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The MedSecurance project focus on identifying new challenges in cyber security with focus on hardware and software medical devices in the context of emerging healthcare architectures. In addition, the project will review best practice and identify gaps in the guidance, particularly the guidance stipulated by the medical device regulation and directives. Finally, the project will develop comprehensive methodology and tooling for the engineering of trustworthy networks of inter-operating medical devices, that shall have security-for-safety by design, with a strategy for device certification and certifiable dynamic network composition, ensuring that patient safety is safeguarded from malicious cyber actors and technology “accidents”.

Objective: Investigate the performance of qualitative review (QR) for assessing dynamic susceptibility contrast (DSC-) MRI data quality in paediatric normal brain and develop an automated alternative to QR. Methods: 1027 signal–time courses were assessed by Reviewer 1 using QR. 243 were additionally assessed by Reviewer 2 and % disagreements and Cohen’s κ (κ) were calculated. The signal drop-to-noise ratio (SDNR), root mean square error (RMSE), full width half maximum (FWHM) and percentage signal recovery (PSR) were calculated for the 1027 signal–time courses. Data quality thresholds for each measure were determined using QR results. The measures and QR results trained machine learning classifiers. Sensitivity, specificity, precision, classification error and area under the curve from a receiver operating characteristic curve were calculated for each threshold and classifier. Results: Comparing reviewers gave 7% disagreements and κ = 0.83. Data quality thresholds of: 7.6 for SDNR; 0.019 for RMSE; 3 s and 19 s for FWHM; and 42.9 and 130.4% for PSR were produced. SDNR gave the best sensitivity, specificity, precision, classification error and area under the curve values of 0.86, 0.86, 0.93, 14.2% and 0.83. Random forest was the best machine learning classifier, giving sensitivity, specificity, precision, classification error and area under the curve of 0.94, 0.83, 0.93, 9.3% and 0.89. Conclusion: The reviewers showed good agreement. Machine learning classifiers trained on signal–time course measures and QR can assess quality. Combining multiple measures reduces misclassification. Advances in knowledge: A new automated quality control method was developed, which trained machine learning classifiers using QR results.

Purpose: Medulloblastoma is the most common malignant brain tumor occurring in childhood and is a significant cause of morbidity and mortality in pediatric oncology. More intense treatment strategies are recommended for patients displaying high-risk factors; however, considerable variation in outcome remains, indicating a need for improved predictive markers. In this study, 1H magnetic resonance spectroscopy (MRS) was used to investigate noninvasive molecular biomarkers of survival in medulloblastoma.Experimental Design: MRS was performed on a series of 35 biopsy-confirmed medulloblastoma cases. One case was excluded because of poor quality MRS. The prognostic value of MRS detectable biomarkers was investigated using Cox regression, retrospectively (N = 15). A subsequent validation analysis (N = 19) was also performed to reduce the chance of type I errors. Where available, high-resolution ex vivo MRS of biopsy tissue was used to confirm biomarker assignments.Results: The retrospective analysis revealed that creatine, glutamate, and glycine were markers of survival (P < 0.01). The validation analysis showed that glutamate was a robust marker, with a hazard ration (HR) of 8.0 for the full dataset (P = 0.0003, N = 34). A good correlation between in vivo and ex vivo MRS glutamate/total-choline was found (P = 0.001), validating the in vivo assignment. Ex vivo glutamate/total-choline was also associated with survival (P < 0.01).Conclusion: The identification of glutamate as a predictive biomarker of survival in pediatric medulloblastoma provides a clinically viable risk factor and highlights the importance of more detailed studies into the metabolism of this disease. Noninvasive biomarker detection using MRS may offer improved disease monitoring and potential for widespread use following multicenter validation. Clin Cancer Res; 20(17); 4532–9. ©2014 AACR.

Supplementary Methods (Text 1). A description of the treatment protocols used during the study period. Supplementary Figure 1. Example medulloblastoma fits where the lowest smooth black line is the baseline; the middle black line represents the acquired spectrum following pre-processing; the red line shows the spectral fit and the top black line shows the residual between the fit and acquired spectrum. A) shows a distorted baseline leading to qc failure whereas B) is an example of an ideal smooth baseline.

The role of the field of informatics in medical imaging is vital; novel or adapted informatics' core methods can be employed to realise innovative information processing and engineering of medical images. As such, imaging informatics can assist in the interpretation of image-based, clinically recorded evidence. This, in turn, leads to the generation of associated actionable knowledge to achieve precision medicine practice. The discipline of informatics has the power to transform data to useful clinical information patterns of observable evidence and, subsequently to generate actionable knowledge in terms of diagnosis, prognosis, and disease management. This paper presents the author's personal viewpoint and distinct contributions to innovations in the acquisition and collection of imaging data; storage, retrieval, and management of imaging information objects; quantitative analysis, classification, and dissemination of imaging observable evidence.

The role of the field of informatics in medical imaging is vital; novel or adapted informatics’ core methods can be employed to realise innovative information processing and engineering of medical images. As such, imaging informatics can assist in the interpretation of image-based, clinically recorded evidence. This, in turn, leads to the generation of associated actionable knowledge to achieve precision medicine practice. The discipline of informatics has the power to transform data to useful clinical information patterns of observable evidence and, subsequently to generate actionable knowledge in terms of diagnosis, prognosis, and disease management. This paper presents the author’s personal viewpoint and distinct contributions to innovations in the acquisition and collection of imaging data; storage, retrieval, and management of imaging information objects; quantitative analysis, classification, and dissemination of imaging observable evidence.

Hydrogels are cross-linked networks of hydrophilic polymer chains with a three-dimensional structure. Owing to their unique features, the application of hydrogels for bacterial/antibacterial studies and bacterial infection management has grown in importance in recent years. This trend is likely to continue due to the rise in bacterial infections and antimicrobial resistance. By exploiting their physicochemical characteristics and inherent nature, hydrogels have been developed to achieve bacterial capture and detection, bacterial growth or elimination, antibiotic delivery, or bacterial sensing. Traditionally, the development of hydrogels for bacterial/antibacterial studies has focused on achieving a single function such as antibiotic delivery, antibacterial activity, bacterial growth, or bacterial detection. However, recent studies demonstrate the fabrication of multifunctional hydrogels, where a single hydrogel is capable of performing more than one bacterial/antibacterial function, or composite hydrogels consisting of a number of single functionalized hydrogels, which exhibit bacterial/antibacterial function synergistically. In this review, we first highlight the hydrogel features critical for bacterial studies and infection management. Then, we specifically address unique hydrogel properties, their surface/network functionalization, and their mode of action for bacterial capture, adhesion/growth, antibacterial activity, and bacterial sensing, respectively. Finally, we provide insights into different strategies for developing multifunctional hydrogels and how such systems can help tackle, manage, and understand bacterial infections and antimicrobial resistance. We also note that the strategies highlighted in this review can be adapted to other cell types and are therefore likely to find applications beyond the field of microbiology.

