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Abstract Introduction This paper outlines the design, implementation, and usability study results of the patient empowerment process for chronic disease management, using Patient Reported Outcome Measurements and Shared Decision-Making Processes. Background The ADLIFE project aims to develop innovative, digital health solutions to support personalized, integrated care for patients with severe long-term conditions such as Chronic Obstructive Pulmonary Disease, and/or Chronic Heart Failure. Successful long-term management of patients with chronic conditions requires active patient self-management and a proactive involvement of patients in their healthcare and treatment. This calls for a patient-provider partnership within an integrated system of collaborative care, supporting self-management, shared-decision making, collection of patient reported outcome measures, education, and follow-up. Methods ADLIFE follows an outcome-based and patient-centered approach where PROMs represent an especially valuable tool to evaluate the outcomes of the care delivered. We have selected 11 standardized PROMs for evaluating the most recent patients’ clinical context, enabling the decision-making process, and personalized care planning. The ADLIFE project implements the "SHARE approach’ for enabling shared decision-making via two digital platforms for healthcare professionals and patients. We have successfully integrated PROMs and shared decision-making processes into our digital toolbox, based on an international interoperability standard, namely HL7 FHIR. A usability study was conducted with 3 clinical sites with 20 users in total to gather feedback and to subsequently prioritize updates to the ADLIFE toolbox. Results User satisfaction is measured in the QUIS7 questionnaire on a 9-point scale in the following aspects: overall reaction, screen, terminology and tool feedback, learning, multimedia, training material and system capabilities. With all the average scores above 6 in all categories, most respondents have a positive reaction to the ADLIFE PEP platform and find it easy to use. We have identified shortcomings and have prioritized updates to the platform before clinical pilot studies are initiated. Conclusions Having finalized design, implementation, and pre-deployment usability studies, and updated the tool based on further feedback, our patient empowerment mechanisms enabled via PROMs and shared decision-making processes are ready to be piloted in clinal settings. Clinical studies will be conducted based at six healthcare settings across Spain, UK, Germany, Denmark, and Israel.

Objective Establish a relationship between digital health intervention (DHI) and health system challenges (HSCs), as defined by the World Health Organization; within the context of hazard identification (HazID), leading to safety claims. To improve the justification of safety of DHIs and provide a standardised approach to hazard assessment through common terminology, ontology and simplification of safety claims. Articulation of results, to provide guidance for health strategy and regulatory/standards-based compliance. Methods We categorise and analyse hazards using a qualitative HazID study. This method utilises a synergy between simplicity of DHI intended use and the interaction from a multidisciplinary team (technologists and health informaticians) in the hazard analysis of the subject under assessment as an influencing factor. Although there are other methodologies available for hazard assessment. We examine the hazards identified and associated failures to articulate the improvements in the quality of safety claims. Results Applying the method provides the hazard assessment and helps generate the assurance case. Justification of safety is made and elicits confidence in safety claim. Controls to hazards contribute to meeting the HSC. Conclusions This method of hazard assessment, analysis and the use of ontologies (DHI & HSC) improves the justification of safety claim and evidence articulation.

IntroductionThe CAREPATH Project aims to develop a patient-centered integrated care platform tailored to older adults with multimorbidity, including mild cognitive impairment (MCI) or mild dementia. Our goal is to empower multidisciplinary care teams to craft personalized holistic care plans while adhering to evidence-based guidelines. This necessitates the creation of clear specifications for clinical decision support (CDS) services, consolidating guidance from multiple evidence-based clinical guidelines. Thus, a co-creation approach involving both clinical and technical experts is essential.MethodsThis paper outlines a robust methodology for generating implementable specifications for CDS services to automate clinical guidelines. We have established a co-creation framework to facilitate collaborative exploration of clinical guidelines between clinical experts and software engineers. We have proposed an open, repeatable, and traceable method for translating evidence-based guideline narratives into implementable specifications of CDS services. Our approach, based on international standards such as CDS-Hooks and HL7 FHIR, enhances interoperability and potential adoption of CDS services across diverse healthcare systems.ResultsThis methodology has been followed to create implementable specifications for 65 CDS services, automating CAREPATH consensus guideline consolidating guidance from 25 selected evidence-based guidelines. A total of 296 CDS rules have been formally defined, with input parameters defined as clinical concepts bound to FHIR resources and international code systems. Outputs include 346 well-defined CDS Cards, offering clear guidance for care plan activities and goal suggestions. These specifications have led to the implementation of 65 CDS services integrated into the CAREPATH Adaptive Integrated Care Platform.DiscussionOur methodology offers a systematic, replicable process for generating CDS specifications, ensuring consistency and reliability across implementation. By fostering collaboration between clinical expertise and technical proficiency, we enhance the quality and relevance of generated specifications. Clear traceability enables stakeholders to track the development process and ensure adherence to guideline recommendations.

