admissions per year, it appears realistic to increase the percentage of mothers attending this type of birth centre from 17% to about 75% while maintaining reasonable travel times. Planning at a local level leads to suboptimal solutions.

Background and motivation: Combining Deep Reinforcement Learning (Deep RL)
and Health Systems Simulations has significant potential, for both research
into improving Deep RL performance and safety, and in operational practice.
While individual toolkits exist for Deep RL and Health Systems Simulations, no
framework to integrate the two has been established.
. Aim: Provide a framework for integrating Deep RL Networks with Health System
Simulations, and to ensure this framework is compatible with Deep RL agents
that have been developed and tested using OpenAI Gym.
. Methods: We developed our framework based on the OpenAI Gym framework, and
demonstrate its use on a simple hospital bed capacity model. We built the Deep
RL agents using PyTorch, and the Hospital Simulatation using SimPy.
. Results: We demonstrate example models using a Double Deep Q Network or a
Duelling Double Deep Q Network as the Deep RL agent.
. Conclusion: SimPy may be used to create Health System Simulations that are
compatible with agents developed and tested on OpenAI Gym environments.
. GitHub repository of code:
https://github.com/MichaelAllen1966/learninghospital

OBJECTIVE: to evaluate the application of clinical pathway simulation in machine learning, using clinical audit data, in order to identify key drivers for improving use and speed of thrombolysis at individual hospitals. DESIGN: Computer simulation modelling and machine learning. SETTING: Seven acute stroke units. PARTICIPANTS: Anonymised clinical audit data for 7864 patients. RESULTS: Three factors were pivotal in governing thrombolysis use: (1) the proportion of patients with a known stroke onset time (range 44%-73%), (2) pathway speed (for patients arriving within 4 hours of onset: per-hospital median arrival-to-scan ranged from 11 to 56 min; median scan-to-thrombolysis ranged from 21 to 44 min) and (3) predisposition to use thrombolysis (thrombolysis use ranged from 31% to 52% for patients with stroke scanned with 30 min left to administer thrombolysis). A pathway simulation model could predict the potential benefit of improving individual stages of the clinical pathway speed, whereas a machine learning model could predict the benefit of 'exporting' clinical decision making from one hospital to another, while allowing for differences in patient population between hospitals. By applying pathway simulation and machine learning together, we found a realistic ceiling of 15%-25% use of thrombolysis across different hospitals and, in the seven hospitals studied, a realistic opportunity to double the number of patients with no significant disability that may be attributed to thrombolysis. CONCLUSIONS: National clinical audit may be enhanced by a combination of pathway simulation and machine learning, which best allows for an understanding of key levers for improvement in hyperacute stroke pathways, allowing for differences between local patient populations. These models, based on standard clinical audit data, may be applied at scale while providing results at individual hospital level. The models facilitate understanding of variation and levers for improvement in stroke pathways, and help set realistic targets tailored to local populations.

BACKGROUND: We have previously modelled that the optimal number of comprehensive stroke centres (CSC) providing endovascular thrombectomy (EVT) in England would be 30 (net 6 new centres). We now estimate the relative effectiveness and cost-effectiveness of increasing the number of centres from 24 to 30. METHODS: We constructed a discrete event simulation (DES) to estimate the effectiveness and lifetime cost-effectiveness (from a payer perspective) using 1 year's incidence of stroke in England. 2000 iterations of the simulation were performed comparing baseline 24 centres to 30. RESULTS: of 80,800 patients admitted to hospital with acute stroke/year, 21,740 would be affected by the service reconfiguration. The median time to treatment for eligible early presenters (