It is typical for many digital health research projects to develop IT architectures that will implement integrated care services that may also deliver interventions. As part of compliance with the requirements of the regulation, the components that are considered as a medical device will need to be classified to a medical device category. This is often seen as task that may increase the business risk and a major barrier of the project, particularly during the earlier stages when not all information is available. The paper offers a method assisting with classification of such architectures in the context of the Medical Devices Rregulation, offering a structured way to identifying how the initial deliverables of a project can be used to provide assurance to the justification of the classification.

The Tommy's National Centre for Miscarriage Research aims to support the diagnosis and treatment for couples suffering from recurrent miscarriage. Tommy's Net is an electronic data gathering tool, collecting miscarriage data and links with hospital Clinical Information System databases. The gathering of patient reported data is an important aspect, especially as data relating to pregnancy and miscarriage events are often left unreported.Both traditional paper-based and electronic patient reported outcome (ePRO) solutions have been explored to improve response rates, minimize data redundancy and reduce burden on staff. Popular ePRO survey solutions have been compared, including REDCap, SurveyMonkey, Qualtrics and LimeSurvey.LimeSurvey was selected as the most appropriate solution as it provided self-hosting capability, SMS integration and ease of use.We have implemented a LimeSurvey based ePRO system for collection of baseline and follow-up data for participants on the Tommy's study.

The Tommy’s National Centre for Miscarriage Research aims to support the diagnosis and treatment for couples suffering from recurrent miscarriage. Tommy’s Net is an electronic data gathering tool, collecting miscarriage data and links with hospital Clinical Information System databases. The gathering of patient reported data is an important aspect, especially as data relating to pregnancy and miscarriage events are often left unreported. Methods: Both traditional paper-based and electronic patient reported outcome (ePRO) solutions have been explored to improve response rates, minimize data redundancy and reduce burden on staff. Popular ePRO survey solutions have been compared, including REDCap, SurveyMonkey, Qualtrics and LimeSurvey. Results: LimeSurvey was selected as the most appropriate solution as it provided self-hosting capability, SMS integration and ease of use. Conclusion: We have implemented a LimeSurvey based ePRO system for collection of baseline and follow-up data for participants on the Tommy’s study.

Modern healthcare providers rely upon Electronic Healthcare Records (EHR) systems to record patient data inside their own organization. Some healthcare providers share this data to facilitate patient care with other providers. Medical devices and healthcare providers can use differing standards of recording healthcare information. The Structural and Semantic Mapper Proxy API solution offers a practical way to tackles the issues of Structural and Semantic mapping of Application Programing Interfaces (API) in a healthcare context to enable connection of all existing systems to a healthcare providers EHR creating a single source of truth regarding the treatment of patients and enabling healthcare providers to bridge the gap between external EHR systems.

CIGs languages consist of approach specific concepts. More widely used concepts, such as those in UMLS are not typically used.An evaluation of UMLS concept sufficiency for CIG definition.A popular guideline is mapped to UMLS concepts with NLP. Results are reviewed to evaluate gaps, and appropriateness.A significant number of the guideline text mapped to UMLS concepts.The approach has shown promise and highlighted further challenges.

Background:\ud CIGs languages consist of approach specific concepts. More widely used concepts, such as those in UMLS are not typically used.\ud \ud Objective:\ud An evaluation of UMLS concept sufficiency for CIG definition.\ud \ud Method:\ud A popular guideline is mapped to UMLS concepts with NLP. Results are reviewed to evaluate gaps, and appropriateness.\ud \ud Results:\ud A significant number of the guideline text mapped to UMLS concepts.\ud \ud Conclusions:\ud The approach has shown promise and highlighted further challenges.

To determine if apparent diffusion coefficients (ADC) can discriminate between posterior fossa brain tumours on a multicentre basis. A total of 124 paediatric patients with posterior fossa tumours (including 55 Medulloblastomas, 36 Pilocytic Astrocytomas and 26 Ependymomas) were scanned using diffusion weighted imaging across 12 different hospitals using a total of 18 different scanners. Apparent diffusion coefficient maps were produced and histogram data was extracted from tumour regions of interest. Total histograms and histogram metrics (mean, variance, skew, kurtosis and 10th, 20th and 50th quantiles) were used as data input for classifiers with accuracy determined by tenfold cross validation. Mean ADC values from the tumour regions of interest differed between tumour types, (ANOVA P −3 mm2 s−1 with sensitivity 80.8% and specificity 80.0%. Overall classification for the ADC histogram metrics were 85% using Naïve Bayes and 84% for Random Forest classifiers. The most commonly occurring posterior fossa paediatric brain tumours can be classified using Apparent Diffusion Coefficient histogram values to a high accuracy on a multicentre basis.

The technology enabled distributed model in Kerala is based on an innovative partnership model between Karkinos Healthcare and private health centers. The model is designed to address the barriers to cancer screening by generating demand and by bringing together the private health centers and service providers at various levels to create a network for continued care. This paper describes the implementation process and presents some preliminary findings. Methods: The model follows the hub-and-spoke and further spoke framework. In the pilot phases, from July 2021 to December 2021, five private health centers (partners) collaborated with Karkinos Healthcare across two districts in Kerala. Screening camps were organized across the districts at the community level where the target groups were administered a risk assessment questionnaire followed by screening tests at the spoke hospitals based on a defined clinical protocol. The screened positive patients were examined further for confirmatory diagnosis at the spoke centers. Patients requiring chemotherapy or minor surgeries were treated at the spokes. For radiation therapy and complex surgeries the patients were referred to the hubs.A total of 2,459 individuals were screened for cancer at the spokes and 299 were screened positive. Capacity was built at the spokes for cancer surgery and chemotherapy. A total of 189 chemotherapy sessions and 17 surgeries were performed at the spokes for cancer patients. 70 patients were referred to the hub.Initial results demonstrate the ability of the technology Distributed Cancer Care Network (DCCN) system to successfully screen and detect cancer and to converge the actions of various private health facilities towards providing a continuum of cancer care. The lessons learnt from this study will be useful for replicating the process in other States.