Objective The COVID-19 pandemic accelerated changes to clinical research methodology, with clinical studies being carried out via online/remote means. This mixed-methods study aimed to identify which digital tools are currently used across all stages of clinical research by stakeholders in clinical, health and social care research and investigate their experience using digital tools.Design Two online surveys followed by semistructured interviews were conducted. Interviews were audiorecorded, transcribed and analysed thematically.Setting, participants To explore the digital tools used since the pandemic, survey participants (researchers and related staff (n=41), research and development staff (n=25)), needed to have worked on clinical, health or social care research studies over the past 2 years (2020–2022) in an employing organisation based in the West Midlands region of England (due to funding from a regional clinical research network (CRN)). Survey participants had the opportunity to participate in an online qualitative interview to explore their experiences of digital tools in greater depth (n=8).Results Six themes were identified in the qualitative interviews: ‘definition of a digital tool in clinical research’; ‘impact of the COVID-19 pandemic’; ‘perceived benefits/drawbacks of digital tools’; ‘selection of a digital tool’; ‘barriers and overcoming barriers’ and ‘future digital tool use’. The context of each theme is discussed, based on the interview results.Conclusions Findings demonstrate how digital tools are becoming embedded in clinical research, as well as the breadth of tools used across different research stages. The majority of participants viewed the tools positively, noting their ability to enhance research efficiency. Several considerations were highlighted; concerns about digital exclusion; need for collaboration with digital expertise/clinical staff, research on tool effectiveness and recommendations to aid future tool selection. There is a need for the development of resources to help optimise the selection and use of appropriate digital tools for clinical research staff and participants.

Summary: Background: The malignant childhood brain tumour, medulloblastoma, is classified clinically into molecular groups which guide therapy. DNA-methylation profiling is the current classification ‘gold-standard’, typically delivered 3–4 weeks post-surgery. Pre-surgery non-invasive diagnostics thus offer significant potential to improve early diagnosis and clinical management. Here, we determine tumour metabolite profiles of the four medulloblastoma groups, assess their diagnostic utility using tumour tissue and potential for non-invasive diagnosis using in vivo magnetic resonance spectroscopy (MRS). Methods: Metabolite profiles were acquired by high-resolution magic-angle spinning NMR spectroscopy (MAS) from 86 medulloblastomas (from 59 male and 27 female patients), previously classified by DNA-methylation array (WNT (n = 9), SHH (n = 22), Group3 (n = 21), Group4 (n = 34)); RNA-seq data was available for sixty. Unsupervised class-discovery was performed and a support vector machine (SVM) constructed to assess diagnostic performance. The SVM classifier was adapted to use only metabolites (n = 10) routinely quantified from in vivo MRS data, and re-tested. Glutamate was assessed as a predictor of overall survival. Findings: Group-specific metabolite profiles were identified; tumours clustered with good concordance to their reference molecular group (93%). GABA was only detected in WNT, taurine was low in SHH and lipids were high in Group3. The tissue-based metabolite SVM classifier had a cross-validated accuracy of 89% (100% for WNT) and, adapted to use metabolites routinely quantified in vivo, gave a combined classification accuracy of 90% for SHH, Group3 and Group4. Glutamate predicted survival after incorporating known risk-factors (HR = 3.39, 95% CI 1.4–8.1, p = 0.025). Interpretation: Tissue metabolite profiles characterise medulloblastoma molecular groups. Their combination with machine learning can aid rapid diagnosis from tissue and potentially in vivo. Specific metabolites provide important information; GABA identifying WNT and glutamate conferring poor prognosis. Funding: Children with Cancer UK, Cancer Research UK, Children’s Cancer North and a Newcastle University PhD studentship.