Purpose: Both intravenous thrombolysis (IVT) and intra-arterial endovascular thrombectomy (ET) improve the outcome of patients with acute ischaemic stroke, with endovascular thrombectomy being an option for those patients with large vessel occlusions. We sought to understand how organisation of services affects time to treatment for both intravenous thrombolysis and endovascular thrombectomy. Method: a multi-objective optimisation approach was used to explore the relationship between the number of intravenous thrombolysis and endovascular thrombectomy centres and times to treatment. The analysis is based on 238,887 emergency stroke admissions in England over 3 years (2013–2015). Results: Providing hyper-acute care only in comprehensive stroke centres (CSC, providing both intravenous thrombolysis and endovascular thrombectomy, and performing >150 endovascular thrombectomy per year, maximum 40 centres) in England would lead to 15% of patients being more than 45 min away from care, and would create centres with up to 4300 stroke admissions/year. Mixing hyper-acute stroke units (providing intravenous thrombolysis only) with comprehensive stroke centres speeds time to intravenous thrombolysis and mitigates admission numbers to comprehensive stroke centres, but at the expense of increasing time to endovascular thrombectomy. With 24 comprehensive stroke centres and all remaining current acute stroke units as hyper-acute stroke units, redirecting patients directly to attend a comprehensive stroke centre by accepting a small delay (15-min maximum) in intravenous thrombolysis reduces time to endovascular thrombectomy: 25% of all patients would be redirected from hyper-acute stroke units to a comprehensive stroke centre, with an average delay in intravenous thrombolysis of 8 min, and an average improvement in time to endovascular thrombectomy of 80 min. The balance of comprehensive stroke centre:hyper-acute stroke unit admissions would change from 24:76 to 49:51. Conclusion: Planning of hyper-acute stroke services is best achieved when considering all forms of acute care and ambulance protocol together. Times to treatment need to be considered alongside manageable and sustainable admission numbers.

Background
There is an inherent tension in neonatal services between the efficiency and specialised care that comes with centralisation and the provision of local services with associated ease of access and community benefits. This study builds on previous work in South West England to address these issues at a national scale.

Objectives
(1) to develop an analytical framework to address key issues of neonatal service configuration in England, (2) to investigate visualisation tools to facilitate the communication of findings to stakeholder groups and (3) to assess parental preferences in relation to service configuration alternatives.

Main outcome measures
The ability to meet nurse staffing guidelines, volumes of units, costs, mortality, number and distance of transfers, travel distances and travel times for parents.

Design
Descriptive statistics, location analysis, mathematical modelling, discrete event simulation and economic analysis were used. Qualitative methods were used to interview policy-makers and parents. A parent advisory group supported the study.

Setting
NHS neonatal services across England.

Data
Neonatal care data were sourced from the National Neonatal Research Database. Information on neonatal units was drawn from the National Neonatal Audit Programme. Geographic and demographic data were sourced from the Office for National Statistics. Travel time data were retrieved via a geographic information system. Birth data were sourced from Hospital Episode Statistics. Parental cost data were collected via a survey.

Results
Location analysis shows that to achieve 100% of births in units with ≥ 6000 births per year, the number of birth centres would need to be reduced from 161 to approximately 72, with more parents travelling > 30 minutes. The maximum number of neonatal intensive care units (NICUs) needed to achieve 100% of very low-birthweight infants attending high-volume units is 36 with existing NICUs, or 48 if NICUs are located wherever there is currently a neonatal unit of any level. Simulation modelling further demonstrated the workforce implications of different configurations. Mortality modelling shows that the birth of very preterm infants in high-volume hospitals reduces mortality (a conservative estimate of a 1.2-percentage-point lower risk) relative to these births in other hospitals. It is currently not possible to estimate the impact of mortality for infants transferred into NICUs. Cost modelling shows that the mean length of stay following a birth in a high-volume hospital is 9 days longer and the mean cost is £5715 more than for a birth in another neonatal unit. In addition, the incremental cost per neonatal life saved is £460,887, which is comparable to other similar life-saving interventions. The analysis of parent costs identified unpaid leave entitlement, food, travel, accommodation, baby care and parking as key factors. The qualitative study suggested that central concerns were the health of the baby and mother, communication by medical teams and support for families.