IntroductionCOVID-19 pandemic has caused major disruptions to delivery of various cancer care services as efforts were put to control the outbreak of the pandemic. Although the pandemic has highlighted the inadequacies of the system but has also led to emergence of a new cancer care delivery model which relies heavily on digital mediums. Digital health is not only restricted to virtual dissemination of information and consultation but has provided additional benefits ranging from support to cancer screening, early and more accurate diagnosis to increasing access to specialized care. This paper evaluates the challenges in the adoption of digital technologies to deliver cancer care services and provides recommendation for large-scale adoption in the Indian healthcare context.MethodsWe performed a search of PubMed and Google Scholar for numerous terms related to adoption of digital health technologies for cancer care during pandemic. We also analyze various socio-ecological challenges—from individual to community, provider and systematic level—for digital adoption of cancer care service which have existed prior to pandemic and lead to digital inequalities.ResultsDespite encouraging benefits accruing from the adoption of digital health key challenges remain for large scale adoption. With respect to user the socio-economic characteristics such as age, literacy and socio-cultural norms are the major barriers. The key challenges faced by providers include regulatory issues, data security and the inconvenience associated with transition to a new system.Policy SummaryFor equitable digital healthcare, the need is to have a participatory approach of all stakeholders and urgently addressing the digital divide adequately. Sharing of health data of public and private hospitals, within the framework of the Indian regulations and Data Protection Act, is critical to the development of digital health in India and it can go a long way in better forecasting and managing cancer burden.

ObjectivesTo measure pregnancy outcome following attendance at a recurrent miscarriage service and identify factors that influence outcome.DesignProspective, observational electronic cohort study.SettingParticipants attending a specialist recurrent miscarriage clinic, with a history of two or more pregnancy losses. 857 new patients attended over a 30-month period and were invited to participate. Participant data were recorded on a bespoke study database, ‘Tommy’s Net’.Participants777 women consented to participate (90.7% of new patients). 639 (82%) women continued within the cohort, and 138 were lost to follow-up. Mean age of active participants was 34 years for women and 37 years for partners, with a mean of 3.5 (1–19) previous pregnancy losses. Rates of obesity (maternal: 23.8%, paternal: 22.4%), smoking (maternal:7.4%, paternal: 19.4%) and alcohol consumption (maternal: 50%, paternal: 79.2%) were high and 55% of participants were not taking folic acid.Outcome measuresBiannual collection of pregnancy outcomes, either through prompted self-reporting, or existing hospital systems.Results639 (82%) women were followed up. 404 (83.4%) reported conception and 106 (16.6%) reported no pregnancy, at least 6 months following registration. Of those that conceived, 72.8% (294/404) had a viable pregnancy. Maternal smoking and body mass index (BMI) over 30 were significantly higher in those who did not conceive (p=0.001)ConclusionsTommy’s Net provides a secure electronic repository on data for couples with recurrent pregnancy loss and associated outcomes. The study identified that subfertility, as well as repeated miscarriage, maternal BMI and smoking status, contributed to failure to achieve live birth. Study findings may enable comparison of clinic outcomes and inform the development of a personalised holistic care package.

Background Urinary tract infection (UTI) is one of the most common bacterial infections responsible for increased annual incidence of antimicrobial resistance (AMR) cases. Clinical diagnosis of UTI AMR relies heavily on conventional urine culture and antibiotic susceptibility testing (AST) which has a turnaround time of ∼3 days. Often, irrespective of the infection status, antibiotics are prescribed to patients even before the test results are available, leading to non-judicious use of antibiotics. Over the years, several technologies have been developed for the rapid detection and diagnosis of UTI AMR, however, most of them are limited to traditional microbiological techniques and large laboratory equipment that are not readily available in low-to-middle income countries (LMICs). To address these diagnostic limitations, we are developing a rapid and affordable UTI-AMR diagnostic microfluidic device that is clinical friendly aimed at improving UTI management and AMR stewardship. Results Our device enables the flow of a large volume of urine specimens for the capture/enrichment of uropathogenic bacteria and determination of AST via a porous membrane that is augmented with a multifunctional polymer-based material. Important objectives for the development of UTI AMR diagnostic microfluidic device are: (i) development of a multifunctional polymer-based material; and (ii) validation of UTI AMR diagnostic device. We have successfully developed a polysaccharide-based platform to (i) selectively capture uropathogenic bacteria from urine specimen by immobilizing concanavalin A (con A) lectin as bacterial capture agent on the polymer surface via chemical modification; (ii) encapsulate and release bacterial nutrient media and antibiotics for AST; and (iii) detect AST via encapsulation of bacterial growth indicator. In addition, we have also determined the development of methacrylate-based and acrylamide-based synthetic polymer-based material for our application. Further, we have demonstrated the uniform augmentation of the polysaccharide-based polymer onto porous membrane via dip-coating technique for on-chip bacterial capture/enrichment and AST in fluid (urine) flow conditions. The porous membrane is a conducting material which enables us to perform electrochemical measurements such as impedance spectroscopy that accelerates the detection process of antibiotic susceptibility. As a proof-of-concept, we have determined the capture of biosafety level I Escherichia coli expressing kanamycin resistance gene on chemically surface modified polysaccharide-based polymer containing con A and the antibiotic susceptibility of captured bacteria against different antibiotics with and without the porous membrane. We have quantitatively determined the limit of detection of E. coli on multifunctional polysaccharide-based polymer material. Conclusions The utility of the UTI AMR microfluidic device in clinical settings enables clinicians to make informed decisions on the most appropriate antibiotic for treatment in less than a day. Integration of impedance spectroscopy will further accelerate the detection by significantly reducing the time of detection. Further, the device allows for off-chip analysis by retrieving the captured uropathogenic bacteria to perform high throughput sequencing for identifying AMR genetic determinants. Therefore, with the ability to selectively capture uropathogenic bacteria and determine AST in a short time, our technology has the potential to overcome some of the current limitations in UTI AMR diagnostics.

1 H-magnetic resonance spectroscopy (MRS) provides noninvasive metabolite profiles with the potential to aid the diagnosis of brain tumours. Prospective studies of diagnostic accuracy and comparisons with conventional MRI are lacking. The aim of the current study was to evaluate, prospectively, the diagnostic accuracy of a previously established classifier for diagnosing the three major childhood cerebellar tumours, and to determine added value compared with standard reporting of conventional imaging. Single-voxel MRS (1.5 T, PRESS, TE 30 ms, TR 1500 ms, spectral resolution 1 Hz/point) was acquired prospectively on 39 consecutive cerebellar tumours with histopathological diagnoses of pilocytic astrocytoma, ependymoma or medulloblastoma. Spectra were analysed with LCModel and predefined quality control criteria were applied, leaving 33 cases in the analysis. The MRS diagnostic classifier was applied to this dataset. A retrospective analysis was subsequently undertaken by three radiologists, blind to histopathological diagnosis, to determine the change in diagnostic certainty when sequentially viewing conventional imaging, MRS and a decision support tool, based on the classifier. The overall classifier accuracy, evaluated prospectively, was 91%. Incorrectly classified cases, two anaplastic ependymomas, and a rare histological variant of medulloblastoma, were not well represented in the original training set. On retrospective review of conventional MRI, MRS and the classifier result, all radiologists showed a significant increase (Wilcoxon signed rank test, p