Objective Integrated care and digital health technology interventions are promising approaches to coordinate services for people living with chronic conditions, across different care settings and providers. The EU-funded ADLIFE project intends to provide digitally integrated personalized care to improve and maintain patients’ health with advanced chronic conditions. This study conducted a qualitative assessment of contextual factors prior to the implementation of the ADLIFE digital health platforms at the German pilot site. The results of the assessment are then used to derive recommendations for action for the subsequent implementation, and for evaluation of the other pilot sites. Methods Qualitative interviews with healthcare professionals and IT experts were conducted at the German pilot site. The interviews followed a semi-structured interview guideline, based on the HOT-fit framework, focusing on organizational, technological, and human factors. All interviews were audio recorded, transcribed, and subsequently analysed following qualitative content analysis. Results The results of the 18 interviews show the interviewees’ high openness and motivation to use new innovative digital solutions, as well as an apparent willingness of cooperation between different healthcare professionals. Challenges include limited technical infrastructure and large variability of software to record health data, lacking standards and interfaces. Conclusions Considering contextual factors on different levels is critical for the success of implementing innovations in healthcare and the transfer into other settings. In our study, the HOT-fit framework proved suitable for assessing contextual factors, when implementing IT innovations in healthcare. In a next step, the methodological approach will be transferred to the six other European pilot sites, participating in the project, for a cross-national assessment of contextual factors.

Background Mobile health (mHealth) technology is being used predominantly in low- and middle-income countries. Developing countries with low level of investment in health infrastructure can augment existing capacity by adopting low-cost affordable technology. The aim of the review was to summarize the available evidence on mHealth interventions that aimed at increasing the utilization of Maternal and Child Health (MCH) care services. Further, this review investigated the barriers which prevent the use of mHealth among both health care workers as well as beneficiaries. Methodology A scoping review of literature was undertaken using the five-stage framework developed by Arksey and O’Malley. The articles published between 1990 and 2021 were retrieved from three databases (PubMed, Cochrane Reviews, and Google Scholar) and grey literature for this review. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist was followed to present the findings. Result A total of 573 studies were identified. After removing duplicates, studies not related to mHealth and MCH and publications of systematic reviews and protocols for studies, a total of 28 studies were selected for review. The study design of the research articles which appeared during the search process were mostly observational, cross-sectional, and randomized controlled trials (RCTs). We have classified the studies into four categories based on the outcomes for which the mHealth intervention was implemented: MCH care services, child immunization, nutrition services, and perceptions of stakeholders toward using technology for improving MCH outcomes. Conclusion This brief review concludes that mHealth interventions can improve access to MCH services. However, further studies based on large sample size and strong research design are recommended.

Abstract Brain tumors represent the highest cause of mortality in the pediatric oncological population. Diagnosis is commonly performed with magnetic resonance imaging. Survival biomarkers are challenging to identify due to the relatively low numbers of individual tumor types. 69 children with biopsy-confirmed brain tumors were recruited into this study. All participants had perfusion and diffusion weighted imaging performed at diagnosis. Imaging data were processed using conventional methods, and a Bayesian survival analysis performed. Unsupervised and supervised machine learning were performed with the survival features, to determine novel sub-groups related to survival. Sub-group analysis was undertaken to understand differences in imaging features. Survival analysis showed that a combination of diffusion and perfusion imaging were able to determine two novel sub-groups of brain tumors with different survival characteristics (p