Limitations
The following factors could not be modelled because of a paucity of data – morbidity outcomes, the impact of transfers and the maternity/neonatal service interface.

Conclusions
An evidence-based framework was developed to inform the configuration of neonatal services and model system performance from the perspectives of both service providers and parents.

Future work
To extend the modelling to encompass the interface between maternity and neonatal services.

Stroke care has been identified as an area where operations research has great potential. In recent years there has been a small but sustained stream of discrete-event simulation case studies in modelling hyperacute stroke systems. The nature of such case studies has led to a fragmented knowledge base and high entry cost to stroke modelling research. Two common issues have faced researchers in stroke care: understanding the logistics and clinical aspects of stroke care and moving from these findings to an appropriately detailed model. We aim to accelerate studies in this area by introducing a conceptual modelling framework that is domain specific for stroke. A domain specific framework trades-off the wide applicability of a general framework against increased efficiency and reuse to support modelling in the problem domain. This compromise is appropriate when the problem domain is complex, of high value to society, and where the saving in future modelling effort is likely to be greater than the effort to create the framework. We detail the requirements of a domain specific conceptual model and then provide domain specific knowledge to support modellers in gaining an understanding of the problem situation, translating this knowledge into selected model outputs, inputs and content in the case of hyperacute stroke. We illustrate the use of the framework with an example based at a large hospital in the United Kingdom.

OBJECTIVES: the policy of centralising hyperacute stroke units (HASUs) in England aims to provide stroke care in units that are both large enough to sustain expertise (>600 admissions/year) and dispersed enough to rapidly deliver time-critical treatments (

Background: Increasing pressure in the United Kingdom (UK) urgent care system has led to Emergency Departments (EDs) failing to meet the national requirement that 95% of patients are admitted, discharged or transferred within 4-h of arrival. Despite the target being the same for all acute hospitals, individual Trusts organise their services in different ways. The impact of this variation on patient journey time and waiting is unknown. Our study aimed to apply the Lean technique of Value Stream Mapping (VSM) to investigate care processes and delays in patient journeys at four contrasting hospitals. Methods: VSM timing data were collected for patients accessing acute care at four hospitals in South West England. Data were categorised according to waits and activities, which were compared across sites to identify variations in practice from the patient viewpoint. We included Public and Patient Involvement (PPI) to fully interpret our findings; observations and initial findings were considered in a PPI workshop. Results: One hundred eight patients were recruited, comprising 25,432 min of patient time containing 4098 episodes of care or waiting. The median patient journey was 223 min (3 h, 43 min); just within the 4-h target. Although total patient journey times were similar between sites, the stage where the greatest proportion of waiting occurred varied. Reasons for waiting were dominated by waits for beds, investigations or results to be available. From our sample we observed that EDs without a discharge/clinical decision area exhibited a greater proportion of waiting time following an admission or discharge decision. PPI interpretation indicated that patients who experience waits at the beginning of their journey feel more anxious because they are 'not in the system yet'. Conclusions: the novel application of VSM analysis across different hospitals, coupled with PPI interpretation, provides important insight into the impact of care provision on patient experience. Measures that could reduce patient waiting include automatic notification of test results, and the option of discharge/clinical decision areas for patients awaiting results or departure. To enhance patient experience, good communication with patients and relatives about reasons for waits is essential.