Artificial Intelligence (AI) has seen an increased application within digital healthcare interventions (DHIs). DHIs use entails challenges about their safety assurance. Exacerbated by regulatory requirements, in the UK, this places the onus of safety assurance not only on the manufacturer, but also on the operator of a DHI. Clinical Safety claims and evidencing safe implementation and use of AI-based DHIs require expertise, to understand and act to control or mitigate risk. Current health software standards, regulation, and guidance do not provide the insight necessary for safer implementation.To interpret published guidance and policy related to AI and justify clinical safety assurance of DHIs.Assessment of UK health regulation policy, standards, and AI institution insights, utilizing a published Hazard Assessment framework, to structure safety justifications, and articulate hazards relating to AI-based DHIs.AI enabled DHI hazard identification, relating to implementation and use within healthcare delivery organizations.By application of the method, we postulate that UK research of AI DHIs highlighted issues that may affect safety, in need of consideration to justify safety of a DHI.

Background:\ud Artificial Intelligence (AI) has seen an increased application within digital healthcare interventions (DHIs). DHIs use entails challenges about their safety assurance. Exacerbated by regulatory requirements, in the UK, this places the onus of safety assurance not only on the manufacturer, but also on the operator of a DHI. Clinical Safety claims and evidencing safe implementation and use of AI-based DHIs require expertise, to understand and act to control or mitigate risk. Current health software standards, regulation, and guidance do not provide the insight necessary for safer implementation.\ud \ud Objective:\ud To interpret published guidance and policy related to AI and justify clinical safety assurance of DHIs.\ud \ud Method:\ud Assessment of UK health regulation policy, standards, and AI institution insights, utilizing a published Hazard Assessment framework, to structure safety justifications, and articulate hazards relating to AI-based DHIs.\ud \ud Results:\ud AI enabled DHI hazard identification, relating to implementation and use within healthcare delivery organizations.\ud \ud Conclusion:\ud By application of the method, we postulate that UK research of AI DHIs highlighted issues that may affect safety, in need of consideration to justify safety of a DHI.\ud

BackgroundDigital health applications can improve quality and effectiveness of healthcare, by offering a number of tools to patients, professionals, and the healthcare system. Introduction of new technologies is not without risk, and digital health applications are often considered a medical device. Assuring their safe operation requires, amongst others, clinical validation, which needs large datasets to test their application in realistic clinical scenarios. Access to such datasets is challenging, due to concerns about patient privacy. Development of synthetic datasets, which will be sufficiently realistic to test digital applications, is seen as a potential alternative, enabling their deployment.ObjectiveThe aim of work was to develop a method for the generation of realistic synthetic datasets, statistically equivalent to real clinical datasets, and demonstrate that Generative Adversarial Network based approach is fit for purpose.MethodA generative adversarial network was implemented and trained, in a series of six experiments, using numerical and categorical variables from three clinically relevant datasets, including ICD-9 and laboratory codes from the MIMIC III dataset. A number of contextual steps provided the success criteria for the synthetic dataset.ResultsThe approach created a synthetic dataset that exhibits very similar statistical characteristics with the real dataset. Pairwise association of variables is very similar. A high degree of Jaccard similarity and a successful K-S test further support this.ConclusionsThe proof of concept of generating realistic synthetic datasets was successful, with the approach showing promise for further work.

It is typical for many digital health research projects to develop IT architectures that will implement integrated care services that may also deliver interventions. As part of compliance with the requirements of the regulation, the components that are considered as a medical device will need to be classified to a medical device category. This is often seen as task that may increase the business risk and a major barrier of the project, particularly during the earlier stages when not all information is available. The paper offers a method assisting with classification of such architectures in the context of the Medical Devices Rregulation, offering a structured way to identifying how the initial deliverables of a project can be used to provide assurance to the justification of the classification.

Due to population ageing and medical advances, people with advanced chronic diseases (ACD) live longer. Such patients are even more likely to face either temporary or permanent reduced functional reserve, which typically further increases their healthcare resource use and the burden of care on their caregiver(s). Accordingly, these patients and their caregiver(s) may benefit from integrated supportive care provided via digitally supported interventions. This approach may either maintain or improve their quality of life, increase their independence, and optimize the healthcare resource use from early stages. ADLIFE is an EU-funded project, aiming to improve the quality of life of older people with ACD by providing integrated personalized care via a digitally enabled toolbox. Indeed, the ADLIFE toolbox is a digital solution which provides patients, caregivers, and health professionals with digitally enabled, integrated, and personalized care, supporting clinical decisions, and encouraging independence and self-management. Here we present the protocol of the ADLIFE study, which is designed to provide robust scientific evidence on the assessment of the effectiveness, socio-economic, implementation, and technology acceptance aspects of the ADLIFE intervention compared to the current standard of care (SoC) when applied in real-life settings of seven different pilot sites across six countries. A quasi-experimental trial following a multicenter, non-randomized, non-concurrent, unblinded, and controlled design will be implemented. Patients in the intervention group will receive the ADLIFE intervention, while patients in the control group will receive SoC. The assessment of the ADLIFE intervention will be conducted using a mixed-methods approach.

MRS can provide high accuracy in the diagnosis of childhood brain tumours when combined with machine learning. A feature selection method such as principal component analysis is commonly used to reduce the dimensionality of metabolite profiles prior to classification. However, an alternative approach of identifying the optimal set of metabolites has not been fully evaluated, possibly due to the challenges of defining this for a multi‐class problem. This study aims to investigate metabolite selection from in vivo MRS for childhood brain tumour classification. Multi‐site 1.5 T and 3 T cohorts of patients with a brain tumour and histological diagnosis of ependymoma, medulloblastoma and pilocytic astrocytoma were retrospectively evaluated. Dimensionality reduction was undertaken by selecting metabolite concentrations through multi‐class receiver operating characteristics and compared with principal component analysis. Classification accuracy was determined through leave‐one‐out and k‐fold cross‐validation. Metabolites identified as crucial in tumour classification include myo‐inositol (P < 0.05, AUC = 0 . 81 ± 0 . 01 ), total lipids and macromolecules at 0.9 ppm (P < 0.05, AUC = 0 . 78 ± 0 . 01 ) and total creatine (P < 0.05, AUC = 0 . 77 ± 0 . 01 ) for the 1.5 T cohort, and glycine (P < 0.05, AUC = 0 . 79 ± 0 . 01 ), total N‐acetylaspartate (P < 0.05, AUC = 0 . 79 ± 0 . 01 ) and total choline (P < 0.05, AUC = 0 . 75 ± 0 . 01 ) for the 3 T cohort. Compared with the principal components, the selected metabolites were able to provide significantly improved discrimination between the tumours through most classifiers (P < 0.05). The highest balanced classification accuracy determined through leave‐one‐out cross‐validation was 85% for 1.5 T 1H‐MRS through support vector machine and 75% for 3 T 1H‐MRS through linear discriminant analysis after oversampling the minority. The study suggests that a group of crucial metabolites helps to achieve better discrimination between childhood brain tumours.