BackgroundThere is an increasing need to organize the care around the patient and not the disease, while considering the complex realities of multiple physical and psychosocial conditions, and polypharmacy. Integrated patient-centered care delivery platforms have been developed for both patients and clinicians. These platforms could provide a promising way to achieve a collaborative environment that improves the provision of integrated care for patients via enhanced information and communication technology solutions for semiautomated clinical decision support. ObjectiveThe Collaborative Care and Cure Cloud project (C3-Cloud) has developed 2 collaborative computer platforms for patients and members of the multidisciplinary team (MDT) and deployed these in 3 different European settings. The objective of this study is to pilot test the platforms and evaluate their impact on patients with 2 or more chronic conditions (diabetes mellitus type 2, heart failure, kidney failure, depression), their informal caregivers, health care professionals, and, to some extent, health care systems. MethodsThis paper describes the protocol for conducting an evaluation of user experience, acceptability, and usefulness of the platforms. For this, 2 “testing and evaluation” phases have been defined, involving multiple qualitative methods (focus groups and surveys) and advanced impact modeling (predictive modeling and cost-benefit analysis). Patients and health care professionals were identified and recruited from 3 partnering regions in Spain, Sweden, and the United Kingdom via electronic health record screening. ResultsThe technology trial in this 4-year funded project (2016-2020) concluded in April 2020. The pilot technology trial for evaluation phases 3 and 4 was launched in November 2019 and carried out until April 2020. Data collection for these phases is completed with promising results on platform acceptance and socioeconomic impact. We believe that the phased, iterative approach taken is useful as it involves relevant stakeholders at crucial stages in the platform development and allows for a sound user acceptance assessment of the final product. ConclusionsPatients with multiple chronic conditions often experience shortcomings in the care they receive. It is hoped that personalized care plan platforms for patients and collaboration platforms for members of MDTs can help tackle the specific challenges of clinical guideline reconciliation for patients with multimorbidity and improve the management of polypharmacy. The initial evaluative phases have indicated promising results of platform usability. Results of phases 3 and 4 were methodologically useful, yet limited due to the COVID-19 pandemic. Trial RegistrationClinicalTrials.gov NCT03834207; https://clinicaltrials.gov/ct2/show/NCT03834207 International Registered Report Identifier (IRRID)RR1-10.2196/21994

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.

Background Image 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. Purpose To determine what automated brain tumour segmentation techniques can medical imaging specialists and clinicians use to identify tumour components, compared to manual segmentation. Methods We 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 tests We compared median Dice score in segmenting the whole tumour, tumour core and enhanced tumour. Results We 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. Conclusion U-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.

Abstract Background Patients with diabetes are at an increased risk of readmission and mortality when discharged from hospital. Existing research identifies statistically significant risk factors that are thought to underpin these outcomes. Increasingly, these risk factors are being used to create risk prediction models, and target risk modifying interventions. These risk factors are typically reported in the literature accompanied by unstandardized effect sizes, which makes comparisons difficult. We demonstrate an assessment of variation between standardised effect sizes for such risk factors across care outcomes and patient cohorts. Such an approach will support development of more rigorous risk stratification tools and better targeting of intervention measures. Methods Data was extracted from the electronic health record of a major tertiary referral centre, over a 3-year period, for all patients discharged from hospital with a concurrent diagnosis of diabetes mellitus. Risk factors selected for extraction were pre-specified according to a systematic review of the research literature. Standardised effect sizes were calculated for all statistically significant risk factors, and compared across patient cohorts and both readmission & mortality outcome measures. Results Data was extracted for 46,357 distinct admissions patients, creating a large dataset of approximately 10,281,400 data points. The calculation of standardized effect size measures allowed direct comparison. Effect sizes were noted to be larger for mortality compared to readmission, as well as for being larger for surgical and type 1 diabetes cohorts of patients. Conclusions The calculation of standardised effect sizes is an important step in evaluating risk factors for healthcare events. This will improve our understanding of risk and support the development of more effective risk stratification tools to support patients to make better informed decisions at discharge from hospital.

Background COVID-19 placed significant challenges on healthcare systems. People with diabetes are at high risk of severe COVID-19 with poor outcomes. We describe the first reported use of inpatient digital flash glucose monitoring devices in a UK NHS hospital to support management of people with diabetes hospitalized for COVID-19. Methods Inpatients at University Hospitals Coventry & Warwickshire (UHCW) NHS Trust with COVID-19 and diabetes were considered for digitally enabled flash glucose monitoring during their hospitalization. Glucose monitoring data were analysed, and potential associations were explored between relevant parameters, including time in hypoglycaemia, hyperglycaemia, and in range, glycated haemoglobin (HbA1c), average glucose, body mass index (BMI), and length of stay. Results During this pilot, digital flash glucose monitoring devices were offered to 25 inpatients, of whom 20 (type 2/type 1: 19/1; mean age: 70.6 years; mean HbA1c: 68.2 mmol/mol; mean BMI: 28.2 kg/m 2 ) accepted and used these (80% uptake). In total, over 2788 h of flash glucose monitoring were recorded for these inpatients with COVID-19 and diabetes. Length of stay was not associated with any of the studied variables (all p-values >0.05). Percentage of time in hyperglycaemia exhibited significant associations with both percentage of time in hypoglycaemia and percentage of time in range, as well as with HbA1c (all p-values

Objective Remote 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. Methods We 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. Results We 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. Conclusions There 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.