BACKGROUND: Mathematical capacity planning methods that can take account of variations in patient complexity, admission rates and delayed discharges have long been available, but their implementation in complex pathways such as stroke care remains limited. Instead simple average based estimates are commonplace. These methods often substantially underestimate capacity requirements. We analyse the capacity requirements for acute and community stroke services in a pathway with over 630 admissions per year. We sought to identify current capacity bottlenecks affecting patient flow, future capacity requirements in the presence of increased admissions, the impact of co-location and pooling of the acute and rehabilitation units and the impact of patient subgroups on capacity requirements. We contrast these results to the often used method of planning by average occupancy, often with arbitrary uplifts to cater for variability. METHODS: We developed a discrete-event simulation model using aggregate parameter values derived from routine administrative data on over 2000 anonymised admission and discharge timestamps. The model mimicked the flow of stroke, high risk TIA and complex neurological patients from admission to an acute ward through to community rehab and early supported discharge, and predicted the probability of admission delays. RESULTS: an increase from 10 to 14 acute beds reduces the number of patients experiencing a delay to the acute stroke unit from 1 in every 7 to 1 in 50. Co-location of the acute and rehabilitation units and pooling eight beds out of a total bed stock of 26 reduce the number of delayed acute admissions to 1 in every 29 and the number of delayed rehabilitation admissions to 1 in every 20. Planning by average occupancy would resulted in delays for one in every five patients in the acute stroke unit. CONCLUSIONS: Planning by average occupancy fails to provide appropriate reserve capacity to manage the variations seen in stroke pathways to desired service levels. An appropriate uplift from the average cannot be based simply on occupancy figures. Our method draws on long available, intuitive, but underused mathematical techniques for capacity planning. Implementation via simulation at our study hospital provided valuable decision support for planners to assess future bed numbers and organisation of the acute and rehabilitation services.

BackgroundHospital emergency admissions have risen annually, exacerbating pressures on emergency departments (EDs) and acute medical units. These pressures have an adverse impact on patient experience and potentially lead to suboptimal clinical decision-making. In response, a variety of innovations have been developed, but whether or not these reduce inappropriate admissions or improve patient and clinician experience is largely unknown.AimsTo investigate the interplay of service factors influencing decision-making about emergency admissions, and to understand how the medical assessment process is experienced by patients, carers and practitioners.MethodsThe project used a multiple case study design for a mixed-methods analysis of decision-making about admissions in four acute hospitals. The primary research comprised two parts: value stream mapping to measure time spent by practitioners on key activities in 108 patient pathways, including an embedded study of cost; and an ethnographic study incorporating data from 65 patients, 30 carers and 282 practitioners of different specialties and levels. Additional data were collected through a clinical panel, learning sets, stakeholder workshops, reading groups and review of site data and documentation. We used a realist synthesis approach to integrate findings from all sources.FindingsPatients’ experiences of emergency care were positive and they often did not raise concerns, whereas carers were more vocal. Staff’s focus on patient flow sometimes limited time for basic care, optimal communication and shared decision-making. Practitioners admitted or discharged few patients during the first hour, but decision-making increased rapidly towards the 4-hour target. Overall, patients’ journey times were similar, although waiting before being seen, for tests or after admission decisions, varied considerably. The meaning of what constituted an ‘admission’ varied across sites and sometimes within a site. Medical and social complexity, targets and ‘bed pressure’, patient safety and risk, each influenced admission/discharge decision-making. Each site responded to these pressures with different initiatives designed to expedite appropriate decision-making. New ways of using hospital ‘space’ were identified. Clinical decision units and observation wards allow potentially dischargeable patients with medical and/or social complexity to be ‘off the clock’, allowing time for tests, observation or safe discharge. New teams supported admission avoidance: an acute general practitioner service filtered patients prior to arrival; discharge teams linked with community services; specialist teams for the elderly facilitated outpatient treatment. Senior doctors had a range of roles: evaluating complex patients, advising and training juniors, and overseeing ED activity.ConclusionsThis research shows how hospitals under pressure manage complexity, safety and risk in emergency care by developing ‘ground-up’ initiatives that facilitate timely, appropriate and safe decision-making, and alternative care pathways for lower-risk, ambulatory patients. New teams and ‘off the clock’ spaces contribute to safely reducing avoidable admissions; frontline expertise brings value not only by placing senior experienced practitioners at the front door of EDs, but also by using seniors in advisory roles. Although the principal limitation of this research is its observational design, so that causation cannot be inferred, its strength is hypothesis generation. Further research should test whether or not the service and care innovations identified here can improve patient experience of acute care and safely reduce avoidable admissions.FundingThe National Institute for Health Research (NIHR) Health Services and Delivery Research programme (project number 10/1010/06). This research was supported by the NIHR Collaboration for Leadership in Applied Health Research and Care South West Peninsula.