INTRODUCTION: Perfusion is associated with grade and survival in children’s brain tumours. Dynamic susceptibility-contrast (DSC-) MRI measures perfusion non-invasively, estimating relative cerebral blood volume (rCBV). We previously showed significant differences between pre-treatment rCBV in low- and high-grade tumours in a multicentre study. Contrast agent leakage from tumour vessels during acquisition affects rCBV accuracy. A contrast agent pre-bolus can be given but this can be challenging in a clinical environment, introducing variability. Alternatively, a single bolus can be administered with leakage correction applied when processing the data. We investigated pre-treatment rCBV values in a multicentre study without pre-bolus administration. METHODS: Thirty-six patients underwent pre-treatment DSC-MRI scans at 2 centres on 4 different scanners. Protocols were variable. Pixel-by-pixel contrast agent concentration time courses were analysed. Maps of uncorrected (rCBVuncorr) and leakage-corrected rCBV (rCBVcorr) were produced. Whole-tumour regions-of-interest were defined and median whole-tumour DSC-MRI parameters calculated. Patients subsequently underwent surgery / biopsy. Tumours were classified and graded. RESULTS: Twelve tumours were classified as low-grade; 24 as high-grade. Median whole-tumour rCBVuncorr was significantly higher in high-grade tumours than in low-grade tumours (1.628 vs -0.167, p

ObjectiveRemote or mobile consulting is being promoted to strengthen health systems, deliver universal health coverage and facilitate safe clinical communication during coronavirus disease 2019 and beyond. We explored whether mobile consulting is a viable option for communities with minimal resources in low- and middle-income countries.MethodsWe reviewed evidence published since 2018 about mobile consulting in low- and middle-income countries and undertook a scoping study (pre-coronavirus disease) in two rural settings (Pakistan and Tanzania) and five urban slums (Kenya, Nigeria and Bangladesh), using policy/document review, secondary analysis of survey data (from the urban sites) and thematic analysis of interviews/workshops with community members, healthcare workers, digital/telecommunications experts, mobile consulting providers, and local and national decision-makers. Project advisory groups guided the study in each country.ResultsWe reviewed four empirical studies and seven reviews, analysed data from 5322 urban slum households and engaged with 424 stakeholders in rural and urban sites. Regulatory frameworks are available in each country. Mobile consulting services are operating through provider platforms ( n = 5–17) and, at the community level, some direct experience of mobile consulting with healthcare workers using their own phones was reported – for emergencies, advice and care follow-up. Stakeholder willingness was high, provided challenges are addressed in technology, infrastructure, data security, confidentiality, acceptability and health system integration. Mobile consulting can reduce affordability barriers and facilitate care-seeking practices.ConclusionsThere are indications of readiness for mobile consulting in communities with minimal resources. However, wider system strengthening is needed to bolster referrals, specialist services, laboratories and supply chains to fully realise the continuity of care and responsiveness that mobile consulting services offer, particularly during/beyond coronavirus disease 2019.

BackgroundImage segmentation is an essential step in the analysis and subsequent characterisation of brain tumours through magnetic resonance imaging. In the literature, segmentation methods are empowered by open-access magnetic resonance imaging datasets, such as the brain tumour segmentation dataset. Moreover, with the increased use of artificial intelligence methods in medical imaging, access to larger data repositories has become vital in method development.PurposeTo determine what automated brain tumour segmentation techniques can medical imaging specialists and clinicians use to identify tumour components, compared to manual segmentation.MethodsWe conducted a systematic review of 572 brain tumour segmentation studies during 2015–2020. We reviewed segmentation techniques using T1-weighted, T2-weighted, gadolinium-enhanced T1-weighted, fluid-attenuated inversion recovery, diffusion-weighted and perfusion-weighted magnetic resonance imaging sequences. Moreover, we assessed physics or mathematics-based methods, deep learning methods, and software-based or semi-automatic methods, as applied to magnetic resonance imaging techniques. Particularly, we synthesised each method as per the utilised magnetic resonance imaging sequences, study population, technical approach (such as deep learning) and performance score measures (such as Dice score).Statistical testsWe compared median Dice score in segmenting the whole tumour, tumour core and enhanced tumour.ResultsWe found that T1-weighted, gadolinium-enhanced T1-weighted, T2-weighted and fluid-attenuated inversion recovery magnetic resonance imaging are used the most in various segmentation algorithms. However, there is limited use of perfusion-weighted and diffusion-weighted magnetic resonance imaging. Moreover, we found that the U-Net deep learning technology is cited the most, and has high accuracy (Dice score 0.9) for magnetic resonance imaging-based brain tumour segmentation.ConclusionU-Net is a promising deep learning technology for magnetic resonance imaging-based brain tumour segmentation. The community should be encouraged to contribute open-access datasets so training, testing and validation of deep learning algorithms can be improved, particularly for diffusion- and perfusion-weighted magnetic resonance imaging, where there are limited datasets available.

Brain tumours are the most common cause of cancer death in children. Molecular studies have greatly improved our understanding of these tumours but tumour metabolism is underexplored. Metabolites measured in vivo have been reported as prognostic biomarkers of these tumours but analysis of surgically resected tumour tissue allows a more extensive set of metabolites to be measured aiding biomarker discovery and providing validation of in vivo findings. In this study, metabolites were quantified across a range of paediatric brain tumours using 1H-High-Resolution Magic Angle Spinning nuclear magnetic resonance spectroscopy (HR-MAS) and their prognostic potential investigated. HR-MAS was performed on pre-treatment frozen tumour tissue from a single centre. Univariate and multivariate Cox regression was used to examine the ability of metabolites to predict survival. The models were cross validated using C-indices and further validated by splitting the cohort into two. Higher concentrations of glutamine were predictive of a longer overall survival, whilst higher concentrations of lipids were predictive of a shorter overall survival. These metabolites were predictive independent of diagnosis, as demonstrated in multivariate Cox regression models. Whilst accurate quantification of metabolites such as glutamine in vivo is challenging, metabolites show promise as prognostic markers due to development of optimised detection methods and increasing use of 3 T clinical scanners.