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. Keywords: Integrated care, Chronic disease management, Clinical decision support systems, Evidence based clinical guidelines

Aims/Objectives Extensive research considers associations between inpatient glycaemic control and outcomes during hospital admission; this cautions against overly tight glycaemic targets. Little research considers glycaemic control following hospital discharge. This is despite a clear understanding that people with diabetes are at increased risk of negative outcomes, following discharge. We evaluate absolute and relative Hba1c values, and frequency of Hba1c monitoring, on readmission and mortality rates for people discharged from hospital with diabetes. Methods All discharges (n = 46,357) with diabetes from a major tertiary referral centre over 3 years were extracted, including biochemistry data. We conducted an evaluation of association between Hba1c, mortality and readmission, statistical significance and standardised Cohen’s D effect size calculations. Results 399 patients had a Hba1c performed during their admission. 3,138 patients had a Hba1c within 1 year of discharge. Mean average Hba1c for readmissions was 57.82 vs 60.39 for not readmitted (p = 0.009, Cohen’s D 0.28). Mean average number of days to Hba1c testing in readmitted was 97 vs 113 for those not readmitted (p = 0.00006, Cohen’s D 0.39). Further evaluation of mortality outcomes, cohorts of T1DM and T2DM and association of relative change in Hba1c was performed. Conclusions Lower Hba1c values following discharge from hospital are significantly associated with increased risk of readmission, as is a shorter duration until testing. Similar patterns observed for mortality. Findings particularly prominent for T1DM. Further research needed to consider underlying causation and design of appropriate risk stratification models.

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.

Objective The poorest populations of the world lack access to quality healthcare. We defined the key components of consulting via mobile technology (mConsulting), explored whether mConsulting can fill gaps in access to quality healthcare for poor and spatially marginalised populations (specifically rural and slum populations) of low- and middle-income countries, and considered the implications of its take-up. Methods We utilised realist methodology. First, we undertook a scoping review of mobile health literature and searched for examples of mConsulting. Second, we formed our programme theories and identified potential benefits and hazards for deployment of mConsulting for poor and spatially marginalised populations. Finally, we tested our programme theories against existing frameworks and identified published evidence on how and why these benefits/hazards are likely to accrue. Results We identified the components of mConsulting, including their characteristics and range. We discuss the implications of mConsulting for poor and spatially marginalised populations in terms of competent care, user experience, cost, workforce, technology, and the wider health system. Conclusions For the many dimensions of mConsulting, how it is structured and deployed will make a difference to the benefits and hazards of its use. There is a lack of evidence of the impact of mConsulting in populations that are poor and spatially marginalised, as most research on mConsulting has been undertaken where quality healthcare exists. We suggest that mConsulting could improve access to quality healthcare for these populations and, with attention to how it is deployed, potential hazards for the populations and wider health system could be mitigated.

Research into the ability to coordinate one's movements with external cues has focussed on the use of simple rhythmic, auditory and visual stimuli, or interpersonal coordination with another person. Coordinating movements with a virtual avatar has not been explored, in the context of responses to temporal cues. To determine whether cueing of movements using a virtual avatar is effective, people's ability to accurately coordinate with the stimuli needs to be investigated. Here we focus on temporal cues, as we know from timing studies that visual cues can be difficult to follow in the timing context. Real stepping movements were mapped onto an avatar using motion capture data. Healthy participants were then motion captured whilst stepping in time with the avatar's movements, as viewed through a virtual reality headset. The timing of one of the avatar step cycles was accelerated or decelerated by 15% to create a temporal perturbation, for which participants would need to correct to, in order to remain in time. Step onset times of participants relative to the corresponding step-onsets of the avatar were used to measure the timing errors (asynchronies) between them. Participants completed either a visual-only condition, or auditory-visual with footstep sounds included, at two stepping tempo conditions (Fast: 400ms interval, Slow: 800ms interval). Participants' asynchronies exhibited slow drift in the Visual-Only condition, but became stable in the Auditory-Visual condition. Moreover, we observed a clear corrective response to the phase perturbation in both the fast and slow tempo auditory-visual conditions. We conclude that an avatar's movements can be used to influence a person's own motion, but should include relevant auditory cues congruent with the movement to ensure a suitable level of entrainment is achieved. This approach has applications in physiotherapy, where virtual avatars present an opportunity to provide the guidance to assist patients in adhering to prescribed exercises.