KEYNOTE PRESENTATIONS K1 Researching complex interventions: the need for robust approaches Peter Craig K2 Complex intervention studies: an important step in developing knowledge for practice Ingalill Rahm-Hallberg K3 Public and patient involvement in research: what, why and how? Nicky Britten K4 Mixed methods in health service research – where do we go from here? Gunilla Borglin SPEAKER PRESENTATIONS S1 Exploring complexity in systematic reviews of complex interventions Gabriele Meyer, Sascha Köpke, Jane Noyes, Jackie Chandler S2 can complex health interventions be optimised before moving to a definitive RCT? Strategies and methods currently in use Sara Levati S3 a systematic approach to develop theory based implementation interventions Anne Sales S4 Pilot studies and feasibility studies for complex interventions: an introduction Lehana Thabane, Lora Giangregorio S5 What can be done to pilot complex interventions? Nancy Feeley, Sylvie Cossette S6 Using feasibility and pilot trials to test alternative methodologies and methodological procedures prior to full scale trials Rod Taylor S7 a mixed methods feasibility study in practice Jacqueline Hill, David a Richards, Willem Kuyken S8 Non-standard experimental designs and preference designs Louise von Essen S9 Evaluation gone wild: using natural experimental approaches to evaluate complex interventions Andrew Williams S10 the stepped wedge cluster randomised trial: an opportunity to increase the quality of evaluations of service delivery and public policy interventions Karla Hemming, Richard Lilford, Alan Girling, Monica Taljaard S11 Adaptive designs in confirmatory clinical trials: opportunities in investigating complex interventions Munyaradzi Dimairo S12 Processes, contexts and outcomes in complex interventions, and the implications for evaluation Mark Petticrew S13 Processes, contexts and outcomes in complex interventions, and the implications for evaluation Janis Baird, Graham Moore S14 Qualitative evaluation alongside RCTs: what to consider to get relevant and valuable results Willem Odendaal, Salla Atkins, Elizabeth Lutge, Natalie Leon, Simon Lewin S15 Using economic evaluations to understand the value of complex interventions: when maximising health status is not sufficient Katherine Payne S16 How to arrive at an implementation plan Theo van Achterberg S17 Modelling process and outcomes in complex interventions Walter Sermeus S18 Systems modelling for improving health care Martin Pitt, Thomas Monks

BackgroundThere is a tension in many health-care services between the expertise and efficiency that comes with centralising services and the ease of access for patients. Neonatal care is further complicated by the organisation of care into networks where different hospitals offer different levels of care and where capacity across, or between, networks may be used when local capacity is exhausted.ObjectivesTo develop a computer model that could mimic the performance of a neonatal network and predict the effect of altering network configuration on neonatal unit workloads, ability to meet nurse staffing guidelines, and distance from the parents’ home location to the point of care. The aim is to provide a model to assist in planning of capacity, location and type of neonatal services.DesignDescriptive analysis of a current network, economic analysis and discrete event simulation. During the course of the project, two meetings with parents were held to allow parent input.SettingThe Peninsula neonatal network (Devon and Cornwall) with additional work extending to the Western network.Main outcome measuresAbility to meet nurse staffing guidelines, cost of service provision, number and distance of transfers, average travel distances for parents, and numbers of parents with an infant over 50 km from home.Data sourcesAnonymised neonatal data for 7629 infants admitted into a neonatal unit between January 2011 and June 2013 were accessed from Badger patient care records. Nurse staffing data were obtained from a daily ring-around audit. Further background data were accessed from NHS England general practitioner (GP) Practice Profiles, Hospital Episode Statistics, Office for National Statistics and NHS Connecting for Health. Access to patient care records was approved by the Research Ethics Committee and the local Caldicott Guardian at the point of access to the data.ResultsWhen the model was tested against a period of data not used for building the model, the model was able to predict the occupancy of each hospital and care level with good precision (R2 > 0.85 for all comparisons). The average distance from the parents’ home location (GP location used as a surrogate) was predicted to within 2 km. The number of transfers was predicted to within 2%. The model was used to forecast the effect of centralisation. Centralisation led to reduced nurse requirements but was accompanied by a significant increase in parent travel distances. Costs of nursing depend on how much of the time nursing guidelines are to be met, rising from £4500 per infant to meet guidelines 80% of the time, to £5500 per infant to meet guidelines 95% of the time. Using network capacity, rather than local spare capacity, to meet local peaks in workloads can reduce the number of nurses required, but the number of transfers and the travel distance for parents start to rise significantly above ≈ 70% network capacity utilisation.ConclusionsWe have developed a model that predicts performance of a neonatal network from the perspectives of both the service provider and the parents of infants in care.Future workApplication of the model at a national level.FundingThe National Institute for Health Research Health Services and Delivery Research programme.