Aim\ud \ud We have limited understanding of which risk factors contribute to increased readmission rates amongst people discharged from hospital with diabetes. We aim to complete the first review of its kind, to identify, in a systematic way, known risk factors for hospital readmission amongst people with diabetes, in order to better understand this costly complication.\ud \ud Method\ud \ud The review was prospectively registered in the PROSPERO database. Risk factors were identified through systematic review of literature in PubMed, EMBASE & SCOPUS databases, performed independently by two authors prior to data extraction, with quality assessment and semi-quantitative synthesis according to PRISMA guidelines.\ud \ud Results\ud \ud Eighty-three studies were selected for inclusion, predominantly from the United States, and utilising retrospective analysis of local or regional data sets. 76 distinct statistically significant risk factors were identified across 48 studies. The most commonly identified risk factors were; co-morbidity burden, age, race and insurance type. Few studies conducted power calculations; unstandardized effect sizes were calculated for the majority of statistically significant risk factors.\ud \ud Conclusion\ud \ud This review is important in assessing the current state of the literature and in supporting development of interventions to reduce readmission risk. Furthermore, it provides an important foundation for development of rigorous, pre-specified risk prediction models.

Older age is associated with an increased accumulation of multiple chronic conditions. The clinical management of patients suffering from multiple chronic conditions is very complex, disconnected and time-consuming with the traditional care settings. Integrated care is a means to address the growing demand for improved patient experience and health outcomes of multimorbid and long-term care patients. Care planning is a prevalent approach of integrated care, where the aim is to deliver more personalized and targeted care creating shared care plans by clearly articulating the role of each provider and patient in the care process. In this paper, we present a method and corresponding implementation of a semi-automatic care plan management tool, integrated with clinical decision support services which can seamlessly access and assess the electronic health records (EHRs) of the patient in comparison with evidence based clinical guidelines to suggest personalized recommendations for goals and interventions to be added to the individualized care plans. We also report the results of usability studies carried out in four pilot sites by patients and clinicians., Graphical abstract Unlabelled Image

Multimorbid patients are prescribed a number of medications in parallel, which may often interact with each other, resulting in adverse effects. However, clinical guidelines on prescription of medications predominantly focus on individual conditions do not consider the guidance in the context of other guidelines, resulting in conflicts. C3-Cloud is an integrated care architecture managing multimorbidity, which amongst others, provides clinical decision support, based on reconciled guidelines, and active monitoring of drug interactions. To identify the severe interactions that resulted from multimorbidity management, in order to reevaluate guidelines as well as to identify knowledge gaps in prescribing practice. Method: Descriptive statistical analysis of interactions identified by the C3-Cloud clinical decision support, collected from the C3-Cloud FHIR repository. As part of a feasibility study, a number of interactions were identified, along with variable practice in how chemicals are represented in the EHR. 191 known severe interactions were identified. The Atorvastatin/Verapamil interaction was the most frequent. The approach has identified a number of interactions where the severity was not available, highlighting the need for further clinical review.

Multimorbid patients are prescribed a number of medications in parallel, which may often interact with each other, resulting in adverse effects. However, clinical guidelines on prescription of medications predominantly focus on individual conditions do not consider the guidance in the context of other guidelines, resulting in conflicts. C3-Cloud is an integrated care architecture managing multimorbidity, which amongst others, provides clinical decision support, based on reconciled guidelines, and active monitoring of drug interactions. To identify the severe interactions that resulted from multimorbidity management, in order to reevaluate guidelines as well as to identify knowledge gaps in prescribing practice. Method: Descriptive statistical analysis of interactions identified by the C3-Cloud clinical decision support, collected from the C3-Cloud FHIR repository. As part of a feasibility study, a number of interactions were identified, along with variable practice in how chemicals are represented in the EHR. 191 known severe interactions were identified. The Atorvastatin/Verapamil interaction was the most frequent. The approach has identified a number of interactions where the severity was not available, highlighting the need for further clinical review.

ObjectivesTo measure pregnancy outcome following attendance at a recurrent miscarriage service and identify factors that influence outcome.DesignProspective, observational electronic cohort study.SettingParticipants attending specialist recurrent miscarriage clinic, within a tertiary centre, with a history of two or more pregnancy losses. The clinic serves a diverse population (33% of residents belong in a minority ethnic group and over 33% in low-income households). Participant data were recorded on a bespoke study database, ‘Tommy’s Net’.Participants777 women consented to participate. 639 (82%) women continued within the cohort, and 138 were lost to follow up. Mean age of active participants was 34 years for women and 37 years for partners, with a mean of 3.5 (1-19) previous pregnancy losses. Rates of obesity, BMI>30 (maternal: 23.8%, paternal: 22.4%), smoking (maternal:7.4%, paternal: 19.4%) and alcohol consumption (maternal: 50%, paternal: 79.2%) were high and 55% of participants were not taking folic acid.Outcome measuresBiannual collection of pregnancy outcomes (ongoing pregnancy, live birth, still birth, pregnancy loss prior to 24 weeks), either through prompted self-reporting, or existing hospital systems.Results639 (82%) women were followed up. 404 reported conception and 106 reported no pregnancy, at least 6 months following registration. Of those that conceived, 72.8% (294/404) had a viable pregnancy. Analysis identified a conception of rate of over 80% and viable pregnancy rate of 60% two years after attending the recurrent miscarriage clinic. 30% of couples had potentially modifiable risk factors for miscarriage.ConclusionsTommy’s Net provides a secure electronic repository on data for couples with recurrent pregnancy loss and associated outcomes. The study identified that subfertility, as well as repeated miscarriage, contributed to failure to achieve live birth. Study findings can enable comparison of clinic management strategies and inform the development of a personalized holistic care package.FunderTommy’s CharitySponsorUniversity Hospitals Coventry and Warwickshire (UHCW) NHS TrustTrial RegistrationInternational Standard Randomized Controlled Trial Number (ISRCTN) Registry ISRCTN17732518; https://doi.org/10.1186/ISRCTN17732518EthicsApproval from West Midlands-South Birmingham Regional Ethics Committee IRAS No: 213740, 2225751 REC Ref: 17/WM/0050: 17/WM/208Strengths and Limitations of this study (related to the method)The ‘Tommy’s Net’ e-repository and associated database contains baseline and prospective pregnancy outcome data from the largest known population of couples with recurrent miscarriage in the UK.Time to conception and viable pregnancy can be calculated from this data using time to event analysis.Obtaining follow up data is challenging but can be improved by using a variety of data collection methods.Follow up data is only requested biannually, therefore this is an inevitable lag in data collection.Limited use of the English language can be a barrier for participants completing the initial lengthy questionnaire.Key points20% of this recurrent miscarriage population do not conceive and two years after first consultation 40% have not had a viable pregnancy. Early identification of this group could help facilitate early referral to fertility services or targeted research.Miscarriage is physically and psychologically challenging. Some couples may decide not to try to conceive again because of this. Ensuring appropriate psychological support is essential.Preconception care is inadequate. Over one third of couples attend their initial consultation with modifiable risk factors known to impact on miscarriage. Tackling these should be a priority.Having a BMI over 30 and being a smoker is more common within this cohort in women that do not conceive. Targeting of these risk factors may improve conception rate.