The imaging and subsequent accurate diagnosis of paediatric brain tumours presents a radiological challenge, with magnetic resonance imaging playing a key role in providing tumour specific imaging information. Diffusion weighted and perfusion imaging are commonly used to aid the non-invasive diagnosis of children's brain tumours, but are usually evaluated by expert qualitative review. Quantitative studies are mainly single centre and single modality.The aim of this work was to combine multi-centre diffusion and perfusion imaging, with machine learning, to develop machine learning based classifiers to discriminate between three common paediatric tumour types.The results show that diffusion and perfusion weighted imaging of both the tumour and whole brain provide significant features which differ between tumour types, and that combining these features gives the optimal machine learning classifier with >80% predictive precision. This work represents a step forward to aid in the non-invasive diagnosis of paediatric brain tumours, using advanced clinical imaging. Keywords: Perfusion, Diffusion, Machine learning

Introduction: A growing share of the population in OECD countries is of age 65 and over, expected to reach 22% by 2030 (compared to 15% in 2010). Life expectancy has also significantly increased. People at age of 65 are expected to live for an average of 21 and 17 years for women and men; an almost 40% increase since 1960. The profound success in improving life expectancy has resulted in a new set of challenges. Challenge: Shift of resources was necessary, redirected to address the complex needs of multi-morbidity patients. Furthermore, patients’ needs are not effectively met by current care models, which tend to operate in isolation. This results in static services that patients need to wander. It is common for patients to revisit all levels of care discussing their needs, and reconciling potentially conflicting objectives amongst their conditions (e.g., incompatible lifestyle goals, adverse drug effects and side-effects, undetected conditions). Optimal collaboration and coordination between professionals in the delivery of integrated care have become essential requirements for the provision of high-quality care. Coordinated care aims for the orderly arrangement of individual and group efforts providing unity of action in pursuit of a common goal. Method: C3-Cloud is an e-health based ICT system, offering integrated, patient-centred care, considering all aspects of multi-morbidity and creating a collaborative environment, for all involved stakeholders. The navel of the system consists of the patient care plan, a digital shared picture of the patients’ needs and care regime. The care plan allows all professionals to review and understand the implications of one condition in the presence of others; this by its nature is complex, containing a considerable amount of diverse information. Navigating, understanding, and interpreting all the information can be confounding. The C3-Cloud Clinical Decision Support Service (CDS) offers an automated means of interpreting the available data. CDSS connects to the care plan repository, and continuously searches records for relevant data. The algorithms and integration of recommendations to the service were reviewed and validated by clinicians. Human computer interaction methods were employed to ensure optimal interaction between C3-Cloud and its users. Results: C3-Cloud offers CDSS for diabetes, renal failure, depression and congenital heart failure, with over 300 rules and checks that deliver four best practice guidelines in parallel; whilst reconciling their objectives, and monitoring their outcomes. It creates warnings or recommendations for the patient as well as for formal and informal carers. Discussion and Conclusions: C3-Cloud offers a powerful way to ensure that subtle, as well as critical, information about the patient, is presented to healthcare professionals, along with guideline based recommendations. The rules reconcile potential conflicts amongst conditions. Combined with a single patient and professionals interface, it provides a seamless experience throughout the health and care service. The C3-Cloud CDS service provides support to three pilot sites throughout Europe, currently undergoing evaluation. Acknowledgements: C3-Cloud is funded from the EU Horizon 2020 research and innovation project C3-Cloud, under grant agreement No 6891810. This abstract is based on the work and material of the entire C3-Cloud consortium.

