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[This corrects the article DOI 10.1017/jns.2017.7.].Without timely assessments of the number of COVID-19 cases requiring hospitalisation, healthcare providers will struggle to ensure an appropriate number of beds are made available. Too few could cause excess deaths while too many could result in additional waits for elective treatment. As well as supporting capacity considerations, reliably projecting future "waves" is important to inform the nature, timing and magnitude of any localised restrictions to reduce transmission. In making the case for locally owned and locally configurable models, this paper details the approach taken by one major healthcare system in founding a multi-disciplinary "Scenario Review Working Group", comprising commissioners, public health officials and academic epidemiologists. find more The role of this group, which met weekly during the pandemic, was to define and maintain an evolving library of plausible scenarios to underpin projections obtained through an SEIR-based compartmental model. Outputs have informed decision-making at the system's major incident Bronze, Silver and Gold Commands. This paper presents illustrated examples of use and offers practical considerations for other healthcare systems that may benefit from such a framework.Increasing efficiency and reducing risk in radiotherapy cancer treatment is of high importance. User assistance systems within a digitally connected radiotherapy environment can support all involved professionals to perform their individual tasks faster and better. This paper presents a qualitative analysis of radiotherapy workflows and a corresponding process modelling in order to identify hypothetical user assistance systems for specific process activities. In addition, the results of an empirical study on the identified systems are presented together with derived requirements and design principles for these systems. A structured online survey with 50 medical physicists in Germany has been conducted. Among others the acceptance, the increase of perceived efficiency and the risk reduction while using the assistance systems are analysed and discussed. The results support the creation of value adding user assistance systems for radiotherapy that improve efficiency, reduce treatment risks and reach high user acceptance levels.Home testing is an emerging innovation that can enable nations and health care systems to safely and efficiently test large numbers of patients to manage COVID-19 and other viral outbreaks. In this position paper, we explore the process of moving home testing across the translational continuum from labs to households, and ultimately into practice and communities for optimal public health impact. We focus on the four translational science drivers to accelerate the implementation of systems-wide home testing programmes 1) collaboration and team science, 2) technology, 3) multilevel interventions, and 4) knowledge integration. We use the Socio Ecological Model (SEM) as a framework to illustrate our vision for the ideal future state of a comprehensive system of stakeholders utilising tech-enabled home testing for COVID-19 and other virus outbreaks, and we suggest SEM as a tool to address key translational readiness and response questions.In hospitals, scheduled operations can often be cancelled in large numbers due to the unavailability of beds for post-operation recovery. Operating theatre scheduling is known to be an N P -hard optimisation problem. Previous studies have shown that the correct scheduling of surgical procedures can have a positive impact on the availability of beds in hospital wards, thereby allowing a reduction in number of elective operation cancellations. This study proposes an exact technique based on the partitioned graph colouring problem for constructing optimal master surgery schedules, with the goal of minimising the number of cancellations. The resultant schedules are then simulated in order to measure how well they cope with the stochastic nature of patient arrivals. Our results show that the utilisation of post-operative beds can be increased, whilst the number of cancellations can be decreased, which may ultimately lead to greater patient throughput and reduced waiting times. A scenario-based model has also been employed to integrate the stochastic-nature associated with the bed requirements into the optimisation process. The results indicate that the proposed model can lead to more robust solutions.A primary goal of emergency services is to minimise the response times to emergencies whilst managing operational costs. This paper is motivated by real data from the Welsh Ambulance Service which in recent years has been criticised for not meeting its eight-minute response target. In this study, four forecasting approaches (ARIMA, Holt Winters, Multiple Regression and Singular Spectrum Analysis (SSA)) are considered to investigate whether they can provide more accurate predictions to the call volume demand (total and by category) than the current approach on a selection of planning horizons (weekly, monthly and 3-monthly). Each method is applied to a training and test set and root mean square error (RMSE) and mean absolute percentage error (MAPE) error statistics are determined. Results showed that ARIMA is the best forecasting method for weekly and monthly prediction of demand and the long-term demand is best predicted using the SSA method.Patients diagnosed with rheumatoid arthritis require lifelong monitoring by a rheumatologist. Initiation of the disease-modifying anti-rheumatic drug therapy within twelve weeks of the onset of symptoms is crucial to prevent joint damage and functional disability. We examine the impact of the engagement of alternate care providers (ACP) in alleviating delay due to limited rheumatologist capacity. Using queueing theory and discrete-event simulation, we model rheumatologist-only