Aims Magnetic resonance imaging (MRI) is a valuable tool for non-invasive diagnosis of paediatric brain tumours. The rarity of the disease dictates multi-centre studies and imaging biomarkers that are robust to protocol variability. We investigated diffusion tensor MRI (DT-MRI), combined with machine learning, as an aid to diagnosis and evaluated the robustness of the imaging metrics. Method A multi-centre cohort of 52 clinical DT-MRI scans (20 medulloblastomas (MB), 21 pilocytic astrocytomas (PA), 11 ependymomas (EP)) were analysed retrospectively. Histograms for regions of solid tumour for fractional anisotropy (FA), mean diffusivity (MD), pure anisotropic diffusion (q) and pure isotropic diffusion (p) were compared to assess diagnostic capability. Linear discriminate analysis (LDA) was used for classification and validated using leave-one-out-cross-validation (LOOCV). Results Histogram medians for FA, MD, q and p were all different between tumor groups (P Conclusion DT-MRI metrics from multi-centre acquisitions can classify paediatric brain tumours. FA is the least robust metric to protocol variability and q provides the most robust quantification of anisotropic behaviour.

Background Myocardial infarction (MI; location and extent of infarction) can be determined by late enhancement cardiac magnetic resonance (CMR) imaging, which requires the injection of a potentially harmful gadolinium-based contrast agent (GBCA). Alternatively, emerging research in the area of myocardial strain has shown potential to identify MI using strain values. Objective This study aims to identify the location of MI by developing an applied algorithmic method of circumferential strain (CS) values, which are derived through a novel hierarchical template matching (HTM) method. Methods HTM-based CS H-spread from end-diastole to end-systole was used to develop an applied method. Grid-tagging magnetic resonance imaging was used to calculate strain values in the left ventricular (LV) myocardium, followed by the 16-segment American Heart Association model. The data set was used with k-fold cross-validation to estimate the percentage reduction of H-spread among infarcted and noninfarcted LV segments. A total of 43 participants (38 MI and 5 healthy) who underwent CMR imaging were retrospectively selected. Infarcted segments detected by using this method were validated by comparison with late enhancement CMR, and the diagnostic performance of the applied algorithmic method was evaluated with a receiver operating characteristic curve test. Results The H-spread of the CS was reduced in infarcted segments compared with noninfarcted segments of the LV. The reductions were 30% in basal segments, 30% in midventricular segments, and 20% in apical LV segments. The diagnostic accuracy of detection, using the reported method, was represented by area under the curve values, which were 0.85, 0.82, and 0.87 for basal, midventricular, and apical slices, respectively, demonstrating good agreement with the late-gadolinium enhancement–based detections. Conclusions The proposed applied algorithmic method has the potential to accurately identify the location of infarcted LV segments without the administration of late-gadolinium enhancement. Such an approach adds the potential to safely identify MI, potentially reduce patient scanning time, and extend the utility of CMR in patients who are contraindicated for the use of GBCA.

ObjectiveRemote or mobile consulting (mConsulting) is being promoted to strengthen health systems, deliver universal health coverage and facilitate safe clinical communication during COVID-19 and beyond. We explored whether mConsulting is a viable option for communities with minimal resources in low- and middle-income countries (LMICs).MethodsWe reviewed evidence published since 2018 about mConsulting in LMICs and undertook a scoping study (pre-COVID) in two rural settings (Pakistan, Tanzania) and five urban slums (Kenya, Nigeria, Bangladesh), using policy/document review, secondary analysis of survey data (from the urban sites), and thematic analysis of interviews/workshops with community members, healthcare workers, digital/telecommunications experts, mConsulting providers, local and national decision-makers. Project advisory groups guided the study in each country.ResultsWe reviewed five empirical studies and seven reviews, analysed data from 5,219 urban slum households and engaged with 419 stakeholders in rural and urban sites. Regulatory frameworks are available in each country. mConsulting services are operating through provider platforms (n=5-17) and, at community-level, some direct experience of mConsulting with healthcare workers using their own phones was reported - for emergencies, advice and care follow-up. Stakeholder willingness was high, provided challenges are addressed in technology, infrastructure, data security, confidentiality, acceptability and health system integration. mConsulting can reduce affordability barriers and facilitate care-seeking practices.ConclusionsThere are indications of readiness for mConsulting in communities with minimal resources. However, wider system strengthening is needed to bolster referrals, specialist services, laboratories and supply-chains to fully realise the continuity of care and responsiveness that mConsulting services offer, particularly during/beyond COVID-19.

Background Relative cerebral blood volume (rCBV) measured using dynamic susceptibility-contrast MRI can differentiate between low- and high-grade pediatric brain tumors. Multicenter studies are required for translation into clinical practice. Objective We compared leakage-corrected dynamic susceptibility-contrast MRI perfusion parameters acquired at multiple centers in low- and high-grade pediatric brain tumors. Materials and methods Eighty-five pediatric patients underwent pre-treatment dynamic susceptibility-contrast MRI scans at four centers. MRI protocols were variable. We analyzed data using the Boxerman leakage-correction method producing pixel-by-pixel estimates of leakage-uncorrected (rCBVuncorr) and corrected (rCBVcorr) relative cerebral blood volume, and the leakage parameter, K2. Histological diagnoses were obtained. Tumors were classified by high-grade tumor. We compared whole-tumor median perfusion parameters between low- and high-grade tumors and across tumor types. Results Forty tumors were classified as low grade, 45 as high grade. Mean whole-tumor median rCBVuncorr was higher in high-grade tumors than low-grade tumors (mean ± standard deviation [SD] = 2.37±2.61 vs. –0.14±5.55; PP=0.010), remaining higher in high-grade tumors than low grade-tumors. Low-grade tumors, particularly pilocytic astrocytomas, showed T1-dominant leakage effects; high-grade tumors showed T2*-dominance (mean K2=0.017±0.049 vs. 0.002±0.017). Parameters varied with tumor type but not center. Median rCBVuncorr was higher (mean = 1.49 vs. 0.49; P=0.015) and K2 lower (mean = 0.005 vs. 0.016; P=0.013) in children who received a pre-bolus of contrast agent compared to those who did not. Leakage correction removed the difference. Conclusion Dynamic susceptibility-contrast MRI acquired at multiple centers helped distinguish between children’s brain tumors. Relative cerebral blood volume was significantly higher in high-grade compared to low-grade tumors and differed among common tumor types. Vessel leakage correction is required to provide accurate rCBV, particularly in low-grade enhancing tumors.