With ever increasing concern over ambulance handover delays this paper looks at the impact of dedicated A&E nurses for ambulance handovers and the effect it can have on ambulance waiting times. It demonstrates that although such roles can bring about reduced waiting times, it also suggests that using this as a sole method to achieve these targets would require unacceptably low staff utilisation.

Interest is growing internationally in the potential benefits of patient and public involvement (PPI) in research. In the United Kingdom (UK) health and social care services are now committed to involving patients and service users in the planning, development and evaluation of their services. Many funders require PPI as a prerequisite for funding. What does healthcare operational research miss by not involving patients and the public in the development, refinement and implementation of models? We believe PPI is important for healthcare OR for model design and validation, and ethical and economic reasons. It also has a distinct contribution that goes beyond the incorporation of behavioural parameters into models. Case studies in neonatal care and a fractured neck of femur pathway highlight PPI’s contribution to model design and validation, but a recent conference session also identified a number of obstacles. We suggest a provisional model for the implementation of PPI in healthcare OR that emphasises a facilitative approach. We acknowledge this is a significant challenge, but argue that it must be met for ethical and economic reasons that are ultimately rooted in modellers’ construction of valid models. Crucially, it has the potential to enhance our ability to bring about change which can benefit health services and, most importantly, the patients they serve.

Please cite this paper as: Allen M, Thornton S. Providing one-to-one care in labour. Analysis of 'BirthratePlus' labour ward staffing in real and simulated labour ward environments. BJOG 2012; DOI: 10.1111/j.1471-0528.2012. 03483.x. Objective to assess the ability of the 'Birthrate Plus' (BR+) midwife staffing system to cope with variability of workload on labour wards. Design Retrospective analysis of labour ward workload and computer simulation of labour wards. Setting the labour ward of a city hospital. Population a total of 5800 births (1 year). Methods the variation in births by time and day was analysed over a 1-year period. Three months of BR+ data were analysed for variation of workload by case mix. A computer simulation model was built to allow prediction of the impact of changing resource levels or shift patterns, and to forecast the impact of changing number of births per year. Main outcome measures Labour ward overloading (when either the number of women or the BR+ Workload Index exceeds the scheduled midwife availability). Results Labour ward overload occurred 37% of the time when applying the BR+ method. Underlying patterns of workload were present and simulation indicated that overload could be reduced by 15-25% if available resources were matched more closely to known patterns of workload. Simulation also indicated that smaller units are predicted to suffer from overload more often than larger units, and are more prone to severe overload. Conclusions the BR+ formula for midwife staffing leaves labour wards vulnerable to significant periods of overload. Matching resource levels to known patterns of workload may reduce the occurrence of overload. Simulation indicates that smaller units need higher relative staffing levels to provide the same level of 1:1 care to mothers in labour. © 2012 the Authors BJOG an International Journal of Obstetrics and Gynaecology © 2012 RCOG.