and rheumatologist-with-ACP system configurations as closed, multi-class queueing networks with class switching.Using summary data from an actual rheumatology clinic for illustration, we analyze various parameter conditions to aid clinic managers and policymakers in decisions concerning capacity allocations and feasible patient panel size that impact timeliness of care and resource utilization.Results not only confirm that a substantial increase in RA patient panel size with an ACP involved in the care of follow-up patients but also demonstrates the boundaries for feasible panel sizes and workload allocation.During intra-hospital transfers, multiple clinicians perform coordinated tasks that leave patients vulnerable to undesirable outcomes. Communication has been established as a challenge to care transitions, but less is known about the organisational complexities within which transfers take place. We performed a qualitative assessment that included various professions to capture a multi-faceted understanding of intra-hospital transfers. Ethnographic observations and semi-structured interviews were conducted with clinicians and staff from the Medical Intensive Care Unit, Emergency Department, and general medicine units at a large, urban, academic, tertiary medical centre. Results highlight the organisational factors that stakeholders view as important for successful transfers the development, dissemination, and application of protocols; robustness of technology; degree of teamwork; hospital capacity; and the ways in which competing hospital priorities are managed. These factors broaden our understanding of the organisational context of intra-hospital transfers and informed the development of a practical guide that can be used prior to embarking on quality improvement efforts around transitions of care.Content available Author Interview and Audio Recording.Content available Author Interview and Audio Recording.Content available Author Audio Recording.Content available Author Audio Recording.Content available Author Interview and Audio Recording.Content available Author Interview and Audio Recording.Content available Author Audio Recording.Content available Author Audio Recording.Content available Author Interview and Audio Recording.Content available Author Interview and Audio Recording.Content available Author Audio Recording.Content available Author Interview and Audio Recording.Content available Author Audio Recording.Content available Author Audio Recording.The use of magnetic resonance imaging (MRI) and spectroscopy (MRS) in the clinical setting enables the acquisition of valuable anatomical information in a rapid, non-invasive fashion. However, MRI applications for identifying disease-related biomarkers are limited due to low sensitivity at clinical magnetic field strengths. The development of hyperpolarized (hp) 129Xe MRI/MRS techniques as complements to traditional 1H-based imaging has been a burgeoning area of research over the past two decades. Pioneering experiments have shown that hp 129Xe can be encapsulated within host molecules to generate ultrasensitive biosensors. In particular, xenon has high affinity for cryptophanes, which are small organic cages that can be functionalized with affinity tags, fluorophores, solubilizing groups, and other moieties to identify biomedically relevant analytes. Cryptophane sensors designed for proteins, metal ions, nucleic acids, pH, and temperature have achieved nanomolar-to-femtomolar limits of detection via a combination of 129Xe hyperpolarization and chemical exchange saturation transfer (CEST) techniques. This review aims to summarize the development of cryptophane biosensors for 129Xe MRI applications, while highlighting innovative biosensor designs and the consequent enhancements in detection sensitivity, which will be invaluable in expanding the scope of 129Xe MRI.The COVID-19 pandemic has posed and is continuously posing enormous societal and health challenges worldwide. The research community has mobilized to develop novel projects to find a cure or a vaccine, as well as to contribute to mass testing, which has been a critical measure to contain the infection in several countries. Through this article, we share our experiences and learnings as a group of volunteers at the Centre for Genomic Regulation (CRG) in Barcelona, Spain. As members of the ORFEU project, an initiative by the Government of Catalonia to achieve mass testing of people at risk and contain the epidemic in Spain, we share our motivations, challenges and the key lessons learnt, which we feel will help better prepare the global society to address similar situations in the future.Background Previous studies of migraine classification have focused on the analysis of brain waves, leading to the development of complex tests that are not accessible to the majority of the population. In the early stages of this pathology, patients tend to go to the emergency services or outpatient department, where timely identification largely depends on the expertise of the physician and continuous monitoring of the patient. However, owing to the lack of time to make a proper diagnosis or the inexperience of the physician, migraines are often misdiagnosed either because they are wrongly classified or because the disease severity is underestimated or disparaged. Both cases can lead to inappropriate, unnecessary, or imprecise therapies, which can result in damage to patients' health. Methods This study focuses on designing and testing an early classification system capable of distinguishing between seven types of migraines based on the patient's symptoms. The methodology proposed comprises four steps data collection based on symptoms and diagnosis by the treating physician, selection of the most relevant variables, use of artificial neural network models for automatic classification, and selection of the best model based on the accuracy and precision of the diagnosis.