Introduction: Effective discharge of patients from hospital represents a key step in provision of effective integrated care. The discharge process is rarely evaluated, from an informatics perspective, due to data acquisition challenges. A particular challenge is the increasing prevalence of patients with multiple interacting medical conditions (“multi-morbidity”). This requires management by multiple care teams, often including those not directly involved with the main cause for admission. An important example of this is patients with diabetes admitted under surgery, who may require medical and specialist social input during admission and subsequent discharge. Methods: Data was extracted from the electronic patient record for all patients discharged with a diagnosis of diabetes from a UK tertiary centre from 2015-2017. Pathology data was extracted from a regional pathology database crossing hospital & community care. Patients were filtered to those discharged from surgery. Pre-specified risk factors were evaluated for impact on readmission and mortality. Results: 5351 patients were discharged from surgical teams with a multi-morbid diagnosis of type 2 diabetes, 627 patients had a multi-morbid diagnosis of type 1 diabetes under surgical teams. Patients were evaluated by disease cohort, with statistically significant risk factors (p

Clinical practice guidelines (CPGs) document evidence-based information and recommendations on treatment and management of conditions. CPGs usually focus on management of a single condition; however, in many cases a patient will be at the centre of multiple health conditions (multimorbidity). Multiple CPGs need to be followed in parallel, each managing a separate condition, which often results in instructions that may interact with each other, such as conflicts in medication. Furthermore, the impetus to deliver customised care based on patient-specific information, results in the need to be able to offer guidelines in an integrated manner, identifying and managing their interactions. In recent years, CPGs have been formatted as computer-interpretable guidelines (CIGs). This enables developing CIG-driven clinical decision support systems (CDSSs), which allow the development of IT applications that contribute to the systematic and reliable management of multiple guidelines. This study focuses on understanding the use of CIG-based CDSSs, in order to manage care complexities of patients with multimorbidity. The literature between 2011 and 2017 is reviewed, which covers: (a) the challenges and barriers in the care of multimorbid patients, (b) the role of CIGs in CDSS augmented delivery of care, and (c) the approaches to alleviating care complexities of multimorbid patients. Generating integrated care plans, detecting and resolving adverse interactions between treatments and medications, dealing with temporal constraints in care steps, supporting patient-caregiver shared decision making and maintaining the continuity of care are some of the approaches that are enabled using a CIG-based CDSS.

Introduction Electronic health records provide an unparalleled opportunity for the use of patient data that is routinely collected and stored, in order to drive research and develop an epidemiological understanding of disease. Diabetes, in particular, stands to benefit, being a data-rich, chronic-disease state. This article aims to provide an understanding of the extent to which the healthcare sector is using routinely collected and stored data to inform research and epidemiological understanding of diabetes mellitus. Methods Narrative literature review of articles, published in both the medical- and engineering-based informatics literature. Results There has been a significant increase in the number of papers published, which utilise electronic health records as a direct data source for diabetes research. These articles consider a diverse range of research questions. Internationally, the secondary use of electronic health records, as a research tool, is most prominent in the USA. The barriers most commonly described in research studies include missing values and misclassification, alongside challenges of establishing the generalisability of results. Discussion Electronic health record research is an important and expanding area of healthcare research. Much of the research output remains in the form of conference abstracts and proceedings, rather than journal articles. There is enormous opportunity within the United Kingdom to develop these research methodologies, due to national patient identifiers. Such a healthcare context may enable UK researchers to overcome many of the barriers encountered elsewhere and thus to truly unlock the potential of electronic health records.

Background Digitally enabled healthcare services combine socio-technical resources to deliver the required outcomes to patients. Unintended operation of these services may result in adverse effects to the patient. Eliminating avoidable harm requires a systematic way of analysing the causal conditions, identifying opportunities for intervention. Operators of such services may be required to justify, and communicate, their safety. For example, the UK Standardisation Committee for Care Information (SCCI) standards 0129 and 0160 require a safety justification for health IT (superseded versions were known as the Information Standards Board (ISB) 0129 & 0160. Initial as well as current standards are maintained by the NHS Digital. Method A framework was designed, and applied as proof of concept, to an IT-supported clinical emergencies (A&E) service. Evaluation was done qualitatively based on the authors’ experience, identifying potential benefits of the approach. Results The applied framework encapsulates analysis, and structures the generated information, into a skeleton of an evidence-based case for safety. The framework improved management of the safety activities, assigning ownership to stakeholders (e.g. IT developer), also creating a clear and compelling safety justification. Conclusions Application of the framework significantly contributed to systematising an exploratory approach for analysing the service, in addition to existing methods such as reporting. Its application made the causal chain to harm more diaphanous. Constructing a safety case contributed to: (a) identifying potential assurance gaps, (b) planning production of information and evidence, and (c) communication of the justification by graphical unambiguous means.