BACKGROUND Myocardial infarction (MI; location and extent of infarction) can be determined by late enhancement cardiac magnetic resonance (CMR) imaging, which requires the injection of a potentially harmful gadolinium-based contrast agent (GBCA). Alternatively, emerging research in the area of myocardial strain has shown potential to identify MI using strain values. OBJECTIVE This study aims to identify the location of MI by developing an applied algorithmic method of circumferential strain (CS) values, which are derived through a novel hierarchical template matching (HTM) method. METHODS HTM-based CS H-spread from end-diastole to end-systole was used to develop an applied method. Grid-tagging magnetic resonance imaging was used to calculate strain values in the left ventricular (LV) myocardium, followed by the 16-segment American Heart Association model. The data set was used with k-fold cross-validation to estimate the percentage reduction of H-spread among infarcted and noninfarcted LV segments. A total of 43 participants (38 MI and 5 healthy) who underwent CMR imaging were retrospectively selected. Infarcted segments detected by using this method were validated by comparison with late enhancement CMR, and the diagnostic performance of the applied algorithmic method was evaluated with a receiver operating characteristic curve test. RESULTS The H-spread of the CS was reduced in infarcted segments compared with noninfarcted segments of the LV. The reductions were 30% in basal segments, 30% in midventricular segments, and 20% in apical LV segments. The diagnostic accuracy of detection, using the reported method, was represented by area under the curve values, which were 0.85, 0.82, and 0.87 for basal, midventricular, and apical slices, respectively, demonstrating good agreement with the late-gadolinium enhancement–based detections. CONCLUSIONS The proposed applied algorithmic method has the potential to accurately identify the location of infarcted LV segments without the administration of late-gadolinium enhancement. Such an approach adds the potential to safely identify MI, potentially reduce patient scanning time, and extend the utility of CMR in patients who are contraindicated for the use of GBCA.

Increasing numbers of intelligent healthcare applications are developed by analysing big data, on which they are trained. It is necessary to assure that such applications will be safe for patients; this entails validation against datasets. But datasets cannot be shared easily, due to privacy, and consent issues, resulting in delaying innovation. Realistic Synthetic Datasets (RSDs), equivalent to the real datasets, are seen as a solution to this.To develop the outline for safety justification of an application, validated with an RSD, and identify the safety evidence the RSD developers will need to generate.Assurance case argument development approaches were used, including high level data related risk identification.An outline of the justification of such applications, focusing on the contribution of the RSD.Use of RSD will require specific arguments and evidence, which will affect the adopted methods. Mutually supporting arguments can result in a compelling justification.

Assurance of digital health interventions involves, amongst others, clinical validation, which requires large datasets to test the application in realistic clinical scenarios. Development of such datasets is time consuming and challenging in terms of maintaining patient anonymity and consent.The development of synthetic datasets that maintain the statistical properties of the real datasets.An artificial neural network based, generative adversarial network was implemented and trained, using numerical and categorical variables, including ICD-9 codes from the MIMIC III dataset, to produce a synthetic dataset.The synthetic dataset, exhibits a correlation matrix highly similar to the real dataset, good Jaccard similarity and passing the KS test.The proof of concept was successful with the approach being promising for further work.

Digital health interventions (DHIs) enable improvements in health strategy and address health system challenges. The World Health Organization provides a formal classification for DHIs. However, safety claims, about such interventions, vary in quality and are often vague as to how they are communicated between technical, clinical experts and stakeholders. By combining the classifications with a method of safety analysis and justification, we postulate confidence in the safety of digital technology. Confidence is resulting from the application of the framework to the DHI, using defined health system challenges. The framework and derived safety justifications can be applied to any DHI. It can serve as guideline for health strategy, regulatory and standards based compliance.

Objective To analyse mortality statistics in the United Kingdom during the initial phases of the severe acute respiratory coronavirus 2 (SARS-CoV-2) pandemic and to understand the impact of the pandemic on national mortality. Methods Retrospective review of weekly national mortality statistics in the United Kingdom over the past 5 years, including subgroup analysis of respiratory mortality rates. Results During the early phases of the SARS-CoV-2 pandemic in the first months of 2020, there were consistently fewer deaths per week compared with the preceding 5 years. This pattern was not observed at any other time within the past 5 years. We have termed this phenomenon the “SARS-CoV-2 paradox.” We postulate potential explanations for this seeming paradox and explore the implications of these data. Conclusions Paradoxically, but potentially importantly, lower rather than higher weekly mortality rates were observed during the early stages of the SARS-CoV-2 pandemic. This paradox may have implications for current and future healthcare utilisation. A rebound increase in non-SARS-CoV-2 mortality later this year might coincide with the peak of SARS-CoV-2 admissions and mortality.

Background New patient-centric integrated care models are enabled by the capability to exchange the patient’s data amongst stakeholders, who each specialise in different aspects of the patient’s care. This requires a robust, trusted and flexible mechanism for patients to offer consent to share their data. Furthermore, new IT technologies make it easier to give patients more control over their data, including the right to revoke consent. These characteristics challenge the traditional paper-based, single-organisation-led consent process. The Dovetail digital consent application uses a mobile application and blockchain based infrastructure to offer this capability, as part of a pilot allowing patients to have their data shared amongst digital tools, empowering patients to manage their condition within an integrated care setting. Objective To evaluate patient perceptions towards existing consent processes, and the Dovetail blockchain based digital consent application as a means to manage data sharing in the context of diabetes care. Method Patients with diabetes at a General Practitioner practice were recruited. Data were collected using focus groups and questionnaires. Thematic analysis of the focus group transcripts and descriptive statistics of the questionnaires was performed. Results There was a lack of understanding of existing consent processes in place, and many patients did not have any recollection of having previously given consent. The digital consent application received favourable feedback, with patients recognising the value of the capability offered by the application. Patients overwhelmingly favoured the digital consent application over existing practice. Conclusions Digital consent was received favourably, with patients recognising that it addresses the main limitations of the current process. Feedback on potential improvements was received. Future work includes confirmation of results in a broader demographic sample and across multiple conditions.