OBJECTIVE: to assess the ability of the 'Birthrate Plus' (BR+) midwife staffing system to cope with variability of workload on labour wards. DESIGN: Retrospective analysis of labour ward workload and computer simulation of labour wards. SETTING: the labour ward of a city hospital. POPULATION: a total of 5800 births (1 year). METHODS: the variation in births by time and day was analysed over a 1-year period. Three months of BR+ data were analysed for variation of workload by case mix. A computer simulation model was built to allow prediction of the impact of changing resource levels or shift patterns, and to forecast the impact of changing number of births per year. MAIN OUTCOME MEASURES: Labour ward overloading (when either the number of women or the BR+ Workload Index exceeds the scheduled midwife availability). RESULTS: Labour ward overload occurred 37% of the time when applying the BR+ method. Underlying patterns of workload were present and simulation indicated that overload could be reduced by 15-25% if available resources were matched more closely to known patterns of workload. Simulation also indicated that smaller units are predicted to suffer from overload more often than larger units, and are more prone to severe overload. CONCLUSIONS: the BR+ formula for midwife staffing leaves labour wards vulnerable to significant periods of overload. Matching resource levels to known patterns of workload may reduce the occurrence of overload. Simulation indicates that smaller units need higher relative staffing levels to provide the same level of 1:1 care to mothers in labour.

Historically, sample management successfully focused on providing compound quality and tracking distribution within a diverse geographic. However, if a competitive advantage is to be delivered in a changing environment of outsourcing, efficiency and customer service must now improve or face reconstruction. The authors have used discrete event simulation to model the compound process from chemistry to assay and applied lean manufacturing techniques to analyze and improve these processes. In doing so, they identified a value-adding process time of just 11 min within a procedure that took days. Modeling also allowed the analysis of equipment and human resources necessary to complete the expected demand in an acceptable cycle time. Layout and location of sample management and screening departments are key in allowing process integration, creating rapid flow of work, and delivering these efficiencies. Following this analysis and minor process changes, the authors have demonstrated for 2 programs that solid compounds can be converted to assay-ready plates in less than 4 h. In addition, it is now possible to deliver assay data from these compounds within the same working day, allowing chemistry teams more flexibility and more time to execute the next chemistry round. Additional application of lean manufacturing principles has the potential to further decrease cycle times while using fewer resources.

In this study we investigated the spatial and temporal relationship among oxytocin (OT), oxytocin receptor (OTR), and estradiol (E2) at term, with (LAB) and without labor (NIL), in human amnion (AM), chorio-decidua (CD), fundal (FU), and lower segment (LS) myometrium. RT-PCR and RIA demonstrated a labor-associated increase in OT mRNA and peptide in CD, AM, and FU, but not LS. HPLC purification and mass spectrometry analysis confirmed that immunoreactive OT corresponded to alpha-amidated OT. Immunohistochemistry localized OT to chorionic trophoblast, decidual stroma, and glandular epithelium. RT-PCR analysis of OTR mRNA demonstrated a significant difference between FU and LS samples, which remained unchanged with labor in all tissues. Immunohistochemistry localized OTR to amniotic epithelium, decidual stroma, and myometrium. Tissue E2 concentrations, as determined by ELISA, demonstrated a significant increase with labor in all tissues. E2 was highest in CD, followed by FU, AM, and LS, respectively. E2 correlated with OT in samples of FU and CD taken from NIL women and in FU, CD, and AM taken from LAB women. We conclude that a significant increase in both OT and E2 occurs at the myometrial decidual interface with labor, and this increase is reflected in both the fundal and lower segments of the uterus. In contrast to OT and E2 the OTR is spatially regulated, with significantly greater expression in the fundal region of the uterus. Paracrine OT production stimulated by E2 may be important in activating the uterus at term.